"),
+ "local_preview": f"{path}.{shared.opts.samples_format}",
+ "metadata": json.dumps(l.metadata, indent=4) if l.metadata else None,
+ "mtime": os.path.getmtime(l.filename),
+ "size": os.path.getsize(l.filename),
+ }
+ info = self.find_info(l.filename)
+
+ tags = {}
+ possible_tags = l.metadata.get('ss_tag_frequency', {}) if l.metadata is not None else {} # tags from model metedata
+ if isinstance(possible_tags, str):
+ possible_tags = {}
+ for k, v in possible_tags.items():
+ words = k.split('_', 1) if '_' in k else [v, k]
+ words = [str(w).replace('.json', '') for w in words]
+ if words[0] == '{}':
+ words[0] = 0
+ tag = ' '.join(words[1:]).lower()
+ tags[tag] = words[0]
+
+ for v in info.get('modelVersions', []): # trigger words from info json
+ possible_tags = v.get('trainedWords', [])
+ if isinstance(possible_tags, list):
+ for tag_str in possible_tags:
+ for tag in tag_str.split(','):
+ tag = tag.strip().lower()
+ if tag not in tags:
+ tags[tag] = 0
+
+ possible_tags = info.get('tags', []) # tags from info json
+ if not isinstance(possible_tags, list):
+ possible_tags = [v for v in possible_tags.values()]
+ for tag in possible_tags:
+ tag = tag.strip().lower()
+ if tag not in tags:
+ tags[tag] = 0
+
+ bad_chars = [';', ':', '<', ">", "*", '?', '\'', '\"', '(', ')', '[', ']', '{', '}', '\\', '/']
+ clean_tags = {}
+ for k, v in tags.items():
+ tag = ''.join(i for i in k if not i in bad_chars).strip()
+ clean_tags[tag] = v
+
+ clean_tags.pop('img', None)
+ clean_tags.pop('dataset', None)
+
+ item["info"] = info
+ item["description"] = self.find_description(l.filename, info) # use existing info instead of double-read
+ item["tags"] = clean_tags
+
+ return item
+ except Exception as e:
+ shared.log.debug(f"Extra networks error: type=lora file={name} {e}")
+ from modules import errors
+ errors.display('e', 'Lora')
+ return None
+
+ def list_items(self):
+ with concurrent.futures.ThreadPoolExecutor(max_workers=shared.max_workers) as executor:
+ future_items = {executor.submit(self.create_item, net): net for net in networks.available_networks}
+ for future in concurrent.futures.as_completed(future_items):
+ item = future.result()
+ if item is not None:
+ yield item
+
+ def allowed_directories_for_previews(self):
+ return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir]
diff --git a/extensions-builtin/sd-extension-chainner b/extensions-builtin/sd-extension-chainner
index 7814d8d89..da3232b3f 160000
--- a/extensions-builtin/sd-extension-chainner
+++ b/extensions-builtin/sd-extension-chainner
@@ -1 +1 @@
-Subproject commit 7814d8d893ec9a29e7322c3228cf88fa3537c4ea
+Subproject commit da3232b3fdaddf37a602348d4f1f5dee60652975
diff --git a/extensions-builtin/sd-extension-system-info b/extensions-builtin/sd-extension-system-info
index f93480869..99ad4030b 160000
--- a/extensions-builtin/sd-extension-system-info
+++ b/extensions-builtin/sd-extension-system-info
@@ -1 +1 @@
-Subproject commit f934808698523465e7abb2d1fb0d290065def776
+Subproject commit 99ad4030b176dbf0b5c1d446c6b191eb0e99c64d
diff --git a/extensions-builtin/sd-webui-controlnet b/extensions-builtin/sd-webui-controlnet
index a5b3fa931..1d03353d9 160000
--- a/extensions-builtin/sd-webui-controlnet
+++ b/extensions-builtin/sd-webui-controlnet
@@ -1 +1 @@
-Subproject commit a5b3fa931fe8d3f18ce372a7bb1a692905d3affc
+Subproject commit 1d03353d9cc464e7d2f166a67eeb7c4514b44c57
diff --git a/html/licenses.html b/html/licenses.html
index 2ad803052..66b9c0bc6 100644
--- a/html/licenses.html
+++ b/html/licenses.html
@@ -1,690 +1,690 @@
-
-
-
-Parts of CodeFormer code had to be copied to be compatible with GFPGAN.
-
-S-Lab License 1.0
-
-Copyright 2022 S-Lab
-
-Redistribution and use for non-commercial purpose in source and
-binary forms, with or without modification, are permitted provided
-that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in
- the documentation and/or other materials provided with the
- distribution.
-
-3. Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived
- from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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-source or binary forms, with or without modification is required,
-please contact the contributor(s) of the work.
-
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-
-
-Code for architecture and reading models copied.
-
-MIT License
-
-Copyright (c) 2021 victorca25
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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-
-
-
-Some code is copied to support ESRGAN models.
-
-BSD 3-Clause License
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-All rights reserved.
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-Redistribution and use in source and binary forms, with or without
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-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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-
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-
-Some code for compatibility with OSX is taken from lstein's repository.
-
-MIT License
-
-Copyright (c) 2022 InvokeAI Team
-
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-
-
-
-Code added by contirubtors, most likely copied from this repository.
-
-MIT License
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-Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
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-SOFTWARE.
-
-
-
-Some small amounts of code borrowed and reworked.
-
-MIT License
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-Copyright (c) 2022 pharmapsychotic
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
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+
+The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers
+
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
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+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+
+
+Tiny AutoEncoder for Stable Diffusion option for live previews
+
+MIT License
+
+Copyright (c) 2023 Ollin Boer Bohan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
diff --git a/html/locale_en.json b/html/locale_en.json
index 0807a2ab9..994d7a784 100644
--- a/html/locale_en.json
+++ b/html/locale_en.json
@@ -1,12 +1,4 @@
{"icons": [
- {"id":"","label":"📘","localized":"","hint":"Read parameters from last generated image"},
- {"id":"","label":"🚮","localized":"","hint":"Clear prompt"},
- {"id":"","label":"🌐","localized":"","hint":"Show/hide extra networks"},
- {"id":"","label":"⏩","localized":"","hint":"Apply selected styles to current prompt"},
- {"id":"","label":"⬇️","localized":"","hint":"Save parameters from last generated image as style template"},
- {"id":"","label":"🔄","localized":"","hint":"Refresh"},
- {"id":"","label":"🛗","localized":"","hint":"Close"},
- {"id":"","label":"⏫","localized":"","hint":"Fill"},
{"id":"","label":"🎲️","localized":"","hint":"Use random seed"},
{"id":"","label":"♻️","localized":"","hint":"Reuse previous seed"},
{"id":"","label":"⇅","localized":"","hint":"Swap values"},
@@ -36,11 +28,7 @@
"common keywords": [
{"id":"","label":"fp16","localized":"","hint":"Number representation in 16-bit floating point format"},
{"id":"","label":"fp32","localized":"","hint":"Number representation in 32-bit floating point format"},
- {"id":"","label":"bf16","localized":"","hint":"Number representation in alternative 16-bit floating point format"},
- {"id":"","label":"Run","localized":"","hint":""},
- {"id":"","label":"all","localized":"","hint":""},
- {"id":"","label":"none","localized":"","hint":""},
- {"id":"","label":"disabled","localized":"","hint":""}
+ {"id":"","label":"bf16","localized":"","hint":"Number representation in alternative 16-bit floating point format"}
],
"tabs": [
{"id":"","label":"Text","localized":"","hint":"Create image from text"},
@@ -63,39 +51,21 @@
{"id":"","label":"Enqueue","localized":"","hint":"Add task to background queue in Agent Scheduler"},
{"id":"","label":"Stop","localized":"","hint":"Stop processing"},
{"id":"","label":"Skip","localized":"","hint":"Stop processing current job and continue processing"},
- {"id":"","label":"Interrupt","localized":"","hint":"Interrupt current processing job"},
{"id":"","label":"Pause","localized":"","hint":"Pause processing"},
{"id":"","label":"Restore","localized":"","hint":"Restore parameters from current prompt or last known generated image"},
{"id":"","label":"Clear","localized":"","hint":"Clear prompts"},
- {"id":"","label":"Networks","localized":"","hint":"Open extra network interface"},
- {"id":"","label":"Interrogate\nCLIP","localized":"","hint":"Run interrogate using CLIP model"},
- {"id":"","label":"Interrogate\nDeepBooru","localized":"","hint":"Run interrogate using DeepBooru model"}
+ {"id":"","label":"Networks","localized":"","hint":"Open extra network interface"}
],
"extra networks": [
- {"id":"","label":"UI position","localized":"","hint":"Location of extra networks"},
+ {"id":"","label":"ui position","localized":"","hint":"Location of extra networks"},
{"id":"","label":"cover","localized":"","hint":"cover full area"},
{"id":"","label":"inline","localized":"","hint":"inline with all additional elements (scrollable)"},
{"id":"","label":"sidebar","localized":"","hint":"sidebar on the right side of the screen"},
- {"id":"","label":"UI height (%)","localized":"","hint":""},
- {"id":"","label":"UI sidebar width (%)","localized":"","hint":""},
- {"id":"","label":"UI card preview lazy loading","localized":"","hint":""},
- {"id":"","label":"UI card size (px)","localized":"","hint":""},
- {"id":"","label":"UI disable variable aspect ratio","localized":"","hint":""},
- {"id":"","label":"UI image contain method","localized":"","hint":""},
- {"id":"","label":"Do not automatically build extra network pages","localized":"","hint":""},
- {"id":"","label":"Use LyCoris handler for all Lora types","localized":"","hint":""},
- {"id":"","label":"Use Kohya method for handling multiple LoRA","localized":"","hint":""},
{"id":"","label":"Default multiplier for extra networks","localized":"","hint":"When adding extra network such as Lora to prompt, use this multiplier for it"},
- {"id":"","label":"Add hypernetwork to prompt","localized":"","hint":""},
- {"id":"","label":"Add Lora to prompt","localized":"","hint":""},
- {"id":"","label":"shuffle tags by ',' when creating prompts.","localized":"","hint":""},
- {"id":"","label":"When adding to prompt, refer to Lora by","localized":"","hint":""},
- {"id":"","label":"add lora hashes to infotext","localized":"","hint":""},
{"id":"","label":"Model","localized":"","hint":"Trained model checkpoints"},
{"id":"","label":"Style","localized":"","hint":"Additional styles to be applied on selected generation paramters"},
{"id":"","label":"Styles","localized":"","hint":"Additional styles to be applied on selected generation paramters"},
{"id":"","label":"Lora","localized":"","hint":"LoRA: Low-Rank Adaptation. Fine-tuned model that is applied on top of a loaded model"},
- {"id":"","label":"LyCORIS","localized":"","hint":"LyCORIS: Lora beYond Conventional methods. Fine-tuned model that is applied on top of a loaded model"},
{"id":"","label":"Embedding","localized":"","hint":"Textual inversion embedding is a trained embedded information about the subject"},
{"id":"","label":"Hypernetwork","localized":"","hint":"Small trained neural network that modifies behavior of the loaded model"}
],
@@ -108,7 +78,7 @@
{"id":"","label":"➠ image","localized":"","hint":"Transfer image to image interface"},
{"id":"","label":"➠ inpaint","localized":"","hint":"Transfer image to inpaint interface"},
{"id":"","label":"➠ sketch","localized":"","hint":"Transfer image to sketch interface"},
- {"id":"","label":"➠ inpaint sketch","localized":"","hint":"Transfer image to inpaint sketch interface"},
+ {"id":"","label":"➠ composite","localized":"","hint":"Transfer image to inpaint sketch interface"},
{"id":"","label":"➠ process","localized":"","hint":"Transfer image to process interface"}
],
"extensions": [
@@ -126,26 +96,17 @@
{"id":"","label":"install","localized":"","hint":"install this extension"},
{"id":"","label":"uninstall","localized":"","hint":"uninstall this extension"},
{"id":"","label":"User interface","localized":"","hint":"Review and set current values as default values for the user interface"},
- {"id":"","label":"View changes","localized":"","hint":"Review changes between default user interface values and current values"},
{"id":"","label":"Set new defaults","localized":"","hint":"Set current values as default values for the user interface"},
{"id":"","label":"Benchmark","localized":"","hint":"Run benchmarks"},
{"id":"","label":"Models & Networks","localized":"","hint":"View lists of all available models and networks"},
- {"id":"","label":"Restore system defaults","localized":"","hint":"Restore default user interface values"}
+ {"id":"","label":"Restore defaults","localized":"","hint":"Restore default user interface values"}
],
"txt2img tab": [
- {"id":"","label":"Batch","localized":"","hint":"Additional batching options"},
- {"id":"","label":"Sampler","localized":"","hint":"Additional sampling options"},
- {"id":"","label":"Advanced","localized":"","hint":"Additional advanced options"},
{"id":"","label":"Sampling method","localized":"","hint":"Which algorithm to use to produce the image"},
{"id":"","label":"Sampling steps","localized":"","hint":"How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results"},
- {"id":"","label":"Sampler options","localized":"","hint":"Advanced sampler options"},
- {"id":"","label":"Sigma algorithm","localized":"","hint":"Algorithm to progress noise resolution on each sampler step"},
- {"id":"","label":"Restore faces","localized":"","hint":"Use a pre-trained model to correct the generated faces. See GFPGAN or Codeformer."},
{"id":"","label":"Tiling","localized":"","hint":"Produce an image that can be tiled"},
{"id":"","label":"full quality","localized":"","hint":"Use full quality VAE to decode latent samples"},
{"id":"","label":"face restore","localized":"","hint":"Run processed image through additional face restoration model"},
- {"id":"","label":"denoise","localized":"","hint":"Denoising details for img2img"},
- {"id":"","label":"remove background","localized":"","hint":"Run processed image through additional background removal model"},
{"id":"","label":"Enable second pass","localized":"","hint":"Use a similar process as image to image to upscale and/or add detail to the final image. Optionally uses refiner model to enhance image details."},
{"id":"","label":"Denoising strength","localized":"","hint":"Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies"},
{"id":"","label":"Denoise start","localized":"","hint":"Override denoise strength by stating how early base model should finish and when refiner should start. Only applicable to refiner usage. If set to 0 or 1, denoising strength will be used"},
@@ -169,7 +130,6 @@
{"id":"","label":"cfg scale","localized":"","hint":"Classifier Free Guidance scale: how strongly the image should conform to prompt. Lower values produce more creative results, higher values make it follow the prompt more strictly; recommended values between 5-10"},
{"id":"","label":"CLIP skip","localized":"","hint":"Clip skip is a feature that allows users to control the level of specificity of the prompt, the higher the CLIP skip value, the less deep the prompt will be interpreted. CLIP Skip 1 is typical while some anime models produce better results at CLIP skip 2"},
{"id":"","label":"Initial seed","localized":"","hint":"A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result"},
- {"id":"","label":"Extra","localized":"","hint":"Show additional options"},
{"id":"","label":"Variation","localized":"","hint":"Second seed to be mixed with primary seed"},
{"id":"","label":"Variation strength","localized":"","hint":"How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something)"},
{"id":"","label":"Resize seed from width","localized":"","hint":"Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution"},
@@ -200,66 +160,20 @@
{"id":"sett_reload_sd_model","label":"Reload checkpoint","localized":"","hint":"Reload currently selected model checkpoint"}
],
"settings sections": [
- {"id":"","label":"execution & models","localized":"","hint":""},
- {"id":"","label":"Optimizations","localized":"","hint":""},
- {"id":"","label":"Compute Settings","localized":"","hint":""},
- {"id":"","label":"Diffusers Settings","localized":"","hint":""},
- {"id":"","label":"System Paths","localized":"","hint":""},
- {"id":"","label":"Image Options","localized":"","hint":""},
- {"id":"","label":"image naming & paths","localized":"","hint":""},
- {"id":"","label":"User Interface","localized":"","hint":""},
- {"id":"","label":"Live Previews","localized":"","hint":""},
- {"id":"","label":"Sampler Settings","localized":"","hint":""},
- {"id":"","label":"Postprocessing","localized":"","hint":""},
- {"id":"","label":"Training","localized":"","hint":""},
- {"id":"","label":"Interrogate","localized":"","hint":""},
- {"id":"","label":"Extra Networks","localized":"","hint":""},
{"id":"","label":"Licenses","localized":"","hint":"View licenses of all additional included libraries"},
- {"id":"","label":"Show all pages","localized":"","hint":"Show all settings pages"},
- {"id":"","label":"Request browser notifications","localized":"","hint":""}
-],
-"img2img tabs": [
- {"id":"","label":"Image","localized":"","hint":""},
- {"id":"","label":"Sketch","localized":"","hint":""},
- {"id":"","label":"Inpaint","localized":"","hint":""},
- {"id":"","label":"Inpaint sketch","localized":"","hint":""},
- {"id":"","label":"Inpaint upload","localized":"","hint":""}
+ {"id":"","label":"Show all pages","localized":"","hint":"Show all settings pages"}
],
"img2img tab": [
- {"id":"","label":"Inpaint batch input directory","localized":"","hint":""},
- {"id":"","label":"Inpaint batch output directory","localized":"","hint":""},
- {"id":"","label":"Inpaint batch mask directory","localized":"","hint":""},
{"id":"","label":"Fixed","localized":"","hint":"Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio"},
{"id":"","label":"Crop","localized":"","hint":"Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out"},
{"id":"","label":"Fill","localized":"","hint":"Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors"},
- {"id":"","label":"Latent","localized":"","hint":""},
{"id":"","label":"Mask blur","localized":"","hint":"How much to blur the mask before processing, in pixels"},
- {"id":"","label":"Mask transparency","localized":"","hint":""},
- {"id":"","label":"Inpaint masked","localized":"","hint":""},
- {"id":"","label":"Inpaint not masked","localized":"","hint":""},
- {"id":"","label":"fill","localized":"","hint":"fill"},
{"id":"","label":"original","localized":"","hint":"keep whatever was there originally"},
{"id":"","label":"latent noise","localized":"","hint":"fill it with latent space noise"},
- {"id":"","label":"latent nothing","localized":"","hint":"fill it with latent space zeroes"},
- {"id":"","label":"Whole picture","localized":"","hint":""},
- {"id":"","label":"Only masked","localized":"","hint":""},
- {"id":"","label":"Masked padding","localized":"","hint":""},
- {"id":"","label":"Scale","localized":"","hint":""},
- {"id":"","label":"Unused","localized":"","hint":""},
- {"id":"","label":"Image CFG scale","localized":"","hint":""}
+ {"id":"","label":"latent nothing","localized":"","hint":"fill it with latent space zeroes"}
],
"models tabs": [
- {"id":"","label":"Convert","localized":"","hint":""},
- {"id":"","label":"Merge","localized":"","hint":""},
- {"id":"","label":"Validate","localized":"","hint":""},
- {"id":"","label":"List model details","localized":"","hint":""},
{"id":"","label":"Calculate hash for all models","localized":"","hint":"Calculates hash for all available models which may take a very long time"},
- {"id":"","label":"search models","localized":"","hint":""},
- {"id":"","label":"select model","localized":"","hint":""},
- {"id":"","label":"specify model variant","localized":"","hint":""},
- {"id":"","label":"specify model revision","localized":"","hint":""},
- {"id":"","label":"huggingface token","localized":"","hint":""},
- {"id":"","label":"huggingface mirror","localized":"","hint":""},
{"id":"","label":"Weights Clip","localized":"","hint":"Forced merged weights to be no heavier than the original model, preventing burn in and overly saturated models"},
{"id":"","label":"ReBasin","localized":"","hint":"Performs multiple merges with permutations in order to keep more features from both models"},
{"id":"","label":"Number of ReBasin Iterations","localized":"","hint":"Number of times to merge and permute the model before saving"},
@@ -272,130 +186,26 @@
{"id":"","label":"Out Block","localized":"","hint":"Upsampling Blocks of the UNet (12 values for SD1.5, 9 values for SDXL)"},
{"id":"","label":"Preset Interpolation Ratio","localized":"","hint":"If two presets are selected, interpolate between them"}
],
-"train tabs": [
- {"id":"","label":"Preprocess","localized":"","hint":""},
- {"id":"","label":"Preprocess images","localized":"","hint":""},
- {"id":"","label":"Train embedding","localized":"","hint":""},
- {"id":"","label":"Train hypernetwork","localized":"","hint":""},
- {"id":"","label":"Create embedding","localized":"","hint":""},
- {"id":"","label":"Create hypernetwork","localized":"","hint":""}
-],
"train tab": [
- {"id":"","label":"Primary model","localized":"","hint":""},
- {"id":"","label":"Secondary model","localized":"","hint":""},
- {"id":"","label":"Tertiary model","localized":"","hint":""},
- {"id":"","label":"New model name","localized":"","hint":""},
- {"id":"","label":"No interpolation","localized":"","hint":"Requires one model. No interpolation will be used, allows for format conversion and VAE baking"},
- {"id":"","label":"Weighted sum","localized":"","hint":"Requires two models. Weighted sum will be used for interpolation, result is calculated as A * (1 - M) + B * M"},
- {"id":"","label":"Add difference","localized":"","hint":"Requires three models. Difference between the last two models will be added to the first, result is calculated as A + (B - C) * M"},
- {"id":"","label":"Interpolation ratio from Primary to Secondary","localized":"","hint":""},
- {"id":"","label":"ckpt","localized":"","hint":""},
- {"id":"","label":"safetensors","localized":"","hint":""},
- {"id":"","label":"Save metadata","localized":"","hint":""},
- {"id":"","label":"Primary","localized":"","hint":""},
- {"id":"","label":"Secondary","localized":"","hint":""},
- {"id":"","label":"Tertiary","localized":"","hint":""},
- {"id":"","label":"Bake in VAE","localized":"","hint":""},
- {"id":"","label":"Discard weights with matching name","localized":"","hint":"Regular expression; if weight's name matches it, the weights are not written to the resulting checkpoint. Use ^model_ema to discard EMA weights"},
- {"id":"","label":"Name","localized":"","hint":""},
{"id":"","label":"Initialization text","localized":"","hint":"If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors"},
- {"id":"","label":"Number of vectors per token","localized":"","hint":""},
- {"id":"","label":"Overwrite Old Embedding","localized":"","hint":""},
- {"id":"","label":"Enter hypernetwork layer structure","localized":"","hint":""},
{"id":"","label":"Select activation function of hypernetwork","localized":"","hint":"Recommended : Swish / Linear(none)"},
{"id":"","label":"Select Layer weights initialization","localized":"","hint":"Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise"},
- {"id":"","label":"Add layer normalization","localized":"","hint":""},
- {"id":"","label":"Use dropout","localized":"","hint":""},
{"id":"","label":"Enter hypernetwork Dropout structure","localized":"","hint":"Recommended : leave empty or 0~0.35 incrementing sequence: 0, 0.05, 0.15"},
- {"id":"","label":"Overwrite Old Hypernetwork","localized":"","hint":""},
- {"id":"","label":"Source directory","localized":"","hint":""},
- {"id":"","label":"Destination directory","localized":"","hint":""},
- {"id":"","label":"Existing caption text action","localized":"","hint":""},
- {"id":"","label":"Keep original size","localized":"","hint":""},
- {"id":"","label":"Keep original image channels","localized":"","hint":""},
- {"id":"","label":"Create flipped copies","localized":"","hint":""},
- {"id":"","label":"Split oversized images","localized":"","hint":""},
- {"id":"","label":"Auto focal point crop","localized":"","hint":""},
- {"id":"","label":"Auto-sized crop","localized":"","hint":""},
- {"id":"","label":"Create captions only","localized":"","hint":""},
- {"id":"","label":"Create BLIP captions","localized":"","hint":""},
- {"id":"","label":"Create Deepbooru captions","localized":"","hint":""},
- {"id":"","label":"Split image threshold","localized":"","hint":""},
- {"id":"","label":"Split image overlap ratio","localized":"","hint":""},
- {"id":"","label":"Focal point face weight","localized":"","hint":""},
- {"id":"","label":"Focal point entropy weight","localized":"","hint":""},
- {"id":"","label":"Focal point edges weight","localized":"","hint":""},
- {"id":"","label":"Create debug image","localized":"","hint":""},
- {"id":"","label":"Dimension lower bound","localized":"","hint":""},
- {"id":"","label":"Dimension upper bound","localized":"","hint":""},
- {"id":"","label":"Area lower bound","localized":"","hint":""},
- {"id":"","label":"Area upper bound","localized":"","hint":""},
- {"id":"","label":"Maximize area","localized":"","hint":""},
- {"id":"","label":"Minimize error","localized":"","hint":""},
- {"id":"","label":"Error threshold","localized":"","hint":""},
- {"id":"","label":"Hypernetwork","localized":"","hint":""},
- {"id":"","label":"Embedding Learning rate","localized":"","hint":""},
- {"id":"","label":"Hypernetwork Learning rate","localized":"","hint":""},
- {"id":"","label":"Gradient Clipping","localized":"","hint":""},
- {"id":"","label":"Gradient accumulation steps","localized":"","hint":""},
- {"id":"","label":"Dataset directory","localized":"","hint":""},
- {"id":"","label":"Log directory","localized":"","hint":""},
- {"id":"","label":"Prompt template","localized":"","hint":""},
- {"id":"","label":"Do not resize images","localized":"","hint":""},
- {"id":"","label":"Max steps","localized":"","hint":""},
{"id":"","label":"Create interim images","localized":"","hint":"Save an image to log directory every N steps, 0 to disable"},
{"id":"","label":"Create interim embeddings","localized":"","hint":"Save a copy of embedding to log directory every N steps, 0 to disable"},
- {"id":"","label":"Use PNG alpha channel as loss weight","localized":"","hint":""},
- {"id":"","label":"Save images with embedding in PNG chunks","localized":"","hint":""},
{"id":"","label":"Use current settings for previews","localized":"","hint":"Read parameters (prompt, etc...) from txt2img tab when making previews"},
- {"id":"","label":"Shuffle tags","localized":"","hint":"Shuffle tags by ',' when creating prompts"},
- {"id":"","label":"Drop out tags when creating prompts","localized":"","hint":""},
- {"id":"","label":"once","localized":"","hint":""},
- {"id":"","label":"deterministic","localized":"","hint":""},
- {"id":"","label":"select embedding","localized":"","hint":""},
- {"id":"","label":"create embedding","localized":"","hint":""},
- {"id":"","label":"gradient clip value","localized":"","hint":""},
- {"id":"","label":"create interim hypernetworks","localized":"","hint":""},
- {"id":"","label":"Original model","localized":"","hint":""}
+ {"id":"","label":"Shuffle tags","localized":"","hint":"Shuffle tags by ',' when creating prompts"}
],
"settings": [
{"id":"","label":"base model","localized":"","hint":"Main model used for all operations"},
{"id":"","label":"refiner model","localized":"","hint":"Refiner model used for second-pass operations"},
- {"id":"","label":"model autoload on server start","localized":"","hint":""},
- {"id":"","label":"use baseline data from a different model","localized":"","hint":""},
- {"id":"","label":"Disallow usage of models in ckpt format","localized":"","hint":""},
- {"id":"","label":"model compile fullgraph","localized":"","hint":""},
- {"id":"","label":"create zip archive when downloading multiple images","localized":"","hint":""},
- {"id":"","label":"samplers solver order where applicable","localized":"","hint":""},
- {"id":"","label":"samplers use karras sigmas where applicable","localized":"","hint":""},
- {"id":"","label":"samplers use simplified solvers in final steps where applicable","localized":"","hint":""},
- {"id":"","label":"samplers use dynamic thresholding where applicable","localized":"","hint":""},
- {"id":"","label":"Number of cached models","localized":"","hint":"The amount of models to store in RAM for quick access"},
- {"id":"","label":"Number of cached VAEs","localized":"","hint":"The amount of VAE files to store in RAM for quick access"},
+ {"id":"","label":"Cached models","localized":"","hint":"The number of models to store in RAM for quick access"},
+ {"id":"","label":"Cached VAEs","localized":"","hint":"The number of VAE files to store in RAM for quick access"},
{"id":"","label":"VAE model","localized":"","hint":"VAE helps with fine details in the final image and may also alter colors"},
- {"id":"","label":"Enable splitting of hires batch processing","localized":"","hint":"Reduces VRAM usage when using hires fix on batches of images"},
{"id":"","label":"Load models using stream loading method","localized":"","hint":"When loading models attempt stream loading optimized for slow or network storage"},
- {"id":"","label":"When loading models attempt to reuse previous model dictionary","localized":"","hint":""},
- {"id":"","label":"Disabled","localized":"","hint":""},
{"id":"","label":"xFormers","localized":"","hint":"Memory optimization. Non-Deterministic (different results each time)"},
{"id":"","label":"Scaled-Dot-Product","localized":"","hint":"Memory optimization. Non-Deterministic unless SDP memory attention is disabled."},
- {"id":"","label":"Doggettx's","localized":"","hint":""},
- {"id":"","label":"InvokeAI's","localized":"","hint":""},
- {"id":"","label":"Sub-quadratic","localized":"","hint":""},
- {"id":"","label":"Split attention","localized":"","hint":""},
- {"id":"","label":"xFormers enable flash Attention","localized":"","hint":""},
- {"id":"","label":"SDP disable memory attention","localized":"","hint":""},
- {"id":"","label":"cross-attention query chunk size","localized":"","hint":""},
- {"id":"","label":"cross-attention kv chunk size","localized":"","hint":""},
- {"id":"","label":"cross-attention chunking threshold","localized":"","hint":""},
- {"id":"","label":"Full parser","localized":"","hint":""},
- {"id":"","label":"Compel parser","localized":"","hint":""},
- {"id":"","label":"A1111 parser","localized":"","hint":""},
- {"id":"","label":"Fixed attention","localized":"","hint":""},
- {"id":"","label":"Prompt attention mean normalization","localized":"","hint":""},
- {"id":"","label":"Disable conditional batching enabled on low memory systems","localized":"","hint":""},
- {"id":"","label":"Enable samplers quantization for sharper and cleaner results","localized":"","hint":""},
- {"id":"","label":"Prompt padding for long prompts","localized":"","hint":"Increase coherency by padding from the last comma within n tokens when using more than 75 tokens"},
+ {"id":"","label":"Prompt padding","localized":"","hint":"Increase coherency by padding from the last comma within n tokens when using more than 75 tokens"},
{"id":"","label":"Original","localized":"","hint":"Original LDM backend"},
{"id":"","label":"Diffusers","localized":"","hint":"Diffusers backend"},
{"id":"","label":"Autocast","localized":"","hint":"Automatically determine precision during runtime"},
@@ -403,275 +213,67 @@
{"id":"","label":"FP32","localized":"","hint":"Use 32-bit floating point precision for calculations"},
{"id":"","label":"FP16","localized":"","hint":"Use 16-bit floating point precision for calculations"},
{"id":"","label":"BF16","localized":"","hint":"Use modified 16-bit floating point precision for calculations"},
- {"id":"","label":"Use full precision for model (--no-half)","localized":"","hint":"Uses FP32 for the model. May produce better results while using more VRAM and slower generation"},
- {"id":"","label":"Use full precision for VAE (--no-half-vae)","localized":"","hint":"Uses FP32 for the VAE. May produce better results while using more VRAM and slower generation"},
- {"id":"","label":"Enable upcast sampling","localized":"","hint":"Usually produces similar results to --no-half with better performance while using less memory"},
- {"id":"","label":"Enable upcast cross attention layer","localized":"","hint":""},
- {"id":"","label":"Disable NaN check in produced images/latent spaces","localized":"","hint":""},
- {"id":"","label":"Attempt VAE roll back when produced NaN values","localized":"","hint":"Requires Torch 2.1 and NaN check enabled"},
- {"id":"","label":"Use channels last as torch memory format","localized":"","hint":""},
- {"id":"","label":"Enable full-depth cuDNN benchmark feature","localized":"","hint":""},
- {"id":"","label":"Enable model compile","localized":"","hint":""},
- {"id":"","label":"inductor","localized":"","hint":""},
- {"id":"","label":"cudagraphs","localized":"","hint":""},
- {"id":"","label":"aot_ts_nvfuser","localized":"","hint":""},
- {"id":"","label":"hidet","localized":"","hint":""},
- {"id":"","label":"ipex","localized":"","hint":""},
- {"id":"","label":"Model compile verbose mode","localized":"","hint":""},
- {"id":"","label":"Model compile suppress errors","localized":"","hint":""},
- {"id":"","label":"Directory for temporary images; leave empty for default","localized":"","hint":""},
- {"id":"","label":"Enable IPEX Optimize for Intel GPUs","localized":"","hint":""},
- {"id":"","label":"Cleanup non-default temporary directory when starting webui","localized":"","hint":""},
- {"id":"","label":"Folder with stable diffusion models","localized":"","hint":""},
- {"id":"","label":"Folder with stable diffusion diffusers","localized":"","hint":""},
- {"id":"","label":"Folder with VAE files","localized":"","hint":""},
- {"id":"","label":"Folder with textual inversion embeddings","localized":"","hint":""},
- {"id":"","label":"Folder with Hypernetwork models","localized":"","hint":""},
- {"id":"","label":"Folder with codeformer Folder","localized":"","hint":""},
- {"id":"","label":"Folder with GFPGAN models","localized":"","hint":""},
- {"id":"","label":"Folder with ESRGAN models","localized":"","hint":""},
- {"id":"","label":"Folder with BSRGAN models","localized":"","hint":""},
- {"id":"","label":"Folder with RealESRGAN models","localized":"","hint":""},
- {"id":"","label":"Folder with SCUNet models","localized":"","hint":""},
- {"id":"","label":"Folder with SwinIR models","localized":"","hint":""},
- {"id":"","label":"Folder with LDSR models","localized":"","hint":""},
- {"id":"","label":"Folder with CLIP models","localized":"","hint":""},
- {"id":"","label":"Folder with Lora networks","localized":"","hint":""},
- {"id":"","label":"Folder with LyCORIS networks","localized":"","hint":""},
- {"id":"","label":"Path to user-defined styles file","localized":"","hint":""},
- {"id":"","label":"Always save all generated images","localized":"","hint":""},
- {"id":"","label":"File format for generated images","localized":"","hint":"Select file format for images"},
- {"id":"","label":"Include metadata in saved images","localized":"","hint":"Save image create parameters as metadata tags inside image file"},
+ {"id":"","label":"Full precision for model (--no-half)","localized":"","hint":"Uses FP32 for the model. May produce better results while using more VRAM and slower generation"},
+ {"id":"","label":"Full precision for VAE (--no-half-vae)","localized":"","hint":"Uses FP32 for the VAE. May produce better results while using more VRAM and slower generation"},
+ {"id":"","label":"Upcast sampling","localized":"","hint":"Usually produces similar results to --no-half with better performance while using less memory"},
+ {"id":"","label":"Attempt VAE roll back for NaN values","localized":"","hint":"Requires Torch 2.1 and NaN check enabled"},
+ {"id":"","label":"File format","localized":"","hint":"Select file format for images"},
+ {"id":"","label":"Include metadata","localized":"","hint":"Save image create parameters as metadata tags inside image file"},
{"id":"","label":"Images filename pattern","localized":"","hint":"Use following tags to define how filenames for images are chosen:seq, uuid "},
- {"id":"","label":"Add number to filename when saving","localized":"","hint":""},
- {"id":"","label":"Always save all generated image grids","localized":"","hint":""},
- {"id":"","label":"File format for grids","localized":"","hint":""},
- {"id":"","label":"Add extended info (seed, prompt) to filename when saving grid","localized":"","hint":""},
- {"id":"","label":"Grid row count","localized":"","hint":"Use -1 for autodetect and 0 for it to be same as batch size"},
- {"id":"","label":"Create info file for each every image","localized":"","hint":""},
- {"id":"","label":"Create JSON log file for each saved image","localized":"","hint":"Save image information to a JSON file"},
- {"id":"","label":"Save copy of image before doing face restoration","localized":"","hint":""},
- {"id":"","label":"Save copy of image before applying hires","localized":"","hint":""},
- {"id":"","label":"Save copy of image before applying color correction","localized":"","hint":""},
- {"id":"","label":"Save copy of the inpainting greyscale mask","localized":"","hint":""},
- {"id":"","label":"Save copy of inpainting masked composite","localized":"","hint":""},
- {"id":"","label":"Save copy of img2img init images","localized":"","hint":""},
- {"id":"","label":"Quality for saved jpeg images","localized":"","hint":""},
- {"id":"","label":"Use lossless compression for webp images","localized":"","hint":""},
- {"id":"","label":"Maximum allowed image size in megapixels","localized":"","hint":""},
- {"id":"","label":"Use original name for output filename during batch process","localized":"","hint":""},
- {"id":"","label":"Use upscaler name as filename suffix in the extras tab","localized":"","hint":""},
- {"id":"","label":"When using 'Save' button, only save a single selected image","localized":"","hint":""},
- {"id":"","label":"Save images to a subdirectory","localized":"","hint":""},
- {"id":"","label":"Save grids to a subdirectory","localized":"","hint":""},
- {"id":"","label":"Save images to a subdirectory when using Save button","localized":"","hint":""},
+ {"id":"","label":"Row count","localized":"","hint":"Use -1 for autodetect and 0 for it to be same as batch size"},
+ {"id":"","label":"Update JSON log file per image","localized":"","hint":"Save image information to a JSON file"},
{"id":"","label":"Directory name pattern","localized":"","hint":"Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime], [datetime], [job_timestamp]; leave empty for default"},
- {"id":"","label":"Max prompt words for [prompt_words] pattern","localized":"","hint":""},
- {"id":"","label":"Apply color correction to match original colors","localized":"","hint":""},
- {"id":"","label":"For image processing do exact number of steps as specified","localized":"","hint":""},
- {"id":"","label":"Image transparent color fill","localized":"","hint":""},
{"id":"","label":"Inpainting conditioning mask strength","localized":"","hint":"Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked (default). 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes"},
- {"id":"","label":"Noise multiplier for image processing","localized":"","hint":""},
{"id":"","label":"Clip skip","localized":"","hint":"Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc"},
- {"id":"","label":"Output directory for images","localized":"","hint":"If empty, defaults to three directories below"},
- {"id":"","label":"Output directory for txt2img images","localized":"","hint":""},
- {"id":"","label":"Output directory for img2img images","localized":"","hint":""},
- {"id":"","label":"Output directory for images from extras tab","localized":"","hint":""},
- {"id":"","label":"Output directory for grids","localized":"","hint":"If empty, defaults to two directories below"},
- {"id":"","label":"Output directory for txt2img grids","localized":"","hint":""},
- {"id":"","label":"Output directory for img2img grids","localized":"","hint":""},
- {"id":"","label":"Directory for saving images using the Save button","localized":"","hint":""},
- {"id":"","label":"Directory for saving init images when using img2img","localized":"","hint":""},
- {"id":"","label":"UI theme","localized":"","hint":""},
- {"id":"","label":"Auto","localized":"","hint":""},
- {"id":"","label":"Dark","localized":"","hint":""},
- {"id":"","label":"Light","localized":"","hint":""},
- {"id":"","label":"Show grid in results","localized":"","hint":""},
- {"id":"","label":"Inpainting include greyscale mask in results","localized":"","hint":""},
- {"id":"","label":"Inpainting include masked composite in results","localized":"","hint":""},
- {"id":"","label":"Do not change selected model when reading generation parameters","localized":"","hint":""},
- {"id":"","label":"Send seed when sending prompt or image to other interface","localized":"","hint":""},
- {"id":"","label":"Send size when sending prompt or image to another interface","localized":"","hint":""},
- {"id":"","label":"Font for image grids that have text","localized":"","hint":""},
- {"id":"","label":"Ctrl+up/down precision when editing (attention:1.1)","localized":"","hint":""},
- {"id":"","label":"Ctrl+up/down precision when editing ","localized":"","hint":""},
- {"id":"","label":"Ctrl+up/down word delimiters","localized":"","hint":""},
+ {"id":"","label":"Images folder","localized":"","hint":"If empty, defaults to three directories below"},
+ {"id":"","label":"Grids folder","localized":"","hint":"If empty, defaults to two directories below"},
{"id":"","label":"Quicksettings list","localized":"","hint":"List of setting names, separated by commas, for settings that should go to the quick access bar at the top instead the setting tab"},
- {"id":"","label":"UI scripts order","localized":"","hint":""},
- {"id":"","label":"Show progressbar","localized":"","hint":""},
- {"id":"","label":"Show live previews of the created image","localized":"","hint":""},
- {"id":"","label":"Show previews of all images generated in a batch as a grid","localized":"","hint":""},
- {"id":"","label":"Play a sound when images are finished generating","localized":"","hint":""},
- {"id":"","label":"Path to notification sound","localized":"","hint":""},
{"id":"","label":"Live preview display period","localized":"","hint":"Request preview image every n steps, set to 0 to disable"},
- {"id":"","label":"Full VAE","localized":"","hint":""},
{"id":"","label":"Approximate","localized":"","hint":"Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resolution and lower quality"},
{"id":"","label":"Simple","localized":"","hint":"Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resolution and extremely low quality"},
- {"id":"","label":"TAESD","localized":"","hint":""},
- {"id":"","label":"Combined","localized":"","hint":""},
{"id":"","label":"Progress update period","localized":"","hint":"Update period for UI progress bar and preview checks, in miliseconds"},
{"id":"","label":"Euler a","localized":"","hint":"Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help"},
- {"id":"","label":"Euler","localized":"","hint":""},
- {"id":"","label":"LMS","localized":"","hint":""},
- {"id":"","label":"Heun","localized":"","hint":""},
- {"id":"","label":"DPM2","localized":"","hint":""},
- {"id":"","label":"DPM2 a","localized":"","hint":""},
- {"id":"","label":"DPM++ 2S a","localized":"","hint":""},
- {"id":"","label":"DPM++ 2M","localized":"","hint":""},
- {"id":"","label":"DPM++ SDE","localized":"","hint":""},
- {"id":"","label":"DPM++ 2M SDE","localized":"","hint":""},
- {"id":"","label":"DPM fast","localized":"","hint":""},
{"id":"","label":"DPM adaptive","localized":"","hint":"Ignores step count - uses a number of steps determined by the CFG and resolution"},
- {"id":"","label":"LMS Karras","localized":"","hint":""},
- {"id":"","label":"DPM2 Karras","localized":"","hint":""},
- {"id":"","label":"DPM2 a Karras","localized":"","hint":""},
- {"id":"","label":"DPM++ 2S a Karras","localized":"","hint":""},
- {"id":"","label":"DPM++ 2M Karras","localized":"","hint":""},
- {"id":"","label":"DPM++ SDE Karras","localized":"","hint":""},
- {"id":"","label":"DPM++ 2M SDE Karras","localized":"","hint":""},
{"id":"","label":"DDIM","localized":"","hint":"Denoising Diffusion Implicit Models - best at inpainting"},
{"id":"","label":"UniPC","localized":"","hint":"Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models"},
- {"id":"","label":"Noise multiplier for ancestral samplers (eta)","localized":"","hint":""},
- {"id":"","label":"Noise multiplier for DDIM (eta)","localized":"","hint":""},
- {"id":"","label":"uniform","localized":"","hint":""},
- {"id":"","label":"quad","localized":"","hint":""},
- {"id":"","label":"sigma churn","localized":"","hint":""},
{"id":"","label":"sigma negative guidance minimum","localized":"","hint":"Skip negative prompt for some steps when the image is almost ready, 0=disable"},
- {"id":"","label":"sigma tmin","localized":"","hint":""},
- {"id":"","label":"sigma noise","localized":"","hint":""},
- {"id":"","label":"Noise seed delta (eta)","localized":"","hint":""},
- {"id":"","label":"Always discard next-to-last sigma","localized":"","hint":""},
- {"id":"","label":"bh1","localized":"","hint":""},
- {"id":"","label":"bh2","localized":"","hint":""},
- {"id":"","label":"vary_coeff","localized":"","hint":""},
- {"id":"","label":"time_uniform","localized":"","hint":""},
- {"id":"","label":"time_quadratic","localized":"","hint":""},
- {"id":"","label":"logSNR","localized":"","hint":""},
- {"id":"","label":"UniPC order (must be < sampling steps)","localized":"","hint":""},
- {"id":"","label":"UniPC lower order final","localized":"","hint":""},
- {"id":"","label":"Enable additional postprocessing operations","localized":"","hint":""},
- {"id":"","label":"Postprocessing operation order","localized":"","hint":""},
- {"id":"","label":"Maximum number of images in upscaling cache","localized":"","hint":""},
- {"id":"","label":"Move VAE and CLIP to RAM when training if possible","localized":"","hint":""},
- {"id":"","label":"Pin training dataset to memory","localized":"","hint":""},
- {"id":"","label":"Save resumable optimizer state when training","localized":"","hint":""},
- {"id":"","label":"Save training settings to a text file on training start","localized":"","hint":""},
{"id":"","label":"Filename word regex","localized":"","hint":"This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is"},
{"id":"","label":"Filename join string","localized":"","hint":"This string will be used to join split words into a single line if the option above is enabled"},
- {"id":"","label":"Embeddings train templates directory","localized":"","hint":""},
- {"id":"","label":"Enable tensorboard logging","localized":"","hint":""},
- {"id":"","label":"Save generated images within tensorboard","localized":"","hint":""},
{"id":"","label":"Tensorboard flush period","localized":"","hint":"How often, in seconds, to flush the pending Tensorboard events and summaries to disk"},
- {"id":"","label":"Interrogate: keep models in VRAM","localized":"","hint":""},
- {"id":"","label":"Interrogate: include ranks of model tags matches in results","localized":"","hint":""},
- {"id":"","label":"Interrogate: num_beams for BLIP","localized":"","hint":""},
{"id":"","label":"Interrogate: minimum description length","localized":"","hint":"Interrogate: minimum description length (excluding artists, etc..)"},
- {"id":"","label":"Interrogate: maximum description length","localized":"","hint":""},
{"id":"","label":"CLIP: maximum number of lines in text file","localized":"","hint":"CLIP: maximum number of lines in text file (0 = No limit)"},
- {"id":"","label":"flavors","localized":"","hint":""},
- {"id":"","label":"artists","localized":"","hint":""},
- {"id":"","label":"mediums","localized":"","hint":""},
- {"id":"","label":"movements","localized":"","hint":""},
- {"id":"","label":"Interrogate: deepbooru score threshold","localized":"","hint":""},
- {"id":"","label":"Interrogate: deepbooru sort alphabetically","localized":"","hint":""},
- {"id":"","label":"Use spaces for tags in deepbooru","localized":"","hint":""},
{"id":"","label":"Escape brackets in deepbooru","localized":"","hint":"Escape (\\) brackets in deepbooru so they are used as literal brackets and not for emphasis"},
{"id":"","label":"Filter out tags from deepbooru output","localized":"","hint":"Filter out those tags from deepbooru output (separated by comma)"},
- {"id":"","label":"Default upscaler for image resize operations","localized":"","hint":""},
{"id":"","label":"Upscaler tile size","localized":"","hint":"0 = no tiling"},
{"id":"","label":"Upscaler tile overlap","localized":"","hint":"Low values = visible seam"},
- {"id":"","label":"Do not fix prompt schedule for second order samplers","localized":"","hint":""},
- {"id":"","label":"CodeFormer","localized":"","hint":""},
{"id":"","label":"GFPGAN","localized":"","hint":"Restore low quality faces using GFPGAN neural network"},
{"id":"","label":"CodeFormer weight parameter","localized":"","hint":"0 = maximum effect; 1 = minimum effect"},
- {"id":"","label":"Move face restoration model from VRAM into RAM after processing","localized":"","hint":""},
- {"id":"","label":"Token merging ratio (txt2img)","localized":"","hint":"Enable redundant token merging via tomesd for speed and memory improvements, 0=disabled"},
- {"id":"","label":"Token merging ratio (img2img)","localized":"","hint":"Enable redundant token merging for img2img via tomesd for speed and memory improvements, 0=disabled"},
- {"id":"","label":"Token merging ratio (hires)","localized":"","hint":"Enable redundant token merging for hires pass via tomesd for speed and memory improvements, 0=disabled"},
+ {"id":"","label":"Token merging ratio for txt2img","localized":"","hint":"Enable redundant token merging via tomesd for speed and memory improvements, 0=disabled"},
+ {"id":"","label":"Token merging ratio for img2img","localized":"","hint":"Enable redundant token merging for img2img via tomesd for speed and memory improvements, 0=disabled"},
+ {"id":"","label":"Token merging ratio for hires","localized":"","hint":"Enable redundant token merging for hires pass via tomesd for speed and memory improvements, 0=disabled"},
{"id":"","label":"Diffusers pipeline","localized":"","hint":"If autodetect does not detect model automatically, select model type before loading a model"},
- {"id":"","label":"Move base model to CPU when using refiner","localized":"","hint":""},
- {"id":"","label":"Move base model to CPU when using VAE","localized":"","hint":""},
- {"id":"","label":"Move refiner model to CPU when not in use","localized":"","hint":""},
- {"id":"","label":"Use model EMA weights when possible","localized":"","hint":""},
- {"id":"","label":"Generator device","localized":"","hint":""},
- {"id":"","label":"Enable sequential CPU offload (--lowvram)","localized":"","hint":"Reduces GPU memory usage by transferring weights to the CPU. Increases inference time approximately 10%"},
- {"id":"","label":"Enable model CPU offload (--medvram)","localized":"","hint":"Transferring of entire models to the CPU, negligible impact on inference time while still providing some memory savings"},
- {"id":"","label":"Enable VAE slicing","localized":"","hint":"Decodes batch latents one image at a time with limited VRAM. Small performance boost in VAE decode on multi-image batches"},
- {"id":"","label":"Enable VAE tiling","localized":"","hint":"Divide large images into overlapping tiles with limited VRAM. Results in a minor increase in processing time"},
- {"id":"","label":"Enable attention slicing","localized":"","hint":"Performs attention computation in steps instead of all at once. Slower inference times, but greatly reduced memory usage"},
- {"id":"","label":"Diffusers force inpaint pipeline","localized":"","hint":"Force inpaint pipeline for all img2img tasks. Results in slightly different results that may be more precise"},
- {"id":"","label":"Diffusers model loading variant","localized":"","hint":""},
- {"id":"","label":"Diffusers VAE loading variant","localized":"","hint":""},
- {"id":"","label":"Diffusers LoRA loading variant","localized":"","hint":"'sequential apply' loads and applies each LoRA in order of appearance, 'merge and apply' loads all LoRAs and merges them in-memory before applying to model, 'diffusers' uses diffusers default LoRA loading method"},
- {"id":"","label":"Torch inference mode","localized":"","hint":"Use torch inference mode"},
+ {"id":"","label":"Sequential CPU offload (--lowvram)","localized":"","hint":"Reduces GPU memory usage by transferring weights to the CPU. Increases inference time approximately 10%"},
+ {"id":"","label":"Model CPU offload (--medvram)","localized":"","hint":"Transferring of entire models to the CPU, negligible impact on inference time while still providing some memory savings"},
+ {"id":"","label":"VAE slicing","localized":"","hint":"Decodes batch latents one image at a time with limited VRAM. Small performance boost in VAE decode on multi-image batches"},
+ {"id":"","label":"VAE tiling","localized":"","hint":"Divide large images into overlapping tiles with limited VRAM. Results in a minor increase in processing time"},
+ {"id":"","label":"Attention slicing","localized":"","hint":"Performs attention computation in steps instead of all at once. Slower inference times, but greatly reduced memory usage"},
{"id":"","label":"inference-mode","localized":"","hint":"Use torch.inference_mode"},
{"id":"","label":"no-grad","localized":"","hint":"Use torch.no_grad"},
- {"id":"","label":"vae slicing (original)","localized":"","hint":"Run VAE on sliced samples to reduce memory requirements when processing high resolution images"},
- {"id":"","label":"use fixed unet precision","localized":"","hint":""},
- {"id":"","label":"enable model compile","localized":"","hint":"Enable usage of torch.compile"},
- {"id":"","label":"reduce-overhead","localized":"","hint":""},
- {"id":"","label":"max-autotune","localized":"","hint":""},
{"id":"","label":"model compile precompile","localized":"","hint":"Run model compile immediately on model load instead of first use"},
- {"id":"","label":"sequential apply","localized":"","hint":"When loading multiple LoRAs, apply each in order of loading"},
- {"id":"","label":"merge and apply","localized":"","hint":"When loading multiple LoRAs, load all and merge them before applying to model"},
{"id":"","label":"force zeros for prompts when empty","localized":"","hint":"Force full zero tensor when prompt is empty to remove any residual noise"},
{"id":"","label":"require aesthetics score","localized":"","hint":"Automatically guide model towards higher-pleasing results, applicable only to refiner model"},
- {"id":"","label":"include watermark in saved images","localized":"","hint":"Add invisible watermark to image by altering some pixel values"},
- {"id":"","label":"image watermark string","localized":"","hint":"Watermark string to add to image. Keep very short to avoid image corruption."},
+ {"id":"","label":"include watermark","localized":"","hint":"Add invisible watermark to image by altering some pixel values"},
+ {"id":"","label":"watermark string","localized":"","hint":"Watermark string to add to image. Keep very short to avoid image corruption."},
{"id":"","label":"show log view","localized":"","hint":"Show log view at the bottom of the main window"},
{"id":"","label":"Log view update period","localized":"","hint":"Log view update period, in miliseconds"}
],
"scripts": [
- {"id":"","label":"Swap X/Y","localized":"","hint":""},
- {"id":"","label":"Swap Y/Z","localized":"","hint":""},
- {"id":"","label":"Swap X/Z","localized":"","hint":""},
- {"id":"","label":"Resize to","localized":"","hint":""},
- {"id":"","label":"Resize by","localized":"","hint":""},
- {"id":"","label":"Use via API","localized":"","hint":""},
- {"id":"","label":"Put variable parts at start of prompt","localized":"","hint":""},
- {"id":"","label":"Use different seed for each picture","localized":"","hint":""},
- {"id":"","label":"positive","localized":"","hint":""},
- {"id":"","label":"negative","localized":"","hint":""},
- {"id":"","label":"comma","localized":"","hint":""},
- {"id":"","label":"space","localized":"","hint":""},
- {"id":"","label":"Iterate seed every line","localized":"","hint":""},
- {"id":"","label":"Use same random seed for all lines","localized":"","hint":""},
- {"id":"","label":"List of prompt inputs","localized":"","hint":""},
- {"id":"","label":"X type","localized":"","hint":""},
{"id":"","label":"X values","localized":"","hint":"Separate values for X axis using commas"},
- {"id":"","label":"Y type","localized":"","hint":""},
{"id":"","label":"Y values","localized":"","hint":"Separate values for Y axis using commas"},
- {"id":"","label":"Z type","localized":"","hint":""},
- {"id":"","label":"Z values","localized":"","hint":""},
- {"id":"","label":"Draw legend","localized":"","hint":""},
- {"id":"","label":"Keep random for seeds","localized":"","hint":""},
- {"id":"","label":"Do not create grid","localized":"","hint":""},
- {"id":"","label":"Include Sub Images","localized":"","hint":""},
- {"id":"","label":"Include Sub Grids","localized":"","hint":""},
{"id":"","label":"Override `Sampling method` to Euler","localized":"","hint":"(this method is built for it)"},
- {"id":"","label":"Override `prompt` to the same value as `original prompt`","localized":"","hint":""},
- {"id":"","label":"Original prompt","localized":"","hint":""},
- {"id":"","label":"Original negative prompt","localized":"","hint":""},
- {"id":"","label":"Override `Sampling Steps` to the same value as `Decode steps`?","localized":"","hint":""},
- {"id":"","label":"Decode steps","localized":"","hint":""},
- {"id":"","label":"Override `Denoising strength` to 1?","localized":"","hint":""},
- {"id":"","label":"Decode CFG scale","localized":"","hint":""},
- {"id":"","label":"Randomness","localized":"","hint":""},
- {"id":"","label":"Sigma adjustment for finding noise for image","localized":"","hint":""},
{"id":"","label":"Loops","localized":"","hint":"How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used"},
{"id":"","label":"Final denoising strength","localized":"","hint":"The denoising strength for the final loop of each image in the batch"},
{"id":"","label":"Denoising strength curve","localized":"","hint":"The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops"},
- {"id":"","label":"Append interrogated prompt at each iteration","localized":"","hint":""},
- {"id":"","label":"Pixels to expand","localized":"","hint":""},
- {"id":"","label":"left","localized":"","hint":""},
- {"id":"","label":"right","localized":"","hint":""},
- {"id":"","label":"up","localized":"","hint":""},
- {"id":"","label":"down","localized":"","hint":""},
- {"id":"","label":"Fall-off exponent (lower=higher detail)","localized":"","hint":""},
- {"id":"","label":"Color variation","localized":"","hint":""},
- {"id":"","label":"Tile overlap","localized":"","hint":"For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam"},
- {"id":"","label":"Scale Factor","localized":"","hint":""},
- {"id":"","label":"None","localized":"","hint":"Do not do anything special"},
- {"id":"","label":"Grid margins","localized":"","hint":""}
+ {"id":"","label":"Tile overlap","localized":"","hint":"For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam"}
]
}
diff --git a/html/locale_ko.json b/html/locale_ko.json
index c36545816..bc64c3ad9 100644
--- a/html/locale_ko.json
+++ b/html/locale_ko.json
@@ -105,7 +105,7 @@
{"id":"","label":"User interface defaults","localized":"유저 인터페이스 기본값","hint":"Review and set current values as default values for the user interface"},
{"id":"","label":"View changes","localized":"변경 사항 확인","hint":"기본값과 현재 값 간의 변경 사항을 확인한다."},
{"id":"","label":"Set new defaults","localized":"새 기본값 설정","hint":"현재 UI 설정을 기본값으로 저장한다."},
- {"id":"","label":"Restore system defaults","localized":"시스템 기본값 복원","hint":"유저 인터페이스 기본값을 복원한다."}
+ {"id":"","label":"Restore defaults","localized":"시스템 기본값 복원","hint":"유저 인터페이스 기본값을 복원한다."}
],
"txt2img tab": [
{"id":"","label":"Sampling method","localized":"샘플링 방법(샘플러)","hint":"이미지 생성 시 사용할 알고리즘을 지정한다."},
diff --git a/html/reference.json b/html/reference.json
index 1ab1291f7..6aa309e8e 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -99,6 +99,11 @@
"desc": "PixArt-α is a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), and the training speed markedly surpasses existing large-scale T2I models. Extensive experiments demonstrate that PIXART-α excels in image quality, artistry, and semantic control. It can directly generate 1024px images from text prompts within a single sampling process.",
"preview": "PixArt-alpha--PixArt-XL-2-1024-MS.jpg"
},
+ "Pixart-α XL 2 Large LCM": {
+ "path": "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
+ "desc": "Pixart-α consists of pure transformer blocks for latent diffusion: It can directly generate 1024px images from text prompts within a single sampling process. LCMs is a diffusion distillation method which predict PF-ODE's solution directly in latent space, achieving super fast inference with few steps. Following LCM LoRA, we illustrative of the generation speed we achieve on various computers. Let us stress again how liberating it is to explore image generation so easily with PixArt-LCM.",
+ "preview": "PixArt-alpha--PixArt-XL-2-1024-MS.jpg"
+ },
"Warp Wuerstchen": {
"path": "warp-ai/wuerstchen",
"desc": "Würstchen is a diffusion model whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images, is way more expensive than training at 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the paper). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, allowing also cheaper and faster inference.",
diff --git a/installer.py b/installer.py
index d74b65568..1389d1175 100644
--- a/installer.py
+++ b/installer.py
@@ -272,19 +272,21 @@ def branch(folder):
# update git repository
-def update(folder, current_branch = False):
+def update(folder, current_branch = False, rebase = True):
try:
git('config rebase.Autostash true')
except Exception:
pass
+ arg = '--rebase --force' if rebase else ''
if current_branch:
- git('pull --rebase --force', folder)
- return
+ res = git(f'pull {arg}', folder)
+ return res
b = branch(folder)
if branch is None:
- git('pull --rebase --force', folder)
+ res = git(f'pull {arg}', folder)
else:
- git(f'pull origin {b} --rebase --force', folder)
+ res = git(f'pull origin {b} {arg}', folder)
+ return res
# clone git repository
@@ -433,8 +435,9 @@ def check_torch():
if "linux" in sys.platform:
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.1.0a0 torchvision==0.16.0a0 intel-extension-for-pytorch==2.1.10+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/')
os.environ.setdefault('TENSORFLOW_PACKAGE', 'tensorflow==2.14.0 intel-extension-for-tensorflow[xpu]==2.14.0.1')
- install(os.environ.get('MKL_PACKAGE', 'mkl==2024.0.0'), 'mkl')
- install(os.environ.get('DPCPP_PACKAGE', 'mkl-dpcpp==2024.0.0'), 'mkl-dpcpp')
+ if os.environ.get('DISABLE_VENV_LIBS', None) is None:
+ install(os.environ.get('MKL_PACKAGE', 'mkl==2024.0.0'), 'mkl')
+ install(os.environ.get('DPCPP_PACKAGE', 'mkl-dpcpp==2024.0.0'), 'mkl-dpcpp')
else:
if sys.version_info[1] == 11:
pytorch_pip = 'https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.1.10%2Bxpu/torch-2.1.0a0+cxx11.abi-cp311-cp311-win_amd64.whl'
@@ -448,8 +451,9 @@ def check_torch():
pytorch_pip = 'torch==2.1.0a0'
torchvision_pip = 'torchvision==0.16.0a0'
ipex_pip = 'intel-extension-for-pytorch==2.1.10+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/'
- install(os.environ.get('MKL_PACKAGE', 'mkl==2024.0.0'), 'mkl')
- install(os.environ.get('DPCPP_PACKAGE', 'mkl-dpcpp==2024.0.0'), 'mkl-dpcpp')
+ if os.environ.get('DISABLE_VENV_LIBS', None) is None:
+ install(os.environ.get('MKL_PACKAGE', 'mkl==2024.0.0'), 'mkl')
+ install(os.environ.get('DPCPP_PACKAGE', 'mkl-dpcpp==2024.0.0'), 'mkl-dpcpp')
torch_command = os.environ.get('TORCH_COMMAND', f'{pytorch_pip} {torchvision_pip} {ipex_pip}')
install(os.environ.get('OPENVINO_PACKAGE', 'openvino==2023.2.0'), 'openvino', ignore=True)
install('nncf==2.7.0', 'nncf', ignore=True)
@@ -457,6 +461,12 @@ def check_torch():
elif allow_openvino and args.use_openvino:
log.info('Using OpenVINO')
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu')
+ install(os.environ.get('OPENVINO_PACKAGE', 'openvino==2023.2.0'), 'openvino')
+ install('onnxruntime-openvino', 'onnxruntime-openvino', ignore=True) # TODO openvino: numpy version conflicts with tensorflow and doesn't support Python 3.11
+ install('nncf==2.7.0', 'nncf')
+ os.environ.setdefault('PYTORCH_TRACING_MODE', 'TORCHFX')
+ os.environ.setdefault('NEOReadDebugKeys', '1')
+ os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')
else:
machine = platform.machine()
if sys.platform == 'darwin':
@@ -523,13 +533,8 @@ def check_torch():
log.debug(f'Cannot install xformers package: {e}')
if opts.get('cuda_compile_backend', '') == 'hidet':
install('hidet', 'hidet')
- if args.use_openvino or opts.get('cuda_compile_backend', '') == 'openvino_fx':
- install(os.environ.get('OPENVINO_PACKAGE', 'openvino==2023.2.0'), 'openvino')
+ if opts.get('nncf_compress_weights', False):
install('nncf==2.7.0', 'nncf')
- install('onnxruntime-openvino', 'onnxruntime-openvino', ignore=True) # TODO openvino: numpy version conflicts with tensorflow and doesn't support Python 3.11
- os.environ.setdefault('PYTORCH_TRACING_MODE', 'TORCHFX')
- os.environ.setdefault('NEOReadDebugKeys', '1')
- os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')
if args.profile:
print_profile(pr, 'Torch')
@@ -611,7 +616,7 @@ def list_extensions_folder(folder, quiet=False):
# run installer for each installed and enabled extension and optionally update them
-def install_extensions():
+def install_extensions(force=False):
if args.profile:
pr = cProfile.Profile()
pr.enable()
@@ -622,6 +627,7 @@ def install_extensions():
extensions_duplicates = []
extensions_enabled = []
extension_folders = [extensions_builtin_dir] if args.safe else [extensions_builtin_dir, extensions_dir]
+ res = []
for folder in extension_folders:
if not os.path.isdir(folder):
continue
@@ -632,10 +638,11 @@ def install_extensions():
extensions_duplicates.append(ext)
continue
extensions_enabled.append(ext)
- if args.upgrade:
+ if args.upgrade or force:
try:
- update(os.path.join(folder, ext))
+ res.append(update(os.path.join(folder, ext)))
except Exception:
+ res.append(f'Error updating extension: {os.path.join(folder, ext)}')
log.error(f'Error updating extension: {os.path.join(folder, ext)}')
if not args.skip_extensions:
run_extension_installer(os.path.join(folder, ext))
@@ -653,17 +660,18 @@ def install_extensions():
log.warning(f'Extensions duplicates: {extensions_duplicates}')
if args.profile:
print_profile(pr, 'Extensions')
+ return '\n'.join(res)
# initialize and optionally update submodules
-def install_submodules():
+def install_submodules(force=True):
if args.profile:
pr = cProfile.Profile()
pr.enable()
log.info('Verifying submodules')
txt = git('submodule')
# log.debug(f'Submodules list: {txt}')
- if 'no submodule mapping found' in txt:
+ if force and 'no submodule mapping found' in txt:
log.warning('Attempting repository recover')
git('add .')
git('stash')
@@ -676,17 +684,19 @@ def install_submodules():
git('submodule --quiet update --init --recursive')
git('submodule --quiet sync --recursive')
submodules = txt.splitlines()
+ res = []
for submodule in submodules:
try:
name = submodule.split()[1].strip()
if args.upgrade:
- update(name)
+ res.append(update(name))
else:
branch(name)
except Exception:
log.error(f'Error updating submodule: {submodule}')
if args.profile:
print_profile(pr, 'Submodule')
+ return '\n'.join(res)
def ensure_base_requirements():
@@ -750,7 +760,7 @@ def set_environment():
os.environ.setdefault('KINETO_LOG_LEVEL', '3')
os.environ.setdefault('DO_NOT_TRACK', '1')
os.environ.setdefault('HF_HUB_CACHE', opts.get('hfcache_dir', os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'hub')))
- log.debug(f'Cache folder: {os.environ.get("HF_HUB_CACHE")}')
+ log.debug(f'HF cache folder: {os.environ.get("HF_HUB_CACHE")}')
if sys.platform == 'darwin':
os.environ.setdefault('PYTORCH_ENABLE_MPS_FALLBACK', '1')
diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index 404b44922..9ded4a006 100644
--- a/javascript/contextMenus.js
+++ b/javascript/contextMenus.js
@@ -85,7 +85,7 @@ const appendContextMenuOption = initResponse[0];
const removeContextMenuOption = initResponse[1];
const addContextMenuEventListener = initResponse[2];
-function initContextMenu() {
+async function initContextMenu() {
const generateForever = (genbuttonid, interruptbuttonid) => {
if (window.generateOnRepeatInterval) {
log('generateForever: cancel');
diff --git a/javascript/control.js b/javascript/control.js
index 495e3a771..56cc5dce6 100644
--- a/javascript/control.js
+++ b/javascript/control.js
@@ -10,7 +10,7 @@ function controlInputMode(inputMode, ...args) {
return [inputMode, ...args];
}
-function setupControlUI() {
+async function setupControlUI() {
const tabs = ['input', 'output', 'preview'];
for (const tab of tabs) {
const btn = gradioApp().getElementById(`control-${tab}-button`);
diff --git a/javascript/emerald-paradise.css b/javascript/emerald-paradise.css
index db96216f1..1fe9a7424 100644
--- a/javascript/emerald-paradise.css
+++ b/javascript/emerald-paradise.css
@@ -1,297 +1,297 @@
-/* generic html tags */
-:root, .light, .dark {
- --font: 'system-ui', 'ui-sans-serif', 'system-ui', "Roboto", sans-serif;
- --font-mono: 'ui-monospace', 'Consolas', monospace;
- --font-size: 16px;
- --primary-100: #1e2223; /* bg color*/
- --primary-200: #242a2c; /* drop down menu/ prompt window fill*/
- --primary-300: #0a0c0e; /* black */
- --primary-400: #2a302c; /* small buttons*/
- --primary-500: #4b695d; /* main accent color green*/
- --primary-700: #273538; /* extension box fill*/
- --primary-800: #d15e84; /* pink(hover accent)*/
- --highlight-color: var(--primary-500);
- --inactive-color: var(--primary--800);
- --body-text-color: var(--neutral-100);
- --body-text-color-subdued: var(--neutral-300);
- --background-color: var(--primary-100);
- --background-fill-primary: var(--input-background-fill);
- --input-padding: 8px;
- --input-background-fill: var(--primary-200);
- --input-shadow: none;
- --button-secondary-text-color: white;
- --button-secondary-background-fill: var(--primary-400);
- --button-secondary-background-fill-hover: var(--primary-700);
- --block-title-text-color: var(--neutral-300);
- --radius-sm: 1px;
- --radius-lg: 6px;
- --spacing-md: 4px;
- --spacing-xxl: 8px;
- --line-sm: 1.2em;
- --line-md: 1.4em;
-}
-
-html { font-size: var(--font-size); }
-body, button, input, select, textarea { font-family: var(--font);}
-button { max-width: 400px; }
-img { background-color: var(--background-color); }
-input[type=range] { height: var(--line-sm); appearance: none; margin-top: 0; min-width: 160px; background-color: var(--background-color); width: 100%; background: transparent; }
-input[type=range]::-webkit-slider-runnable-track, input[type=range]::-moz-range-track { width: 100%; height: 6px; cursor: pointer; background: var(--primary-400); border-radius: var(--radius-lg); border: 0px solid #222222; }
-input[type=range]::-webkit-slider-thumb, input[type=range]::-moz-range-thumb { border: 0px solid #000000; height: var(--line-sm); width: 8px; border-radius: var(--radius-lg); background: white; cursor: pointer; appearance: none; margin-top: 0px; }
-input[type=range]::-moz-range-progress { background-color: var(--primary-500); height: 6px; border-radius: var(--radius-lg); }
-::-webkit-scrollbar-track { background: #333333; }
-::-webkit-scrollbar-thumb { background-color: var(--highlight-color); border-radius: var(--radius-lg); border-width: 0; box-shadow: 2px 2px 3px #111111; }
-div.form { border-width: 0; box-shadow: none; background: transparent; overflow: visible; margin-bottom: 6px; }
-div.compact { gap: 1em; }
-
-/* gradio style classes */
-fieldset .gr-block.gr-box, label.block span { padding: 0; margin-top: -4px; }
-.border-2 { border-width: 0; }
-.border-b-2 { border-bottom-width: 2px; border-color: var(--highlight-color) !important; padding-bottom: 2px; margin-bottom: 8px; }
-.bg-white { color: lightyellow; background-color: var(--inactive-color); }
-.gr-box { border-radius: var(--radius-sm) !important; background-color: #111111 !important; box-shadow: 2px 2px 3px #111111; border-width: 0; padding: 4px; margin: 12px 0px 12px 0px }
-.gr-button { font-weight: normal; box-shadow: 2px 2px 3px #111111; font-size: 0.8rem; min-width: 32px; min-height: 32px; padding: 3px; margin: 3px; }
-.gr-check-radio { background-color: var(--inactive-color); border-width: 0; border-radius: var(--radius-lg); box-shadow: 2px 2px 3px #111111; }
-.gr-check-radio:checked { background-color: var(--highlight-color); }
-.gr-compact { background-color: var(--background-color); }
-.gr-form { border-width: 0; }
-.gr-input { background-color: #333333 !important; padding: 4px; margin: 4px; }
-.gr-input-label { color: lightyellow; border-width: 0; background: transparent; padding: 2px !important; }
-.gr-panel { background-color: var(--background-color); }
-.eta-bar { display: none !important }
-svg.feather.feather-image, .feather .feather-image { display: none }
-.gap-2 { padding-top: 8px; }
-.gr-box > div > div > input.gr-text-input { right: 0; width: 4em; padding: 0; top: -12px; border: none; max-height: 20px; }
-.output-html { line-height: 1.2rem; overflow-x: hidden; }
-.output-html > div { margin-bottom: 8px; }
-.overflow-hidden .flex .flex-col .relative col .gap-4 { min-width: var(--left-column); max-width: var(--left-column); } /* this is a problematic one */
-.p-2 { padding: 0; }
-.px-4 { padding-lefT: 1rem; padding-right: 1rem; }
-.py-6 { padding-bottom: 0; }
-.tabs { background-color: var(--background-color); }
-.block.token-counter span { background-color: var(--input-background-fill) !important; box-shadow: 2px 2px 2px #111; border: none !important; font-size: 0.8rem; }
-.tab-nav { zoom: 110%; margin-top: 10px; margin-bottom: 10px; border-bottom: 2px solid var(--highlight-color) !important; padding-bottom: 2px; }
-div.tab-nav button.selected {background-color: var(--button-primary-background-fill);}
-#settings div.tab-nav button.selected {background-color: var(--background-color); color: var(--primary-800); font-weight: bold;}
-.label-wrap { background-color: #191919; /* extension tab color*/ padding: 16px 8px 8px 8px; border-radius: var(--radius-lg); padding-left: 8px !important; }
-.small-accordion .label-wrap { padding: 8px 0px 8px 0px; }
-.small-accordion .label-wrap .icon { margin-right: 1em; }
-.gradio-button.tool { border: none; box-shadow: none; border-radius: var(--radius-lg);}
-button.selected {background: var(--button-primary-background-fill);}
-.center.boundedheight.flex {background-color: var(--input-background-fill);}
-.compact {border-radius: var(--border-radius-lg);}
-#logMonitorData {background-color: var(--input-background-fill);}
-#tab_extensions table td, #tab_extensions table th, #tab_config table td, #tab_config table th { border: none; padding: 0.5em; background-color: var(--primary-200); }
-#tab_extensions table, #tab_config table { width: 96vw; }
-#tab_extensions table input[type=checkbox] {appearance: none; border-radius: 0px;}
-#tab_extensions button:hover { background-color: var(--button-secondary-background-fill-hover);}
-
-/* automatic style classes */
-.progressDiv { border-radius: var(--radius-sm) !important; position: fixed; top: 44px; right: 26px; max-width: 262px; height: 48px; z-index: 99; box-shadow: var(--button-shadow); }
-.progressDiv .progress { border-radius: var(--radius-lg) !important; background: var(--highlight-color); line-height: 3rem; height: 48px; }
-.gallery-item { box-shadow: none !important; }
-.performance { color: #888; }
-.extra-networks { border-left: 2px solid var(--highlight-color) !important; padding-left: 4px; }
-.image-buttons { gap: 10px !important; justify-content: center; }
-.image-buttons > button { max-width: 160px; }
-.tooltip { background: var(--primary-800); color: white; border: none; border-radius: var(--radius-lg) }
-#system_row > button, #settings_row > button, #config_row > button { max-width: 10em; }
-
-/* gradio elements overrides */
-#div.gradio-container { overflow-x: hidden; }
-#img2img_label_copy_to_img2img { font-weight: normal; }
-#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt { background-color: var(--background-color); box-shadow: 4px 4px 4px 0px #333333 !important; }
-#txt2img_prompt > label > textarea, #txt2img_neg_prompt > label > textarea, #img2img_prompt > label > textarea, #img2img_neg_prompt > label > textarea { font-size: 1.1rem; }
-#img2img_settings { min-width: calc(2 * var(--left-column)); max-width: calc(2 * var(--left-column)); background-color: #111111; padding-top: 16px; }
-#interrogate, #deepbooru { margin: 0 0px 10px 0px; max-width: 80px; max-height: 80px; font-weight: normal; font-size: 0.95em; }
-#quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-top: -2px; height: 2.4em; }
-#quicksettings button {padding: 0 0.5em 0.1em 0.5em;}
-#open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h { display: none; }
-#save-animation { border-radius: var(--radius-sm) !important; margin-bottom: 16px; background-color: #111111; }
-#script_list { padding: 4px; margin-top: 16px; margin-bottom: 8px; }
-#settings > div.flex-wrap { width: 15em; }
-#txt2img_cfg_scale { min-width: 200px; }
-#txt2img_checkboxes, #img2img_checkboxes { background-color: transparent; }
-#txt2img_checkboxes, #img2img_checkboxes { margin-bottom: 0.2em; }
-#txt2img_actions_column, #img2img_actions_column { flex-flow: wrap; justify-content: space-between; }
-#txt2img_enqueue_wrapper, #img2img_enqueue_wrapper { min-width: unset; width: 48%; }
-#txt2img_generate_box, #img2img_generate_box { min-width: unset; width: 48%; }
-
-#extras_upscale { margin-top: 10px }
-#txt2img_progress_row > div { min-width: var(--left-column); max-width: var(--left-column); }
-#txt2img_settings { min-width: var(--left-column); max-width: var(--left-column); background-color: #111111; padding-top: 16px; }
-#pnginfo_html2_info { margin-top: -18px; background-color: var(--input-background-fill); padding: var(--input-padding) }
-#txt2img_tools, #img2img_tools { margin-top: -4px; margin-bottom: -4px; }
-#txt2img_styles_row, #img2img_styles_row { margin-top: -6px; z-index: 200; }
-
-/* based on gradio built-in dark theme */
-:root, .light, .dark {
- --body-background-fill: var(--background-color);
- --color-accent-soft: var(--neutral-700);
- --background-fill-secondary: none;
- --border-color-accent: var(--background-color);
- --border-color-primary: var(--background-color);
- --link-text-color-active: var(--primary-500);
- --link-text-color: var(--secondary-500);
- --link-text-color-hover: var(--secondary-400);
- --link-text-color-visited: var(--secondary-600);
- --shadow-spread: 1px;
- --block-background-fill: None;
- --block-border-color: var(--border-color-primary);
- --block_border_width: None;
- --block-info-text-color: var(--body-text-color-subdued);
- --block-label-background-fill: var(--background-fill-secondary);
- --block-label-border-color: var(--border-color-primary);
- --block_label_border_width: None;
- --block-label-text-color: var(--neutral-200);
- --block_shadow: None;
- --block_title_background_fill: None;
- --block_title_border_color: None;
- --block_title_border_width: None;
- --panel-background-fill: var(--background-fill-secondary);
- --panel-border-color: var(--border-color-primary);
- --panel_border_width: None;
- --checkbox-background-color: var(--primary-200);
- --checkbox-background-color-focus: var(--primary-700);
- --checkbox-background-color-hover: var(--primary-700);
- --checkbox-background-color-selected: var(--primary-500);
- --checkbox-border-color: transparent;
- --checkbox-border-color-focus: var(--primary-800);
- --checkbox-border-color-hover: var(--primary-800);
- --checkbox-border-color-selected: var(--primary-800);
- --checkbox-border-width: var(--input-border-width);
- --checkbox-label-background-fill: None;
- --checkbox-label-background-fill-hover: None;
- --checkbox-label-background-fill-selected: var(--checkbox-label-background-fill);
- --checkbox-label-border-color: var(--border-color-primary);
- --checkbox-label-border-color-hover: var(--checkbox-label-border-color);
- --checkbox-label-border-width: var(--input-border-width);
- --checkbox-label-text-color: var(--body-text-color);
- --checkbox-label-text-color-selected: var(--checkbox-label-text-color);
- --error-background-fill: var(--background-fill-primary);
- --error-border-color: var(--border-color-primary);
- --error-text-color: #f768b7; /*was ef4444*/
- --input-background-fill-focus: var(--secondary-600);
- --input-background-fill-hover: var(--input-background-fill);
- --input-border-color: var(--background-color);
- --input-border-color-focus: var(--primary-800);
- --input-placeholder-color: var(--neutral-500);
- --input-shadow-focus: None;
- --loader_color: None;
- --slider_color: None;
- --stat-background-fill: linear-gradient(to right, var(--primary-400), var(--primary-800));
- --table-border-color: var(--neutral-700);
- --table-even-background-fill: var(--primary-300);
- --table-odd-background-fill: var(--primary-200);
- --table-row-focus: var(--color-accent-soft);
- --button-border-width: var(--input-border-width);
- --button-cancel-background-fill: linear-gradient(to bottom right, #dc2626, #b91c1c);
- --button-cancel-background-fill-hover: linear-gradient(to bottom right, #dc2626, #dc2626);
- --button-cancel-border-color: #dc2626;
- --button-cancel-border-color-hover: var(--button-cancel-border-color);
- --button-cancel-text-color: white;
- --button-cancel-text-color-hover: var(--button-cancel-text-color);
- --button-primary-background-fill: var(--primary-500);
- --button-primary-background-fill-hover: var(--primary-800);
- --button-primary-border-color: var(--primary-500);
- --button-primary-border-color-hover: var(--button-primary-border-color);
- --button-primary-text-color: white;
- --button-primary-text-color-hover: var(--button-primary-text-color);
- --button-secondary-border-color: var(--neutral-600);
- --button-secondary-border-color-hover: var(--button-secondary-border-color);
- --button-secondary-text-color-hover: var(--button-secondary-text-color);
- --secondary-50: #eff6ff;
- --secondary-100: #dbeafe;
- --secondary-200: #bfdbfe;
- --secondary-300: #93c5fd;
- --secondary-400: #60a5fa;
- --secondary-500: #3b82f6;
- --secondary-600: #2563eb;
- --secondary-700: #1d4ed8;
- --secondary-800: #1e40af;
- --secondary-900: #1e3a8a;
- --secondary-950: #1d3660;
- --neutral-50: #f0f0f0; /* */
- --neutral-100: #e8e8e3;/* majority of text (neutral gray yellow) */
- --neutral-200: #d0d0d0;
- --neutral-300: #b3b5ac; /* top tab /sub text (light accent) */
- --neutral-400: #ffba85;/* tab title (bright orange) */
- --neutral-500: #48665b; /* prompt text (desat accent)*/
- --neutral-600: #373f39; /* tab outline color (accent color)*/
- --neutral-700: #2b373b; /* small settings tab accent */
- --neutral-800: #f379c2; /* bright pink accent */
- --neutral-900: #111827;
- --neutral-950: #0b0f19;
- --radius-xxs: 0;
- --radius-xs: 0;
- --radius-md: 0;
- --radius-xl: 0;
- --radius-xxl: 0;
- --body-text-size: var(--text-md);
- --body-text-weight: 400;
- --embed-radius: var(--radius-lg);
- --color-accent: var(--primary-500);
- --shadow-drop: 0;
- --shadow-drop-lg: 0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1);
- --shadow-inset: rgba(0,0,0,0.05) 0px 2px 4px 0px inset;
- --block-border-width: 1px;
- --block-info-text-size: var(--text-sm);
- --block-info-text-weight: 400;
- --block-label-border-width: 1px;
- --block-label-margin: 0;
- --block-label-padding: var(--spacing-sm) var(--spacing-lg);
- --block-label-radius: calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px) 0;
- --block-label-right-radius: 0 calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px);
- --block-label-text-size: var(--text-sm);
- --block-label-text-weight: 400;
- --block-padding: var(--spacing-xl) calc(var(--spacing-xl) + 2px);
- --block-radius: var(--radius-lg);
- --block-shadow: var(--shadow-drop);
- --block-title-background-fill: none;
- --block-title-border-color: none;
- --block-title-border-width: 0;
- --block-title-padding: 0;
- --block-title-radius: none;
- --block-title-text-size: var(--text-md);
- --block-title-text-weight: 400;
- --container-radius: var(--radius-lg);
- --form-gap-width: 1px;
- --layout-gap: var(--spacing-xxl);
- --panel-border-width: 0;
- --section-header-text-size: var(--text-md);
- --section-header-text-weight: 400;
- --checkbox-border-radius: var(--radius-sm);
- --checkbox-label-gap: 2px;
- --checkbox-label-padding: var(--spacing-md);
- --checkbox-label-shadow: var(--shadow-drop);
- --checkbox-label-text-size: var(--text-md);
- --checkbox-label-text-weight: 400;
- --checkbox-check: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e");
- --radio-circle: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e");
- --checkbox-shadow: var(--input-shadow);
- --error-border-width: 1px;
- --input-border-width: 1px;
- --input-radius: var(--radius-lg);
- --input-text-size: var(--text-md);
- --input-text-weight: 400;
- --loader-color: var(--color-accent);
- --prose-text-size: var(--text-md);
- --prose-text-weight: 400;
- --prose-header-text-weight: 600;
- --slider-color: ;
- --table-radius: var(--radius-lg);
- --button-large-padding: 2px 6px;
- --button-large-radius: var(--radius-lg);
- --button-large-text-size: var(--text-lg);
- --button-large-text-weight: 400;
- --button-shadow: none;
- --button-shadow-active: none;
- --button-shadow-hover: none;
- --button-small-padding: var(--spacing-sm) calc(2 * var(--spacing-sm));
- --button-small-radius: var(--radius-lg);
- --button-small-text-size: var(--text-md);
- --button-small-text-weight: 400;
- --button-transition: none;
- --size-9: 64px;
- --size-14: 64px;
-}
+/* generic html tags */
+:root, .light, .dark {
+ --font: 'system-ui', 'ui-sans-serif', 'system-ui', "Roboto", sans-serif;
+ --font-mono: 'ui-monospace', 'Consolas', monospace;
+ --font-size: 16px;
+ --primary-100: #1e2223; /* bg color*/
+ --primary-200: #242a2c; /* drop down menu/ prompt window fill*/
+ --primary-300: #0a0c0e; /* black */
+ --primary-400: #2a302c; /* small buttons*/
+ --primary-500: #4b695d; /* main accent color green*/
+ --primary-700: #273538; /* extension box fill*/
+ --primary-800: #d15e84; /* pink(hover accent)*/
+ --highlight-color: var(--primary-500);
+ --inactive-color: var(--primary--800);
+ --body-text-color: var(--neutral-100);
+ --body-text-color-subdued: var(--neutral-300);
+ --background-color: var(--primary-100);
+ --background-fill-primary: var(--input-background-fill);
+ --input-padding: 8px;
+ --input-background-fill: var(--primary-200);
+ --input-shadow: none;
+ --button-secondary-text-color: white;
+ --button-secondary-background-fill: var(--primary-400);
+ --button-secondary-background-fill-hover: var(--primary-700);
+ --block-title-text-color: var(--neutral-300);
+ --radius-sm: 1px;
+ --radius-lg: 6px;
+ --spacing-md: 4px;
+ --spacing-xxl: 8px;
+ --line-sm: 1.2em;
+ --line-md: 1.4em;
+}
+
+html { font-size: var(--font-size); }
+body, button, input, select, textarea { font-family: var(--font);}
+button { max-width: 400px; }
+img { background-color: var(--background-color); }
+input[type=range] { height: var(--line-sm); appearance: none; margin-top: 0; min-width: 160px; background-color: var(--background-color); width: 100%; background: transparent; }
+input[type=range]::-webkit-slider-runnable-track, input[type=range]::-moz-range-track { width: 100%; height: 6px; cursor: pointer; background: var(--primary-400); border-radius: var(--radius-lg); border: 0px solid #222222; }
+input[type=range]::-webkit-slider-thumb, input[type=range]::-moz-range-thumb { border: 0px solid #000000; height: var(--line-sm); width: 8px; border-radius: var(--radius-lg); background: white; cursor: pointer; appearance: none; margin-top: 0px; }
+input[type=range]::-moz-range-progress { background-color: var(--primary-500); height: 6px; border-radius: var(--radius-lg); }
+::-webkit-scrollbar-track { background: #333333; }
+::-webkit-scrollbar-thumb { background-color: var(--highlight-color); border-radius: var(--radius-lg); border-width: 0; box-shadow: 2px 2px 3px #111111; }
+div.form { border-width: 0; box-shadow: none; background: transparent; overflow: visible; margin-bottom: 6px; }
+div.compact { gap: 1em; }
+
+/* gradio style classes */
+fieldset .gr-block.gr-box, label.block span { padding: 0; margin-top: -4px; }
+.border-2 { border-width: 0; }
+.border-b-2 { border-bottom-width: 2px; border-color: var(--highlight-color) !important; padding-bottom: 2px; margin-bottom: 8px; }
+.bg-white { color: lightyellow; background-color: var(--inactive-color); }
+.gr-box { border-radius: var(--radius-sm) !important; background-color: #111111 !important; box-shadow: 2px 2px 3px #111111; border-width: 0; padding: 4px; margin: 12px 0px 12px 0px }
+.gr-button { font-weight: normal; box-shadow: 2px 2px 3px #111111; font-size: 0.8rem; min-width: 32px; min-height: 32px; padding: 3px; margin: 3px; }
+.gr-check-radio { background-color: var(--inactive-color); border-width: 0; border-radius: var(--radius-lg); box-shadow: 2px 2px 3px #111111; }
+.gr-check-radio:checked { background-color: var(--highlight-color); }
+.gr-compact { background-color: var(--background-color); }
+.gr-form { border-width: 0; }
+.gr-input { background-color: #333333 !important; padding: 4px; margin: 4px; }
+.gr-input-label { color: lightyellow; border-width: 0; background: transparent; padding: 2px !important; }
+.gr-panel { background-color: var(--background-color); }
+.eta-bar { display: none !important }
+svg.feather.feather-image, .feather .feather-image { display: none }
+.gap-2 { padding-top: 8px; }
+.gr-box > div > div > input.gr-text-input { right: 0; width: 4em; padding: 0; top: -12px; border: none; max-height: 20px; }
+.output-html { line-height: 1.2rem; overflow-x: hidden; }
+.output-html > div { margin-bottom: 8px; }
+.overflow-hidden .flex .flex-col .relative col .gap-4 { min-width: var(--left-column); max-width: var(--left-column); } /* this is a problematic one */
+.p-2 { padding: 0; }
+.px-4 { padding-lefT: 1rem; padding-right: 1rem; }
+.py-6 { padding-bottom: 0; }
+.tabs { background-color: var(--background-color); }
+.block.token-counter span { background-color: var(--input-background-fill) !important; box-shadow: 2px 2px 2px #111; border: none !important; font-size: 0.8rem; }
+.tab-nav { zoom: 110%; margin-top: 10px; margin-bottom: 10px; border-bottom: 2px solid var(--highlight-color) !important; padding-bottom: 2px; }
+div.tab-nav button.selected {background-color: var(--button-primary-background-fill);}
+#settings div.tab-nav button.selected {background-color: var(--background-color); color: var(--primary-800); font-weight: bold;}
+.label-wrap { background-color: #191919; /* extension tab color*/ padding: 16px 8px 8px 8px; border-radius: var(--radius-lg); padding-left: 8px !important; }
+.small-accordion .label-wrap { padding: 8px 0px 8px 0px; }
+.small-accordion .label-wrap .icon { margin-right: 1em; }
+.gradio-button.tool { border: none; box-shadow: none; border-radius: var(--radius-lg);}
+button.selected {background: var(--button-primary-background-fill);}
+.center.boundedheight.flex {background-color: var(--input-background-fill);}
+.compact {border-radius: var(--border-radius-lg);}
+#logMonitorData {background-color: var(--input-background-fill);}
+#tab_extensions table td, #tab_extensions table th, #tab_config table td, #tab_config table th { border: none; padding: 0.5em; background-color: var(--primary-200); }
+#tab_extensions table, #tab_config table { width: 96vw; }
+#tab_extensions table input[type=checkbox] {appearance: none; border-radius: 0px;}
+#tab_extensions button:hover { background-color: var(--button-secondary-background-fill-hover);}
+
+/* automatic style classes */
+.progressDiv { border-radius: var(--radius-sm) !important; position: fixed; top: 44px; right: 26px; max-width: 262px; height: 48px; z-index: 99; box-shadow: var(--button-shadow); }
+.progressDiv .progress { border-radius: var(--radius-lg) !important; background: var(--highlight-color); line-height: 3rem; height: 48px; }
+.gallery-item { box-shadow: none !important; }
+.performance { color: #888; }
+.extra-networks { border-left: 2px solid var(--highlight-color) !important; padding-left: 4px; }
+.image-buttons { gap: 10px !important; justify-content: center; }
+.image-buttons > button { max-width: 160px; }
+.tooltip { background: var(--primary-800); color: white; border: none; border-radius: var(--radius-lg) }
+#system_row > button, #settings_row > button, #config_row > button { max-width: 10em; }
+
+/* gradio elements overrides */
+#div.gradio-container { overflow-x: hidden; }
+#img2img_label_copy_to_img2img { font-weight: normal; }
+#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt { background-color: var(--background-color); box-shadow: 4px 4px 4px 0px #333333 !important; }
+#txt2img_prompt > label > textarea, #txt2img_neg_prompt > label > textarea, #img2img_prompt > label > textarea, #img2img_neg_prompt > label > textarea { font-size: 1.1rem; }
+#img2img_settings { min-width: calc(2 * var(--left-column)); max-width: calc(2 * var(--left-column)); background-color: #111111; padding-top: 16px; }
+#interrogate, #deepbooru { margin: 0 0px 10px 0px; max-width: 80px; max-height: 80px; font-weight: normal; font-size: 0.95em; }
+#quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-top: -2px; height: 2.4em; }
+#quicksettings button {padding: 0 0.5em 0.1em 0.5em;}
+#open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h { display: none; }
+#save-animation { border-radius: var(--radius-sm) !important; margin-bottom: 16px; background-color: #111111; }
+#script_list { padding: 4px; margin-top: 16px; margin-bottom: 8px; }
+#settings > div.flex-wrap { width: 15em; }
+#txt2img_cfg_scale { min-width: 200px; }
+#txt2img_checkboxes, #img2img_checkboxes { background-color: transparent; }
+#txt2img_checkboxes, #img2img_checkboxes { margin-bottom: 0.2em; }
+#txt2img_actions_column, #img2img_actions_column { flex-flow: wrap; justify-content: space-between; }
+#txt2img_enqueue_wrapper, #img2img_enqueue_wrapper { min-width: unset; width: 48%; }
+#txt2img_generate_box, #img2img_generate_box { min-width: unset; width: 48%; }
+
+#extras_upscale { margin-top: 10px }
+#txt2img_progress_row > div { min-width: var(--left-column); max-width: var(--left-column); }
+#txt2img_settings { min-width: var(--left-column); max-width: var(--left-column); background-color: #111111; padding-top: 16px; }
+#pnginfo_html2_info { margin-top: -18px; background-color: var(--input-background-fill); padding: var(--input-padding) }
+#txt2img_tools, #img2img_tools { margin-top: -4px; margin-bottom: -4px; }
+#txt2img_styles_row, #img2img_styles_row { margin-top: -6px; z-index: 200; }
+
+/* based on gradio built-in dark theme */
+:root, .light, .dark {
+ --body-background-fill: var(--background-color);
+ --color-accent-soft: var(--neutral-700);
+ --background-fill-secondary: none;
+ --border-color-accent: var(--background-color);
+ --border-color-primary: var(--background-color);
+ --link-text-color-active: var(--primary-500);
+ --link-text-color: var(--secondary-500);
+ --link-text-color-hover: var(--secondary-400);
+ --link-text-color-visited: var(--secondary-600);
+ --shadow-spread: 1px;
+ --block-background-fill: None;
+ --block-border-color: var(--border-color-primary);
+ --block_border_width: None;
+ --block-info-text-color: var(--body-text-color-subdued);
+ --block-label-background-fill: var(--background-fill-secondary);
+ --block-label-border-color: var(--border-color-primary);
+ --block_label_border_width: None;
+ --block-label-text-color: var(--neutral-200);
+ --block_shadow: None;
+ --block_title_background_fill: None;
+ --block_title_border_color: None;
+ --block_title_border_width: None;
+ --panel-background-fill: var(--background-fill-secondary);
+ --panel-border-color: var(--border-color-primary);
+ --panel_border_width: None;
+ --checkbox-background-color: var(--primary-200);
+ --checkbox-background-color-focus: var(--primary-700);
+ --checkbox-background-color-hover: var(--primary-700);
+ --checkbox-background-color-selected: var(--primary-500);
+ --checkbox-border-color: transparent;
+ --checkbox-border-color-focus: var(--primary-800);
+ --checkbox-border-color-hover: var(--primary-800);
+ --checkbox-border-color-selected: var(--primary-800);
+ --checkbox-border-width: var(--input-border-width);
+ --checkbox-label-background-fill: None;
+ --checkbox-label-background-fill-hover: None;
+ --checkbox-label-background-fill-selected: var(--checkbox-label-background-fill);
+ --checkbox-label-border-color: var(--border-color-primary);
+ --checkbox-label-border-color-hover: var(--checkbox-label-border-color);
+ --checkbox-label-border-width: var(--input-border-width);
+ --checkbox-label-text-color: var(--body-text-color);
+ --checkbox-label-text-color-selected: var(--checkbox-label-text-color);
+ --error-background-fill: var(--background-fill-primary);
+ --error-border-color: var(--border-color-primary);
+ --error-text-color: #f768b7; /*was ef4444*/
+ --input-background-fill-focus: var(--secondary-600);
+ --input-background-fill-hover: var(--input-background-fill);
+ --input-border-color: var(--background-color);
+ --input-border-color-focus: var(--primary-800);
+ --input-placeholder-color: var(--neutral-500);
+ --input-shadow-focus: None;
+ --loader_color: None;
+ --slider_color: None;
+ --stat-background-fill: linear-gradient(to right, var(--primary-400), var(--primary-800));
+ --table-border-color: var(--neutral-700);
+ --table-even-background-fill: var(--primary-300);
+ --table-odd-background-fill: var(--primary-200);
+ --table-row-focus: var(--color-accent-soft);
+ --button-border-width: var(--input-border-width);
+ --button-cancel-background-fill: linear-gradient(to bottom right, #dc2626, #b91c1c);
+ --button-cancel-background-fill-hover: linear-gradient(to bottom right, #dc2626, #dc2626);
+ --button-cancel-border-color: #dc2626;
+ --button-cancel-border-color-hover: var(--button-cancel-border-color);
+ --button-cancel-text-color: white;
+ --button-cancel-text-color-hover: var(--button-cancel-text-color);
+ --button-primary-background-fill: var(--primary-500);
+ --button-primary-background-fill-hover: var(--primary-800);
+ --button-primary-border-color: var(--primary-500);
+ --button-primary-border-color-hover: var(--button-primary-border-color);
+ --button-primary-text-color: white;
+ --button-primary-text-color-hover: var(--button-primary-text-color);
+ --button-secondary-border-color: var(--neutral-600);
+ --button-secondary-border-color-hover: var(--button-secondary-border-color);
+ --button-secondary-text-color-hover: var(--button-secondary-text-color);
+ --secondary-50: #eff6ff;
+ --secondary-100: #dbeafe;
+ --secondary-200: #bfdbfe;
+ --secondary-300: #93c5fd;
+ --secondary-400: #60a5fa;
+ --secondary-500: #3b82f6;
+ --secondary-600: #2563eb;
+ --secondary-700: #1d4ed8;
+ --secondary-800: #1e40af;
+ --secondary-900: #1e3a8a;
+ --secondary-950: #1d3660;
+ --neutral-50: #f0f0f0; /* */
+ --neutral-100: #e8e8e3;/* majority of text (neutral gray yellow) */
+ --neutral-200: #d0d0d0;
+ --neutral-300: #b3b5ac; /* top tab /sub text (light accent) */
+ --neutral-400: #ffba85;/* tab title (bright orange) */
+ --neutral-500: #48665b; /* prompt text (desat accent)*/
+ --neutral-600: #373f39; /* tab outline color (accent color)*/
+ --neutral-700: #2b373b; /* small settings tab accent */
+ --neutral-800: #f379c2; /* bright pink accent */
+ --neutral-900: #111827;
+ --neutral-950: #0b0f19;
+ --radius-xxs: 0;
+ --radius-xs: 0;
+ --radius-md: 0;
+ --radius-xl: 0;
+ --radius-xxl: 0;
+ --body-text-size: var(--text-md);
+ --body-text-weight: 400;
+ --embed-radius: var(--radius-lg);
+ --color-accent: var(--primary-500);
+ --shadow-drop: 0;
+ --shadow-drop-lg: 0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1);
+ --shadow-inset: rgba(0,0,0,0.05) 0px 2px 4px 0px inset;
+ --block-border-width: 1px;
+ --block-info-text-size: var(--text-sm);
+ --block-info-text-weight: 400;
+ --block-label-border-width: 1px;
+ --block-label-margin: 0;
+ --block-label-padding: var(--spacing-sm) var(--spacing-lg);
+ --block-label-radius: calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px) 0;
+ --block-label-right-radius: 0 calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px);
+ --block-label-text-size: var(--text-sm);
+ --block-label-text-weight: 400;
+ --block-padding: var(--spacing-xl) calc(var(--spacing-xl) + 2px);
+ --block-radius: var(--radius-lg);
+ --block-shadow: var(--shadow-drop);
+ --block-title-background-fill: none;
+ --block-title-border-color: none;
+ --block-title-border-width: 0;
+ --block-title-padding: 0;
+ --block-title-radius: none;
+ --block-title-text-size: var(--text-md);
+ --block-title-text-weight: 400;
+ --container-radius: var(--radius-lg);
+ --form-gap-width: 1px;
+ --layout-gap: var(--spacing-xxl);
+ --panel-border-width: 0;
+ --section-header-text-size: var(--text-md);
+ --section-header-text-weight: 400;
+ --checkbox-border-radius: var(--radius-sm);
+ --checkbox-label-gap: 2px;
+ --checkbox-label-padding: var(--spacing-md);
+ --checkbox-label-shadow: var(--shadow-drop);
+ --checkbox-label-text-size: var(--text-md);
+ --checkbox-label-text-weight: 400;
+ --checkbox-check: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e");
+ --radio-circle: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e");
+ --checkbox-shadow: var(--input-shadow);
+ --error-border-width: 1px;
+ --input-border-width: 1px;
+ --input-radius: var(--radius-lg);
+ --input-text-size: var(--text-md);
+ --input-text-weight: 400;
+ --loader-color: var(--color-accent);
+ --prose-text-size: var(--text-md);
+ --prose-text-weight: 400;
+ --prose-header-text-weight: 600;
+ --slider-color: ;
+ --table-radius: var(--radius-lg);
+ --button-large-padding: 2px 6px;
+ --button-large-radius: var(--radius-lg);
+ --button-large-text-size: var(--text-lg);
+ --button-large-text-weight: 400;
+ --button-shadow: none;
+ --button-shadow-active: none;
+ --button-shadow-hover: none;
+ --button-small-padding: var(--spacing-sm) calc(2 * var(--spacing-sm));
+ --button-small-radius: var(--radius-lg);
+ --button-small-text-size: var(--text-md);
+ --button-small-text-weight: 400;
+ --button-transition: none;
+ --size-9: 64px;
+ --size-14: 64px;
+}
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js
index 18e401fde..f45662668 100644
--- a/javascript/extraNetworks.js
+++ b/javascript/extraNetworks.js
@@ -61,15 +61,23 @@ function getCardDetails(...args) {
}
function readCardTags(el, tags) {
+ const replaceOutsideBrackets = (input, target, replacement) => input.split(/(<[^>]*>|\{[^}]*\})/g).map((part, i) => {
+ if (i % 2 === 0) return part.split(target).join(replacement); // Only replace in the parts that are not inside brackets (which are at even indices)
+ return part;
+ }).join('');
+
const clickTag = (e, tag) => {
e.preventDefault();
e.stopPropagation();
const textarea = activePromptTextarea[getENActiveTab()];
- if (textarea.value.indexOf(` ${tag}`) !== -1) textarea.value = textarea.value.replace(` ${tag}`, '');
- else if (textarea.value.indexOf(`${tag} `) !== -1) textarea.value = textarea.value.replace(` ${tag} `, '');
- else textarea.value += ` ${tag}`;
+ let new_prompt = textarea.value;
+ new_prompt = replaceOutsideBrackets(new_prompt, ` ${tag}`, ''); // try to remove tag
+ new_prompt = replaceOutsideBrackets(new_prompt, `${tag} `, '');
+ if (new_prompt === textarea.value) new_prompt += ` ${tag}`; // if not removed, then append it
+ textarea.value = new_prompt;
updateInput(textarea);
};
+
if (tags.length === 0) return;
const cardTags = tags.split('|');
if (!cardTags || cardTags.length === 0) return;
@@ -119,8 +127,12 @@ async function filterExtraNetworksForTab(searchTerm) {
if (searchTerm === '') {
elem.style.display = '';
} else {
- let text = elem.dataset.search.toLowerCase();
- text = text.toLowerCase().replace('models--', 'Diffusers').replace('\\', '/');
+ let text = '';
+ if (elem.dataset.filename) text += `${elem.dataset.filename} `;
+ if (elem.dataset.name) text += `${elem.dataset.name} `;
+ if (elem.dataset.title) text += `${elem.dataset.title} `;
+ if (elem.dataset.tags) text += `${elem.dataset.title} `;
+ text = text.toLowerCase().replace('models--', 'diffusers').replaceAll('\\', '/');
if (text.indexOf(searchTerm) === -1) {
elem.style.display = 'none';
} else {
@@ -131,7 +143,7 @@ async function filterExtraNetworksForTab(searchTerm) {
});
}
const t1 = performance.now();
- if (found > 0) log(`filterExtraNetworks: text=${searchTerm} items=${items} match=${found} time=${Math.round(1000 * (t1 - t0)) / 1000000}`);
+ if (searchTerm !== '') log(`filterExtraNetworks: text=${searchTerm} items=${items} match=${found} time=${Math.round(1000 * (t1 - t0)) / 1000000}`);
else log(`filterExtraNetworks: text=all items=${items} time=${Math.round(1000 * (t1 - t0)) / 1000000}`);
}
@@ -180,8 +192,8 @@ function sortExtraNetworks() {
if (num === 0) return 'sort: no cards';
cards.sort((a, b) => { // eslint-disable-line no-loop-func
switch (sortVal) {
- case 0: return a.dataset.search ? a.dataset.search.localeCompare(b.dataset.search) : 0;
- case 1: return b.dataset.search ? b.dataset.search.localeCompare(a.dataset.search) : 0;
+ case 0: return a.dataset.name ? a.dataset.name.localeCompare(b.dataset.name) : 0;
+ case 1: return b.dataset.name ? b.dataset.name.localeCompare(a.dataset.name) : 0;
case 2: return a.dataset.mtime && !isNaN(a.dataset.mtime) ? parseFloat(b.dataset.mtime) - parseFloat(a.dataset.mtime) : 0;
case 3: return b.dataset.mtime && !isNaN(b.dataset.mtime) ? parseFloat(a.dataset.mtime) - parseFloat(b.dataset.mtime) : 0;
case 4: return a.dataset.size && !isNaN(a.dataset.size) ? parseFloat(b.dataset.size) - parseFloat(a.dataset.size) : 0;
@@ -398,7 +410,7 @@ function setupExtraNetworksForTab(tabname) {
intersectionObserver.observe(en); // monitor visibility of
}
-function setupExtraNetworks() {
+async function setupExtraNetworks() {
setupExtraNetworksForTab('txt2img');
setupExtraNetworksForTab('img2img');
setupExtraNetworksForTab('control');
diff --git a/javascript/generationParams.js b/javascript/generationParams.js
index 93cd07bd0..b9d38a514 100644
--- a/javascript/generationParams.js
+++ b/javascript/generationParams.js
@@ -18,7 +18,7 @@ let img2img_gallery;
let modal;
let generationParamsInitialized = false;
-function initiGenerationParams() {
+async function initiGenerationParams() {
if (generationParamsInitialized) return;
if (!modal) modal = gradioApp().getElementById('lightboxModal');
if (!modal) return;
diff --git a/javascript/orchid-dreams.css b/javascript/orchid-dreams.css
index ef40d8e3a..05ff97a3b 100644
--- a/javascript/orchid-dreams.css
+++ b/javascript/orchid-dreams.css
@@ -1,297 +1,297 @@
-/* generic html tags */
-:root, .light, .dark {
- --font: 'system-ui', 'ui-sans-serif', 'system-ui', "Roboto", sans-serif;
- --font-mono: 'ui-monospace', 'Consolas', monospace;
- --font-size: 16px;
- --primary-100: #2a2a34; /* bg color*/
- --primary-200: #1f2028; /* drop down menu/ prompt*/
- --primary-300: #0a0c0e; /* black */
- --primary-400: #40435c; /* small buttons*/
- --primary-500: #4c48b5; /* main accent color purple*/
- --primary-700: #1f2028; /* darker hover accent*/
- --primary-800: #e95ee3; /* pink accent*/
- --highlight-color: var(--primary-500);
- --inactive-color: var(--primary--800);
- --body-text-color: var(--neutral-100);
- --body-text-color-subdued: var(--neutral-300);
- --background-color: var(--primary-100);
- --background-fill-primary: var(--input-background-fill);
- --input-padding: 8px;
- --input-background-fill: var(--primary-200);
- --input-shadow: none;
- --button-secondary-text-color: white;
- --button-secondary-background-fill: var(--primary-400);
- --button-secondary-background-fill-hover: var(--primary-700);
- --block-title-text-color: var(--neutral-300);
- --radius-sm: 1px;
- --radius-lg: 6px;
- --spacing-md: 4px;
- --spacing-xxl: 8px;
- --line-sm: 1.2em;
- --line-md: 1.4em;
-}
-
-html { font-size: var(--font-size); }
-body, button, input, select, textarea { font-family: var(--font);}
-button { max-width: 400px; }
-img { background-color: var(--background-color); }
-input[type=range] { height: var(--line-sm); appearance: none; margin-top: 0; min-width: 160px; background-color: var(--background-color); width: 100%; background: transparent; }
-input[type=range]::-webkit-slider-runnable-track, input[type=range]::-moz-range-track { width: 100%; height: 6px; cursor: pointer; background: var(--primary-400); border-radius: var(--radius-lg); border: 0px solid #222222; }
-input[type=range]::-webkit-slider-thumb, input[type=range]::-moz-range-thumb { border: 0px solid #000000; height: var(--line-sm); width: 8px; border-radius: var(--radius-lg); background: white; cursor: pointer; appearance: none; margin-top: 0px; }
-input[type=range]::-moz-range-progress { background-color: var(--primary-500); height: 6px; border-radius: var(--radius-lg); }
-::-webkit-scrollbar-track { background: #333333; }
-::-webkit-scrollbar-thumb { background-color: var(--highlight-color); border-radius: var(--radius-lg); border-width: 0; box-shadow: 2px 2px 3px #111111; }
-div.form { border-width: 0; box-shadow: none; background: transparent; overflow: visible; margin-bottom: 6px; }
-div.compact { gap: 1em; }
-
-/* gradio style classes */
-fieldset .gr-block.gr-box, label.block span { padding: 0; margin-top: -4px; }
-.border-2 { border-width: 0; }
-.border-b-2 { border-bottom-width: 2px; border-color: var(--highlight-color) !important; padding-bottom: 2px; margin-bottom: 8px; }
-.bg-white { color: lightyellow; background-color: var(--inactive-color); }
-.gr-box { border-radius: var(--radius-sm) !important; background-color: #111111 !important; box-shadow: 2px 2px 3px #111111; border-width: 0; padding: 4px; margin: 12px 0px 12px 0px }
-.gr-button { font-weight: normal; box-shadow: 2px 2px 3px #111111; font-size: 0.8rem; min-width: 32px; min-height: 32px; padding: 3px; margin: 3px; }
-.gr-check-radio { background-color: var(--inactive-color); border-width: 0; border-radius: var(--radius-lg); box-shadow: 2px 2px 3px #111111; }
-.gr-check-radio:checked { background-color: var(--highlight-color); }
-.gr-compact { background-color: var(--background-color); }
-.gr-form { border-width: 0; }
-.gr-input { background-color: #333333 !important; padding: 4px; margin: 4px; }
-.gr-input-label { color: lightyellow; border-width: 0; background: transparent; padding: 2px !important; }
-.gr-panel { background-color: var(--background-color); }
-.eta-bar { display: none !important }
-svg.feather.feather-image, .feather .feather-image { display: none }
-.gap-2 { padding-top: 8px; }
-.gr-box > div > div > input.gr-text-input { right: 0; width: 4em; padding: 0; top: -12px; border: none; max-height: 20px; }
-.output-html { line-height: 1.2rem; overflow-x: hidden; }
-.output-html > div { margin-bottom: 8px; }
-.overflow-hidden .flex .flex-col .relative col .gap-4 { min-width: var(--left-column); max-width: var(--left-column); } /* this is a problematic one */
-.p-2 { padding: 0; }
-.px-4 { padding-lefT: 1rem; padding-right: 1rem; }
-.py-6 { padding-bottom: 0; }
-.tabs { background-color: var(--background-color); }
-.block.token-counter span { background-color: var(--input-background-fill) !important; box-shadow: 2px 2px 2px #111; border: none !important; font-size: 0.8rem; }
-.tab-nav { zoom: 110%; margin-top: 10px; margin-bottom: 10px; border-bottom: 2px solid var(--highlight-color) !important; padding-bottom: 2px; }
-div.tab-nav button.selected {background-color: var(--button-primary-background-fill);}
-#settings div.tab-nav button.selected {background-color: var(--background-color); color: var(--primary-800); font-weight: bold;}
-.label-wrap { background-color: #18181e; /* extension tab color*/ padding: 16px 8px 8px 8px; border-radius: var(--radius-lg); padding-left: 8px !important; }
-.small-accordion .label-wrap { padding: 8px 0px 8px 0px; }
-.small-accordion .label-wrap .icon { margin-right: 1em; }
-.gradio-button.tool { border: none; box-shadow: none; border-radius: var(--radius-lg);}
-button.selected {background: var(--button-primary-background-fill);}
-.center.boundedheight.flex {background-color: var(--input-background-fill);}
-.compact {border-radius: var(--border-radius-lg);}
-#logMonitorData {background-color: var(--input-background-fill);}
-#tab_extensions table td, #tab_extensions table th, #tab_config table td, #tab_config table th { border: none; padding: 0.5em; background-color: var(--primary-200); }
-#tab_extensions table, #tab_config table { width: 96vw; }
-#tab_extensions table input[type=checkbox] {appearance: none; border-radius: 0px;}
-#tab_extensions button:hover { background-color: var(--button-secondary-background-fill-hover);}
-
-/* automatic style classes */
-.progressDiv { border-radius: var(--radius-sm) !important; position: fixed; top: 44px; right: 26px; max-width: 262px; height: 48px; z-index: 99; box-shadow: var(--button-shadow); }
-.progressDiv .progress { border-radius: var(--radius-lg) !important; background: var(--highlight-color); line-height: 3rem; height: 48px; }
-.gallery-item { box-shadow: none !important; }
-.performance { color: #888; }
-.extra-networks { border-left: 2px solid var(--highlight-color) !important; padding-left: 4px; }
-.image-buttons { gap: 10px !important; justify-content: center; }
-.image-buttons > button { max-width: 160px; }
-.tooltip { background: var(--primary-800); color: white; border: none; border-radius: var(--radius-lg) }
-#system_row > button, #settings_row > button, #config_row > button { max-width: 10em; }
-
-/* gradio elements overrides */
-#div.gradio-container { overflow-x: hidden; }
-#img2img_label_copy_to_img2img { font-weight: normal; }
-#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt { background-color: var(--background-color); box-shadow: 4px 4px 4px 0px #333333 !important; }
-#txt2img_prompt > label > textarea, #txt2img_neg_prompt > label > textarea, #img2img_prompt > label > textarea, #img2img_neg_prompt > label > textarea { font-size: 1.1rem; }
-#img2img_settings { min-width: calc(2 * var(--left-column)); max-width: calc(2 * var(--left-column)); background-color: #111111; padding-top: 16px; }
-#interrogate, #deepbooru { margin: 0 0px 10px 0px; max-width: 80px; max-height: 80px; font-weight: normal; font-size: 0.95em; }
-#quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-top: -2px; height: 2.4em; }
-#quicksettings button {padding: 0 0.5em 0.1em 0.5em;}
-#open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h { display: none; }
-#save-animation { border-radius: var(--radius-sm) !important; margin-bottom: 16px; background-color: #111111; }
-#script_list { padding: 4px; margin-top: 16px; margin-bottom: 8px; }
-#settings > div.flex-wrap { width: 15em; }
-#txt2img_cfg_scale { min-width: 200px; }
-#txt2img_checkboxes, #img2img_checkboxes { background-color: transparent; }
-#txt2img_checkboxes, #img2img_checkboxes { margin-bottom: 0.2em; }
-#txt2img_actions_column, #img2img_actions_column { flex-flow: wrap; justify-content: space-between; }
-#txt2img_enqueue_wrapper, #img2img_enqueue_wrapper { min-width: unset; width: 48%; }
-#txt2img_generate_box, #img2img_generate_box { min-width: unset; width: 48%; }
-
-#extras_upscale { margin-top: 10px }
-#txt2img_progress_row > div { min-width: var(--left-column); max-width: var(--left-column); }
-#txt2img_settings { min-width: var(--left-column); max-width: var(--left-column); background-color: #111111; padding-top: 16px; }
-#pnginfo_html2_info { margin-top: -18px; background-color: var(--input-background-fill); padding: var(--input-padding) }
-#txt2img_tools, #img2img_tools { margin-top: -4px; margin-bottom: -4px; }
-#txt2img_styles_row, #img2img_styles_row { margin-top: -6px; z-index: 200; }
-
-/* based on gradio built-in dark theme */
-:root, .light, .dark {
- --body-background-fill: var(--background-color);
- --color-accent-soft: var(--neutral-700);
- --background-fill-secondary: none;
- --border-color-accent: var(--background-color);
- --border-color-primary: var(--background-color);
- --link-text-color-active: var(--primary-500);
- --link-text-color: var(--secondary-500);
- --link-text-color-hover: var(--secondary-400);
- --link-text-color-visited: var(--secondary-600);
- --shadow-spread: 1px;
- --block-background-fill: None;
- --block-border-color: var(--border-color-primary);
- --block_border_width: None;
- --block-info-text-color: var(--body-text-color-subdued);
- --block-label-background-fill: var(--background-fill-secondary);
- --block-label-border-color: var(--border-color-primary);
- --block_label_border_width: None;
- --block-label-text-color: var(--neutral-200);
- --block_shadow: None;
- --block_title_background_fill: None;
- --block_title_border_color: None;
- --block_title_border_width: None;
- --panel-background-fill: var(--background-fill-secondary);
- --panel-border-color: var(--border-color-primary);
- --panel_border_width: None;
- --checkbox-background-color: var(--primary-200);
- --checkbox-background-color-focus: var(--primary-400);
- --checkbox-background-color-hover: var(--primary-200);
- --checkbox-background-color-selected: var(--primary-400);
- --checkbox-border-color: transparent;
- --checkbox-border-color-focus: var(--primary-800);
- --checkbox-border-color-hover: var(--primary-800);
- --checkbox-border-color-selected: var(--primary-800);
- --checkbox-border-width: var(--input-border-width);
- --checkbox-label-background-fill: None;
- --checkbox-label-background-fill-hover: None;
- --checkbox-label-background-fill-selected: var(--checkbox-label-background-fill);
- --checkbox-label-border-color: var(--border-color-primary);
- --checkbox-label-border-color-hover: var(--checkbox-label-border-color);
- --checkbox-label-border-width: var(--input-border-width);
- --checkbox-label-text-color: var(--body-text-color);
- --checkbox-label-text-color-selected: var(--checkbox-label-text-color);
- --error-background-fill: var(--background-fill-primary);
- --error-border-color: var(--border-color-primary);
- --error-text-color: #f768b7; /*was ef4444*/
- --input-background-fill-focus: var(--secondary-600);
- --input-background-fill-hover: var(--input-background-fill);
- --input-border-color: var(--background-color);
- --input-border-color-focus: var(--primary-800);
- --input-placeholder-color: var(--neutral-500);
- --input-shadow-focus: None;
- --loader_color: None;
- --slider_color: None;
- --stat-background-fill: linear-gradient(to right, var(--primary-400), var(--primary-800));
- --table-border-color: var(--neutral-700);
- --table-even-background-fill: var(--primary-300);
- --table-odd-background-fill: var(--primary-200);
- --table-row-focus: var(--color-accent-soft);
- --button-border-width: var(--input-border-width);
- --button-cancel-background-fill: linear-gradient(to bottom right, #dc2626, #b91c1c);
- --button-cancel-background-fill-hover: linear-gradient(to bottom right, #dc2626, #dc2626);
- --button-cancel-border-color: #dc2626;
- --button-cancel-border-color-hover: var(--button-cancel-border-color);
- --button-cancel-text-color: white;
- --button-cancel-text-color-hover: var(--button-cancel-text-color);
- --button-primary-background-fill: var(--primary-500);
- --button-primary-background-fill-hover: var(--primary-800);
- --button-primary-border-color: var(--primary-500);
- --button-primary-border-color-hover: var(--button-primary-border-color);
- --button-primary-text-color: white;
- --button-primary-text-color-hover: var(--button-primary-text-color);
- --button-secondary-border-color: var(--neutral-600);
- --button-secondary-border-color-hover: var(--button-secondary-border-color);
- --button-secondary-text-color-hover: var(--button-secondary-text-color);
- --secondary-50: #eff6ff;
- --secondary-100: #dbeafe;
- --secondary-200: #bfdbfe;
- --secondary-300: #93c5fd;
- --secondary-400: #60a5fa;
- --secondary-500: #3b82f6;
- --secondary-600: #2563eb;
- --secondary-700: #1d4ed8;
- --secondary-800: #1e40af;
- --secondary-900: #1e3a8a;
- --secondary-950: #1d3660;
- --neutral-50: #f0f0f0; /* */
- --neutral-100: #ddd5e8;/* majority of text (neutral gray purple) */
- --neutral-200: #d0d0d0;
- --neutral-300: #bfbad6; /* top tab text (light accent) */
- --neutral-400: #ffba85;/* tab title (bright orange) */
- --neutral-500: #545b94; /* prompt text (desat accent)*/
- --neutral-600: #1f2028; /* tab outline color (accent color)*/
- --neutral-700: #20212c; /* unchanged settings tab accent (dark)*/
- --neutral-800: #e055dc; /* bright pink accent */
- --neutral-900: #111827;
- --neutral-950: #0b0f19;
- --radius-xxs: 0;
- --radius-xs: 0;
- --radius-md: 0;
- --radius-xl: 0;
- --radius-xxl: 0;
- --body-text-size: var(--text-md);
- --body-text-weight: 400;
- --embed-radius: var(--radius-lg);
- --color-accent: var(--primary-500);
- --shadow-drop: 0;
- --shadow-drop-lg: 0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1);
- --shadow-inset: rgba(0,0,0,0.05) 0px 2px 4px 0px inset;
- --block-border-width: 1px;
- --block-info-text-size: var(--text-sm);
- --block-info-text-weight: 400;
- --block-label-border-width: 1px;
- --block-label-margin: 0;
- --block-label-padding: var(--spacing-sm) var(--spacing-lg);
- --block-label-radius: calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px) 0;
- --block-label-right-radius: 0 calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px);
- --block-label-text-size: var(--text-sm);
- --block-label-text-weight: 400;
- --block-padding: var(--spacing-xl) calc(var(--spacing-xl) + 2px);
- --block-radius: var(--radius-lg);
- --block-shadow: var(--shadow-drop);
- --block-title-background-fill: none;
- --block-title-border-color: none;
- --block-title-border-width: 0;
- --block-title-padding: 0;
- --block-title-radius: none;
- --block-title-text-size: var(--text-md);
- --block-title-text-weight: 400;
- --container-radius: var(--radius-lg);
- --form-gap-width: 1px;
- --layout-gap: var(--spacing-xxl);
- --panel-border-width: 0;
- --section-header-text-size: var(--text-md);
- --section-header-text-weight: 400;
- --checkbox-border-radius: var(--radius-sm);
- --checkbox-label-gap: 2px;
- --checkbox-label-padding: var(--spacing-md);
- --checkbox-label-shadow: var(--shadow-drop);
- --checkbox-label-text-size: var(--text-md);
- --checkbox-label-text-weight: 400;
- --checkbox-check: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e");
- --radio-circle: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e");
- --checkbox-shadow: var(--input-shadow);
- --error-border-width: 1px;
- --input-border-width: 1px;
- --input-radius: var(--radius-lg);
- --input-text-size: var(--text-md);
- --input-text-weight: 400;
- --loader-color: var(--color-accent);
- --prose-text-size: var(--text-md);
- --prose-text-weight: 400;
- --prose-header-text-weight: 600;
- --slider-color: ;
- --table-radius: var(--radius-lg);
- --button-large-padding: 2px 6px;
- --button-large-radius: var(--radius-lg);
- --button-large-text-size: var(--text-lg);
- --button-large-text-weight: 400;
- --button-shadow: none;
- --button-shadow-active: none;
- --button-shadow-hover: none;
- --button-small-padding: var(--spacing-sm) calc(2 * var(--spacing-sm));
- --button-small-radius: var(--radius-lg);
- --button-small-text-size: var(--text-md);
- --button-small-text-weight: 400;
- --button-transition: none;
- --size-9: 64px;
- --size-14: 64px;
-}
+/* generic html tags */
+:root, .light, .dark {
+ --font: 'system-ui', 'ui-sans-serif', 'system-ui', "Roboto", sans-serif;
+ --font-mono: 'ui-monospace', 'Consolas', monospace;
+ --font-size: 16px;
+ --primary-100: #2a2a34; /* bg color*/
+ --primary-200: #1f2028; /* drop down menu/ prompt*/
+ --primary-300: #0a0c0e; /* black */
+ --primary-400: #40435c; /* small buttons*/
+ --primary-500: #4c48b5; /* main accent color purple*/
+ --primary-700: #1f2028; /* darker hover accent*/
+ --primary-800: #e95ee3; /* pink accent*/
+ --highlight-color: var(--primary-500);
+ --inactive-color: var(--primary--800);
+ --body-text-color: var(--neutral-100);
+ --body-text-color-subdued: var(--neutral-300);
+ --background-color: var(--primary-100);
+ --background-fill-primary: var(--input-background-fill);
+ --input-padding: 8px;
+ --input-background-fill: var(--primary-200);
+ --input-shadow: none;
+ --button-secondary-text-color: white;
+ --button-secondary-background-fill: var(--primary-400);
+ --button-secondary-background-fill-hover: var(--primary-700);
+ --block-title-text-color: var(--neutral-300);
+ --radius-sm: 1px;
+ --radius-lg: 6px;
+ --spacing-md: 4px;
+ --spacing-xxl: 8px;
+ --line-sm: 1.2em;
+ --line-md: 1.4em;
+}
+
+html { font-size: var(--font-size); }
+body, button, input, select, textarea { font-family: var(--font);}
+button { max-width: 400px; }
+img { background-color: var(--background-color); }
+input[type=range] { height: var(--line-sm); appearance: none; margin-top: 0; min-width: 160px; background-color: var(--background-color); width: 100%; background: transparent; }
+input[type=range]::-webkit-slider-runnable-track, input[type=range]::-moz-range-track { width: 100%; height: 6px; cursor: pointer; background: var(--primary-400); border-radius: var(--radius-lg); border: 0px solid #222222; }
+input[type=range]::-webkit-slider-thumb, input[type=range]::-moz-range-thumb { border: 0px solid #000000; height: var(--line-sm); width: 8px; border-radius: var(--radius-lg); background: white; cursor: pointer; appearance: none; margin-top: 0px; }
+input[type=range]::-moz-range-progress { background-color: var(--primary-500); height: 6px; border-radius: var(--radius-lg); }
+::-webkit-scrollbar-track { background: #333333; }
+::-webkit-scrollbar-thumb { background-color: var(--highlight-color); border-radius: var(--radius-lg); border-width: 0; box-shadow: 2px 2px 3px #111111; }
+div.form { border-width: 0; box-shadow: none; background: transparent; overflow: visible; margin-bottom: 6px; }
+div.compact { gap: 1em; }
+
+/* gradio style classes */
+fieldset .gr-block.gr-box, label.block span { padding: 0; margin-top: -4px; }
+.border-2 { border-width: 0; }
+.border-b-2 { border-bottom-width: 2px; border-color: var(--highlight-color) !important; padding-bottom: 2px; margin-bottom: 8px; }
+.bg-white { color: lightyellow; background-color: var(--inactive-color); }
+.gr-box { border-radius: var(--radius-sm) !important; background-color: #111111 !important; box-shadow: 2px 2px 3px #111111; border-width: 0; padding: 4px; margin: 12px 0px 12px 0px }
+.gr-button { font-weight: normal; box-shadow: 2px 2px 3px #111111; font-size: 0.8rem; min-width: 32px; min-height: 32px; padding: 3px; margin: 3px; }
+.gr-check-radio { background-color: var(--inactive-color); border-width: 0; border-radius: var(--radius-lg); box-shadow: 2px 2px 3px #111111; }
+.gr-check-radio:checked { background-color: var(--highlight-color); }
+.gr-compact { background-color: var(--background-color); }
+.gr-form { border-width: 0; }
+.gr-input { background-color: #333333 !important; padding: 4px; margin: 4px; }
+.gr-input-label { color: lightyellow; border-width: 0; background: transparent; padding: 2px !important; }
+.gr-panel { background-color: var(--background-color); }
+.eta-bar { display: none !important }
+svg.feather.feather-image, .feather .feather-image { display: none }
+.gap-2 { padding-top: 8px; }
+.gr-box > div > div > input.gr-text-input { right: 0; width: 4em; padding: 0; top: -12px; border: none; max-height: 20px; }
+.output-html { line-height: 1.2rem; overflow-x: hidden; }
+.output-html > div { margin-bottom: 8px; }
+.overflow-hidden .flex .flex-col .relative col .gap-4 { min-width: var(--left-column); max-width: var(--left-column); } /* this is a problematic one */
+.p-2 { padding: 0; }
+.px-4 { padding-lefT: 1rem; padding-right: 1rem; }
+.py-6 { padding-bottom: 0; }
+.tabs { background-color: var(--background-color); }
+.block.token-counter span { background-color: var(--input-background-fill) !important; box-shadow: 2px 2px 2px #111; border: none !important; font-size: 0.8rem; }
+.tab-nav { zoom: 110%; margin-top: 10px; margin-bottom: 10px; border-bottom: 2px solid var(--highlight-color) !important; padding-bottom: 2px; }
+div.tab-nav button.selected {background-color: var(--button-primary-background-fill);}
+#settings div.tab-nav button.selected {background-color: var(--background-color); color: var(--primary-800); font-weight: bold;}
+.label-wrap { background-color: #18181e; /* extension tab color*/ padding: 16px 8px 8px 8px; border-radius: var(--radius-lg); padding-left: 8px !important; }
+.small-accordion .label-wrap { padding: 8px 0px 8px 0px; }
+.small-accordion .label-wrap .icon { margin-right: 1em; }
+.gradio-button.tool { border: none; box-shadow: none; border-radius: var(--radius-lg);}
+button.selected {background: var(--button-primary-background-fill);}
+.center.boundedheight.flex {background-color: var(--input-background-fill);}
+.compact {border-radius: var(--border-radius-lg);}
+#logMonitorData {background-color: var(--input-background-fill);}
+#tab_extensions table td, #tab_extensions table th, #tab_config table td, #tab_config table th { border: none; padding: 0.5em; background-color: var(--primary-200); }
+#tab_extensions table, #tab_config table { width: 96vw; }
+#tab_extensions table input[type=checkbox] {appearance: none; border-radius: 0px;}
+#tab_extensions button:hover { background-color: var(--button-secondary-background-fill-hover);}
+
+/* automatic style classes */
+.progressDiv { border-radius: var(--radius-sm) !important; position: fixed; top: 44px; right: 26px; max-width: 262px; height: 48px; z-index: 99; box-shadow: var(--button-shadow); }
+.progressDiv .progress { border-radius: var(--radius-lg) !important; background: var(--highlight-color); line-height: 3rem; height: 48px; }
+.gallery-item { box-shadow: none !important; }
+.performance { color: #888; }
+.extra-networks { border-left: 2px solid var(--highlight-color) !important; padding-left: 4px; }
+.image-buttons { gap: 10px !important; justify-content: center; }
+.image-buttons > button { max-width: 160px; }
+.tooltip { background: var(--primary-800); color: white; border: none; border-radius: var(--radius-lg) }
+#system_row > button, #settings_row > button, #config_row > button { max-width: 10em; }
+
+/* gradio elements overrides */
+#div.gradio-container { overflow-x: hidden; }
+#img2img_label_copy_to_img2img { font-weight: normal; }
+#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt { background-color: var(--background-color); box-shadow: 4px 4px 4px 0px #333333 !important; }
+#txt2img_prompt > label > textarea, #txt2img_neg_prompt > label > textarea, #img2img_prompt > label > textarea, #img2img_neg_prompt > label > textarea { font-size: 1.1rem; }
+#img2img_settings { min-width: calc(2 * var(--left-column)); max-width: calc(2 * var(--left-column)); background-color: #111111; padding-top: 16px; }
+#interrogate, #deepbooru { margin: 0 0px 10px 0px; max-width: 80px; max-height: 80px; font-weight: normal; font-size: 0.95em; }
+#quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-top: -2px; height: 2.4em; }
+#quicksettings button {padding: 0 0.5em 0.1em 0.5em;}
+#open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h { display: none; }
+#save-animation { border-radius: var(--radius-sm) !important; margin-bottom: 16px; background-color: #111111; }
+#script_list { padding: 4px; margin-top: 16px; margin-bottom: 8px; }
+#settings > div.flex-wrap { width: 15em; }
+#txt2img_cfg_scale { min-width: 200px; }
+#txt2img_checkboxes, #img2img_checkboxes { background-color: transparent; }
+#txt2img_checkboxes, #img2img_checkboxes { margin-bottom: 0.2em; }
+#txt2img_actions_column, #img2img_actions_column { flex-flow: wrap; justify-content: space-between; }
+#txt2img_enqueue_wrapper, #img2img_enqueue_wrapper { min-width: unset; width: 48%; }
+#txt2img_generate_box, #img2img_generate_box { min-width: unset; width: 48%; }
+
+#extras_upscale { margin-top: 10px }
+#txt2img_progress_row > div { min-width: var(--left-column); max-width: var(--left-column); }
+#txt2img_settings { min-width: var(--left-column); max-width: var(--left-column); background-color: #111111; padding-top: 16px; }
+#pnginfo_html2_info { margin-top: -18px; background-color: var(--input-background-fill); padding: var(--input-padding) }
+#txt2img_tools, #img2img_tools { margin-top: -4px; margin-bottom: -4px; }
+#txt2img_styles_row, #img2img_styles_row { margin-top: -6px; z-index: 200; }
+
+/* based on gradio built-in dark theme */
+:root, .light, .dark {
+ --body-background-fill: var(--background-color);
+ --color-accent-soft: var(--neutral-700);
+ --background-fill-secondary: none;
+ --border-color-accent: var(--background-color);
+ --border-color-primary: var(--background-color);
+ --link-text-color-active: var(--primary-500);
+ --link-text-color: var(--secondary-500);
+ --link-text-color-hover: var(--secondary-400);
+ --link-text-color-visited: var(--secondary-600);
+ --shadow-spread: 1px;
+ --block-background-fill: None;
+ --block-border-color: var(--border-color-primary);
+ --block_border_width: None;
+ --block-info-text-color: var(--body-text-color-subdued);
+ --block-label-background-fill: var(--background-fill-secondary);
+ --block-label-border-color: var(--border-color-primary);
+ --block_label_border_width: None;
+ --block-label-text-color: var(--neutral-200);
+ --block_shadow: None;
+ --block_title_background_fill: None;
+ --block_title_border_color: None;
+ --block_title_border_width: None;
+ --panel-background-fill: var(--background-fill-secondary);
+ --panel-border-color: var(--border-color-primary);
+ --panel_border_width: None;
+ --checkbox-background-color: var(--primary-200);
+ --checkbox-background-color-focus: var(--primary-400);
+ --checkbox-background-color-hover: var(--primary-200);
+ --checkbox-background-color-selected: var(--primary-400);
+ --checkbox-border-color: transparent;
+ --checkbox-border-color-focus: var(--primary-800);
+ --checkbox-border-color-hover: var(--primary-800);
+ --checkbox-border-color-selected: var(--primary-800);
+ --checkbox-border-width: var(--input-border-width);
+ --checkbox-label-background-fill: None;
+ --checkbox-label-background-fill-hover: None;
+ --checkbox-label-background-fill-selected: var(--checkbox-label-background-fill);
+ --checkbox-label-border-color: var(--border-color-primary);
+ --checkbox-label-border-color-hover: var(--checkbox-label-border-color);
+ --checkbox-label-border-width: var(--input-border-width);
+ --checkbox-label-text-color: var(--body-text-color);
+ --checkbox-label-text-color-selected: var(--checkbox-label-text-color);
+ --error-background-fill: var(--background-fill-primary);
+ --error-border-color: var(--border-color-primary);
+ --error-text-color: #f768b7; /*was ef4444*/
+ --input-background-fill-focus: var(--secondary-600);
+ --input-background-fill-hover: var(--input-background-fill);
+ --input-border-color: var(--background-color);
+ --input-border-color-focus: var(--primary-800);
+ --input-placeholder-color: var(--neutral-500);
+ --input-shadow-focus: None;
+ --loader_color: None;
+ --slider_color: None;
+ --stat-background-fill: linear-gradient(to right, var(--primary-400), var(--primary-800));
+ --table-border-color: var(--neutral-700);
+ --table-even-background-fill: var(--primary-300);
+ --table-odd-background-fill: var(--primary-200);
+ --table-row-focus: var(--color-accent-soft);
+ --button-border-width: var(--input-border-width);
+ --button-cancel-background-fill: linear-gradient(to bottom right, #dc2626, #b91c1c);
+ --button-cancel-background-fill-hover: linear-gradient(to bottom right, #dc2626, #dc2626);
+ --button-cancel-border-color: #dc2626;
+ --button-cancel-border-color-hover: var(--button-cancel-border-color);
+ --button-cancel-text-color: white;
+ --button-cancel-text-color-hover: var(--button-cancel-text-color);
+ --button-primary-background-fill: var(--primary-500);
+ --button-primary-background-fill-hover: var(--primary-800);
+ --button-primary-border-color: var(--primary-500);
+ --button-primary-border-color-hover: var(--button-primary-border-color);
+ --button-primary-text-color: white;
+ --button-primary-text-color-hover: var(--button-primary-text-color);
+ --button-secondary-border-color: var(--neutral-600);
+ --button-secondary-border-color-hover: var(--button-secondary-border-color);
+ --button-secondary-text-color-hover: var(--button-secondary-text-color);
+ --secondary-50: #eff6ff;
+ --secondary-100: #dbeafe;
+ --secondary-200: #bfdbfe;
+ --secondary-300: #93c5fd;
+ --secondary-400: #60a5fa;
+ --secondary-500: #3b82f6;
+ --secondary-600: #2563eb;
+ --secondary-700: #1d4ed8;
+ --secondary-800: #1e40af;
+ --secondary-900: #1e3a8a;
+ --secondary-950: #1d3660;
+ --neutral-50: #f0f0f0; /* */
+ --neutral-100: #ddd5e8;/* majority of text (neutral gray purple) */
+ --neutral-200: #d0d0d0;
+ --neutral-300: #bfbad6; /* top tab text (light accent) */
+ --neutral-400: #ffba85;/* tab title (bright orange) */
+ --neutral-500: #545b94; /* prompt text (desat accent)*/
+ --neutral-600: #1f2028; /* tab outline color (accent color)*/
+ --neutral-700: #20212c; /* unchanged settings tab accent (dark)*/
+ --neutral-800: #e055dc; /* bright pink accent */
+ --neutral-900: #111827;
+ --neutral-950: #0b0f19;
+ --radius-xxs: 0;
+ --radius-xs: 0;
+ --radius-md: 0;
+ --radius-xl: 0;
+ --radius-xxl: 0;
+ --body-text-size: var(--text-md);
+ --body-text-weight: 400;
+ --embed-radius: var(--radius-lg);
+ --color-accent: var(--primary-500);
+ --shadow-drop: 0;
+ --shadow-drop-lg: 0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1);
+ --shadow-inset: rgba(0,0,0,0.05) 0px 2px 4px 0px inset;
+ --block-border-width: 1px;
+ --block-info-text-size: var(--text-sm);
+ --block-info-text-weight: 400;
+ --block-label-border-width: 1px;
+ --block-label-margin: 0;
+ --block-label-padding: var(--spacing-sm) var(--spacing-lg);
+ --block-label-radius: calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px) 0;
+ --block-label-right-radius: 0 calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px);
+ --block-label-text-size: var(--text-sm);
+ --block-label-text-weight: 400;
+ --block-padding: var(--spacing-xl) calc(var(--spacing-xl) + 2px);
+ --block-radius: var(--radius-lg);
+ --block-shadow: var(--shadow-drop);
+ --block-title-background-fill: none;
+ --block-title-border-color: none;
+ --block-title-border-width: 0;
+ --block-title-padding: 0;
+ --block-title-radius: none;
+ --block-title-text-size: var(--text-md);
+ --block-title-text-weight: 400;
+ --container-radius: var(--radius-lg);
+ --form-gap-width: 1px;
+ --layout-gap: var(--spacing-xxl);
+ --panel-border-width: 0;
+ --section-header-text-size: var(--text-md);
+ --section-header-text-weight: 400;
+ --checkbox-border-radius: var(--radius-sm);
+ --checkbox-label-gap: 2px;
+ --checkbox-label-padding: var(--spacing-md);
+ --checkbox-label-shadow: var(--shadow-drop);
+ --checkbox-label-text-size: var(--text-md);
+ --checkbox-label-text-weight: 400;
+ --checkbox-check: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e");
+ --radio-circle: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e");
+ --checkbox-shadow: var(--input-shadow);
+ --error-border-width: 1px;
+ --input-border-width: 1px;
+ --input-radius: var(--radius-lg);
+ --input-text-size: var(--text-md);
+ --input-text-weight: 400;
+ --loader-color: var(--color-accent);
+ --prose-text-size: var(--text-md);
+ --prose-text-weight: 400;
+ --prose-header-text-weight: 600;
+ --slider-color: ;
+ --table-radius: var(--radius-lg);
+ --button-large-padding: 2px 6px;
+ --button-large-radius: var(--radius-lg);
+ --button-large-text-size: var(--text-lg);
+ --button-large-text-weight: 400;
+ --button-shadow: none;
+ --button-shadow-active: none;
+ --button-shadow-hover: none;
+ --button-small-padding: var(--spacing-sm) calc(2 * var(--spacing-sm));
+ --button-small-radius: var(--radius-lg);
+ --button-small-text-size: var(--text-md);
+ --button-small-text-weight: 400;
+ --button-transition: none;
+ --size-9: 64px;
+ --size-14: 64px;
+}
diff --git a/javascript/sdnext.css b/javascript/sdnext.css
index fd93c6729..70b10c988 100644
--- a/javascript/sdnext.css
+++ b/javascript/sdnext.css
@@ -1,343 +1,343 @@
-@font-face { font-family: 'Roboto'; font-display: swap; font-style: normal; font-weight: 100; src: local('Roboto'), url('roboto.ttf') }
-:root { --left-column: 500px; }
-a { font-weight: bold; cursor: pointer; }
-h2 { margin-top: 1em !important; font-size: var(--text-xxl) !important; }
-footer { display: none; }
-table { overflow-x: auto !important; overflow-y: auto !important; }
-td { border-bottom: none !important; padding: 0.1em 0.5em !important; }
-tr { border-bottom: none !important; padding: 0.1em 0.5em !important; }
-textarea { overflow-y: auto !important; }
-span { font-size: var(--text-md) !important; }
-button { font-size: var(--text-lg) !important; }
-
-/* gradio elements */
-.block .padded:not(.gradio-accordion) { padding: 0 !important; margin-right: 0; min-width: 90px !important; }
-.compact { gap: 1em 0.2em; background: transparent !important; padding: 0 !important; }
-.flex-break { flex-basis: 100% !important; }
-.form { border-width: 0; box-shadow: none; background: transparent; overflow: visible; gap: 0.5em 1em; flex-grow: 1 !important; }
-.form-compact { margin-bottom: 0 !important; gap: 0.2em 1em !important; }
-.gap .compact{ padding: 0; gap: 0.2em 0; }
-.hidden { display: none; }
-.tabitem { padding: 0 !important; }
-
-.gradio-dropdown, .block.gradio-slider, .block.gradio-checkbox, .block.gradio-textbox, .block.gradio-radio, .block.gradio-checkboxgroup, .block.gradio-number, .block.gradio-colorpicker { border-width: 0 !important; box-shadow: none !important;}
-.gradio-accordion { padding-top: var(--spacing-md) !important; padding-right: 0 !important; padding-bottom: 0 !important; color: var(--body-text-color); }
-.gradio-accordion .label-wrap .icon { color: var(--button-primary-border-color); }
-.gradio-button { border-radius: var(--radius-lg) !important; }
-.gradio-button.secondary-down { background: var(--button-secondary-background-fill); color: var(--button-secondary-text-color); }
-.gradio-button.secondary-down, .gradio-button.secondary-down:hover { box-shadow: 1px 1px 1px rgba(0,0,0,0.25) inset, 0px 0px 3px rgba(0,0,0,0.15) inset; }
-.gradio-button.secondary-down:hover { background: var(--button-secondary-background-fill-hover); color: var(--button-secondary-text-color-hover); }
-.gradio-button.tool { max-width: min-content; min-width: min-content !important; align-self: end; font-size: var(--text-xxl); color: var(--body-text-color) !important; margin-top: auto; margin-bottom: var(--spacing-md); align-self: center; }
-.gradio-checkbox { margin: 0.75em 1.5em 0 0; align-self: center; }
-.gradio-column { min-width: min(160px, 100%) !important; }
-.gradio-container { max-width: unset !important; padding: var(--block-label-padding) !important; }
-.gradio-container .prose a, .gradio-container .prose a:visited{ color: unset; text-decoration: none; }
-.gradio-dropdown { margin-right: var(--spacing-sm) !important; min-width:160px; max-width:fit-content }
-.gradio-dropdown ul.options { z-index: 1000; min-width: fit-content; max-height: 33vh !important; white-space: nowrap; }
-.gradio-dropdown ul.options li.item { padding: var(--spacing-xs); }
-.gradio-dropdown ul.options li.item:not(:has(.hide)) { background-color: var(--primary-500); }
-.gradio-dropdown .token { padding: var(--spacing-xs); }
-.gradio-dropdown span { margin-bottom: 0 !important; font-size: var(--text-sm); }
-.gradio-dropdown .reference { margin-bottom: var(--spacing-sm) !important; }
-.gradio-html { color: var(--body-text-color); }
-.gradio-html .min { min-height: 0; }
-.gradio-html div.wrap { height: 100%; }
-.gradio-number { min-width: unset !important; max-width: 5em !important; }
-.gradio-textbox { overflow: visible !important; }
-.gradio-radio { padding: 0 !important; width: max-content !important; }
-.gradio-slider { margin-right: var(--spacing-sm) !important; width: max-content !important }
-.gradio-slider input[type="number"] { width: 6em; font-size: var(--text-xs); height: 16px; text-align: right; }
-
-/* custom gradio elements */
-.accordion-compact { padding: 8px 0px 4px 0px !important; }
-.settings-accordion>div { flex-flow: wrap; }
-.small-accordion .form { min-width: var(--left-column) !important; max-width: max-content; }
-.small-accordion .label-wrap .icon { margin-right: 1.6em; margin-left: 0.6em; color: var(--button-primary-border-color); }
-.small-accordion .label-wrap { padding: 16px 0px 8px 0px; margin: 0; border-top: 2px solid var(--button-secondary-border-color); }
-.small-accordion { width: fit-content !important; min-width: fit-content !important; padding-left: 0 !important; }
-.extension-script { max-width: 48vw; }
-button.custom-button{ border-radius: var(--button-large-radius); padding: var(--button-large-padding); font-weight: var(--button-large-text-weight); border: var(--button-border-width) solid var(--button-secondary-border-color);
- background: var(--button-secondary-background-fill); color: var(--button-secondary-text-color); font-size: var(--text-lg);
- display: inline-flex; justify-content: center; align-items: center; transition: var(--button-transition); box-shadow: var(--button-shadow); text-align: center; }
-
-/* themes */
-.theme-preview { display: none; position: fixed; border: var(--spacing-sm) solid var(--neutral-600); box-shadow: 2px 2px 2px 2px var(--neutral-700); top: 0; bottom: 0; left: 0; right: 0; margin: auto; max-width: 75vw; z-index: 999; }
-
-/* txt2img/img2img specific */
-.block.token-counter{ position: absolute; display: inline-block; right: 1em; min-width: 0 !important; width: auto; z-index: 100; top: -0.5em; }
-.block.token-counter span{ background: var(--input-background-fill) !important; box-shadow: 0 0 0.0 0.3em rgba(192,192,192,0.15), inset 0 0 0.6em rgba(192,192,192,0.075); border: 2px solid rgba(192,192,192,0.4) !important; }
-.block.token-counter.error span{ box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075); border: 2px solid rgba(255,0,0,0.4) !important; }
-.block.token-counter div{ display: inline; }
-.block.token-counter span{ padding: 0.1em 0.75em; }
-.performance { font-size: var(--text-xs); color: #444; }
-.performance p { display: inline-block; color: var(--body-text-color-subdued) !important }
-.performance .time { margin-right: 0; }
-.thumbnails { background: var(--body-background-fill); }
-#control_gallery { height: 564px; }
-#control-result { padding: 0.5em; }
-#control-inputs { margin-top: 1em; }
-#txt2img_prompt_container, #img2img_prompt_container, #control_prompt_container { margin-right: var(--layout-gap) }
-#txt2img_footer, #img2img_footer, #control_footer { height: fit-content; display: none; }
-#txt2img_generate_box, #img2img_generate_box, #control_general_box { gap: 0.5em; flex-wrap: wrap-reverse; height: fit-content; }
-#txt2img_actions_column, #img2img_actions_column, #control_actions_column { gap: 0.3em; height: fit-content; }
-#txt2img_generate_box>button, #img2img_generate_box>button, #control_generate_box>button, #txt2img_enqueue, #img2img_enqueue { min-height: 42px; max-height: 42px; line-height: 1em; }
-#txt2img_generate_line2, #img2img_generate_line2, #txt2img_tools, #img2img_tools, #control_generate_line2, #control_tools { display: flex; }
-#txt2img_generate_line2>button, #img2img_generate_line2>button, #extras_generate_box>button, #control_generate_line2>button, #txt2img_tools>button, #img2img_tools>button, #control_tools>button { height: 2em; line-height: 0; font-size: var(--text-md);
- min-width: unset; display: block !important; }
-#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt, #control_prompt, #control_neg_prompt { display: contents; }
-#txt2img_generate_box, #img2img_generate_box { min-width: unset; width: 48%; }
-#control_generate_box { min-width: unset; width: 100%; }
-#txt2img_actions_column, #img2img_actions_column, #control_actions { flex-flow: wrap; justify-content: space-between; }
-#txt2img_enqueue_wrapper, #img2img_enqueue_wrapper, #control_enqueue_wrapper { min-width: unset !important; width: 48%; }
-.interrogate-clip { position: absolute; right: 3em; top: -2.7em; max-width: fit-content; }
-.interrogate-blip { position: absolute; right: 1em; top: -2.7em; max-width: fit-content; }
-.interrogate-col{ min-width: 0 !important; max-width: fit-content; margin-right: var(--spacing-xxl); }
-.interrogate-col>button{ flex: 1; width: 7em; max-height: 84px; }
-#sampler_selection_img2img { margin-top: 1em; }
-#txtimg_hr_finalres{ min-height: 0 !important; }
-#img2img_scale_resolution_preview.block{ display: flex; align-items: end; }
-#txtimg_hr_finalres .resolution, #img2img_scale_resolution_preview .resolution{ font-weight: bold; }
-div#extras_scale_to_tab div.form{ flex-direction: row; }
-#img2img_unused_scale_by_slider { visibility: hidden; width: 0.5em; max-width: 0.5em; min-width: 0.5em; }
-.inactive{ opacity: 0.5; }
-div#extras_scale_to_tab div.form{ flex-direction: row; }
-#mode_img2img .gradio-image>div.fixed-height, #mode_img2img .gradio-image>div.fixed-height img{ height: 480px !important; max-height: 480px !important; min-height: 480px !important; }
-#img2img_sketch, #img2maskimg, #inpaint_sketch { overflow: overlay !important; resize: auto; background: var(--panel-background-fill); z-index: 5; }
-.image-buttons button{ min-width: auto; }
-.infotext { overflow-wrap: break-word; line-height: 1.5em; }
-.infotext>p { padding-left: 1em; text-indent: -1em; white-space: pre-wrap; }
-.tooltip { display: block; position: fixed; top: 1em; right: 1em; padding: 0.5em; background: var(--input-background-fill); color: var(--body-text-color); border: 1pt solid var(--button-primary-border-color);
- width: 22em; min-height: 1.3em; font-size: var(--text-xs); transition: opacity 0.2s ease-in; pointer-events: none; opacity: 0; z-index: 999; }
-.tooltip-show { opacity: 0.9; }
-.toolbutton-selected { background: var(--background-fill-primary) !important; }
-
-/* settings */
-#si-sparkline-memo, #si-sparkline-load { background-color: #111; }
-#quicksettings { width: fit-content; }
-#quicksettings>button { padding: 0 1em 0 0; align-self: end; margin-bottom: var(--text-sm); }
-#settings { display: flex; gap: var(--layout-gap); }
-#settings div { border: none; gap: 0; margin: 0 0 var(--layout-gap) 0px; padding: 0; }
-#settings>div.tab-content { flex: 10 0 75%; display: grid; }
-#settings>div.tab-content>div { border: none; padding: 0; }
-#settings>div.tab-content>div>div>div>div>div { flex-direction: unset; }
-#settings>div.tab-nav { display: grid; grid-template-columns: repeat(auto-fill, .5em minmax(10em, 1fr)); flex: 1 0 auto; width: 12em; align-self: flex-start; gap: var(--spacing-xxl); }
-#settings>div.tab-nav button { display: block; border: none; text-align: left; white-space: initial; padding: 0; }
-#settings>div.tab-nav>#settings_show_all_pages { padding: var(--size-2) var(--size-4); }
-#settings .block.gradio-checkbox { margin: 0; width: auto; }
-#settings .dirtyable { gap: .5em; }
-#settings .dirtyable.hidden { display: none; }
-#settings .modification-indicator { height: 1.2em; border-radius: 1em !important; padding: 0; width: 0; margin-right: 0.5em; }
-#settings .modification-indicator:disabled { visibility: hidden; }
-#settings .modification-indicator.saved { background: var(--color-accent-soft); width: var(--spacing-sm); }
-#settings .modification-indicator.changed { background: var(--color-accent); width: var(--spacing-sm); }
-#settings .modification-indicator.changed.unsaved { background-image: linear-gradient(var(--color-accent) 25%, var(--color-accent-soft) 75%); width: var(--spacing-sm); }
-#settings_result { margin: 0 1.2em; }
-.licenses { display: block !important; }
-
-/* live preview */
-.progressDiv{ position: relative; height: 20px; background: #b4c0cc; margin-bottom: -3px; }
-.dark .progressDiv{ background: #424c5b; }
-.progressDiv .progress{ width: 0%; height: 20px; background: #0060df; color: white; font-weight: bold; line-height: 20px; padding: 0 8px 0 0; text-align: right; overflow: visible; white-space: nowrap; padding: 0 0.5em; }
-.livePreview { position: absolute; z-index: 50; background-color: transparent; width: -moz-available; width: -webkit-fill-available; }
-.livePreview img { position: absolute; object-fit: contain; width: 100%; height: 100%; }
-.dark .livePreview { background-color: rgb(17 24 39 / var(--tw-bg-opacity)); }
-.popup-metadata { color: white; background: #0000; display: inline-block; white-space: pre-wrap; font-size: var(--text-xxs); }
-.global-popup{ display: flex; position: fixed; z-index: 10001; left: 0; top: 0; width: 100%; height: 100%; overflow: auto; background-color: rgba(20, 20, 20, 0.95);}
-.global-popup-close:before { content: "×"; }
-.global-popup-close{ position: fixed; right: 0.5em; top: 0; cursor: pointer; color: white; font-size: 32pt; }
-.global-popup-inner{ display: inline-block; margin: auto; padding: 2em; }
-
-/* fullpage image viewer */
-#lightboxModal{ display: none; position: fixed; z-index: 1001; left: 0; top: 0; width: 100%; height: 100%; overflow: auto; background-color: rgba(20, 20, 20, 0.75); backdrop-filter: blur(6px);
- user-select: none; -webkit-user-select: none; flex-direction: row; }
-.modalControls { display: flex; justify-content: space-evenly; background-color: transparent; position: absolute; width: 99%; z-index: 1; }
-.modalControls:hover { background-color: #50505050; }
-.modalControls span { color: white; font-size: 2em; font-weight: bold; cursor: pointer; filter: grayscale(100%); }
-.modalControls span:hover, .modalControls span:focus { color: var(--highlight-color); filter: none; }
-.lightboxModalPreviewZone { display: flex; width: 100%; height: 100%; }
-.lightboxModalPreviewZone:focus-visible { outline: none; }
-.lightboxModalPreviewZone>img { display: block; margin: auto; width: auto; }
-.lightboxModalPreviewZone>img.modalImageFullscreen{ object-fit: contain; height: 100%; width: 100%; min-height: 0; background: transparent; }
-table.settings-value-table { background: white; border-collapse: collapse; margin: 1em; border: var(--spacing-sm) solid white; }
-table.settings-value-table td { padding: 0.4em; border: 1px solid #ccc; max-width: 36em; }
-.modalPrev, .modalNext { cursor: pointer; position: relative; z-index: 1; top: 0; width: auto; height: 100vh; line-height: 100vh; text-align: center; padding: 16px;
- margin-top: -50px; color: white; font-weight: bold; font-size: 20px; transition: 0.6s ease; user-select: none; -webkit-user-select: none; }
-.modalNext { right: 0; }
-.modalPrev:hover, .modalNext:hover { background-color: rgba(0, 0, 0, 0.8); }
-#imageARPreview { position: absolute; top: 0px; left: 0px; border: 2px solid red; background: rgba(255, 0, 0, 0.3); z-index: 900; pointer-events: none; display: none; }
-
-/* context menu (ie for the generate button) */
-#context-menu { z-index: 9999; position: absolute; display: block; padding: var(--spacing-md); border: 2px solid var(--highlight-color); background: var(--background-fill-primary); color: var(--body-text-color); }
-.context-menu-items { list-style: none; margin: 0; padding: 0; font-size: var(--text-sm); }
-.context-menu-items a { display: block; padding: var(--spacing-md); cursor: pointer; font-weight: normal; }
-.context-menu-items a:hover { background: var(--highlight-color) }
-
-/* extensions */
-#tab_extensions table, #tab_config table{ border-collapse: collapse; }
-#tab_extensions table td, #tab_extensions table th, #tab_config table td, #tab_config table th { border: 1px solid #ccc; padding: 0.25em 0.5em; }
-#tab_extensions table tr:hover, #tab_config table tr:hover { background-color: var(--neutral-500) !important; }
-#tab_extensions table input[type="checkbox"] { margin-right: 0.5em; appearance: checkbox; }
-#tab_extensions button{ max-width: 16em; }
-#tab_extensions input[disabled="disabled"]{ opacity: 0.5; }
-.extension-tag{ font-weight: bold; font-size: var(--text-sm); }
-.extension-button { font-size: var(--text-sm) !important; width: 6em; }
-#extensions .name{ font-size: var(--text-lg) }
-#extensions .type{ opacity: 0.5; font-size: var(--text-sm); text-align: center; }
-#extensions .version{ opacity: 0.7; }
-#extensions .info{ margin: 0; }
-#extensions .date{ opacity: 0.85; font-size: var(--text-sm); }
-
-/* extra networks */
-.extra-networks>div { margin: 0; border-bottom: none !important; gap: 0.3em 0; }
-.extra-networks .second-line { display: flex; width: -moz-available; width: -webkit-fill-available; gap: 0.3em; box-shadow: var(--input-shadow); }
-.extra-networks .search { flex: 1; }
-.extra-networks .description { flex: 3; }
-.extra-networks .tab-nav>button { margin-right: 0; height: 24px; padding: 2px 4px 2px 4px; }
-.extra-networks .buttons { position: absolute; right: 0; margin: -4px; background: var(--background-color); }
-.extra-networks .buttons>button { margin-left: -0.2em; height: 1.4em; color: var(--primary-300) !important; }
-.extra-networks .custom-button { width: 120px; width: 100%; background: none; justify-content: left; text-align: left; padding: 3px 3px 3px 12px; text-indent: -6px; box-shadow: none; line-break: auto; }
-.extra-networks .custom-button:hover { background: var(--button-primary-background-fill) }
-.extra-networks-tab { padding: 0 !important; }
-.extra-network-subdirs { background: var(--input-background-fill); overflow-x: hidden; overflow-y: auto; min-width: max(15%, 120px); padding-top: 0.5em; margin-top: -4px !important; }
-.extra-networks-page { display: flex }
-.extra-network-cards { display: flex; flex-wrap: wrap; overflow-y: auto; overflow-x: hidden; align-content: flex-start; width: -moz-available; width: -webkit-fill-available; }
-.extra-network-cards .card { height: fit-content; margin: 0 0 0.5em 0.5em; position: relative; scroll-snap-align: start; scroll-margin-top: 0; }
-.extra-network-cards .card .overlay { position: absolute; bottom: 0; padding: 0.2em; z-index: 10; width: 100%; background: none; }
-.extra-network-cards .card .overlay .name { font-size: var(--text-lg); font-weight: bold; text-shadow: 1px 1px black; color: white; overflow-wrap: break-word; }
-.extra-network-cards .card .preview { box-shadow: var(--button-shadow); min-height: 30px; }
-.extra-network-cards .card:hover .overlay { background: rgba(0, 0, 0, 0.40); }
-.extra-network-cards .card:hover .preview { box-shadow: none; filter: grayscale(100%); }
-.extra-network-cards .card:hover .overlay { background: rgba(0, 0, 0, 0.40); }
-.extra-network-cards .card .overlay .tags { display: none; overflow-wrap: break-word; }
-.extra-network-cards .card .overlay .tag { padding: 2px; margin: 2px; background: rgba(70, 70, 70, 0.60); font-size: var(--text-md); cursor: pointer; display: inline-block; }
-.extra-network-cards .card .actions>span { padding: 4px; font-size: 34px !important; }
-.extra-network-cards .card .actions>span:hover { color: var(--highlight-color); }
-.extra-network-cards .card:hover .actions { display: block; }
-.extra-network-cards .card:hover .overlay .tags { display: block; }
-.extra-network-cards .card .actions { font-size: 3em; display: none; text-align-last: right; cursor: pointer; font-variant: unicase; position: absolute; z-index: 80; right: 0; height: 0.7em; width: 100%; background: rgba(0, 0, 0, 0.40); }
-.extra-network-cards .card-list { display: flex; margin: 0.3em; padding: 0.3em; background: var(--input-background-fill); cursor: pointer; border-radius: var(--button-large-radius); }
-.extra-network-cards .card-list .tag { color: var(--primary-500); margin-left: 0.8em; }
-.extra-details-close { position: fixed; top: 0.2em; right: 0.2em; z-index: 99; background: var(--button-secondary-background-fill) !important; }
-#txt2img_description, #img2img_description, #control_description { max-height: 63px; overflow-y: auto !important; }
-#txt2img_description>label>textarea, #img2img_description>label>textarea, #control_description>label>textarea { font-size: var(--text-sm) }
-
-#txt2img_extra_details>div, #img2img_extra_details>div { overflow-y: auto; min-height: 40vh; max-height: 80vh; align-self: flex-start; }
-#txt2img_extra_details, #img2img_extra_details { position: fixed; bottom: 50%; left: 50%; transform: translate(-50%, 50%); padding: 0.8em; border: var(--block-border-width) solid var(--highlight-color) !important;
- z-index: 100; box-shadow: var(--button-shadow); }
-#txt2img_extra_details td:first-child, #img2img_extra_details td:first-child { font-weight: bold; vertical-align: top; }
-#txt2img_extra_details .gradio-image, #img2img_extra_details .gradio-image { max-height: 70vh; }
-
-
-/* specific elements */
-#modelmerger_interp_description { margin-top: 1em; margin-bottom: 1em; }
-#scripts_alwayson_txt2img, #scripts_alwayson_img2img { padding: 0 }
-#scripts_alwayson_txt2img>.label-wrap, #scripts_alwayson_img2img>.label-wrap { background: var(--input-background-fill); padding: 0; margin: 0; border-radius: var(--radius-lg); }
-#scripts_alwayson_txt2img>.label-wrap>span, #scripts_alwayson_img2img>.label-wrap>span { padding: var(--spacing-xxl); }
-#scripts_alwayson_txt2img div { max-width: var(--left-column); }
-#script_txt2img_agent_scheduler { display: none; }
-#refresh_tac_refreshTempFiles { display: none; }
-#train_tab { flex-flow: row-reverse; }
-#models_tab { flex-flow: row-reverse; }
-#swap_axes>button { min-width: 100px; font-size: var(--text-md); }
-#ui_defaults_review { margin: 1em; }
-
-/* extras */
-.extras { gap: 0.2em 1em !important }
-#extras_generate, #extras_interrupt, #extras_skip { display: block !important; position: relative; height: 36px; }
-#extras_upscale { margin-top: 10px }
-#pnginfo_html_info .gradio-html > div { margin: 0.5em; }
-
-/* log monitor */
-.log-monitor { display: none; justify-content: unset !important; overflow: hidden; padding: 0; margin-top: auto; font-family: monospace; font-size: var(--text-xs); }
-.log-monitor td, .log-monitor th { padding-left: 1em; }
-
-/* changelog */
-.md h2 { background-color: var(--background-fill-primary); padding: 0.5em; }
-.md ul { list-style-type: square !important; text-indent: 1em; margin-left: 4em; }
-.md li { list-style-position: outside !important; text-indent: 0; }
-.md p { margin-left: 2em; }
-
-/* custom component */
-.folder-selector textarea { height: 2em !important; padding: 6px !important; }
-.nvml { position: fixed; bottom: 10px; right: 10px; background: var(--background-fill-primary); border: 1px solid var(--button-primary-border-color); padding: 6px; color: var(--button-primary-text-color);
- font-size: var(--text-xxs); z-index: 50; font-family: monospace; display: none; }
-
-/* control */
-#control_input_type { max-width: 18em }
-#control_settings .small-accordion .form { min-width: 350px !important }
-.control-button { min-height: 42px; max-height: 42px; line-height: 1em; }
-.control-tabs > .tab-nav { margin-bottom: 0; margin-top: 0; }
-.control-unit { max-width: 1200px; padding: 0 !important; margin-top: -10px !important; }
-.control-unit > .label-wrap { margin-bottom: 0 !important; }
-.processor-settings { padding: 0 !important; max-width: 300px; }
-.processor-group>div { flex-flow: wrap;gap: 1em; }
-
-/* main info */
-.main-info { font-weight: var(--section-header-text-weight); color: var(--body-text-color-subdued); padding: 1em !important; margin-top: 2em !important; line-height: var(--line-lg) !important; }
-
-/* loader */
-.splash { position: fixed; top: 0; left: 0; width: 100vw; height: 100vh; z-index: 1000; display: block; text-align: center; }
-.motd { margin-top: 2em; color: var(--body-text-color-subdued); font-family: monospace; font-variant: all-petite-caps; }
-.splash-img { margin: 10% auto 0 auto; width: 512px; background-repeat: no-repeat; height: 512px; animation: color 10s infinite alternate; }
-.loading { color: white; position: absolute; top: 20%; left: 50%; transform: translateX(-50%); }
-.loader { width: 300px; height: 300px; border: var(--spacing-md) solid transparent; border-radius: 50%; border-top: var(--spacing-md) solid var(--primary-600); animation: spin 4s linear infinite; position: relative; }
-.loader::before, .loader::after { content: ""; position: absolute; top: 6px; bottom: 6px; left: 6px; right: 6px; border-radius: 50%; border: var(--spacing-md) solid transparent; }
-.loader::before { border-top-color: var(--primary-900); animation: 3s spin linear infinite; }
-.loader::after { border-top-color: var(--primary-300); animation: spin 1.5s linear infinite; }
-@keyframes move { from { background-position-x: 0, -40px; } to { background-position-x: 0, 40px; } }
-@keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } }
-@keyframes color { from { filter: hue-rotate(0deg) } to { filter: hue-rotate(360deg) } }
-
-:root, .light, .dark {
- --text-xxs: 9px;
- --text-xs: 10px;
- --text-sm: 12px;
- --text-md: 14px;
- --text-lg: 15px;
- --text-xl: 16px;
- --text-xxl: 17px;
- --spacing-xxs: 1px;
- --spacing-xs: 2px;
- --spacing-sm: 3px;
- --spacing-lg: 4px;
- --spacing-xl: 5px;
- --spacing-xxl: 6px;
-}
-
-@media (hover: none) and (pointer: coarse) { /* Apply different styles for devices with coarse pointers dependant on screen resolution */
- @media (max-width: 1024px) { /* Do not affect displays larger than 1024px wide. */
- @media (max-width: 399px) { /* Screens smaller than 400px wide */
- :root, .light, .dark { --left-column: 100%; }
- #txt2img_results, #img2img_results, #extras_results { min-width: calc(min(320px, 100%)) !important;} /* maintain single column for from image operations on larger mobile devices */
- #txt2img_footer p { text-wrap: wrap; }
- }
- @media (min-width: 400px) { /* Screens larger than 400px wide */
- :root, .light, .dark {--left-column: 50%;}
- #txt2img_results, #extras_results, #txt2im g_footer p {text-wrap: wrap; max-width: 100% !important; } /* maintain side by side split on larger mobile displays for from text */
- }
- #scripts_alwayson_txt2img div, #scripts_alwayson_img2img div { max-width: 100%; }
- #txt2img_prompt_container, #img2img_prompt_container, #control_prompt_container { resize:vertical !important; }
- #txt2img_generate_box, #txt2img_enqueue_wrapper { min-width: 100% !important;} /* make generate and enqueue buttons take up the entire width of their rows. */
- #img2img_toprow>div.gradio-column {flex-grow: 1 !important;} /*make interrogate buttons take up appropriate space. */
- #img2img_actions_column {display: flex; min-width: fit-content !important; flex-direction: row;justify-content: space-evenly; align-items: center;}
- #txt2img_generate_box, #img2img_generate_box, #txt2img_enqueue_wrapper,#img2img_enqueue_wrapper {display: flex;flex-direction: column;height: 4em !important;align-items: stretch;justify-content: space-evenly;}
- #img2img_interface, #img2img_results, #img2img_footer p {text-wrap: wrap; min-width: 100% !important; max-width: 100% !important;} /* maintain single column for from image operations on larger mobile devices */
- #img2img_sketch, #img2maskimg, #inpaint_sketch {display: flex; overflow: auto !important; resize: none !important; } /* fix inpaint image display being too large for mobile displays */
- #img2maskimg canvas { width: auto !important; max-height: 100% !important; height: auto !important; }
- #txt2img_sampler, #txt2img_batch, #txt2img_seed_group, #txt2img_advanced, #txt2img_second_pass, #img2img_sampling_group, #img2img_resize_group, #img2img_batch_group, #img2img_seed_group, #img2img_denoise_group, #img2img_advanced_group { width: 100% !important; } /* fix from text/image UI elements to prevent them from moving around within the UI */
- #img2img_resize_group .gradio-radio>div { display: flex; flex-direction: column; width: unset !important; }
- #inpaint_controls div {display:flex;flex-direction: row;}
- #inpaint_controls .gradio-radio>div { display: flex; flex-direction: column !important; }
- #models_tab { flex-direction: column-reverse !important; } /* move image preview/output on models page to bottom of page */
- #enqueue_keyboard_shortcut_modifiers, #enqueue_keyboard_shortcut_key div { max-width: 40% !important;} /* fix settings for agent scheduler */
- #settings { display: flex; flex-direction: row; flex-wrap: wrap; max-width: 100% !important; } /* adjust width of certain settings item to allow aligning as row, but not have it go off the screen */
- #settings div.tab-content>div>div>div { max-width: 80% !important;}
- #settings div .gradio-radio { width: unset !important; }
- #tab_extensions table { border-collapse: collapse; display: block; overflow-x:auto !important;} /* enable scrolling on extensions tab */
- ::-webkit-scrollbar { width: 25px !important; height:25px; } /* increase scrollbar size to make it finger friendly */
- .gradio-dropdown ul.options {max-height: 41vh !important; } /* adjust dropdown size to make them easier to select individual items on mobile. */
- .gradio-dropdown ul.options li.item {height: 40px !important; display: flex; align-items: center;}
- .gradio-slider input[type="number"] { width: 4em; font-size: var(--text-xs); height: 16px; text-align: center; } /* adjust slider input fields as they were too large for mobile devices. */
- #txt2img_settings .block .padded:not(.gradio-accordion) {padding: 0 !important;margin-right: 0; min-width: 100% !important; width:100% !important;}
- }
-}
+@font-face { font-family: 'NotoSans'; font-display: swap; font-style: normal; font-weight: 100; src: local('NotoSans'), url('notosans-nerdfont-regular.ttf') }
+:root { --left-column: 500px; }
+a { font-weight: bold; cursor: pointer; }
+h2 { margin-top: 1em !important; font-size: var(--text-xxl) !important; }
+footer { display: none; }
+table { overflow-x: auto !important; overflow-y: auto !important; }
+td { border-bottom: none !important; padding: 0.1em 0.5em !important; }
+tr { border-bottom: none !important; padding: 0.1em 0.5em !important; }
+textarea { overflow-y: auto !important; }
+span { font-size: var(--text-md) !important; }
+button { font-size: var(--text-lg) !important; }
+
+/* gradio elements */
+.block .padded:not(.gradio-accordion) { padding: 0 !important; margin-right: 0; min-width: 90px !important; }
+.compact { gap: 1em 0.2em; background: transparent !important; padding: 0 !important; }
+.flex-break { flex-basis: 100% !important; }
+.form { border-width: 0; box-shadow: none; background: transparent; overflow: visible; gap: 0.5em 1em; flex-grow: 1 !important; }
+.form-compact { margin-bottom: 0 !important; gap: 0.2em 1em !important; }
+.gap .compact{ padding: 0; gap: 0.2em 0; }
+.hidden { display: none; }
+.tabitem { padding: 0 !important; }
+
+.gradio-dropdown, .block.gradio-slider, .block.gradio-checkbox, .block.gradio-textbox, .block.gradio-radio, .block.gradio-checkboxgroup, .block.gradio-number, .block.gradio-colorpicker { border-width: 0 !important; box-shadow: none !important;}
+.gradio-accordion { padding-top: var(--spacing-md) !important; padding-right: 0 !important; padding-bottom: 0 !important; color: var(--body-text-color); }
+.gradio-accordion .label-wrap .icon { color: var(--button-primary-border-color); }
+.gradio-button { border-radius: var(--radius-lg) !important; }
+.gradio-button.secondary-down { background: var(--button-secondary-background-fill); color: var(--button-secondary-text-color); }
+.gradio-button.secondary-down, .gradio-button.secondary-down:hover { box-shadow: 1px 1px 1px rgba(0,0,0,0.25) inset, 0px 0px 3px rgba(0,0,0,0.15) inset; }
+.gradio-button.secondary-down:hover { background: var(--button-secondary-background-fill-hover); color: var(--button-secondary-text-color-hover); }
+.gradio-button.tool { max-width: min-content; min-width: min-content !important; align-self: end; font-size: 20px !important; color: var(--body-text-color) !important; margin-top: auto; margin-bottom: var(--spacing-md); align-self: center; }
+.gradio-checkbox { margin: 0.75em 1.5em 0 0; align-self: center; }
+.gradio-column { min-width: min(160px, 100%) !important; }
+.gradio-container { max-width: unset !important; padding: var(--block-label-padding) !important; }
+.gradio-container .prose a, .gradio-container .prose a:visited{ color: unset; text-decoration: none; }
+.gradio-dropdown { margin-right: var(--spacing-sm) !important; min-width:160px; max-width:fit-content }
+.gradio-dropdown ul.options { z-index: 1000; min-width: fit-content; max-height: 33vh !important; white-space: nowrap; }
+.gradio-dropdown ul.options li.item { padding: var(--spacing-xs); }
+.gradio-dropdown ul.options li.item:not(:has(.hide)) { background-color: var(--primary-500); }
+.gradio-dropdown .token { padding: var(--spacing-xs); }
+.gradio-dropdown span { margin-bottom: 0 !important; font-size: var(--text-sm); }
+.gradio-dropdown .reference { margin-bottom: var(--spacing-sm) !important; }
+.gradio-html { color: var(--body-text-color); }
+.gradio-html .min { min-height: 0; }
+.gradio-html div.wrap { height: 100%; }
+.gradio-number { min-width: unset !important; max-width: 5em !important; }
+.gradio-textbox { overflow: visible !important; }
+.gradio-radio { padding: 0 !important; width: max-content !important; }
+.gradio-slider { margin-right: var(--spacing-sm) !important; width: max-content !important }
+.gradio-slider input[type="number"] { width: 6em; font-size: var(--text-xs); height: 16px; text-align: right; }
+
+/* custom gradio elements */
+.accordion-compact { padding: 8px 0px 4px 0px !important; }
+.settings-accordion>div { flex-flow: wrap; }
+.small-accordion .form { min-width: var(--left-column) !important; max-width: max-content; }
+.small-accordion .label-wrap .icon { margin-right: 1.6em; margin-left: 0.6em; color: var(--button-primary-border-color); }
+.small-accordion .label-wrap { padding: 16px 0px 8px 0px; margin: 0; border-top: 2px solid var(--button-secondary-border-color); }
+.small-accordion { width: fit-content !important; min-width: fit-content !important; padding-left: 0 !important; }
+.extension-script { max-width: 48vw; }
+button.custom-button{ border-radius: var(--button-large-radius); padding: var(--button-large-padding); font-weight: var(--button-large-text-weight); border: var(--button-border-width) solid var(--button-secondary-border-color);
+ background: var(--button-secondary-background-fill); color: var(--button-secondary-text-color); font-size: var(--text-lg);
+ display: inline-flex; justify-content: center; align-items: center; transition: var(--button-transition); box-shadow: var(--button-shadow); text-align: center; }
+
+/* themes */
+.theme-preview { display: none; position: fixed; border: var(--spacing-sm) solid var(--neutral-600); box-shadow: 2px 2px 2px 2px var(--neutral-700); top: 0; bottom: 0; left: 0; right: 0; margin: auto; max-width: 75vw; z-index: 999; }
+
+/* txt2img/img2img specific */
+.block.token-counter{ position: absolute; display: inline-block; right: 1em; min-width: 0 !important; width: auto; z-index: 100; top: -0.5em; }
+.block.token-counter span{ background: var(--input-background-fill) !important; box-shadow: 0 0 0.0 0.3em rgba(192,192,192,0.15), inset 0 0 0.6em rgba(192,192,192,0.075); border: 2px solid rgba(192,192,192,0.4) !important; }
+.block.token-counter.error span{ box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075); border: 2px solid rgba(255,0,0,0.4) !important; }
+.block.token-counter div{ display: inline; }
+.block.token-counter span{ padding: 0.1em 0.75em; }
+.performance { font-size: var(--text-xs); color: #444; }
+.performance p { display: inline-block; color: var(--body-text-color-subdued) !important }
+.performance .time { margin-right: 0; }
+.thumbnails { background: var(--body-background-fill); }
+#control_gallery { height: 564px; }
+#control-result { padding: 0.5em; }
+#control-inputs { margin-top: 1em; }
+#txt2img_prompt_container, #img2img_prompt_container, #control_prompt_container { margin-right: var(--layout-gap) }
+#txt2img_footer, #img2img_footer, #control_footer { height: fit-content; display: none; }
+#txt2img_generate_box, #img2img_generate_box, #control_general_box { gap: 0.5em; flex-wrap: wrap-reverse; height: fit-content; }
+#txt2img_actions_column, #img2img_actions_column, #control_actions_column { gap: 0.3em; height: fit-content; }
+#txt2img_generate_box>button, #img2img_generate_box>button, #control_generate_box>button, #txt2img_enqueue, #img2img_enqueue { min-height: 42px; max-height: 42px; line-height: 1em; }
+#txt2img_generate_line2, #img2img_generate_line2, #txt2img_tools, #img2img_tools, #control_generate_line2, #control_tools { display: flex; }
+#txt2img_generate_line2>button, #img2img_generate_line2>button, #extras_generate_box>button, #control_generate_line2>button, #txt2img_tools>button, #img2img_tools>button, #control_tools>button { height: 2em; line-height: 0; font-size: var(--text-md);
+ min-width: unset; display: block !important; }
+#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt, #control_prompt, #control_neg_prompt { display: contents; }
+#txt2img_generate_box, #img2img_generate_box { min-width: unset; width: 48%; }
+#control_generate_box { min-width: unset; width: 100%; }
+#txt2img_actions_column, #img2img_actions_column, #control_actions { flex-flow: wrap; justify-content: space-between; }
+#txt2img_enqueue_wrapper, #img2img_enqueue_wrapper, #control_enqueue_wrapper { min-width: unset !important; width: 48%; }
+.interrogate-clip { position: absolute; right: 3em; top: -2.7em; max-width: fit-content; }
+.interrogate-blip { position: absolute; right: 1em; top: -2.7em; max-width: fit-content; }
+.interrogate-col{ min-width: 0 !important; max-width: fit-content; margin-right: var(--spacing-xxl); }
+.interrogate-col>button{ flex: 1; width: 7em; max-height: 84px; }
+#sampler_selection_img2img { margin-top: 1em; }
+#txtimg_hr_finalres{ min-height: 0 !important; }
+#img2img_scale_resolution_preview.block{ display: flex; align-items: end; }
+#txtimg_hr_finalres .resolution, #img2img_scale_resolution_preview .resolution{ font-weight: bold; }
+div#extras_scale_to_tab div.form{ flex-direction: row; }
+#img2img_unused_scale_by_slider { visibility: hidden; width: 0.5em; max-width: 0.5em; min-width: 0.5em; }
+.inactive{ opacity: 0.5; }
+div#extras_scale_to_tab div.form{ flex-direction: row; }
+#mode_img2img .gradio-image>div.fixed-height, #mode_img2img .gradio-image>div.fixed-height img{ height: 480px !important; max-height: 480px !important; min-height: 480px !important; }
+#img2img_sketch, #img2maskimg, #inpaint_sketch { overflow: overlay !important; resize: auto; background: var(--panel-background-fill); z-index: 5; }
+.image-buttons button{ min-width: auto; }
+.infotext { overflow-wrap: break-word; line-height: 1.5em; }
+.infotext>p { padding-left: 1em; text-indent: -1em; white-space: pre-wrap; }
+.tooltip { display: block; position: fixed; top: 1em; right: 1em; padding: 0.5em; background: var(--input-background-fill); color: var(--body-text-color); border: 1pt solid var(--button-primary-border-color);
+ width: 22em; min-height: 1.3em; font-size: var(--text-xs); transition: opacity 0.2s ease-in; pointer-events: none; opacity: 0; z-index: 999; }
+.tooltip-show { opacity: 0.9; }
+.toolbutton-selected { background: var(--background-fill-primary) !important; }
+
+/* settings */
+#si-sparkline-memo, #si-sparkline-load { background-color: #111; }
+#quicksettings { width: fit-content; }
+#quicksettings>button { padding: 0 1em 0 0; align-self: end; margin-bottom: var(--text-sm); }
+#settings { display: flex; gap: var(--layout-gap); }
+#settings div { border: none; gap: 0; margin: 0 0 var(--layout-gap) 0px; padding: 0; }
+#settings>div.tab-content { flex: 10 0 75%; display: grid; }
+#settings>div.tab-content>div { border: none; padding: 0; }
+#settings>div.tab-content>div>div>div>div>div { flex-direction: unset; }
+#settings>div.tab-nav { display: grid; grid-template-columns: repeat(auto-fill, .5em minmax(10em, 1fr)); flex: 1 0 auto; width: 12em; align-self: flex-start; gap: var(--spacing-xxl); }
+#settings>div.tab-nav button { display: block; border: none; text-align: left; white-space: initial; padding: 0; }
+#settings>div.tab-nav>#settings_show_all_pages { padding: var(--size-2) var(--size-4); }
+#settings .block.gradio-checkbox { margin: 0; width: auto; }
+#settings .dirtyable { gap: .5em; }
+#settings .dirtyable.hidden { display: none; }
+#settings .modification-indicator { height: 1.2em; border-radius: 1em !important; padding: 0; width: 0; margin-right: 0.5em; }
+#settings .modification-indicator:disabled { visibility: hidden; }
+#settings .modification-indicator.saved { background: var(--color-accent-soft); width: var(--spacing-sm); }
+#settings .modification-indicator.changed { background: var(--color-accent); width: var(--spacing-sm); }
+#settings .modification-indicator.changed.unsaved { background-image: linear-gradient(var(--color-accent) 25%, var(--color-accent-soft) 75%); width: var(--spacing-sm); }
+#settings_result { margin: 0 1.2em; }
+.licenses { display: block !important; }
+
+/* live preview */
+.progressDiv{ position: relative; height: 20px; background: #b4c0cc; margin-bottom: -3px; }
+.dark .progressDiv{ background: #424c5b; }
+.progressDiv .progress{ width: 0%; height: 20px; background: #0060df; color: white; font-weight: bold; line-height: 20px; padding: 0 8px 0 0; text-align: right; overflow: visible; white-space: nowrap; padding: 0 0.5em; }
+.livePreview { position: absolute; z-index: 50; background-color: transparent; width: -moz-available; width: -webkit-fill-available; }
+.livePreview img { position: absolute; object-fit: contain; width: 100%; height: 100%; }
+.dark .livePreview { background-color: rgb(17 24 39 / var(--tw-bg-opacity)); }
+.popup-metadata { color: white; background: #0000; display: inline-block; white-space: pre-wrap; font-size: var(--text-xxs); }
+.global-popup{ display: flex; position: fixed; z-index: 10001; left: 0; top: 0; width: 100%; height: 100%; overflow: auto; background-color: rgba(20, 20, 20, 0.95);}
+.global-popup-close:before { content: "×"; }
+.global-popup-close{ position: fixed; right: 0.5em; top: 0; cursor: pointer; color: white; font-size: 32pt; }
+.global-popup-inner{ display: inline-block; margin: auto; padding: 2em; }
+
+/* fullpage image viewer */
+#lightboxModal{ display: none; position: fixed; z-index: 1001; left: 0; top: 0; width: 100%; height: 100%; overflow: auto; background-color: rgba(20, 20, 20, 0.75); backdrop-filter: blur(6px);
+ user-select: none; -webkit-user-select: none; flex-direction: row; }
+.modalControls { display: flex; justify-content: space-evenly; background-color: transparent; position: absolute; width: 99%; z-index: 1; }
+.modalControls:hover { background-color: #50505050; }
+.modalControls span { color: white; font-size: 2em; font-weight: bold; cursor: pointer; filter: grayscale(100%); }
+.modalControls span:hover, .modalControls span:focus { color: var(--highlight-color); filter: none; }
+.lightboxModalPreviewZone { display: flex; width: 100%; height: 100%; }
+.lightboxModalPreviewZone:focus-visible { outline: none; }
+.lightboxModalPreviewZone>img { display: block; margin: auto; width: auto; }
+.lightboxModalPreviewZone>img.modalImageFullscreen{ object-fit: contain; height: 100%; width: 100%; min-height: 0; background: transparent; }
+table.settings-value-table { background: white; border-collapse: collapse; margin: 1em; border: var(--spacing-sm) solid white; }
+table.settings-value-table td { padding: 0.4em; border: 1px solid #ccc; max-width: 36em; }
+.modalPrev, .modalNext { cursor: pointer; position: relative; z-index: 1; top: 0; width: auto; height: 100vh; line-height: 100vh; text-align: center; padding: 16px;
+ margin-top: -50px; color: white; font-weight: bold; font-size: 20px; transition: 0.6s ease; user-select: none; -webkit-user-select: none; }
+.modalNext { right: 0; }
+.modalPrev:hover, .modalNext:hover { background-color: rgba(0, 0, 0, 0.8); }
+#imageARPreview { position: absolute; top: 0px; left: 0px; border: 2px solid red; background: rgba(255, 0, 0, 0.3); z-index: 900; pointer-events: none; display: none; }
+
+/* context menu (ie for the generate button) */
+#context-menu { z-index: 9999; position: absolute; display: block; padding: var(--spacing-md); border: 2px solid var(--highlight-color); background: var(--background-fill-primary); color: var(--body-text-color); }
+.context-menu-items { list-style: none; margin: 0; padding: 0; font-size: var(--text-sm); }
+.context-menu-items a { display: block; padding: var(--spacing-md); cursor: pointer; font-weight: normal; }
+.context-menu-items a:hover { background: var(--highlight-color) }
+
+/* extensions */
+#tab_extensions table, #tab_config table{ border-collapse: collapse; }
+#tab_extensions table td, #tab_extensions table th, #tab_config table td, #tab_config table th { border: 1px solid #ccc; padding: 0.25em 0.5em; }
+#tab_extensions table tr:hover, #tab_config table tr:hover { background-color: var(--neutral-500) !important; }
+#tab_extensions table input[type="checkbox"] { margin-right: 0.5em; appearance: checkbox; }
+#tab_extensions button{ max-width: 16em; }
+#tab_extensions input[disabled="disabled"]{ opacity: 0.5; }
+.extension-tag{ font-weight: bold; font-size: var(--text-sm); }
+.extension-button { font-size: var(--text-sm) !important; width: 6em; }
+#extensions .name{ font-size: var(--text-lg) }
+#extensions .type{ opacity: 0.5; font-size: var(--text-sm); text-align: center; }
+#extensions .version{ opacity: 0.7; }
+#extensions .info{ margin: 0; }
+#extensions .date{ opacity: 0.85; font-size: var(--text-sm); }
+
+/* extra networks */
+.extra-networks>div { margin: 0; border-bottom: none !important; gap: 0.3em 0; }
+.extra-networks .second-line { display: flex; width: -moz-available; width: -webkit-fill-available; gap: 0.3em; box-shadow: var(--input-shadow); }
+.extra-networks .search { flex: 1; }
+.extra-networks .description { flex: 3; }
+.extra-networks .tab-nav>button { margin-right: 0; height: 24px; padding: 2px 4px 2px 4px; }
+.extra-networks .buttons { position: absolute; right: 0; margin: -4px; background: var(--background-color); }
+.extra-networks .buttons>button { margin-left: -0.2em; height: 1.4em; color: var(--primary-300) !important; font-size: 20px !important; }
+.extra-networks .custom-button { width: 120px; width: 100%; background: none; justify-content: left; text-align: left; padding: 3px 3px 3px 12px; text-indent: -6px; box-shadow: none; line-break: auto; }
+.extra-networks .custom-button:hover { background: var(--button-primary-background-fill) }
+.extra-networks-tab { padding: 0 !important; }
+.extra-network-subdirs { background: var(--input-background-fill); overflow-x: hidden; overflow-y: auto; min-width: max(15%, 120px); padding-top: 0.5em; margin-top: -4px !important; }
+.extra-networks-page { display: flex }
+.extra-network-cards { display: flex; flex-wrap: wrap; overflow-y: auto; overflow-x: hidden; align-content: flex-start; width: -moz-available; width: -webkit-fill-available; }
+.extra-network-cards .card { height: fit-content; margin: 0 0 0.5em 0.5em; position: relative; scroll-snap-align: start; scroll-margin-top: 0; }
+.extra-network-cards .card .overlay { z-index: 10; width: 100%; background: none; }
+.extra-network-cards .card .overlay .name { font-size: var(--text-lg); font-weight: bold; text-shadow: 1px 1px black; color: white; overflow-wrap: break-word; position: absolute; bottom: 0; padding: 0.2em; z-index: 10; }
+.extra-network-cards .card .preview { box-shadow: var(--button-shadow); min-height: 30px; }
+.extra-network-cards .card:hover .overlay { background: rgba(0, 0, 0, 0.70); }
+.extra-network-cards .card:hover .preview { box-shadow: none; filter: grayscale(100%); }
+.extra-network-cards .card .overlay .tags { display: none; overflow-wrap: anywhere; position: absolute; top: 100%; z-index: 20; background: var(--body-background-fill); }
+.extra-network-cards .card .overlay .tag { padding: 2px; margin: 2px; background: rgba(70, 70, 70, 0.60); font-size: var(--text-md); cursor: pointer; display: inline-block; }
+.extra-network-cards .card .actions>span { padding: 4px; font-size: 34px !important; }
+.extra-network-cards .card .actions>span:hover { color: var(--highlight-color); }
+.extra-network-cards .card:hover .actions { display: block; }
+.extra-network-cards .card:hover .overlay .tags { display: block; }
+.extra-network-cards .card .actions { font-size: 3em; display: none; text-align-last: right; cursor: pointer; font-variant: unicase; position: absolute; z-index: 80; right: 0; height: 0.7em; width: 100%; background: rgba(0, 0, 0, 0.40); }
+.extra-network-cards .card-list { display: flex; margin: 0.3em; padding: 0.3em; background: var(--input-background-fill); cursor: pointer; border-radius: var(--button-large-radius); }
+.extra-network-cards .card-list .tag { color: var(--primary-500); margin-left: 0.8em; }
+.extra-details-close { position: fixed; top: 0.2em; right: 0.2em; z-index: 99; background: var(--button-secondary-background-fill) !important; }
+#txt2img_description, #img2img_description, #control_description { max-height: 63px; overflow-y: auto !important; }
+#txt2img_description>label>textarea, #img2img_description>label>textarea, #control_description>label>textarea { font-size: var(--text-sm) }
+
+#txt2img_extra_details>div, #img2img_extra_details>div { overflow-y: auto; min-height: 40vh; max-height: 80vh; align-self: flex-start; }
+#txt2img_extra_details, #img2img_extra_details { position: fixed; bottom: 50%; left: 50%; transform: translate(-50%, 50%); padding: 0.8em; border: var(--block-border-width) solid var(--highlight-color) !important;
+ z-index: 100; box-shadow: var(--button-shadow); }
+#txt2img_extra_details td:first-child, #img2img_extra_details td:first-child { font-weight: bold; vertical-align: top; }
+#txt2img_extra_details .gradio-image, #img2img_extra_details .gradio-image { max-height: 70vh; }
+
+
+/* specific elements */
+#modelmerger_interp_description { margin-top: 1em; margin-bottom: 1em; }
+#scripts_alwayson_txt2img, #scripts_alwayson_img2img { padding: 0 }
+#scripts_alwayson_txt2img>.label-wrap, #scripts_alwayson_img2img>.label-wrap { background: var(--input-background-fill); padding: 0; margin: 0; border-radius: var(--radius-lg); }
+#scripts_alwayson_txt2img>.label-wrap>span, #scripts_alwayson_img2img>.label-wrap>span { padding: var(--spacing-xxl); }
+#scripts_alwayson_txt2img div { max-width: var(--left-column); }
+#script_txt2img_agent_scheduler { display: none; }
+#refresh_tac_refreshTempFiles { display: none; }
+#train_tab { flex-flow: row-reverse; }
+#models_tab { flex-flow: row-reverse; }
+#swap_axes>button { min-width: 100px; font-size: var(--text-md); }
+#ui_defaults_review { margin: 1em; }
+
+/* extras */
+.extras { gap: 0.2em 1em !important }
+#extras_generate, #extras_interrupt, #extras_skip { display: block !important; position: relative; height: 36px; }
+#extras_upscale { margin-top: 10px }
+#pnginfo_html_info .gradio-html > div { margin: 0.5em; }
+
+/* log monitor */
+.log-monitor { display: none; justify-content: unset !important; overflow: hidden; padding: 0; margin-top: auto; font-family: monospace; font-size: var(--text-xs); }
+.log-monitor td, .log-monitor th { padding-left: 1em; }
+
+/* changelog */
+.md h2 { background-color: var(--background-fill-primary); padding: 0.5em; }
+.md ul { list-style-type: square !important; text-indent: 1em; margin-left: 4em; }
+.md li { list-style-position: outside !important; text-indent: 0; }
+.md p { margin-left: 2em; }
+
+/* custom component */
+.folder-selector textarea { height: 2em !important; padding: 6px !important; }
+.nvml { position: fixed; bottom: 10px; right: 10px; background: var(--background-fill-primary); border: 1px solid var(--button-primary-border-color); padding: 6px; color: var(--button-primary-text-color);
+ font-size: var(--text-xxs); z-index: 50; font-family: monospace; display: none; }
+
+/* control */
+#control_input_type { max-width: 18em }
+#control_settings .small-accordion .form { min-width: 350px !important }
+.control-button { min-height: 42px; max-height: 42px; line-height: 1em; }
+.control-tabs > .tab-nav { margin-bottom: 0; margin-top: 0; }
+.control-unit { max-width: 1200px; padding: 0 !important; margin-top: -10px !important; }
+.control-unit > .label-wrap { margin-bottom: 0 !important; }
+.processor-settings { padding: 0 !important; max-width: 300px; }
+.processor-group>div { flex-flow: wrap;gap: 1em; }
+
+/* main info */
+.main-info { font-weight: var(--section-header-text-weight); color: var(--body-text-color-subdued); padding: 1em !important; margin-top: 2em !important; line-height: var(--line-lg) !important; }
+.update-status { margin: 1em; line-height: 1.5em; }
+
+/* loader */
+.splash { position: fixed; top: 0; left: 0; width: 100vw; height: 100vh; z-index: 1000; display: block; text-align: center; }
+.motd { margin-top: 2em; color: var(--body-text-color-subdued); font-family: monospace; font-variant: all-petite-caps; }
+.splash-img { margin: 10% auto 0 auto; width: 512px; background-repeat: no-repeat; height: 512px; animation: color 10s infinite alternate; }
+.loading { color: white; position: absolute; top: 20%; left: 50%; transform: translateX(-50%); }
+.loader { width: 300px; height: 300px; border: var(--spacing-md) solid transparent; border-radius: 50%; border-top: var(--spacing-md) solid var(--primary-600); animation: spin 4s linear infinite; position: relative; }
+.loader::before, .loader::after { content: ""; position: absolute; top: 6px; bottom: 6px; left: 6px; right: 6px; border-radius: 50%; border: var(--spacing-md) solid transparent; }
+.loader::before { border-top-color: var(--primary-900); animation: 3s spin linear infinite; }
+.loader::after { border-top-color: var(--primary-300); animation: spin 1.5s linear infinite; }
+@keyframes move { from { background-position-x: 0, -40px; } to { background-position-x: 0, 40px; } }
+@keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } }
+@keyframes color { from { filter: hue-rotate(0deg) } to { filter: hue-rotate(360deg) } }
+
+:root, .light, .dark {
+ --text-xxs: 9px;
+ --text-xs: 10px;
+ --text-sm: 12px;
+ --text-md: 14px;
+ --text-lg: 15px;
+ --text-xl: 16px;
+ --text-xxl: 17px;
+ --spacing-xxs: 1px;
+ --spacing-xs: 2px;
+ --spacing-sm: 3px;
+ --spacing-lg: 4px;
+ --spacing-xl: 5px;
+ --spacing-xxl: 6px;
+}
+
+@media (hover: none) and (pointer: coarse) { /* Apply different styles for devices with coarse pointers dependant on screen resolution */
+ @media (max-width: 1024px) { /* Do not affect displays larger than 1024px wide. */
+ @media (max-width: 399px) { /* Screens smaller than 400px wide */
+ :root, .light, .dark { --left-column: 100%; }
+ #txt2img_results, #img2img_results, #extras_results { min-width: calc(min(320px, 100%)) !important;} /* maintain single column for from image operations on larger mobile devices */
+ #txt2img_footer p { text-wrap: wrap; }
+ }
+ @media (min-width: 400px) { /* Screens larger than 400px wide */
+ :root, .light, .dark {--left-column: 50%;}
+ #txt2img_results, #extras_results, #txt2im g_footer p {text-wrap: wrap; max-width: 100% !important; } /* maintain side by side split on larger mobile displays for from text */
+ }
+ #scripts_alwayson_txt2img div, #scripts_alwayson_img2img div { max-width: 100%; }
+ #txt2img_prompt_container, #img2img_prompt_container, #control_prompt_container { resize:vertical !important; }
+ #txt2img_generate_box, #txt2img_enqueue_wrapper { min-width: 100% !important;} /* make generate and enqueue buttons take up the entire width of their rows. */
+ #img2img_toprow>div.gradio-column {flex-grow: 1 !important;} /*make interrogate buttons take up appropriate space. */
+ #img2img_actions_column {display: flex; min-width: fit-content !important; flex-direction: row;justify-content: space-evenly; align-items: center;}
+ #txt2img_generate_box, #img2img_generate_box, #txt2img_enqueue_wrapper,#img2img_enqueue_wrapper {display: flex;flex-direction: column;height: 4em !important;align-items: stretch;justify-content: space-evenly;}
+ #img2img_interface, #img2img_results, #img2img_footer p {text-wrap: wrap; min-width: 100% !important; max-width: 100% !important;} /* maintain single column for from image operations on larger mobile devices */
+ #img2img_sketch, #img2maskimg, #inpaint_sketch {display: flex; overflow: auto !important; resize: none !important; } /* fix inpaint image display being too large for mobile displays */
+ #img2maskimg canvas { width: auto !important; max-height: 100% !important; height: auto !important; }
+ #txt2img_sampler, #txt2img_batch, #txt2img_seed_group, #txt2img_advanced, #txt2img_second_pass, #img2img_sampling_group, #img2img_resize_group, #img2img_batch_group, #img2img_seed_group, #img2img_denoise_group, #img2img_advanced_group { width: 100% !important; } /* fix from text/image UI elements to prevent them from moving around within the UI */
+ #img2img_resize_group .gradio-radio>div { display: flex; flex-direction: column; width: unset !important; }
+ #inpaint_controls div {display:flex;flex-direction: row;}
+ #inpaint_controls .gradio-radio>div { display: flex; flex-direction: column !important; }
+ #models_tab { flex-direction: column-reverse !important; } /* move image preview/output on models page to bottom of page */
+ #enqueue_keyboard_shortcut_modifiers, #enqueue_keyboard_shortcut_key div { max-width: 40% !important;} /* fix settings for agent scheduler */
+ #settings { display: flex; flex-direction: row; flex-wrap: wrap; max-width: 100% !important; } /* adjust width of certain settings item to allow aligning as row, but not have it go off the screen */
+ #settings div.tab-content>div>div>div { max-width: 80% !important;}
+ #settings div .gradio-radio { width: unset !important; }
+ #tab_extensions table { border-collapse: collapse; display: block; overflow-x:auto !important;} /* enable scrolling on extensions tab */
+ ::-webkit-scrollbar { width: 25px !important; height:25px; } /* increase scrollbar size to make it finger friendly */
+ .gradio-dropdown ul.options {max-height: 41vh !important; } /* adjust dropdown size to make them easier to select individual items on mobile. */
+ .gradio-dropdown ul.options li.item {height: 40px !important; display: flex; align-items: center;}
+ .gradio-slider input[type="number"] { width: 4em; font-size: var(--text-xs); height: 16px; text-align: center; } /* adjust slider input fields as they were too large for mobile devices. */
+ #txt2img_settings .block .padded:not(.gradio-accordion) {padding: 0 !important;margin-right: 0; min-width: 100% !important; width:100% !important;}
+ }
+}
diff --git a/javascript/setHints.js b/javascript/setHints.js
index 726d83dfa..e7bbe0d7b 100644
--- a/javascript/setHints.js
+++ b/javascript/setHints.js
@@ -6,7 +6,7 @@ const localeData = {
el: null,
};
-function tooltipCreate() {
+async function tooltipCreate() {
localeData.el = document.createElement('div');
localeData.el.className = 'tooltip';
localeData.el.id = 'tooltip-container';
diff --git a/javascript/settings.js b/javascript/settings.js
index c4bf88e4b..9dd6cfaba 100644
--- a/javascript/settings.js
+++ b/javascript/settings.js
@@ -9,12 +9,13 @@ const monitoredOpts = [
const AppyOpts = [
{ compact_view: (val) => toggleCompact(val) },
+ { gradio_theme: (val, old) => setTheme(val, old) },
{ font_size: (val) => setFontSize(val) },
];
async function updateOpts(json_string) {
const settings_data = JSON.parse(json_string);
- opts = settings_data.values;
+ const new_opts = settings_data.values;
opts_metadata = settings_data.metadata;
for (const op of monitoredOpts) {
@@ -22,16 +23,18 @@ async function updateOpts(json_string) {
const callback = op[key];
if (opts[key] && opts[key] !== settings_data.values[key]) {
log('updateOpts', key, opts[key], settings_data.values[key]);
- if (callback) callback(opts[key]);
+ if (callback) callback(new_opts[key], opts[key]);
}
}
for (const op of AppyOpts) {
const key = Object.keys(op)[0];
const callback = op[key];
- if (callback) callback(settings_data.values[key]);
+ if (callback) callback(new_opts[key], opts[key]);
}
+ opts = new_opts;
+
Object.entries(opts_metadata).forEach(([opt, meta]) => {
if (!opts_tabs[meta.tab_name]) opts_tabs[meta.tab_name] = {};
if (!opts_tabs[meta.tab_name].unsaved_keys) opts_tabs[meta.tab_name].unsaved_keys = new Set();
diff --git a/javascript/timeless-beige.css b/javascript/timeless-beige.css
index d9a17bd00..d0142bdce 100644
--- a/javascript/timeless-beige.css
+++ b/javascript/timeless-beige.css
@@ -1,297 +1,297 @@
-/* generic html tags */
-:root, .light, .dark {
- --font: 'system-ui', 'ui-sans-serif', 'system-ui', "Roboto", sans-serif;
- --font-mono: 'ui-monospace', 'Consolas', monospace;
- --font-size: 16px;
- --primary-100: #212226; /* bg color*/
- --primary-200: #17181b; /* drop down menu/ prompt window fill*/
- --primary-300: #0a0c0e; /* black */
- --primary-400: #2f3034; /* small buttons*/
- --primary-500: #434242; /* main accent color retro beige*/
- --primary-700: #e75d5d; /* light blue gray*/
- --primary-800: #e75d5d; /* sat orange(hover accent)*/
- --highlight-color: var(--primary-500);
- --inactive-color: var(--primary--800);
- --body-text-color: var(--neutral-100);
- --body-text-color-subdued: var(--neutral-300);
- --background-color: var(--primary-100);
- --background-fill-primary: var(--input-background-fill);
- --input-padding: 8px;
- --input-background-fill: var(--primary-200);
- --input-shadow: none;
- --button-secondary-text-color: white;
- --button-secondary-background-fill: var(--primary-400);
- --button-secondary-background-fill-hover: var(--primary-700);
- --block-title-text-color: var(--neutral-300);
- --radius-sm: 1px;
- --radius-lg: 6px;
- --spacing-md: 4px;
- --spacing-xxl: 8px;
- --line-sm: 1.2em;
- --line-md: 1.4em;
-}
-
-html { font-size: var(--font-size); }
-body, button, input, select, textarea { font-family: var(--font);}
-button { max-width: 400px; }
-img { background-color: var(--background-color); }
-input[type=range] { height: var(--line-sm); appearance: none; margin-top: 0; min-width: 160px; background-color: var(--background-color); width: 100%; background: transparent; }
-input[type=range]::-webkit-slider-runnable-track, input[type=range]::-moz-range-track { width: 100%; height: 6px; cursor: pointer; background: var(--primary-400); border-radius: var(--radius-lg); border: 0px solid #222222; }
-input[type=range]::-webkit-slider-thumb, input[type=range]::-moz-range-thumb { border: 0px solid #000000; height: var(--line-sm); width: 8px; border-radius: var(--radius-lg); background: white; cursor: pointer; appearance: none; margin-top: 0px; }
-input[type=range]::-moz-range-progress { background-color: var(--primary-500); height: 6px; border-radius: var(--radius-lg); }
-::-webkit-scrollbar-track { background: #333333; }
-::-webkit-scrollbar-thumb { background-color: var(--highlight-color); border-radius: var(--radius-lg); border-width: 0; box-shadow: 2px 2px 3px #111111; }
-div.form { border-width: 0; box-shadow: none; background: transparent; overflow: visible; margin-bottom: 6px; }
-div.compact { gap: 1em; }
-
-/* gradio style classes */
-fieldset .gr-block.gr-box, label.block span { padding: 0; margin-top: -4px; }
-.border-2 { border-width: 0; }
-.border-b-2 { border-bottom-width: 2px; border-color: var(--highlight-color) !important; padding-bottom: 2px; margin-bottom: 8px; }
-.bg-white { color: lightyellow; background-color: var(--inactive-color); }
-.gr-box { border-radius: var(--radius-sm) !important; background-color: #111111 !important; box-shadow: 2px 2px 3px #111111; border-width: 0; padding: 4px; margin: 12px 0px 12px 0px }
-.gr-button { font-weight: normal; box-shadow: 2px 2px 3px #111111; font-size: 0.8rem; min-width: 32px; min-height: 32px; padding: 3px; margin: 3px; }
-.gr-check-radio { background-color: var(--inactive-color); border-width: 0; border-radius: var(--radius-lg); box-shadow: 2px 2px 3px #111111; }
-.gr-check-radio:checked { background-color: var(--highlight-color); }
-.gr-compact { background-color: var(--background-color); }
-.gr-form { border-width: 0; }
-.gr-input { background-color: #333333 !important; padding: 4px; margin: 4px; }
-.gr-input-label { color: lightyellow; border-width: 0; background: transparent; padding: 2px !important; }
-.gr-panel { background-color: var(--background-color); }
-.eta-bar { display: none !important }
-svg.feather.feather-image, .feather .feather-image { display: none }
-.gap-2 { padding-top: 8px; }
-.gr-box > div > div > input.gr-text-input { right: 0; width: 4em; padding: 0; top: -12px; border: none; max-height: 20px; }
-.output-html { line-height: 1.2rem; overflow-x: hidden; }
-.output-html > div { margin-bottom: 8px; }
-.overflow-hidden .flex .flex-col .relative col .gap-4 { min-width: var(--left-column); max-width: var(--left-column); } /* this is a problematic one */
-.p-2 { padding: 0; }
-.px-4 { padding-lefT: 1rem; padding-right: 1rem; }
-.py-6 { padding-bottom: 0; }
-.tabs { background-color: var(--background-color); }
-.block.token-counter span { background-color: var(--input-background-fill) !important; box-shadow: 2px 2px 2px #111; border: none !important; font-size: 0.8rem; }
-.tab-nav { zoom: 110%; margin-top: 10px; margin-bottom: 10px; border-bottom: 2px solid var(--highlight-color) !important; padding-bottom: 2px; }
-div.tab-nav button.selected {background-color: var(--button-primary-background-fill);}
-#settings div.tab-nav button.selected {background-color: var(--background-color); color: var(--primary-800); font-weight: bold;}
-.label-wrap { background-color: #292b30; /* extension tab color*/ padding: 16px 8px 8px 8px; border-radius: var(--radius-lg); padding-left: 8px !important; }
-.small-accordion .label-wrap { padding: 8px 0px 8px 0px; }
-.small-accordion .label-wrap .icon { margin-right: 1em; }
-.gradio-button.tool { border: none; box-shadow: none; border-radius: var(--radius-lg);}
-button.selected {background: var(--button-primary-background-fill);}
-.center.boundedheight.flex {background-color: var(--input-background-fill);}
-.compact {border-radius: var(--border-radius-lg);}
-#logMonitorData {background-color: var(--input-background-fill);}
-#tab_extensions table td, #tab_extensions table th, #tab_config table td, #tab_config table th { border: none; padding: 0.5em; background-color: var(--primary-200); }
-#tab_extensions table, #tab_config table { width: 96vw; }
-#tab_extensions table input[type=checkbox] {appearance: none; border-radius: 0px;}
-#tab_extensions button:hover { background-color: var(--button-secondary-background-fill-hover);}
-
-/* automatic style classes */
-.progressDiv { border-radius: var(--radius-sm) !important; position: fixed; top: 44px; right: 26px; max-width: 262px; height: 48px; z-index: 99; box-shadow: var(--button-shadow); }
-.progressDiv .progress { border-radius: var(--radius-lg) !important; background: var(--highlight-color); line-height: 3rem; height: 48px; }
-.gallery-item { box-shadow: none !important; }
-.performance { color: #888; }
-.extra-networks { border-left: 2px solid var(--highlight-color) !important; padding-left: 4px; }
-.image-buttons { gap: 10px !important; justify-content: center; }
-.image-buttons > button { max-width: 160px; }
-.tooltip { background: var(--primary-800); color: white; border: none; border-radius: var(--radius-lg) }
-#system_row > button, #settings_row > button, #config_row > button { max-width: 10em; }
-
-/* gradio elements overrides */
-#div.gradio-container { overflow-x: hidden; }
-#img2img_label_copy_to_img2img { font-weight: normal; }
-#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt { background-color: var(--background-color); box-shadow: 4px 4px 4px 0px #333333 !important; }
-#txt2img_prompt > label > textarea, #txt2img_neg_prompt > label > textarea, #img2img_prompt > label > textarea, #img2img_neg_prompt > label > textarea { font-size: 1.1rem; }
-#img2img_settings { min-width: calc(2 * var(--left-column)); max-width: calc(2 * var(--left-column)); background-color: #111111; padding-top: 16px; }
-#interrogate, #deepbooru { margin: 0 0px 10px 0px; max-width: 80px; max-height: 80px; font-weight: normal; font-size: 0.95em; }
-#quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-top: -2px; height: 2.4em; }
-#quicksettings button {padding: 0 0.5em 0.1em 0.5em;}
-#open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h { display: none; }
-#save-animation { border-radius: var(--radius-sm) !important; margin-bottom: 16px; background-color: #111111; }
-#script_list { padding: 4px; margin-top: 16px; margin-bottom: 8px; }
-#settings > div.flex-wrap { width: 15em; }
-#txt2img_cfg_scale { min-width: 200px; }
-#txt2img_checkboxes, #img2img_checkboxes { background-color: transparent; }
-#txt2img_checkboxes, #img2img_checkboxes { margin-bottom: 0.2em; }
-#txt2img_actions_column, #img2img_actions_column { flex-flow: wrap; justify-content: space-between; }
-#txt2img_enqueue_wrapper, #img2img_enqueue_wrapper { min-width: unset; width: 48%; }
-#txt2img_generate_box, #img2img_generate_box { min-width: unset; width: 48%; }
-
-#extras_upscale { margin-top: 10px }
-#txt2img_progress_row > div { min-width: var(--left-column); max-width: var(--left-column); }
-#txt2img_settings { min-width: var(--left-column); max-width: var(--left-column); background-color: #111111; padding-top: 16px; }
-#pnginfo_html2_info { margin-top: -18px; background-color: var(--input-background-fill); padding: var(--input-padding) }
-#txt2img_tools, #img2img_tools { margin-top: -4px; margin-bottom: -4px; }
-#txt2img_styles_row, #img2img_styles_row { margin-top: -6px; z-index: 200; }
-
-/* based on gradio built-in dark theme */
-:root, .light, .dark {
- --body-background-fill: var(--background-color);
- --color-accent-soft: var(--neutral-700);
- --background-fill-secondary: none;
- --border-color-accent: var(--background-color);
- --border-color-primary: var(--background-color);
- --link-text-color-active: var(--primary-500);
- --link-text-color: var(--secondary-500);
- --link-text-color-hover: var(--secondary-400);
- --link-text-color-visited: var(--secondary-600);
- --shadow-spread: 1px;
- --block-background-fill: None;
- --block-border-color: var(--border-color-primary);
- --block_border_width: None;
- --block-info-text-color: var(--body-text-color-subdued);
- --block-label-background-fill: var(--background-fill-secondary);
- --block-label-border-color: var(--border-color-primary);
- --block_label_border_width: None;
- --block-label-text-color: var(--neutral-200);
- --block_shadow: None;
- --block_title_background_fill: None;
- --block_title_border_color: None;
- --block_title_border_width: None;
- --panel-background-fill: var(--background-fill-secondary);
- --panel-border-color: var(--border-color-primary);
- --panel_border_width: None;
- --checkbox-background-color: var(--primary-400);
- --checkbox-background-color-focus: var(--primary-700);
- --checkbox-background-color-hover: var(--primary-700);
- --checkbox-background-color-selected: var(--primary-500);
- --checkbox-border-color: transparent;
- --checkbox-border-color-focus: var(--primary-800);
- --checkbox-border-color-hover: var(--primary-800);
- --checkbox-border-color-selected: var(--primary-800);
- --checkbox-border-width: var(--input-border-width);
- --checkbox-label-background-fill: None;
- --checkbox-label-background-fill-hover: None;
- --checkbox-label-background-fill-selected: var(--checkbox-label-background-fill);
- --checkbox-label-border-color: var(--border-color-primary);
- --checkbox-label-border-color-hover: var(--checkbox-label-border-color);
- --checkbox-label-border-width: var(--input-border-width);
- --checkbox-label-text-color: var(--body-text-color);
- --checkbox-label-text-color-selected: var(--checkbox-label-text-color);
- --error-background-fill: var(--background-fill-primary);
- --error-border-color: var(--border-color-primary);
- --error-text-color: #f768b7; /*was ef4444*/
- --input-background-fill-focus: var(--secondary-600);
- --input-background-fill-hover: var(--input-background-fill);
- --input-border-color: var(--background-color);
- --input-border-color-focus: var(--primary-800);
- --input-placeholder-color: var(--neutral-500);
- --input-shadow-focus: None;
- --loader_color: None;
- --slider_color: None;
- --stat-background-fill: linear-gradient(to right, var(--primary-400), var(--primary-800));
- --table-border-color: var(--neutral-700);
- --table-even-background-fill: var(--primary-300);
- --table-odd-background-fill: var(--primary-200);
- --table-row-focus: var(--color-accent-soft);
- --button-border-width: var(--input-border-width);
- --button-cancel-background-fill: linear-gradient(to bottom right, #dc2626, #b91c1c);
- --button-cancel-background-fill-hover: linear-gradient(to bottom right, #dc2626, #dc2626);
- --button-cancel-border-color: #dc2626;
- --button-cancel-border-color-hover: var(--button-cancel-border-color);
- --button-cancel-text-color: white;
- --button-cancel-text-color-hover: var(--button-cancel-text-color);
- --button-primary-background-fill: var(--primary-500);
- --button-primary-background-fill-hover: var(--primary-800);
- --button-primary-border-color: var(--primary-500);
- --button-primary-border-color-hover: var(--button-primary-border-color);
- --button-primary-text-color: white;
- --button-primary-text-color-hover: var(--button-primary-text-color);
- --button-secondary-border-color: var(--neutral-600);
- --button-secondary-border-color-hover: var(--button-secondary-border-color);
- --button-secondary-text-color-hover: var(--button-secondary-text-color);
- --secondary-50: #eff6ff;
- --secondary-100: #dbeafe;
- --secondary-200: #bfdbfe;
- --secondary-300: #93c5fd;
- --secondary-400: #60a5fa;
- --secondary-500: #3b82f6;
- --secondary-600: #2563eb;
- --secondary-700: #1d4ed8;
- --secondary-800: #1e40af;
- --secondary-900: #1e3a8a;
- --secondary-950: #1d3660;
- --neutral-50: #f0f0f0; /* */
- --neutral-100: #e0dedc;/* majority of text (neutral gray yellow) */
- --neutral-200: #d0d0d0;
- --neutral-300: #9d9dab; /* top tab text (light accent) */
- --neutral-400: #ffba85;/* tab title (light beige) */
- --neutral-500: #484746; /* prompt text (desat accent)*/
- --neutral-600: #605a54; /* tab outline color (accent color)*/
- --neutral-700: #1b1c1e; /* small settings tab accent (dark)*/
- --neutral-800: #e75d5d; /* bright orange accent */
- --neutral-900: #111827;
- --neutral-950: #0b0f19;
- --radius-xxs: 0;
- --radius-xs: 0;
- --radius-md: 0;
- --radius-xl: 0;
- --radius-xxl: 0;
- --body-text-size: var(--text-md);
- --body-text-weight: 400;
- --embed-radius: var(--radius-lg);
- --color-accent: var(--primary-500);
- --shadow-drop: 0;
- --shadow-drop-lg: 0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1);
- --shadow-inset: rgba(0,0,0,0.05) 0px 2px 4px 0px inset;
- --block-border-width: 1px;
- --block-info-text-size: var(--text-sm);
- --block-info-text-weight: 400;
- --block-label-border-width: 1px;
- --block-label-margin: 0;
- --block-label-padding: var(--spacing-sm) var(--spacing-lg);
- --block-label-radius: calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px) 0;
- --block-label-right-radius: 0 calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px);
- --block-label-text-size: var(--text-sm);
- --block-label-text-weight: 400;
- --block-padding: var(--spacing-xl) calc(var(--spacing-xl) + 2px);
- --block-radius: var(--radius-lg);
- --block-shadow: var(--shadow-drop);
- --block-title-background-fill: none;
- --block-title-border-color: none;
- --block-title-border-width: 0;
- --block-title-padding: 0;
- --block-title-radius: none;
- --block-title-text-size: var(--text-md);
- --block-title-text-weight: 400;
- --container-radius: var(--radius-lg);
- --form-gap-width: 1px;
- --layout-gap: var(--spacing-xxl);
- --panel-border-width: 0;
- --section-header-text-size: var(--text-md);
- --section-header-text-weight: 400;
- --checkbox-border-radius: var(--radius-sm);
- --checkbox-label-gap: 2px;
- --checkbox-label-padding: var(--spacing-md);
- --checkbox-label-shadow: var(--shadow-drop);
- --checkbox-label-text-size: var(--text-md);
- --checkbox-label-text-weight: 400;
- --checkbox-check: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e");
- --radio-circle: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e");
- --checkbox-shadow: var(--input-shadow);
- --error-border-width: 1px;
- --input-border-width: 1px;
- --input-radius: var(--radius-lg);
- --input-text-size: var(--text-md);
- --input-text-weight: 400;
- --loader-color: var(--color-accent);
- --prose-text-size: var(--text-md);
- --prose-text-weight: 400;
- --prose-header-text-weight: 600;
- --slider-color: ;
- --table-radius: var(--radius-lg);
- --button-large-padding: 2px 6px;
- --button-large-radius: var(--radius-lg);
- --button-large-text-size: var(--text-lg);
- --button-large-text-weight: 400;
- --button-shadow: none;
- --button-shadow-active: none;
- --button-shadow-hover: none;
- --button-small-padding: var(--spacing-sm) calc(2 * var(--spacing-sm));
- --button-small-radius: var(--radius-lg);
- --button-small-text-size: var(--text-md);
- --button-small-text-weight: 400;
- --button-transition: none;
- --size-9: 64px;
- --size-14: 64px;
-}
+/* generic html tags */
+:root, .light, .dark {
+ --font: 'system-ui', 'ui-sans-serif', 'system-ui', "Roboto", sans-serif;
+ --font-mono: 'ui-monospace', 'Consolas', monospace;
+ --font-size: 16px;
+ --primary-100: #212226; /* bg color*/
+ --primary-200: #17181b; /* drop down menu/ prompt window fill*/
+ --primary-300: #0a0c0e; /* black */
+ --primary-400: #2f3034; /* small buttons*/
+ --primary-500: #434242; /* main accent color retro beige*/
+ --primary-700: #e75d5d; /* light blue gray*/
+ --primary-800: #e75d5d; /* sat orange(hover accent)*/
+ --highlight-color: var(--primary-500);
+ --inactive-color: var(--primary--800);
+ --body-text-color: var(--neutral-100);
+ --body-text-color-subdued: var(--neutral-300);
+ --background-color: var(--primary-100);
+ --background-fill-primary: var(--input-background-fill);
+ --input-padding: 8px;
+ --input-background-fill: var(--primary-200);
+ --input-shadow: none;
+ --button-secondary-text-color: white;
+ --button-secondary-background-fill: var(--primary-400);
+ --button-secondary-background-fill-hover: var(--primary-700);
+ --block-title-text-color: var(--neutral-300);
+ --radius-sm: 1px;
+ --radius-lg: 6px;
+ --spacing-md: 4px;
+ --spacing-xxl: 8px;
+ --line-sm: 1.2em;
+ --line-md: 1.4em;
+}
+
+html { font-size: var(--font-size); }
+body, button, input, select, textarea { font-family: var(--font);}
+button { max-width: 400px; }
+img { background-color: var(--background-color); }
+input[type=range] { height: var(--line-sm); appearance: none; margin-top: 0; min-width: 160px; background-color: var(--background-color); width: 100%; background: transparent; }
+input[type=range]::-webkit-slider-runnable-track, input[type=range]::-moz-range-track { width: 100%; height: 6px; cursor: pointer; background: var(--primary-400); border-radius: var(--radius-lg); border: 0px solid #222222; }
+input[type=range]::-webkit-slider-thumb, input[type=range]::-moz-range-thumb { border: 0px solid #000000; height: var(--line-sm); width: 8px; border-radius: var(--radius-lg); background: white; cursor: pointer; appearance: none; margin-top: 0px; }
+input[type=range]::-moz-range-progress { background-color: var(--primary-500); height: 6px; border-radius: var(--radius-lg); }
+::-webkit-scrollbar-track { background: #333333; }
+::-webkit-scrollbar-thumb { background-color: var(--highlight-color); border-radius: var(--radius-lg); border-width: 0; box-shadow: 2px 2px 3px #111111; }
+div.form { border-width: 0; box-shadow: none; background: transparent; overflow: visible; margin-bottom: 6px; }
+div.compact { gap: 1em; }
+
+/* gradio style classes */
+fieldset .gr-block.gr-box, label.block span { padding: 0; margin-top: -4px; }
+.border-2 { border-width: 0; }
+.border-b-2 { border-bottom-width: 2px; border-color: var(--highlight-color) !important; padding-bottom: 2px; margin-bottom: 8px; }
+.bg-white { color: lightyellow; background-color: var(--inactive-color); }
+.gr-box { border-radius: var(--radius-sm) !important; background-color: #111111 !important; box-shadow: 2px 2px 3px #111111; border-width: 0; padding: 4px; margin: 12px 0px 12px 0px }
+.gr-button { font-weight: normal; box-shadow: 2px 2px 3px #111111; font-size: 0.8rem; min-width: 32px; min-height: 32px; padding: 3px; margin: 3px; }
+.gr-check-radio { background-color: var(--inactive-color); border-width: 0; border-radius: var(--radius-lg); box-shadow: 2px 2px 3px #111111; }
+.gr-check-radio:checked { background-color: var(--highlight-color); }
+.gr-compact { background-color: var(--background-color); }
+.gr-form { border-width: 0; }
+.gr-input { background-color: #333333 !important; padding: 4px; margin: 4px; }
+.gr-input-label { color: lightyellow; border-width: 0; background: transparent; padding: 2px !important; }
+.gr-panel { background-color: var(--background-color); }
+.eta-bar { display: none !important }
+svg.feather.feather-image, .feather .feather-image { display: none }
+.gap-2 { padding-top: 8px; }
+.gr-box > div > div > input.gr-text-input { right: 0; width: 4em; padding: 0; top: -12px; border: none; max-height: 20px; }
+.output-html { line-height: 1.2rem; overflow-x: hidden; }
+.output-html > div { margin-bottom: 8px; }
+.overflow-hidden .flex .flex-col .relative col .gap-4 { min-width: var(--left-column); max-width: var(--left-column); } /* this is a problematic one */
+.p-2 { padding: 0; }
+.px-4 { padding-lefT: 1rem; padding-right: 1rem; }
+.py-6 { padding-bottom: 0; }
+.tabs { background-color: var(--background-color); }
+.block.token-counter span { background-color: var(--input-background-fill) !important; box-shadow: 2px 2px 2px #111; border: none !important; font-size: 0.8rem; }
+.tab-nav { zoom: 110%; margin-top: 10px; margin-bottom: 10px; border-bottom: 2px solid var(--highlight-color) !important; padding-bottom: 2px; }
+div.tab-nav button.selected {background-color: var(--button-primary-background-fill);}
+#settings div.tab-nav button.selected {background-color: var(--background-color); color: var(--primary-800); font-weight: bold;}
+.label-wrap { background-color: #292b30; /* extension tab color*/ padding: 16px 8px 8px 8px; border-radius: var(--radius-lg); padding-left: 8px !important; }
+.small-accordion .label-wrap { padding: 8px 0px 8px 0px; }
+.small-accordion .label-wrap .icon { margin-right: 1em; }
+.gradio-button.tool { border: none; box-shadow: none; border-radius: var(--radius-lg);}
+button.selected {background: var(--button-primary-background-fill);}
+.center.boundedheight.flex {background-color: var(--input-background-fill);}
+.compact {border-radius: var(--border-radius-lg);}
+#logMonitorData {background-color: var(--input-background-fill);}
+#tab_extensions table td, #tab_extensions table th, #tab_config table td, #tab_config table th { border: none; padding: 0.5em; background-color: var(--primary-200); }
+#tab_extensions table, #tab_config table { width: 96vw; }
+#tab_extensions table input[type=checkbox] {appearance: none; border-radius: 0px;}
+#tab_extensions button:hover { background-color: var(--button-secondary-background-fill-hover);}
+
+/* automatic style classes */
+.progressDiv { border-radius: var(--radius-sm) !important; position: fixed; top: 44px; right: 26px; max-width: 262px; height: 48px; z-index: 99; box-shadow: var(--button-shadow); }
+.progressDiv .progress { border-radius: var(--radius-lg) !important; background: var(--highlight-color); line-height: 3rem; height: 48px; }
+.gallery-item { box-shadow: none !important; }
+.performance { color: #888; }
+.extra-networks { border-left: 2px solid var(--highlight-color) !important; padding-left: 4px; }
+.image-buttons { gap: 10px !important; justify-content: center; }
+.image-buttons > button { max-width: 160px; }
+.tooltip { background: var(--primary-800); color: white; border: none; border-radius: var(--radius-lg) }
+#system_row > button, #settings_row > button, #config_row > button { max-width: 10em; }
+
+/* gradio elements overrides */
+#div.gradio-container { overflow-x: hidden; }
+#img2img_label_copy_to_img2img { font-weight: normal; }
+#txt2img_prompt, #txt2img_neg_prompt, #img2img_prompt, #img2img_neg_prompt { background-color: var(--background-color); box-shadow: 4px 4px 4px 0px #333333 !important; }
+#txt2img_prompt > label > textarea, #txt2img_neg_prompt > label > textarea, #img2img_prompt > label > textarea, #img2img_neg_prompt > label > textarea { font-size: 1.1rem; }
+#img2img_settings { min-width: calc(2 * var(--left-column)); max-width: calc(2 * var(--left-column)); background-color: #111111; padding-top: 16px; }
+#interrogate, #deepbooru { margin: 0 0px 10px 0px; max-width: 80px; max-height: 80px; font-weight: normal; font-size: 0.95em; }
+#quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-top: -2px; height: 2.4em; }
+#quicksettings button {padding: 0 0.5em 0.1em 0.5em;}
+#open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h { display: none; }
+#save-animation { border-radius: var(--radius-sm) !important; margin-bottom: 16px; background-color: #111111; }
+#script_list { padding: 4px; margin-top: 16px; margin-bottom: 8px; }
+#settings > div.flex-wrap { width: 15em; }
+#txt2img_cfg_scale { min-width: 200px; }
+#txt2img_checkboxes, #img2img_checkboxes { background-color: transparent; }
+#txt2img_checkboxes, #img2img_checkboxes { margin-bottom: 0.2em; }
+#txt2img_actions_column, #img2img_actions_column { flex-flow: wrap; justify-content: space-between; }
+#txt2img_enqueue_wrapper, #img2img_enqueue_wrapper { min-width: unset; width: 48%; }
+#txt2img_generate_box, #img2img_generate_box { min-width: unset; width: 48%; }
+
+#extras_upscale { margin-top: 10px }
+#txt2img_progress_row > div { min-width: var(--left-column); max-width: var(--left-column); }
+#txt2img_settings { min-width: var(--left-column); max-width: var(--left-column); background-color: #111111; padding-top: 16px; }
+#pnginfo_html2_info { margin-top: -18px; background-color: var(--input-background-fill); padding: var(--input-padding) }
+#txt2img_tools, #img2img_tools { margin-top: -4px; margin-bottom: -4px; }
+#txt2img_styles_row, #img2img_styles_row { margin-top: -6px; z-index: 200; }
+
+/* based on gradio built-in dark theme */
+:root, .light, .dark {
+ --body-background-fill: var(--background-color);
+ --color-accent-soft: var(--neutral-700);
+ --background-fill-secondary: none;
+ --border-color-accent: var(--background-color);
+ --border-color-primary: var(--background-color);
+ --link-text-color-active: var(--primary-500);
+ --link-text-color: var(--secondary-500);
+ --link-text-color-hover: var(--secondary-400);
+ --link-text-color-visited: var(--secondary-600);
+ --shadow-spread: 1px;
+ --block-background-fill: None;
+ --block-border-color: var(--border-color-primary);
+ --block_border_width: None;
+ --block-info-text-color: var(--body-text-color-subdued);
+ --block-label-background-fill: var(--background-fill-secondary);
+ --block-label-border-color: var(--border-color-primary);
+ --block_label_border_width: None;
+ --block-label-text-color: var(--neutral-200);
+ --block_shadow: None;
+ --block_title_background_fill: None;
+ --block_title_border_color: None;
+ --block_title_border_width: None;
+ --panel-background-fill: var(--background-fill-secondary);
+ --panel-border-color: var(--border-color-primary);
+ --panel_border_width: None;
+ --checkbox-background-color: var(--primary-400);
+ --checkbox-background-color-focus: var(--primary-700);
+ --checkbox-background-color-hover: var(--primary-700);
+ --checkbox-background-color-selected: var(--primary-500);
+ --checkbox-border-color: transparent;
+ --checkbox-border-color-focus: var(--primary-800);
+ --checkbox-border-color-hover: var(--primary-800);
+ --checkbox-border-color-selected: var(--primary-800);
+ --checkbox-border-width: var(--input-border-width);
+ --checkbox-label-background-fill: None;
+ --checkbox-label-background-fill-hover: None;
+ --checkbox-label-background-fill-selected: var(--checkbox-label-background-fill);
+ --checkbox-label-border-color: var(--border-color-primary);
+ --checkbox-label-border-color-hover: var(--checkbox-label-border-color);
+ --checkbox-label-border-width: var(--input-border-width);
+ --checkbox-label-text-color: var(--body-text-color);
+ --checkbox-label-text-color-selected: var(--checkbox-label-text-color);
+ --error-background-fill: var(--background-fill-primary);
+ --error-border-color: var(--border-color-primary);
+ --error-text-color: #f768b7; /*was ef4444*/
+ --input-background-fill-focus: var(--secondary-600);
+ --input-background-fill-hover: var(--input-background-fill);
+ --input-border-color: var(--background-color);
+ --input-border-color-focus: var(--primary-800);
+ --input-placeholder-color: var(--neutral-500);
+ --input-shadow-focus: None;
+ --loader_color: None;
+ --slider_color: None;
+ --stat-background-fill: linear-gradient(to right, var(--primary-400), var(--primary-800));
+ --table-border-color: var(--neutral-700);
+ --table-even-background-fill: var(--primary-300);
+ --table-odd-background-fill: var(--primary-200);
+ --table-row-focus: var(--color-accent-soft);
+ --button-border-width: var(--input-border-width);
+ --button-cancel-background-fill: linear-gradient(to bottom right, #dc2626, #b91c1c);
+ --button-cancel-background-fill-hover: linear-gradient(to bottom right, #dc2626, #dc2626);
+ --button-cancel-border-color: #dc2626;
+ --button-cancel-border-color-hover: var(--button-cancel-border-color);
+ --button-cancel-text-color: white;
+ --button-cancel-text-color-hover: var(--button-cancel-text-color);
+ --button-primary-background-fill: var(--primary-500);
+ --button-primary-background-fill-hover: var(--primary-800);
+ --button-primary-border-color: var(--primary-500);
+ --button-primary-border-color-hover: var(--button-primary-border-color);
+ --button-primary-text-color: white;
+ --button-primary-text-color-hover: var(--button-primary-text-color);
+ --button-secondary-border-color: var(--neutral-600);
+ --button-secondary-border-color-hover: var(--button-secondary-border-color);
+ --button-secondary-text-color-hover: var(--button-secondary-text-color);
+ --secondary-50: #eff6ff;
+ --secondary-100: #dbeafe;
+ --secondary-200: #bfdbfe;
+ --secondary-300: #93c5fd;
+ --secondary-400: #60a5fa;
+ --secondary-500: #3b82f6;
+ --secondary-600: #2563eb;
+ --secondary-700: #1d4ed8;
+ --secondary-800: #1e40af;
+ --secondary-900: #1e3a8a;
+ --secondary-950: #1d3660;
+ --neutral-50: #f0f0f0; /* */
+ --neutral-100: #e0dedc;/* majority of text (neutral gray yellow) */
+ --neutral-200: #d0d0d0;
+ --neutral-300: #9d9dab; /* top tab text (light accent) */
+ --neutral-400: #ffba85;/* tab title (light beige) */
+ --neutral-500: #484746; /* prompt text (desat accent)*/
+ --neutral-600: #605a54; /* tab outline color (accent color)*/
+ --neutral-700: #1b1c1e; /* small settings tab accent (dark)*/
+ --neutral-800: #e75d5d; /* bright orange accent */
+ --neutral-900: #111827;
+ --neutral-950: #0b0f19;
+ --radius-xxs: 0;
+ --radius-xs: 0;
+ --radius-md: 0;
+ --radius-xl: 0;
+ --radius-xxl: 0;
+ --body-text-size: var(--text-md);
+ --body-text-weight: 400;
+ --embed-radius: var(--radius-lg);
+ --color-accent: var(--primary-500);
+ --shadow-drop: 0;
+ --shadow-drop-lg: 0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1);
+ --shadow-inset: rgba(0,0,0,0.05) 0px 2px 4px 0px inset;
+ --block-border-width: 1px;
+ --block-info-text-size: var(--text-sm);
+ --block-info-text-weight: 400;
+ --block-label-border-width: 1px;
+ --block-label-margin: 0;
+ --block-label-padding: var(--spacing-sm) var(--spacing-lg);
+ --block-label-radius: calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px) 0;
+ --block-label-right-radius: 0 calc(var(--radius-lg) - 1px) 0 calc(var(--radius-lg) - 1px);
+ --block-label-text-size: var(--text-sm);
+ --block-label-text-weight: 400;
+ --block-padding: var(--spacing-xl) calc(var(--spacing-xl) + 2px);
+ --block-radius: var(--radius-lg);
+ --block-shadow: var(--shadow-drop);
+ --block-title-background-fill: none;
+ --block-title-border-color: none;
+ --block-title-border-width: 0;
+ --block-title-padding: 0;
+ --block-title-radius: none;
+ --block-title-text-size: var(--text-md);
+ --block-title-text-weight: 400;
+ --container-radius: var(--radius-lg);
+ --form-gap-width: 1px;
+ --layout-gap: var(--spacing-xxl);
+ --panel-border-width: 0;
+ --section-header-text-size: var(--text-md);
+ --section-header-text-weight: 400;
+ --checkbox-border-radius: var(--radius-sm);
+ --checkbox-label-gap: 2px;
+ --checkbox-label-padding: var(--spacing-md);
+ --checkbox-label-shadow: var(--shadow-drop);
+ --checkbox-label-text-size: var(--text-md);
+ --checkbox-label-text-weight: 400;
+ --checkbox-check: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e");
+ --radio-circle: url("data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e");
+ --checkbox-shadow: var(--input-shadow);
+ --error-border-width: 1px;
+ --input-border-width: 1px;
+ --input-radius: var(--radius-lg);
+ --input-text-size: var(--text-md);
+ --input-text-weight: 400;
+ --loader-color: var(--color-accent);
+ --prose-text-size: var(--text-md);
+ --prose-text-weight: 400;
+ --prose-header-text-weight: 600;
+ --slider-color: ;
+ --table-radius: var(--radius-lg);
+ --button-large-padding: 2px 6px;
+ --button-large-radius: var(--radius-lg);
+ --button-large-text-size: var(--text-lg);
+ --button-large-text-weight: 400;
+ --button-shadow: none;
+ --button-shadow-active: none;
+ --button-shadow-hover: none;
+ --button-small-padding: var(--spacing-sm) calc(2 * var(--spacing-sm));
+ --button-small-radius: var(--radius-lg);
+ --button-small-text-size: var(--text-md);
+ --button-small-text-weight: 400;
+ --button-transition: none;
+ --size-9: 64px;
+ --size-14: 64px;
+}
diff --git a/javascript/ui.js b/javascript/ui.js
index d7a85d93d..107879b07 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -7,6 +7,7 @@ let img2img_textarea;
const wait_time = 800;
const token_timeouts = {};
let uiLoaded = false;
+window.args_to_array = Array.from; // Compatibility with e.g. extensions that may expect this to be around
function rememberGallerySelection(name) {
// dummy
@@ -64,7 +65,20 @@ function extract_image_from_gallery(gallery) {
return [gallery[index]];
}
-window.args_to_array = Array.from; // Compatibility with e.g. extensions that may expect this to be around
+async function setTheme(val, old) {
+ if (!old) return;
+ const links = Array.from(document.getElementsByTagName('link')).filter((l) => l.href.includes(old));
+ for (const link of links) {
+ const href = link.href.replace(old, val);
+ const res = await fetch(href);
+ if (res.ok) {
+ log('setTheme:', old, val);
+ link.href = link.href.replace(old, val);
+ } else {
+ log('setTheme: CSS not found', val);
+ }
+ }
+}
function setFontSize(val) {
const size = val || opts.font_size;
diff --git a/modules/api/api.py b/modules/api/api.py
index 6ecaebce1..979774d13 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -264,7 +264,16 @@ class Api:
def prepare_img_gen_request(self, request, img_gen_type: str):
if hasattr(request, "face_id") and request.face_id and not request.script_name and (not request.alwayson_scripts or "FaceID" not in request.alwayson_scripts.keys()):
request.script_name = "FaceID"
- request.script_args = [request.face_id.scale, request.face_id.image]
+ request.script_args = [
+ request.face_id.model,
+ request.face_id.scale,
+ request.face_id.image,
+ request.face_id.override_sampler,
+ request.face_id.rank,
+ request.face_id.tokens,
+ request.face_id.structure,
+ request.face_id.cache_model
+ ]
del request.face_id
if hasattr(request, "ip_adapter") and request.ip_adapter and request.script_name != "IP Adapter" and (not request.alwayson_scripts or "IP Adapter" not in request.alwayson_scripts.keys()):
@@ -284,7 +293,7 @@ class Api:
for script_name in request.alwayson_scripts.keys():
script_obj = request.alwayson_scripts[script_name]
- if "args" in script_obj and script_obj["args"]:
+ if script_obj and "args" in script_obj and script_obj["args"]:
self.sanitize_args(script_obj["args"])
if hasattr(request, "script_args") and request.script_args:
diff --git a/modules/api/models.py b/modules/api/models.py
index 0cd38fddc..ff7cbe39c 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -99,8 +99,14 @@ class IPAdapterItem(BaseModel):
class FaceIDItem(BaseModel):
+ model: str = Field(title="Model", default="FaceID Base",description="The FaceID model to use.")
image: str = Field(title="Image", default="", description="Source face image, must be a base64 string containing the image's data.")
- scale: float = Field(title="Scale", default=0.5, gt=0, le=1, description="Scale of the source face, must be between 0 and 1.")
+ scale: float = Field(title="Scale", default=1, gt=0, le=1, description="Scale of the source face, must be between 0 and 1.")
+ structure: float = Field(title="Structure", default=1, gt=0, le=1, description="Structure to use, must be between 0 and 1.")
+ rank: float = Field(title="Rank", default=128, ge=4, le=256, description="Rank to use, must be between 4 and 256.")
+ override_sampler: bool = Field(title="Override Sampler", default=True, description="Should the sampler be overriden?")
+ tokens: int = Field(title="Tokens", default=4, ge=1, le=16, description="Amount of tokens to use, must be between 1 and 16.")
+ cache_model: bool = Field(title="Cache", default=True, description="Should the model be cached?")
StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
diff --git a/modules/call_queue.py b/modules/call_queue.py
index 482a87e82..04dbd81d0 100644
--- a/modules/call_queue.py
+++ b/modules/call_queue.py
@@ -1,86 +1,86 @@
-import html
-import threading
-import time
-import cProfile
-from modules import shared, progress, errors
-
-queue_lock = threading.Lock()
-
-
-def wrap_queued_call(func):
- def f(*args, **kwargs):
- with queue_lock:
- res = func(*args, **kwargs)
- return res
- return f
-
-
-def wrap_gradio_gpu_call(func, extra_outputs=None):
- name = func.__name__
- def f(*args, **kwargs):
- # if the first argument is a string that says "task(...)", it is treated as a job id
- if len(args) > 0 and type(args[0]) == str and args[0][0:5] == "task(" and args[0][-1] == ")":
- id_task = args[0]
- progress.add_task_to_queue(id_task)
- else:
- id_task = None
- with queue_lock:
- progress.start_task(id_task)
- res = [None, '', '', '']
- try:
- res = func(*args, **kwargs)
- progress.record_results(id_task, res)
- except Exception as e:
- shared.log.error(f"Exception: {e}")
- shared.log.error(f"Arguments: args={str(args)[:10240]} kwargs={str(kwargs)[:10240]}")
- errors.display(e, 'gradio call')
- res[-1] = f"{html.escape(str(e))}
"
- finally:
- progress.finish_task(id_task)
- return res
- return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True, name=name)
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False, name=None):
- job_name = name if name is not None else func.__name__
- def f(*args, extra_outputs_array=extra_outputs, **kwargs):
- t = time.perf_counter()
- shared.mem_mon.reset()
- shared.state.begin(job_name)
- try:
- if shared.cmd_opts.profile:
- pr = cProfile.Profile()
- pr.enable()
- res = func(*args, **kwargs)
- if res is None:
- msg = "No result returned from function"
- shared.log.warning(msg)
- res = [None, '', '', f"{html.escape(msg)}
"]
- else:
- res = list(res)
- if shared.cmd_opts.profile:
- errors.profile(pr, 'Wrap')
- except Exception as e:
- errors.display(e, 'gradio call')
- if extra_outputs_array is None:
- extra_outputs_array = [None, '']
- res = extra_outputs_array + [f"{html.escape(type(e).__name__+': '+str(e))}
"]
- shared.state.end()
- if not add_stats:
- return tuple(res)
- elapsed = time.perf_counter() - t
- elapsed_m = int(elapsed // 60)
- elapsed_s = elapsed % 60
- elapsed_text = f"{elapsed_m}m {elapsed_s:.2f}s" if elapsed_m > 0 else f"{elapsed_s:.2f}s"
- vram_html = ''
- if not shared.mem_mon.disabled:
- vram = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.read().items()}
- if vram.get('active_peak', 0) > 0:
- vram_html = " | "
- vram_html += f"GPU active {max(vram['active_peak'], vram['reserved_peak'])} MB reserved {vram['reserved']} | used {vram['used']} MB free {vram['free']} MB total {vram['total']} MB"
- vram_html += f" | retries {vram['retries']} oom {vram['oom']}" if vram.get('retries', 0) > 0 or vram.get('oom', 0) > 0 else ''
- vram_html += "
"
- if isinstance(res, list):
- res[-1] += f""
- return tuple(res)
- return f
+import html
+import threading
+import time
+import cProfile
+from modules import shared, progress, errors
+
+queue_lock = threading.Lock()
+
+
+def wrap_queued_call(func):
+ def f(*args, **kwargs):
+ with queue_lock:
+ res = func(*args, **kwargs)
+ return res
+ return f
+
+
+def wrap_gradio_gpu_call(func, extra_outputs=None):
+ name = func.__name__
+ def f(*args, **kwargs):
+ # if the first argument is a string that says "task(...)", it is treated as a job id
+ if len(args) > 0 and type(args[0]) == str and args[0][0:5] == "task(" and args[0][-1] == ")":
+ id_task = args[0]
+ progress.add_task_to_queue(id_task)
+ else:
+ id_task = None
+ with queue_lock:
+ progress.start_task(id_task)
+ res = [None, '', '', '']
+ try:
+ res = func(*args, **kwargs)
+ progress.record_results(id_task, res)
+ except Exception as e:
+ shared.log.error(f"Exception: {e}")
+ shared.log.error(f"Arguments: args={str(args)[:10240]} kwargs={str(kwargs)[:10240]}")
+ errors.display(e, 'gradio call')
+ res[-1] = f"{html.escape(str(e))}
"
+ finally:
+ progress.finish_task(id_task)
+ return res
+ return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True, name=name)
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False, name=None):
+ job_name = name if name is not None else func.__name__
+ def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+ t = time.perf_counter()
+ shared.mem_mon.reset()
+ shared.state.begin(job_name)
+ try:
+ if shared.cmd_opts.profile:
+ pr = cProfile.Profile()
+ pr.enable()
+ res = func(*args, **kwargs)
+ if res is None:
+ msg = "No result returned from function"
+ shared.log.warning(msg)
+ res = [None, '', '', f"{html.escape(msg)}
"]
+ else:
+ res = list(res)
+ if shared.cmd_opts.profile:
+ errors.profile(pr, 'Wrap')
+ except Exception as e:
+ errors.display(e, 'gradio call')
+ if extra_outputs_array is None:
+ extra_outputs_array = [None, '']
+ res = extra_outputs_array + [f"{html.escape(type(e).__name__+': '+str(e))}
"]
+ shared.state.end()
+ if not add_stats:
+ return tuple(res)
+ elapsed = time.perf_counter() - t
+ elapsed_m = int(elapsed // 60)
+ elapsed_s = elapsed % 60
+ elapsed_text = f"{elapsed_m}m {elapsed_s:.2f}s" if elapsed_m > 0 else f"{elapsed_s:.2f}s"
+ vram_html = ''
+ if not shared.mem_mon.disabled:
+ vram = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.read().items()}
+ if vram.get('active_peak', 0) > 0:
+ vram_html = " | "
+ vram_html += f"GPU active {max(vram['active_peak'], vram['reserved_peak'])} MB reserved {vram['reserved']} | used {vram['used']} MB free {vram['free']} MB total {vram['total']} MB"
+ vram_html += f" | retries {vram['retries']} oom {vram['oom']}" if vram.get('retries', 0) > 0 or vram.get('oom', 0) > 0 else ''
+ vram_html += "
"
+ if isinstance(res, list):
+ res[-1] += f""
+ return tuple(res)
+ return f
diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index 40aeeebce..ae9ecbc85 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -1,127 +1,126 @@
-import os
-import argparse
-from modules.paths import data_path
-
-parser = argparse.ArgumentParser(description="SD.Next", conflict_handler='resolve', epilog='For other options see UI Settings page', prog='', add_help=True, formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=55, indent_increment=2, width=200))
-parser._optionals = parser.add_argument_group('Other options') # pylint: disable=protected-access
-group = parser.add_argument_group('Server options')
-
-# main server args
-group.add_argument("--config", type=str, default=os.environ.get("SD_CONFIG", os.path.join(data_path, 'config.json')), help="Use specific server configuration file, default: %(default)s")
-group.add_argument("--ui-config", type=str, default=os.environ.get("SD_UICONFIG", os.path.join(data_path, 'ui-config.json')), help="Use specific UI configuration file, default: %(default)s")
-group.add_argument("--medvram", default=os.environ.get("SD_MEDVRAM", False), action='store_true', help="Split model stages and keep only active part in VRAM, default: %(default)s")
-group.add_argument("--lowvram", default=os.environ.get("SD_LOWVRAM", False), action='store_true', help="Split model components and keep only active part in VRAM, default: %(default)s")
-group.add_argument("--ckpt", type=str, default=os.environ.get("SD_MODEL", None), help="Path to model checkpoint to load immediately, default: %(default)s")
-group.add_argument('--vae', type=str, default=os.environ.get("SD_VAE", None), help='Path to VAE checkpoint to load immediately, default: %(default)s')
-group.add_argument("--data-dir", type=str, default=os.environ.get("SD_DATADIR", ''), help="Base path where all user data is stored, default: %(default)s")
-group.add_argument("--models-dir", type=str, default=os.environ.get("SD_MODELSDIR", 'models'), help="Base path where all models are stored, default: %(default)s",)
-group.add_argument("--allow-code", default=os.environ.get("SD_ALLOWCODE", False), action='store_true', help="Allow custom script execution, default: %(default)s")
-group.add_argument("--share", default=os.environ.get("SD_SHARE", False), action='store_true', help="Enable UI accessible through Gradio site, default: %(default)s")
-group.add_argument("--insecure", default=os.environ.get("SD_INSECURE", False), action='store_true', help="Enable extensions tab regardless of other options, default: %(default)s")
-group.add_argument("--use-cpu", nargs='+', default=[], type=str.lower, help="Force use CPU for specified modules, default: %(default)s")
-group.add_argument("--listen", default=os.environ.get("SD_LISTEN", False), action='store_true', help="Launch web server using public IP address, default: %(default)s")
-group.add_argument("--port", type=int, default=os.environ.get("SD_PORT", 7860), help="Launch web server with given server port, default: %(default)s")
-group.add_argument("--freeze", default=os.environ.get("SD_FREEZE", False), action='store_true', help="Disable editing settings")
-group.add_argument("--auth", type=str, default=os.environ.get("SD_AUTH", None), help='Set access authentication like "user:pwd,user:pwd""')
-group.add_argument("--auth-file", type=str, default=os.environ.get("SD_AUTHFILE", None), help='Set access authentication using file, default: %(default)s')
-group.add_argument("--autolaunch", default=os.environ.get("SD_AUTOLAUNCH", False), action='store_true', help="Open the UI URL in the system's default browser upon launch")
-group.add_argument('--docs', default=os.environ.get("SD_DOCS", False), action='store_true', help = "Mount Gradio docs at /docs, default: %(default)s")
-group.add_argument('--api-only', default=os.environ.get("SD_APIONLY", False), action='store_true', help = "Run in API only mode without starting UI")
-group.add_argument("--api-log", default=os.environ.get("SD_APILOG", False), action='store_true', help="Enable logging of all API requests, default: %(default)s")
-group.add_argument("--device-id", type=str, default=os.environ.get("SD_DEVICEID", None), help="Select the default CUDA device to use, default: %(default)s")
-group.add_argument("--cors-origins", type=str, default=os.environ.get("SD_CORSORIGINS", None), help="Allowed CORS origins as comma-separated list, default: %(default)s")
-group.add_argument("--cors-regex", type=str, default=os.environ.get("SD_CORSREGEX", None), help="Allowed CORS origins as regular expression, default: %(default)s")
-group.add_argument("--tls-keyfile", type=str, default=os.environ.get("SD_TLSKEYFILE", None), help="Enable TLS and specify key file, default: %(default)s")
-group.add_argument("--tls-certfile", type=str, default=os.environ.get("SD_TLSCERTFILE", None), help="Enable TLS and specify cert file, default: %(default)s")
-group.add_argument("--tls-selfsign", action="store_true", default=os.environ.get("SD_TLSSELFSIGN", False), help="Enable TLS with self-signed certificates, default: %(default)s")
-group.add_argument("--server-name", type=str, default=os.environ.get("SD_SERVERNAME", None), help="Sets hostname of server, default: %(default)s")
-group.add_argument("--no-hashing", default=os.environ.get("SD_NOHASHING", False), action='store_true', help="Disable hashing of checkpoints, default: %(default)s")
-group.add_argument("--no-metadata", default=os.environ.get("SD_NOMETADATA", False), action='store_true', help="Disable reading of metadata from models, default: %(default)s")
-group.add_argument("--no-download", default=os.environ.get("SD_DOWNLOAD", False), action='store_true', help="Disable download of default model, default: %(default)s")
-group.add_argument("--profile", default=os.environ.get("SD_PROFILE", False), action='store_true', help="Run profiler, default: %(default)s")
-group.add_argument("--disable-queue", default=os.environ.get("SD_DISABLEQUEUE", False), action='store_true', help="Disable queues, default: %(default)s")
-group.add_argument('--debug', default=os.environ.get("SD_DEBUG", False), action='store_true', help = "Run installer with debug logging, default: %(default)s")
-group.add_argument('--use-directml', default=os.environ.get("SD_USEDIRECTML", False), action='store_true', help = "Use DirectML if no compatible GPU is detected, default: %(default)s")
-group.add_argument("--use-openvino", default=os.environ.get("SD_USEOPENVINO", False), action='store_true', help="Use Intel OpenVINO backend, default: %(default)s")
-group.add_argument("--use-ipex", default=os.environ.get("SD_USEIPX", False), action='store_true', help="Force use Intel OneAPI XPU backend, default: %(default)s")
-group.add_argument("--use-cuda", default=os.environ.get("SD_USECUDA", False), action='store_true', help="Force use nVidia CUDA backend, default: %(default)s")
-group.add_argument("--use-rocm", default=os.environ.get("SD_USEROCM", False), action='store_true', help="Force use AMD ROCm backend, default: %(default)s")
-group.add_argument('--subpath', type=str, default=os.environ.get("SD_SUBPATH", None), help='Customize the URL subpath for usage with reverse proxy')
-group.add_argument('--backend', type=str, default=os.environ.get("SD_BACKEND", None), choices=['original', 'diffusers'], required=False, help='force model pipeline type')
-
-
-# removed args are added here as hidden in fixed format for compatbility reasons
-group.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui
-group.add_argument("--ui-settings-file", type=str, help=argparse.SUPPRESS, default=os.path.join(data_path, 'config.json'))
-group.add_argument("--ui-config-file", type=str, help=argparse.SUPPRESS, default=os.path.join(data_path, 'ui-config.json'))
-group.add_argument("--hide-ui-dir-config", action='store_true', help=argparse.SUPPRESS, default=False)
-group.add_argument("--theme", type=str, help=argparse.SUPPRESS, default=None)
-group.add_argument("--disable-console-progressbars", action='store_true', help=argparse.SUPPRESS, default=True)
-group.add_argument("--disable-safe-unpickle", action='store_true', help=argparse.SUPPRESS, default=True)
-group.add_argument("--lowram", action='store_true', help=argparse.SUPPRESS)
-group.add_argument("--disable-extension-access", default=False, action='store_true', help=argparse.SUPPRESS)
-group.add_argument("--api", help=argparse.SUPPRESS, default=True)
-group.add_argument("--api-auth", type=str, help=argparse.SUPPRESS, default=None)
-
-
-def compatibility_args(opts, args):
- # removed args that have been moved to opts are added here as hidden with default values as defined in opts
- group.add_argument("--ckpt-dir", type=str, help=argparse.SUPPRESS, default=opts.ckpt_dir)
- group.add_argument("--vae-dir", type=str, help=argparse.SUPPRESS, default=opts.vae_dir)
- group.add_argument("--embeddings-dir", type=str, help=argparse.SUPPRESS, default=opts.embeddings_dir)
- group.add_argument("--embeddings-templates-dir", type=str, help=argparse.SUPPRESS, default=opts.embeddings_templates_dir)
- group.add_argument("--hypernetwork-dir", type=str, help=argparse.SUPPRESS, default=opts.hypernetwork_dir)
- group.add_argument("--codeformer-models-path", type=str, help=argparse.SUPPRESS, default=opts.codeformer_models_path)
- group.add_argument("--gfpgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.gfpgan_models_path)
- group.add_argument("--esrgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.esrgan_models_path)
- group.add_argument("--bsrgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.bsrgan_models_path)
- group.add_argument("--realesrgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.realesrgan_models_path)
- group.add_argument("--scunet-models-path", help=argparse.SUPPRESS, default=opts.scunet_models_path)
- group.add_argument("--swinir-models-path", help=argparse.SUPPRESS, default=opts.swinir_models_path)
- group.add_argument("--ldsr-models-path", help=argparse.SUPPRESS, default=opts.ldsr_models_path)
- group.add_argument("--clip-models-path", type=str, help=argparse.SUPPRESS, default=opts.clip_models_path)
- group.add_argument("--opt-channelslast", help=argparse.SUPPRESS, action='store_true', default=opts.opt_channelslast)
- group.add_argument("--xformers", default=(opts.cross_attention_optimization == "xFormers"), action='store_true', help=argparse.SUPPRESS)
- group.add_argument("--disable-nan-check", help=argparse.SUPPRESS, action='store_true', default=opts.disable_nan_check)
- group.add_argument("--rollback-vae", help=argparse.SUPPRESS, default=opts.rollback_vae)
- group.add_argument("--no-half", help=argparse.SUPPRESS, action='store_true', default=opts.no_half)
- group.add_argument("--no-half-vae", help=argparse.SUPPRESS, action='store_true', default=opts.no_half_vae)
- group.add_argument("--precision", help=argparse.SUPPRESS, default=opts.precision)
- group.add_argument("--sub-quad-q-chunk-size", help=argparse.SUPPRESS, default=opts.sub_quad_q_chunk_size)
- group.add_argument("--sub-quad-kv-chunk-size", help=argparse.SUPPRESS, default=opts.sub_quad_kv_chunk_size)
- group.add_argument("--sub-quad-chunk-threshold", help=argparse.SUPPRESS, default=opts.sub_quad_chunk_threshold)
- group.add_argument("--lora-dir", help=argparse.SUPPRESS, default=opts.lora_dir)
- group.add_argument("--lyco-dir", help=argparse.SUPPRESS, default=opts.lyco_dir)
- group.add_argument("--embeddings-dir", help=argparse.SUPPRESS, default=opts.embeddings_dir)
- group.add_argument("--hypernetwork-dir", help=argparse.SUPPRESS, default=opts.hypernetwork_dir)
- group.add_argument("--lyco-patch-lora", help=argparse.SUPPRESS, action='store_true', default=False)
- group.add_argument("--lyco-debug", help=argparse.SUPPRESS, action='store_true', default=False)
- group.add_argument("--enable-console-prompts", help=argparse.SUPPRESS, action='store_true', default=False)
- group.add_argument("--safe", help=argparse.SUPPRESS, action='store_true', default=False)
- group.add_argument("--use-xformers", help=argparse.SUPPRESS, action='store_true', default=False)
-
- # removed opts are added here with fixed values for compatibility reasons
- opts.use_old_emphasis_implementation = False
- opts.use_old_karras_scheduler_sigmas = False
- opts.no_dpmpp_sde_batch_determinism = False
- opts.lora_apply_to_outputs = False
- opts.do_not_show_images = False
- opts.add_model_hash_to_info = True
- opts.add_model_name_to_info = True
- opts.js_modal_lightbox = True
- opts.js_modal_lightbox_initially_zoomed = True
- opts.show_progress_in_title = False
- opts.sd_vae_as_default = True
- opts.enable_emphasis = True
- opts.enable_batch_seeds = True
- # opts.multiple_tqdm = False
- opts.print_hypernet_extra = False
- opts.dimensions_and_batch_together = True
- opts.enable_pnginfo = True
- opts.data['clip_skip'] = 1
-
- opts.onchange("lora_dir", lambda: setattr(args, "lora_dir", opts.lora_dir))
- opts.onchange("lyco_dir", lambda: setattr(args, "lyco_dir", opts.lyco_dir))
-
- args = parser.parse_args()
- return args
+import os
+import argparse
+from modules.paths import data_path
+
+parser = argparse.ArgumentParser(description="SD.Next", conflict_handler='resolve', epilog='For other options see UI Settings page', prog='', add_help=True, formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=55, indent_increment=2, width=200))
+parser._optionals = parser.add_argument_group('Other options') # pylint: disable=protected-access
+group = parser.add_argument_group('Server options')
+
+# main server args
+group.add_argument("--config", type=str, default=os.environ.get("SD_CONFIG", os.path.join(data_path, 'config.json')), help="Use specific server configuration file, default: %(default)s")
+group.add_argument("--ui-config", type=str, default=os.environ.get("SD_UICONFIG", os.path.join(data_path, 'ui-config.json')), help="Use specific UI configuration file, default: %(default)s")
+group.add_argument("--medvram", default=os.environ.get("SD_MEDVRAM", False), action='store_true', help="Split model stages and keep only active part in VRAM, default: %(default)s")
+group.add_argument("--lowvram", default=os.environ.get("SD_LOWVRAM", False), action='store_true', help="Split model components and keep only active part in VRAM, default: %(default)s")
+group.add_argument("--ckpt", type=str, default=os.environ.get("SD_MODEL", None), help="Path to model checkpoint to load immediately, default: %(default)s")
+group.add_argument('--vae', type=str, default=os.environ.get("SD_VAE", None), help='Path to VAE checkpoint to load immediately, default: %(default)s')
+group.add_argument("--data-dir", type=str, default=os.environ.get("SD_DATADIR", ''), help="Base path where all user data is stored, default: %(default)s")
+group.add_argument("--models-dir", type=str, default=os.environ.get("SD_MODELSDIR", 'models'), help="Base path where all models are stored, default: %(default)s",)
+group.add_argument("--allow-code", default=os.environ.get("SD_ALLOWCODE", False), action='store_true', help="Allow custom script execution, default: %(default)s")
+group.add_argument("--share", default=os.environ.get("SD_SHARE", False), action='store_true', help="Enable UI accessible through Gradio site, default: %(default)s")
+group.add_argument("--insecure", default=os.environ.get("SD_INSECURE", False), action='store_true', help="Enable extensions tab regardless of other options, default: %(default)s")
+group.add_argument("--use-cpu", nargs='+', default=[], type=str.lower, help="Force use CPU for specified modules, default: %(default)s")
+group.add_argument("--listen", default=os.environ.get("SD_LISTEN", False), action='store_true', help="Launch web server using public IP address, default: %(default)s")
+group.add_argument("--port", type=int, default=os.environ.get("SD_PORT", 7860), help="Launch web server with given server port, default: %(default)s")
+group.add_argument("--freeze", default=os.environ.get("SD_FREEZE", False), action='store_true', help="Disable editing settings")
+group.add_argument("--auth", type=str, default=os.environ.get("SD_AUTH", None), help='Set access authentication like "user:pwd,user:pwd""')
+group.add_argument("--auth-file", type=str, default=os.environ.get("SD_AUTHFILE", None), help='Set access authentication using file, default: %(default)s')
+group.add_argument("--autolaunch", default=os.environ.get("SD_AUTOLAUNCH", False), action='store_true', help="Open the UI URL in the system's default browser upon launch")
+group.add_argument('--docs', default=os.environ.get("SD_DOCS", False), action='store_true', help = "Mount Gradio docs at /docs, default: %(default)s")
+group.add_argument('--api-only', default=os.environ.get("SD_APIONLY", False), action='store_true', help = "Run in API only mode without starting UI")
+group.add_argument("--api-log", default=os.environ.get("SD_APILOG", False), action='store_true', help="Enable logging of all API requests, default: %(default)s")
+group.add_argument("--device-id", type=str, default=os.environ.get("SD_DEVICEID", None), help="Select the default CUDA device to use, default: %(default)s")
+group.add_argument("--cors-origins", type=str, default=os.environ.get("SD_CORSORIGINS", None), help="Allowed CORS origins as comma-separated list, default: %(default)s")
+group.add_argument("--cors-regex", type=str, default=os.environ.get("SD_CORSREGEX", None), help="Allowed CORS origins as regular expression, default: %(default)s")
+group.add_argument("--tls-keyfile", type=str, default=os.environ.get("SD_TLSKEYFILE", None), help="Enable TLS and specify key file, default: %(default)s")
+group.add_argument("--tls-certfile", type=str, default=os.environ.get("SD_TLSCERTFILE", None), help="Enable TLS and specify cert file, default: %(default)s")
+group.add_argument("--tls-selfsign", action="store_true", default=os.environ.get("SD_TLSSELFSIGN", False), help="Enable TLS with self-signed certificates, default: %(default)s")
+group.add_argument("--server-name", type=str, default=os.environ.get("SD_SERVERNAME", None), help="Sets hostname of server, default: %(default)s")
+group.add_argument("--no-hashing", default=os.environ.get("SD_NOHASHING", False), action='store_true', help="Disable hashing of checkpoints, default: %(default)s")
+group.add_argument("--no-metadata", default=os.environ.get("SD_NOMETADATA", False), action='store_true', help="Disable reading of metadata from models, default: %(default)s")
+group.add_argument("--profile", default=os.environ.get("SD_PROFILE", False), action='store_true', help="Run profiler, default: %(default)s")
+group.add_argument("--disable-queue", default=os.environ.get("SD_DISABLEQUEUE", False), action='store_true', help="Disable queues, default: %(default)s")
+group.add_argument('--debug', default=os.environ.get("SD_DEBUG", False), action='store_true', help = "Run installer with debug logging, default: %(default)s")
+group.add_argument('--use-directml', default=os.environ.get("SD_USEDIRECTML", False), action='store_true', help = "Use DirectML if no compatible GPU is detected, default: %(default)s")
+group.add_argument("--use-openvino", default=os.environ.get("SD_USEOPENVINO", False), action='store_true', help="Use Intel OpenVINO backend, default: %(default)s")
+group.add_argument("--use-ipex", default=os.environ.get("SD_USEIPX", False), action='store_true', help="Force use Intel OneAPI XPU backend, default: %(default)s")
+group.add_argument("--use-cuda", default=os.environ.get("SD_USECUDA", False), action='store_true', help="Force use nVidia CUDA backend, default: %(default)s")
+group.add_argument("--use-rocm", default=os.environ.get("SD_USEROCM", False), action='store_true', help="Force use AMD ROCm backend, default: %(default)s")
+group.add_argument('--subpath', type=str, default=os.environ.get("SD_SUBPATH", None), help='Customize the URL subpath for usage with reverse proxy')
+group.add_argument('--backend', type=str, default=os.environ.get("SD_BACKEND", None), choices=['original', 'diffusers'], required=False, help='force model pipeline type')
+
+
+# removed args are added here as hidden in fixed format for compatbility reasons
+group.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui
+group.add_argument("--ui-settings-file", type=str, help=argparse.SUPPRESS, default=os.path.join(data_path, 'config.json'))
+group.add_argument("--ui-config-file", type=str, help=argparse.SUPPRESS, default=os.path.join(data_path, 'ui-config.json'))
+group.add_argument("--hide-ui-dir-config", action='store_true', help=argparse.SUPPRESS, default=False)
+group.add_argument("--theme", type=str, help=argparse.SUPPRESS, default=None)
+group.add_argument("--disable-console-progressbars", action='store_true', help=argparse.SUPPRESS, default=True)
+group.add_argument("--disable-safe-unpickle", action='store_true', help=argparse.SUPPRESS, default=True)
+group.add_argument("--lowram", action='store_true', help=argparse.SUPPRESS)
+group.add_argument("--disable-extension-access", default=False, action='store_true', help=argparse.SUPPRESS)
+group.add_argument("--api", help=argparse.SUPPRESS, default=True)
+group.add_argument("--api-auth", type=str, help=argparse.SUPPRESS, default=None)
+
+
+def compatibility_args(opts, args):
+ # removed args that have been moved to opts are added here as hidden with default values as defined in opts
+ group.add_argument("--ckpt-dir", type=str, help=argparse.SUPPRESS, default=opts.ckpt_dir)
+ group.add_argument("--vae-dir", type=str, help=argparse.SUPPRESS, default=opts.vae_dir)
+ group.add_argument("--embeddings-dir", type=str, help=argparse.SUPPRESS, default=opts.embeddings_dir)
+ group.add_argument("--embeddings-templates-dir", type=str, help=argparse.SUPPRESS, default=opts.embeddings_templates_dir)
+ group.add_argument("--hypernetwork-dir", type=str, help=argparse.SUPPRESS, default=opts.hypernetwork_dir)
+ group.add_argument("--codeformer-models-path", type=str, help=argparse.SUPPRESS, default=opts.codeformer_models_path)
+ group.add_argument("--gfpgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.gfpgan_models_path)
+ group.add_argument("--esrgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.esrgan_models_path)
+ group.add_argument("--bsrgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.bsrgan_models_path)
+ group.add_argument("--realesrgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.realesrgan_models_path)
+ group.add_argument("--scunet-models-path", help=argparse.SUPPRESS, default=opts.scunet_models_path)
+ group.add_argument("--swinir-models-path", help=argparse.SUPPRESS, default=opts.swinir_models_path)
+ group.add_argument("--ldsr-models-path", help=argparse.SUPPRESS, default=opts.ldsr_models_path)
+ group.add_argument("--clip-models-path", type=str, help=argparse.SUPPRESS, default=opts.clip_models_path)
+ group.add_argument("--opt-channelslast", help=argparse.SUPPRESS, action='store_true', default=opts.opt_channelslast)
+ group.add_argument("--xformers", default=(opts.cross_attention_optimization == "xFormers"), action='store_true', help=argparse.SUPPRESS)
+ group.add_argument("--disable-nan-check", help=argparse.SUPPRESS, action='store_true', default=opts.disable_nan_check)
+ group.add_argument("--rollback-vae", help=argparse.SUPPRESS, default=opts.rollback_vae)
+ group.add_argument("--no-half", help=argparse.SUPPRESS, action='store_true', default=opts.no_half)
+ group.add_argument("--no-half-vae", help=argparse.SUPPRESS, action='store_true', default=opts.no_half_vae)
+ group.add_argument("--precision", help=argparse.SUPPRESS, default=opts.precision)
+ group.add_argument("--sub-quad-q-chunk-size", help=argparse.SUPPRESS, default=opts.sub_quad_q_chunk_size)
+ group.add_argument("--sub-quad-kv-chunk-size", help=argparse.SUPPRESS, default=opts.sub_quad_kv_chunk_size)
+ group.add_argument("--sub-quad-chunk-threshold", help=argparse.SUPPRESS, default=opts.sub_quad_chunk_threshold)
+ group.add_argument("--lora-dir", help=argparse.SUPPRESS, default=opts.lora_dir)
+ group.add_argument("--lyco-dir", help=argparse.SUPPRESS, default=opts.lyco_dir)
+ group.add_argument("--embeddings-dir", help=argparse.SUPPRESS, default=opts.embeddings_dir)
+ group.add_argument("--hypernetwork-dir", help=argparse.SUPPRESS, default=opts.hypernetwork_dir)
+ group.add_argument("--lyco-patch-lora", help=argparse.SUPPRESS, action='store_true', default=False)
+ group.add_argument("--lyco-debug", help=argparse.SUPPRESS, action='store_true', default=False)
+ group.add_argument("--enable-console-prompts", help=argparse.SUPPRESS, action='store_true', default=False)
+ group.add_argument("--safe", help=argparse.SUPPRESS, action='store_true', default=False)
+ group.add_argument("--use-xformers", help=argparse.SUPPRESS, action='store_true', default=False)
+
+ # removed opts are added here with fixed values for compatibility reasons
+ opts.use_old_emphasis_implementation = False
+ opts.use_old_karras_scheduler_sigmas = False
+ opts.no_dpmpp_sde_batch_determinism = False
+ opts.lora_apply_to_outputs = False
+ opts.do_not_show_images = False
+ opts.add_model_hash_to_info = True
+ opts.add_model_name_to_info = True
+ opts.js_modal_lightbox = True
+ opts.js_modal_lightbox_initially_zoomed = True
+ opts.show_progress_in_title = False
+ opts.sd_vae_as_default = True
+ opts.enable_emphasis = True
+ opts.enable_batch_seeds = True
+ # opts.multiple_tqdm = False
+ opts.print_hypernet_extra = False
+ opts.dimensions_and_batch_together = True
+ opts.enable_pnginfo = True
+ opts.data['clip_skip'] = 1
+
+ opts.onchange("lora_dir", lambda: setattr(args, "lora_dir", opts.lora_dir))
+ opts.onchange("lyco_dir", lambda: setattr(args, "lyco_dir", opts.lyco_dir))
+
+ args = parser.parse_args()
+ return args
diff --git a/modules/control/proc/dpt.py b/modules/control/proc/dpt.py
new file mode 100644
index 000000000..c297fc80c
--- /dev/null
+++ b/modules/control/proc/dpt.py
@@ -0,0 +1,35 @@
+from PIL import Image
+import numpy as np
+import torch
+from transformers import AutoImageProcessor, DPTForDepthEstimation
+from modules import devices
+
+
+image_processor: AutoImageProcessor = None
+dpt_model: DPTForDepthEstimation = None
+
+
+class DPTDetector:
+ def __call__(self, input_image=None):
+ global image_processor, dpt_model # pylint: disable=global-statement
+ from modules.control.processors import cache_dir
+ if image_processor is None:
+ image_processor = AutoImageProcessor.from_pretrained("Intel/dpt-large", cache_dir=cache_dir)
+ if dpt_model is None:
+ dpt_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", cache_dir=cache_dir)
+
+ with devices.inference_context():
+ inputs = image_processor(images=input_image, return_tensors="pt")
+ outputs = dpt_model(**inputs)
+ predicted_depth = outputs.predicted_depth
+ prediction = torch.nn.functional.interpolate(
+ predicted_depth.unsqueeze(1),
+ size=input_image.size[::-1],
+ mode="bicubic",
+ align_corners=False,
+ )
+ output = prediction.squeeze().cpu().numpy()
+ formatted = (output * 255 / np.max(output)).astype("uint8")
+ depth = Image.fromarray(formatted)
+ depth = depth.convert('RGB')
+ return depth
diff --git a/modules/control/proc/glpn.py b/modules/control/proc/glpn.py
new file mode 100644
index 000000000..13e9ee3e2
--- /dev/null
+++ b/modules/control/proc/glpn.py
@@ -0,0 +1,35 @@
+from PIL import Image
+import numpy as np
+import torch
+from transformers import AutoImageProcessor, GLPNForDepthEstimation
+from modules import devices
+
+
+image_processor: AutoImageProcessor = None
+glpn_model: GLPNForDepthEstimation = None
+
+
+class GLPNDetector:
+ def __call__(self, input_image=None):
+ global image_processor, glpn_model # pylint: disable=global-statement
+ from modules.control.processors import cache_dir
+ if image_processor is None:
+ image_processor = AutoImageProcessor.from_pretrained("vinvino02/glpn-kitti", cache_dir=cache_dir)
+ if glpn_model is None:
+ glpn_model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti", cache_dir=cache_dir)
+
+ with devices.inference_context():
+ inputs = image_processor(images=input_image, return_tensors="pt")
+ outputs = glpn_model(**inputs)
+ predicted_depth = outputs.predicted_depth
+ prediction = torch.nn.functional.interpolate(
+ predicted_depth.unsqueeze(1),
+ size=input_image.size[::-1],
+ mode="bicubic",
+ align_corners=False,
+ )
+ output = prediction.squeeze().cpu().numpy()
+ formatted = 255 - (output * 255 / np.max(output)).astype("uint8")
+ depth = Image.fromarray(formatted)
+ depth = depth.convert('RGB')
+ return depth
diff --git a/modules/control/processors.py b/modules/control/processors.py
index a17ffe0dd..76a3b6e19 100644
--- a/modules/control/processors.py
+++ b/modules/control/processors.py
@@ -23,6 +23,8 @@ from modules.control.proc.dwpose import DWposeDetector
from modules.control.proc.segment_anything import SamDetector
from modules.control.proc.zoe import ZoeDetector
from modules.control.proc.marigold import MarigoldDetector
+from modules.control.proc.dpt import DPTDetector
+from modules.control.proc.glpn import GLPNDetector
models = {}
@@ -52,6 +54,8 @@ config = {
# other models
'MLSD': {'class': MLSDdetector, 'checkpoint': True, 'params': {'thr_v': 0.1, 'thr_d': 0.1}},
'Shuffle': {'class': ContentShuffleDetector, 'checkpoint': False, 'params': {}},
+ 'DPT Depth Hybrid': {'class': DPTDetector, 'checkpoint': False, 'params': {}},
+ 'GLPN Depth': {'class': GLPNDetector, 'checkpoint': False, 'params': {}},
# 'Midas Depth Large': {'class': MidasDetector, 'checkpoint': True, 'params': {'bg_th': 0.1, 'depth_and_normal': False}, 'load_config': {'pretrained_model_or_path': 'Intel/dpt-large', 'model_type': "dpt_large", 'filename': ''}},
# 'Zoe Depth Zoe': {'class': ZoeDetector, 'checkpoint': True, 'params': {}},
# 'Zoe Depth NK': {'class': ZoeDetector, 'checkpoint': True, 'params': {}, 'load_config': {'pretrained_model_or_path': 'halffried/gyre_zoedepth', 'filename': 'ZoeD_M12_NK.safetensors', 'model_type': "zoedepth_nk"}},
@@ -114,19 +118,13 @@ def update_settings(*settings):
class Processor():
- def __init__(self, processor_id: str = None, resize = True, load_config = None):
+ def __init__(self, processor_id: str = None, resize = True):
self.model = None
+ self.processor_id = None
+ self.override = None
self.resize = resize
- self.processor_id = processor_id
- self.override = None # override input image
- self.load_config = { 'cache_dir': cache_dir }
- from_config = config.get(processor_id, {}).get('load_config', None)
- if load_config is not None:
- for k, v in load_config.items():
- self.load_config[k] = v
- if from_config is not None:
- for k, v in from_config.items():
- self.load_config[k] = v
+ self.reset()
+ self.config(processor_id)
if processor_id is not None:
self.load()
@@ -136,6 +134,20 @@ class Processor():
self.model = None
self.processor_id = None
self.override = None
+ self.load_config = { 'cache_dir': cache_dir }
+
+ def config(self, processor_id = None):
+ if processor_id is not None:
+ self.processor_id = processor_id
+ from_config = config.get(self.processor_id, {}).get('load_config', None)
+ """
+ if load_config is not None:
+ for k, v in load_config.items():
+ self.load_config[k] = v
+ """
+ if from_config is not None:
+ for k, v in from_config.items():
+ self.load_config[k] = v
def load(self, processor_id: str = None) -> str:
try:
@@ -144,10 +156,9 @@ class Processor():
if processor_id is None or processor_id == 'None':
self.reset()
return ''
- from_config = config.get(processor_id, {}).get('load_config', None)
- if from_config is not None:
- for k, v in from_config.items():
- self.load_config[k] = v
+ if self.processor_id != processor_id:
+ self.reset()
+ self.config(processor_id)
cls = config[processor_id]['class']
log.debug(f'Control Processor loading: id="{processor_id}" class={cls.__name__}')
debug(f'Control Processor config={self.load_config}')
diff --git a/modules/control/run.py b/modules/control/run.py
index 5c5d6da30..7c4ccc16f 100644
--- a/modules/control/run.py
+++ b/modules/control/run.py
@@ -80,7 +80,7 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
hdr_clamp, hdr_boundary, hdr_threshold, hdr_center, hdr_channel_shift, hdr_full_shift, hdr_maximize, hdr_max_center, hdr_max_boundry,
resize_mode_before, resize_name_before, width_before, height_before, scale_by_before, selected_scale_tab_before,
resize_mode_after, resize_name_after, width_after, height_after, scale_by_after, selected_scale_tab_after,
- denoising_strength, batch_count, batch_size, mask_blur, mask_overlap,
+ denoising_strength, batch_count, batch_size,
video_skip_frames, video_type, video_duration, video_loop, video_pad, video_interpolate,
ip_adapter, ip_scale, ip_image,
*input_script_args
@@ -135,7 +135,6 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
denoising_strength = denoising_strength,
n_iter = batch_count,
batch_size = batch_size,
- mask_blur=mask_blur,
outpath_samples=shared.opts.outdir_samples or shared.opts.outdir_control_samples,
outpath_grids=shared.opts.outdir_grids or shared.opts.outdir_control_grids,
)
diff --git a/modules/control/units/xs_pipe.py b/modules/control/units/xs_pipe.py
index 51e6d8191..0d701c441 100644
--- a/modules/control/units/xs_pipe.py
+++ b/modules/control/units/xs_pipe.py
@@ -16,7 +16,7 @@ import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
-import PIL.Image
+from PIL import Image
import torch
import torch.nn.functional as F
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPImageProcessor
@@ -541,10 +541,10 @@ class StableDiffusionXLControlNetXSPipeline(
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
def check_image(self, image, prompt, prompt_embeds):
- image_is_pil = isinstance(image, PIL.Image.Image)
+ image_is_pil = isinstance(image, Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(image, np.ndarray)
- image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
+ image_is_pil_list = isinstance(image, list) and isinstance(image[0], Image.Image)
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
@@ -1550,10 +1550,10 @@ class StableDiffusionControlNetXSPipeline(
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
def check_image(self, image, prompt, prompt_embeds):
- image_is_pil = isinstance(image, PIL.Image.Image)
+ image_is_pil = isinstance(image, Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(image, np.ndarray)
- image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
+ image_is_pil_list = isinstance(image, list) and isinstance(image[0], Image.Image)
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
diff --git a/modules/deepbooru_model.py b/modules/deepbooru_model.py
index edeb81866..2963385c3 100644
--- a/modules/deepbooru_model.py
+++ b/modules/deepbooru_model.py
@@ -1,674 +1,674 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from modules import devices
-
-# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
-
-
-class DeepDanbooruModel(nn.Module):
- def __init__(self):
- super().__init__()
- self.tags = []
- self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
- self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
- self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
- self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
- self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
- self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
- self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
- self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
- self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
- self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
- self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
- self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
- self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
- self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
- self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
- self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
- self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
- self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
- self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
- self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
- self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
- self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
- self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
- self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
- self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
- self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
- self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
- self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
- self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
- self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
- self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
- self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
- self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
- self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
- self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
- self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
- self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
- self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
- self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
- self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
- self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
- self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
- self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
- self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
- self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
- self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
- self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
- self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
- self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
- self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
- self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
- self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
- self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
- self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
- self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
- self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
- self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
- self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
- self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
- self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
- self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
- self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
- self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
- self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
- self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
- self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
-
- def forward(self, *inputs):
- t_358, = inputs
- t_359 = t_358.permute(*[0, 3, 1, 2])
- t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
- t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
- t_361 = F.relu(t_360)
- t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
- t_362 = self.n_MaxPool_0(t_361)
- t_363 = self.n_Conv_1(t_362)
- t_364 = self.n_Conv_2(t_362)
- t_365 = F.relu(t_364)
- t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
- t_366 = self.n_Conv_3(t_365_padded)
- t_367 = F.relu(t_366)
- t_368 = self.n_Conv_4(t_367)
- t_369 = torch.add(t_368, t_363)
- t_370 = F.relu(t_369)
- t_371 = self.n_Conv_5(t_370)
- t_372 = F.relu(t_371)
- t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
- t_373 = self.n_Conv_6(t_372_padded)
- t_374 = F.relu(t_373)
- t_375 = self.n_Conv_7(t_374)
- t_376 = torch.add(t_375, t_370)
- t_377 = F.relu(t_376)
- t_378 = self.n_Conv_8(t_377)
- t_379 = F.relu(t_378)
- t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
- t_380 = self.n_Conv_9(t_379_padded)
- t_381 = F.relu(t_380)
- t_382 = self.n_Conv_10(t_381)
- t_383 = torch.add(t_382, t_377)
- t_384 = F.relu(t_383)
- t_385 = self.n_Conv_11(t_384)
- t_386 = self.n_Conv_12(t_384)
- t_387 = F.relu(t_386)
- t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
- t_388 = self.n_Conv_13(t_387_padded)
- t_389 = F.relu(t_388)
- t_390 = self.n_Conv_14(t_389)
- t_391 = torch.add(t_390, t_385)
- t_392 = F.relu(t_391)
- t_393 = self.n_Conv_15(t_392)
- t_394 = F.relu(t_393)
- t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
- t_395 = self.n_Conv_16(t_394_padded)
- t_396 = F.relu(t_395)
- t_397 = self.n_Conv_17(t_396)
- t_398 = torch.add(t_397, t_392)
- t_399 = F.relu(t_398)
- t_400 = self.n_Conv_18(t_399)
- t_401 = F.relu(t_400)
- t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
- t_402 = self.n_Conv_19(t_401_padded)
- t_403 = F.relu(t_402)
- t_404 = self.n_Conv_20(t_403)
- t_405 = torch.add(t_404, t_399)
- t_406 = F.relu(t_405)
- t_407 = self.n_Conv_21(t_406)
- t_408 = F.relu(t_407)
- t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
- t_409 = self.n_Conv_22(t_408_padded)
- t_410 = F.relu(t_409)
- t_411 = self.n_Conv_23(t_410)
- t_412 = torch.add(t_411, t_406)
- t_413 = F.relu(t_412)
- t_414 = self.n_Conv_24(t_413)
- t_415 = F.relu(t_414)
- t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
- t_416 = self.n_Conv_25(t_415_padded)
- t_417 = F.relu(t_416)
- t_418 = self.n_Conv_26(t_417)
- t_419 = torch.add(t_418, t_413)
- t_420 = F.relu(t_419)
- t_421 = self.n_Conv_27(t_420)
- t_422 = F.relu(t_421)
- t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
- t_423 = self.n_Conv_28(t_422_padded)
- t_424 = F.relu(t_423)
- t_425 = self.n_Conv_29(t_424)
- t_426 = torch.add(t_425, t_420)
- t_427 = F.relu(t_426)
- t_428 = self.n_Conv_30(t_427)
- t_429 = F.relu(t_428)
- t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
- t_430 = self.n_Conv_31(t_429_padded)
- t_431 = F.relu(t_430)
- t_432 = self.n_Conv_32(t_431)
- t_433 = torch.add(t_432, t_427)
- t_434 = F.relu(t_433)
- t_435 = self.n_Conv_33(t_434)
- t_436 = F.relu(t_435)
- t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
- t_437 = self.n_Conv_34(t_436_padded)
- t_438 = F.relu(t_437)
- t_439 = self.n_Conv_35(t_438)
- t_440 = torch.add(t_439, t_434)
- t_441 = F.relu(t_440)
- t_442 = self.n_Conv_36(t_441)
- t_443 = self.n_Conv_37(t_441)
- t_444 = F.relu(t_443)
- t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
- t_445 = self.n_Conv_38(t_444_padded)
- t_446 = F.relu(t_445)
- t_447 = self.n_Conv_39(t_446)
- t_448 = torch.add(t_447, t_442)
- t_449 = F.relu(t_448)
- t_450 = self.n_Conv_40(t_449)
- t_451 = F.relu(t_450)
- t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
- t_452 = self.n_Conv_41(t_451_padded)
- t_453 = F.relu(t_452)
- t_454 = self.n_Conv_42(t_453)
- t_455 = torch.add(t_454, t_449)
- t_456 = F.relu(t_455)
- t_457 = self.n_Conv_43(t_456)
- t_458 = F.relu(t_457)
- t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
- t_459 = self.n_Conv_44(t_458_padded)
- t_460 = F.relu(t_459)
- t_461 = self.n_Conv_45(t_460)
- t_462 = torch.add(t_461, t_456)
- t_463 = F.relu(t_462)
- t_464 = self.n_Conv_46(t_463)
- t_465 = F.relu(t_464)
- t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
- t_466 = self.n_Conv_47(t_465_padded)
- t_467 = F.relu(t_466)
- t_468 = self.n_Conv_48(t_467)
- t_469 = torch.add(t_468, t_463)
- t_470 = F.relu(t_469)
- t_471 = self.n_Conv_49(t_470)
- t_472 = F.relu(t_471)
- t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
- t_473 = self.n_Conv_50(t_472_padded)
- t_474 = F.relu(t_473)
- t_475 = self.n_Conv_51(t_474)
- t_476 = torch.add(t_475, t_470)
- t_477 = F.relu(t_476)
- t_478 = self.n_Conv_52(t_477)
- t_479 = F.relu(t_478)
- t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
- t_480 = self.n_Conv_53(t_479_padded)
- t_481 = F.relu(t_480)
- t_482 = self.n_Conv_54(t_481)
- t_483 = torch.add(t_482, t_477)
- t_484 = F.relu(t_483)
- t_485 = self.n_Conv_55(t_484)
- t_486 = F.relu(t_485)
- t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
- t_487 = self.n_Conv_56(t_486_padded)
- t_488 = F.relu(t_487)
- t_489 = self.n_Conv_57(t_488)
- t_490 = torch.add(t_489, t_484)
- t_491 = F.relu(t_490)
- t_492 = self.n_Conv_58(t_491)
- t_493 = F.relu(t_492)
- t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
- t_494 = self.n_Conv_59(t_493_padded)
- t_495 = F.relu(t_494)
- t_496 = self.n_Conv_60(t_495)
- t_497 = torch.add(t_496, t_491)
- t_498 = F.relu(t_497)
- t_499 = self.n_Conv_61(t_498)
- t_500 = F.relu(t_499)
- t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
- t_501 = self.n_Conv_62(t_500_padded)
- t_502 = F.relu(t_501)
- t_503 = self.n_Conv_63(t_502)
- t_504 = torch.add(t_503, t_498)
- t_505 = F.relu(t_504)
- t_506 = self.n_Conv_64(t_505)
- t_507 = F.relu(t_506)
- t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
- t_508 = self.n_Conv_65(t_507_padded)
- t_509 = F.relu(t_508)
- t_510 = self.n_Conv_66(t_509)
- t_511 = torch.add(t_510, t_505)
- t_512 = F.relu(t_511)
- t_513 = self.n_Conv_67(t_512)
- t_514 = F.relu(t_513)
- t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
- t_515 = self.n_Conv_68(t_514_padded)
- t_516 = F.relu(t_515)
- t_517 = self.n_Conv_69(t_516)
- t_518 = torch.add(t_517, t_512)
- t_519 = F.relu(t_518)
- t_520 = self.n_Conv_70(t_519)
- t_521 = F.relu(t_520)
- t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
- t_522 = self.n_Conv_71(t_521_padded)
- t_523 = F.relu(t_522)
- t_524 = self.n_Conv_72(t_523)
- t_525 = torch.add(t_524, t_519)
- t_526 = F.relu(t_525)
- t_527 = self.n_Conv_73(t_526)
- t_528 = F.relu(t_527)
- t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
- t_529 = self.n_Conv_74(t_528_padded)
- t_530 = F.relu(t_529)
- t_531 = self.n_Conv_75(t_530)
- t_532 = torch.add(t_531, t_526)
- t_533 = F.relu(t_532)
- t_534 = self.n_Conv_76(t_533)
- t_535 = F.relu(t_534)
- t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
- t_536 = self.n_Conv_77(t_535_padded)
- t_537 = F.relu(t_536)
- t_538 = self.n_Conv_78(t_537)
- t_539 = torch.add(t_538, t_533)
- t_540 = F.relu(t_539)
- t_541 = self.n_Conv_79(t_540)
- t_542 = F.relu(t_541)
- t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
- t_543 = self.n_Conv_80(t_542_padded)
- t_544 = F.relu(t_543)
- t_545 = self.n_Conv_81(t_544)
- t_546 = torch.add(t_545, t_540)
- t_547 = F.relu(t_546)
- t_548 = self.n_Conv_82(t_547)
- t_549 = F.relu(t_548)
- t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
- t_550 = self.n_Conv_83(t_549_padded)
- t_551 = F.relu(t_550)
- t_552 = self.n_Conv_84(t_551)
- t_553 = torch.add(t_552, t_547)
- t_554 = F.relu(t_553)
- t_555 = self.n_Conv_85(t_554)
- t_556 = F.relu(t_555)
- t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
- t_557 = self.n_Conv_86(t_556_padded)
- t_558 = F.relu(t_557)
- t_559 = self.n_Conv_87(t_558)
- t_560 = torch.add(t_559, t_554)
- t_561 = F.relu(t_560)
- t_562 = self.n_Conv_88(t_561)
- t_563 = F.relu(t_562)
- t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
- t_564 = self.n_Conv_89(t_563_padded)
- t_565 = F.relu(t_564)
- t_566 = self.n_Conv_90(t_565)
- t_567 = torch.add(t_566, t_561)
- t_568 = F.relu(t_567)
- t_569 = self.n_Conv_91(t_568)
- t_570 = F.relu(t_569)
- t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
- t_571 = self.n_Conv_92(t_570_padded)
- t_572 = F.relu(t_571)
- t_573 = self.n_Conv_93(t_572)
- t_574 = torch.add(t_573, t_568)
- t_575 = F.relu(t_574)
- t_576 = self.n_Conv_94(t_575)
- t_577 = F.relu(t_576)
- t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
- t_578 = self.n_Conv_95(t_577_padded)
- t_579 = F.relu(t_578)
- t_580 = self.n_Conv_96(t_579)
- t_581 = torch.add(t_580, t_575)
- t_582 = F.relu(t_581)
- t_583 = self.n_Conv_97(t_582)
- t_584 = F.relu(t_583)
- t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
- t_585 = self.n_Conv_98(t_584_padded)
- t_586 = F.relu(t_585)
- t_587 = self.n_Conv_99(t_586)
- t_588 = self.n_Conv_100(t_582)
- t_589 = torch.add(t_587, t_588)
- t_590 = F.relu(t_589)
- t_591 = self.n_Conv_101(t_590)
- t_592 = F.relu(t_591)
- t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
- t_593 = self.n_Conv_102(t_592_padded)
- t_594 = F.relu(t_593)
- t_595 = self.n_Conv_103(t_594)
- t_596 = torch.add(t_595, t_590)
- t_597 = F.relu(t_596)
- t_598 = self.n_Conv_104(t_597)
- t_599 = F.relu(t_598)
- t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
- t_600 = self.n_Conv_105(t_599_padded)
- t_601 = F.relu(t_600)
- t_602 = self.n_Conv_106(t_601)
- t_603 = torch.add(t_602, t_597)
- t_604 = F.relu(t_603)
- t_605 = self.n_Conv_107(t_604)
- t_606 = F.relu(t_605)
- t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
- t_607 = self.n_Conv_108(t_606_padded)
- t_608 = F.relu(t_607)
- t_609 = self.n_Conv_109(t_608)
- t_610 = torch.add(t_609, t_604)
- t_611 = F.relu(t_610)
- t_612 = self.n_Conv_110(t_611)
- t_613 = F.relu(t_612)
- t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
- t_614 = self.n_Conv_111(t_613_padded)
- t_615 = F.relu(t_614)
- t_616 = self.n_Conv_112(t_615)
- t_617 = torch.add(t_616, t_611)
- t_618 = F.relu(t_617)
- t_619 = self.n_Conv_113(t_618)
- t_620 = F.relu(t_619)
- t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
- t_621 = self.n_Conv_114(t_620_padded)
- t_622 = F.relu(t_621)
- t_623 = self.n_Conv_115(t_622)
- t_624 = torch.add(t_623, t_618)
- t_625 = F.relu(t_624)
- t_626 = self.n_Conv_116(t_625)
- t_627 = F.relu(t_626)
- t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
- t_628 = self.n_Conv_117(t_627_padded)
- t_629 = F.relu(t_628)
- t_630 = self.n_Conv_118(t_629)
- t_631 = torch.add(t_630, t_625)
- t_632 = F.relu(t_631)
- t_633 = self.n_Conv_119(t_632)
- t_634 = F.relu(t_633)
- t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
- t_635 = self.n_Conv_120(t_634_padded)
- t_636 = F.relu(t_635)
- t_637 = self.n_Conv_121(t_636)
- t_638 = torch.add(t_637, t_632)
- t_639 = F.relu(t_638)
- t_640 = self.n_Conv_122(t_639)
- t_641 = F.relu(t_640)
- t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
- t_642 = self.n_Conv_123(t_641_padded)
- t_643 = F.relu(t_642)
- t_644 = self.n_Conv_124(t_643)
- t_645 = torch.add(t_644, t_639)
- t_646 = F.relu(t_645)
- t_647 = self.n_Conv_125(t_646)
- t_648 = F.relu(t_647)
- t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
- t_649 = self.n_Conv_126(t_648_padded)
- t_650 = F.relu(t_649)
- t_651 = self.n_Conv_127(t_650)
- t_652 = torch.add(t_651, t_646)
- t_653 = F.relu(t_652)
- t_654 = self.n_Conv_128(t_653)
- t_655 = F.relu(t_654)
- t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
- t_656 = self.n_Conv_129(t_655_padded)
- t_657 = F.relu(t_656)
- t_658 = self.n_Conv_130(t_657)
- t_659 = torch.add(t_658, t_653)
- t_660 = F.relu(t_659)
- t_661 = self.n_Conv_131(t_660)
- t_662 = F.relu(t_661)
- t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
- t_663 = self.n_Conv_132(t_662_padded)
- t_664 = F.relu(t_663)
- t_665 = self.n_Conv_133(t_664)
- t_666 = torch.add(t_665, t_660)
- t_667 = F.relu(t_666)
- t_668 = self.n_Conv_134(t_667)
- t_669 = F.relu(t_668)
- t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
- t_670 = self.n_Conv_135(t_669_padded)
- t_671 = F.relu(t_670)
- t_672 = self.n_Conv_136(t_671)
- t_673 = torch.add(t_672, t_667)
- t_674 = F.relu(t_673)
- t_675 = self.n_Conv_137(t_674)
- t_676 = F.relu(t_675)
- t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
- t_677 = self.n_Conv_138(t_676_padded)
- t_678 = F.relu(t_677)
- t_679 = self.n_Conv_139(t_678)
- t_680 = torch.add(t_679, t_674)
- t_681 = F.relu(t_680)
- t_682 = self.n_Conv_140(t_681)
- t_683 = F.relu(t_682)
- t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
- t_684 = self.n_Conv_141(t_683_padded)
- t_685 = F.relu(t_684)
- t_686 = self.n_Conv_142(t_685)
- t_687 = torch.add(t_686, t_681)
- t_688 = F.relu(t_687)
- t_689 = self.n_Conv_143(t_688)
- t_690 = F.relu(t_689)
- t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
- t_691 = self.n_Conv_144(t_690_padded)
- t_692 = F.relu(t_691)
- t_693 = self.n_Conv_145(t_692)
- t_694 = torch.add(t_693, t_688)
- t_695 = F.relu(t_694)
- t_696 = self.n_Conv_146(t_695)
- t_697 = F.relu(t_696)
- t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
- t_698 = self.n_Conv_147(t_697_padded)
- t_699 = F.relu(t_698)
- t_700 = self.n_Conv_148(t_699)
- t_701 = torch.add(t_700, t_695)
- t_702 = F.relu(t_701)
- t_703 = self.n_Conv_149(t_702)
- t_704 = F.relu(t_703)
- t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
- t_705 = self.n_Conv_150(t_704_padded)
- t_706 = F.relu(t_705)
- t_707 = self.n_Conv_151(t_706)
- t_708 = torch.add(t_707, t_702)
- t_709 = F.relu(t_708)
- t_710 = self.n_Conv_152(t_709)
- t_711 = F.relu(t_710)
- t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
- t_712 = self.n_Conv_153(t_711_padded)
- t_713 = F.relu(t_712)
- t_714 = self.n_Conv_154(t_713)
- t_715 = torch.add(t_714, t_709)
- t_716 = F.relu(t_715)
- t_717 = self.n_Conv_155(t_716)
- t_718 = F.relu(t_717)
- t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
- t_719 = self.n_Conv_156(t_718_padded)
- t_720 = F.relu(t_719)
- t_721 = self.n_Conv_157(t_720)
- t_722 = torch.add(t_721, t_716)
- t_723 = F.relu(t_722)
- t_724 = self.n_Conv_158(t_723)
- t_725 = self.n_Conv_159(t_723)
- t_726 = F.relu(t_725)
- t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
- t_727 = self.n_Conv_160(t_726_padded)
- t_728 = F.relu(t_727)
- t_729 = self.n_Conv_161(t_728)
- t_730 = torch.add(t_729, t_724)
- t_731 = F.relu(t_730)
- t_732 = self.n_Conv_162(t_731)
- t_733 = F.relu(t_732)
- t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
- t_734 = self.n_Conv_163(t_733_padded)
- t_735 = F.relu(t_734)
- t_736 = self.n_Conv_164(t_735)
- t_737 = torch.add(t_736, t_731)
- t_738 = F.relu(t_737)
- t_739 = self.n_Conv_165(t_738)
- t_740 = F.relu(t_739)
- t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
- t_741 = self.n_Conv_166(t_740_padded)
- t_742 = F.relu(t_741)
- t_743 = self.n_Conv_167(t_742)
- t_744 = torch.add(t_743, t_738)
- t_745 = F.relu(t_744)
- t_746 = self.n_Conv_168(t_745)
- t_747 = self.n_Conv_169(t_745)
- t_748 = F.relu(t_747)
- t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
- t_749 = self.n_Conv_170(t_748_padded)
- t_750 = F.relu(t_749)
- t_751 = self.n_Conv_171(t_750)
- t_752 = torch.add(t_751, t_746)
- t_753 = F.relu(t_752)
- t_754 = self.n_Conv_172(t_753)
- t_755 = F.relu(t_754)
- t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
- t_756 = self.n_Conv_173(t_755_padded)
- t_757 = F.relu(t_756)
- t_758 = self.n_Conv_174(t_757)
- t_759 = torch.add(t_758, t_753)
- t_760 = F.relu(t_759)
- t_761 = self.n_Conv_175(t_760)
- t_762 = F.relu(t_761)
- t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
- t_763 = self.n_Conv_176(t_762_padded)
- t_764 = F.relu(t_763)
- t_765 = self.n_Conv_177(t_764)
- t_766 = torch.add(t_765, t_760)
- t_767 = F.relu(t_766)
- t_768 = self.n_Conv_178(t_767)
- t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
- t_770 = torch.squeeze(t_769, 3)
- t_770 = torch.squeeze(t_770, 2)
- t_771 = torch.sigmoid(t_770)
- return t_771
-
- def load_state_dict(self, state_dict, **kwargs): # pylint: disable=arguments-differ,unused-argument
- self.tags = state_dict.get('tags', [])
- super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'}) # pylint: disable=R1725
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from modules import devices
+
+# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
+
+
+class DeepDanbooruModel(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.tags = []
+ self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
+ self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
+ self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+ self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
+ self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+ self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+ self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
+ self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+ self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+ self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
+ self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+ self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+ self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
+ self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
+ self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
+ self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
+ self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
+ self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
+ self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
+ self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
+ self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
+ self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
+ self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
+ self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+ self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
+ self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
+ self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+ self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
+ self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
+ self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+ self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
+ self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
+ self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
+ self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+ self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
+ self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
+ self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+ self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
+ self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
+ self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+ self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
+
+ def forward(self, *inputs):
+ t_358, = inputs
+ t_359 = t_358.permute(*[0, 3, 1, 2])
+ t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
+ t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
+ t_361 = F.relu(t_360)
+ t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
+ t_362 = self.n_MaxPool_0(t_361)
+ t_363 = self.n_Conv_1(t_362)
+ t_364 = self.n_Conv_2(t_362)
+ t_365 = F.relu(t_364)
+ t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
+ t_366 = self.n_Conv_3(t_365_padded)
+ t_367 = F.relu(t_366)
+ t_368 = self.n_Conv_4(t_367)
+ t_369 = torch.add(t_368, t_363)
+ t_370 = F.relu(t_369)
+ t_371 = self.n_Conv_5(t_370)
+ t_372 = F.relu(t_371)
+ t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
+ t_373 = self.n_Conv_6(t_372_padded)
+ t_374 = F.relu(t_373)
+ t_375 = self.n_Conv_7(t_374)
+ t_376 = torch.add(t_375, t_370)
+ t_377 = F.relu(t_376)
+ t_378 = self.n_Conv_8(t_377)
+ t_379 = F.relu(t_378)
+ t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
+ t_380 = self.n_Conv_9(t_379_padded)
+ t_381 = F.relu(t_380)
+ t_382 = self.n_Conv_10(t_381)
+ t_383 = torch.add(t_382, t_377)
+ t_384 = F.relu(t_383)
+ t_385 = self.n_Conv_11(t_384)
+ t_386 = self.n_Conv_12(t_384)
+ t_387 = F.relu(t_386)
+ t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
+ t_388 = self.n_Conv_13(t_387_padded)
+ t_389 = F.relu(t_388)
+ t_390 = self.n_Conv_14(t_389)
+ t_391 = torch.add(t_390, t_385)
+ t_392 = F.relu(t_391)
+ t_393 = self.n_Conv_15(t_392)
+ t_394 = F.relu(t_393)
+ t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
+ t_395 = self.n_Conv_16(t_394_padded)
+ t_396 = F.relu(t_395)
+ t_397 = self.n_Conv_17(t_396)
+ t_398 = torch.add(t_397, t_392)
+ t_399 = F.relu(t_398)
+ t_400 = self.n_Conv_18(t_399)
+ t_401 = F.relu(t_400)
+ t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
+ t_402 = self.n_Conv_19(t_401_padded)
+ t_403 = F.relu(t_402)
+ t_404 = self.n_Conv_20(t_403)
+ t_405 = torch.add(t_404, t_399)
+ t_406 = F.relu(t_405)
+ t_407 = self.n_Conv_21(t_406)
+ t_408 = F.relu(t_407)
+ t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
+ t_409 = self.n_Conv_22(t_408_padded)
+ t_410 = F.relu(t_409)
+ t_411 = self.n_Conv_23(t_410)
+ t_412 = torch.add(t_411, t_406)
+ t_413 = F.relu(t_412)
+ t_414 = self.n_Conv_24(t_413)
+ t_415 = F.relu(t_414)
+ t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
+ t_416 = self.n_Conv_25(t_415_padded)
+ t_417 = F.relu(t_416)
+ t_418 = self.n_Conv_26(t_417)
+ t_419 = torch.add(t_418, t_413)
+ t_420 = F.relu(t_419)
+ t_421 = self.n_Conv_27(t_420)
+ t_422 = F.relu(t_421)
+ t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
+ t_423 = self.n_Conv_28(t_422_padded)
+ t_424 = F.relu(t_423)
+ t_425 = self.n_Conv_29(t_424)
+ t_426 = torch.add(t_425, t_420)
+ t_427 = F.relu(t_426)
+ t_428 = self.n_Conv_30(t_427)
+ t_429 = F.relu(t_428)
+ t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
+ t_430 = self.n_Conv_31(t_429_padded)
+ t_431 = F.relu(t_430)
+ t_432 = self.n_Conv_32(t_431)
+ t_433 = torch.add(t_432, t_427)
+ t_434 = F.relu(t_433)
+ t_435 = self.n_Conv_33(t_434)
+ t_436 = F.relu(t_435)
+ t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
+ t_437 = self.n_Conv_34(t_436_padded)
+ t_438 = F.relu(t_437)
+ t_439 = self.n_Conv_35(t_438)
+ t_440 = torch.add(t_439, t_434)
+ t_441 = F.relu(t_440)
+ t_442 = self.n_Conv_36(t_441)
+ t_443 = self.n_Conv_37(t_441)
+ t_444 = F.relu(t_443)
+ t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
+ t_445 = self.n_Conv_38(t_444_padded)
+ t_446 = F.relu(t_445)
+ t_447 = self.n_Conv_39(t_446)
+ t_448 = torch.add(t_447, t_442)
+ t_449 = F.relu(t_448)
+ t_450 = self.n_Conv_40(t_449)
+ t_451 = F.relu(t_450)
+ t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
+ t_452 = self.n_Conv_41(t_451_padded)
+ t_453 = F.relu(t_452)
+ t_454 = self.n_Conv_42(t_453)
+ t_455 = torch.add(t_454, t_449)
+ t_456 = F.relu(t_455)
+ t_457 = self.n_Conv_43(t_456)
+ t_458 = F.relu(t_457)
+ t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
+ t_459 = self.n_Conv_44(t_458_padded)
+ t_460 = F.relu(t_459)
+ t_461 = self.n_Conv_45(t_460)
+ t_462 = torch.add(t_461, t_456)
+ t_463 = F.relu(t_462)
+ t_464 = self.n_Conv_46(t_463)
+ t_465 = F.relu(t_464)
+ t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
+ t_466 = self.n_Conv_47(t_465_padded)
+ t_467 = F.relu(t_466)
+ t_468 = self.n_Conv_48(t_467)
+ t_469 = torch.add(t_468, t_463)
+ t_470 = F.relu(t_469)
+ t_471 = self.n_Conv_49(t_470)
+ t_472 = F.relu(t_471)
+ t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
+ t_473 = self.n_Conv_50(t_472_padded)
+ t_474 = F.relu(t_473)
+ t_475 = self.n_Conv_51(t_474)
+ t_476 = torch.add(t_475, t_470)
+ t_477 = F.relu(t_476)
+ t_478 = self.n_Conv_52(t_477)
+ t_479 = F.relu(t_478)
+ t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
+ t_480 = self.n_Conv_53(t_479_padded)
+ t_481 = F.relu(t_480)
+ t_482 = self.n_Conv_54(t_481)
+ t_483 = torch.add(t_482, t_477)
+ t_484 = F.relu(t_483)
+ t_485 = self.n_Conv_55(t_484)
+ t_486 = F.relu(t_485)
+ t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
+ t_487 = self.n_Conv_56(t_486_padded)
+ t_488 = F.relu(t_487)
+ t_489 = self.n_Conv_57(t_488)
+ t_490 = torch.add(t_489, t_484)
+ t_491 = F.relu(t_490)
+ t_492 = self.n_Conv_58(t_491)
+ t_493 = F.relu(t_492)
+ t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
+ t_494 = self.n_Conv_59(t_493_padded)
+ t_495 = F.relu(t_494)
+ t_496 = self.n_Conv_60(t_495)
+ t_497 = torch.add(t_496, t_491)
+ t_498 = F.relu(t_497)
+ t_499 = self.n_Conv_61(t_498)
+ t_500 = F.relu(t_499)
+ t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
+ t_501 = self.n_Conv_62(t_500_padded)
+ t_502 = F.relu(t_501)
+ t_503 = self.n_Conv_63(t_502)
+ t_504 = torch.add(t_503, t_498)
+ t_505 = F.relu(t_504)
+ t_506 = self.n_Conv_64(t_505)
+ t_507 = F.relu(t_506)
+ t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
+ t_508 = self.n_Conv_65(t_507_padded)
+ t_509 = F.relu(t_508)
+ t_510 = self.n_Conv_66(t_509)
+ t_511 = torch.add(t_510, t_505)
+ t_512 = F.relu(t_511)
+ t_513 = self.n_Conv_67(t_512)
+ t_514 = F.relu(t_513)
+ t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
+ t_515 = self.n_Conv_68(t_514_padded)
+ t_516 = F.relu(t_515)
+ t_517 = self.n_Conv_69(t_516)
+ t_518 = torch.add(t_517, t_512)
+ t_519 = F.relu(t_518)
+ t_520 = self.n_Conv_70(t_519)
+ t_521 = F.relu(t_520)
+ t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
+ t_522 = self.n_Conv_71(t_521_padded)
+ t_523 = F.relu(t_522)
+ t_524 = self.n_Conv_72(t_523)
+ t_525 = torch.add(t_524, t_519)
+ t_526 = F.relu(t_525)
+ t_527 = self.n_Conv_73(t_526)
+ t_528 = F.relu(t_527)
+ t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
+ t_529 = self.n_Conv_74(t_528_padded)
+ t_530 = F.relu(t_529)
+ t_531 = self.n_Conv_75(t_530)
+ t_532 = torch.add(t_531, t_526)
+ t_533 = F.relu(t_532)
+ t_534 = self.n_Conv_76(t_533)
+ t_535 = F.relu(t_534)
+ t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
+ t_536 = self.n_Conv_77(t_535_padded)
+ t_537 = F.relu(t_536)
+ t_538 = self.n_Conv_78(t_537)
+ t_539 = torch.add(t_538, t_533)
+ t_540 = F.relu(t_539)
+ t_541 = self.n_Conv_79(t_540)
+ t_542 = F.relu(t_541)
+ t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
+ t_543 = self.n_Conv_80(t_542_padded)
+ t_544 = F.relu(t_543)
+ t_545 = self.n_Conv_81(t_544)
+ t_546 = torch.add(t_545, t_540)
+ t_547 = F.relu(t_546)
+ t_548 = self.n_Conv_82(t_547)
+ t_549 = F.relu(t_548)
+ t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
+ t_550 = self.n_Conv_83(t_549_padded)
+ t_551 = F.relu(t_550)
+ t_552 = self.n_Conv_84(t_551)
+ t_553 = torch.add(t_552, t_547)
+ t_554 = F.relu(t_553)
+ t_555 = self.n_Conv_85(t_554)
+ t_556 = F.relu(t_555)
+ t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
+ t_557 = self.n_Conv_86(t_556_padded)
+ t_558 = F.relu(t_557)
+ t_559 = self.n_Conv_87(t_558)
+ t_560 = torch.add(t_559, t_554)
+ t_561 = F.relu(t_560)
+ t_562 = self.n_Conv_88(t_561)
+ t_563 = F.relu(t_562)
+ t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
+ t_564 = self.n_Conv_89(t_563_padded)
+ t_565 = F.relu(t_564)
+ t_566 = self.n_Conv_90(t_565)
+ t_567 = torch.add(t_566, t_561)
+ t_568 = F.relu(t_567)
+ t_569 = self.n_Conv_91(t_568)
+ t_570 = F.relu(t_569)
+ t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
+ t_571 = self.n_Conv_92(t_570_padded)
+ t_572 = F.relu(t_571)
+ t_573 = self.n_Conv_93(t_572)
+ t_574 = torch.add(t_573, t_568)
+ t_575 = F.relu(t_574)
+ t_576 = self.n_Conv_94(t_575)
+ t_577 = F.relu(t_576)
+ t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
+ t_578 = self.n_Conv_95(t_577_padded)
+ t_579 = F.relu(t_578)
+ t_580 = self.n_Conv_96(t_579)
+ t_581 = torch.add(t_580, t_575)
+ t_582 = F.relu(t_581)
+ t_583 = self.n_Conv_97(t_582)
+ t_584 = F.relu(t_583)
+ t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
+ t_585 = self.n_Conv_98(t_584_padded)
+ t_586 = F.relu(t_585)
+ t_587 = self.n_Conv_99(t_586)
+ t_588 = self.n_Conv_100(t_582)
+ t_589 = torch.add(t_587, t_588)
+ t_590 = F.relu(t_589)
+ t_591 = self.n_Conv_101(t_590)
+ t_592 = F.relu(t_591)
+ t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
+ t_593 = self.n_Conv_102(t_592_padded)
+ t_594 = F.relu(t_593)
+ t_595 = self.n_Conv_103(t_594)
+ t_596 = torch.add(t_595, t_590)
+ t_597 = F.relu(t_596)
+ t_598 = self.n_Conv_104(t_597)
+ t_599 = F.relu(t_598)
+ t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
+ t_600 = self.n_Conv_105(t_599_padded)
+ t_601 = F.relu(t_600)
+ t_602 = self.n_Conv_106(t_601)
+ t_603 = torch.add(t_602, t_597)
+ t_604 = F.relu(t_603)
+ t_605 = self.n_Conv_107(t_604)
+ t_606 = F.relu(t_605)
+ t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
+ t_607 = self.n_Conv_108(t_606_padded)
+ t_608 = F.relu(t_607)
+ t_609 = self.n_Conv_109(t_608)
+ t_610 = torch.add(t_609, t_604)
+ t_611 = F.relu(t_610)
+ t_612 = self.n_Conv_110(t_611)
+ t_613 = F.relu(t_612)
+ t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
+ t_614 = self.n_Conv_111(t_613_padded)
+ t_615 = F.relu(t_614)
+ t_616 = self.n_Conv_112(t_615)
+ t_617 = torch.add(t_616, t_611)
+ t_618 = F.relu(t_617)
+ t_619 = self.n_Conv_113(t_618)
+ t_620 = F.relu(t_619)
+ t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
+ t_621 = self.n_Conv_114(t_620_padded)
+ t_622 = F.relu(t_621)
+ t_623 = self.n_Conv_115(t_622)
+ t_624 = torch.add(t_623, t_618)
+ t_625 = F.relu(t_624)
+ t_626 = self.n_Conv_116(t_625)
+ t_627 = F.relu(t_626)
+ t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
+ t_628 = self.n_Conv_117(t_627_padded)
+ t_629 = F.relu(t_628)
+ t_630 = self.n_Conv_118(t_629)
+ t_631 = torch.add(t_630, t_625)
+ t_632 = F.relu(t_631)
+ t_633 = self.n_Conv_119(t_632)
+ t_634 = F.relu(t_633)
+ t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
+ t_635 = self.n_Conv_120(t_634_padded)
+ t_636 = F.relu(t_635)
+ t_637 = self.n_Conv_121(t_636)
+ t_638 = torch.add(t_637, t_632)
+ t_639 = F.relu(t_638)
+ t_640 = self.n_Conv_122(t_639)
+ t_641 = F.relu(t_640)
+ t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
+ t_642 = self.n_Conv_123(t_641_padded)
+ t_643 = F.relu(t_642)
+ t_644 = self.n_Conv_124(t_643)
+ t_645 = torch.add(t_644, t_639)
+ t_646 = F.relu(t_645)
+ t_647 = self.n_Conv_125(t_646)
+ t_648 = F.relu(t_647)
+ t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
+ t_649 = self.n_Conv_126(t_648_padded)
+ t_650 = F.relu(t_649)
+ t_651 = self.n_Conv_127(t_650)
+ t_652 = torch.add(t_651, t_646)
+ t_653 = F.relu(t_652)
+ t_654 = self.n_Conv_128(t_653)
+ t_655 = F.relu(t_654)
+ t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
+ t_656 = self.n_Conv_129(t_655_padded)
+ t_657 = F.relu(t_656)
+ t_658 = self.n_Conv_130(t_657)
+ t_659 = torch.add(t_658, t_653)
+ t_660 = F.relu(t_659)
+ t_661 = self.n_Conv_131(t_660)
+ t_662 = F.relu(t_661)
+ t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
+ t_663 = self.n_Conv_132(t_662_padded)
+ t_664 = F.relu(t_663)
+ t_665 = self.n_Conv_133(t_664)
+ t_666 = torch.add(t_665, t_660)
+ t_667 = F.relu(t_666)
+ t_668 = self.n_Conv_134(t_667)
+ t_669 = F.relu(t_668)
+ t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
+ t_670 = self.n_Conv_135(t_669_padded)
+ t_671 = F.relu(t_670)
+ t_672 = self.n_Conv_136(t_671)
+ t_673 = torch.add(t_672, t_667)
+ t_674 = F.relu(t_673)
+ t_675 = self.n_Conv_137(t_674)
+ t_676 = F.relu(t_675)
+ t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
+ t_677 = self.n_Conv_138(t_676_padded)
+ t_678 = F.relu(t_677)
+ t_679 = self.n_Conv_139(t_678)
+ t_680 = torch.add(t_679, t_674)
+ t_681 = F.relu(t_680)
+ t_682 = self.n_Conv_140(t_681)
+ t_683 = F.relu(t_682)
+ t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
+ t_684 = self.n_Conv_141(t_683_padded)
+ t_685 = F.relu(t_684)
+ t_686 = self.n_Conv_142(t_685)
+ t_687 = torch.add(t_686, t_681)
+ t_688 = F.relu(t_687)
+ t_689 = self.n_Conv_143(t_688)
+ t_690 = F.relu(t_689)
+ t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
+ t_691 = self.n_Conv_144(t_690_padded)
+ t_692 = F.relu(t_691)
+ t_693 = self.n_Conv_145(t_692)
+ t_694 = torch.add(t_693, t_688)
+ t_695 = F.relu(t_694)
+ t_696 = self.n_Conv_146(t_695)
+ t_697 = F.relu(t_696)
+ t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
+ t_698 = self.n_Conv_147(t_697_padded)
+ t_699 = F.relu(t_698)
+ t_700 = self.n_Conv_148(t_699)
+ t_701 = torch.add(t_700, t_695)
+ t_702 = F.relu(t_701)
+ t_703 = self.n_Conv_149(t_702)
+ t_704 = F.relu(t_703)
+ t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
+ t_705 = self.n_Conv_150(t_704_padded)
+ t_706 = F.relu(t_705)
+ t_707 = self.n_Conv_151(t_706)
+ t_708 = torch.add(t_707, t_702)
+ t_709 = F.relu(t_708)
+ t_710 = self.n_Conv_152(t_709)
+ t_711 = F.relu(t_710)
+ t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
+ t_712 = self.n_Conv_153(t_711_padded)
+ t_713 = F.relu(t_712)
+ t_714 = self.n_Conv_154(t_713)
+ t_715 = torch.add(t_714, t_709)
+ t_716 = F.relu(t_715)
+ t_717 = self.n_Conv_155(t_716)
+ t_718 = F.relu(t_717)
+ t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
+ t_719 = self.n_Conv_156(t_718_padded)
+ t_720 = F.relu(t_719)
+ t_721 = self.n_Conv_157(t_720)
+ t_722 = torch.add(t_721, t_716)
+ t_723 = F.relu(t_722)
+ t_724 = self.n_Conv_158(t_723)
+ t_725 = self.n_Conv_159(t_723)
+ t_726 = F.relu(t_725)
+ t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
+ t_727 = self.n_Conv_160(t_726_padded)
+ t_728 = F.relu(t_727)
+ t_729 = self.n_Conv_161(t_728)
+ t_730 = torch.add(t_729, t_724)
+ t_731 = F.relu(t_730)
+ t_732 = self.n_Conv_162(t_731)
+ t_733 = F.relu(t_732)
+ t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
+ t_734 = self.n_Conv_163(t_733_padded)
+ t_735 = F.relu(t_734)
+ t_736 = self.n_Conv_164(t_735)
+ t_737 = torch.add(t_736, t_731)
+ t_738 = F.relu(t_737)
+ t_739 = self.n_Conv_165(t_738)
+ t_740 = F.relu(t_739)
+ t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
+ t_741 = self.n_Conv_166(t_740_padded)
+ t_742 = F.relu(t_741)
+ t_743 = self.n_Conv_167(t_742)
+ t_744 = torch.add(t_743, t_738)
+ t_745 = F.relu(t_744)
+ t_746 = self.n_Conv_168(t_745)
+ t_747 = self.n_Conv_169(t_745)
+ t_748 = F.relu(t_747)
+ t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
+ t_749 = self.n_Conv_170(t_748_padded)
+ t_750 = F.relu(t_749)
+ t_751 = self.n_Conv_171(t_750)
+ t_752 = torch.add(t_751, t_746)
+ t_753 = F.relu(t_752)
+ t_754 = self.n_Conv_172(t_753)
+ t_755 = F.relu(t_754)
+ t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
+ t_756 = self.n_Conv_173(t_755_padded)
+ t_757 = F.relu(t_756)
+ t_758 = self.n_Conv_174(t_757)
+ t_759 = torch.add(t_758, t_753)
+ t_760 = F.relu(t_759)
+ t_761 = self.n_Conv_175(t_760)
+ t_762 = F.relu(t_761)
+ t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
+ t_763 = self.n_Conv_176(t_762_padded)
+ t_764 = F.relu(t_763)
+ t_765 = self.n_Conv_177(t_764)
+ t_766 = torch.add(t_765, t_760)
+ t_767 = F.relu(t_766)
+ t_768 = self.n_Conv_178(t_767)
+ t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
+ t_770 = torch.squeeze(t_769, 3)
+ t_770 = torch.squeeze(t_770, 2)
+ t_771 = torch.sigmoid(t_770)
+ return t_771
+
+ def load_state_dict(self, state_dict, **kwargs): # pylint: disable=arguments-differ,unused-argument
+ self.tags = state_dict.get('tags', [])
+ super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'}) # pylint: disable=R1725
diff --git a/modules/errors.py b/modules/errors.py
index 122628bff..8cb2eb599 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -1,96 +1,96 @@
-import logging
-import warnings
-from rich.console import Console
-from rich.theme import Theme
-from rich.pretty import install as pretty_install
-from rich.traceback import install as traceback_install
-from installer import log as installer_log, setup_logging
-
-
-setup_logging()
-log = installer_log
-console = Console(log_time=True, log_time_format='%H:%M:%S-%f', theme=Theme({
- "traceback.border": "black",
- "traceback.border.syntax_error": "black",
- "inspect.value.border": "black",
-}))
-
-pretty_install(console=console)
-traceback_install(console=console, extra_lines=1, width=console.width, word_wrap=False, indent_guides=False)
-already_displayed = {}
-
-
-def install(suppress=[]): # noqa: B006
- warnings.filterwarnings("ignore", category=UserWarning)
- pretty_install(console=console)
- traceback_install(console=console, extra_lines=1, width=console.width, word_wrap=False, indent_guides=False, suppress=suppress)
- logging.basicConfig(level=logging.ERROR, format='%(asctime)s | %(levelname)s | %(pathname)s | %(message)s')
- # for handler in logging.getLogger().handlers:
- # handler.setLevel(logging.INFO)
-
-
-def print_error_explanation(message):
- lines = message.strip().split("\n")
- for line in lines:
- log.error(line)
-
-
-def display(e: Exception, task, suppress=[]): # noqa: B006
- log.error(f"{task or 'error'}: {type(e).__name__}")
- console.print_exception(show_locals=False, max_frames=10, extra_lines=1, suppress=suppress, theme="ansi_dark", word_wrap=False, width=min([console.width, 200]))
-
-
-def display_once(e: Exception, task):
- if task in already_displayed:
- return
- display(e, task)
- already_displayed[task] = 1
-
-
-def run(code, task):
- try:
- code()
- except Exception as e:
- display(e, task)
-
-
-def exception(suppress=[]): # noqa: B006
- console.print_exception(show_locals=False, max_frames=10, extra_lines=2, suppress=suppress, theme="ansi_dark", word_wrap=False, width=min([console.width, 200]))
-
-
-def profile(profiler, msg: str):
- profiler.disable()
- import io
- import pstats
- stream = io.StringIO() # pylint: disable=abstract-class-instantiated
- p = pstats.Stats(profiler, stream=stream)
- p.sort_stats(pstats.SortKey.CUMULATIVE)
- p.print_stats(100)
- # p.print_title()
- # p.print_call_heading(10, 'time')
- # p.print_callees(10)
- # p.print_callers(10)
- profiler = None
- lines = stream.getvalue().split('\n')
- lines = [x for x in lines if '
- Where name matches the name of this ExtraNetwork object, and arg1:arg2:arg3 are any natural number of text arguments separated by colon.
- Even if the user does not mention this ExtraNetwork in his prompt, the call will stil be made, with empty params_list - in this case, all effects of this extra networks should be disabled.
- Can be called multiple times before deactivate() - each new call should override the previous call completely.
- For example, if this ExtraNetwork's name is 'hypernet' and user's prompt is:
- > "1girl, "
- params_list will be:
- [
- ExtraNetworkParams(items=["agm", "1.1"]),
- ExtraNetworkParams(items=["ray"])
- ]
- """
- raise NotImplementedError
-
- def deactivate(self, p):
- """
- Called at the end of processing for housekeeping. No need to do anything here.
- """
- raise NotImplementedError
-
-
-def activate(p, extra_network_data):
- """call activate for extra networks in extra_network_data in specified order, then call activate for all remaining registered networks with an empty argument list"""
- if extra_network_data is None:
- return
- for extra_network_name, extra_network_args in extra_network_data.items():
- extra_network = extra_network_registry.get(extra_network_name, None)
- if extra_network is None:
- print(f"Skipping unknown extra network: {extra_network_name}")
- continue
- try:
- extra_network.activate(p, extra_network_args)
- except Exception as e:
- errors.display(e, f"activating extra network: name={extra_network_name} args:{extra_network_args}")
-
- for extra_network_name, extra_network in extra_network_registry.items():
- args = extra_network_data.get(extra_network_name, None)
- if args is not None:
- continue
- try:
- extra_network.activate(p, [])
- except Exception as e:
- errors.display(e, f"activating extra network: name={extra_network_name}")
-
-
-def deactivate(p, extra_network_data):
- """call deactivate for extra networks in extra_network_data in specified order, then call deactivate for all remaining registered networks"""
- if extra_network_data is None:
- return
- for extra_network_name in extra_network_data:
- extra_network = extra_network_registry.get(extra_network_name, None)
- if extra_network is None:
- continue
- try:
- extra_network.deactivate(p)
- except Exception as e:
- errors.display(e, f"deactivating extra network {extra_network_name}")
-
- for extra_network_name, extra_network in extra_network_registry.items():
- args = extra_network_data.get(extra_network_name, None)
- if args is not None:
- continue
- try:
- extra_network.deactivate(p)
- except Exception as e:
- errors.display(e, f"deactivating unmentioned extra network {extra_network_name}")
-
-
-re_extra_net = re.compile(r"<(\w+):([^>]+)>")
-
-
-def parse_prompt(prompt):
- res = defaultdict(list)
-
- def found(m):
- name = m.group(1)
- args = m.group(2)
- res[name].append(ExtraNetworkParams(items=args.split(":")))
- return ""
- prompt = re.sub(re_extra_net, found, prompt)
- return prompt, res
-
-
-def parse_prompts(prompts):
- res = []
- extra_data = None
-
- for prompt in prompts:
- updated_prompt, parsed_extra_data = parse_prompt(prompt)
- if extra_data is None:
- extra_data = parsed_extra_data
- res.append(updated_prompt)
-
- return res, extra_data
+import re
+from collections import defaultdict
+
+from modules import errors
+
+extra_network_registry = {}
+
+
+def initialize():
+ extra_network_registry.clear()
+
+
+def register_extra_network(extra_network):
+ extra_network_registry[extra_network.name] = extra_network
+
+
+def register_default_extra_networks():
+ from modules.extra_networks_hypernet import ExtraNetworkHypernet
+ register_extra_network(ExtraNetworkHypernet())
+ from modules.ui_extra_networks_styles import ExtraNetworkStyles
+ register_extra_network(ExtraNetworkStyles())
+
+
+class ExtraNetworkParams:
+ def __init__(self, items=None):
+ self.items = items or []
+ self.positional = []
+ self.named = {}
+ for item in self.items:
+ parts = item.split('=', 2) if isinstance(item, str) else [item]
+ if len(parts) == 2:
+ self.named[parts[0]] = parts[1]
+ else:
+ self.positional.append(item)
+
+
+class ExtraNetwork:
+ def __init__(self, name):
+ self.name = name
+
+ def activate(self, p, params_list):
+ """
+ Called by processing on every run. Whatever the extra network is meant to do should be activated here. Passes arguments related to this extra network in params_list. User passes arguments by specifying this in his prompt:
+
+ Where name matches the name of this ExtraNetwork object, and arg1:arg2:arg3 are any natural number of text arguments separated by colon.
+ Even if the user does not mention this ExtraNetwork in his prompt, the call will stil be made, with empty params_list - in this case, all effects of this extra networks should be disabled.
+ Can be called multiple times before deactivate() - each new call should override the previous call completely.
+ For example, if this ExtraNetwork's name is 'hypernet' and user's prompt is:
+ > "1girl, "
+ params_list will be:
+ [
+ ExtraNetworkParams(items=["agm", "1.1"]),
+ ExtraNetworkParams(items=["ray"])
+ ]
+ """
+ raise NotImplementedError
+
+ def deactivate(self, p):
+ """
+ Called at the end of processing for housekeeping. No need to do anything here.
+ """
+ raise NotImplementedError
+
+
+def activate(p, extra_network_data):
+ """call activate for extra networks in extra_network_data in specified order, then call activate for all remaining registered networks with an empty argument list"""
+ if extra_network_data is None:
+ return
+ for extra_network_name, extra_network_args in extra_network_data.items():
+ extra_network = extra_network_registry.get(extra_network_name, None)
+ if extra_network is None:
+ print(f"Skipping unknown extra network: {extra_network_name}")
+ continue
+ try:
+ extra_network.activate(p, extra_network_args)
+ except Exception as e:
+ errors.display(e, f"activating extra network: name={extra_network_name} args:{extra_network_args}")
+
+ for extra_network_name, extra_network in extra_network_registry.items():
+ args = extra_network_data.get(extra_network_name, None)
+ if args is not None:
+ continue
+ try:
+ extra_network.activate(p, [])
+ except Exception as e:
+ errors.display(e, f"activating extra network: name={extra_network_name}")
+
+
+def deactivate(p, extra_network_data):
+ """call deactivate for extra networks in extra_network_data in specified order, then call deactivate for all remaining registered networks"""
+ if extra_network_data is None:
+ return
+ for extra_network_name in extra_network_data:
+ extra_network = extra_network_registry.get(extra_network_name, None)
+ if extra_network is None:
+ continue
+ try:
+ extra_network.deactivate(p)
+ except Exception as e:
+ errors.display(e, f"deactivating extra network {extra_network_name}")
+
+ for extra_network_name, extra_network in extra_network_registry.items():
+ args = extra_network_data.get(extra_network_name, None)
+ if args is not None:
+ continue
+ try:
+ extra_network.deactivate(p)
+ except Exception as e:
+ errors.display(e, f"deactivating unmentioned extra network {extra_network_name}")
+
+
+re_extra_net = re.compile(r"<(\w+):([^>]+)>")
+
+
+def parse_prompt(prompt):
+ res = defaultdict(list)
+
+ def found(m):
+ name = m.group(1)
+ args = m.group(2)
+ res[name].append(ExtraNetworkParams(items=args.split(":")))
+ return ""
+ prompt = re.sub(re_extra_net, found, prompt)
+ return prompt, res
+
+
+def parse_prompts(prompts):
+ res = []
+ extra_data = None
+
+ for prompt in prompts:
+ updated_prompt, parsed_extra_data = parse_prompt(prompt)
+ if extra_data is None:
+ extra_data = parsed_extra_data
+ res.append(updated_prompt)
+
+ return res, extra_data
diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py
index aa2a14efd..dce11b68a 100644
--- a/modules/extra_networks_hypernet.py
+++ b/modules/extra_networks_hypernet.py
@@ -1,28 +1,28 @@
-from modules import extra_networks, shared
-from modules.hypernetworks import hypernetwork
-
-
-class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
- def __init__(self):
- super().__init__('hypernet')
-
- def activate(self, p, params_list):
- additional = shared.opts.sd_hypernetwork
-
- if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
- hypernet_prompt_text = f""
- p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
- params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
-
- names = []
- multipliers = []
- for params in params_list:
- assert len(params.items) > 0
-
- names.append(params.items[0])
- multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
-
- hypernetwork.load_hypernetworks(names, multipliers)
-
- def deactivate(self, p):
- pass
+from modules import extra_networks, shared
+from modules.hypernetworks import hypernetwork
+
+
+class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
+ def __init__(self):
+ super().__init__('hypernet')
+
+ def activate(self, p, params_list):
+ additional = shared.opts.sd_hypernetwork
+
+ if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+ hypernet_prompt_text = f""
+ p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
+ params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
+ names = []
+ multipliers = []
+ for params in params_list:
+ assert len(params.items) > 0
+
+ names.append(params.items[0])
+ multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
+
+ hypernetwork.load_hypernetworks(names, multipliers)
+
+ def deactivate(self, p):
+ pass
diff --git a/modules/extras.py b/modules/extras.py
index fa3e07378..8e24635ff 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,348 +1,348 @@
-import os
-import html
-import json
-import time
-import shutil
-
-import torch
-import tqdm
-import gradio as gr
-import safetensors.torch
-from modules.merging.merge import merge_models
-from modules.merging.merge_utils import TRIPLE_METHODS
-
-from modules import shared, images, sd_models, sd_vae, sd_models_config, devices
-
-
-def run_pnginfo(image):
- if image is None:
- return '', '', ''
- geninfo, items = images.read_info_from_image(image)
- items = {**{'parameters': geninfo}, **items}
- info = ''
- for key, text in items.items():
- if key != 'UserComment':
- info += f"{html.escape(str(key))} : {html.escape(str(text))}
"
- return '', geninfo, info
-
-
-def create_config(ckpt_result, config_source, a, b, c):
- def config(x):
- res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None
- return res if res != shared.sd_default_config else None
-
- if config_source == 0:
- cfg = config(a) or config(b) or config(c)
- elif config_source == 1:
- cfg = config(b)
- elif config_source == 2:
- cfg = config(c)
- else:
- cfg = None
- if cfg is None:
- return
- filename, _ = os.path.splitext(ckpt_result)
- checkpoint_filename = filename + ".yaml"
- shared.log.info("Copying config: {cfg} -> {checkpoint_filename}")
- shutil.copyfile(cfg, checkpoint_filename)
-
-
-def to_half(tensor, enable):
- if enable and tensor.dtype == torch.float:
- return tensor.half()
- return tensor
-
-
-def run_modelmerger(id_task, **kwargs): # pylint: disable=unused-argument
- shared.state.begin('merge')
- t0 = time.time()
-
- def fail(message):
- shared.state.textinfo = message
- shared.state.end()
- return [*[gr.update() for _ in range(4)], message]
-
- kwargs["models"] = {
- "model_a": sd_models.get_closet_checkpoint_match(kwargs.get("primary_model_name", None)).filename,
- "model_b": sd_models.get_closet_checkpoint_match(kwargs.get("secondary_model_name", None)).filename,
- }
-
- if kwargs.get("primary_model_name", None) in [None, 'None']:
- return fail("Failed: Merging requires a primary model.")
- primary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("primary_model_name", None))
- if kwargs.get("secondary_model_name", None) in [None, 'None']:
- return fail("Failed: Merging requires a secondary model.")
- secondary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("secondary_model_name", None))
- if kwargs.get("tertiary_model_name", None) in [None, 'None'] and kwargs.get("merge_mode", None) in TRIPLE_METHODS:
- return fail(f"Failed: Interpolation method ({kwargs.get('merge_mode', None)}) requires a tertiary model.")
- tertiary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("tertiary_model_name", None)) if kwargs.get("merge_mode", None) in TRIPLE_METHODS else None
-
- del kwargs["primary_model_name"]
- del kwargs["secondary_model_name"]
- if kwargs.get("tertiary_model_name", None) is not None:
- kwargs["models"] |= {"model_c": sd_models.get_closet_checkpoint_match(kwargs.get("tertiary_model_name", None)).filename}
- del kwargs["tertiary_model_name"]
-
- if hasattr(kwargs, "alpha_base") and hasattr(kwargs, "alpha_in_blocks") and hasattr(kwargs, "alpha_mid_block") and hasattr(kwargs, "alpha_out_blocks"):
- try:
- alpha = [float(x) for x in
- [kwargs["alpha_base"]] + kwargs["alpha_in_blocks"].split(",") + [kwargs["alpha_mid_block"]] + kwargs["alpha_out_blocks"].split(",")]
- assert len(alpha) == 26 or len(alpha) == 20, "Alpha Block Weights are wrong length (26 or 20 for SDXL) falling back"
- kwargs["alpha"] = alpha
- except KeyError as ke:
- shared.log.warning(f"Merge: Malformed manual block weight: {ke}")
- elif hasattr(kwargs, "alpha_preset") or hasattr(kwargs, "alpha"):
- kwargs["alpha"] = kwargs.get("alpha_preset", kwargs["alpha"])
-
- kwargs.pop("alpha_base", None)
- kwargs.pop("alpha_in_blocks", None)
- kwargs.pop("alpha_mid_block", None)
- kwargs.pop("alpha_out_blocks", None)
- kwargs.pop("alpha_preset", None)
-
- if hasattr(kwargs, "beta_base") and hasattr(kwargs, "beta_in_blocks") and hasattr(kwargs, "beta_mid_block") and hasattr(kwargs, "beta_out_blocks"):
- try:
- beta = [float(x) for x in
- [kwargs["beta_base"]] + kwargs["beta_in_blocks"].split(",") + [kwargs["beta_mid_block"]] + kwargs["beta_out_blocks"].split(",")]
- assert len(beta) == 26 or len(beta) == 20, "Beta Block Weights are wrong length (26 or 20 for SDXL) falling back"
- kwargs["beta"] = beta
- except KeyError as ke:
- shared.log.warning(f"Merge: Malformed manual block weight: {ke}")
- elif hasattr(kwargs, "beta_preset") or hasattr(kwargs, "beta"):
- kwargs["beta"] = kwargs.get("beta_preset", kwargs["beta"])
-
- kwargs.pop("beta_base", None)
- kwargs.pop("beta_in_blocks", None)
- kwargs.pop("beta_mid_block", None)
- kwargs.pop("beta_out_blocks", None)
- kwargs.pop("beta_preset", None)
-
- if kwargs["device"] == "gpu":
- kwargs["device"] = devices.device
- elif kwargs["device"] == "shuffle":
- kwargs["device"] = torch.device("cpu")
- kwargs["work_device"] = devices.device
- else:
- kwargs["device"] = torch.device("cpu")
- if kwargs.pop("unload", False):
- sd_models.unload_model_weights()
-
- try:
- theta_0 = merge_models(**kwargs)
- except Exception as e:
- return fail(f"{e}")
-
- try:
- theta_0 = theta_0.to_dict() #TensorDict -> Dict if necessary
- except Exception:
- pass
-
- bake_in_vae_filename = sd_vae.vae_dict.get(kwargs.get("bake_in_vae", None), None)
- if bake_in_vae_filename is not None:
- shared.log.info(f"Merge VAE='{bake_in_vae_filename}'")
- shared.state.textinfo = 'Merge VAE'
- vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename)
- for key in vae_dict.keys():
- theta_0_key = 'first_stage_model.' + key
- if theta_0_key in theta_0:
- theta_0[theta_0_key] = to_half(vae_dict[key], kwargs.get("precision", "fp16") == "fp16")
- del vae_dict
-
- ckpt_dir = shared.opts.ckpt_dir or sd_models.model_path
- filename = kwargs.get("custom_name", "Unnamed_Merge")
- filename += "." + kwargs.get("checkpoint_format", None)
- output_modelname = os.path.join(ckpt_dir, filename)
- shared.state.textinfo = "merge saving"
- metadata = None
- if kwargs.get("save_metadata", False):
- metadata = {"format": "pt", "sd_merge_models": {}}
- merge_recipe = {
- "type": "SDNext", # indicate this model was merged with webui's built-in merger
- "primary_model_hash": primary_model_info.sha256,
- "secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
- "tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
- "merge_mode": kwargs.get('merge_mode', None),
- "alpha": kwargs.get('alpha', None),
- "beta": kwargs.get('beta', None),
- "precision": kwargs.get('precision', None),
- "custom_name": kwargs.get("custom_name", "Unamed_Merge"),
- }
- metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
-
- def add_model_metadata(checkpoint_info):
- checkpoint_info.calculate_shorthash()
- metadata["sd_merge_models"][checkpoint_info.sha256] = {
- "name": checkpoint_info.name,
- "legacy_hash": checkpoint_info.hash,
- "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
- }
- metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
-
- add_model_metadata(primary_model_info)
- if secondary_model_info:
- add_model_metadata(secondary_model_info)
- if tertiary_model_info:
- add_model_metadata(tertiary_model_info)
- metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
-
- _, extension = os.path.splitext(output_modelname)
-
- if os.path.exists(output_modelname) and not kwargs.get("overwrite", False):
- return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Model alredy exists: {output_modelname}"]
- if extension.lower() == ".safetensors":
- safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
- else:
- torch.save(theta_0, output_modelname)
-
- t1 = time.time()
- shared.log.info(f"Merge complete: saved='{output_modelname}' time={t1-t0:.2f}")
- sd_models.list_models()
- created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
- if created_model:
- created_model.calculate_shorthash()
- devices.torch_gc(force=True)
- shared.state.end()
- return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Model saved to {output_modelname}"]
-
-
-def run_modelconvert(model, checkpoint_formats, precision, conv_type, custom_name, unet_conv, text_encoder_conv,
- vae_conv, others_conv, fix_clip):
- # position_ids in clip is int64. model_ema.num_updates is int32
- dtypes_to_fp16 = {torch.float32, torch.float64, torch.bfloat16}
- dtypes_to_bf16 = {torch.float32, torch.float64, torch.float16}
-
- def conv_fp16(t: torch.Tensor):
- return t.half() if t.dtype in dtypes_to_fp16 else t
-
- def conv_bf16(t: torch.Tensor):
- return t.bfloat16() if t.dtype in dtypes_to_bf16 else t
-
- def conv_full(t):
- return t
-
- _g_precision_func = {
- "full": conv_full,
- "fp32": conv_full,
- "fp16": conv_fp16,
- "bf16": conv_bf16,
- }
-
- def check_weight_type(k: str) -> str:
- if k.startswith("model.diffusion_model"):
- return "unet"
- elif k.startswith("first_stage_model"):
- return "vae"
- elif k.startswith("cond_stage_model"):
- return "clip"
- return "other"
-
- def load_model(path):
- if path.endswith(".safetensors"):
- m = safetensors.torch.load_file(path, device="cpu")
- else:
- m = torch.load(path, map_location="cpu")
- state_dict = m["state_dict"] if "state_dict" in m else m
- return state_dict
-
- def fix_model(model, fix_clip=False):
- # code from model-toolkit
- nai_keys = {
- 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
- 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
- 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.'
- }
- for k in list(model.keys()):
- for r in nai_keys:
- if type(k) == str and k.startswith(r):
- new_key = k.replace(r, nai_keys[r])
- model[new_key] = model[k]
- del model[k]
- shared.log.warning(f"Model convert: fixed NovelAI error key: {k}")
- break
- if fix_clip:
- i = "cond_stage_model.transformer.text_model.embeddings.position_ids"
- if i in model:
- correct = torch.Tensor([list(range(77))]).to(torch.int64)
- now = model[i].to(torch.int64)
-
- broken = correct.ne(now)
- broken = [i for i in range(77) if broken[0][i]]
- model[i] = correct
- if len(broken) != 0:
- shared.log.warning(f"Model convert: fixed broken CLiP: {broken}")
-
- return model
-
- if model == "":
- return "Error: you must choose a model"
- if len(checkpoint_formats) == 0:
- return "Error: at least choose one model save format"
-
- extra_opt = {
- "unet": unet_conv,
- "clip": text_encoder_conv,
- "vae": vae_conv,
- "other": others_conv
- }
- shared.state.begin('convert')
- model_info = sd_models.checkpoints_list[model]
- shared.state.textinfo = f"Loading {model_info.filename}..."
- shared.log.info(f"Model convert loading: {model_info.filename}")
- state_dict = load_model(model_info.filename)
-
- ok = {} # {"state_dict": {}}
-
- conv_func = _g_precision_func[precision]
-
- def _hf(wk: str, t: torch.Tensor):
- if not isinstance(t, torch.Tensor):
- return
- w_t = check_weight_type(wk)
- conv_t = extra_opt[w_t]
- if conv_t == "convert":
- ok[wk] = conv_func(t)
- elif conv_t == "copy":
- ok[wk] = t
- elif conv_t == "delete":
- return
-
- shared.log.info("Model convert: running")
- if conv_type == "ema-only":
- for k in tqdm.tqdm(state_dict):
- ema_k = "___"
- try:
- ema_k = "model_ema." + k[6:].replace(".", "")
- except Exception:
- pass
- if ema_k in state_dict:
- _hf(k, state_dict[ema_k])
- elif not k.startswith("model_ema.") or k in ["model_ema.num_updates", "model_ema.decay"]:
- _hf(k, state_dict[k])
- elif conv_type == "no-ema":
- for k, v in tqdm.tqdm(state_dict.items()):
- if "model_ema." not in k:
- _hf(k, v)
- else:
- for k, v in tqdm.tqdm(state_dict.items()):
- _hf(k, v)
-
- ok = fix_model(ok, fix_clip=fix_clip)
- output = ""
- ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
- save_name = f"{model_info.model_name}-{precision}"
- if conv_type != "disabled":
- save_name += f"-{conv_type}"
- if custom_name != "":
- save_name = custom_name
- for fmt in checkpoint_formats:
- ext = ".safetensors" if fmt == "safetensors" else ".ckpt"
- _save_name = save_name + ext
- save_path = os.path.join(ckpt_dir, _save_name)
- shared.log.info(f"Model convert saving: {save_path}")
- if fmt == "safetensors":
- safetensors.torch.save_file(ok, save_path)
- else:
- torch.save({"state_dict": ok}, save_path)
- output += f"Checkpoint saved to {save_path}{html.escape(str(key))} : {html.escape(str(text))}
"
+ return '', geninfo, info
+
+
+def create_config(ckpt_result, config_source, a, b, c):
+ def config(x):
+ res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None
+ return res if res != shared.sd_default_config else None
+
+ if config_source == 0:
+ cfg = config(a) or config(b) or config(c)
+ elif config_source == 1:
+ cfg = config(b)
+ elif config_source == 2:
+ cfg = config(c)
+ else:
+ cfg = None
+ if cfg is None:
+ return
+ filename, _ = os.path.splitext(ckpt_result)
+ checkpoint_filename = filename + ".yaml"
+ shared.log.info("Copying config: {cfg} -> {checkpoint_filename}")
+ shutil.copyfile(cfg, checkpoint_filename)
+
+
+def to_half(tensor, enable):
+ if enable and tensor.dtype == torch.float:
+ return tensor.half()
+ return tensor
+
+
+def run_modelmerger(id_task, **kwargs): # pylint: disable=unused-argument
+ shared.state.begin('merge')
+ t0 = time.time()
+
+ def fail(message):
+ shared.state.textinfo = message
+ shared.state.end()
+ return [*[gr.update() for _ in range(4)], message]
+
+ kwargs["models"] = {
+ "model_a": sd_models.get_closet_checkpoint_match(kwargs.get("primary_model_name", None)).filename,
+ "model_b": sd_models.get_closet_checkpoint_match(kwargs.get("secondary_model_name", None)).filename,
+ }
+
+ if kwargs.get("primary_model_name", None) in [None, 'None']:
+ return fail("Failed: Merging requires a primary model.")
+ primary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("primary_model_name", None))
+ if kwargs.get("secondary_model_name", None) in [None, 'None']:
+ return fail("Failed: Merging requires a secondary model.")
+ secondary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("secondary_model_name", None))
+ if kwargs.get("tertiary_model_name", None) in [None, 'None'] and kwargs.get("merge_mode", None) in TRIPLE_METHODS:
+ return fail(f"Failed: Interpolation method ({kwargs.get('merge_mode', None)}) requires a tertiary model.")
+ tertiary_model_info = sd_models.get_closet_checkpoint_match(kwargs.get("tertiary_model_name", None)) if kwargs.get("merge_mode", None) in TRIPLE_METHODS else None
+
+ del kwargs["primary_model_name"]
+ del kwargs["secondary_model_name"]
+ if kwargs.get("tertiary_model_name", None) is not None:
+ kwargs["models"] |= {"model_c": sd_models.get_closet_checkpoint_match(kwargs.get("tertiary_model_name", None)).filename}
+ del kwargs["tertiary_model_name"]
+
+ if hasattr(kwargs, "alpha_base") and hasattr(kwargs, "alpha_in_blocks") and hasattr(kwargs, "alpha_mid_block") and hasattr(kwargs, "alpha_out_blocks"):
+ try:
+ alpha = [float(x) for x in
+ [kwargs["alpha_base"]] + kwargs["alpha_in_blocks"].split(",") + [kwargs["alpha_mid_block"]] + kwargs["alpha_out_blocks"].split(",")]
+ assert len(alpha) == 26 or len(alpha) == 20, "Alpha Block Weights are wrong length (26 or 20 for SDXL) falling back"
+ kwargs["alpha"] = alpha
+ except KeyError as ke:
+ shared.log.warning(f"Merge: Malformed manual block weight: {ke}")
+ elif hasattr(kwargs, "alpha_preset") or hasattr(kwargs, "alpha"):
+ kwargs["alpha"] = kwargs.get("alpha_preset", kwargs["alpha"])
+
+ kwargs.pop("alpha_base", None)
+ kwargs.pop("alpha_in_blocks", None)
+ kwargs.pop("alpha_mid_block", None)
+ kwargs.pop("alpha_out_blocks", None)
+ kwargs.pop("alpha_preset", None)
+
+ if hasattr(kwargs, "beta_base") and hasattr(kwargs, "beta_in_blocks") and hasattr(kwargs, "beta_mid_block") and hasattr(kwargs, "beta_out_blocks"):
+ try:
+ beta = [float(x) for x in
+ [kwargs["beta_base"]] + kwargs["beta_in_blocks"].split(",") + [kwargs["beta_mid_block"]] + kwargs["beta_out_blocks"].split(",")]
+ assert len(beta) == 26 or len(beta) == 20, "Beta Block Weights are wrong length (26 or 20 for SDXL) falling back"
+ kwargs["beta"] = beta
+ except KeyError as ke:
+ shared.log.warning(f"Merge: Malformed manual block weight: {ke}")
+ elif hasattr(kwargs, "beta_preset") or hasattr(kwargs, "beta"):
+ kwargs["beta"] = kwargs.get("beta_preset", kwargs["beta"])
+
+ kwargs.pop("beta_base", None)
+ kwargs.pop("beta_in_blocks", None)
+ kwargs.pop("beta_mid_block", None)
+ kwargs.pop("beta_out_blocks", None)
+ kwargs.pop("beta_preset", None)
+
+ if kwargs["device"] == "gpu":
+ kwargs["device"] = devices.device
+ elif kwargs["device"] == "shuffle":
+ kwargs["device"] = torch.device("cpu")
+ kwargs["work_device"] = devices.device
+ else:
+ kwargs["device"] = torch.device("cpu")
+ if kwargs.pop("unload", False):
+ sd_models.unload_model_weights()
+
+ try:
+ theta_0 = merge_models(**kwargs)
+ except Exception as e:
+ return fail(f"{e}")
+
+ try:
+ theta_0 = theta_0.to_dict() #TensorDict -> Dict if necessary
+ except Exception:
+ pass
+
+ bake_in_vae_filename = sd_vae.vae_dict.get(kwargs.get("bake_in_vae", None), None)
+ if bake_in_vae_filename is not None:
+ shared.log.info(f"Merge VAE='{bake_in_vae_filename}'")
+ shared.state.textinfo = 'Merge VAE'
+ vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename)
+ for key in vae_dict.keys():
+ theta_0_key = 'first_stage_model.' + key
+ if theta_0_key in theta_0:
+ theta_0[theta_0_key] = to_half(vae_dict[key], kwargs.get("precision", "fp16") == "fp16")
+ del vae_dict
+
+ ckpt_dir = shared.opts.ckpt_dir or sd_models.model_path
+ filename = kwargs.get("custom_name", "Unnamed_Merge")
+ filename += "." + kwargs.get("checkpoint_format", None)
+ output_modelname = os.path.join(ckpt_dir, filename)
+ shared.state.textinfo = "merge saving"
+ metadata = None
+ if kwargs.get("save_metadata", False):
+ metadata = {"format": "pt", "sd_merge_models": {}}
+ merge_recipe = {
+ "type": "SDNext", # indicate this model was merged with webui's built-in merger
+ "primary_model_hash": primary_model_info.sha256,
+ "secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
+ "tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
+ "merge_mode": kwargs.get('merge_mode', None),
+ "alpha": kwargs.get('alpha', None),
+ "beta": kwargs.get('beta', None),
+ "precision": kwargs.get('precision', None),
+ "custom_name": kwargs.get("custom_name", "Unamed_Merge"),
+ }
+ metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
+
+ def add_model_metadata(checkpoint_info):
+ checkpoint_info.calculate_shorthash()
+ metadata["sd_merge_models"][checkpoint_info.sha256] = {
+ "name": checkpoint_info.name,
+ "legacy_hash": checkpoint_info.hash,
+ "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
+ }
+ metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
+
+ add_model_metadata(primary_model_info)
+ if secondary_model_info:
+ add_model_metadata(secondary_model_info)
+ if tertiary_model_info:
+ add_model_metadata(tertiary_model_info)
+ metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
+
+ _, extension = os.path.splitext(output_modelname)
+
+ if os.path.exists(output_modelname) and not kwargs.get("overwrite", False):
+ return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Model alredy exists: {output_modelname}"]
+ if extension.lower() == ".safetensors":
+ safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
+ else:
+ torch.save(theta_0, output_modelname)
+
+ t1 = time.time()
+ shared.log.info(f"Merge complete: saved='{output_modelname}' time={t1-t0:.2f}")
+ sd_models.list_models()
+ created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
+ if created_model:
+ created_model.calculate_shorthash()
+ devices.torch_gc(force=True)
+ shared.state.end()
+ return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Model saved to {output_modelname}"]
+
+
+def run_modelconvert(model, checkpoint_formats, precision, conv_type, custom_name, unet_conv, text_encoder_conv,
+ vae_conv, others_conv, fix_clip):
+ # position_ids in clip is int64. model_ema.num_updates is int32
+ dtypes_to_fp16 = {torch.float32, torch.float64, torch.bfloat16}
+ dtypes_to_bf16 = {torch.float32, torch.float64, torch.float16}
+
+ def conv_fp16(t: torch.Tensor):
+ return t.half() if t.dtype in dtypes_to_fp16 else t
+
+ def conv_bf16(t: torch.Tensor):
+ return t.bfloat16() if t.dtype in dtypes_to_bf16 else t
+
+ def conv_full(t):
+ return t
+
+ _g_precision_func = {
+ "full": conv_full,
+ "fp32": conv_full,
+ "fp16": conv_fp16,
+ "bf16": conv_bf16,
+ }
+
+ def check_weight_type(k: str) -> str:
+ if k.startswith("model.diffusion_model"):
+ return "unet"
+ elif k.startswith("first_stage_model"):
+ return "vae"
+ elif k.startswith("cond_stage_model"):
+ return "clip"
+ return "other"
+
+ def load_model(path):
+ if path.endswith(".safetensors"):
+ m = safetensors.torch.load_file(path, device="cpu")
+ else:
+ m = torch.load(path, map_location="cpu")
+ state_dict = m["state_dict"] if "state_dict" in m else m
+ return state_dict
+
+ def fix_model(model, fix_clip=False):
+ # code from model-toolkit
+ nai_keys = {
+ 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
+ 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
+ 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.'
+ }
+ for k in list(model.keys()):
+ for r in nai_keys:
+ if type(k) == str and k.startswith(r):
+ new_key = k.replace(r, nai_keys[r])
+ model[new_key] = model[k]
+ del model[k]
+ shared.log.warning(f"Model convert: fixed NovelAI error key: {k}")
+ break
+ if fix_clip:
+ i = "cond_stage_model.transformer.text_model.embeddings.position_ids"
+ if i in model:
+ correct = torch.Tensor([list(range(77))]).to(torch.int64)
+ now = model[i].to(torch.int64)
+
+ broken = correct.ne(now)
+ broken = [i for i in range(77) if broken[0][i]]
+ model[i] = correct
+ if len(broken) != 0:
+ shared.log.warning(f"Model convert: fixed broken CLiP: {broken}")
+
+ return model
+
+ if model == "":
+ return "Error: you must choose a model"
+ if len(checkpoint_formats) == 0:
+ return "Error: at least choose one model save format"
+
+ extra_opt = {
+ "unet": unet_conv,
+ "clip": text_encoder_conv,
+ "vae": vae_conv,
+ "other": others_conv
+ }
+ shared.state.begin('convert')
+ model_info = sd_models.checkpoints_list[model]
+ shared.state.textinfo = f"Loading {model_info.filename}..."
+ shared.log.info(f"Model convert loading: {model_info.filename}")
+ state_dict = load_model(model_info.filename)
+
+ ok = {} # {"state_dict": {}}
+
+ conv_func = _g_precision_func[precision]
+
+ def _hf(wk: str, t: torch.Tensor):
+ if not isinstance(t, torch.Tensor):
+ return
+ w_t = check_weight_type(wk)
+ conv_t = extra_opt[w_t]
+ if conv_t == "convert":
+ ok[wk] = conv_func(t)
+ elif conv_t == "copy":
+ ok[wk] = t
+ elif conv_t == "delete":
+ return
+
+ shared.log.info("Model convert: running")
+ if conv_type == "ema-only":
+ for k in tqdm.tqdm(state_dict):
+ ema_k = "___"
+ try:
+ ema_k = "model_ema." + k[6:].replace(".", "")
+ except Exception:
+ pass
+ if ema_k in state_dict:
+ _hf(k, state_dict[ema_k])
+ elif not k.startswith("model_ema.") or k in ["model_ema.num_updates", "model_ema.decay"]:
+ _hf(k, state_dict[k])
+ elif conv_type == "no-ema":
+ for k, v in tqdm.tqdm(state_dict.items()):
+ if "model_ema." not in k:
+ _hf(k, v)
+ else:
+ for k, v in tqdm.tqdm(state_dict.items()):
+ _hf(k, v)
+
+ ok = fix_model(ok, fix_clip=fix_clip)
+ output = ""
+ ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
+ save_name = f"{model_info.model_name}-{precision}"
+ if conv_type != "disabled":
+ save_name += f"-{conv_type}"
+ if custom_name != "":
+ save_name = custom_name
+ for fmt in checkpoint_formats:
+ ext = ".safetensors" if fmt == "safetensors" else ".ckpt"
+ _save_name = save_name + ext
+ save_path = os.path.join(ckpt_dir, _save_name)
+ shared.log.info(f"Model convert saving: {save_path}")
+ if fmt == "safetensors":
+ safetensors.torch.save_file(ok, save_path)
+ else:
+ torch.save({"state_dict": ok}, save_path)
+ output += f"Checkpoint saved to {save_path}'} using fallback: {upscaler.name}")
- if im.width != w or im.height != h:
- im = im.resize((w, h), resample=Image.Resampling.LANCZOS)
- return im
-
- if resize_mode == 0 or (im.width == width and im.height == height):
- res = im.copy()
- elif resize_mode == 1:
- res = resize(im, width, height)
- elif resize_mode == 2:
- ratio = width / height
- src_ratio = im.width / im.height
- src_w = width if ratio > src_ratio else im.width * height // im.height
- src_h = height if ratio <= src_ratio else im.height * width // im.width
- resized = resize(im, src_w, src_h)
- res = Image.new(im.mode, (width, height))
- res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
- else:
- ratio = width / height
- src_ratio = im.width / im.height
- src_w = width if ratio < src_ratio else im.width * height // im.height
- src_h = height if ratio >= src_ratio else im.height * width // im.width
- resized = resize(im, src_w, src_h)
- res = Image.new(im.mode, (width, height))
- res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
- if ratio < src_ratio:
- fill_height = height // 2 - src_h // 2
- res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
- res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
- elif ratio > src_ratio:
- fill_width = width // 2 - src_w // 2
- res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
- res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
- if output_type == 'np':
- return np.array(res)
- return res
-
-
-re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
-re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
-re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
-re_attention = re.compile(r'[\(*\[*](\w+)(:\d+(\.\d+))?[\)*\]*]|')
-re_network = re.compile(r'\<\w+:(\w+)(:\d+(\.\d+))?\>|')
-re_brackets = re.compile(r'[\([{})\]]')
-
-NOTHING = object()
-
-
-class FilenameGenerator:
- replacements = {
- 'width': lambda self: self.image.width,
- 'height': lambda self: self.image.height,
- 'batch_number': lambda self: self.batch_number,
- 'iter_number': lambda self: self.iter_number,
- 'num': lambda self: NOTHING if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
- 'generation_number': lambda self: NOTHING if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
- 'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
- 'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime], [datetime]
- 'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt..]
- 'hash': lambda self: self.image_hash(),
- 'image_hash': lambda self: self.image_hash(),
- 'timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
- 'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
-
- 'model': lambda self: shared.sd_model.sd_checkpoint_info.title,
- 'model_shortname': lambda self: shared.sd_model.sd_checkpoint_info.model_name,
- 'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name,
- 'model_hash': lambda self: shared.sd_model.sd_checkpoint_info.shorthash,
-
- 'prompt': lambda self: self.prompt_full(),
- 'prompt_no_styles': lambda self: self.prompt_no_style(),
- 'prompt_words': lambda self: self.prompt_words(),
- 'prompt_hash': lambda self: hashlib.sha256(self.prompt.encode()).hexdigest()[0:8],
-
- 'sampler': lambda self: self.p and self.p.sampler_name,
- 'seed': lambda self: self.seed and str(self.seed) or '',
- 'steps': lambda self: self.p and self.p.steps,
- 'styles': lambda self: self.p and ", ".join([style for style in self.p.styles if not style == "None"]) or "None",
- 'uuid': lambda self: str(uuid.uuid4()),
- }
- default_time_format = '%Y%m%d%H%M%S'
-
- def __init__(self, p, seed, prompt, image, grid=False):
- if p is None:
- debug('Filename generator init skip')
- else:
- debug(f'Filename generator init: {seed} {prompt}')
- self.p = p
- if seed is not None and seed > 0:
- self.seed = seed
- elif hasattr(p, 'all_seeds'):
- self.seed = p.all_seeds[0]
- else:
- self.seed = 0
- self.prompt = prompt
- self.image = image
- if not grid:
- self.batch_number = NOTHING if self.p is None or getattr(self.p, 'batch_size', 1) == 1 else (self.p.batch_index + 1 if hasattr(self.p, 'batch_index') else NOTHING)
- self.iter_number = NOTHING if self.p is None or getattr(self.p, 'n_iter', 1) == 1 else (self.p.iteration + 1 if hasattr(self.p, 'iteration') else NOTHING)
- else:
- self.batch_number = NOTHING
- self.iter_number = NOTHING
-
- def hasprompt(self, *args):
- lower = self.prompt.lower()
- if getattr(self, 'p', None) is None or getattr(self, 'prompt', None) is None:
- return None
- outres = ""
- for arg in args:
- if arg != "":
- division = arg.split("|")
- expected = division[0].lower()
- default = division[1] if len(division) > 1 else ""
- if lower.find(expected) >= 0:
- outres = f'{outres}{expected}'
- else:
- outres = outres if default == "" else f'{outres}{default}'
- return outres
-
- def image_hash(self):
- if getattr(self, 'image', None) is None:
- return None
- import base64
- from io import BytesIO
- buffered = BytesIO()
- self.image.save(buffered, format="JPEG")
- img_str = base64.b64encode(buffered.getvalue())
- shorthash = hashlib.sha256(img_str).hexdigest()[0:8]
- return shorthash
-
- def prompt_full(self):
- return self.prompt_sanitize(self.prompt)
-
- def prompt_words(self):
- if getattr(self, 'prompt', None) is None:
- return ''
- no_attention = re_attention.sub(r'\1', self.prompt)
- no_network = re_network.sub(r'\1', no_attention)
- no_brackets = re_brackets.sub('', no_network)
- words = [x for x in re_nonletters.split(no_brackets or "") if len(x) > 0]
- prompt = " ".join(words[0:shared.opts.directories_max_prompt_words])
- return self.prompt_sanitize(prompt)
-
- def prompt_no_style(self):
- if getattr(self, 'p', None) is None or getattr(self, 'prompt', None) is None:
- return None
- prompt_no_style = self.prompt
- for style in shared.prompt_styles.get_style_prompts(self.p.styles):
- if len(style) > 0:
- for part in style.split("{prompt}"):
- prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",")
- prompt_no_style = prompt_no_style.replace(style, "")
- return self.prompt_sanitize(prompt_no_style)
-
- def datetime(self, *args):
- import pytz
- time_datetime = datetime.datetime.now()
- time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
- try:
- time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
- except pytz.exceptions.UnknownTimeZoneError:
- time_zone = None
- time_zone_time = time_datetime.astimezone(time_zone)
- try:
- formatted_time = time_zone_time.strftime(time_format)
- except (ValueError, TypeError):
- formatted_time = time_zone_time.strftime(self.default_time_format)
- return formatted_time
-
- def prompt_sanitize(self, prompt):
- invalid_chars = '#<>:\'"\\|?*\n\t\r'
- sanitized = prompt.translate({ ord(x): '_' for x in invalid_chars }).strip()
- debug(f'Prompt sanitize: input="{prompt}" output={sanitized}')
- return sanitized
-
- def sanitize(self, filename):
- invalid_chars = '\'"|?*\n\t\r' #
- invalid_folder = ':'
- invalid_files = ['CON', 'PRN', 'AUX', 'NUL', 'NULL', 'COM0', 'COM1', 'LPT0', 'LPT1']
- invalid_prefix = ', '
- invalid_suffix = '.,_ '
- fn, ext = os.path.splitext(filename)
- parts = Path(fn).parts
- newparts = []
- for i, part in enumerate(parts):
- part = part.translate({ ord(x): '_' for x in invalid_chars })
- if i > 0 or (len(part) >= 2 and part[1] != invalid_folder): # skip drive, otherwise remove
- part = part.translate({ ord(x): '_' for x in invalid_folder })
- part = part.lstrip(invalid_prefix).rstrip(invalid_suffix)
- if part in invalid_files: # reserved names
- [part := part.replace(word, '_') for word in invalid_files] # pylint: disable=expression-not-assigned
- newparts.append(part)
- fn = str(Path(*newparts))
- max_length = max(256 - len(ext), os.statvfs(__file__).f_namemax - 32 if hasattr(os, 'statvfs') else 256 - len(ext))
- while len(os.path.abspath(fn)) > max_length:
- fn = fn[:-1]
- fn += ext
- debug(f'Filename sanitize: input="{filename}" parts={parts} output="{fn}" ext={ext} max={max_length} len={len(fn)}')
- return fn
-
- def sequence(self, x, dirname, basename):
- if shared.opts.save_images_add_number or '[seq]' in x:
- if '[seq]' not in x:
- x = os.path.join(os.path.dirname(x), f"[seq]-{os.path.basename(x)}")
- basecount = get_next_sequence_number(dirname, basename)
- for i in range(9999):
- seq = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
- filename = x.replace('[seq]', seq)
- if not os.path.exists(filename):
- debug(f'Prompt sequence: input="{x}" seq={seq} output="{filename}"')
- x = filename
- break
- return x
-
- def apply(self, x):
- res = ''
- for m in re_pattern.finditer(x):
- text, pattern = m.groups()
- if pattern is None:
- res += text
- continue
- pattern_args = []
- while True:
- m = re_pattern_arg.match(pattern)
- if m is None:
- break
- pattern, arg = m.groups()
- pattern_args.insert(0, arg)
- fun = self.replacements.get(pattern.lower(), None)
- if fun is not None:
- try:
- debug(f'Filename apply: pattern={pattern.lower()} args={pattern_args}')
- replacement = fun(self, *pattern_args)
- except Exception as e:
- replacement = None
- shared.log.error(f'Filename apply pattern: {x} {e}')
- if replacement == NOTHING:
- continue
- if replacement is not None:
- res += text + str(replacement).replace('/', '-').replace('\\', '-')
- continue
- else:
- res += text + f'[{pattern}]' # reinsert unknown pattern
- return res
-
-
-def get_next_sequence_number(path, basename):
- """
- Determines and returns the next sequence number to use when saving an image in the specified directory.
- """
- result = -1
- if basename != '':
- basename = f"{basename}-"
- prefix_length = len(basename)
- if not os.path.isdir(path):
- return 0
- for p in os.listdir(path):
- if p.startswith(basename):
- parts = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
- try:
- result = max(int(parts[0]), result)
- except ValueError:
- pass
- return result + 1
-
-
-def atomically_save_image():
- Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
- while True:
- image, filename, extension, params, exifinfo, filename_txt = save_queue.get()
- with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
- file.write(exifinfo)
- fn = filename + extension
- filename = filename.strip()
- if extension[0] != '.': # add dot if missing
- extension = '.' + extension
- try:
- image_format = Image.registered_extensions()[extension]
- except Exception:
- shared.log.warning(f'Unknown image format: {extension}')
- image_format = 'JPEG'
- if shared.opts.image_watermark_enabled:
- image = set_watermark(image, shared.opts.image_watermark)
- size = os.path.getsize(fn) if os.path.exists(fn) else 0
- shared.log.debug(f'Saving: image="{fn}" type={image_format} resolution={image.width}x{image.height} size={size}')
- # additional metadata saved in files
- if shared.opts.save_txt and len(exifinfo) > 0:
- try:
- with open(filename_txt, "w", encoding="utf8") as file:
- file.write(f"{exifinfo}\n")
- shared.log.debug(f'Saving: text="{filename_txt}" len={len(exifinfo)}')
- except Exception as e:
- shared.log.warning(f'Image description save failed: {filename_txt} {e}')
- # actual save
- exifinfo = (exifinfo or "") if shared.opts.image_metadata else ""
- if image_format == 'PNG':
- pnginfo_data = PngImagePlugin.PngInfo()
- for k, v in params.pnginfo.items():
- pnginfo_data.add_text(k, str(v))
- try:
- image.save(fn, format=image_format, compress_level=6, pnginfo=pnginfo_data if shared.opts.image_metadata else None)
- except Exception as e:
- shared.log.error(f'Image save failed: file="{fn}" {e}')
- elif image_format == 'JPEG':
- if image.mode == 'RGBA':
- shared.log.warning('Saving RGBA image as JPEG: Alpha channel will be lost')
- image = image.convert("RGB")
- elif image.mode == 'I;16':
- image = image.point(lambda p: p * 0.0038910505836576).convert("L")
- exif_bytes = piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(exifinfo, encoding="unicode") } })
- try:
- image.save(fn, format=image_format, optimize=True, quality=shared.opts.jpeg_quality, exif=exif_bytes)
- except Exception as e:
- shared.log.error(f'Image save failed: file="{fn}" {e}')
- elif image_format == 'WEBP':
- if image.mode == 'I;16':
- image = image.point(lambda p: p * 0.0038910505836576).convert("RGB")
- exif_bytes = piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(exifinfo, encoding="unicode") } })
- try:
- image.save(fn, format=image_format, quality=shared.opts.jpeg_quality, lossless=shared.opts.webp_lossless, exif=exif_bytes)
- except Exception as e:
- shared.log.error(f'Image save failed: file="{fn}" {e}')
- else:
- # shared.log.warning(f'Unrecognized image format: {extension} attempting save as {image_format}')
- try:
- image.save(fn, format=image_format, quality=shared.opts.jpeg_quality)
- except Exception as e:
- shared.log.error(f'Image save failed: file="{fn}" {e}')
- if shared.opts.save_log_fn != '' and len(exifinfo) > 0:
- fn = os.path.join(paths.data_path, shared.opts.save_log_fn)
- if not fn.endswith('.json'):
- fn += '.json'
- entries = shared.readfile(fn, silent=True)
- idx = len(list(entries))
- if idx == 0:
- entries = []
- entry = { 'id': idx, 'filename': filename, 'time': datetime.datetime.now().isoformat(), 'info': exifinfo }
- entries.append(entry)
- shared.writefile(entries, fn, mode='w', silent=True)
- shared.log.debug(f'Saving: json="{fn}" records={len(entries)}')
- save_queue.task_done()
-
-
-save_queue = queue.Queue()
-save_thread = threading.Thread(target=atomically_save_image, daemon=True)
-save_thread.start()
-
-
-def save_image(image, path, basename='', seed=None, prompt=None, extension=shared.opts.samples_format, info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix='', save_to_dirs=None): # pylint: disable=unused-argument
- debug(f'Save from function={sys._getframe(1).f_code.co_name}') # pylint: disable=protected-access
- if image is None:
- shared.log.warning('Image is none')
- return None, None
- if not check_grid_size([image]):
- return None, None
- if path is None or len(path) == 0: # set default path to avoid errors when functions are triggered manually or via api and param is not set
- path = shared.opts.outdir_save
- namegen = FilenameGenerator(p, seed, prompt, image, grid=grid)
- suffix = suffix if suffix is not None else ''
- basename = basename if basename is not None else ''
- if shared.opts.save_to_dirs:
- dirname = namegen.apply(shared.opts.directories_filename_pattern or "[prompt_words]")
- path = os.path.join(path, dirname)
- if forced_filename is None:
- if shared.opts.samples_filename_pattern and len(shared.opts.samples_filename_pattern) > 0:
- file_decoration = shared.opts.samples_filename_pattern
- else:
- file_decoration = "[seq]-[prompt_words]"
- file_decoration = namegen.apply(file_decoration)
- file_decoration += suffix if suffix is not None else ''
- filename = os.path.join(path, f"{file_decoration}.{extension}") if basename == '' else os.path.join(path, f"{basename}-{file_decoration}.{extension}")
- else:
- forced_filename += suffix if suffix is not None else ''
- filename = os.path.join(path, f"{forced_filename}.{extension}") if basename == '' else os.path.join(path, f"{basename}-{forced_filename}.{extension}")
- pnginfo = existing_info or {}
- if info is not None:
- pnginfo[pnginfo_section_name] = info
- params = script_callbacks.ImageSaveParams(image, p, filename, pnginfo)
- params.filename = namegen.sanitize(filename)
- dirname = os.path.dirname(params.filename)
- if dirname is not None and len(dirname) > 0:
- os.makedirs(dirname, exist_ok=True)
- params.filename = namegen.sequence(params.filename, dirname, basename)
- params.filename = namegen.sanitize(params.filename)
- # callbacks
- script_callbacks.before_image_saved_callback(params)
- exifinfo = params.pnginfo.get('UserComment', '')
- exifinfo = (exifinfo + ', ' if len(exifinfo) > 0 else '') + params.pnginfo.get(pnginfo_section_name, '')
- filename, extension = os.path.splitext(params.filename)
- filename_txt = f"{filename}.txt" if shared.opts.save_txt and len(exifinfo) > 0 else None
- save_queue.put((params.image, filename, extension, params, exifinfo, filename_txt)) # actual save is executed in a thread that polls data from queue
- save_queue.join()
- if not hasattr(params.image, 'already_saved_as'):
- debug(f'Image marked: "{params.filename}"')
- params.image.already_saved_as = params.filename
- script_callbacks.image_saved_callback(params)
- return params.filename, filename_txt
-
-
-def save_video_atomic(images, filename, video_type: str = 'none', duration: float = 2.0, loop: bool = False, interpolate: int = 0, scale: float = 1.0, pad: int = 1, change: float = 0.3):
- try:
- import cv2
- except Exception as e:
- shared.log.error(f'Save video: cv2: {e}')
- return
- os.makedirs(os.path.dirname(filename), exist_ok=True)
- if video_type.lower() == 'mp4':
- frames = images
- if interpolate > 0:
- try:
- import modules.rife
- frames = modules.rife.interpolate(images, count=interpolate, scale=scale, pad=pad, change=change)
- except Exception as e:
- shared.log.error(f'RIFE interpolation: {e}')
- errors.display(e, 'RIFE interpolation')
- video_frames = [np.array(frame) for frame in frames]
- fourcc = "mp4v"
- h, w, _c = video_frames[0].shape
- video_writer = cv2.VideoWriter(filename, fourcc=cv2.VideoWriter_fourcc(*fourcc), fps=len(frames)/duration, frameSize=(w, h))
- for i in range(len(video_frames)):
- img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)
- video_writer.write(img)
- size = os.path.getsize(filename)
- shared.log.info(f'Save video: file="{filename}" frames={len(frames)} duration={duration} fourcc={fourcc} size={size}')
- if video_type.lower() == 'gif' or video_type.lower() == 'png':
- append = images.copy()
- image = append.pop(0)
- if loop:
- append += append[::-1]
- frames=len(append) + 1
- image.save(
- filename,
- save_all = True,
- append_images = append,
- optimize = False,
- duration = 1000.0 * duration / frames,
- loop = 0 if loop else 1,
- )
- size = os.path.getsize(filename)
- shared.log.info(f'Save video: file="{filename}" frames={len(append) + 1} duration={duration} loop={loop} size={size}')
-
-
-def save_video(p, images, filename = None, video_type: str = 'none', duration: float = 2.0, loop: bool = False, interpolate: int = 0, scale: float = 1.0, pad: int = 1, change: float = 0.3, sync: bool = False):
- if images is None or len(images) < 2 or video_type is None or video_type.lower() == 'none':
- return
- image = images[0]
- if p is not None:
- namegen = FilenameGenerator(p, seed=p.all_seeds[0], prompt=p.all_prompts[0], image=image)
- else:
- namegen = FilenameGenerator(None, seed=0, prompt='', image=image)
- if filename is None and p is not None:
- filename = namegen.apply(shared.opts.samples_filename_pattern if shared.opts.samples_filename_pattern and len(shared.opts.samples_filename_pattern) > 0 else "[seq]-[prompt_words]")
- filename = os.path.join(shared.opts.outdir_video, filename)
- filename = namegen.sequence(filename, shared.opts.outdir_video, '')
- else:
- if os.pathsep not in filename:
- filename = os.path.join(shared.opts.outdir_video, filename)
- if not filename.lower().endswith(video_type.lower()):
- filename += f'.{video_type.lower()}'
- filename = namegen.sanitize(filename)
- if not sync:
- threading.Thread(target=save_video_atomic, args=(images, filename, video_type, duration, loop, interpolate, scale, pad, change)).start()
- else:
- save_video_atomic(images, filename, video_type, duration, loop, interpolate, scale, pad, change)
- return filename
-
-
-def safe_decode_string(s: bytes):
- remove_prefix = lambda text, prefix: text[len(prefix):] if text.startswith(prefix) else text # pylint: disable=unnecessary-lambda-assignment
- for encoding in ['utf-8', 'utf-16', 'ascii', 'latin_1', 'cp1252', 'cp437']: # try different encodings
- try:
- s = remove_prefix(s, b'UNICODE')
- s = remove_prefix(s, b'ASCII')
- s = remove_prefix(s, b'\x00')
- val = s.decode(encoding, errors="strict")
- val = re.sub(r'[\x00-\x09]', '', val).strip() # remove remaining special characters
- if len(val) == 0: # remove empty strings
- val = None
- return val
- except Exception:
- pass
- return None
-
-
-def read_info_from_image(image: Image):
- items = image.info or {}
- geninfo = items.pop('parameters', None)
- if geninfo is None:
- geninfo = items.pop('UserComment', None)
- if geninfo is not None and len(geninfo) > 0:
- if 'UserComment' in geninfo:
- geninfo = geninfo['UserComment']
- items['UserComment'] = geninfo
-
- if "exif" in items:
- try:
- exif = piexif.load(items["exif"])
- except Exception as e:
- shared.log.error(f'Error loading EXIF data: {e}')
- exif = {}
- for _key, subkey in exif.items():
- if isinstance(subkey, dict):
- for key, val in subkey.items():
- if isinstance(val, bytes): # decode bytestring
- val = safe_decode_string(val)
- if isinstance(val, tuple) and isinstance(val[0], int) and isinstance(val[1], int) and val[1] > 0: # convert camera ratios
- val = round(val[0] / val[1], 2)
- if val is not None and key in ExifTags.TAGS: # add known tags
- if ExifTags.TAGS[key] == 'UserComment': # add geninfo from UserComment
- geninfo = val
- items['parameters'] = val
- else:
- items[ExifTags.TAGS[key]] = val
- elif val is not None and key in ExifTags.GPSTAGS:
- items[ExifTags.GPSTAGS[key]] = val
- wm = get_watermark(image)
- if wm != '':
- # geninfo += f' Watermark: {wm}'
- items['watermark'] = wm
-
- for key, val in items.items():
- if isinstance(val, bytes): # decode bytestring
- items[key] = safe_decode_string(val)
-
- for key in ['exif', 'ExifOffset', 'JpegIFOffset', 'JpegIFByteCount', 'ExifVersion', 'icc_profile', 'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'adobe', 'photoshop', 'loop', 'duration', 'dpi']: # remove unwanted tags
- items.pop(key, None)
-
- if items.get("Software", None) == "NovelAI":
- try:
- json_info = json.loads(items["Comment"])
- sampler = sd_samplers.samplers_map.get(json_info["sampler"], "Euler a")
- geninfo = f"""{items["Description"]}
-Negative prompt: {json_info["uc"]}
-Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
- except Exception as e:
- errors.display(e, 'novelai image parser')
-
- try:
- items['width'] = image.width
- items['height'] = image.height
- items['mode'] = image.mode
- except Exception:
- pass
-
- return geninfo, items
-
-
-def image_data(data):
- import gradio as gr
- if data is None:
- return gr.update(), None
- err1 = None
- err2 = None
- try:
- image = Image.open(io.BytesIO(data))
- errors.log.debug(f'Decoded object: image={image}')
- textinfo, _ = read_info_from_image(image)
- return textinfo, None
- except Exception as e:
- err1 = e
- try:
- if len(data) > 1024 * 10:
- errors.log.warning(f'Error decoding object: data too long: {len(data)}')
- return gr.update(), None
- text = data.decode('utf8')
- errors.log.debug(f'Decoded object: size={len(text)}')
- return text, None
- except Exception as e:
- err2 = e
- errors.log.error(f'Error decoding object: {err1 or err2}')
- return gr.update(), None
-
-
-def flatten(img, bgcolor):
- """replaces transparency with bgcolor (example: "#ffffff"), returning an RGB mode image with no transparency"""
- if img.mode == "RGBA":
- background = Image.new('RGBA', img.size, bgcolor)
- background.paste(img, mask=img)
- img = background
- return img.convert('RGB')
-
-
-def set_watermark(image, watermark):
- from imwatermark import WatermarkEncoder
- wm_type = 'bytes'
- wm_method = 'dwtDctSvd'
- wm_length = 32
- length = wm_length // 8
- info = image.info
- data = np.asarray(image)
- encoder = WatermarkEncoder()
- text = f"{watermark:<{length}}"[:length]
- bytearr = text.encode(encoding='ascii', errors='ignore')
- try:
- encoder.set_watermark(wm_type, bytearr)
- encoded = encoder.encode(data, wm_method)
- image = Image.fromarray(encoded)
- image.info = info
- shared.log.debug(f'Set watermark: {watermark} method={wm_method} bits={wm_length}')
- except Exception as e:
- shared.log.warning(f'Set watermark error: {watermark} method={wm_method} bits={wm_length} {e}')
- return image
-
-
-def get_watermark(image):
- from imwatermark import WatermarkDecoder
- wm_type = 'bytes'
- wm_method = 'dwtDctSvd'
- wm_length = 32
- data = np.asarray(image)
- decoder = WatermarkDecoder(wm_type, wm_length)
- try:
- decoded = decoder.decode(data, wm_method)
- wm = decoded.decode(encoding='ascii', errors='ignore')
- except Exception:
- wm = ''
- return wm
+import io
+import re
+import os
+import sys
+import math
+import json
+import uuid
+import queue
+import string
+import hashlib
+import datetime
+import threading
+from pathlib import Path
+from collections import namedtuple
+import numpy as np
+import piexif
+import piexif.helper
+from PIL import Image, ImageFont, ImageDraw, PngImagePlugin, ExifTags
+from modules import sd_samplers, shared, script_callbacks, errors, paths
+
+
+debug = errors.log.trace if os.environ.get('SD_PATH_DEBUG', None) is not None else lambda *args, **kwargs: None
+try:
+ from pi_heif import register_heif_opener
+ register_heif_opener()
+except Exception:
+ pass
+
+
+def check_grid_size(imgs):
+ mp = 0
+ for img in imgs:
+ mp += img.width * img.height
+ mp = round(mp / 1000000)
+ ok = mp <= shared.opts.img_max_size_mp
+ if not ok:
+ shared.log.warning(f'Maximum image size exceded: size={mp} maximum={shared.opts.img_max_size_mp} MPixels')
+ return ok
+
+
+def image_grid(imgs, batch_size=1, rows=None):
+ if rows is None:
+ if shared.opts.n_rows > 0:
+ rows = shared.opts.n_rows
+ elif shared.opts.n_rows == 0:
+ rows = batch_size
+ else:
+ rows = math.floor(math.sqrt(len(imgs)))
+ while len(imgs) % rows != 0:
+ rows -= 1
+ if rows > len(imgs):
+ rows = len(imgs)
+ cols = math.ceil(len(imgs) / rows)
+ params = script_callbacks.ImageGridLoopParams(imgs, cols, rows)
+ script_callbacks.image_grid_callback(params)
+ w, h = imgs[0].size
+ grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color=shared.opts.grid_background)
+ for i, img in enumerate(params.imgs):
+ grid.paste(img, box=(i % params.cols * w, i // params.cols * h))
+ return grid
+
+
+Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
+
+
+def split_grid(image, tile_w=512, tile_h=512, overlap=64):
+ w = image.width
+ h = image.height
+ non_overlap_width = tile_w - overlap
+ non_overlap_height = tile_h - overlap
+ cols = math.ceil((w - overlap) / non_overlap_width)
+ rows = math.ceil((h - overlap) / non_overlap_height)
+ dx = (w - tile_w) / (cols - 1) if cols > 1 else 0
+ dy = (h - tile_h) / (rows - 1) if rows > 1 else 0
+ grid = Grid([], tile_w, tile_h, w, h, overlap)
+ for row in range(rows):
+ row_images = []
+ y = int(row * dy)
+ if y + tile_h >= h:
+ y = h - tile_h
+ for col in range(cols):
+ x = int(col * dx)
+ if x + tile_w >= w:
+ x = w - tile_w
+ tile = image.crop((x, y, x + tile_w, y + tile_h))
+ row_images.append([x, tile_w, tile])
+ grid.tiles.append([y, tile_h, row_images])
+ return grid
+
+
+def combine_grid(grid):
+ def make_mask_image(r):
+ r = r * 255 / grid.overlap
+ r = r.astype(np.uint8)
+ return Image.fromarray(r, 'L')
+
+ mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
+ mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
+ combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
+ for y, h, row in grid.tiles:
+ combined_row = Image.new("RGB", (grid.image_w, h))
+ for x, w, tile in row:
+ if x == 0:
+ combined_row.paste(tile, (0, 0))
+ continue
+ combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
+ combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))
+ if y == 0:
+ combined_image.paste(combined_row, (0, 0))
+ continue
+ combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h)
+ combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
+ return combined_image
+
+
+class GridAnnotation:
+ def __init__(self, text='', is_active=True):
+ self.text = text
+ self.is_active = is_active
+ self.size = None
+
+
+def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0, title=None):
+ def wrap(drawing, text, font, line_length):
+ lines = ['']
+ for word in text.split():
+ line = f'{lines[-1]} {word}'.strip()
+ if drawing.textlength(line, font=font) <= line_length:
+ lines[-1] = line
+ else:
+ lines.append(word)
+ return lines
+
+ def get_font(fontsize):
+ try:
+ return ImageFont.truetype(shared.opts.font or 'javascript/notosans-nerdfont-regular.ttf', fontsize)
+ except Exception:
+ return ImageFont.truetype('javascript/notosans-nerdfont-regular.ttf', fontsize)
+
+ def draw_texts(drawing: ImageDraw, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
+ for line in lines:
+ font = initial_fnt
+ fontsize = initial_fontsize
+ while drawing.multiline_textbbox((0,0), text=line.text, font=font)[0] > line.allowed_width and fontsize > 0:
+ fontsize -= 1
+ font = get_font(fontsize)
+ drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=font, fill=shared.opts.font_color if line.is_active else color_inactive, anchor="mm", align="center")
+ if not line.is_active:
+ drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4)
+ draw_y += line.size[1] + line_spacing
+
+ fontsize = (width + height) // 25
+ line_spacing = fontsize // 2
+ font = get_font(fontsize)
+ color_inactive = (127, 127, 127)
+ pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
+ cols = im.width // width
+ rows = im.height // height
+ assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
+ assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
+ calc_img = Image.new("RGB", (1, 1), shared.opts.grid_background)
+ calc_d = ImageDraw.Draw(calc_img)
+ title_texts = [title] if title else [[GridAnnotation()]]
+ for texts, allowed_width in zip(hor_texts + ver_texts + title_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts) + [(width+margin)*cols]):
+ items = [] + texts
+ texts.clear()
+ for line in items:
+ wrapped = wrap(calc_d, line.text, font, allowed_width)
+ texts += [GridAnnotation(x, line.is_active) for x in wrapped]
+ for line in texts:
+ bbox = calc_d.multiline_textbbox((0, 0), line.text, font=font)
+ line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
+ line.allowed_width = allowed_width
+ hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
+ ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
+ pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
+ title_pad = 0
+ if title:
+ title_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in title_texts] # pylint: disable=unsubscriptable-object
+ title_pad = 0 if sum(title_text_heights) == 0 else max(title_text_heights) + line_spacing * 2
+ result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + title_pad + margin * (rows-1)), shared.opts.grid_background)
+ for row in range(rows):
+ for col in range(cols):
+ cell = im.crop((width * col, height * row, width * (col+1), height * (row+1)))
+ result.paste(cell, (pad_left + (width + margin) * col, pad_top + title_pad + (height + margin) * row))
+ d = ImageDraw.Draw(result)
+ if title:
+ x = pad_left + ((width+margin)*cols) / 2
+ y = title_pad / 2 - title_text_heights[0] / 2
+ draw_texts(d, x, y, title_texts[0], font, fontsize)
+ for col in range(cols):
+ x = pad_left + (width + margin) * col + width / 2
+ y = (pad_top / 2 - hor_text_heights[col] / 2) + title_pad
+ draw_texts(d, x, y, hor_texts[col], font, fontsize)
+ for row in range(rows):
+ x = pad_left / 2
+ y = (pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2) + title_pad
+ draw_texts(d, x, y, ver_texts[row], font, fontsize)
+ return result
+
+
+def draw_prompt_matrix(im, width, height, all_prompts, margin=0):
+ prompts = all_prompts[1:]
+ boundary = math.ceil(len(prompts) / 2)
+ prompts_horiz = prompts[:boundary]
+ prompts_vert = prompts[boundary:]
+ hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
+ ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
+ return draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin)
+
+
+def resize_image(resize_mode, im, width, height, upscaler_name=None, output_type='image'):
+ shared.log.debug(f'Image resize: mode={resize_mode} resolution={width}x{height} upscaler={upscaler_name} function={sys._getframe(1).f_code.co_name}') # pylint: disable=protected-access
+ """
+ Resizes an image with the specified resize_mode, width, and height.
+ Args:
+ resize_mode: The mode to use when resizing the image.
+ 0: No resize
+ 1: Resize the image to the specified width and height.
+ 2: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
+ 3: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
+ im: The image to resize.
+ width: The width to resize the image to.
+ height: The height to resize the image to.
+ upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img.
+ """
+ upscaler_name = upscaler_name or shared.opts.upscaler_for_img2img
+
+ def latent(im, w, h, upscaler):
+ from modules.processing_vae import vae_encode, vae_decode
+ import torch
+ latents = vae_encode(im, shared.sd_model, full_quality=False) # TODO enable full VAE mode
+ latents = torch.nn.functional.interpolate(latents, size=(h // 8, w // 8), mode=upscaler["mode"], antialias=upscaler["antialias"])
+ im = vae_decode(latents, shared.sd_model, output_type='pil', full_quality=False)[0]
+ return im
+
+ def resize(im, w, h):
+ if upscaler_name is None or upscaler_name == "None" or im.mode == 'L':
+ return im.resize((w, h), resample=Image.Resampling.LANCZOS)
+ scale = max(w / im.width, h / im.height)
+ if scale > 1.0:
+ upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
+ if len(upscalers) > 0:
+ upscaler = upscalers[0]
+ im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
+ else:
+ upscaler = shared.latent_upscale_modes.get(upscaler_name, None)
+ if upscaler is not None:
+ im = latent(im, w, h, upscaler)
+ else:
+ shared.log.warning(f"Could not find upscaler: {upscaler_name or ''} using fallback: {upscaler.name}")
+ if im.width != w or im.height != h:
+ im = im.resize((w, h), resample=Image.Resampling.LANCZOS)
+ return im
+
+ if resize_mode == 0 or (im.width == width and im.height == height):
+ res = im.copy()
+ elif resize_mode == 1:
+ res = resize(im, width, height)
+ elif resize_mode == 2:
+ ratio = width / height
+ src_ratio = im.width / im.height
+ src_w = width if ratio > src_ratio else im.width * height // im.height
+ src_h = height if ratio <= src_ratio else im.height * width // im.width
+ resized = resize(im, src_w, src_h)
+ res = Image.new(im.mode, (width, height))
+ res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+ else:
+ ratio = width / height
+ src_ratio = im.width / im.height
+ src_w = width if ratio < src_ratio else im.width * height // im.height
+ src_h = height if ratio >= src_ratio else im.height * width // im.width
+ resized = resize(im, src_w, src_h)
+ res = Image.new(im.mode, (width, height))
+ res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+ if ratio < src_ratio:
+ fill_height = height // 2 - src_h // 2
+ res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
+ res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
+ elif ratio > src_ratio:
+ fill_width = width // 2 - src_w // 2
+ res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
+ res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
+ if output_type == 'np':
+ return np.array(res)
+ return res
+
+
+re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
+re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
+re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
+re_attention = re.compile(r'[\(*\[*](\w+)(:\d+(\.\d+))?[\)*\]*]|')
+re_network = re.compile(r'\<\w+:(\w+)(:\d+(\.\d+))?\>|')
+re_brackets = re.compile(r'[\([{})\]]')
+
+NOTHING = object()
+
+
+class FilenameGenerator:
+ replacements = {
+ 'width': lambda self: self.image.width,
+ 'height': lambda self: self.image.height,
+ 'batch_number': lambda self: self.batch_number,
+ 'iter_number': lambda self: self.iter_number,
+ 'num': lambda self: NOTHING if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+ 'generation_number': lambda self: NOTHING if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+ 'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
+ 'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime], [datetime]
+ 'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt..]
+ 'hash': lambda self: self.image_hash(),
+ 'image_hash': lambda self: self.image_hash(),
+ 'timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
+ 'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
+
+ 'model': lambda self: shared.sd_model.sd_checkpoint_info.title,
+ 'model_shortname': lambda self: shared.sd_model.sd_checkpoint_info.model_name,
+ 'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name,
+ 'model_hash': lambda self: shared.sd_model.sd_checkpoint_info.shorthash,
+
+ 'prompt': lambda self: self.prompt_full(),
+ 'prompt_no_styles': lambda self: self.prompt_no_style(),
+ 'prompt_words': lambda self: self.prompt_words(),
+ 'prompt_hash': lambda self: hashlib.sha256(self.prompt.encode()).hexdigest()[0:8],
+
+ 'sampler': lambda self: self.p and self.p.sampler_name,
+ 'seed': lambda self: self.seed and str(self.seed) or '',
+ 'steps': lambda self: self.p and self.p.steps,
+ 'styles': lambda self: self.p and ", ".join([style for style in self.p.styles if not style == "None"]) or "None",
+ 'uuid': lambda self: str(uuid.uuid4()),
+ }
+ default_time_format = '%Y%m%d%H%M%S'
+
+ def __init__(self, p, seed, prompt, image, grid=False):
+ if p is None:
+ debug('Filename generator init skip')
+ else:
+ debug(f'Filename generator init: {seed} {prompt}')
+ self.p = p
+ if seed is not None and seed > 0:
+ self.seed = seed
+ elif hasattr(p, 'all_seeds'):
+ self.seed = p.all_seeds[0]
+ else:
+ self.seed = 0
+ self.prompt = prompt
+ self.image = image
+ if not grid:
+ self.batch_number = NOTHING if self.p is None or getattr(self.p, 'batch_size', 1) == 1 else (self.p.batch_index + 1 if hasattr(self.p, 'batch_index') else NOTHING)
+ self.iter_number = NOTHING if self.p is None or getattr(self.p, 'n_iter', 1) == 1 else (self.p.iteration + 1 if hasattr(self.p, 'iteration') else NOTHING)
+ else:
+ self.batch_number = NOTHING
+ self.iter_number = NOTHING
+
+ def hasprompt(self, *args):
+ lower = self.prompt.lower()
+ if getattr(self, 'p', None) is None or getattr(self, 'prompt', None) is None:
+ return None
+ outres = ""
+ for arg in args:
+ if arg != "":
+ division = arg.split("|")
+ expected = division[0].lower()
+ default = division[1] if len(division) > 1 else ""
+ if lower.find(expected) >= 0:
+ outres = f'{outres}{expected}'
+ else:
+ outres = outres if default == "" else f'{outres}{default}'
+ return outres
+
+ def image_hash(self):
+ if getattr(self, 'image', None) is None:
+ return None
+ import base64
+ from io import BytesIO
+ buffered = BytesIO()
+ self.image.save(buffered, format="JPEG")
+ img_str = base64.b64encode(buffered.getvalue())
+ shorthash = hashlib.sha256(img_str).hexdigest()[0:8]
+ return shorthash
+
+ def prompt_full(self):
+ return self.prompt_sanitize(self.prompt)
+
+ def prompt_words(self):
+ if getattr(self, 'prompt', None) is None:
+ return ''
+ no_attention = re_attention.sub(r'\1', self.prompt)
+ no_network = re_network.sub(r'\1', no_attention)
+ no_brackets = re_brackets.sub('', no_network)
+ words = [x for x in re_nonletters.split(no_brackets or "") if len(x) > 0]
+ prompt = " ".join(words[0:shared.opts.directories_max_prompt_words])
+ return self.prompt_sanitize(prompt)
+
+ def prompt_no_style(self):
+ if getattr(self, 'p', None) is None or getattr(self, 'prompt', None) is None:
+ return None
+ prompt_no_style = self.prompt
+ for style in shared.prompt_styles.get_style_prompts(self.p.styles):
+ if len(style) > 0:
+ for part in style.split("{prompt}"):
+ prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",")
+ prompt_no_style = prompt_no_style.replace(style, "")
+ return self.prompt_sanitize(prompt_no_style)
+
+ def datetime(self, *args):
+ import pytz
+ time_datetime = datetime.datetime.now()
+ time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
+ try:
+ time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
+ except pytz.exceptions.UnknownTimeZoneError:
+ time_zone = None
+ time_zone_time = time_datetime.astimezone(time_zone)
+ try:
+ formatted_time = time_zone_time.strftime(time_format)
+ except (ValueError, TypeError):
+ formatted_time = time_zone_time.strftime(self.default_time_format)
+ return formatted_time
+
+ def prompt_sanitize(self, prompt):
+ invalid_chars = '#<>:\'"\\|?*\n\t\r'
+ sanitized = prompt.translate({ ord(x): '_' for x in invalid_chars }).strip()
+ debug(f'Prompt sanitize: input="{prompt}" output={sanitized}')
+ return sanitized
+
+ def sanitize(self, filename):
+ invalid_chars = '\'"|?*\n\t\r' #
+ invalid_folder = ':'
+ invalid_files = ['CON', 'PRN', 'AUX', 'NUL', 'NULL', 'COM0', 'COM1', 'LPT0', 'LPT1']
+ invalid_prefix = ', '
+ invalid_suffix = '.,_ '
+ fn, ext = os.path.splitext(filename)
+ parts = Path(fn).parts
+ newparts = []
+ for i, part in enumerate(parts):
+ part = part.translate({ ord(x): '_' for x in invalid_chars })
+ if i > 0 or (len(part) >= 2 and part[1] != invalid_folder): # skip drive, otherwise remove
+ part = part.translate({ ord(x): '_' for x in invalid_folder })
+ part = part.lstrip(invalid_prefix).rstrip(invalid_suffix)
+ if part in invalid_files: # reserved names
+ [part := part.replace(word, '_') for word in invalid_files] # pylint: disable=expression-not-assigned
+ newparts.append(part)
+ fn = str(Path(*newparts))
+ max_length = max(256 - len(ext), os.statvfs(__file__).f_namemax - 32 if hasattr(os, 'statvfs') else 256 - len(ext))
+ while len(os.path.abspath(fn)) > max_length:
+ fn = fn[:-1]
+ fn += ext
+ debug(f'Filename sanitize: input="{filename}" parts={parts} output="{fn}" ext={ext} max={max_length} len={len(fn)}')
+ return fn
+
+ def sequence(self, x, dirname, basename):
+ if shared.opts.save_images_add_number or '[seq]' in x:
+ if '[seq]' not in x:
+ x = os.path.join(os.path.dirname(x), f"[seq]-{os.path.basename(x)}")
+ basecount = get_next_sequence_number(dirname, basename)
+ for i in range(9999):
+ seq = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
+ filename = x.replace('[seq]', seq)
+ if not os.path.exists(filename):
+ debug(f'Prompt sequence: input="{x}" seq={seq} output="{filename}"')
+ x = filename
+ break
+ return x
+
+ def apply(self, x):
+ res = ''
+ for m in re_pattern.finditer(x):
+ text, pattern = m.groups()
+ if pattern is None:
+ res += text
+ continue
+ pattern_args = []
+ while True:
+ m = re_pattern_arg.match(pattern)
+ if m is None:
+ break
+ pattern, arg = m.groups()
+ pattern_args.insert(0, arg)
+ fun = self.replacements.get(pattern.lower(), None)
+ if fun is not None:
+ try:
+ debug(f'Filename apply: pattern={pattern.lower()} args={pattern_args}')
+ replacement = fun(self, *pattern_args)
+ except Exception as e:
+ replacement = None
+ shared.log.error(f'Filename apply pattern: {x} {e}')
+ if replacement == NOTHING:
+ continue
+ if replacement is not None:
+ res += text + str(replacement).replace('/', '-').replace('\\', '-')
+ continue
+ else:
+ res += text + f'[{pattern}]' # reinsert unknown pattern
+ return res
+
+
+def get_next_sequence_number(path, basename):
+ """
+ Determines and returns the next sequence number to use when saving an image in the specified directory.
+ """
+ result = -1
+ if basename != '':
+ basename = f"{basename}-"
+ prefix_length = len(basename)
+ if not os.path.isdir(path):
+ return 0
+ for p in os.listdir(path):
+ if p.startswith(basename):
+ parts = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+ try:
+ result = max(int(parts[0]), result)
+ except ValueError:
+ pass
+ return result + 1
+
+
+def atomically_save_image():
+ Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
+ while True:
+ image, filename, extension, params, exifinfo, filename_txt = save_queue.get()
+ with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+ file.write(exifinfo)
+ fn = filename + extension
+ filename = filename.strip()
+ if extension[0] != '.': # add dot if missing
+ extension = '.' + extension
+ try:
+ image_format = Image.registered_extensions()[extension]
+ except Exception:
+ shared.log.warning(f'Unknown image format: {extension}')
+ image_format = 'JPEG'
+ if shared.opts.image_watermark_enabled:
+ image = set_watermark(image, shared.opts.image_watermark)
+ size = os.path.getsize(fn) if os.path.exists(fn) else 0
+ shared.log.debug(f'Saving: image="{fn}" type={image_format} resolution={image.width}x{image.height} size={size}')
+ # additional metadata saved in files
+ if shared.opts.save_txt and len(exifinfo) > 0:
+ try:
+ with open(filename_txt, "w", encoding="utf8") as file:
+ file.write(f"{exifinfo}\n")
+ shared.log.debug(f'Saving: text="{filename_txt}" len={len(exifinfo)}')
+ except Exception as e:
+ shared.log.warning(f'Image description save failed: {filename_txt} {e}')
+ # actual save
+ exifinfo = (exifinfo or "") if shared.opts.image_metadata else ""
+ if image_format == 'PNG':
+ pnginfo_data = PngImagePlugin.PngInfo()
+ for k, v in params.pnginfo.items():
+ pnginfo_data.add_text(k, str(v))
+ try:
+ image.save(fn, format=image_format, compress_level=6, pnginfo=pnginfo_data if shared.opts.image_metadata else None)
+ except Exception as e:
+ shared.log.error(f'Image save failed: file="{fn}" {e}')
+ elif image_format == 'JPEG':
+ if image.mode == 'RGBA':
+ shared.log.warning('Saving RGBA image as JPEG: Alpha channel will be lost')
+ image = image.convert("RGB")
+ elif image.mode == 'I;16':
+ image = image.point(lambda p: p * 0.0038910505836576).convert("L")
+ exif_bytes = piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(exifinfo, encoding="unicode") } })
+ try:
+ image.save(fn, format=image_format, optimize=True, quality=shared.opts.jpeg_quality, exif=exif_bytes)
+ except Exception as e:
+ shared.log.error(f'Image save failed: file="{fn}" {e}')
+ elif image_format == 'WEBP':
+ if image.mode == 'I;16':
+ image = image.point(lambda p: p * 0.0038910505836576).convert("RGB")
+ exif_bytes = piexif.dump({ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(exifinfo, encoding="unicode") } })
+ try:
+ image.save(fn, format=image_format, quality=shared.opts.jpeg_quality, lossless=shared.opts.webp_lossless, exif=exif_bytes)
+ except Exception as e:
+ shared.log.error(f'Image save failed: file="{fn}" {e}')
+ else:
+ # shared.log.warning(f'Unrecognized image format: {extension} attempting save as {image_format}')
+ try:
+ image.save(fn, format=image_format, quality=shared.opts.jpeg_quality)
+ except Exception as e:
+ shared.log.error(f'Image save failed: file="{fn}" {e}')
+ if shared.opts.save_log_fn != '' and len(exifinfo) > 0:
+ fn = os.path.join(paths.data_path, shared.opts.save_log_fn)
+ if not fn.endswith('.json'):
+ fn += '.json'
+ entries = shared.readfile(fn, silent=True)
+ idx = len(list(entries))
+ if idx == 0:
+ entries = []
+ entry = { 'id': idx, 'filename': filename, 'time': datetime.datetime.now().isoformat(), 'info': exifinfo }
+ entries.append(entry)
+ shared.writefile(entries, fn, mode='w', silent=True)
+ shared.log.debug(f'Saving: json="{fn}" records={len(entries)}')
+ save_queue.task_done()
+
+
+save_queue = queue.Queue()
+save_thread = threading.Thread(target=atomically_save_image, daemon=True)
+save_thread.start()
+
+
+def save_image(image, path, basename='', seed=None, prompt=None, extension=shared.opts.samples_format, info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix='', save_to_dirs=None): # pylint: disable=unused-argument
+ debug(f'Save from function={sys._getframe(1).f_code.co_name}') # pylint: disable=protected-access
+ if image is None:
+ shared.log.warning('Image is none')
+ return None, None
+ if not check_grid_size([image]):
+ return None, None
+ if path is None or len(path) == 0: # set default path to avoid errors when functions are triggered manually or via api and param is not set
+ path = shared.opts.outdir_save
+ namegen = FilenameGenerator(p, seed, prompt, image, grid=grid)
+ suffix = suffix if suffix is not None else ''
+ basename = basename if basename is not None else ''
+ if shared.opts.save_to_dirs:
+ dirname = namegen.apply(shared.opts.directories_filename_pattern or "[prompt_words]")
+ path = os.path.join(path, dirname)
+ if forced_filename is None:
+ if shared.opts.samples_filename_pattern and len(shared.opts.samples_filename_pattern) > 0:
+ file_decoration = shared.opts.samples_filename_pattern
+ else:
+ file_decoration = "[seq]-[prompt_words]"
+ file_decoration = namegen.apply(file_decoration)
+ file_decoration += suffix if suffix is not None else ''
+ filename = os.path.join(path, f"{file_decoration}.{extension}") if basename == '' else os.path.join(path, f"{basename}-{file_decoration}.{extension}")
+ else:
+ forced_filename += suffix if suffix is not None else ''
+ filename = os.path.join(path, f"{forced_filename}.{extension}") if basename == '' else os.path.join(path, f"{basename}-{forced_filename}.{extension}")
+ pnginfo = existing_info or {}
+ if info is not None:
+ pnginfo[pnginfo_section_name] = info
+ params = script_callbacks.ImageSaveParams(image, p, filename, pnginfo)
+ params.filename = namegen.sanitize(filename)
+ dirname = os.path.dirname(params.filename)
+ if dirname is not None and len(dirname) > 0:
+ os.makedirs(dirname, exist_ok=True)
+ params.filename = namegen.sequence(params.filename, dirname, basename)
+ params.filename = namegen.sanitize(params.filename)
+ # callbacks
+ script_callbacks.before_image_saved_callback(params)
+ exifinfo = params.pnginfo.get('UserComment', '')
+ exifinfo = (exifinfo + ', ' if len(exifinfo) > 0 else '') + params.pnginfo.get(pnginfo_section_name, '')
+ filename, extension = os.path.splitext(params.filename)
+ filename_txt = f"{filename}.txt" if shared.opts.save_txt and len(exifinfo) > 0 else None
+ save_queue.put((params.image, filename, extension, params, exifinfo, filename_txt)) # actual save is executed in a thread that polls data from queue
+ save_queue.join()
+ if not hasattr(params.image, 'already_saved_as'):
+ debug(f'Image marked: "{params.filename}"')
+ params.image.already_saved_as = params.filename
+ script_callbacks.image_saved_callback(params)
+ return params.filename, filename_txt
+
+
+def save_video_atomic(images, filename, video_type: str = 'none', duration: float = 2.0, loop: bool = False, interpolate: int = 0, scale: float = 1.0, pad: int = 1, change: float = 0.3):
+ try:
+ import cv2
+ except Exception as e:
+ shared.log.error(f'Save video: cv2: {e}')
+ return
+ os.makedirs(os.path.dirname(filename), exist_ok=True)
+ if video_type.lower() == 'mp4':
+ frames = images
+ if interpolate > 0:
+ try:
+ import modules.rife
+ frames = modules.rife.interpolate(images, count=interpolate, scale=scale, pad=pad, change=change)
+ except Exception as e:
+ shared.log.error(f'RIFE interpolation: {e}')
+ errors.display(e, 'RIFE interpolation')
+ video_frames = [np.array(frame) for frame in frames]
+ fourcc = "mp4v"
+ h, w, _c = video_frames[0].shape
+ video_writer = cv2.VideoWriter(filename, fourcc=cv2.VideoWriter_fourcc(*fourcc), fps=len(frames)/duration, frameSize=(w, h))
+ for i in range(len(video_frames)):
+ img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)
+ video_writer.write(img)
+ size = os.path.getsize(filename)
+ shared.log.info(f'Save video: file="{filename}" frames={len(frames)} duration={duration} fourcc={fourcc} size={size}')
+ if video_type.lower() == 'gif' or video_type.lower() == 'png':
+ append = images.copy()
+ image = append.pop(0)
+ if loop:
+ append += append[::-1]
+ frames=len(append) + 1
+ image.save(
+ filename,
+ save_all = True,
+ append_images = append,
+ optimize = False,
+ duration = 1000.0 * duration / frames,
+ loop = 0 if loop else 1,
+ )
+ size = os.path.getsize(filename)
+ shared.log.info(f'Save video: file="{filename}" frames={len(append) + 1} duration={duration} loop={loop} size={size}')
+
+
+def save_video(p, images, filename = None, video_type: str = 'none', duration: float = 2.0, loop: bool = False, interpolate: int = 0, scale: float = 1.0, pad: int = 1, change: float = 0.3, sync: bool = False):
+ if images is None or len(images) < 2 or video_type is None or video_type.lower() == 'none':
+ return
+ image = images[0]
+ if p is not None:
+ namegen = FilenameGenerator(p, seed=p.all_seeds[0], prompt=p.all_prompts[0], image=image)
+ else:
+ namegen = FilenameGenerator(None, seed=0, prompt='', image=image)
+ if filename is None and p is not None:
+ filename = namegen.apply(shared.opts.samples_filename_pattern if shared.opts.samples_filename_pattern and len(shared.opts.samples_filename_pattern) > 0 else "[seq]-[prompt_words]")
+ filename = os.path.join(shared.opts.outdir_video, filename)
+ filename = namegen.sequence(filename, shared.opts.outdir_video, '')
+ else:
+ if os.pathsep not in filename:
+ filename = os.path.join(shared.opts.outdir_video, filename)
+ if not filename.lower().endswith(video_type.lower()):
+ filename += f'.{video_type.lower()}'
+ filename = namegen.sanitize(filename)
+ if not sync:
+ threading.Thread(target=save_video_atomic, args=(images, filename, video_type, duration, loop, interpolate, scale, pad, change)).start()
+ else:
+ save_video_atomic(images, filename, video_type, duration, loop, interpolate, scale, pad, change)
+ return filename
+
+
+def safe_decode_string(s: bytes):
+ remove_prefix = lambda text, prefix: text[len(prefix):] if text.startswith(prefix) else text # pylint: disable=unnecessary-lambda-assignment
+ for encoding in ['utf-8', 'utf-16', 'ascii', 'latin_1', 'cp1252', 'cp437']: # try different encodings
+ try:
+ s = remove_prefix(s, b'UNICODE')
+ s = remove_prefix(s, b'ASCII')
+ s = remove_prefix(s, b'\x00')
+ val = s.decode(encoding, errors="strict")
+ val = re.sub(r'[\x00-\x09]', '', val).strip() # remove remaining special characters
+ if len(val) == 0: # remove empty strings
+ val = None
+ return val
+ except Exception:
+ pass
+ return None
+
+
+def read_info_from_image(image: Image):
+ items = image.info or {}
+ geninfo = items.pop('parameters', None)
+ if geninfo is None:
+ geninfo = items.pop('UserComment', None)
+ if geninfo is not None and len(geninfo) > 0:
+ if 'UserComment' in geninfo:
+ geninfo = geninfo['UserComment']
+ items['UserComment'] = geninfo
+
+ if "exif" in items:
+ try:
+ exif = piexif.load(items["exif"])
+ except Exception as e:
+ shared.log.error(f'Error loading EXIF data: {e}')
+ exif = {}
+ for _key, subkey in exif.items():
+ if isinstance(subkey, dict):
+ for key, val in subkey.items():
+ if isinstance(val, bytes): # decode bytestring
+ val = safe_decode_string(val)
+ if isinstance(val, tuple) and isinstance(val[0], int) and isinstance(val[1], int) and val[1] > 0: # convert camera ratios
+ val = round(val[0] / val[1], 2)
+ if val is not None and key in ExifTags.TAGS: # add known tags
+ if ExifTags.TAGS[key] == 'UserComment': # add geninfo from UserComment
+ geninfo = val
+ items['parameters'] = val
+ else:
+ items[ExifTags.TAGS[key]] = val
+ elif val is not None and key in ExifTags.GPSTAGS:
+ items[ExifTags.GPSTAGS[key]] = val
+ wm = get_watermark(image)
+ if wm != '':
+ # geninfo += f' Watermark: {wm}'
+ items['watermark'] = wm
+
+ for key, val in items.items():
+ if isinstance(val, bytes): # decode bytestring
+ items[key] = safe_decode_string(val)
+
+ for key in ['exif', 'ExifOffset', 'JpegIFOffset', 'JpegIFByteCount', 'ExifVersion', 'icc_profile', 'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'adobe', 'photoshop', 'loop', 'duration', 'dpi']: # remove unwanted tags
+ items.pop(key, None)
+
+ if items.get("Software", None) == "NovelAI":
+ try:
+ json_info = json.loads(items["Comment"])
+ sampler = sd_samplers.samplers_map.get(json_info["sampler"], "Euler a")
+ geninfo = f"""{items["Description"]}
+Negative prompt: {json_info["uc"]}
+Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
+ except Exception as e:
+ errors.display(e, 'novelai image parser')
+
+ try:
+ items['width'] = image.width
+ items['height'] = image.height
+ items['mode'] = image.mode
+ except Exception:
+ pass
+
+ return geninfo, items
+
+
+def image_data(data):
+ import gradio as gr
+ if data is None:
+ return gr.update(), None
+ err1 = None
+ err2 = None
+ try:
+ image = Image.open(io.BytesIO(data))
+ errors.log.debug(f'Decoded object: image={image}')
+ textinfo, _ = read_info_from_image(image)
+ return textinfo, None
+ except Exception as e:
+ err1 = e
+ try:
+ if len(data) > 1024 * 10:
+ errors.log.warning(f'Error decoding object: data too long: {len(data)}')
+ return gr.update(), None
+ text = data.decode('utf8')
+ errors.log.debug(f'Decoded object: size={len(text)}')
+ return text, None
+ except Exception as e:
+ err2 = e
+ errors.log.error(f'Error decoding object: {err1 or err2}')
+ return gr.update(), None
+
+
+def flatten(img, bgcolor):
+ """replaces transparency with bgcolor (example: "#ffffff"), returning an RGB mode image with no transparency"""
+ if img.mode == "RGBA":
+ background = Image.new('RGBA', img.size, bgcolor)
+ background.paste(img, mask=img)
+ img = background
+ return img.convert('RGB')
+
+
+def set_watermark(image, watermark):
+ from imwatermark import WatermarkEncoder
+ wm_type = 'bytes'
+ wm_method = 'dwtDctSvd'
+ wm_length = 32
+ length = wm_length // 8
+ info = image.info
+ data = np.asarray(image)
+ encoder = WatermarkEncoder()
+ text = f"{watermark:<{length}}"[:length]
+ bytearr = text.encode(encoding='ascii', errors='ignore')
+ try:
+ encoder.set_watermark(wm_type, bytearr)
+ encoded = encoder.encode(data, wm_method)
+ image = Image.fromarray(encoded)
+ image.info = info
+ shared.log.debug(f'Set watermark: {watermark} method={wm_method} bits={wm_length}')
+ except Exception as e:
+ shared.log.warning(f'Set watermark error: {watermark} method={wm_method} bits={wm_length} {e}')
+ return image
+
+
+def get_watermark(image):
+ from imwatermark import WatermarkDecoder
+ wm_type = 'bytes'
+ wm_method = 'dwtDctSvd'
+ wm_length = 32
+ data = np.asarray(image)
+ decoder = WatermarkDecoder(wm_type, wm_length)
+ try:
+ decoded = decoder.decode(data, wm_method)
+ wm = decoded.decode(encoding='ascii', errors='ignore')
+ except Exception:
+ wm = ''
+ return wm
diff --git a/modules/img2img.py b/modules/img2img.py
index bb51119d3..c2e14b6e4 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -1,264 +1,264 @@
-import os
-import itertools # SBM Batch frames
-import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
-import modules.scripts
-from modules import shared, processing, images
-from modules.generation_parameters_copypaste import create_override_settings_dict
-from modules.ui import plaintext_to_html
-from modules.memstats import memory_stats
-
-
-debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
-debug('Trace: PROCESS')
-
-
-def process_batch(p, input_files, input_dir, output_dir, inpaint_mask_dir, args):
- shared.log.debug(f'batch: {input_files}|{input_dir}|{output_dir}|{inpaint_mask_dir}')
- processing.fix_seed(p)
- if input_files is not None and len(input_files) > 0:
- image_files = [f.name for f in input_files]
- else:
- if not os.path.isdir(input_dir):
- shared.log.error(f"Process batch: directory not found: {input_dir}")
- return
- image_files = os.listdir(input_dir)
- is_inpaint_batch = False
- if inpaint_mask_dir:
- inpaint_masks = os.listdir(inpaint_mask_dir)
- is_inpaint_batch = len(inpaint_masks) > 0
- if is_inpaint_batch:
- shared.log.info(f"Process batch: inpaint batch masks={len(inpaint_masks)}")
- save_normally = output_dir == ''
- p.do_not_save_grid = True
- p.do_not_save_samples = not save_normally
- shared.state.job_count = len(image_files) * p.n_iter
- if shared.opts.batch_frame_mode: # SBM Frame mode is on, process each image in batch with same seed
- window_size = p.batch_size
- btcrept = 1
- p.seed = [p.seed] * window_size # SBM MONKEYPATCH: Need to change processing to support a fixed seed value.
- p.subseed = [p.subseed] * window_size # SBM MONKEYPATCH
- shared.log.info(f"Process batch: inputs={len(image_files)} parallel={window_size} outputs={p.n_iter} per input ")
- else: # SBM Frame mode is off, standard operation of repeating same images with sequential seed.
- window_size = 1
- btcrept = p.batch_size
- shared.log.info(f"Process batch: inputs={len(image_files)} outputs={p.n_iter * p.batch_size} per input")
- for i in range(0, len(image_files), window_size):
- if shared.state.skipped:
- shared.state.skipped = False
- if shared.state.interrupted:
- break
- batch_image_files = image_files[i:i+window_size]
- batch_images = []
- for image_file in batch_image_files:
- try:
- img = Image.open(image_file)
- if p.scale_by != 1:
- p.width = int(img.width * p.scale_by)
- p.height = int(img.height * p.scale_by)
- except UnidentifiedImageError as e:
- shared.log.error(f"Image error: {e}")
- continue
- img = ImageOps.exif_transpose(img)
- batch_images.append(img)
- batch_images = batch_images * btcrept # Standard mode sends the same image per batchsize.
- p.init_images = batch_images
-
- if is_inpaint_batch:
- # try to find corresponding mask for an image using simple filename matching
- batch_mask_images = []
- for image_file in batch_image_files:
- mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image_file))
- # if not found use first one ("same mask for all images" use-case)
- if mask_image_path not in inpaint_masks:
- mask_image_path = inpaint_masks[0]
- mask_image = Image.open(mask_image_path)
- batch_mask_images.append(mask_image)
- batch_mask_images = batch_mask_images * btcrept
- p.image_mask = batch_mask_images
-
- batch_image_files = batch_image_files * btcrept # List used for naming later.
-
- proc = modules.scripts.scripts_img2img.run(p, *args)
- if proc is None:
- proc = processing.process_images(p)
- for n, (image, image_file) in enumerate(itertools.zip_longest(proc.images,batch_image_files)):
- basename = ''
- if shared.opts.use_original_name_batch:
- forced_filename, ext = os.path.splitext(os.path.basename(image_file))
- else:
- forced_filename = None
- ext = shared.opts.samples_format
- if len(proc.images) > 1:
- basename = f'{n + i}' if shared.opts.batch_frame_mode else f'{n}'
- else:
- basename = ''
- if output_dir == '':
- output_dir = shared.opts.outdir_img2img_samples
- if not save_normally:
- os.makedirs(output_dir, exist_ok=True)
- geninfo, items = images.read_info_from_image(image)
- for k, v in items.items():
- image.info[k] = v
- images.save_image(image, path=output_dir, basename=basename, seed=None, prompt=None, extension=ext, info=geninfo, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=image.info, forced_filename=forced_filename)
- shared.log.debug(f'Processed: images={len(batch_image_files)} memory={memory_stats()} batch')
-
-
-def img2img(id_task: str, mode: int,
- prompt, negative_prompt, prompt_styles,
- init_img,
- sketch,
- init_img_with_mask,
- inpaint_color_sketch,
- inpaint_color_sketch_orig,
- init_img_inpaint,
- init_mask_inpaint,
- steps,
- sampler_index,
- mask_blur, mask_alpha,
- inpainting_fill,
- full_quality, restore_faces, tiling,
- n_iter, batch_size,
- cfg_scale, image_cfg_scale,
- diffusers_guidance_rescale,
- sag_scale,
- refiner_start,
- clip_skip,
- denoising_strength,
- seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
- selected_scale_tab,
- height, width,
- scale_by,
- resize_mode, resize_name,
- inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert,
- img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir,
- hdr_clamp, hdr_boundary, hdr_threshold, hdr_center, hdr_channel_shift, hdr_full_shift, hdr_maximize, hdr_max_center, hdr_max_boundry,
- override_settings_texts,
- *args): # pylint: disable=unused-argument
-
- if shared.sd_model is None:
- shared.log.warning('Model not loaded')
- return [], '', '', 'Error: model not loaded'
-
- debug(f'img2img: id_task={id_task}|mode={mode}|prompt={prompt}|negative_prompt={negative_prompt}|prompt_styles={prompt_styles}|init_img={init_img}|sketch={sketch}|init_img_with_mask={init_img_with_mask}|inpaint_color_sketch={inpaint_color_sketch}|inpaint_color_sketch_orig={inpaint_color_sketch_orig}|init_img_inpaint={init_img_inpaint}|init_mask_inpaint={init_mask_inpaint}|steps={steps}|sampler_index={sampler_index}||mask_blur={mask_blur}|mask_alpha={mask_alpha}|inpainting_fill={inpainting_fill}|full_quality={full_quality}|restore_faces={restore_faces}|tiling={tiling}|n_iter={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|image_cfg_scale={image_cfg_scale}|clip_skip={clip_skip}|denoising_strength={denoising_strength}|seed={seed}|subseed{subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|selected_scale_tab={selected_scale_tab}|height={height}|width={width}|scale_by={scale_by}|resize_mode={resize_mode}|resize_name={resize_name}|inpaint_full_res={inpaint_full_res}|inpaint_full_res_padding={inpaint_full_res_padding}|inpainting_mask_invert={inpainting_mask_invert}|img2img_batch_files={img2img_batch_files}|img2img_batch_input_dir={img2img_batch_input_dir}|img2img_batch_output_dir={img2img_batch_output_dir}|img2img_batch_inpaint_mask_dir={img2img_batch_inpaint_mask_dir}|override_settings_texts={override_settings_texts}')
-
- if mode == 5:
- if img2img_batch_files is None or len(img2img_batch_files) == 0:
- shared.log.debug('Init bactch images not set')
- elif init_img:
- shared.log.debug('Init image not set')
-
- if sampler_index is None:
- sampler_index = 0
-
- override_settings = create_override_settings_dict(override_settings_texts)
-
- if mode == 0: # img2img
- if init_img is None:
- return [], '', '', 'Error: init image not provided'
- image = init_img.convert("RGB")
- mask = None
- elif mode == 1: # img2img sketch
- if sketch is None:
- return [], '', '', 'Error: sketch image not provided'
- image = sketch.convert("RGB")
- mask = None
- elif mode == 2: # inpaint
- if init_img_with_mask is None:
- return [], '', '', 'Error: init image with mask not provided'
- image = init_img_with_mask["image"]
- mask = init_img_with_mask["mask"]
- alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
- mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
- image = image.convert("RGB")
- elif mode == 3: # inpaint sketch
- if inpaint_color_sketch is None:
- return [], '', '', 'Error: color sketch image not provided'
- image = inpaint_color_sketch
- orig = inpaint_color_sketch_orig or inpaint_color_sketch
- pred = np.any(np.array(image) != np.array(orig), axis=-1)
- mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
- mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
- blur = ImageFilter.GaussianBlur(mask_blur)
- image = Image.composite(image.filter(blur), orig, mask.filter(blur))
- image = image.convert("RGB")
- elif mode == 4: # inpaint upload mask
- if init_img_inpaint is None:
- return [], '', '', 'Error: inpaint image not provided'
- image = init_img_inpaint
- mask = init_mask_inpaint
- else:
- shared.log.error(f'Image processing unknown mode: {mode}')
- image = None
- mask = None
- if image is not None:
- image = ImageOps.exif_transpose(image)
- if selected_scale_tab == 1 and resize_mode != 0:
- width = int(image.width * scale_by)
- height = int(image.height * scale_by)
-
- p = processing.StableDiffusionProcessingImg2Img(
- sd_model=shared.sd_model,
- outpath_samples=shared.opts.outdir_samples or shared.opts.outdir_img2img_samples,
- outpath_grids=shared.opts.outdir_grids or shared.opts.outdir_img2img_grids,
- prompt=prompt,
- negative_prompt=negative_prompt,
- styles=prompt_styles,
- seed=seed,
- subseed=subseed,
- subseed_strength=subseed_strength,
- seed_resize_from_h=seed_resize_from_h,
- seed_resize_from_w=seed_resize_from_w,
- seed_enable_extras=True,
- sampler_name = processing.get_sampler_name(sampler_index, img=True),
- batch_size=batch_size,
- n_iter=n_iter,
- steps=steps,
- cfg_scale=cfg_scale,
- clip_skip=clip_skip,
- width=width,
- height=height,
- full_quality=full_quality,
- restore_faces=restore_faces,
- tiling=tiling,
- init_images=[image],
- mask=mask,
- mask_blur=mask_blur,
- inpainting_fill=inpainting_fill,
- resize_mode=resize_mode,
- resize_name=resize_name,
- denoising_strength=denoising_strength,
- image_cfg_scale=image_cfg_scale,
- diffusers_guidance_rescale=diffusers_guidance_rescale,
- sag_scale=sag_scale,
- refiner_start=refiner_start,
- inpaint_full_res=inpaint_full_res != 0,
- inpaint_full_res_padding=inpaint_full_res_padding,
- inpainting_mask_invert=inpainting_mask_invert,
- hdr_clamp=hdr_clamp, hdr_boundary=hdr_boundary, hdr_threshold=hdr_threshold,
- hdr_center=hdr_center, hdr_channel_shift=hdr_channel_shift, hdr_full_shift=hdr_full_shift,
- hdr_maximize=hdr_maximize, hdr_max_center=hdr_max_center, hdr_max_boundry=hdr_max_boundry,
- override_settings=override_settings,
- )
- if selected_scale_tab == 1 and resize_mode != 0:
- p.scale_by = scale_by
- p.scripts = modules.scripts.scripts_img2img
- p.script_args = args
- if mask:
- p.extra_generation_params["Mask blur"] = mask_blur
- p.extra_generation_params["Mask alpha"] = mask_alpha
- p.extra_generation_params["Mask invert"] = inpainting_mask_invert
- p.extra_generation_params["Mask content"] = inpainting_fill
- p.extra_generation_params["Mask area"] = inpaint_full_res
- p.extra_generation_params["Mask padding"] = inpaint_full_res_padding
- p.is_batch = mode == 5
- if p.is_batch:
- process_batch(p, img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
- processed = processing.Processed(p, [], p.seed, "")
- else:
- processed = modules.scripts.scripts_img2img.run(p, *args)
- if processed is None:
- processed = processing.process_images(p)
- p.close()
- generation_info_js = processed.js() if processed is not None else ''
- return processed.images, generation_info_js, processed.info, plaintext_to_html(processed.comments)
+import os
+import itertools # SBM Batch frames
+import numpy as np
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
+import modules.scripts
+from modules import shared, processing, images
+from modules.generation_parameters_copypaste import create_override_settings_dict
+from modules.ui import plaintext_to_html
+from modules.memstats import memory_stats
+
+
+debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
+debug('Trace: PROCESS')
+
+
+def process_batch(p, input_files, input_dir, output_dir, inpaint_mask_dir, args):
+ shared.log.debug(f'batch: {input_files}|{input_dir}|{output_dir}|{inpaint_mask_dir}')
+ processing.fix_seed(p)
+ if input_files is not None and len(input_files) > 0:
+ image_files = [f.name for f in input_files]
+ else:
+ if not os.path.isdir(input_dir):
+ shared.log.error(f"Process batch: directory not found: {input_dir}")
+ return
+ image_files = os.listdir(input_dir)
+ is_inpaint_batch = False
+ if inpaint_mask_dir:
+ inpaint_masks = os.listdir(inpaint_mask_dir)
+ is_inpaint_batch = len(inpaint_masks) > 0
+ if is_inpaint_batch:
+ shared.log.info(f"Process batch: inpaint batch masks={len(inpaint_masks)}")
+ save_normally = output_dir == ''
+ p.do_not_save_grid = True
+ p.do_not_save_samples = not save_normally
+ shared.state.job_count = len(image_files) * p.n_iter
+ if shared.opts.batch_frame_mode: # SBM Frame mode is on, process each image in batch with same seed
+ window_size = p.batch_size
+ btcrept = 1
+ p.seed = [p.seed] * window_size # SBM MONKEYPATCH: Need to change processing to support a fixed seed value.
+ p.subseed = [p.subseed] * window_size # SBM MONKEYPATCH
+ shared.log.info(f"Process batch: inputs={len(image_files)} parallel={window_size} outputs={p.n_iter} per input ")
+ else: # SBM Frame mode is off, standard operation of repeating same images with sequential seed.
+ window_size = 1
+ btcrept = p.batch_size
+ shared.log.info(f"Process batch: inputs={len(image_files)} outputs={p.n_iter * p.batch_size} per input")
+ for i in range(0, len(image_files), window_size):
+ if shared.state.skipped:
+ shared.state.skipped = False
+ if shared.state.interrupted:
+ break
+ batch_image_files = image_files[i:i+window_size]
+ batch_images = []
+ for image_file in batch_image_files:
+ try:
+ img = Image.open(image_file)
+ if p.scale_by != 1:
+ p.width = int(img.width * p.scale_by)
+ p.height = int(img.height * p.scale_by)
+ except UnidentifiedImageError as e:
+ shared.log.error(f"Image error: {e}")
+ continue
+ img = ImageOps.exif_transpose(img)
+ batch_images.append(img)
+ batch_images = batch_images * btcrept # Standard mode sends the same image per batchsize.
+ p.init_images = batch_images
+
+ if is_inpaint_batch:
+ # try to find corresponding mask for an image using simple filename matching
+ batch_mask_images = []
+ for image_file in batch_image_files:
+ mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image_file))
+ # if not found use first one ("same mask for all images" use-case)
+ if mask_image_path not in inpaint_masks:
+ mask_image_path = inpaint_masks[0]
+ mask_image = Image.open(mask_image_path)
+ batch_mask_images.append(mask_image)
+ batch_mask_images = batch_mask_images * btcrept
+ p.image_mask = batch_mask_images
+
+ batch_image_files = batch_image_files * btcrept # List used for naming later.
+
+ proc = modules.scripts.scripts_img2img.run(p, *args)
+ if proc is None:
+ proc = processing.process_images(p)
+ for n, (image, image_file) in enumerate(itertools.zip_longest(proc.images,batch_image_files)):
+ basename = ''
+ if shared.opts.use_original_name_batch:
+ forced_filename, ext = os.path.splitext(os.path.basename(image_file))
+ else:
+ forced_filename = None
+ ext = shared.opts.samples_format
+ if len(proc.images) > 1:
+ basename = f'{n + i}' if shared.opts.batch_frame_mode else f'{n}'
+ else:
+ basename = ''
+ if output_dir == '':
+ output_dir = shared.opts.outdir_img2img_samples
+ if not save_normally:
+ os.makedirs(output_dir, exist_ok=True)
+ geninfo, items = images.read_info_from_image(image)
+ for k, v in items.items():
+ image.info[k] = v
+ images.save_image(image, path=output_dir, basename=basename, seed=None, prompt=None, extension=ext, info=geninfo, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=image.info, forced_filename=forced_filename)
+ shared.log.debug(f'Processed: images={len(batch_image_files)} memory={memory_stats()} batch')
+
+
+def img2img(id_task: str, mode: int,
+ prompt, negative_prompt, prompt_styles,
+ init_img,
+ sketch,
+ init_img_with_mask,
+ inpaint_color_sketch,
+ inpaint_color_sketch_orig,
+ init_img_inpaint,
+ init_mask_inpaint,
+ steps,
+ sampler_index,
+ mask_blur, mask_alpha,
+ inpainting_fill,
+ full_quality, restore_faces, tiling,
+ n_iter, batch_size,
+ cfg_scale, image_cfg_scale,
+ diffusers_guidance_rescale,
+ sag_scale,
+ refiner_start,
+ clip_skip,
+ denoising_strength,
+ seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
+ selected_scale_tab,
+ height, width,
+ scale_by,
+ resize_mode, resize_name,
+ inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert,
+ img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir,
+ hdr_clamp, hdr_boundary, hdr_threshold, hdr_center, hdr_channel_shift, hdr_full_shift, hdr_maximize, hdr_max_center, hdr_max_boundry,
+ override_settings_texts,
+ *args): # pylint: disable=unused-argument
+
+ if shared.sd_model is None:
+ shared.log.warning('Model not loaded')
+ return [], '', '', 'Error: model not loaded'
+
+ debug(f'img2img: id_task={id_task}|mode={mode}|prompt={prompt}|negative_prompt={negative_prompt}|prompt_styles={prompt_styles}|init_img={init_img}|sketch={sketch}|init_img_with_mask={init_img_with_mask}|inpaint_color_sketch={inpaint_color_sketch}|inpaint_color_sketch_orig={inpaint_color_sketch_orig}|init_img_inpaint={init_img_inpaint}|init_mask_inpaint={init_mask_inpaint}|steps={steps}|sampler_index={sampler_index}||mask_blur={mask_blur}|mask_alpha={mask_alpha}|inpainting_fill={inpainting_fill}|full_quality={full_quality}|restore_faces={restore_faces}|tiling={tiling}|n_iter={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|image_cfg_scale={image_cfg_scale}|clip_skip={clip_skip}|denoising_strength={denoising_strength}|seed={seed}|subseed{subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|selected_scale_tab={selected_scale_tab}|height={height}|width={width}|scale_by={scale_by}|resize_mode={resize_mode}|resize_name={resize_name}|inpaint_full_res={inpaint_full_res}|inpaint_full_res_padding={inpaint_full_res_padding}|inpainting_mask_invert={inpainting_mask_invert}|img2img_batch_files={img2img_batch_files}|img2img_batch_input_dir={img2img_batch_input_dir}|img2img_batch_output_dir={img2img_batch_output_dir}|img2img_batch_inpaint_mask_dir={img2img_batch_inpaint_mask_dir}|override_settings_texts={override_settings_texts}')
+
+ if mode == 5:
+ if img2img_batch_files is None or len(img2img_batch_files) == 0:
+ shared.log.debug('Init bactch images not set')
+ elif init_img:
+ shared.log.debug('Init image not set')
+
+ if sampler_index is None:
+ sampler_index = 0
+
+ override_settings = create_override_settings_dict(override_settings_texts)
+
+ if mode == 0: # img2img
+ if init_img is None:
+ return [], '', '', 'Error: init image not provided'
+ image = init_img.convert("RGB")
+ mask = None
+ elif mode == 1: # img2img sketch
+ if sketch is None:
+ return [], '', '', 'Error: sketch image not provided'
+ image = sketch.convert("RGB")
+ mask = None
+ elif mode == 2: # inpaint
+ if init_img_with_mask is None:
+ return [], '', '', 'Error: init image with mask not provided'
+ image = init_img_with_mask["image"]
+ mask = init_img_with_mask["mask"]
+ alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
+ mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
+ image = image.convert("RGB")
+ elif mode == 3: # inpaint sketch
+ if inpaint_color_sketch is None:
+ return [], '', '', 'Error: color sketch image not provided'
+ image = inpaint_color_sketch
+ orig = inpaint_color_sketch_orig or inpaint_color_sketch
+ pred = np.any(np.array(image) != np.array(orig), axis=-1)
+ mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
+ mask = ImageEnhance.Brightness(mask).enhance(mask_alpha)
+ blur = ImageFilter.GaussianBlur(mask_blur)
+ image = Image.composite(image.filter(blur), orig, mask.filter(blur))
+ image = image.convert("RGB")
+ elif mode == 4: # inpaint upload mask
+ if init_img_inpaint is None:
+ return [], '', '', 'Error: inpaint image not provided'
+ image = init_img_inpaint
+ mask = init_mask_inpaint
+ else:
+ shared.log.error(f'Image processing unknown mode: {mode}')
+ image = None
+ mask = None
+ if image is not None:
+ image = ImageOps.exif_transpose(image)
+ if selected_scale_tab == 1 and resize_mode != 0:
+ width = int(image.width * scale_by)
+ height = int(image.height * scale_by)
+
+ p = processing.StableDiffusionProcessingImg2Img(
+ sd_model=shared.sd_model,
+ outpath_samples=shared.opts.outdir_samples or shared.opts.outdir_img2img_samples,
+ outpath_grids=shared.opts.outdir_grids or shared.opts.outdir_img2img_grids,
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ styles=prompt_styles,
+ seed=seed,
+ subseed=subseed,
+ subseed_strength=subseed_strength,
+ seed_resize_from_h=seed_resize_from_h,
+ seed_resize_from_w=seed_resize_from_w,
+ seed_enable_extras=True,
+ sampler_name = processing.get_sampler_name(sampler_index, img=True),
+ batch_size=batch_size,
+ n_iter=n_iter,
+ steps=steps,
+ cfg_scale=cfg_scale,
+ clip_skip=clip_skip,
+ width=width,
+ height=height,
+ full_quality=full_quality,
+ restore_faces=restore_faces,
+ tiling=tiling,
+ init_images=[image],
+ mask=mask,
+ mask_blur=mask_blur,
+ inpainting_fill=inpainting_fill,
+ resize_mode=resize_mode,
+ resize_name=resize_name,
+ denoising_strength=denoising_strength,
+ image_cfg_scale=image_cfg_scale,
+ diffusers_guidance_rescale=diffusers_guidance_rescale,
+ sag_scale=sag_scale,
+ refiner_start=refiner_start,
+ inpaint_full_res=inpaint_full_res != 0,
+ inpaint_full_res_padding=inpaint_full_res_padding,
+ inpainting_mask_invert=inpainting_mask_invert,
+ hdr_clamp=hdr_clamp, hdr_boundary=hdr_boundary, hdr_threshold=hdr_threshold,
+ hdr_center=hdr_center, hdr_channel_shift=hdr_channel_shift, hdr_full_shift=hdr_full_shift,
+ hdr_maximize=hdr_maximize, hdr_max_center=hdr_max_center, hdr_max_boundry=hdr_max_boundry,
+ override_settings=override_settings,
+ )
+ if selected_scale_tab == 1 and resize_mode != 0:
+ p.scale_by = scale_by
+ p.scripts = modules.scripts.scripts_img2img
+ p.script_args = args
+ if mask:
+ p.extra_generation_params["Mask blur"] = mask_blur
+ p.extra_generation_params["Mask alpha"] = mask_alpha
+ p.extra_generation_params["Mask invert"] = inpainting_mask_invert
+ p.extra_generation_params["Mask content"] = inpainting_fill
+ p.extra_generation_params["Mask area"] = inpaint_full_res
+ p.extra_generation_params["Mask padding"] = inpaint_full_res_padding
+ p.is_batch = mode == 5
+ if p.is_batch:
+ process_batch(p, img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
+ processed = processing.Processed(p, [], p.seed, "")
+ else:
+ processed = modules.scripts.scripts_img2img.run(p, *args)
+ if processed is None:
+ processed = processing.process_images(p)
+ p.close()
+ generation_info_js = processed.js() if processed is not None else ''
+ return processed.images, generation_info_js, processed.info, plaintext_to_html(processed.comments)
diff --git a/modules/intel/ipex/__init__.py b/modules/intel/ipex/__init__.py
index 333504935..50ca55769 100644
--- a/modules/intel/ipex/__init__.py
+++ b/modules/intel/ipex/__init__.py
@@ -159,7 +159,7 @@ def ipex_init(): # pylint: disable=too-many-statements
torch.cuda.utilization = lambda *args, **kwargs: 0
ipex_hijacks()
- if not torch.xpu.has_fp64_dtype():
+ if not torch.xpu.has_fp64_dtype() or os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None) is not None:
try:
from .diffusers import ipex_diffusers
ipex_diffusers()
diff --git a/modules/intel/ipex/attention.py b/modules/intel/ipex/attention.py
index e9f927a9c..b46b3ab61 100644
--- a/modules/intel/ipex/attention.py
+++ b/modules/intel/ipex/attention.py
@@ -124,6 +124,7 @@ def torch_bmm_32_bit(input, mat2, *, out=None):
)
else:
return original_torch_bmm(input, mat2, out=out)
+ torch.xpu.synchronize(input.device)
return hidden_states
original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
@@ -172,4 +173,5 @@ def scaled_dot_product_attention_32_bit(query, key, value, attn_mask=None, dropo
)
else:
return original_scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
+ torch.xpu.synchronize(query.device)
return hidden_states
diff --git a/modules/intel/ipex/diffusers.py b/modules/intel/ipex/diffusers.py
index 47b0375ae..732a18568 100644
--- a/modules/intel/ipex/diffusers.py
+++ b/modules/intel/ipex/diffusers.py
@@ -149,6 +149,7 @@ class SlicedAttnProcessor: # pylint: disable=too-few-public-methods
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = attn_slice
del attn_slice
+ torch.xpu.synchronize(query.device)
else:
query_slice = query[start_idx:end_idx]
key_slice = key[start_idx:end_idx]
@@ -283,6 +284,7 @@ class AttnProcessor:
hidden_states[start_idx:end_idx] = attn_slice
del attn_slice
+ torch.xpu.synchronize(query.device)
else:
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
diff --git a/modules/intel/ipex/hijacks.py b/modules/intel/ipex/hijacks.py
index 34522941f..d4fa8441c 100644
--- a/modules/intel/ipex/hijacks.py
+++ b/modules/intel/ipex/hijacks.py
@@ -1,3 +1,4 @@
+import os
from functools import wraps
from contextlib import nullcontext
import torch
@@ -60,7 +61,7 @@ def from_numpy(ndarray):
else:
return original_from_numpy(ndarray)
-if torch.xpu.has_fp64_dtype():
+if torch.xpu.has_fp64_dtype() and os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None) is None:
original_torch_bmm = torch.bmm
original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
else:
diff --git a/modules/intel/openvino/__init__.py b/modules/intel/openvino/__init__.py
index 753ea4daa..fc9551ba1 100644
--- a/modules/intel/openvino/__init__.py
+++ b/modules/intel/openvino/__init__.py
@@ -70,19 +70,22 @@ class OpenVINOGraphModule(torch.nn.Module):
result = openvino_execute(self.gm, *args, executor_parameters=self.executor_parameters, partition_id=self.partition_id, file_name=self.file_name)
return result
-def get_device():
+def get_device_list():
core = Core()
- if os.getenv("OPENVINO_TORCH_BACKEND_DEVICE") is not None:
- device = os.getenv("OPENVINO_TORCH_BACKEND_DEVICE")
- elif shared.opts.openvino_hetero_gpu:
+ return core.available_devices
+
+def get_device():
+ if hasattr(shared, "opts") and len(shared.opts.openvino_devices) == 1:
+ return shared.opts.openvino_devices[0]
+
+ core = Core()
+ if hasattr(shared, "opts") and len(shared.opts.openvino_devices) > 1:
device = ""
- available_devices = core.available_devices
+ available_devices = shared.opts.openvino_devices.copy()
available_devices.remove("CPU")
- if shared.opts.openvino_remove_igpu_from_hetero and "GPU.0" in available_devices:
- available_devices.remove("GPU.0")
- for gpu in available_devices:
- device = f"{device},{gpu}"
- if not shared.opts.openvino_remove_cpu_from_hetero:
+ for hetero_device in available_devices:
+ device = f"{device},{hetero_device}"
+ if "CPU" in shared.opts.openvino_devices:
device = f"{device},CPU"
device = f"HETERO:{device[1:]}"
elif any(openvino_cpu in cpu_module.lower() for cpu_module in shared.cmd_opts.use_cpu for openvino_cpu in ["openvino", "all"]):
@@ -98,7 +101,6 @@ def get_device():
else:
device = core.available_devices[-1]
shared.log.warning(f"OpenVINO: No compatible GPU detected! Using {device}")
- os.environ.setdefault('OPENVINO_TORCH_BACKEND_DEVICE', device)
return device
def get_openvino_device():
@@ -182,6 +184,8 @@ def openvino_compile(gm: GraphModule, *args, model_hash_str: str = None, file_na
device = get_device()
cache_root = shared.opts.openvino_cache_path
+ global dont_use_4bit_nncf
+ global dont_use_nncf
if file_name is not None and os.path.isfile(file_name + ".xml") and os.path.isfile(file_name + ".bin"):
om = core.read_model(file_name + ".xml")
@@ -229,16 +233,17 @@ def openvino_compile(gm: GraphModule, *args, model_hash_str: str = None, file_na
om.inputs[idx].get_node().set_element_type(dtype_mapping[input_data.dtype])
om.inputs[idx].get_node().set_partial_shape(PartialShape(list(input_data.shape)))
om.validate_nodes_and_infer_types()
- if shared.opts.nncf_compress_weights and not (shared.compiled_model_state.compile_dont_use_4bit and not shared.opts.nncf_compress_vae_weights):
- if shared.compiled_model_state.compile_dont_use_4bit or shared.opts.nncf_compress_weights_mode == "INT8":
+ if shared.opts.nncf_compress_weights and not dont_use_nncf:
+ if dont_use_4bit_nncf or shared.opts.nncf_compress_weights_mode == "INT8":
om = nncf.compress_weights(om)
else:
om = nncf.compress_weights(om, mode=getattr(nncf.CompressWeightsMode, shared.opts.nncf_compress_weights_mode), group_size=8, ratio=shared.opts.nncf_compress_weights_raito)
if model_hash_str is not None:
core.set_property({'CACHE_DIR': cache_root + '/blob'})
+ dont_use_nncf = False
+ dont_use_4bit_nncf = False
- shared.compiled_model_state.compile_dont_use_4bit = False
compiled_model = core.compile_model(om, device)
return compiled_model
@@ -246,6 +251,9 @@ def openvino_compile_cached_model(cached_model_path, *example_inputs):
core = Core()
om = core.read_model(cached_model_path + ".xml")
+ global dont_use_4bit_nncf
+ global dont_use_nncf
+
dtype_mapping = {
torch.float32: Type.f32,
torch.float64: Type.f64,
@@ -261,15 +269,16 @@ def openvino_compile_cached_model(cached_model_path, *example_inputs):
om.inputs[idx].get_node().set_element_type(dtype_mapping[input_data.dtype])
om.inputs[idx].get_node().set_partial_shape(PartialShape(list(input_data.shape)))
om.validate_nodes_and_infer_types()
- if shared.opts.nncf_compress_weights and not (shared.compiled_model_state.compile_dont_use_4bit and not shared.opts.nncf_compress_vae_weights):
- if shared.compiled_model_state.compile_dont_use_4bit or shared.opts.nncf_compress_weights_mode == "INT8":
+ if shared.opts.nncf_compress_weights and not dont_use_nncf:
+ if dont_use_4bit_nncf or shared.opts.nncf_compress_weights_mode == "INT8":
om = nncf.compress_weights(om)
else:
om = nncf.compress_weights(om, mode=getattr(nncf.CompressWeightsMode, shared.opts.nncf_compress_weights_mode), group_size=8, ratio=shared.opts.nncf_compress_weights_raito)
core.set_property({'CACHE_DIR': shared.opts.openvino_cache_path + '/blob'})
+ dont_use_nncf = False
+ dont_use_4bit_nncf = False
- shared.compiled_model_state.compile_dont_use_4bit = False
compiled_model = core.compile_model(om, get_device())
return compiled_model
@@ -344,52 +353,69 @@ def partition_graph(gm: GraphModule, use_python_fusion_cache: bool, model_hash_s
def generate_subgraph_str(tensor):
if hasattr(tensor, "weight"):
- shared.compiled_model_state.model_str = shared.compiled_model_state.model_str + sha256(str(tensor.weight).encode('utf-8')).hexdigest()
+ shared.compiled_model_state.model_hash_str = shared.compiled_model_state.model_hash_str + sha256(str(tensor.weight).encode('utf-8')).hexdigest()
return tensor
def get_subgraph_type(tensor):
- shared.compiled_model_state.subgraph_type.append(type(tensor))
+ global subgraph_type
+ subgraph_type.append(type(tensor))
return tensor
@register_backend
@fake_tensor_unsupported
def openvino_fx(subgraph, example_inputs):
+ global dont_use_4bit_nncf
+ global dont_use_nncf
+ global subgraph_type
+
+ dont_use_4bit_nncf = False
+ dont_use_nncf = False
+ dont_use_faketensors = False
executor_parameters = None
inputs_reversed = False
maybe_fs_cached_name = None
- shared.compiled_model_state.subgraph_type = []
+ subgraph_type = []
subgraph.apply(get_subgraph_type)
# SD 1.5 / SDXL VAE
- if (shared.compiled_model_state.subgraph_type[0] is torch.nn.modules.conv.Conv2d and
- shared.compiled_model_state.subgraph_type[1] is torch.nn.modules.conv.Conv2d and
- shared.compiled_model_state.subgraph_type[2] is torch.nn.modules.normalization.GroupNorm and
- shared.compiled_model_state.subgraph_type[3] is torch.nn.modules.activation.SiLU):
+ if (subgraph_type[0] is torch.nn.modules.conv.Conv2d and
+ subgraph_type[1] is torch.nn.modules.conv.Conv2d and
+ subgraph_type[2] is torch.nn.modules.normalization.GroupNorm and
+ subgraph_type[3] is torch.nn.modules.activation.SiLU):
- shared.compiled_model_state.compile_dont_use_4bit = True
+ dont_use_4bit_nncf = True
+ dont_use_nncf = bool("VAE" not in shared.opts.nncf_compress_weights)
+
+ # SD 1.5 / SDXL Text Encoder
+ elif (subgraph_type[0] is torch.nn.modules.sparse.Embedding and
+ subgraph_type[1] is torch.nn.modules.sparse.Embedding and
+ subgraph_type[2] is torch.nn.modules.normalization.LayerNorm and
+ subgraph_type[3] is torch.nn.modules.linear.Linear):
+
+ dont_use_faketensors = True
+ dont_use_nncf = bool("Text Encoder" not in shared.opts.nncf_compress_weights)
if not shared.opts.openvino_disable_model_caching:
os.environ.setdefault('OPENVINO_TORCH_MODEL_CACHING', "1")
- shared.compiled_model_state.model_str = ""
# Create a hash to be used for caching
subgraph.apply(generate_subgraph_str)
- shared.compiled_model_state.model_str = shared.compiled_model_state.model_str + sha256(subgraph.code.encode('utf-8')).hexdigest()
- model_hash_str = sha256(shared.compiled_model_state.model_str.encode('utf-8')).hexdigest()
- shared.compiled_model_state.model_str = ""
+ shared.compiled_model_state.model_hash_str = shared.compiled_model_state.model_hash_str + sha256(subgraph.code.encode('utf-8')).hexdigest()
+ model_hash_str = sha256(shared.compiled_model_state.model_hash_str.encode('utf-8')).hexdigest()
+ shared.compiled_model_state.model_hash_str = ""
if (shared.compiled_model_state.cn_model != [] and shared.compiled_model_state.partition_id == 0):
- model_hash_str = model_hash_str + str(shared.compiled_model_state.cn_model)
+ shared.compiled_model_state.shared.compiled_model_state.model_hash_str = model_hash_str + str(shared.compiled_model_state.cn_model)
if (shared.compiled_model_state.lora_model != []):
- model_hash_str = model_hash_str + str(shared.compiled_model_state.lora_model)
+ shared.compiled_model_state.model_hash_str = shared.compiled_model_state.model_hash_str + str(shared.compiled_model_state.lora_model)
- executor_parameters = {"model_hash_str": model_hash_str}
+ executor_parameters = {"model_hash_str": shared.compiled_model_state.model_hash_str}
# Check if the model was fully supported and already cached
example_inputs.reverse()
inputs_reversed = True
- maybe_fs_cached_name = cached_model_name(model_hash_str + "_fs", get_device(), example_inputs, shared.opts.openvino_cache_path)
+ maybe_fs_cached_name = cached_model_name(shared.compiled_model_state.model_hash_str + "_fs", get_device(), example_inputs, shared.opts.openvino_cache_path)
if os.path.isfile(maybe_fs_cached_name + ".xml") and os.path.isfile(maybe_fs_cached_name + ".bin"):
example_inputs_reordered = []
@@ -403,13 +429,8 @@ def openvino_fx(subgraph, example_inputs):
example_inputs_reordered.append(example_inputs[idx1])
example_inputs = example_inputs_reordered
- # SD 1.5 / SDXL Text Encoder
- if (shared.compiled_model_state.subgraph_type[0] is torch.nn.modules.sparse.Embedding and
- shared.compiled_model_state.subgraph_type[1] is torch.nn.modules.sparse.Embedding and
- shared.compiled_model_state.subgraph_type[2] is torch.nn.modules.normalization.LayerNorm and
- shared.compiled_model_state.subgraph_type[3] is torch.nn.modules.linear.Linear):
-
- pass # Fails with FakeTensors or Downcast
+ if dont_use_faketensors:
+ pass
else:
# Delete unused subgraphs
subgraph = subgraph.apply(sd_models.convert_to_faketensors)
diff --git a/modules/loader.py b/modules/loader.py
index 31cadcd56..a3fdb04ea 100644
--- a/modules/loader.py
+++ b/modules/loader.py
@@ -41,6 +41,9 @@ import gradio # pylint: disable=W0611,C0411
timer.startup.record("gradio")
errors.install([gradio])
+import pydantic # pylint: disable=W0611,C0411
+timer.startup.record("pydantic")
+
import diffusers # pylint: disable=W0611,C0411
timer.startup.record("diffusers")
errors.log.info(f'Load packages: torch={getattr(torch, "__long_version__", torch.__version__)} diffusers={diffusers.__version__} gradio={gradio.__version__}')
diff --git a/modules/localization.py b/modules/localization.py
index d18d5137b..a385ccbd7 100644
--- a/modules/localization.py
+++ b/modules/localization.py
@@ -1,38 +1,38 @@
-import json
-import sys
-import modules.errors as errors
-
-
-localizations = {}
-
-
-def list_localizations(dirname): # pylint: disable=unused-argument
- localizations.clear()
- """
- for file in os.listdir(dirname):
- fn, ext = os.path.splitext(file)
- if ext.lower() != ".json":
- continue
-
- localizations[fn] = os.path.join(dirname, file)
-
- from modules import scripts
- for file in scripts.list_scripts("localizations", ".json"):
- fn, ext = os.path.splitext(file.filename)
- localizations[fn] = file.path
- """
- return localizations
-
-
-def localization_js(current_localization_name):
- fn = localizations.get(current_localization_name, None)
- data = {}
- if fn is not None:
- try:
- with open(fn, "r", encoding="utf8") as file:
- data = json.load(file)
- except Exception as e:
- print(f"Error loading localization from {fn}:", file=sys.stderr)
- errors.display(e, 'localization')
-
- return f"var localization = {json.dumps(data)}\n"
+import json
+import sys
+import modules.errors as errors
+
+
+localizations = {}
+
+
+def list_localizations(dirname): # pylint: disable=unused-argument
+ localizations.clear()
+ """
+ for file in os.listdir(dirname):
+ fn, ext = os.path.splitext(file)
+ if ext.lower() != ".json":
+ continue
+
+ localizations[fn] = os.path.join(dirname, file)
+
+ from modules import scripts
+ for file in scripts.list_scripts("localizations", ".json"):
+ fn, ext = os.path.splitext(file.filename)
+ localizations[fn] = file.path
+ """
+ return localizations
+
+
+def localization_js(current_localization_name):
+ fn = localizations.get(current_localization_name, None)
+ data = {}
+ if fn is not None:
+ try:
+ with open(fn, "r", encoding="utf8") as file:
+ data = json.load(file)
+ except Exception as e:
+ print(f"Error loading localization from {fn}:", file=sys.stderr)
+ errors.display(e, 'localization')
+
+ return f"var localization = {json.dumps(data)}\n"
diff --git a/modules/lowvram.py b/modules/lowvram.py
index 5afb7e7c8..143893ce6 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -1,98 +1,98 @@
-import torch
-from modules import devices
-
-module_in_gpu = None
-cpu = torch.device("cpu")
-
-
-def send_everything_to_cpu():
- global module_in_gpu # pylint: disable=global-statement
-
- if module_in_gpu is not None:
- module_in_gpu.to(cpu)
-
- module_in_gpu = None
-
-
-def setup_for_low_vram(sd_model, use_medvram):
- parents = {}
-
- def send_me_to_gpu(module, _):
- """send this module to GPU; send whatever tracked module was previous in GPU to CPU;
- we add this as forward_pre_hook to a lot of modules and this way all but one of them will
- be in CPU
- """
- global module_in_gpu # pylint: disable=global-statement
-
- module = parents.get(module, module)
-
- if module_in_gpu == module:
- return
-
- if module_in_gpu is not None:
- module_in_gpu.to(cpu)
-
- module.to(devices.device)
- module_in_gpu = module
-
- # see below for register_forward_pre_hook;
- # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
- # useless here, and we just replace those methods
-
- first_stage_model = sd_model.first_stage_model
- first_stage_model_encode = sd_model.first_stage_model.encode
- first_stage_model_decode = sd_model.first_stage_model.decode
-
- def first_stage_model_encode_wrap(x):
- send_me_to_gpu(first_stage_model, None)
- return first_stage_model_encode(x)
-
- def first_stage_model_decode_wrap(z):
- send_me_to_gpu(first_stage_model, None)
- return first_stage_model_decode(z)
-
- # for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
- if hasattr(sd_model, 'cond_stage_model') and hasattr(sd_model.cond_stage_model, 'model'):
- sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
-
- # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
- # send the model to GPU. Then put modules back. the modules will be in CPU.
- stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
- sd_model.to(devices.device)
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
-
- # register hooks for those the first three models
- sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
- sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
- sd_model.first_stage_model.encode = first_stage_model_encode_wrap
- sd_model.first_stage_model.decode = first_stage_model_decode_wrap
- if sd_model.depth_model:
- sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
- if sd_model.embedder:
- sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
- parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
-
- if hasattr(sd_model, 'cond_stage_model') and hasattr(sd_model.cond_stage_model, 'model'):
- sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
- del sd_model.cond_stage_model.transformer
-
- if use_medvram:
- sd_model.model.register_forward_pre_hook(send_me_to_gpu)
- else:
- diff_model = sd_model.model.diffusion_model
-
- # the third remaining model is still too big for 4 GB, so we also do the same for its submodules
- # so that only one of them is in GPU at a time
- stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
- diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
- sd_model.model.to(devices.device)
- diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
-
- # install hooks for bits of third model
- diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
- for block in diff_model.input_blocks:
- block.register_forward_pre_hook(send_me_to_gpu)
- diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
- for block in diff_model.output_blocks:
- block.register_forward_pre_hook(send_me_to_gpu)
+import torch
+from modules import devices
+
+module_in_gpu = None
+cpu = torch.device("cpu")
+
+
+def send_everything_to_cpu():
+ global module_in_gpu # pylint: disable=global-statement
+
+ if module_in_gpu is not None:
+ module_in_gpu.to(cpu)
+
+ module_in_gpu = None
+
+
+def setup_for_low_vram(sd_model, use_medvram):
+ parents = {}
+
+ def send_me_to_gpu(module, _):
+ """send this module to GPU; send whatever tracked module was previous in GPU to CPU;
+ we add this as forward_pre_hook to a lot of modules and this way all but one of them will
+ be in CPU
+ """
+ global module_in_gpu # pylint: disable=global-statement
+
+ module = parents.get(module, module)
+
+ if module_in_gpu == module:
+ return
+
+ if module_in_gpu is not None:
+ module_in_gpu.to(cpu)
+
+ module.to(devices.device)
+ module_in_gpu = module
+
+ # see below for register_forward_pre_hook;
+ # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
+ # useless here, and we just replace those methods
+
+ first_stage_model = sd_model.first_stage_model
+ first_stage_model_encode = sd_model.first_stage_model.encode
+ first_stage_model_decode = sd_model.first_stage_model.decode
+
+ def first_stage_model_encode_wrap(x):
+ send_me_to_gpu(first_stage_model, None)
+ return first_stage_model_encode(x)
+
+ def first_stage_model_decode_wrap(z):
+ send_me_to_gpu(first_stage_model, None)
+ return first_stage_model_decode(z)
+
+ # for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
+ if hasattr(sd_model, 'cond_stage_model') and hasattr(sd_model.cond_stage_model, 'model'):
+ sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
+
+ # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
+ # send the model to GPU. Then put modules back. the modules will be in CPU.
+ stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
+ sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
+ sd_model.to(devices.device)
+ sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
+
+ # register hooks for those the first three models
+ sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+ sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
+ sd_model.first_stage_model.encode = first_stage_model_encode_wrap
+ sd_model.first_stage_model.decode = first_stage_model_decode_wrap
+ if sd_model.depth_model:
+ sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
+ if sd_model.embedder:
+ sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
+ parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
+
+ if hasattr(sd_model, 'cond_stage_model') and hasattr(sd_model.cond_stage_model, 'model'):
+ sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
+ del sd_model.cond_stage_model.transformer
+
+ if use_medvram:
+ sd_model.model.register_forward_pre_hook(send_me_to_gpu)
+ else:
+ diff_model = sd_model.model.diffusion_model
+
+ # the third remaining model is still too big for 4 GB, so we also do the same for its submodules
+ # so that only one of them is in GPU at a time
+ stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
+ diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
+ sd_model.model.to(devices.device)
+ diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
+
+ # install hooks for bits of third model
+ diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
+ for block in diff_model.input_blocks:
+ block.register_forward_pre_hook(send_me_to_gpu)
+ diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
+ for block in diff_model.output_blocks:
+ block.register_forward_pre_hook(send_me_to_gpu)
diff --git a/modules/masking.py b/modules/masking.py
index ce1de4c22..1c33e9724 100644
--- a/modules/masking.py
+++ b/modules/masking.py
@@ -1,85 +1,356 @@
-from PIL import Image, ImageFilter, ImageOps
-
-
-def get_crop_region(mask, pad=0):
- """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
- For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
- h, w = mask.shape
- crop_left = 0
- for i in range(w):
- if not (mask[:, i] == 0).all():
- break
- crop_left += 1
- crop_right = 0
- for i in reversed(range(w)):
- if not (mask[:, i] == 0).all():
- break
- crop_right += 1
- crop_top = 0
- for i in range(h):
- if not (mask[i] == 0).all():
- break
- crop_top += 1
- crop_bottom = 0
- for i in reversed(range(h)):
- if not (mask[i] == 0).all():
- break
- crop_bottom += 1
- return (
- int(max(crop_left-pad, 0)),
- int(max(crop_top-pad, 0)),
- int(min(w - crop_right + pad, w)),
- int(min(h - crop_bottom + pad, h))
- )
-
-
-def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):
- """expands crop region get_crop_region() to match the ratio of the image the region will processed in; returns expanded region
- for example, if user drew mask in a 128x32 region, and the dimensions for processing are 512x512, the region will be expanded to 128x128."""
- x1, y1, x2, y2 = crop_region
- ratio_crop_region = (x2 - x1) / (y2 - y1)
- ratio_processing = processing_width / processing_height
-
- if ratio_crop_region > ratio_processing:
- desired_height = (x2 - x1) / ratio_processing
- desired_height_diff = int(desired_height - (y2-y1))
- y1 -= desired_height_diff//2
- y2 += desired_height_diff - desired_height_diff//2
- if y2 >= image_height:
- diff = y2 - image_height
- y2 -= diff
- y1 -= diff
- if y1 < 0:
- y2 -= y1
- y1 -= y1
- if y2 >= image_height:
- y2 = image_height
- else:
- desired_width = (y2 - y1) * ratio_processing
- desired_width_diff = int(desired_width - (x2-x1))
- x1 -= desired_width_diff//2
- x2 += desired_width_diff - desired_width_diff//2
- if x2 >= image_width:
- diff = x2 - image_width
- x2 -= diff
- x1 -= diff
- if x1 < 0:
- x2 -= x1
- x1 -= x1
- if x2 >= image_width:
- x2 = image_width
-
- return x1, y1, x2, y2
-
-
-def fill(image, mask):
- """fills masked regions with colors from image using blur. Not extremely effective."""
- image_mod = Image.new('RGBA', (image.width, image.height))
- image_masked = Image.new('RGBa', (image.width, image.height))
- image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
- image_masked = image_masked.convert('RGBa')
- for radius, repeats in [(256, 1), (64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
- blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA')
- for _ in range(repeats):
- image_mod.alpha_composite(blurred)
- return image_mod.convert("RGB")
+from types import SimpleNamespace
+import os
+import time
+import gradio as gr
+import numpy as np
+import cv2
+from PIL import Image, ImageFilter, ImageOps
+from transformers import SamModel, SamImageProcessor, MaskGenerationPipeline
+from modules import shared, errors, devices, ui_components, ui_symbols
+
+
+def get_crop_region(mask, pad=0):
+ """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
+ For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
+ h, w = mask.shape
+ crop_left = 0
+ for i in range(w):
+ if not (mask[:, i] == 0).all():
+ break
+ crop_left += 1
+ crop_right = 0
+ for i in reversed(range(w)):
+ if not (mask[:, i] == 0).all():
+ break
+ crop_right += 1
+ crop_top = 0
+ for i in range(h):
+ if not (mask[i] == 0).all():
+ break
+ crop_top += 1
+ crop_bottom = 0
+ for i in reversed(range(h)):
+ if not (mask[i] == 0).all():
+ break
+ crop_bottom += 1
+ return (
+ int(max(crop_left-pad, 0)),
+ int(max(crop_top-pad, 0)),
+ int(min(w - crop_right + pad, w)),
+ int(min(h - crop_bottom + pad, h))
+ )
+
+
+def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):
+ """expands crop region get_crop_region() to match the ratio of the image the region will processed in; returns expanded region
+ for example, if user drew mask in a 128x32 region, and the dimensions for processing are 512x512, the region will be expanded to 128x128."""
+ x1, y1, x2, y2 = crop_region
+ ratio_crop_region = (x2 - x1) / (y2 - y1)
+ ratio_processing = processing_width / processing_height
+
+ if ratio_crop_region > ratio_processing:
+ desired_height = (x2 - x1) / ratio_processing
+ desired_height_diff = int(desired_height - (y2-y1))
+ y1 -= desired_height_diff//2
+ y2 += desired_height_diff - desired_height_diff//2
+ if y2 >= image_height:
+ diff = y2 - image_height
+ y2 -= diff
+ y1 -= diff
+ if y1 < 0:
+ y2 -= y1
+ y1 -= y1
+ if y2 >= image_height:
+ y2 = image_height
+ else:
+ desired_width = (y2 - y1) * ratio_processing
+ desired_width_diff = int(desired_width - (x2-x1))
+ x1 -= desired_width_diff//2
+ x2 += desired_width_diff - desired_width_diff//2
+ if x2 >= image_width:
+ diff = x2 - image_width
+ x2 -= diff
+ x1 -= diff
+ if x1 < 0:
+ x2 -= x1
+ x1 -= x1
+ if x2 >= image_width:
+ x2 = image_width
+
+ return x1, y1, x2, y2
+
+
+def fill(image, mask):
+ """fills masked regions with colors from image using blur. Not extremely effective."""
+ image_mod = Image.new('RGBA', (image.width, image.height))
+ image_masked = Image.new('RGBa', (image.width, image.height))
+ image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
+ image_masked = image_masked.convert('RGBa')
+ for radius, repeats in [(256, 1), (64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
+ blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA')
+ for _ in range(repeats):
+ image_mod.alpha_composite(blurred)
+ return image_mod.convert("RGB")
+
+
+"""
+[docs](https://huggingface.co/docs/transformers/v4.36.1/en/model_doc/sam#overview)
+TODO:
+- PerSAM
+- https://huggingface.co/docs/transformers/tasks/semantic_segmentation
+- transformers.pipeline.MaskGenerationPipeline: https://huggingface.co/models?pipeline_tag=mask-generation
+- transformers.pipeline.ImageSegmentationPipeline: https://huggingface.co/models?pipeline_tag=image-segmentation
+"""
+
+MODELS = {
+ 'None': None,
+ 'Facebook SAM ViT Base': 'facebook/sam-vit-base',
+ 'Facebook SAM ViT Large': 'facebook/sam-vit-large',
+ 'Facebook SAM ViT Huge': 'facebook/sam-vit-huge',
+ 'SlimSAM Uniform': 'Zigeng/SlimSAM-uniform-50',
+ 'SlimSAM Uniform Tiny': 'Zigeng/SlimSAM-uniform-77',
+ # 'Tiny Random': 'fxmarty/sam-vit-tiny-random',
+}
+COLORMAP = ['autumn', 'bone', 'jet', 'winter', 'rainbow', 'ocean', 'summer', 'spring', 'cool', 'hsv', 'pink', 'hot', 'parula', 'magma', 'inferno', 'plasma', 'viridis', 'cividis', 'twilight', 'shifted', 'turbo', 'deepgreen']
+cache_dir = 'models/control/segment'
+loaded_model = None
+model: SamModel = None
+processor: SamImageProcessor = None
+generator: MaskGenerationPipeline = None
+debug = shared.log.trace if os.environ.get('SD_MASK_DEBUG', None) is not None else lambda *args, **kwargs: None
+debug('Trace: MASK')
+busy = False
+btn_segment = None
+controls = []
+opts = SimpleNamespace(**{
+ 'mask_blur': 0.01,
+ 'mask_erode': 0.01,
+ 'mask_dilate': 0.01,
+ 'seg_iou_thresh': 0.5,
+ 'seg_score_thresh': 0.5,
+ 'seg_nms_thresh': 0.5,
+ 'seg_overlap_ratio': 0.3,
+ 'seg_points_per_batch': 64,
+ 'seg_topK': 50,
+ 'seg_colormap': 'pink',
+ 'preview_type': 'composite',
+ 'seg_live': True,
+ 'weight_original': 0.5,
+ 'weight_mask': 0.5,
+ 'kernel_iterations': 1,
+})
+
+
+def init_model(selected_model: str):
+ global busy, loaded_model, model, processor, generator # pylint: disable=global-statement
+ if selected_model == "None":
+ if model is not None:
+ shared.log.debug('Segment unloading model')
+ model = None
+ loaded_model = None
+ processor = None
+ generator = None
+ devices.torch_gc()
+ return selected_model
+ model_path = MODELS[selected_model]
+ if model_path is not None and (loaded_model != selected_model or model is None or processor is None):
+ busy = True
+ t0 = time.time()
+ shared.log.debug(f'Segment loading: model={selected_model} path={model_path}')
+ model = SamModel.from_pretrained(model_path, cache_dir=cache_dir).to(device=devices.device)
+ processor = SamImageProcessor.from_pretrained(model_path, cache_dir=cache_dir)
+ generator = MaskGenerationPipeline(
+ model=model,
+ image_processor=processor,
+ device=devices.device,
+ # output_bboxes_mask=False,
+ # output_rle_masks=False,
+ )
+ devices.torch_gc()
+ shared.log.debug(f'Segment loaded: model={selected_model} path={model_path} time={time.time()-t0:.2f}s')
+ busy = False
+ return selected_model
+
+
+def run_segment(input_image: gr.Image, input_mask: np.ndarray):
+ outputs = None
+ with devices.inference_context():
+ try:
+ outputs = generator(
+ input_image,
+ points_per_batch=opts.seg_points_per_batch,
+ pred_iou_thresh=opts.seg_iou_thresh,
+ stability_score_thresh=opts.seg_score_thresh,
+ crops_nms_thresh=opts.seg_nms_thresh,
+ crop_overlap_ratio=opts.seg_overlap_ratio,
+ crops_n_layers=0,
+ crop_n_points_downscale_factor=1,
+ )
+ except Exception as e:
+ shared.log.error(f'Segment error: {e}')
+ errors.display(e, 'Segment')
+ return outputs
+ devices.torch_gc()
+ i = 1
+ combined_mask = np.zeros(input_mask.shape, dtype='uint8')
+ input_mask_size = np.count_nonzero(input_mask)
+ debug(f'Segment: {vars(opts)}')
+ for mask in outputs['masks']:
+ mask = mask.astype('uint8')
+ mask_size = np.count_nonzero(mask)
+ if mask_size == 0:
+ continue
+ overlap = 0
+ if input_mask_size > 0:
+ overlap = cv2.bitwise_and(mask, input_mask)
+ overlap = np.count_nonzero(overlap)
+ if overlap == 0:
+ continue
+ mask = (opts.seg_topK + 1 - i) * mask * (255 // opts.seg_topK) # set grayscale intensity so we can recolor
+ combined_mask = combined_mask + mask
+ debug(f'Segment mask: i={i} size={input_image.width}x{input_image.height} masked={mask_size}px overlap={overlap} score={outputs["scores"][i-1]:.2f}')
+ i += 1
+ if i > opts.seg_topK:
+ break
+ return combined_mask
+
+
+def run_mask(input_image: gr.Image, input_mask: gr.Image = None, return_type: str = None, mask_blur: int = None, mask_padding: int = None, segment_enable=True):
+ if input_image is None:
+ return input_mask
+ if isinstance(input_image, list):
+ input_image = input_image[0]
+ if isinstance(input_image, dict):
+ input_mask = input_image.get('mask', None)
+ input_image = input_image.get('image', None)
+ if input_mask is None:
+ input_mask = input_image.convert('L')
+ input_mask = input_mask.point(lambda x: 255 if x > 127 else 0)
+ else:
+ input_mask = input_mask.convert('L')
+ shared.log.debug(f'Segment mask: input={input_image} mask={input_mask} type={return_type}')
+
+ input_mask = np.array(input_mask) // 255
+ t0 = time.time()
+
+ if mask_blur is not None:
+ opts.mask_blur = mask_blur / min(input_image.width, input_image.height)
+ if mask_padding is not None:
+ opts.mask_dilate = mask_padding / min(input_image.width, input_image.height)
+
+ if generator is None or not segment_enable:
+ mask = input_mask * 255
+ else:
+ mask = run_segment(input_image, input_mask)
+
+ if mask is None:
+ shared.log.error('Segment error: no mask')
+ return input_mask
+
+ debug(f'Segment mask: mask={mask.shape}')
+ if opts.mask_erode > 0:
+ try:
+ kernel = np.ones((int(opts.mask_erode * input_image.height / 4) + 1, int(opts.mask_erode * input_image.width / 4) + 1), np.uint8)
+ cv2_mask = cv2.erode(mask, kernel, iterations=opts.kernel_iterations) # remove noise
+ mask = cv2_mask
+ debug(f'Segment erode={opts.mask_erode} kernel={kernel} mask={mask.shape}')
+ except Exception as e:
+ shared.log.error(f'Segment erode: {e}')
+ if opts.mask_dilate > 0:
+ try:
+ kernel = np.ones((int(opts.mask_dilate * input_image.height / 4) + 1, int(opts.mask_dilate * input_image.width / 4) + 1), np.uint8)
+ cv2_mask = cv2.dilate(mask, kernel, iterations=opts.kernel_iterations) # expand area
+ mask = cv2_mask
+ debug(f'Segment dilate={opts.mask_dilate} kernel={kernel} mask={mask.shape}')
+ except Exception as e:
+ shared.log.error(f'Segment dilate: {e}')
+ if opts.mask_blur > 0:
+ try:
+ sigmax, sigmay = 1 + int(opts.mask_blur * input_image.width / 4), 1 + int(opts.mask_blur * input_image.height / 4)
+ cv2_mask = cv2.GaussianBlur(mask, (0, 0), sigmaX=sigmax, sigmaY=sigmay) # blur mask
+ mask = cv2_mask
+ debug(f'Segment blur={opts.mask_blur} x={sigmax} y={sigmay} mask={mask.shape}')
+ except Exception as e:
+ shared.log.error(f'Segment blur: {e}')
+
+ mask_size = np.count_nonzero(mask)
+ total_size = np.prod(mask.shape)
+ area_size = np.count_nonzero(mask)
+ colored_mask = cv2.applyColorMap(mask, COLORMAP.index(opts.seg_colormap)) # recolor mask
+ combined_image = cv2.addWeighted(np.array(input_image), opts.weight_original, colored_mask, opts.weight_mask, 0)
+ binary_mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] # otsu uses mean instead of threshold
+ t1 = time.time()
+
+ return_type = return_type or opts.preview_type
+ shared.log.debug(f'Segment mask opts: size={input_image.width}x{input_image.height} masked={mask_size}px area={area_size/total_size:.2f} time={t1-t0:.2f}')
+ if return_type == 'none':
+ return input_mask
+ elif return_type == 'binary':
+ return Image.fromarray(binary_mask)
+ elif return_type == 'grayscale':
+ return Image.fromarray(mask)
+ elif return_type == 'color':
+ return Image.fromarray(colored_mask)
+ elif return_type == 'composite':
+ return Image.fromarray(combined_image)
+ return input_mask
+
+
+def run_mask_live(input_image: gr.Image):
+ global busy # pylint: disable=global-statement
+ if opts.seg_live:
+ if not busy:
+ busy = True
+ res = run_mask(input_image)
+ busy = False
+ return res
+ else:
+ return None
+
+
+def create_segment_ui():
+ def update_opts(*args):
+ opts.seg_live = args[0]
+ opts.mask_blur = args[1]
+ opts.mask_erode = args[2]
+ opts.mask_dilate = args[3]
+ opts.seg_score_thresh = args[4]
+ opts.seg_iou_thresh = args[5]
+ opts.seg_nms_thresh = args[6]
+ opts.preview_type = args[7]
+ opts.seg_colormap = args[8]
+
+ def display_controls(selected_model):
+ return 4 * [gr.update(visible=True)] + (len(controls) - 4) * [gr.update(visible=selected_model != 'None')]
+
+ global btn_segment # pylint: disable=global-statement
+ with gr.Accordion(open=False, label="Mask", elem_id="control_mask", elem_classes=["small-accordion"]):
+ controls.clear()
+ with gr.Row():
+ controls.append(gr.Checkbox(label="Live update", value=True))
+ with gr.Row():
+ controls.append(gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Blur', value=0.01, elem_id="control_mask_blur"))
+ controls.append(gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Erode', value=0.01, elem_id="control_mask_erode"))
+ controls.append(gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Dilate', value=0.01, elem_id="control_mask_dilate"))
+ with gr.Row():
+ selected_model = gr.Dropdown(label="Auto-segment", choices=MODELS.keys(), value='None')
+ btn_segment = ui_components.ToolButton(value=ui_symbols.refresh, visible=False)
+ with gr.Row():
+ controls.append(gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Score', value=0.5, visible=False))
+ controls.append(gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='IOU', value=0.5, visible=False))
+ controls.append(gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='NMS', value=0.5, visible=False))
+ with gr.Row():
+ controls.append(gr.Dropdown(label="Preview", choices=['none', 'binary', 'grayscale', 'color', 'composite'], value='composite'))
+ controls.append(gr.Dropdown(label="Colormap", choices=COLORMAP, value='pink'))
+
+ selected_model.change(fn=init_model, inputs=[selected_model], outputs=[selected_model])
+ selected_model.change(fn=display_controls, inputs=[selected_model], outputs=controls)
+ for control in controls:
+ control.change(fn=update_opts, inputs=controls, outputs=[])
+
+
+def bind_controls(input_image: gr.Image, preview_image: gr.Image):
+ btn_segment.click(run_mask, inputs=[input_image], outputs=[preview_image])
+ input_image.edit(fn=run_mask_live, inputs=[input_image], outputs=[preview_image])
+ for control in controls:
+ control.change(fn=run_mask_live, inputs=[input_image], outputs=[preview_image])
diff --git a/modules/middleware.py b/modules/middleware.py
index 9eeebe541..6bebe0e69 100644
--- a/modules/middleware.py
+++ b/modules/middleware.py
@@ -65,16 +65,19 @@ def setup_middleware(app: FastAPI, cmd_opts):
def handle_exception(req: Request, e: Exception):
err = {
"error": type(e).__name__,
+ "code": vars(e).get('status_code', 500),
"detail": vars(e).get('detail', ''),
"body": vars(e).get('body', ''),
"errors": str(e),
}
+ log.error(f"API error: {req.method}: {req.url} {err}")
if not isinstance(e, HTTPException) and err['error'] != 'TypeError': # do not print backtrace on known httpexceptions
- log.error(f"API error: {req.method}: {req.url} {err}")
errors.display(e, 'HTTP API', [anyio, fastapi, uvicorn, starlette])
+ elif err['code'] == 404 or err['code'] == 401:
+ pass
else:
- log.debug(e, exc_info=True)
- return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
+ log.debug(e, exc_info=True) # print stack trace
+ return JSONResponse(status_code=err['code'], content=jsonable_encoder(err))
@app.exception_handler(HTTPException)
async def http_exception_handler(req: Request, e: HTTPException):
diff --git a/modules/modeldata.py b/modules/modeldata.py
index 7fe0e0f31..d04ac2b52 100644
--- a/modules/modeldata.py
+++ b/modules/modeldata.py
@@ -69,10 +69,6 @@ class Shared(sys.modules[__name__].__class__):
import modules.sd_models # pylint: disable=W0621
modules.sd_models.model_data.set_sd_refiner(value)
- @property
- def backend(self):
- return shared.Backend.ORIGINAL if not shared.cmd_opts.use_openvino and shared.opts.data['sd_backend'] == 'original' else shared.Backend.DIFFUSERS
-
@property
def sd_model_type(self):
try:
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 81635737c..b8f4662a9 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -4,7 +4,7 @@ import shutil
import importlib
from typing import Dict
from urllib.parse import urlparse
-import PIL.Image as Image
+from PIL import Image
import rich.progress as p
from modules import shared, errors
from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
@@ -264,8 +264,6 @@ def find_diffuser(name: str):
repo = [r for r in diffuser_repos if name == r['name'] or name == r['friendly'] or name == r['path']]
if len(repo) > 0:
return repo['name']
- if shared.cmd_opts.no_download:
- return None
import huggingface_hub as hf
hf_api = hf.HfApi()
hf_filter = hf.ModelFilter(
diff --git a/modules/patches.py b/modules/patches.py
index cff6bfd64..348759ab7 100644
--- a/modules/patches.py
+++ b/modules/patches.py
@@ -1,63 +1,63 @@
-from collections import defaultdict
-
-
-def patch(key, obj, field, replacement):
- """Replaces a function in a module or a class.
-
- Also stores the original function in this module, possible to be retrieved via original(key, obj, field).
- If the function is already replaced by this caller (key), an exception is raised -- use undo() before that.
-
- Arguments:
- key: identifying information for who is doing the replacement. You can use __name__.
- obj: the module or the class
- field: name of the function as a string
- replacement: the new function
-
- Returns:
- the original function
- """
-
- patch_key = (obj, field)
- if patch_key in originals[key]:
- raise RuntimeError(f"patch for {field} is already applied")
-
- original_func = getattr(obj, field)
- originals[key][patch_key] = original_func
-
- setattr(obj, field, replacement)
-
- return original_func
-
-
-def undo(key, obj, field):
- """Undoes the peplacement by the patch().
-
- If the function is not replaced, raises an exception.
-
- Arguments:
- key: identifying information for who is doing the replacement. You can use __name__.
- obj: the module or the class
- field: name of the function as a string
-
- Returns:
- Always None
- """
-
- patch_key = (obj, field)
-
- if patch_key not in originals[key]:
- raise RuntimeError(f"there is no patch for {field} to undo")
-
- original_func = originals[key].pop(patch_key)
- setattr(obj, field, original_func)
- return None
-
-
-def original(key, obj, field):
- """Returns the original function for the patch created by the patch() function"""
- patch_key = (obj, field)
-
- return originals[key].get(patch_key, None)
-
-
-originals = defaultdict(dict)
+from collections import defaultdict
+
+
+def patch(key, obj, field, replacement):
+ """Replaces a function in a module or a class.
+
+ Also stores the original function in this module, possible to be retrieved via original(key, obj, field).
+ If the function is already replaced by this caller (key), an exception is raised -- use undo() before that.
+
+ Arguments:
+ key: identifying information for who is doing the replacement. You can use __name__.
+ obj: the module or the class
+ field: name of the function as a string
+ replacement: the new function
+
+ Returns:
+ the original function
+ """
+
+ patch_key = (obj, field)
+ if patch_key in originals[key]:
+ raise RuntimeError(f"patch for {field} is already applied")
+
+ original_func = getattr(obj, field)
+ originals[key][patch_key] = original_func
+
+ setattr(obj, field, replacement)
+
+ return original_func
+
+
+def undo(key, obj, field):
+ """Undoes the peplacement by the patch().
+
+ If the function is not replaced, raises an exception.
+
+ Arguments:
+ key: identifying information for who is doing the replacement. You can use __name__.
+ obj: the module or the class
+ field: name of the function as a string
+
+ Returns:
+ Always None
+ """
+
+ patch_key = (obj, field)
+
+ if patch_key not in originals[key]:
+ raise RuntimeError(f"there is no patch for {field} to undo")
+
+ original_func = originals[key].pop(patch_key)
+ setattr(obj, field, original_func)
+ return None
+
+
+def original(key, obj, field):
+ """Returns the original function for the patch created by the patch() function"""
+ patch_key = (obj, field)
+
+ return originals[key].get(patch_key, None)
+
+
+originals = defaultdict(dict)
diff --git a/modules/paths.py b/modules/paths.py
index 3166005b6..7295f6575 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -1,130 +1,130 @@
-# this module must not have any dependencies as it is a very first import before webui even starts
-import os
-import sys
-import json
-import argparse
-from modules.errors import log
-
-
-# parse args, parse again after we have the data-dir and early-read the config file
-parser = argparse.ArgumentParser(add_help=False)
-parser.add_argument("--ckpt", type=str, default=os.environ.get("SD_MODEL", None), help="Path to model checkpoint to load immediately, default: %(default)s")
-parser.add_argument("--data-dir", type=str, default=os.environ.get("SD_DATADIR", ''), help="Base path where all user data is stored, default: %(default)s")
-parser.add_argument("--models-dir", type=str, default=os.environ.get("SD_MODELSDIR", None), help="Base path where all models are stored, default: %(default)s",)
-cli = parser.parse_known_args()[0]
-parser.add_argument("--config", type=str, default=os.environ.get("SD_CONFIG", os.path.join(cli.data_dir, 'config.json')), help="Use specific server configuration file, default: %(default)s")
-cli = parser.parse_known_args()[0]
-config_path = cli.config if os.path.isabs(cli.config) else os.path.join(cli.data_dir, cli.config)
-try:
- with open(config_path, 'r', encoding='utf8') as f:
- config = json.load(f)
-except Exception:
- config = {}
-
-modules_path = os.path.dirname(os.path.realpath(__file__))
-script_path = os.path.dirname(modules_path)
-data_path = cli.data_dir
-models_config = cli.models_dir or config.get('models_dir') or 'models'
-models_path = models_config if os.path.isabs(models_config) else os.path.join(data_path, models_config)
-extensions_dir = os.path.join(data_path, "extensions")
-extensions_builtin_dir = "extensions-builtin"
-sd_configs_path = os.path.join(script_path, "configs")
-sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
-sd_model_file = cli.ckpt or os.path.join(script_path, 'model.ckpt') # not used
-default_sd_model_file = sd_model_file # not used
-debug = log.trace if os.environ.get('SD_PATH_DEBUG', None) is not None else lambda *args, **kwargs: None
-debug('Trace: PATH')
-paths = {}
-
-if os.environ.get('SD_PATH_DEBUG', None) is not None:
- print(f'Paths: script-path="{script_path}" data-dir="{data_path}" models-dir="{models_path}" config="{config_path}"')
-
-
-def register_paths():
- log.debug('Register paths')
- sys.path.insert(0, script_path)
- sd_path = os.path.join(script_path, 'repositories')
- path_dirs = [
- (sd_path, 'ldm', 'ldm', []),
- (sd_path, 'taming', 'Taming Transformers', []),
- (os.path.join(sd_path, 'blip'), 'models/blip.py', 'BLIP', []),
- (os.path.join(sd_path, 'codeformer'), 'inference_codeformer.py', 'CodeFormer', []),
- (os.path.join(modules_path, 'k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
- ]
- for d, must_exist, what, _options in path_dirs:
- must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
- if not os.path.exists(must_exist_path):
- log.error(f'Required path not found: path={must_exist_path} item={what}')
- else:
- d = os.path.abspath(d)
- sys.path.append(d)
- paths[what] = d
-
-
-def create_path(folder):
- if folder is None or folder == '':
- return
- if os.path.exists(folder):
- return
- try:
- os.makedirs(folder, exist_ok=True)
- log.info(f'Create: folder="{folder}"')
- except Exception as e:
- log.error(f'Create failed: folder="{folder}" {e}')
-
-
-def create_paths(opts):
- def fix_path(folder):
- tgt = opts.data.get(folder, None) or opts.data_labels[folder].default
- if tgt is None or tgt == '':
- return tgt
- fix = tgt
- if not os.path.isabs(tgt) and len(data_path) > 0 and not tgt.startswith(data_path): # path is already relative to data_path
- fix = os.path.join(data_path, fix)
- if fix.startswith('..'):
- fix = os.path.abspath(fix)
- fix = fix if os.path.isabs(fix) else os.path.relpath(fix, script_path)
- opts.data[folder] = fix
- debug(f'Paths: folder="{folder}" original="{tgt}" target="{fix}"')
- return opts.data[folder]
-
- create_path(data_path)
- create_path(script_path)
- create_path(models_path)
- create_path(sd_configs_path)
- create_path(extensions_dir)
- create_path(extensions_builtin_dir)
- create_path(fix_path('temp_dir'))
- create_path(fix_path('ckpt_dir'))
- create_path(fix_path('diffusers_dir'))
- create_path(fix_path('vae_dir'))
- create_path(fix_path('lora_dir'))
- create_path(fix_path('embeddings_dir'))
- create_path(fix_path('hypernetwork_dir'))
- create_path(fix_path('outdir_samples'))
- create_path(fix_path('outdir_txt2img_samples'))
- create_path(fix_path('outdir_img2img_samples'))
- create_path(fix_path('outdir_control_samples'))
- create_path(fix_path('outdir_extras_samples'))
- create_path(fix_path('outdir_init_images'))
- create_path(fix_path('outdir_grids'))
- create_path(fix_path('outdir_txt2img_grids'))
- create_path(fix_path('outdir_img2img_grids'))
- create_path(fix_path('outdir_control_grids'))
- create_path(fix_path('outdir_save'))
- create_path(fix_path('outdir_video'))
- create_path(fix_path('styles_dir'))
-
-
-class Prioritize:
- def __init__(self, name):
- self.name = name
- self.path = None
-
- def __enter__(self):
- self.path = sys.path.copy()
- sys.path = [paths[self.name]] + sys.path
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- sys.path = self.path
- self.path = None
+# this module must not have any dependencies as it is a very first import before webui even starts
+import os
+import sys
+import json
+import argparse
+from modules.errors import log
+
+
+# parse args, parse again after we have the data-dir and early-read the config file
+parser = argparse.ArgumentParser(add_help=False)
+parser.add_argument("--ckpt", type=str, default=os.environ.get("SD_MODEL", None), help="Path to model checkpoint to load immediately, default: %(default)s")
+parser.add_argument("--data-dir", type=str, default=os.environ.get("SD_DATADIR", ''), help="Base path where all user data is stored, default: %(default)s")
+parser.add_argument("--models-dir", type=str, default=os.environ.get("SD_MODELSDIR", None), help="Base path where all models are stored, default: %(default)s",)
+cli = parser.parse_known_args()[0]
+parser.add_argument("--config", type=str, default=os.environ.get("SD_CONFIG", os.path.join(cli.data_dir, 'config.json')), help="Use specific server configuration file, default: %(default)s")
+cli = parser.parse_known_args()[0]
+config_path = cli.config if os.path.isabs(cli.config) else os.path.join(cli.data_dir, cli.config)
+try:
+ with open(config_path, 'r', encoding='utf8') as f:
+ config = json.load(f)
+except Exception:
+ config = {}
+
+modules_path = os.path.dirname(os.path.realpath(__file__))
+script_path = os.path.dirname(modules_path)
+data_path = cli.data_dir
+models_config = cli.models_dir or config.get('models_dir') or 'models'
+models_path = models_config if os.path.isabs(models_config) else os.path.join(data_path, models_config)
+extensions_dir = os.path.join(data_path, "extensions")
+extensions_builtin_dir = "extensions-builtin"
+sd_configs_path = os.path.join(script_path, "configs")
+sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
+sd_model_file = cli.ckpt or os.path.join(script_path, 'model.ckpt') # not used
+default_sd_model_file = sd_model_file # not used
+debug = log.trace if os.environ.get('SD_PATH_DEBUG', None) is not None else lambda *args, **kwargs: None
+debug('Trace: PATH')
+paths = {}
+
+if os.environ.get('SD_PATH_DEBUG', None) is not None:
+ print(f'Paths: script-path="{script_path}" data-dir="{data_path}" models-dir="{models_path}" config="{config_path}"')
+
+
+def register_paths():
+ log.debug('Register paths')
+ sys.path.insert(0, script_path)
+ sd_path = os.path.join(script_path, 'repositories')
+ path_dirs = [
+ (sd_path, 'ldm', 'ldm', []),
+ (sd_path, 'taming', 'Taming Transformers', []),
+ (os.path.join(sd_path, 'blip'), 'models/blip.py', 'BLIP', []),
+ (os.path.join(sd_path, 'codeformer'), 'inference_codeformer.py', 'CodeFormer', []),
+ (os.path.join(modules_path, 'k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
+ ]
+ for d, must_exist, what, _options in path_dirs:
+ must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
+ if not os.path.exists(must_exist_path):
+ log.error(f'Required path not found: path={must_exist_path} item={what}')
+ else:
+ d = os.path.abspath(d)
+ sys.path.append(d)
+ paths[what] = d
+
+
+def create_path(folder):
+ if folder is None or folder == '':
+ return
+ if os.path.exists(folder):
+ return
+ try:
+ os.makedirs(folder, exist_ok=True)
+ log.info(f'Create: folder="{folder}"')
+ except Exception as e:
+ log.error(f'Create failed: folder="{folder}" {e}')
+
+
+def create_paths(opts):
+ def fix_path(folder):
+ tgt = opts.data.get(folder, None) or opts.data_labels[folder].default
+ if tgt is None or tgt == '':
+ return tgt
+ fix = tgt
+ if not os.path.isabs(tgt) and len(data_path) > 0 and not tgt.startswith(data_path): # path is already relative to data_path
+ fix = os.path.join(data_path, fix)
+ if fix.startswith('..'):
+ fix = os.path.abspath(fix)
+ fix = fix if os.path.isabs(fix) else os.path.relpath(fix, script_path)
+ opts.data[folder] = fix
+ debug(f'Paths: folder="{folder}" original="{tgt}" target="{fix}"')
+ return opts.data[folder]
+
+ create_path(data_path)
+ create_path(script_path)
+ create_path(models_path)
+ create_path(sd_configs_path)
+ create_path(extensions_dir)
+ create_path(extensions_builtin_dir)
+ create_path(fix_path('temp_dir'))
+ create_path(fix_path('ckpt_dir'))
+ create_path(fix_path('diffusers_dir'))
+ create_path(fix_path('vae_dir'))
+ create_path(fix_path('lora_dir'))
+ create_path(fix_path('embeddings_dir'))
+ create_path(fix_path('hypernetwork_dir'))
+ create_path(fix_path('outdir_samples'))
+ create_path(fix_path('outdir_txt2img_samples'))
+ create_path(fix_path('outdir_img2img_samples'))
+ create_path(fix_path('outdir_control_samples'))
+ create_path(fix_path('outdir_extras_samples'))
+ create_path(fix_path('outdir_init_images'))
+ create_path(fix_path('outdir_grids'))
+ create_path(fix_path('outdir_txt2img_grids'))
+ create_path(fix_path('outdir_img2img_grids'))
+ create_path(fix_path('outdir_control_grids'))
+ create_path(fix_path('outdir_save'))
+ create_path(fix_path('outdir_video'))
+ create_path(fix_path('styles_dir'))
+
+
+class Prioritize:
+ def __init__(self, name):
+ self.name = name
+ self.path = None
+
+ def __enter__(self):
+ self.path = sys.path.copy()
+ sys.path = [paths[self.name]] + sys.path
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ sys.path = self.path
+ self.path = None
diff --git a/modules/paths_internal.py b/modules/paths_internal.py
index 49a055caa..3a408329d 100644
--- a/modules/paths_internal.py
+++ b/modules/paths_internal.py
@@ -1,30 +1,30 @@
-# no longer used, all paths are defined in paths.py
-
-from modules.paths import modules_path, script_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, data_path, models_path, extensions_dir, extensions_builtin_dir # pylint: disable=unused-import
-
-"""
-import argparse
-import os
-
-modules_path = os.path.dirname(os.path.realpath(__file__))
-script_path = os.path.dirname(modules_path)
-sd_configs_path = os.path.join(script_path, "configs")
-sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
-
-# Parse the --data-dir flag first so we can use it as a base for our other argument default values
-parser_pre = argparse.ArgumentParser(add_help=False)
-parser_pre.add_argument("--ckpt", type=str, default=os.environ.get("SD_MODEL", None), help="Path to model checkpoint to load immediately, default: %(default)s")
-parser_pre.add_argument("--data-dir", type=str, default=os.environ.get("SD_DATADIR", ''), help="Base path where all user data is stored, default: %(default)s")
-parser_pre.add_argument("--models-dir", type=str, default=os.environ.get("SD_MODELSDIR", 'models'), help="Base path where all models are stored, default: %(default)s",)
-cmd_opts_pre = parser_pre.parse_known_args()[0]
-
-# parser_pre.add_argument("--config", type=str, default=os.environ.get("SD_CONFIG", os.path.join(data_path, 'config.json')), help="Use specific server configuration file, default: %(default)s")
-
-data_path = cmd_opts_pre.data_dir
-models_path = cmd_opts_pre.models_dir if os.path.isabs(cmd_opts_pre.models_dir) else os.path.join(data_path, cmd_opts_pre.models_dir)
-extensions_dir = os.path.join(data_path, "extensions")
-extensions_builtin_dir = "extensions-builtin"
-
-sd_model_file = cmd_opts_pre.ckpt or os.path.join(script_path, 'model.ckpt') # not used
-default_sd_model_file = sd_model_file # not used
-"""
+# no longer used, all paths are defined in paths.py
+
+from modules.paths import modules_path, script_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, data_path, models_path, extensions_dir, extensions_builtin_dir # pylint: disable=unused-import
+
+"""
+import argparse
+import os
+
+modules_path = os.path.dirname(os.path.realpath(__file__))
+script_path = os.path.dirname(modules_path)
+sd_configs_path = os.path.join(script_path, "configs")
+sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
+
+# Parse the --data-dir flag first so we can use it as a base for our other argument default values
+parser_pre = argparse.ArgumentParser(add_help=False)
+parser_pre.add_argument("--ckpt", type=str, default=os.environ.get("SD_MODEL", None), help="Path to model checkpoint to load immediately, default: %(default)s")
+parser_pre.add_argument("--data-dir", type=str, default=os.environ.get("SD_DATADIR", ''), help="Base path where all user data is stored, default: %(default)s")
+parser_pre.add_argument("--models-dir", type=str, default=os.environ.get("SD_MODELSDIR", 'models'), help="Base path where all models are stored, default: %(default)s",)
+cmd_opts_pre = parser_pre.parse_known_args()[0]
+
+# parser_pre.add_argument("--config", type=str, default=os.environ.get("SD_CONFIG", os.path.join(data_path, 'config.json')), help="Use specific server configuration file, default: %(default)s")
+
+data_path = cmd_opts_pre.data_dir
+models_path = cmd_opts_pre.models_dir if os.path.isabs(cmd_opts_pre.models_dir) else os.path.join(data_path, cmd_opts_pre.models_dir)
+extensions_dir = os.path.join(data_path, "extensions")
+extensions_builtin_dir = "extensions-builtin"
+
+sd_model_file = cmd_opts_pre.ckpt or os.path.join(script_path, 'model.ckpt') # not used
+default_sd_model_file = sd_model_file # not used
+"""
diff --git a/modules/postprocess/codeformer_model.py b/modules/postprocess/codeformer_model.py
index fcf938081..59a990ce2 100644
--- a/modules/postprocess/codeformer_model.py
+++ b/modules/postprocess/codeformer_model.py
@@ -1,111 +1,111 @@
-import os
-import cv2
-import torch
-import modules.face_restoration
-from modules import shared, devices, modelloader, errors
-from modules.paths import models_path
-
-# codeformer people made a choice to include modified basicsr library to their project which makes
-# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
-# I am making a choice to include some files from codeformer to work around this issue.
-model_dir = "Codeformer"
-model_path = os.path.join(models_path, model_dir)
-model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-
-have_codeformer = False
-codeformer = None
-
-
-def setup_model(dirname):
- if not os.path.exists(model_path):
- os.makedirs(model_path)
- path = modules.paths.paths.get("CodeFormer", None)
- if path is None:
- return
-
- try:
- class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
- def name(self):
- return "CodeFormer"
-
- def __init__(self, dirname):
- self.net = None
- self.face_helper = None
- self.cmd_dir = dirname
-
- def create_models(self):
- from modules.postprocess.codeformer_arch import CodeFormer
- from facelib.utils.face_restoration_helper import FaceRestoreHelper
- from facelib.detection.retinaface import retinaface
- if self.net is not None and self.face_helper is not None:
- self.net.to(devices.device_codeformer)
- return self.net, self.face_helper
- model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
- if len(model_paths) != 0:
- ckpt_path = model_paths[0]
- else:
- shared.log.error(f"Model failed loading: type=CodeFormer model={model_path}")
- return None, None
- net = CodeFormer(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
- checkpoint = torch.load(ckpt_path)['params_ema']
- net.load_state_dict(checkpoint)
- net.eval()
- shared.log.info(f"Model loaded: type=CodeFormer model={ckpt_path}")
- if hasattr(retinaface, 'device'):
- retinaface.device = devices.device_codeformer
- face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
- self.net = net
- self.face_helper = face_helper
- return net, face_helper
-
- def send_model_to(self, device):
- self.net.to(device)
- self.face_helper.face_det.to(device) # pylint: disable=no-member
- self.face_helper.face_parse.to(device)
-
- def restore(self, np_image, w=None):
- from torchvision.transforms.functional import normalize
- from basicsr.utils import img2tensor, tensor2img
- np_image = np_image[:, :, ::-1]
- original_resolution = np_image.shape[0:2]
- self.create_models()
- if self.net is None or self.face_helper is None:
- return np_image
- self.send_model_to(devices.device_codeformer)
- self.face_helper.clean_all()
- self.face_helper.read_image(np_image)
- self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
- self.face_helper.align_warp_face()
- for cropped_face in self.face_helper.cropped_faces:
- cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
- normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
- cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
- try:
- with devices.inference_context():
- output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0] # pylint: disable=not-callable
- restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
- del output
- devices.torch_gc()
- except Exception as e:
- shared.log.error(f'CodeForomer error: {e}')
- restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
- restored_face = restored_face.astype('uint8')
- self.face_helper.add_restored_face(restored_face)
- self.face_helper.get_inverse_affine(None)
- restored_img = self.face_helper.paste_faces_to_input_image()
- restored_img = restored_img[:, :, ::-1]
- if original_resolution != restored_img.shape[0:2]:
- restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
- self.face_helper.clean_all()
- if shared.opts.face_restoration_unload:
- self.send_model_to(devices.cpu)
- return restored_img
-
- global have_codeformer # pylint: disable=global-statement
- have_codeformer = True
- global codeformer # pylint: disable=global-statement
- codeformer = FaceRestorerCodeFormer(dirname)
- shared.face_restorers.append(codeformer)
-
- except Exception as e:
- errors.display(e, 'codeformer')
+import os
+import cv2
+import torch
+import modules.face_restoration
+from modules import shared, devices, modelloader, errors
+from modules.paths import models_path
+
+# codeformer people made a choice to include modified basicsr library to their project which makes
+# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
+# I am making a choice to include some files from codeformer to work around this issue.
+model_dir = "Codeformer"
+model_path = os.path.join(models_path, model_dir)
+model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
+
+have_codeformer = False
+codeformer = None
+
+
+def setup_model(dirname):
+ if not os.path.exists(model_path):
+ os.makedirs(model_path)
+ path = modules.paths.paths.get("CodeFormer", None)
+ if path is None:
+ return
+
+ try:
+ class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
+ def name(self):
+ return "CodeFormer"
+
+ def __init__(self, dirname):
+ self.net = None
+ self.face_helper = None
+ self.cmd_dir = dirname
+
+ def create_models(self):
+ from modules.postprocess.codeformer_arch import CodeFormer
+ from facelib.utils.face_restoration_helper import FaceRestoreHelper
+ from facelib.detection.retinaface import retinaface
+ if self.net is not None and self.face_helper is not None:
+ self.net.to(devices.device_codeformer)
+ return self.net, self.face_helper
+ model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
+ if len(model_paths) != 0:
+ ckpt_path = model_paths[0]
+ else:
+ shared.log.error(f"Model failed loading: type=CodeFormer model={model_path}")
+ return None, None
+ net = CodeFormer(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
+ checkpoint = torch.load(ckpt_path)['params_ema']
+ net.load_state_dict(checkpoint)
+ net.eval()
+ shared.log.info(f"Model loaded: type=CodeFormer model={ckpt_path}")
+ if hasattr(retinaface, 'device'):
+ retinaface.device = devices.device_codeformer
+ face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
+ self.net = net
+ self.face_helper = face_helper
+ return net, face_helper
+
+ def send_model_to(self, device):
+ self.net.to(device)
+ self.face_helper.face_det.to(device) # pylint: disable=no-member
+ self.face_helper.face_parse.to(device)
+
+ def restore(self, np_image, w=None):
+ from torchvision.transforms.functional import normalize
+ from basicsr.utils import img2tensor, tensor2img
+ np_image = np_image[:, :, ::-1]
+ original_resolution = np_image.shape[0:2]
+ self.create_models()
+ if self.net is None or self.face_helper is None:
+ return np_image
+ self.send_model_to(devices.device_codeformer)
+ self.face_helper.clean_all()
+ self.face_helper.read_image(np_image)
+ self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
+ self.face_helper.align_warp_face()
+ for cropped_face in self.face_helper.cropped_faces:
+ cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+ normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+ cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
+ try:
+ with devices.inference_context():
+ output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0] # pylint: disable=not-callable
+ restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+ del output
+ devices.torch_gc()
+ except Exception as e:
+ shared.log.error(f'CodeForomer error: {e}')
+ restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+ restored_face = restored_face.astype('uint8')
+ self.face_helper.add_restored_face(restored_face)
+ self.face_helper.get_inverse_affine(None)
+ restored_img = self.face_helper.paste_faces_to_input_image()
+ restored_img = restored_img[:, :, ::-1]
+ if original_resolution != restored_img.shape[0:2]:
+ restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
+ self.face_helper.clean_all()
+ if shared.opts.face_restoration_unload:
+ self.send_model_to(devices.cpu)
+ return restored_img
+
+ global have_codeformer # pylint: disable=global-statement
+ have_codeformer = True
+ global codeformer # pylint: disable=global-statement
+ codeformer = FaceRestorerCodeFormer(dirname)
+ shared.face_restorers.append(codeformer)
+
+ except Exception as e:
+ errors.display(e, 'codeformer')
diff --git a/modules/postprocess/esrgan_model.py b/modules/postprocess/esrgan_model.py
index 29a4cc8a2..c4d3e4ad4 100644
--- a/modules/postprocess/esrgan_model.py
+++ b/modules/postprocess/esrgan_model.py
@@ -1,217 +1,217 @@
-import numpy as np
-import torch
-from PIL import Image
-from rich.progress import Progress, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn, TimeElapsedColumn
-import modules.postprocess.esrgan_model_arch as arch
-from modules import images, devices
-from modules.upscaler import Upscaler, UpscalerData, compile_upscaler
-from modules.shared import opts, log, console
-
-
-def mod2normal(state_dict):
- # this code is copied from https://github.com/victorca25/iNNfer
- if 'conv_first.weight' in state_dict:
- crt_net = {}
- items = list(state_dict)
-
- crt_net['model.0.weight'] = state_dict['conv_first.weight']
- crt_net['model.0.bias'] = state_dict['conv_first.bias']
-
- for k in items.copy():
- if 'RDB' in k:
- ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
- if '.weight' in k:
- ori_k = ori_k.replace('.weight', '.0.weight')
- elif '.bias' in k:
- ori_k = ori_k.replace('.bias', '.0.bias')
- crt_net[ori_k] = state_dict[k]
- items.remove(k)
-
- crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
- crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
- crt_net['model.3.weight'] = state_dict['upconv1.weight']
- crt_net['model.3.bias'] = state_dict['upconv1.bias']
- crt_net['model.6.weight'] = state_dict['upconv2.weight']
- crt_net['model.6.bias'] = state_dict['upconv2.bias']
- crt_net['model.8.weight'] = state_dict['HRconv.weight']
- crt_net['model.8.bias'] = state_dict['HRconv.bias']
- crt_net['model.10.weight'] = state_dict['conv_last.weight']
- crt_net['model.10.bias'] = state_dict['conv_last.bias']
- state_dict = crt_net
- return state_dict
-
-
-def resrgan2normal(state_dict, nb=23):
- # this code is copied from https://github.com/victorca25/iNNfer
- if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
- re8x = 0
- crt_net = {}
- items = list(state_dict)
-
- crt_net['model.0.weight'] = state_dict['conv_first.weight']
- crt_net['model.0.bias'] = state_dict['conv_first.bias']
-
- for k in items.copy():
- if "rdb" in k:
- ori_k = k.replace('body.', 'model.1.sub.')
- ori_k = ori_k.replace('.rdb', '.RDB')
- if '.weight' in k:
- ori_k = ori_k.replace('.weight', '.0.weight')
- elif '.bias' in k:
- ori_k = ori_k.replace('.bias', '.0.bias')
- crt_net[ori_k] = state_dict[k]
- items.remove(k)
-
- crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
- crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
- crt_net['model.3.weight'] = state_dict['conv_up1.weight']
- crt_net['model.3.bias'] = state_dict['conv_up1.bias']
- crt_net['model.6.weight'] = state_dict['conv_up2.weight']
- crt_net['model.6.bias'] = state_dict['conv_up2.bias']
-
- if 'conv_up3.weight' in state_dict:
- # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
- re8x = 3
- crt_net['model.9.weight'] = state_dict['conv_up3.weight']
- crt_net['model.9.bias'] = state_dict['conv_up3.bias']
-
- crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
- crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
- crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
- crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
-
- state_dict = crt_net
- return state_dict
-
-
-def infer_params(state_dict):
- # this code is copied from https://github.com/victorca25/iNNfer
- scale2x = 0
- scalemin = 6
- n_uplayer = 0
- plus = False
-
- for block in list(state_dict):
- parts = block.split(".")
- n_parts = len(parts)
- if n_parts == 5 and parts[2] == "sub":
- nb = int(parts[3])
- elif n_parts == 3:
- part_num = int(parts[1])
- if (part_num > scalemin
- and parts[0] == "model"
- and parts[2] == "weight"):
- scale2x += 1
- if part_num > n_uplayer:
- n_uplayer = part_num
- out_nc = state_dict[block].shape[0]
- if not plus and "conv1x1" in block:
- plus = True
-
- nf = state_dict["model.0.weight"].shape[0]
- in_nc = state_dict["model.0.weight"].shape[1]
- # out_nc = out_nc
- scale = 2 ** scale2x
-
- return in_nc, out_nc, nf, nb, plus, scale
-
-
-class UpscalerESRGAN(Upscaler):
- def __init__(self, dirname):
- self.name = "ESRGAN"
- self.user_path = dirname
- super().__init__()
- self.scalers = self.find_scalers()
- self.models = {}
-
- def do_upscale(self, img, selected_model):
- model = self.load_model(selected_model)
- if model is None:
- return img
- model.to(devices.device_esrgan)
- img = esrgan_upscale(model, img)
- if opts.upscaler_unload and selected_model in self.models:
- del self.models[selected_model]
- log.debug(f"Upscaler unloaded: type={self.name} model={selected_model}")
- devices.torch_gc(force=True)
- return img
-
- def load_model(self, path: str):
- info: UpscalerData = self.find_model(path)
- if info is None:
- return
- if self.models.get(info.local_data_path, None) is not None:
- log.debug(f"Upscaler cached: type={self.name} model={info.local_data_path}")
- return self.models[info.local_data_path]
- state_dict = torch.load(info.local_data_path, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
- log.info(f"Upscaler loaded: type={self.name} model={info.local_data_path}")
-
- if "params_ema" in state_dict:
- state_dict = state_dict["params_ema"]
- elif "params" in state_dict:
- state_dict = state_dict["params"]
- num_conv = 16 if "realesr-animevideov3" in info.local_data_path else 32
- model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
- model.load_state_dict(state_dict)
- model.eval()
- model = compile_upscaler(model, name=self.name)
- self.models[info.local_data_path] = model
- return self.models[info.local_data_path]
-
- if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
- nb = 6 if "RealESRGAN_x4plus_anime_6B" in info.local_data_path else 23
- state_dict = resrgan2normal(state_dict, nb)
- elif "conv_first.weight" in state_dict:
- state_dict = mod2normal(state_dict)
- elif "model.0.weight" not in state_dict:
- raise TypeError("The file is not a recognized ESRGAN model.")
- in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
- model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
- model.load_state_dict(state_dict)
- model.eval()
- model = compile_upscaler(model, name=self.name)
- self.models[info.local_data_path] = model
- return self.models[info.local_data_path]
-
-
-def upscale_without_tiling(model, img):
- img = np.array(img)
- img = img[:, :, ::-1]
- img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
- img = torch.from_numpy(img).float()
- img = img.unsqueeze(0).to(devices.device_esrgan)
- with devices.inference_context():
- output = model(img)
- output = output.squeeze().float().cpu().clamp_(0, 1).detach().numpy()
- output = 255. * np.moveaxis(output, 0, 2)
- output = output.astype(np.uint8)
- output = output[:, :, ::-1]
- return Image.fromarray(output, 'RGB')
-
-
-def esrgan_upscale(model, img):
- if opts.upscaler_tile_size == 0:
- return upscale_without_tiling(model, img)
-
- grid = images.split_grid(img, opts.upscaler_tile_size, opts.upscaler_tile_size, opts.upscaler_tile_overlap)
- newtiles = []
- scale_factor = 1
-
- with Progress(TextColumn('[cyan]{task.description}'), BarColumn(), TaskProgressColumn(), TimeRemainingColumn(), TimeElapsedColumn(), console=console) as progress:
- total = 0
- for _y, _h, row in grid.tiles:
- total += len(row)
- task = progress.add_task(description="Upscaling", total=total)
- for y, h, row in grid.tiles:
- newrow = []
- for tiledata in row:
- x, w, tile = tiledata
- output = upscale_without_tiling(model, tile)
- scale_factor = output.width // tile.width
- newrow.append([x * scale_factor, w * scale_factor, output])
- progress.update(task, advance=1, description="Upscaling")
- newtiles.append([y * scale_factor, h * scale_factor, newrow])
-
- newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
- output = images.combine_grid(newgrid)
- return output
+import numpy as np
+import torch
+from PIL import Image
+from rich.progress import Progress, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn, TimeElapsedColumn
+import modules.postprocess.esrgan_model_arch as arch
+from modules import images, devices
+from modules.upscaler import Upscaler, UpscalerData, compile_upscaler
+from modules.shared import opts, log, console
+
+
+def mod2normal(state_dict):
+ # this code is copied from https://github.com/victorca25/iNNfer
+ if 'conv_first.weight' in state_dict:
+ crt_net = {}
+ items = list(state_dict)
+
+ crt_net['model.0.weight'] = state_dict['conv_first.weight']
+ crt_net['model.0.bias'] = state_dict['conv_first.bias']
+
+ for k in items.copy():
+ if 'RDB' in k:
+ ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+ if '.weight' in k:
+ ori_k = ori_k.replace('.weight', '.0.weight')
+ elif '.bias' in k:
+ ori_k = ori_k.replace('.bias', '.0.bias')
+ crt_net[ori_k] = state_dict[k]
+ items.remove(k)
+
+ crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
+ crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
+ crt_net['model.3.weight'] = state_dict['upconv1.weight']
+ crt_net['model.3.bias'] = state_dict['upconv1.bias']
+ crt_net['model.6.weight'] = state_dict['upconv2.weight']
+ crt_net['model.6.bias'] = state_dict['upconv2.bias']
+ crt_net['model.8.weight'] = state_dict['HRconv.weight']
+ crt_net['model.8.bias'] = state_dict['HRconv.bias']
+ crt_net['model.10.weight'] = state_dict['conv_last.weight']
+ crt_net['model.10.bias'] = state_dict['conv_last.bias']
+ state_dict = crt_net
+ return state_dict
+
+
+def resrgan2normal(state_dict, nb=23):
+ # this code is copied from https://github.com/victorca25/iNNfer
+ if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
+ re8x = 0
+ crt_net = {}
+ items = list(state_dict)
+
+ crt_net['model.0.weight'] = state_dict['conv_first.weight']
+ crt_net['model.0.bias'] = state_dict['conv_first.bias']
+
+ for k in items.copy():
+ if "rdb" in k:
+ ori_k = k.replace('body.', 'model.1.sub.')
+ ori_k = ori_k.replace('.rdb', '.RDB')
+ if '.weight' in k:
+ ori_k = ori_k.replace('.weight', '.0.weight')
+ elif '.bias' in k:
+ ori_k = ori_k.replace('.bias', '.0.bias')
+ crt_net[ori_k] = state_dict[k]
+ items.remove(k)
+
+ crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
+ crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
+ crt_net['model.3.weight'] = state_dict['conv_up1.weight']
+ crt_net['model.3.bias'] = state_dict['conv_up1.bias']
+ crt_net['model.6.weight'] = state_dict['conv_up2.weight']
+ crt_net['model.6.bias'] = state_dict['conv_up2.bias']
+
+ if 'conv_up3.weight' in state_dict:
+ # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
+ re8x = 3
+ crt_net['model.9.weight'] = state_dict['conv_up3.weight']
+ crt_net['model.9.bias'] = state_dict['conv_up3.bias']
+
+ crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
+ crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
+ crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
+ crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
+
+ state_dict = crt_net
+ return state_dict
+
+
+def infer_params(state_dict):
+ # this code is copied from https://github.com/victorca25/iNNfer
+ scale2x = 0
+ scalemin = 6
+ n_uplayer = 0
+ plus = False
+
+ for block in list(state_dict):
+ parts = block.split(".")
+ n_parts = len(parts)
+ if n_parts == 5 and parts[2] == "sub":
+ nb = int(parts[3])
+ elif n_parts == 3:
+ part_num = int(parts[1])
+ if (part_num > scalemin
+ and parts[0] == "model"
+ and parts[2] == "weight"):
+ scale2x += 1
+ if part_num > n_uplayer:
+ n_uplayer = part_num
+ out_nc = state_dict[block].shape[0]
+ if not plus and "conv1x1" in block:
+ plus = True
+
+ nf = state_dict["model.0.weight"].shape[0]
+ in_nc = state_dict["model.0.weight"].shape[1]
+ # out_nc = out_nc
+ scale = 2 ** scale2x
+
+ return in_nc, out_nc, nf, nb, plus, scale
+
+
+class UpscalerESRGAN(Upscaler):
+ def __init__(self, dirname):
+ self.name = "ESRGAN"
+ self.user_path = dirname
+ super().__init__()
+ self.scalers = self.find_scalers()
+ self.models = {}
+
+ def do_upscale(self, img, selected_model):
+ model = self.load_model(selected_model)
+ if model is None:
+ return img
+ model.to(devices.device_esrgan)
+ img = esrgan_upscale(model, img)
+ if opts.upscaler_unload and selected_model in self.models:
+ del self.models[selected_model]
+ log.debug(f"Upscaler unloaded: type={self.name} model={selected_model}")
+ devices.torch_gc(force=True)
+ return img
+
+ def load_model(self, path: str):
+ info: UpscalerData = self.find_model(path)
+ if info is None:
+ return
+ if self.models.get(info.local_data_path, None) is not None:
+ log.debug(f"Upscaler cached: type={self.name} model={info.local_data_path}")
+ return self.models[info.local_data_path]
+ state_dict = torch.load(info.local_data_path, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
+ log.info(f"Upscaler loaded: type={self.name} model={info.local_data_path}")
+
+ if "params_ema" in state_dict:
+ state_dict = state_dict["params_ema"]
+ elif "params" in state_dict:
+ state_dict = state_dict["params"]
+ num_conv = 16 if "realesr-animevideov3" in info.local_data_path else 32
+ model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
+ model.load_state_dict(state_dict)
+ model.eval()
+ model = compile_upscaler(model, name=self.name)
+ self.models[info.local_data_path] = model
+ return self.models[info.local_data_path]
+
+ if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
+ nb = 6 if "RealESRGAN_x4plus_anime_6B" in info.local_data_path else 23
+ state_dict = resrgan2normal(state_dict, nb)
+ elif "conv_first.weight" in state_dict:
+ state_dict = mod2normal(state_dict)
+ elif "model.0.weight" not in state_dict:
+ raise TypeError("The file is not a recognized ESRGAN model.")
+ in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
+ model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
+ model.load_state_dict(state_dict)
+ model.eval()
+ model = compile_upscaler(model, name=self.name)
+ self.models[info.local_data_path] = model
+ return self.models[info.local_data_path]
+
+
+def upscale_without_tiling(model, img):
+ img = np.array(img)
+ img = img[:, :, ::-1]
+ img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
+ img = torch.from_numpy(img).float()
+ img = img.unsqueeze(0).to(devices.device_esrgan)
+ with devices.inference_context():
+ output = model(img)
+ output = output.squeeze().float().cpu().clamp_(0, 1).detach().numpy()
+ output = 255. * np.moveaxis(output, 0, 2)
+ output = output.astype(np.uint8)
+ output = output[:, :, ::-1]
+ return Image.fromarray(output, 'RGB')
+
+
+def esrgan_upscale(model, img):
+ if opts.upscaler_tile_size == 0:
+ return upscale_without_tiling(model, img)
+
+ grid = images.split_grid(img, opts.upscaler_tile_size, opts.upscaler_tile_size, opts.upscaler_tile_overlap)
+ newtiles = []
+ scale_factor = 1
+
+ with Progress(TextColumn('[cyan]{task.description}'), BarColumn(), TaskProgressColumn(), TimeRemainingColumn(), TimeElapsedColumn(), console=console) as progress:
+ total = 0
+ for _y, _h, row in grid.tiles:
+ total += len(row)
+ task = progress.add_task(description="Upscaling", total=total)
+ for y, h, row in grid.tiles:
+ newrow = []
+ for tiledata in row:
+ x, w, tile = tiledata
+ output = upscale_without_tiling(model, tile)
+ scale_factor = output.width // tile.width
+ newrow.append([x * scale_factor, w * scale_factor, output])
+ progress.update(task, advance=1, description="Upscaling")
+ newtiles.append([y * scale_factor, h * scale_factor, newrow])
+
+ newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
+ output = images.combine_grid(newgrid)
+ return output
diff --git a/modules/postprocess/esrgan_model_arch.py b/modules/postprocess/esrgan_model_arch.py
index fb8ed9741..bf9f0ac6e 100644
--- a/modules/postprocess/esrgan_model_arch.py
+++ b/modules/postprocess/esrgan_model_arch.py
@@ -1,465 +1,465 @@
-# this file is adapted from https://github.com/victorca25/iNNfer
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-####################
-# RRDBNet Generator
-####################
-
-class RRDBNet(nn.Module):
- def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
- act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
- finalact=None, gaussian_noise=False, plus=False):
- super(RRDBNet, self).__init__()
- n_upscale = int(math.log(upscale, 2))
- if upscale == 3:
- n_upscale = 1
-
- self.resrgan_scale = 0
- if in_nc % 16 == 0:
- self.resrgan_scale = 1
- elif in_nc != 4 and in_nc % 4 == 0:
- self.resrgan_scale = 2
-
- fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
- rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
- norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
- gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
- LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
-
- if upsample_mode == 'upconv':
- upsample_block = upconv_block
- elif upsample_mode == 'pixelshuffle':
- upsample_block = pixelshuffle_block
- else:
- raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
- if upscale == 3:
- upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
- else:
- upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
- HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
- HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
-
- outact = act(finalact) if finalact else None
-
- self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
- *upsampler, HR_conv0, HR_conv1, outact)
-
- def forward(self, x, outm=None):
- if self.resrgan_scale == 1:
- feat = pixel_unshuffle(x, scale=4)
- elif self.resrgan_scale == 2:
- feat = pixel_unshuffle(x, scale=2)
- else:
- feat = x
-
- return self.model(feat)
-
-
-class RRDB(nn.Module):
- """
- Residual in Residual Dense Block
- (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
- """
-
- def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
- norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
- spectral_norm=False, gaussian_noise=False, plus=False):
- super(RRDB, self).__init__()
- # This is for backwards compatibility with existing models
- if nr == 3:
- self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
- norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
- gaussian_noise=gaussian_noise, plus=plus)
- self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
- norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
- gaussian_noise=gaussian_noise, plus=plus)
- self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
- norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
- gaussian_noise=gaussian_noise, plus=plus)
- else:
- RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
- norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
- gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
- self.RDBs = nn.Sequential(*RDB_list)
-
- def forward(self, x):
- if hasattr(self, 'RDB1'):
- out = self.RDB1(x)
- out = self.RDB2(out)
- out = self.RDB3(out)
- else:
- out = self.RDBs(x)
- return out * 0.2 + x
-
-
-class ResidualDenseBlock_5C(nn.Module):
- """
- Residual Dense Block
- The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
- Modified options that can be used:
- - "Partial Convolution based Padding" arXiv:1811.11718
- - "Spectral normalization" arXiv:1802.05957
- - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
- {Rakotonirina} and A. {Rasoanaivo}
- """
-
- def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
- norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
- spectral_norm=False, gaussian_noise=False, plus=False):
- super(ResidualDenseBlock_5C, self).__init__()
-
- self.noise = GaussianNoise() if gaussian_noise else None
- self.conv1x1 = conv1x1(nf, gc) if plus else None
-
- self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
- self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
- self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
- self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
- if mode == 'CNA':
- last_act = None
- else:
- last_act = act_type
- self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv2(torch.cat((x, x1), 1))
- if self.conv1x1:
- x2 = x2 + self.conv1x1(x)
- x3 = self.conv3(torch.cat((x, x1, x2), 1))
- x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
- if self.conv1x1:
- x4 = x4 + x2
- x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
- if self.noise:
- return self.noise(x5.mul(0.2) + x)
- else:
- return x5 * 0.2 + x
-
-
-####################
-# ESRGANplus
-####################
-
-class GaussianNoise(nn.Module):
- def __init__(self, sigma=0.1, is_relative_detach=False):
- super().__init__()
- self.sigma = sigma
- self.is_relative_detach = is_relative_detach
- self.noise = torch.tensor(0, dtype=torch.float)
-
- def forward(self, x):
- if self.training and self.sigma != 0:
- self.noise = self.noise.to(x.device)
- scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
- sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
- x = x + sampled_noise
- return x
-
-def conv1x1(in_planes, out_planes, stride=1):
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-####################
-# SRVGGNetCompact
-####################
-
-class SRVGGNetCompact(nn.Module):
- """A compact VGG-style network structure for super-resolution.
- This class is copied from https://github.com/xinntao/Real-ESRGAN
- """
-
- def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
- super(SRVGGNetCompact, self).__init__()
- self.num_in_ch = num_in_ch
- self.num_out_ch = num_out_ch
- self.num_feat = num_feat
- self.num_conv = num_conv
- self.upscale = upscale
- self.act_type = act_type
-
- self.body = nn.ModuleList()
- # the first conv
- self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
- # the first activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the body structure
- for _ in range(num_conv):
- self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
- # activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the last conv
- self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
- # upsample
- self.upsampler = nn.PixelShuffle(upscale)
-
- def forward(self, x):
- out = x
- for i in range(0, len(self.body)):
- out = self.body[i](out)
-
- out = self.upsampler(out)
- # add the nearest upsampled image, so that the network learns the residual
- base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
- out += base
- return out
-
-
-####################
-# Upsampler
-####################
-
-class Upsample(nn.Module):
- r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
- The input data is assumed to be of the form
- `minibatch x channels x [optional depth] x [optional height] x width`.
- """
-
- def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
- super(Upsample, self).__init__()
- if isinstance(scale_factor, tuple):
- self.scale_factor = tuple(float(factor) for factor in scale_factor)
- else:
- self.scale_factor = float(scale_factor) if scale_factor else None
- self.mode = mode
- self.size = size
- self.align_corners = align_corners
-
- def forward(self, x):
- return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
-
- def extra_repr(self):
- if self.scale_factor is not None:
- info = f'scale_factor={self.scale_factor}'
- else:
- info = f'size={self.size}'
- info += f', mode={self.mode}'
- return info
-
-
-def pixel_unshuffle(x, scale):
- """ Pixel unshuffle.
- Args:
- x (Tensor): Input feature with shape (b, c, hh, hw).
- scale (int): Downsample ratio.
- Returns:
- Tensor: the pixel unshuffled feature.
- """
- b, c, hh, hw = x.size()
- out_channel = c * (scale**2)
- assert hh % scale == 0 and hw % scale == 0
- h = hh // scale
- w = hw // scale
- x_view = x.view(b, c, h, scale, w, scale)
- return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-
-def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
- pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
- """
- Pixel shuffle layer
- (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
- Neural Network, CVPR17)
- """
- conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
- pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
- pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
- n = norm(norm_type, out_nc) if norm_type else None
- a = act(act_type) if act_type else None
- return sequential(conv, pixel_shuffle, n, a)
-
-
-def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
- pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
- """ Upconv layer """
- upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
- upsample = Upsample(scale_factor=upscale_factor, mode=mode)
- conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
- pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
- return sequential(upsample, conv)
-
-
-
-
-
-
-
-
-####################
-# Basic blocks
-####################
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
- """Make layers by stacking the same blocks.
- Args:
- basic_block (nn.module): nn.module class for basic block. (block)
- num_basic_block (int): number of blocks. (n_layers)
- Returns:
- nn.Sequential: Stacked blocks in nn.Sequential.
- """
- layers = []
- for _ in range(num_basic_block):
- layers.append(basic_block(**kwarg))
- return nn.Sequential(*layers)
-
-
-def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
- """ activation helper """
- act_type = act_type.lower()
- if act_type == 'relu':
- layer = nn.ReLU(inplace)
- elif act_type in ('leakyrelu', 'lrelu'):
- layer = nn.LeakyReLU(neg_slope, inplace)
- elif act_type == 'prelu':
- layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
- elif act_type == 'tanh': # [-1, 1] range output
- layer = nn.Tanh()
- elif act_type == 'sigmoid': # [0, 1] range output
- layer = nn.Sigmoid()
- else:
- raise NotImplementedError(f'activation layer [{act_type}] is not found')
- return layer
-
-
-class Identity(nn.Module):
- def __init__(self, *kwargs):
- super(Identity, self).__init__()
-
- def forward(self, x, *kwargs):
- return x
-
-
-def norm(norm_type, nc):
- """ Return a normalization layer """
- norm_type = norm_type.lower()
- if norm_type == 'batch':
- layer = nn.BatchNorm2d(nc, affine=True)
- elif norm_type == 'instance':
- layer = nn.InstanceNorm2d(nc, affine=False)
- elif norm_type == 'none':
- def norm_layer(x): return Identity()
- else:
- raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
- return layer
-
-
-def pad(pad_type, padding):
- """ padding layer helper """
- pad_type = pad_type.lower()
- if padding == 0:
- return None
- if pad_type == 'reflect':
- layer = nn.ReflectionPad2d(padding)
- elif pad_type == 'replicate':
- layer = nn.ReplicationPad2d(padding)
- elif pad_type == 'zero':
- layer = nn.ZeroPad2d(padding)
- else:
- raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
- return layer
-
-
-def get_valid_padding(kernel_size, dilation):
- kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
- padding = (kernel_size - 1) // 2
- return padding
-
-
-class ShortcutBlock(nn.Module):
- """ Elementwise sum the output of a submodule to its input """
- def __init__(self, submodule):
- super(ShortcutBlock, self).__init__()
- self.sub = submodule
-
- def forward(self, x):
- output = x + self.sub(x)
- return output
-
- def __repr__(self):
- return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
-
-
-def sequential(*args):
- """ Flatten Sequential. It unwraps nn.Sequential. """
- if len(args) == 1:
- from collections import OrderedDict
- if isinstance(args[0], OrderedDict):
- raise NotImplementedError('sequential does not support OrderedDict input.')
- return args[0] # No sequential is needed.
- modules = []
- for module in args:
- if isinstance(module, nn.Sequential):
- for submodule in module.children():
- modules.append(submodule)
- elif isinstance(module, nn.Module):
- modules.append(module)
- return nn.Sequential(*modules)
-
-
-def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
- pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
- spectral_norm=False):
- """ Conv layer with padding, normalization, activation """
- assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
- padding = get_valid_padding(kernel_size, dilation)
- p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
- padding = padding if pad_type == 'zero' else 0
-
- if convtype=='PartialConv2D':
- from torchvision.ops import PartialConv2d
- c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
- dilation=dilation, bias=bias, groups=groups)
- elif convtype=='DeformConv2D':
- from torchvision.ops import DeformConv2d
- c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
- dilation=dilation, bias=bias, groups=groups)
- elif convtype=='Conv3D':
- c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
- dilation=dilation, bias=bias, groups=groups)
- else:
- c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
- dilation=dilation, bias=bias, groups=groups)
-
- if spectral_norm:
- c = nn.utils.spectral_norm(c)
-
- a = act(act_type) if act_type else None
- if 'CNA' in mode:
- n = norm(norm_type, out_nc) if norm_type else None
- return sequential(p, c, n, a)
- elif mode == 'NAC':
- if norm_type is None and act_type is not None:
- a = act(act_type, inplace=False)
- n = norm(norm_type, in_nc) if norm_type else None
- return sequential(n, a, p, c)
+# this file is adapted from https://github.com/victorca25/iNNfer
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+####################
+# RRDBNet Generator
+####################
+
+class RRDBNet(nn.Module):
+ def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
+ act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
+ finalact=None, gaussian_noise=False, plus=False):
+ super(RRDBNet, self).__init__()
+ n_upscale = int(math.log(upscale, 2))
+ if upscale == 3:
+ n_upscale = 1
+
+ self.resrgan_scale = 0
+ if in_nc % 16 == 0:
+ self.resrgan_scale = 1
+ elif in_nc != 4 and in_nc % 4 == 0:
+ self.resrgan_scale = 2
+
+ fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
+ rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+ norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
+ gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
+ LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
+
+ if upsample_mode == 'upconv':
+ upsample_block = upconv_block
+ elif upsample_mode == 'pixelshuffle':
+ upsample_block = pixelshuffle_block
+ else:
+ raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
+ if upscale == 3:
+ upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
+ else:
+ upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
+ HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
+ HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
+
+ outact = act(finalact) if finalact else None
+
+ self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
+ *upsampler, HR_conv0, HR_conv1, outact)
+
+ def forward(self, x, outm=None):
+ if self.resrgan_scale == 1:
+ feat = pixel_unshuffle(x, scale=4)
+ elif self.resrgan_scale == 2:
+ feat = pixel_unshuffle(x, scale=2)
+ else:
+ feat = x
+
+ return self.model(feat)
+
+
+class RRDB(nn.Module):
+ """
+ Residual in Residual Dense Block
+ (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
+ """
+
+ def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+ norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
+ spectral_norm=False, gaussian_noise=False, plus=False):
+ super(RRDB, self).__init__()
+ # This is for backwards compatibility with existing models
+ if nr == 3:
+ self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+ norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+ gaussian_noise=gaussian_noise, plus=plus)
+ self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+ norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+ gaussian_noise=gaussian_noise, plus=plus)
+ self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+ norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+ gaussian_noise=gaussian_noise, plus=plus)
+ else:
+ RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+ norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+ gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
+ self.RDBs = nn.Sequential(*RDB_list)
+
+ def forward(self, x):
+ if hasattr(self, 'RDB1'):
+ out = self.RDB1(x)
+ out = self.RDB2(out)
+ out = self.RDB3(out)
+ else:
+ out = self.RDBs(x)
+ return out * 0.2 + x
+
+
+class ResidualDenseBlock_5C(nn.Module):
+ """
+ Residual Dense Block
+ The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
+ Modified options that can be used:
+ - "Partial Convolution based Padding" arXiv:1811.11718
+ - "Spectral normalization" arXiv:1802.05957
+ - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
+ {Rakotonirina} and A. {Rasoanaivo}
+ """
+
+ def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+ norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
+ spectral_norm=False, gaussian_noise=False, plus=False):
+ super(ResidualDenseBlock_5C, self).__init__()
+
+ self.noise = GaussianNoise() if gaussian_noise else None
+ self.conv1x1 = conv1x1(nf, gc) if plus else None
+
+ self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+ self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+ self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+ self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+ if mode == 'CNA':
+ last_act = None
+ else:
+ last_act = act_type
+ self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+
+ def forward(self, x):
+ x1 = self.conv1(x)
+ x2 = self.conv2(torch.cat((x, x1), 1))
+ if self.conv1x1:
+ x2 = x2 + self.conv1x1(x)
+ x3 = self.conv3(torch.cat((x, x1, x2), 1))
+ x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
+ if self.conv1x1:
+ x4 = x4 + x2
+ x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+ if self.noise:
+ return self.noise(x5.mul(0.2) + x)
+ else:
+ return x5 * 0.2 + x
+
+
+####################
+# ESRGANplus
+####################
+
+class GaussianNoise(nn.Module):
+ def __init__(self, sigma=0.1, is_relative_detach=False):
+ super().__init__()
+ self.sigma = sigma
+ self.is_relative_detach = is_relative_detach
+ self.noise = torch.tensor(0, dtype=torch.float)
+
+ def forward(self, x):
+ if self.training and self.sigma != 0:
+ self.noise = self.noise.to(x.device)
+ scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
+ sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
+ x = x + sampled_noise
+ return x
+
+def conv1x1(in_planes, out_planes, stride=1):
+ return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+####################
+# SRVGGNetCompact
+####################
+
+class SRVGGNetCompact(nn.Module):
+ """A compact VGG-style network structure for super-resolution.
+ This class is copied from https://github.com/xinntao/Real-ESRGAN
+ """
+
+ def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
+ super(SRVGGNetCompact, self).__init__()
+ self.num_in_ch = num_in_ch
+ self.num_out_ch = num_out_ch
+ self.num_feat = num_feat
+ self.num_conv = num_conv
+ self.upscale = upscale
+ self.act_type = act_type
+
+ self.body = nn.ModuleList()
+ # the first conv
+ self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
+ # the first activation
+ if act_type == 'relu':
+ activation = nn.ReLU(inplace=True)
+ elif act_type == 'prelu':
+ activation = nn.PReLU(num_parameters=num_feat)
+ elif act_type == 'leakyrelu':
+ activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+ self.body.append(activation)
+
+ # the body structure
+ for _ in range(num_conv):
+ self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
+ # activation
+ if act_type == 'relu':
+ activation = nn.ReLU(inplace=True)
+ elif act_type == 'prelu':
+ activation = nn.PReLU(num_parameters=num_feat)
+ elif act_type == 'leakyrelu':
+ activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+ self.body.append(activation)
+
+ # the last conv
+ self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
+ # upsample
+ self.upsampler = nn.PixelShuffle(upscale)
+
+ def forward(self, x):
+ out = x
+ for i in range(0, len(self.body)):
+ out = self.body[i](out)
+
+ out = self.upsampler(out)
+ # add the nearest upsampled image, so that the network learns the residual
+ base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
+ out += base
+ return out
+
+
+####################
+# Upsampler
+####################
+
+class Upsample(nn.Module):
+ r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
+ The input data is assumed to be of the form
+ `minibatch x channels x [optional depth] x [optional height] x width`.
+ """
+
+ def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
+ super(Upsample, self).__init__()
+ if isinstance(scale_factor, tuple):
+ self.scale_factor = tuple(float(factor) for factor in scale_factor)
+ else:
+ self.scale_factor = float(scale_factor) if scale_factor else None
+ self.mode = mode
+ self.size = size
+ self.align_corners = align_corners
+
+ def forward(self, x):
+ return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
+
+ def extra_repr(self):
+ if self.scale_factor is not None:
+ info = f'scale_factor={self.scale_factor}'
+ else:
+ info = f'size={self.size}'
+ info += f', mode={self.mode}'
+ return info
+
+
+def pixel_unshuffle(x, scale):
+ """ Pixel unshuffle.
+ Args:
+ x (Tensor): Input feature with shape (b, c, hh, hw).
+ scale (int): Downsample ratio.
+ Returns:
+ Tensor: the pixel unshuffled feature.
+ """
+ b, c, hh, hw = x.size()
+ out_channel = c * (scale**2)
+ assert hh % scale == 0 and hw % scale == 0
+ h = hh // scale
+ w = hw // scale
+ x_view = x.view(b, c, h, scale, w, scale)
+ return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
+
+
+def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+ pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
+ """
+ Pixel shuffle layer
+ (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
+ Neural Network, CVPR17)
+ """
+ conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
+ pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
+ pixel_shuffle = nn.PixelShuffle(upscale_factor)
+
+ n = norm(norm_type, out_nc) if norm_type else None
+ a = act(act_type) if act_type else None
+ return sequential(conv, pixel_shuffle, n, a)
+
+
+def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+ pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
+ """ Upconv layer """
+ upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
+ upsample = Upsample(scale_factor=upscale_factor, mode=mode)
+ conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
+ pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
+ return sequential(upsample, conv)
+
+
+
+
+
+
+
+
+####################
+# Basic blocks
+####################
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+ """Make layers by stacking the same blocks.
+ Args:
+ basic_block (nn.module): nn.module class for basic block. (block)
+ num_basic_block (int): number of blocks. (n_layers)
+ Returns:
+ nn.Sequential: Stacked blocks in nn.Sequential.
+ """
+ layers = []
+ for _ in range(num_basic_block):
+ layers.append(basic_block(**kwarg))
+ return nn.Sequential(*layers)
+
+
+def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
+ """ activation helper """
+ act_type = act_type.lower()
+ if act_type == 'relu':
+ layer = nn.ReLU(inplace)
+ elif act_type in ('leakyrelu', 'lrelu'):
+ layer = nn.LeakyReLU(neg_slope, inplace)
+ elif act_type == 'prelu':
+ layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
+ elif act_type == 'tanh': # [-1, 1] range output
+ layer = nn.Tanh()
+ elif act_type == 'sigmoid': # [0, 1] range output
+ layer = nn.Sigmoid()
+ else:
+ raise NotImplementedError(f'activation layer [{act_type}] is not found')
+ return layer
+
+
+class Identity(nn.Module):
+ def __init__(self, *kwargs):
+ super(Identity, self).__init__()
+
+ def forward(self, x, *kwargs):
+ return x
+
+
+def norm(norm_type, nc):
+ """ Return a normalization layer """
+ norm_type = norm_type.lower()
+ if norm_type == 'batch':
+ layer = nn.BatchNorm2d(nc, affine=True)
+ elif norm_type == 'instance':
+ layer = nn.InstanceNorm2d(nc, affine=False)
+ elif norm_type == 'none':
+ def norm_layer(x): return Identity()
+ else:
+ raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
+ return layer
+
+
+def pad(pad_type, padding):
+ """ padding layer helper """
+ pad_type = pad_type.lower()
+ if padding == 0:
+ return None
+ if pad_type == 'reflect':
+ layer = nn.ReflectionPad2d(padding)
+ elif pad_type == 'replicate':
+ layer = nn.ReplicationPad2d(padding)
+ elif pad_type == 'zero':
+ layer = nn.ZeroPad2d(padding)
+ else:
+ raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
+ return layer
+
+
+def get_valid_padding(kernel_size, dilation):
+ kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
+ padding = (kernel_size - 1) // 2
+ return padding
+
+
+class ShortcutBlock(nn.Module):
+ """ Elementwise sum the output of a submodule to its input """
+ def __init__(self, submodule):
+ super(ShortcutBlock, self).__init__()
+ self.sub = submodule
+
+ def forward(self, x):
+ output = x + self.sub(x)
+ return output
+
+ def __repr__(self):
+ return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
+
+
+def sequential(*args):
+ """ Flatten Sequential. It unwraps nn.Sequential. """
+ if len(args) == 1:
+ from collections import OrderedDict
+ if isinstance(args[0], OrderedDict):
+ raise NotImplementedError('sequential does not support OrderedDict input.')
+ return args[0] # No sequential is needed.
+ modules = []
+ for module in args:
+ if isinstance(module, nn.Sequential):
+ for submodule in module.children():
+ modules.append(submodule)
+ elif isinstance(module, nn.Module):
+ modules.append(module)
+ return nn.Sequential(*modules)
+
+
+def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
+ pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
+ spectral_norm=False):
+ """ Conv layer with padding, normalization, activation """
+ assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
+ padding = get_valid_padding(kernel_size, dilation)
+ p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
+ padding = padding if pad_type == 'zero' else 0
+
+ if convtype=='PartialConv2D':
+ from torchvision.ops import PartialConv2d
+ c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+ dilation=dilation, bias=bias, groups=groups)
+ elif convtype=='DeformConv2D':
+ from torchvision.ops import DeformConv2d
+ c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+ dilation=dilation, bias=bias, groups=groups)
+ elif convtype=='Conv3D':
+ c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+ dilation=dilation, bias=bias, groups=groups)
+ else:
+ c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+ dilation=dilation, bias=bias, groups=groups)
+
+ if spectral_norm:
+ c = nn.utils.spectral_norm(c)
+
+ a = act(act_type) if act_type else None
+ if 'CNA' in mode:
+ n = norm(norm_type, out_nc) if norm_type else None
+ return sequential(p, c, n, a)
+ elif mode == 'NAC':
+ if norm_type is None and act_type is not None:
+ a = act(act_type, inplace=False)
+ n = norm(norm_type, in_nc) if norm_type else None
+ return sequential(n, a, p, c)
diff --git a/modules/postprocess/gfpgan_model.py b/modules/postprocess/gfpgan_model.py
index 989af466f..b85c900e6 100644
--- a/modules/postprocess/gfpgan_model.py
+++ b/modules/postprocess/gfpgan_model.py
@@ -1,107 +1,107 @@
-import os
-
-import modules.face_restoration
-from modules import paths, shared, devices, modelloader, errors
-
-model_dir = "GFPGAN"
-user_path = None
-model_path = os.path.join(paths.models_path, model_dir)
-model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
-have_gfpgan = False
-loaded_gfpgan_model = None
-
-
-def gfpgann():
- import facexlib
- import gfpgan # pylint: disable=unused-import
- global loaded_gfpgan_model # pylint: disable=global-statement
- if loaded_gfpgan_model is not None:
- loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
- return loaded_gfpgan_model
- if gfpgan_constructor is None:
- return None
- models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
- if len(models) == 1 and "http" in models[0]:
- model_file = models[0]
- elif len(models) != 0:
- latest_file = max(models, key=os.path.getctime)
- model_file = latest_file
- else:
- shared.log.error(f"Model failed loading: type=GFPGAN model={model_file}")
- return None
- if hasattr(facexlib.detection.retinaface, 'device'):
- facexlib.detection.retinaface.device = devices.device_gfpgan
- model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
- loaded_gfpgan_model = model
- shared.log.info(f"Model loaded: type=GFPGAN model={model_file}")
- return model
-
-
-def send_model_to(model, device):
- model.gfpgan.to(device)
- model.face_helper.face_det.to(device)
- model.face_helper.face_parse.to(device)
-
-
-def gfpgan_fix_faces(np_image):
- model = gfpgann()
- if model is None:
- return np_image
-
- send_model_to(model, devices.device_gfpgan)
-
- np_image_bgr = np_image[:, :, ::-1]
- _cropped_faces, _restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
- np_image = gfpgan_output_bgr[:, :, ::-1]
-
- model.face_helper.clean_all()
-
- if shared.opts.face_restoration_unload:
- send_model_to(model, devices.cpu)
-
- return np_image
-
-
-gfpgan_constructor = None
-
-
-def setup_model(dirname):
- if not os.path.exists(model_path):
- os.makedirs(model_path)
- try:
- import gfpgan
- import facexlib
-
- global user_path # pylint: disable=global-statement
- global have_gfpgan # pylint: disable=global-statement
- global gfpgan_constructor # pylint: disable=global-statement
- load_file_from_url_orig = gfpgan.utils.load_file_from_url
- facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
- facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
-
- def my_load_file_from_url(**kwargs):
- return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
-
- def facex_load_file_from_url(**kwargs):
- return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
-
- def facex_load_file_from_url2(**kwargs):
- return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
-
- gfpgan.utils.load_file_from_url = my_load_file_from_url
- facexlib.detection.load_file_from_url = facex_load_file_from_url
- facexlib.parsing.load_file_from_url = facex_load_file_from_url2
- user_path = dirname
- have_gfpgan = True
- gfpgan_constructor = gfpgan.GFPGANer
-
- class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
- def name(self):
- return "GFPGAN"
-
- def restore(self, np_image):
- return gfpgan_fix_faces(np_image)
-
- shared.face_restorers.append(FaceRestorerGFPGAN())
- except Exception as e:
- errors.display(e, 'gfpgan')
+import os
+
+import modules.face_restoration
+from modules import paths, shared, devices, modelloader, errors
+
+model_dir = "GFPGAN"
+user_path = None
+model_path = os.path.join(paths.models_path, model_dir)
+model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
+have_gfpgan = False
+loaded_gfpgan_model = None
+
+
+def gfpgann():
+ import facexlib
+ import gfpgan # pylint: disable=unused-import
+ global loaded_gfpgan_model # pylint: disable=global-statement
+ if loaded_gfpgan_model is not None:
+ loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
+ return loaded_gfpgan_model
+ if gfpgan_constructor is None:
+ return None
+ models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
+ if len(models) == 1 and "http" in models[0]:
+ model_file = models[0]
+ elif len(models) != 0:
+ latest_file = max(models, key=os.path.getctime)
+ model_file = latest_file
+ else:
+ shared.log.error(f"Model failed loading: type=GFPGAN model={model_file}")
+ return None
+ if hasattr(facexlib.detection.retinaface, 'device'):
+ facexlib.detection.retinaface.device = devices.device_gfpgan
+ model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
+ loaded_gfpgan_model = model
+ shared.log.info(f"Model loaded: type=GFPGAN model={model_file}")
+ return model
+
+
+def send_model_to(model, device):
+ model.gfpgan.to(device)
+ model.face_helper.face_det.to(device)
+ model.face_helper.face_parse.to(device)
+
+
+def gfpgan_fix_faces(np_image):
+ model = gfpgann()
+ if model is None:
+ return np_image
+
+ send_model_to(model, devices.device_gfpgan)
+
+ np_image_bgr = np_image[:, :, ::-1]
+ _cropped_faces, _restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
+ np_image = gfpgan_output_bgr[:, :, ::-1]
+
+ model.face_helper.clean_all()
+
+ if shared.opts.face_restoration_unload:
+ send_model_to(model, devices.cpu)
+
+ return np_image
+
+
+gfpgan_constructor = None
+
+
+def setup_model(dirname):
+ if not os.path.exists(model_path):
+ os.makedirs(model_path)
+ try:
+ import gfpgan
+ import facexlib
+
+ global user_path # pylint: disable=global-statement
+ global have_gfpgan # pylint: disable=global-statement
+ global gfpgan_constructor # pylint: disable=global-statement
+ load_file_from_url_orig = gfpgan.utils.load_file_from_url
+ facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
+ facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
+
+ def my_load_file_from_url(**kwargs):
+ return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
+
+ def facex_load_file_from_url(**kwargs):
+ return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
+
+ def facex_load_file_from_url2(**kwargs):
+ return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
+
+ gfpgan.utils.load_file_from_url = my_load_file_from_url
+ facexlib.detection.load_file_from_url = facex_load_file_from_url
+ facexlib.parsing.load_file_from_url = facex_load_file_from_url2
+ user_path = dirname
+ have_gfpgan = True
+ gfpgan_constructor = gfpgan.GFPGANer
+
+ class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
+ def name(self):
+ return "GFPGAN"
+
+ def restore(self, np_image):
+ return gfpgan_fix_faces(np_image)
+
+ shared.face_restorers.append(FaceRestorerGFPGAN())
+ except Exception as e:
+ errors.display(e, 'gfpgan')
diff --git a/modules/postprocess/realesrgan_model.py b/modules/postprocess/realesrgan_model.py
index af6bb4705..490148c2b 100644
--- a/modules/postprocess/realesrgan_model.py
+++ b/modules/postprocess/realesrgan_model.py
@@ -1,70 +1,70 @@
-import os
-import numpy as np
-from PIL import Image
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from modules.postprocess.realesrgan_model_arch import SRVGGNetCompact
-from modules.upscaler import Upscaler
-from modules.shared import opts, device, log
-from modules import devices
-
-class UpscalerRealESRGAN(Upscaler):
- def __init__(self, dirname):
- self.name = "RealESRGAN"
- self.user_path = dirname
- super().__init__()
- self.scalers = self.find_scalers()
- self.models = {}
- for scaler in self.scalers:
- if scaler.name == 'RealESRGAN 2x+':
- scaler.model = lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
- scaler.scale = 2
- elif scaler.name == 'RealESRGAN 4x+ Anime6B':
- scaler.model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
- elif scaler.name == 'RealESRGAN 4x General V3':
- scaler.model = lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
- elif scaler.name == 'RealESRGAN 4x General WDN V3':
- scaler.model = lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
- elif scaler.name == 'RealESRGAN AnimeVideo V3':
- scaler.model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
- elif scaler.name == 'RealESRGAN 4x+':
- scaler.model = lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
- else:
- log.error(f"Upscaler unrecognized model: type={self.name} model={scaler.name}")
-
- def load_model(self, path): # pylint: disable=unused-argument
- pass
-
- def do_upscale(self, img, selected_model):
- if not self.enable:
- return img
- try:
- from modules.postprocess.realesrgan_model_arch import RealESRGANer
- except Exception:
- log.error("Error importing Real-ESRGAN:")
- return img
- info = self.find_model(selected_model)
- if info is None or not os.path.exists(info.local_data_path):
- return img
- if self.models.get(info.local_data_path, None) is not None:
- log.debug(f"Upscaler cached: type={self.name} model={info.local_data_path}")
- upsampler=self.models[info.local_data_path]
- else:
- upsampler = RealESRGANer(
- name=info.name,
- scale=info.scale,
- model_path=info.local_data_path,
- model=info.model(),
- half=not opts.no_half and not opts.upcast_sampling,
- tile=opts.upscaler_tile_size,
- tile_pad=opts.upscaler_tile_overlap,
- device=device,
- )
- self.models[info.local_data_path] = upsampler
- upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
- if opts.upscaler_unload and info.local_data_path in self.models:
- del self.models[info.local_data_path]
- log.debug(f"Upscaler unloaded: type={self.name} model={selected_model}")
- devices.torch_gc(force=True)
-
- image = Image.fromarray(upsampled)
- return image
+import os
+import numpy as np
+from PIL import Image
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from modules.postprocess.realesrgan_model_arch import SRVGGNetCompact
+from modules.upscaler import Upscaler
+from modules.shared import opts, device, log
+from modules import devices
+
+class UpscalerRealESRGAN(Upscaler):
+ def __init__(self, dirname):
+ self.name = "RealESRGAN"
+ self.user_path = dirname
+ super().__init__()
+ self.scalers = self.find_scalers()
+ self.models = {}
+ for scaler in self.scalers:
+ if scaler.name == 'RealESRGAN 2x+':
+ scaler.model = lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+ scaler.scale = 2
+ elif scaler.name == 'RealESRGAN 4x+ Anime6B':
+ scaler.model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+ elif scaler.name == 'RealESRGAN 4x General V3':
+ scaler.model = lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+ elif scaler.name == 'RealESRGAN 4x General WDN V3':
+ scaler.model = lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+ elif scaler.name == 'RealESRGAN AnimeVideo V3':
+ scaler.model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+ elif scaler.name == 'RealESRGAN 4x+':
+ scaler.model = lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+ else:
+ log.error(f"Upscaler unrecognized model: type={self.name} model={scaler.name}")
+
+ def load_model(self, path): # pylint: disable=unused-argument
+ pass
+
+ def do_upscale(self, img, selected_model):
+ if not self.enable:
+ return img
+ try:
+ from modules.postprocess.realesrgan_model_arch import RealESRGANer
+ except Exception:
+ log.error("Error importing Real-ESRGAN:")
+ return img
+ info = self.find_model(selected_model)
+ if info is None or not os.path.exists(info.local_data_path):
+ return img
+ if self.models.get(info.local_data_path, None) is not None:
+ log.debug(f"Upscaler cached: type={self.name} model={info.local_data_path}")
+ upsampler=self.models[info.local_data_path]
+ else:
+ upsampler = RealESRGANer(
+ name=info.name,
+ scale=info.scale,
+ model_path=info.local_data_path,
+ model=info.model(),
+ half=not opts.no_half and not opts.upcast_sampling,
+ tile=opts.upscaler_tile_size,
+ tile_pad=opts.upscaler_tile_overlap,
+ device=device,
+ )
+ self.models[info.local_data_path] = upsampler
+ upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
+ if opts.upscaler_unload and info.local_data_path in self.models:
+ del self.models[info.local_data_path]
+ log.debug(f"Upscaler unloaded: type={self.name} model={selected_model}")
+ devices.torch_gc(force=True)
+
+ image = Image.fromarray(upsampled)
+ return image
diff --git a/modules/postprocess/scunet_model.py b/modules/postprocess/scunet_model.py
index e48950afe..566a8009c 100644
--- a/modules/postprocess/scunet_model.py
+++ b/modules/postprocess/scunet_model.py
@@ -1,4 +1,4 @@
-import PIL.Image
+from PIL import Image
import numpy as np
import torch
from rich.progress import Progress, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn, TimeElapsedColumn
@@ -68,7 +68,7 @@ class UpscalerSCUNet(Upscaler):
output = E.div_(W)
return output
- def do_upscale(self, img: PIL.Image.Image, selected_file):
+ def do_upscale(self, img: Image.Image, selected_file):
devices.torch_gc()
model = self.load_model(selected_file)
if model is None:
@@ -90,7 +90,7 @@ class UpscalerSCUNet(Upscaler):
devices.torch_gc()
output = np_output.transpose((1, 2, 0)) # CHW to HWC
output = output[:, :, ::-1] # BGR to RGB
- img = PIL.Image.fromarray((output * 255).astype(np.uint8))
+ img = Image.fromarray((output * 255).astype(np.uint8))
if opts.upscaler_unload and selected_file in self.models:
del self.models[selected_file]
log.debug(f"Upscaler unloaded: type={self.name} model={selected_file}")
diff --git a/modules/postprocess/swinir_model_arch_v2.py b/modules/postprocess/swinir_model_arch_v2.py
index 19bcab441..71c246c3f 100644
--- a/modules/postprocess/swinir_model_arch_v2.py
+++ b/modules/postprocess/swinir_model_arch_v2.py
@@ -1,1015 +1,1015 @@
-# -----------------------------------------------------------------------------------
-# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
-# Written by Conde and Choi et al.
-# -----------------------------------------------------------------------------------
-
-import math
-import numpy as np
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-class WindowAttention(nn.Module):
- r""" Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
- """
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
- pretrained_window_size=(0, 0)):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.pretrained_window_size = pretrained_window_size
- self.num_heads = num_heads
-
- self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
- # mlp to generate continuous relative position bias
- self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
- nn.ReLU(inplace=True),
- nn.Linear(512, num_heads, bias=False))
-
- # get relative_coords_table
- relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
- relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
- relative_coords_table = torch.stack(
- torch.meshgrid([relative_coords_h,
- relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
- if pretrained_window_size[0] > 0:
- relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
- relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
- else:
- relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
- relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
- relative_coords_table *= 8 # normalize to -8, 8
- relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
- torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
- self.register_buffer("relative_coords_table", relative_coords_table)
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=False)
- if qkv_bias:
- self.q_bias = nn.Parameter(torch.zeros(dim))
- self.v_bias = nn.Parameter(torch.zeros(dim))
- else:
- self.q_bias = None
- self.v_bias = None
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv_bias = None
- if self.q_bias is not None:
- qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
- qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
- qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- # cosine attention
- attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
- logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
- attn = attn * logit_scale
-
- relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
- relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
- def extra_repr(self) -> str:
- return f'dim={self.dim}, window_size={self.window_size}, pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
-
- def flops(self, N):
- # calculate flops for 1 window with token length of N
- flops = 0
- # qkv = self.qkv(x)
- flops += N * self.dim * 3 * self.dim
- # attn = (q @ k.transpose(-2, -1))
- flops += self.num_heads * N * (self.dim // self.num_heads) * N
- # x = (attn @ v)
- flops += self.num_heads * N * N * (self.dim // self.num_heads)
- # x = self.proj(x)
- flops += N * self.dim * self.dim
- return flops
-
-class SwinTransformerBlock(nn.Module):
- r""" Swin Transformer Block.
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resulotion.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- pretrained_window_size (int): Window size in pre-training.
- """
-
- def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- if min(self.input_resolution) <= self.window_size:
- # if window size is larger than input resolution, we don't partition windows
- self.shift_size = 0
- self.window_size = min(self.input_resolution)
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
- pretrained_window_size=to_2tuple(pretrained_window_size))
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- if self.shift_size > 0:
- attn_mask = self.calculate_mask(self.input_resolution)
- else:
- attn_mask = None
-
- self.register_buffer("attn_mask", attn_mask)
-
- def calculate_mask(self, x_size):
- # calculate attention mask for SW-MSA
- H, W = x_size
- img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
- return attn_mask
-
- def forward(self, x, x_size):
- H, W = x_size
- B, L, C = x.shape
- #assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = x.view(B, H, W, C)
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- else:
- shifted_x = x
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
- if self.input_resolution == x_size:
- attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
- else:
- attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
- x = x.view(B, H * W, C)
- x = shortcut + self.drop_path(self.norm1(x))
-
- # FFN
- x = x + self.drop_path(self.norm2(self.mlp(x)))
-
- return x
-
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
- def flops(self):
- flops = 0
- H, W = self.input_resolution
- # norm1
- flops += self.dim * H * W
- # W-MSA/SW-MSA
- nW = H * W / self.window_size / self.window_size
- flops += nW * self.attn.flops(self.window_size * self.window_size)
- # mlp
- flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
- # norm2
- flops += self.dim * H * W
- return flops
-
-class PatchMerging(nn.Module):
- r""" Patch Merging Layer.
- Args:
- input_resolution (tuple[int]): Resolution of input feature.
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.input_resolution = input_resolution
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(2 * dim)
-
- def forward(self, x):
- """
- x: B, H*W, C
- """
- H, W = self.input_resolution
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
- assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
- x = x.view(B, H, W, C)
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.reduction(x)
- x = self.norm(x)
-
- return x
-
- def extra_repr(self) -> str:
- return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
- def flops(self):
- H, W = self.input_resolution
- flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
- flops += H * W * self.dim // 2
- return flops
-
-class BasicLayer(nn.Module):
- """ A basic Swin Transformer layer for one stage.
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resolution.
- depth (int): Number of blocks.
- num_heads (int): Number of attention heads.
- window_size (int): Local window size.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- pretrained_window_size (int): Local window size in pre-training.
- """
-
- def __init__(self, dim, input_resolution, depth, num_heads, window_size,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
- drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
- pretrained_window_size=0):
-
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.depth = depth
- self.use_checkpoint = use_checkpoint
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
- num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop, attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer,
- pretrained_window_size=pretrained_window_size)
- for i in range(depth)])
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x, x_size):
- for blk in self.blocks:
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x, x_size)
- else:
- x = blk(x, x_size)
- if self.downsample is not None:
- x = self.downsample(x)
- return x
-
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
- def flops(self):
- flops = 0
- for blk in self.blocks:
- flops += blk.flops()
- if self.downsample is not None:
- flops += self.downsample.flops()
- return flops
-
- def _init_respostnorm(self):
- for blk in self.blocks:
- nn.init.constant_(blk.norm1.bias, 0)
- nn.init.constant_(blk.norm1.weight, 0)
- nn.init.constant_(blk.norm2.bias, 0)
- nn.init.constant_(blk.norm2.weight, 0)
-
-class PatchEmbed(nn.Module):
- r""" Image to Patch Embedding
- Args:
- img_size (int): Image size. Default: 224.
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
- self.img_size = img_size
- self.patch_size = patch_size
- self.patches_resolution = patches_resolution
- self.num_patches = patches_resolution[0] * patches_resolution[1]
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
- if norm_layer is not None:
- self.norm = norm_layer(embed_dim)
- else:
- self.norm = None
-
- def forward(self, x):
- B, C, H, W = x.shape
- # FIXME look at relaxing size constraints
- # assert H == self.img_size[0] and W == self.img_size[1],
- # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
- if self.norm is not None:
- x = self.norm(x)
- return x
-
- def flops(self):
- Ho, Wo = self.patches_resolution
- flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
- if self.norm is not None:
- flops += Ho * Wo * self.embed_dim
- return flops
-
-class RSTB(nn.Module):
- """Residual Swin Transformer Block (RSTB).
-
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resolution.
- depth (int): Number of blocks.
- num_heads (int): Number of attention heads.
- window_size (int): Local window size.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- img_size: Input image size.
- patch_size: Patch size.
- resi_connection: The convolutional block before residual connection.
- """
-
- def __init__(self, dim, input_resolution, depth, num_heads, window_size,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
- drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
- img_size=224, patch_size=4, resi_connection='1conv'):
- super(RSTB, self).__init__()
-
- self.dim = dim
- self.input_resolution = input_resolution
-
- self.residual_group = BasicLayer(dim=dim,
- input_resolution=input_resolution,
- depth=depth,
- num_heads=num_heads,
- window_size=window_size,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop, attn_drop=attn_drop,
- drop_path=drop_path,
- norm_layer=norm_layer,
- downsample=downsample,
- use_checkpoint=use_checkpoint)
-
- if resi_connection == '1conv':
- self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
- elif resi_connection == '3conv':
- # to save parameters and memory
- self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
- nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
- nn.LeakyReLU(negative_slope=0.2, inplace=True),
- nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
- self.patch_embed = PatchEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
- norm_layer=None)
-
- self.patch_unembed = PatchUnEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
- norm_layer=None)
-
- def forward(self, x, x_size):
- return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
- def flops(self):
- flops = 0
- flops += self.residual_group.flops()
- H, W = self.input_resolution
- flops += H * W * self.dim * self.dim * 9
- flops += self.patch_embed.flops()
- flops += self.patch_unembed.flops()
-
- return flops
-
-class PatchUnEmbed(nn.Module):
- r""" Image to Patch Unembedding
-
- Args:
- img_size (int): Image size. Default: 224.
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
- self.img_size = img_size
- self.patch_size = patch_size
- self.patches_resolution = patches_resolution
- self.num_patches = patches_resolution[0] * patches_resolution[1]
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- def forward(self, x, x_size):
- B, HW, C = x.shape
- x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
- return x
-
- def flops(self):
- flops = 0
- return flops
-
-
-class Upsample(nn.Sequential):
- """Upsample module.
-
- Args:
- scale (int): Scale factor. Supported scales: 2^n and 3.
- num_feat (int): Channel number of intermediate features.
- """
-
- def __init__(self, scale, num_feat):
- m = []
- if (scale & (scale - 1)) == 0: # scale = 2^n
- for _ in range(int(math.log(scale, 2))):
- m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(2))
- elif scale == 3:
- m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(3))
- else:
- raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
- super(Upsample, self).__init__(*m)
-
-class Upsample_hf(nn.Sequential):
- """Upsample module.
-
- Args:
- scale (int): Scale factor. Supported scales: 2^n and 3.
- num_feat (int): Channel number of intermediate features.
- """
-
- def __init__(self, scale, num_feat):
- m = []
- if (scale & (scale - 1)) == 0: # scale = 2^n
- for _ in range(int(math.log(scale, 2))):
- m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(2))
- elif scale == 3:
- m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(3))
- else:
- raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
- super(Upsample_hf, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
- """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
- Used in lightweight SR to save parameters.
-
- Args:
- scale (int): Scale factor. Supported scales: 2^n and 3.
- num_feat (int): Channel number of intermediate features.
-
- """
-
- def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
- self.num_feat = num_feat
- self.input_resolution = input_resolution
- m = []
- m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
- m.append(nn.PixelShuffle(scale))
- super(UpsampleOneStep, self).__init__(*m)
-
- def flops(self):
- H, W = self.input_resolution
- flops = H * W * self.num_feat * 3 * 9
- return flops
-
-
-
-class Swin2SR(nn.Module):
- r""" Swin2SR
- A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
-
- Args:
- img_size (int | tuple(int)): Input image size. Default 64
- patch_size (int | tuple(int)): Patch size. Default: 1
- in_chans (int): Number of input image channels. Default: 3
- embed_dim (int): Patch embedding dimension. Default: 96
- depths (tuple(int)): Depth of each Swin Transformer layer.
- num_heads (tuple(int)): Number of attention heads in different layers.
- window_size (int): Window size. Default: 7
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- drop_rate (float): Dropout rate. Default: 0
- attn_drop_rate (float): Attention dropout rate. Default: 0
- drop_path_rate (float): Stochastic depth rate. Default: 0.1
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
- patch_norm (bool): If True, add normalization after patch embedding. Default: True
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
- upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
- img_range: Image range. 1. or 255.
- upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
- resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
- """
-
- def __init__(self, img_size=64, patch_size=1, in_chans=3,
- embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
- window_size=7, mlp_ratio=4., qkv_bias=True,
- drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
- norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
- use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
- **kwargs):
- super(Swin2SR, self).__init__()
- num_in_ch = in_chans
- num_out_ch = in_chans
- num_feat = 64
- self.img_range = img_range
- if in_chans == 3:
- rgb_mean = (0.4488, 0.4371, 0.4040)
- self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
- else:
- self.mean = torch.zeros(1, 1, 1, 1)
- self.upscale = upscale
- self.upsampler = upsampler
- self.window_size = window_size
-
- #####################################################################################################
- ################################### 1, shallow feature extraction ###################################
- self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
- #####################################################################################################
- ################################### 2, deep feature extraction ######################################
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.num_features = embed_dim
- self.mlp_ratio = mlp_ratio
-
- # split image into non-overlapping patches
- self.patch_embed = PatchEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
- num_patches = self.patch_embed.num_patches
- patches_resolution = self.patch_embed.patches_resolution
- self.patches_resolution = patches_resolution
-
- # merge non-overlapping patches into image
- self.patch_unembed = PatchUnEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
-
- # absolute position embedding
- if self.ape:
- self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
- trunc_normal_(self.absolute_pos_embed, std=.02)
-
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- # stochastic depth
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
-
- # build Residual Swin Transformer blocks (RSTB)
- self.layers = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = RSTB(dim=embed_dim,
- input_resolution=(patches_resolution[0],
- patches_resolution[1]),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=self.mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
- norm_layer=norm_layer,
- downsample=None,
- use_checkpoint=use_checkpoint,
- img_size=img_size,
- patch_size=patch_size,
- resi_connection=resi_connection
-
- )
- self.layers.append(layer)
-
- if self.upsampler == 'pixelshuffle_hf':
- self.layers_hf = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = RSTB(dim=embed_dim,
- input_resolution=(patches_resolution[0],
- patches_resolution[1]),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=self.mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
- norm_layer=norm_layer,
- downsample=None,
- use_checkpoint=use_checkpoint,
- img_size=img_size,
- patch_size=patch_size,
- resi_connection=resi_connection
-
- )
- self.layers_hf.append(layer)
-
- self.norm = norm_layer(self.num_features)
-
- # build the last conv layer in deep feature extraction
- if resi_connection == '1conv':
- self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
- elif resi_connection == '3conv':
- # to save parameters and memory
- self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
- nn.LeakyReLU(negative_slope=0.2, inplace=True),
- nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
- nn.LeakyReLU(negative_slope=0.2, inplace=True),
- nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
- #####################################################################################################
- ################################ 3, high quality image reconstruction ################################
- if self.upsampler == 'pixelshuffle':
- # for classical SR
- self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.upsample = Upsample(upscale, num_feat)
- self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
- elif self.upsampler == 'pixelshuffle_aux':
- self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
- self.conv_before_upsample = nn.Sequential(
- nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
- self.conv_after_aux = nn.Sequential(
- nn.Conv2d(3, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.upsample = Upsample(upscale, num_feat)
- self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
- elif self.upsampler == 'pixelshuffle_hf':
- self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.upsample = Upsample(upscale, num_feat)
- self.upsample_hf = Upsample_hf(upscale, num_feat)
- self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
- self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
- self.conv_before_upsample_hf = nn.Sequential(
- nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
- elif self.upsampler == 'pixelshuffledirect':
- # for lightweight SR (to save parameters)
- self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
- (patches_resolution[0], patches_resolution[1]))
- elif self.upsampler == 'nearest+conv':
- # for real-world SR (less artifacts)
- assert self.upscale == 4, 'only support x4 now.'
- self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
- self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
- self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
- self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
- self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
- else:
- # for image denoising and JPEG compression artifact reduction
- self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'absolute_pos_embed'}
-
- @torch.jit.ignore
- def no_weight_decay_keywords(self):
- return {'relative_position_bias_table'}
-
- def check_image_size(self, x):
- _, _, h, w = x.size()
- mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
- mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
- x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
- return x
-
- def forward_features(self, x):
- x_size = (x.shape[2], x.shape[3])
- x = self.patch_embed(x)
- if self.ape:
- x = x + self.absolute_pos_embed
- x = self.pos_drop(x)
-
- for layer in self.layers:
- x = layer(x, x_size)
-
- x = self.norm(x) # B L C
- x = self.patch_unembed(x, x_size)
-
- return x
-
- def forward_features_hf(self, x):
- x_size = (x.shape[2], x.shape[3])
- x = self.patch_embed(x)
- if self.ape:
- x = x + self.absolute_pos_embed
- x = self.pos_drop(x)
-
- for layer in self.layers_hf:
- x = layer(x, x_size)
-
- x = self.norm(x) # B L C
- x = self.patch_unembed(x, x_size)
-
- return x
-
- def forward(self, x):
- H, W = x.shape[2:]
- x = self.check_image_size(x)
-
- self.mean = self.mean.type_as(x)
- x = (x - self.mean) * self.img_range
-
- if self.upsampler == 'pixelshuffle':
- # for classical SR
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x = self.conv_before_upsample(x)
- x = self.conv_last(self.upsample(x))
- elif self.upsampler == 'pixelshuffle_aux':
- bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
- bicubic = self.conv_bicubic(bicubic)
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x = self.conv_before_upsample(x)
- aux = self.conv_aux(x) # b, 3, LR_H, LR_W
- x = self.conv_after_aux(aux)
- x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
- x = self.conv_last(x)
- aux = aux / self.img_range + self.mean
- elif self.upsampler == 'pixelshuffle_hf':
- # for classical SR with HF
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x_before = self.conv_before_upsample(x)
- x_out = self.conv_last(self.upsample(x_before))
-
- x_hf = self.conv_first_hf(x_before)
- x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
- x_hf = self.conv_before_upsample_hf(x_hf)
- x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
- x = x_out + x_hf
- x_hf = x_hf / self.img_range + self.mean
-
- elif self.upsampler == 'pixelshuffledirect':
- # for lightweight SR
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x = self.upsample(x)
- elif self.upsampler == 'nearest+conv':
- # for real-world SR
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x = self.conv_before_upsample(x)
- x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
- x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
- x = self.conv_last(self.lrelu(self.conv_hr(x)))
- else:
- # for image denoising and JPEG compression artifact reduction
- x_first = self.conv_first(x)
- res = self.conv_after_body(self.forward_features(x_first)) + x_first
- x = x + self.conv_last(res)
-
- x = x / self.img_range + self.mean
- if self.upsampler == "pixelshuffle_aux":
- return x[:, :, :H*self.upscale, :W*self.upscale], aux
-
- elif self.upsampler == "pixelshuffle_hf":
- x_out = x_out / self.img_range + self.mean
- return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
-
- else:
- return x[:, :, :H*self.upscale, :W*self.upscale]
-
- def flops(self):
- flops = 0
- H, W = self.patches_resolution
- flops += H * W * 3 * self.embed_dim * 9
- flops += self.patch_embed.flops()
- for layer in self.layers:
- flops += layer.flops()
- flops += H * W * 3 * self.embed_dim * self.embed_dim
- flops += self.upsample.flops()
- return flops
-
-
-if __name__ == '__main__':
- upscale = 4
- window_size = 8
- height = (1024 // upscale // window_size + 1) * window_size
- width = (720 // upscale // window_size + 1) * window_size
- model = Swin2SR(upscale=2, img_size=(height, width),
- window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
- embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
- print(model)
- print(height, width, model.flops() / 1e9)
-
- x = torch.randn((1, 3, height, width))
- x = model(x)
- print(x.shape)
+# -----------------------------------------------------------------------------------
+# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
+# Written by Conde and Choi et al.
+# -----------------------------------------------------------------------------------
+
+import math
+import numpy as np
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+class WindowAttention(nn.Module):
+ r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
+ """
+
+ def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
+ pretrained_window_size=(0, 0)):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.pretrained_window_size = pretrained_window_size
+ self.num_heads = num_heads
+
+ self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
+
+ # mlp to generate continuous relative position bias
+ self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+ nn.ReLU(inplace=True),
+ nn.Linear(512, num_heads, bias=False))
+
+ # get relative_coords_table
+ relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+ relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+ relative_coords_table = torch.stack(
+ torch.meshgrid([relative_coords_h,
+ relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
+ if pretrained_window_size[0] > 0:
+ relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+ relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+ else:
+ relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+ relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+ relative_coords_table *= 8 # normalize to -8, 8
+ relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+ torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+ self.register_buffer("relative_coords_table", relative_coords_table)
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=False)
+ if qkv_bias:
+ self.q_bias = nn.Parameter(torch.zeros(dim))
+ self.v_bias = nn.Parameter(torch.zeros(dim))
+ else:
+ self.q_bias = None
+ self.v_bias = None
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask=None):
+ """
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ qkv_bias = None
+ if self.q_bias is not None:
+ qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+ qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+ qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ # cosine attention
+ attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
+ logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
+ attn = attn * logit_scale
+
+ relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+ relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+ attn = self.softmax(attn)
+ else:
+ attn = self.softmax(attn)
+
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f'dim={self.dim}, window_size={self.window_size}, pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
+
+ def flops(self, N):
+ # calculate flops for 1 window with token length of N
+ flops = 0
+ # qkv = self.qkv(x)
+ flops += N * self.dim * 3 * self.dim
+ # attn = (q @ k.transpose(-2, -1))
+ flops += self.num_heads * N * (self.dim // self.num_heads) * N
+ # x = (attn @ v)
+ flops += self.num_heads * N * N * (self.dim // self.num_heads)
+ # x = self.proj(x)
+ flops += N * self.dim * self.dim
+ return flops
+
+class SwinTransformerBlock(nn.Module):
+ r""" Swin Transformer Block.
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resulotion.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ pretrained_window_size (int): Window size in pre-training.
+ """
+
+ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ if min(self.input_resolution) <= self.window_size:
+ # if window size is larger than input resolution, we don't partition windows
+ self.shift_size = 0
+ self.window_size = min(self.input_resolution)
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+ qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
+ pretrained_window_size=to_2tuple(pretrained_window_size))
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+ if self.shift_size > 0:
+ attn_mask = self.calculate_mask(self.input_resolution)
+ else:
+ attn_mask = None
+
+ self.register_buffer("attn_mask", attn_mask)
+
+ def calculate_mask(self, x_size):
+ # calculate attention mask for SW-MSA
+ H, W = x_size
+ img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
+ h_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ w_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+ return attn_mask
+
+ def forward(self, x, x_size):
+ H, W = x_size
+ B, L, C = x.shape
+ #assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = x.view(B, H, W, C)
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ else:
+ shifted_x = x
+
+ # partition windows
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
+
+ # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+ if self.input_resolution == x_size:
+ attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
+ else:
+ attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+ x = x.view(B, H * W, C)
+ x = shortcut + self.drop_path(self.norm1(x))
+
+ # FFN
+ x = x + self.drop_path(self.norm2(self.mlp(x)))
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+ def flops(self):
+ flops = 0
+ H, W = self.input_resolution
+ # norm1
+ flops += self.dim * H * W
+ # W-MSA/SW-MSA
+ nW = H * W / self.window_size / self.window_size
+ flops += nW * self.attn.flops(self.window_size * self.window_size)
+ # mlp
+ flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+ # norm2
+ flops += self.dim * H * W
+ return flops
+
+class PatchMerging(nn.Module):
+ r""" Patch Merging Layer.
+ Args:
+ input_resolution (tuple[int]): Resolution of input feature.
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.input_resolution = input_resolution
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(2 * dim)
+
+ def forward(self, x):
+ """
+ x: B, H*W, C
+ """
+ H, W = self.input_resolution
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+ assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+ x = x.view(B, H, W, C)
+
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
+
+ x = self.reduction(x)
+ x = self.norm(x)
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+ flops += H * W * self.dim // 2
+ return flops
+
+class BasicLayer(nn.Module):
+ """ A basic Swin Transformer layer for one stage.
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ pretrained_window_size (int): Local window size in pre-training.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+ pretrained_window_size=0):
+
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList([
+ SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+ num_heads=num_heads, window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer,
+ pretrained_window_size=pretrained_window_size)
+ for i in range(depth)])
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, x_size):
+ for blk in self.blocks:
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, x_size)
+ else:
+ x = blk(x, x_size)
+ if self.downsample is not None:
+ x = self.downsample(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+ def flops(self):
+ flops = 0
+ for blk in self.blocks:
+ flops += blk.flops()
+ if self.downsample is not None:
+ flops += self.downsample.flops()
+ return flops
+
+ def _init_respostnorm(self):
+ for blk in self.blocks:
+ nn.init.constant_(blk.norm1.bias, 0)
+ nn.init.constant_(blk.norm1.weight, 0)
+ nn.init.constant_(blk.norm2.bias, 0)
+ nn.init.constant_(blk.norm2.weight, 0)
+
+class PatchEmbed(nn.Module):
+ r""" Image to Patch Embedding
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ B, C, H, W = x.shape
+ # FIXME look at relaxing size constraints
+ # assert H == self.img_size[0] and W == self.img_size[1],
+ # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+ x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
+ if self.norm is not None:
+ x = self.norm(x)
+ return x
+
+ def flops(self):
+ Ho, Wo = self.patches_resolution
+ flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+ if self.norm is not None:
+ flops += Ho * Wo * self.embed_dim
+ return flops
+
+class RSTB(nn.Module):
+ """Residual Swin Transformer Block (RSTB).
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ img_size: Input image size.
+ patch_size: Patch size.
+ resi_connection: The convolutional block before residual connection.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+ img_size=224, patch_size=4, resi_connection='1conv'):
+ super(RSTB, self).__init__()
+
+ self.dim = dim
+ self.input_resolution = input_resolution
+
+ self.residual_group = BasicLayer(dim=dim,
+ input_resolution=input_resolution,
+ depth=depth,
+ num_heads=num_heads,
+ window_size=window_size,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path,
+ norm_layer=norm_layer,
+ downsample=downsample,
+ use_checkpoint=use_checkpoint)
+
+ if resi_connection == '1conv':
+ self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+ norm_layer=None)
+
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+ norm_layer=None)
+
+ def forward(self, x, x_size):
+ return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+ def flops(self):
+ flops = 0
+ flops += self.residual_group.flops()
+ H, W = self.input_resolution
+ flops += H * W * self.dim * self.dim * 9
+ flops += self.patch_embed.flops()
+ flops += self.patch_unembed.flops()
+
+ return flops
+
+class PatchUnEmbed(nn.Module):
+ r""" Image to Patch Unembedding
+
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ def forward(self, x, x_size):
+ B, HW, C = x.shape
+ x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
+ return x
+
+ def flops(self):
+ flops = 0
+ return flops
+
+
+class Upsample(nn.Sequential):
+ """Upsample module.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+ """
+
+ def __init__(self, scale, num_feat):
+ m = []
+ if (scale & (scale - 1)) == 0: # scale = 2^n
+ for _ in range(int(math.log(scale, 2))):
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(2))
+ elif scale == 3:
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(3))
+ else:
+ raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
+ super(Upsample, self).__init__(*m)
+
+class Upsample_hf(nn.Sequential):
+ """Upsample module.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+ """
+
+ def __init__(self, scale, num_feat):
+ m = []
+ if (scale & (scale - 1)) == 0: # scale = 2^n
+ for _ in range(int(math.log(scale, 2))):
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(2))
+ elif scale == 3:
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(3))
+ else:
+ raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
+ super(Upsample_hf, self).__init__(*m)
+
+
+class UpsampleOneStep(nn.Sequential):
+ """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+ Used in lightweight SR to save parameters.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+
+ """
+
+ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+ self.num_feat = num_feat
+ self.input_resolution = input_resolution
+ m = []
+ m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+ m.append(nn.PixelShuffle(scale))
+ super(UpsampleOneStep, self).__init__(*m)
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = H * W * self.num_feat * 3 * 9
+ return flops
+
+
+
+class Swin2SR(nn.Module):
+ r""" Swin2SR
+ A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
+
+ Args:
+ img_size (int | tuple(int)): Input image size. Default 64
+ patch_size (int | tuple(int)): Patch size. Default: 1
+ in_chans (int): Number of input image channels. Default: 3
+ embed_dim (int): Patch embedding dimension. Default: 96
+ depths (tuple(int)): Depth of each Swin Transformer layer.
+ num_heads (tuple(int)): Number of attention heads in different layers.
+ window_size (int): Window size. Default: 7
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+ drop_rate (float): Dropout rate. Default: 0
+ attn_drop_rate (float): Attention dropout rate. Default: 0
+ drop_path_rate (float): Stochastic depth rate. Default: 0.1
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+ upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+ img_range: Image range. 1. or 255.
+ upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+ resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+ """
+
+ def __init__(self, img_size=64, patch_size=1, in_chans=3,
+ embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
+ window_size=7, mlp_ratio=4., qkv_bias=True,
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+ norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+ use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+ **kwargs):
+ super(Swin2SR, self).__init__()
+ num_in_ch = in_chans
+ num_out_ch = in_chans
+ num_feat = 64
+ self.img_range = img_range
+ if in_chans == 3:
+ rgb_mean = (0.4488, 0.4371, 0.4040)
+ self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+ else:
+ self.mean = torch.zeros(1, 1, 1, 1)
+ self.upscale = upscale
+ self.upsampler = upsampler
+ self.window_size = window_size
+
+ #####################################################################################################
+ ################################### 1, shallow feature extraction ###################################
+ self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+ #####################################################################################################
+ ################################### 2, deep feature extraction ######################################
+ self.num_layers = len(depths)
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.num_features = embed_dim
+ self.mlp_ratio = mlp_ratio
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+ num_patches = self.patch_embed.num_patches
+ patches_resolution = self.patch_embed.patches_resolution
+ self.patches_resolution = patches_resolution
+
+ # merge non-overlapping patches into image
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+
+ # absolute position embedding
+ if self.ape:
+ self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+ trunc_normal_(self.absolute_pos_embed, std=.02)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ # stochastic depth
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
+
+ # build Residual Swin Transformer blocks (RSTB)
+ self.layers = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = RSTB(dim=embed_dim,
+ input_resolution=(patches_resolution[0],
+ patches_resolution[1]),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
+ norm_layer=norm_layer,
+ downsample=None,
+ use_checkpoint=use_checkpoint,
+ img_size=img_size,
+ patch_size=patch_size,
+ resi_connection=resi_connection
+
+ )
+ self.layers.append(layer)
+
+ if self.upsampler == 'pixelshuffle_hf':
+ self.layers_hf = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = RSTB(dim=embed_dim,
+ input_resolution=(patches_resolution[0],
+ patches_resolution[1]),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
+ norm_layer=norm_layer,
+ downsample=None,
+ use_checkpoint=use_checkpoint,
+ img_size=img_size,
+ patch_size=patch_size,
+ resi_connection=resi_connection
+
+ )
+ self.layers_hf.append(layer)
+
+ self.norm = norm_layer(self.num_features)
+
+ # build the last conv layer in deep feature extraction
+ if resi_connection == '1conv':
+ self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+ #####################################################################################################
+ ################################ 3, high quality image reconstruction ################################
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ elif self.upsampler == 'pixelshuffle_aux':
+ self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+ self.conv_before_upsample = nn.Sequential(
+ nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.conv_after_aux = nn.Sequential(
+ nn.Conv2d(3, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+ elif self.upsampler == 'pixelshuffle_hf':
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.upsample_hf = Upsample_hf(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+ self.conv_before_upsample_hf = nn.Sequential(
+ nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR (to save parameters)
+ self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+ (patches_resolution[0], patches_resolution[1]))
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR (less artifacts)
+ assert self.upscale == 4, 'only support x4 now.'
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ @torch.jit.ignore
+ def no_weight_decay(self):
+ return {'absolute_pos_embed'}
+
+ @torch.jit.ignore
+ def no_weight_decay_keywords(self):
+ return {'relative_position_bias_table'}
+
+ def check_image_size(self, x):
+ _, _, h, w = x.size()
+ mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+ mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+ x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+ return x
+
+ def forward_features(self, x):
+ x_size = (x.shape[2], x.shape[3])
+ x = self.patch_embed(x)
+ if self.ape:
+ x = x + self.absolute_pos_embed
+ x = self.pos_drop(x)
+
+ for layer in self.layers:
+ x = layer(x, x_size)
+
+ x = self.norm(x) # B L C
+ x = self.patch_unembed(x, x_size)
+
+ return x
+
+ def forward_features_hf(self, x):
+ x_size = (x.shape[2], x.shape[3])
+ x = self.patch_embed(x)
+ if self.ape:
+ x = x + self.absolute_pos_embed
+ x = self.pos_drop(x)
+
+ for layer in self.layers_hf:
+ x = layer(x, x_size)
+
+ x = self.norm(x) # B L C
+ x = self.patch_unembed(x, x_size)
+
+ return x
+
+ def forward(self, x):
+ H, W = x.shape[2:]
+ x = self.check_image_size(x)
+
+ self.mean = self.mean.type_as(x)
+ x = (x - self.mean) * self.img_range
+
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.conv_last(self.upsample(x))
+ elif self.upsampler == 'pixelshuffle_aux':
+ bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
+ bicubic = self.conv_bicubic(bicubic)
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ aux = self.conv_aux(x) # b, 3, LR_H, LR_W
+ x = self.conv_after_aux(aux)
+ x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
+ x = self.conv_last(x)
+ aux = aux / self.img_range + self.mean
+ elif self.upsampler == 'pixelshuffle_hf':
+ # for classical SR with HF
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x_before = self.conv_before_upsample(x)
+ x_out = self.conv_last(self.upsample(x_before))
+
+ x_hf = self.conv_first_hf(x_before)
+ x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
+ x_hf = self.conv_before_upsample_hf(x_hf)
+ x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
+ x = x_out + x_hf
+ x_hf = x_hf / self.img_range + self.mean
+
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.upsample(x)
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ x = self.conv_last(self.lrelu(self.conv_hr(x)))
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ x_first = self.conv_first(x)
+ res = self.conv_after_body(self.forward_features(x_first)) + x_first
+ x = x + self.conv_last(res)
+
+ x = x / self.img_range + self.mean
+ if self.upsampler == "pixelshuffle_aux":
+ return x[:, :, :H*self.upscale, :W*self.upscale], aux
+
+ elif self.upsampler == "pixelshuffle_hf":
+ x_out = x_out / self.img_range + self.mean
+ return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
+
+ else:
+ return x[:, :, :H*self.upscale, :W*self.upscale]
+
+ def flops(self):
+ flops = 0
+ H, W = self.patches_resolution
+ flops += H * W * 3 * self.embed_dim * 9
+ flops += self.patch_embed.flops()
+ for layer in self.layers:
+ flops += layer.flops()
+ flops += H * W * 3 * self.embed_dim * self.embed_dim
+ flops += self.upsample.flops()
+ return flops
+
+
+if __name__ == '__main__':
+ upscale = 4
+ window_size = 8
+ height = (1024 // upscale // window_size + 1) * window_size
+ width = (720 // upscale // window_size + 1) * window_size
+ model = Swin2SR(upscale=2, img_size=(height, width),
+ window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+ embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+ print(model)
+ print(height, width, model.flops() / 1e9)
+
+ x = torch.randn((1, 3, height, width))
+ x = model(x)
+ print(x.shape)
diff --git a/modules/postprocessing.py b/modules/postprocessing.py
index dc2ae3d45..db3645d75 100644
--- a/modules/postprocessing.py
+++ b/modules/postprocessing.py
@@ -1,120 +1,122 @@
-import os
-import tempfile
-from typing import List
-
-from PIL import Image
-
-from modules import shared, images, devices, scripts, scripts_postprocessing, generation_parameters_copypaste
-from modules.shared import opts
-
-
-def run_postprocessing(extras_mode, image, image_folder: List[tempfile.NamedTemporaryFile], input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
- devices.torch_gc()
- shared.state.begin('extras')
- image_data = []
- image_names = []
- image_fullnames = []
- image_ext = []
- outputs = []
- params = {}
- if extras_mode == 1:
- for img in image_folder:
- if isinstance(img, Image.Image):
- image = img
- fn = ''
- ext = None
- else:
- try:
- image = Image.open(os.path.abspath(img.name))
- except Exception as e:
- shared.log.error(f'Failed to open image: file="{img.name}" {e}')
- continue
- fn, ext = os.path.splitext(img.orig_name)
- image_fullnames.append(img.name)
- image_data.append(image)
- image_names.append(fn)
- image_ext.append(ext)
- shared.log.debug(f'Process: mode=batch inputs={len(image_folder)} images={len(image_data)}')
- elif extras_mode == 2:
- assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
- assert input_dir, 'input directory not selected'
- image_list = os.listdir(input_dir)
- for filename in image_list:
- try:
- image = Image.open(filename)
- except Exception as e:
- shared.log.error(f'Failed to open image: file="{filename}" {e}')
- continue
- image_fullnames.append(filename)
- image_data.append(image)
- image_names.append(filename)
- image_ext.append(None)
- shared.log.debug(f'Process: mode=folder inputs={input_dir} files={len(image_list)} images={len(image_data)}')
- else:
- image_data.append(image)
- image_names.append(None)
- image_ext.append(None)
- if extras_mode == 2 and output_dir != '':
- outpath = output_dir
- else:
- outpath = opts.outdir_samples or opts.outdir_extras_samples
- processed_images = []
- for image, name, ext in zip(image_data, image_names, image_ext): # pylint: disable=redefined-argument-from-local
- shared.log.debug(f'Process: image={image} {args}')
- infotext = ''
- if shared.state.interrupted:
- shared.log.debug('Postprocess interrupted')
- break
- if image is None:
- continue
- shared.state.textinfo = name
- pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
- scripts.scripts_postproc.run(pp, args)
- if opts.use_original_name_batch and name is not None:
- basename = os.path.splitext(os.path.basename(name))[0]
- else:
- basename = ''
- geninfo, items = images.read_info_from_image(image)
- params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
- for k, v in items.items():
- pp.image.info[k] = v
- if 'parameters' in items:
- infotext = items['parameters'] + ', '
- infotext = infotext + ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
- pp.image.info["postprocessing"] = infotext
- processed_images.append(pp.image)
- if save_output:
- images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=ext or opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=pp.image.info, forced_filename=None)
- if extras_mode != 2 or show_extras_results:
- outputs.append(pp.image)
- image.close()
- scripts.scripts_postproc.postprocess(processed_images, args)
-
- devices.torch_gc()
- return outputs, infotext, params
-
-
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True): #pylint: disable=unused-argument
- """old handler for API"""
-
- args = scripts.scripts_postproc.create_args_for_run({
- "Upscale": {
- "upscale_mode": resize_mode,
- "upscale_by": upscaling_resize,
- "upscale_to_width": upscaling_resize_w,
- "upscale_to_height": upscaling_resize_h,
- "upscale_crop": upscaling_crop,
- "upscaler_1_name": extras_upscaler_1,
- "upscaler_2_name": extras_upscaler_2,
- "upscaler_2_visibility": extras_upscaler_2_visibility,
- },
- "GFPGAN": {
- "gfpgan_visibility": gfpgan_visibility,
- },
- "CodeFormer": {
- "codeformer_visibility": codeformer_visibility,
- "codeformer_weight": codeformer_weight,
- },
- })
-
- return run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output=save_output)
+import os
+import tempfile
+from typing import List
+
+from PIL import Image
+
+from modules import shared, images, devices, scripts, scripts_postprocessing, generation_parameters_copypaste
+from modules.shared import opts
+
+
+def run_postprocessing(extras_mode, image, image_folder: List[tempfile.NamedTemporaryFile], input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
+ devices.torch_gc()
+ shared.state.begin('extras')
+ image_data = []
+ image_names = []
+ image_fullnames = []
+ image_ext = []
+ outputs = []
+ params = {}
+ infotext = ''
+ if extras_mode == 1:
+ for img in image_folder:
+ if isinstance(img, Image.Image):
+ image = img
+ fn = ''
+ ext = None
+ else:
+ try:
+ image = Image.open(os.path.abspath(img.name))
+ except Exception as e:
+ shared.log.error(f'Failed to open image: file="{img.name}" {e}')
+ continue
+ fn, ext = os.path.splitext(img.orig_name)
+ image_fullnames.append(img.name)
+ image_data.append(image)
+ image_names.append(fn)
+ image_ext.append(ext)
+ shared.log.debug(f'Process: mode=batch inputs={len(image_folder)} images={len(image_data)}')
+ elif extras_mode == 2:
+ assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+ assert input_dir, 'input directory not selected'
+ image_list = os.listdir(input_dir)
+ for filename in image_list:
+ fn = os.path.join(input_dir, filename)
+ try:
+ image = Image.open(fn)
+ except Exception as e:
+ shared.log.error(f'Failed to open image: file="{fn}" {e}')
+ continue
+ image_fullnames.append(fn)
+ image_data.append(image)
+ image_names.append(fn)
+ image_ext.append(None)
+ shared.log.debug(f'Process: mode=folder inputs={input_dir} files={len(image_list)} images={len(image_data)}')
+ else:
+ image_data.append(image)
+ image_names.append(None)
+ image_ext.append(None)
+ if extras_mode == 2 and output_dir != '':
+ outpath = output_dir
+ else:
+ outpath = opts.outdir_samples or opts.outdir_extras_samples
+ processed_images = []
+ for image, name, ext in zip(image_data, image_names, image_ext): # pylint: disable=redefined-argument-from-local
+ shared.log.debug(f'Process: image={image} {args}')
+ infotext = ''
+ if shared.state.interrupted:
+ shared.log.debug('Postprocess interrupted')
+ break
+ if image is None:
+ continue
+ shared.state.textinfo = name
+ pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
+ scripts.scripts_postproc.run(pp, args)
+ geninfo, items = images.read_info_from_image(image)
+ params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
+ for k, v in items.items():
+ pp.image.info[k] = v
+ if 'parameters' in items:
+ infotext = items['parameters'] + ', '
+ infotext = infotext + ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
+ pp.image.info["postprocessing"] = infotext
+ processed_images.append(pp.image)
+ if save_output:
+ if opts.use_original_name_batch and name is not None:
+ forced_filename = os.path.splitext(os.path.basename(name))[0]
+ images.save_image(pp.image, path=outpath, extension=ext or opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=pp.image.info, forced_filename=forced_filename)
+ else:
+ images.save_image(pp.image, path=outpath, extension=ext or opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=pp.image.info)
+ if extras_mode != 2 or show_extras_results:
+ outputs.append(pp.image)
+ image.close()
+ scripts.scripts_postproc.postprocess(processed_images, args)
+
+ devices.torch_gc()
+ return outputs, infotext, params
+
+
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True): #pylint: disable=unused-argument
+ """old handler for API"""
+
+ args = scripts.scripts_postproc.create_args_for_run({
+ "Upscale": {
+ "upscale_mode": resize_mode,
+ "upscale_by": upscaling_resize,
+ "upscale_to_width": upscaling_resize_w,
+ "upscale_to_height": upscaling_resize_h,
+ "upscale_crop": upscaling_crop,
+ "upscaler_1_name": extras_upscaler_1,
+ "upscaler_2_name": extras_upscaler_2,
+ "upscaler_2_visibility": extras_upscaler_2_visibility,
+ },
+ "GFPGAN": {
+ "gfpgan_visibility": gfpgan_visibility,
+ },
+ "CodeFormer": {
+ "codeformer_visibility": codeformer_visibility,
+ "codeformer_weight": codeformer_weight,
+ },
+ })
+
+ return run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output=save_output)
diff --git a/modules/processing.py b/modules/processing.py
index ca8ff520a..a567a81d7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,1404 +1,1413 @@
-import os
-import json
-import math
-import time
-import hashlib
-import random
-import warnings
-from contextlib import nullcontext
-from typing import Any, Dict, List
-from dataclasses import dataclass, field
-import torch
-import numpy as np
-import cv2
-from PIL import Image, ImageOps
-from skimage import exposure
-from ldm.data.util import AddMiDaS
-from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
-from einops import repeat, rearrange
-from blendmodes.blend import blendLayers, BlendType
-from installer import git_commit
-from modules import shared, devices, errors
-import modules.memstats
-import modules.lowvram
-import modules.masking
-import modules.paths
-import modules.scripts
-import modules.script_callbacks
-import modules.prompt_parser
-import modules.extra_networks
-import modules.face_restoration
-import modules.images as images
-import modules.styles
-import modules.sd_hijack
-import modules.sd_hijack_freeu
-import modules.sd_samplers
-import modules.sd_samplers_common
-import modules.sd_models
-import modules.sd_vae
-import modules.sd_vae_approx
-import modules.taesd.sd_vae_taesd
-import modules.generation_parameters_copypaste
-from modules.sd_hijack_hypertile import context_hypertile_vae, context_hypertile_unet, hypertile_set
-
-
-opt_C = 4
-opt_f = 8
-debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
-debug('Trace: PROCESS')
-
-
-def setup_color_correction(image):
- debug("Calibrating color correction")
- correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
- return correction_target
-
-
-def apply_color_correction(correction, original_image):
- shared.log.debug(f"Applying color correction: correction={correction} image={original_image}")
- image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(cv2.cvtColor(np.asarray(original_image), cv2.COLOR_RGB2LAB), correction, channel_axis=2), cv2.COLOR_LAB2RGB).astype("uint8"))
- image = blendLayers(image, original_image, BlendType.LUMINOSITY)
- return image
-
-
-def apply_overlay(image: Image, paste_loc, index, overlays):
- debug(f'Apply overlay: image={image} loc={paste_loc} index={index} overlays={overlays}')
- if overlays is None or index >= len(overlays):
- return image
- overlay = overlays[index]
- if paste_loc is not None:
- x, y, w, h = paste_loc
- if image.width != w or image.height != h or x != 0 or y != 0:
- base_image = Image.new('RGBA', (overlay.width, overlay.height))
- image = images.resize_image(2, image, w, h)
- base_image.paste(image, (x, y))
- image = base_image
- image = image.convert('RGBA')
- image.alpha_composite(overlay)
- image = image.convert('RGB')
- return image
-
-
-def create_binary_mask(image):
- if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
- image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
- else:
- image = image.convert('L')
- return image
-
-
-def images_tensor_to_samples(image, approximation=None, model=None): # pylint: disable=unused-argument
- if model is None:
- model = shared.sd_model
- model.first_stage_model.to(devices.dtype_vae)
- image = image.to(shared.device, dtype=devices.dtype_vae)
- image = image * 2 - 1
- if len(image) > 1:
- x_latent = torch.stack([
- model.get_first_stage_encoding(model.encode_first_stage(torch.unsqueeze(img, 0)))[0]
- for img in image
- ])
- else:
- x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
- return x_latent
-
-
-def txt2img_image_conditioning(sd_model, x, width, height):
- if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
- # The "masked-image" in this case will just be all zeros since the entire image is masked.
- image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
- image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
- # Add the fake full 1s mask to the first dimension.
- image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) # pylint: disable=not-callable
- image_conditioning = image_conditioning.to(x.dtype)
- return image_conditioning
- elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
- return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
- else:
- # Dummy zero conditioning if we're not using inpainting or unclip models.
- # Still takes up a bit of memory, but no encoder call.
- # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
- return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-
-def get_sampler_name(sampler_index: int, img: bool = False) -> str:
- if len(modules.sd_samplers.samplers) > sampler_index:
- sampler_name = modules.sd_samplers.samplers[sampler_index].name
- else:
- sampler_name = "UniPC"
- shared.log.warning(f'Sampler not found: index={sampler_index} available={[s.name for s in modules.sd_samplers.samplers]} fallback={sampler_name}')
- if img and sampler_name == "PLMS":
- sampler_name = "UniPC"
- shared.log.warning(f'Sampler not compatible: name=PLMS fallback={sampler_name}')
- return sampler_name
-
-
-@dataclass(repr=False)
-class StableDiffusionProcessing:
- """
- The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
- """
- def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, hr_sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, image_cfg_scale: float = None, clip_skip: int = 1, width: int = 512, height: int = 512, full_quality: bool = True, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, diffusers_guidance_rescale: float = 0.7, sag_scale: float = 0.0, resize_mode: int = 0, resize_name: str = 'None', scale_by: float = 0, selected_scale_tab: int = 0, hdr_clamp: bool = False, hdr_boundary: float = 4.0, hdr_threshold: float = 3.5, hdr_center: bool = False, hdr_channel_shift: float = 0.8, hdr_full_shift: float = 0.8, hdr_maximize: bool = False, hdr_max_center: float = 0.6, hdr_max_boundry: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): # pylint: disable=unused-argument
- self.outpath_samples: str = outpath_samples
- self.outpath_grids: str = outpath_grids
- self.prompt: str = prompt
- self.prompt_for_display: str = None
- self.negative_prompt: str = (negative_prompt or "")
- self.styles: list = styles or []
- self.seed: int = seed
- self.subseed: int = subseed
- self.subseed_strength: float = subseed_strength
- self.seed_resize_from_h: int = seed_resize_from_h
- self.seed_resize_from_w: int = seed_resize_from_w
- self.sampler_name: str = sampler_name
- self.hr_sampler_name: str = hr_sampler_name
- self.batch_size: int = batch_size
- self.n_iter: int = n_iter
- self.steps: int = steps
- self.hr_second_pass_steps = 0
- self.cfg_scale: float = cfg_scale
- self.scale_by: float = scale_by
- self.image_cfg_scale = image_cfg_scale
- self.diffusers_guidance_rescale = diffusers_guidance_rescale
- self.sag_scale = sag_scale
- if devices.backend == "ipex" and width == 1024 and height == 1024 and os.environ.get('DISABLE_IPEX_1024_WA', None) is None:
- width = 1080
- height = 1080
- self.width: int = width
- self.height: int = height
- self.full_quality: bool = full_quality
- self.restore_faces: bool = restore_faces
- self.tiling: bool = tiling
- self.do_not_save_samples: bool = do_not_save_samples
- self.do_not_save_grid: bool = do_not_save_grid
- self.extra_generation_params: dict = extra_generation_params or {}
- self.overlay_images = overlay_images
- self.eta = eta
- self.do_not_reload_embeddings = do_not_reload_embeddings
- self.paste_to = None
- self.color_corrections = None
- self.denoising_strength: float = denoising_strength
- self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
- self.override_settings_restore_afterwards = override_settings_restore_afterwards
- self.is_using_inpainting_conditioning = False
- self.disable_extra_networks = False
- self.token_merging_ratio = 0
- self.token_merging_ratio_hr = 0
- # self.scripts = modules.scripts.ScriptRunner() # set via property
- # self.script_args = script_args or [] # set via property
- self.per_script_args = {}
- self.all_prompts = None
- self.all_negative_prompts = None
- self.all_seeds = None
- self.all_subseeds = None
- self.clip_skip = clip_skip
- self.iteration = 0
- self.is_control = False
- self.is_hr_pass = False
- self.is_refiner_pass = False
- self.hr_force = False
- self.enable_hr = None
- self.hr_scale = None
- self.hr_upscaler = None
- self.hr_resize_x = 0
- self.hr_resize_y = 0
- self.hr_upscale_to_x = 0
- self.hr_upscale_to_y = 0
- self.truncate_x = 0
- self.truncate_y = 0
- self.applied_old_hires_behavior_to = None
- self.refiner_steps = 5
- self.refiner_start = 0
- self.refiner_prompt = ''
- self.refiner_negative = ''
- self.ops = []
- self.resize_mode: int = resize_mode
- self.resize_name: str = resize_name
- self.ddim_discretize = shared.opts.ddim_discretize
- self.s_min_uncond = shared.opts.s_min_uncond
- self.s_churn = shared.opts.s_churn
- self.s_noise = shared.opts.s_noise
- self.s_min = shared.opts.s_min
- self.s_max = shared.opts.s_max
- self.s_tmin = shared.opts.s_tmin
- self.s_tmax = float('inf') # not representable as a standard ui option
- shared.opts.data['clip_skip'] = clip_skip
- self.task_args = {}
- # a1111 compatibility items
- self.refiner_switch_at = 0
- self.hr_prompt = ''
- self.all_hr_prompts = []
- self.hr_negative_prompt = ''
- self.all_hr_negative_prompts = []
- self.comments = {}
- self.is_api = False
- self.scripts_value: modules.scripts.ScriptRunner = field(default=None, init=False)
- self.script_args_value: list = field(default=None, init=False)
- self.scripts_setup_complete: bool = field(default=False, init=False)
- # hdr
- self.hdr_clamp = hdr_clamp
- self.hdr_boundary = hdr_boundary
- self.hdr_threshold = hdr_threshold
- self.hdr_center = hdr_center
- self.hdr_channel_shift = hdr_channel_shift
- self.hdr_full_shift = hdr_full_shift
- self.hdr_maximize = hdr_maximize
- self.hdr_max_center = hdr_max_center
- self.hdr_max_boundry = hdr_max_boundry
- self.scheduled_prompt: bool = False
- self.prompt_embeds = []
- self.positive_pooleds = []
- self.negative_embeds = []
- self.negative_pooleds = []
-
-
- @property
- def sd_model(self):
- return shared.sd_model
-
- @property
- def scripts(self):
- return self.scripts_value
-
- @scripts.setter
- def scripts(self, value):
- self.scripts_value = value
- if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
- self.setup_scripts()
-
- @property
- def script_args(self):
- return self.script_args_value
-
- @script_args.setter
- def script_args(self, value):
- self.script_args_value = value
- if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
- self.setup_scripts()
-
- def setup_scripts(self):
- self.scripts_setup_complete = True
- self.scripts.setup_scrips(self, is_ui=not self.is_api)
-
- def comment(self, text):
- self.comments[text] = 1
-
- def txt2img_image_conditioning(self, x, width=None, height=None):
- self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
- return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
-
- def depth2img_image_conditioning(self, source_image):
- # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
- transformer = AddMiDaS(model_type="dpt_hybrid")
- transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
- midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
- midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
- conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
- conditioning = torch.nn.functional.interpolate(
- self.sd_model.depth_model(midas_in),
- size=conditioning_image.shape[2:],
- mode="bicubic",
- align_corners=False,
- )
- (depth_min, depth_max) = torch.aminmax(conditioning)
- conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
- return conditioning
-
- def edit_image_conditioning(self, source_image):
- conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
- return conditioning_image
-
- def unclip_image_conditioning(self, source_image):
- c_adm = self.sd_model.embedder(source_image)
- if self.sd_model.noise_augmentor is not None:
- noise_level = 0
- c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
- c_adm = torch.cat((c_adm, noise_level_emb), 1)
- return c_adm
-
- def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
- self.is_using_inpainting_conditioning = True
- # Handle the different mask inputs
- if image_mask is not None:
- if torch.is_tensor(image_mask):
- conditioning_mask = image_mask
- else:
- conditioning_mask = np.array(image_mask.convert("L"))
- conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
- conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
- # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
- conditioning_mask = torch.round(conditioning_mask)
- else:
- conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
- # Create another latent image, this time with a masked version of the original input.
- # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
- conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
- conditioning_image = torch.lerp(
- source_image,
- source_image * (1.0 - conditioning_mask),
- getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
- )
- # Encode the new masked image using first stage of network.
- conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
- # Create the concatenated conditioning tensor to be fed to `c_concat`
- conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
- conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
- image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
- image_conditioning = image_conditioning.to(device=shared.device, dtype=source_image.dtype)
- return image_conditioning
-
- def diffusers_image_conditioning(self, _source_image, latent_image, _image_mask=None):
- # shared.log.warning('Diffusers not implemented: img2img_image_conditioning')
- return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
-
- def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
- source_image = devices.cond_cast_float(source_image)
- # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
- # identify itself with a field common to all models. The conditioning_key is also hybrid.
- if shared.backend == shared.Backend.DIFFUSERS:
- return self.diffusers_image_conditioning(source_image, latent_image, image_mask)
- if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
- return self.depth2img_image_conditioning(source_image)
- if hasattr(self.sd_model, 'cond_stage_key') and self.sd_model.cond_stage_key == "edit":
- return self.edit_image_conditioning(source_image)
- if hasattr(self.sampler, 'conditioning_key') and self.sampler.conditioning_key in {'hybrid', 'concat'}:
- return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
- if hasattr(self.sampler, 'conditioning_key') and self.sampler.conditioning_key == "crossattn-adm":
- return self.unclip_image_conditioning(source_image)
- # Dummy zero conditioning if we're not using inpainting or depth model.
- return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
-
- def init(self, all_prompts, all_seeds, all_subseeds):
- pass
-
- def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
- raise NotImplementedError
-
- def close(self):
- self.sampler = None # pylint: disable=attribute-defined-outside-init
-
- def get_token_merging_ratio(self, for_hr=False):
- if for_hr:
- return self.token_merging_ratio_hr or shared.opts.token_merging_ratio_hr or self.token_merging_ratio or shared.opts.token_merging_ratio
- return self.token_merging_ratio or shared.opts.token_merging_ratio
-
-
-class Processed:
- def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
- self.images = images_list
- self.prompt = p.prompt
- self.negative_prompt = p.negative_prompt
- self.seed = seed
- self.subseed = subseed
- self.subseed_strength = p.subseed_strength
- self.info = info
- self.comments = comments
- self.width = p.width if hasattr(p, 'width') else (self.images[0].width if len(self.images) > 0 else 0)
- self.height = p.height if hasattr(p, 'height') else (self.images[0].height if len(self.images) > 0 else 0)
- self.sampler_name = p.sampler_name
- self.cfg_scale = p.cfg_scale
- self.image_cfg_scale = p.image_cfg_scale
- self.steps = p.steps
- self.batch_size = p.batch_size
- self.restore_faces = p.restore_faces
- self.face_restoration_model = shared.opts.face_restoration_model if p.restore_faces else None
- self.sd_model_hash = getattr(shared.sd_model, 'sd_model_hash', '')
- self.seed_resize_from_w = p.seed_resize_from_w
- self.seed_resize_from_h = p.seed_resize_from_h
- self.denoising_strength = p.denoising_strength
- self.extra_generation_params = p.extra_generation_params
- self.index_of_first_image = index_of_first_image
- self.styles = p.styles
- self.job_timestamp = shared.state.job_timestamp
- self.clip_skip = p.clip_skip
- self.eta = p.eta
- self.ddim_discretize = p.ddim_discretize
- self.s_churn = p.s_churn
- self.s_tmin = p.s_tmin
- self.s_tmax = p.s_tmax
- self.s_noise = p.s_noise
- self.s_min_uncond = p.s_min_uncond
- self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
- self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
- self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
- self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
- self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
- self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
- self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
- self.all_seeds = all_seeds or p.all_seeds or [self.seed]
- self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
- self.token_merging_ratio = p.token_merging_ratio
- self.token_merging_ratio_hr = p.token_merging_ratio_hr
- self.infotexts = infotexts or [info]
-
- def js(self):
- obj = {
- "prompt": self.all_prompts[0],
- "all_prompts": self.all_prompts,
- "negative_prompt": self.all_negative_prompts[0],
- "all_negative_prompts": self.all_negative_prompts,
- "seed": self.seed,
- "all_seeds": self.all_seeds,
- "subseed": self.subseed,
- "all_subseeds": self.all_subseeds,
- "subseed_strength": self.subseed_strength,
- "width": self.width,
- "height": self.height,
- "sampler_name": self.sampler_name,
- "cfg_scale": self.cfg_scale,
- "steps": self.steps,
- "batch_size": self.batch_size,
- "restore_faces": self.restore_faces,
- "face_restoration_model": self.face_restoration_model,
- "sd_model_hash": self.sd_model_hash,
- "seed_resize_from_w": self.seed_resize_from_w,
- "seed_resize_from_h": self.seed_resize_from_h,
- "denoising_strength": self.denoising_strength,
- "extra_generation_params": self.extra_generation_params,
- "index_of_first_image": self.index_of_first_image,
- "infotexts": self.infotexts,
- "styles": self.styles,
- "job_timestamp": self.job_timestamp,
- "clip_skip": self.clip_skip,
- # "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
- }
- return json.dumps(obj)
-
- def infotext(self, p: StableDiffusionProcessing, index):
- return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
-
- def get_token_merging_ratio(self, for_hr=False):
- return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
-
-
-def slerp(val, low, high): # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
- low_norm = low/torch.norm(low, dim=1, keepdim=True)
- high_norm = high/torch.norm(high, dim=1, keepdim=True)
- dot = (low_norm*high_norm).sum(1)
-
- if dot.mean() > 0.9995:
- return low * val + high * (1 - val)
-
- omega = torch.acos(dot)
- so = torch.sin(omega)
- res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
- return res
-
-
-def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
- eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
- xs = []
- # if we have multiple seeds, this means we are working with batch size>1; this then
- # enables the generation of additional tensors with noise that the sampler will use during its processing.
- # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
- # produce the same images as with two batches [100], [101].
- if p is not None and p.sampler is not None and (len(seeds) > 1 and shared.opts.enable_batch_seeds or eta_noise_seed_delta > 0):
- sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
- else:
- sampler_noises = None
- for i, seed in enumerate(seeds):
- noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
- subnoise = None
- if subseeds is not None:
- subseed = 0 if i >= len(subseeds) else subseeds[i]
- subnoise = devices.randn(subseed, noise_shape)
- # randn results depend on device; gpu and cpu get different results for same seed;
- # the way I see it, it's better to do this on CPU, so that everyone gets same result;
- # but the original script had it like this, so I do not dare change it for now because
- # it will break everyone's seeds.
- noise = devices.randn(seed, noise_shape)
- if subnoise is not None:
- noise = slerp(subseed_strength, noise, subnoise)
- if noise_shape != shape:
- x = devices.randn(seed, shape)
- dx = (shape[2] - noise_shape[2]) // 2
- dy = (shape[1] - noise_shape[1]) // 2
- w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
- h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
- tx = 0 if dx < 0 else dx
- ty = 0 if dy < 0 else dy
- dx = max(-dx, 0)
- dy = max(-dy, 0)
- x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
- noise = x
- if sampler_noises is not None:
- cnt = p.sampler.number_of_needed_noises(p)
- if eta_noise_seed_delta > 0:
- torch.manual_seed(seed + eta_noise_seed_delta)
- for j in range(cnt):
- sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
- xs.append(noise)
- if sampler_noises is not None:
- p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
- x = torch.stack(xs).to(shared.device)
- return x
-
-
-def decode_first_stage(model, x, full_quality=True):
- if not shared.opts.keep_incomplete and (shared.state.skipped or shared.state.interrupted):
- shared.log.debug(f'Decode VAE: skipped={shared.state.skipped} interrupted={shared.state.interrupted}')
- x_sample = torch.zeros((len(x), 3, x.shape[2] * 8, x.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
- return x_sample
- prev_job = shared.state.job
- shared.state.job = 'vae'
- with devices.autocast(disable = x.dtype==devices.dtype_vae):
- try:
- if full_quality:
- if hasattr(model, 'decode_first_stage'):
- x_sample = model.decode_first_stage(x)
- elif hasattr(model, 'vae'):
- x_sample = model.vae(x)
- else:
- x_sample = x
- shared.log.error('Decode VAE unknown model')
- else:
- x_sample = torch.zeros((len(x), 3, x.shape[2] * 8, x.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
- for i in range(len(x_sample)):
- x_sample[i] = modules.taesd.sd_vae_taesd.decode(x[i])
- except Exception as e:
- x_sample = x
- shared.log.error(f'Decode VAE: {e}')
- shared.state.job = prev_job
- return x_sample
-
-
-def get_fixed_seed(seed):
- if seed is None or seed == '' or seed == -1:
- return int(random.randrange(4294967294))
- return seed
-
-
-def fix_seed(p):
- p.seed = get_fixed_seed(p.seed)
- p.subseed = get_fixed_seed(p.subseed)
-
-
-def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=None, all_subseeds=None, comments=None, iteration=0, position_in_batch=0, index=None, all_negative_prompts=None):
- if not hasattr(shared.sd_model, 'sd_checkpoint_info'):
- return ''
- if index is None:
- index = position_in_batch + iteration * p.batch_size
- if all_prompts is None:
- all_prompts = p.all_prompts or [p.prompt]
- if all_negative_prompts is None:
- all_negative_prompts = p.all_negative_prompts or [p.negative_prompt]
- if all_seeds is None:
- all_seeds = p.all_seeds or [p.seed]
- if all_subseeds is None:
- all_subseeds = p.all_subseeds or [p.subseed]
- while len(all_prompts) <= index:
- all_prompts.append(all_prompts[-1])
- while len(all_seeds) <= index:
- all_seeds.append(all_seeds[-1])
- while len(all_subseeds) <= index:
- all_subseeds.append(all_subseeds[-1])
- while len(all_negative_prompts) <= index:
- all_negative_prompts.append(all_negative_prompts[-1])
- comment = ', '.join(comments) if comments is not None and type(comments) is list else None
- ops = list(set(p.ops))
- ops.reverse()
- args = {
- # basic
- "Steps": p.steps,
- "Seed": all_seeds[index],
- "Sampler": p.sampler_name,
- "CFG scale": p.cfg_scale,
- "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None,
- "Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None,
- "Index": f'{p.iteration + 1}x{index + 1}' if (p.n_iter > 1 or p.batch_size > 1) and index >= 0 else None,
- "Parser": shared.opts.prompt_attention,
- "Model": None if (not shared.opts.add_model_name_to_info) or (not shared.sd_model.sd_checkpoint_info.model_name) else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''),
- "Model hash": getattr(p, 'sd_model_hash', None if (not shared.opts.add_model_hash_to_info) or (not shared.sd_model.sd_model_hash) else shared.sd_model.sd_model_hash),
- "VAE": (None if not shared.opts.add_model_name_to_info or modules.sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(modules.sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD',
- "Seed resize from": None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}",
- "Clip skip": p.clip_skip if p.clip_skip > 1 else None,
- "Prompt2": p.refiner_prompt if len(p.refiner_prompt) > 0 else None,
- "Negative2": p.refiner_negative if len(p.refiner_negative) > 0 else None,
- "Styles": "; ".join(p.styles) if p.styles is not None and len(p.styles) > 0 else None,
- "Tiling": p.tiling if p.tiling else None,
- # sdnext
- "Backend": 'Diffusers' if shared.backend == shared.Backend.DIFFUSERS else 'Original',
- "App": 'SD.Next',
- "Version": git_commit,
- "Comment": comment,
- "Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none',
- }
- if 'txt2img' in p.ops:
- pass
- if shared.backend == shared.Backend.ORIGINAL:
- args["Variation seed"] = None if p.subseed_strength == 0 else all_subseeds[index],
- args["Variation strength"] = None if p.subseed_strength == 0 else p.subseed_strength,
- if 'hires' in p.ops or 'upscale' in p.ops:
- args["Second pass"] = p.enable_hr
- args["Hires force"] = p.hr_force
- args["Hires steps"] = p.hr_second_pass_steps
- args["Hires upscaler"] = p.hr_upscaler
- args["Hires upscale"] = p.hr_scale
- args["Hires resize"] = f"{p.hr_resize_x}x{p.hr_resize_y}"
- args["Hires size"] = f"{p.hr_upscale_to_x}x{p.hr_upscale_to_y}"
- args["Denoising strength"] = p.denoising_strength
- args["Hires sampler"] = p.hr_sampler_name
- args["Image CFG scale"] = p.image_cfg_scale
- args["CFG rescale"] = p.diffusers_guidance_rescale
- if 'refine' in p.ops:
- args["Second pass"] = p.enable_hr
- args["Refiner"] = None if (not shared.opts.add_model_name_to_info) or (not shared.sd_refiner) or (not shared.sd_refiner.sd_checkpoint_info.model_name) else shared.sd_refiner.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')
- args['Image CFG scale'] = p.image_cfg_scale
- args['Refiner steps'] = p.refiner_steps
- args['Refiner start'] = p.refiner_start
- args["Hires steps"] = p.hr_second_pass_steps
- args["Hires sampler"] = p.hr_sampler_name
- args["CFG rescale"] = p.diffusers_guidance_rescale
- if 'img2img' in p.ops or 'inpaint' in p.ops:
- args["Init image size"] = f"{getattr(p, 'init_img_width', 0)}x{getattr(p, 'init_img_height', 0)}"
- args["Init image hash"] = getattr(p, 'init_img_hash', None)
- args["Mask weight"] = getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None
- args['Resize scale'] = getattr(p, 'scale_by', None)
- args["Mask blur"] = p.mask_blur if getattr(p, 'mask', None) is not None and getattr(p, 'mask_blur', 0) > 0 else None
- args["Denoising strength"] = getattr(p, 'denoising_strength', None)
- if args["Size"] is None:
- args["Size"] = args["Init image size"]
- # lookup by index
- if getattr(p, 'resize_mode', None) is not None:
- args['Resize mode'] = shared.resize_modes[p.resize_mode]
- if 'face' in p.ops:
- args["Face restoration"] = shared.opts.face_restoration_model
- if 'color' in p.ops:
- args["Color correction"] = True
- # embeddings
- if hasattr(modules.sd_hijack.model_hijack, 'embedding_db') and len(modules.sd_hijack.model_hijack.embedding_db.embeddings_used) > 0: # this is for original hijaacked models only, diffusers are handled separately
- args["Embeddings"] = ', '.join(modules.sd_hijack.model_hijack.embedding_db.embeddings_used)
- # samplers
- args["Sampler ENSD"] = shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and modules.sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) else None
- args["Sampler ENSM"] = p.initial_noise_multiplier if getattr(p, 'initial_noise_multiplier', 1.0) != 1.0 else None
- args['Sampler order'] = shared.opts.schedulers_solver_order if shared.opts.schedulers_solver_order != shared.opts.data_labels.get('schedulers_solver_order').default else None
- if shared.backend == shared.Backend.DIFFUSERS:
- args['Sampler beta schedule'] = shared.opts.schedulers_beta_schedule if shared.opts.schedulers_beta_schedule != shared.opts.data_labels.get('schedulers_beta_schedule').default else None
- args['Sampler beta start'] = shared.opts.schedulers_beta_start if shared.opts.schedulers_beta_start != shared.opts.data_labels.get('schedulers_beta_start').default else None
- args['Sampler beta end'] = shared.opts.schedulers_beta_end if shared.opts.schedulers_beta_end != shared.opts.data_labels.get('schedulers_beta_end').default else None
- args['Sampler DPM solver'] = shared.opts.schedulers_dpm_solver if shared.opts.schedulers_dpm_solver != shared.opts.data_labels.get('schedulers_dpm_solver').default else None
- if shared.backend == shared.Backend.ORIGINAL:
- args['Sampler brownian'] = shared.opts.schedulers_brownian_noise if shared.opts.schedulers_brownian_noise != shared.opts.data_labels.get('schedulers_brownian_noise').default else None
- args['Sampler discard'] = shared.opts.schedulers_discard_penultimate if shared.opts.schedulers_discard_penultimate != shared.opts.data_labels.get('schedulers_discard_penultimate').default else None
- args['Sampler dyn threshold'] = shared.opts.schedulers_use_thresholding if shared.opts.schedulers_use_thresholding != shared.opts.data_labels.get('schedulers_use_thresholding').default else None
- args['Sampler karras'] = shared.opts.schedulers_use_karras if shared.opts.schedulers_use_karras != shared.opts.data_labels.get('schedulers_use_karras').default else None
- args['Sampler low order'] = shared.opts.schedulers_use_loworder if shared.opts.schedulers_use_loworder != shared.opts.data_labels.get('schedulers_use_loworder').default else None
- args['Sampler quantization'] = shared.opts.enable_quantization if shared.opts.enable_quantization != shared.opts.data_labels.get('enable_quantization').default else None
- args['Sampler sigma'] = shared.opts.schedulers_sigma if shared.opts.schedulers_sigma != shared.opts.data_labels.get('schedulers_sigma').default else None
- args['Sampler sigma min'] = shared.opts.s_min if shared.opts.s_min != shared.opts.data_labels.get('s_min').default else None
- args['Sampler sigma max'] = shared.opts.s_max if shared.opts.s_max != shared.opts.data_labels.get('s_max').default else None
- args['Sampler sigma churn'] = shared.opts.s_churn if shared.opts.s_churn != shared.opts.data_labels.get('s_churn').default else None
- args['Sampler sigma uncond'] = shared.opts.s_churn if shared.opts.s_churn != shared.opts.data_labels.get('s_churn').default else None
- args['Sampler sigma noise'] = shared.opts.s_noise if shared.opts.s_noise != shared.opts.data_labels.get('s_noise').default else None
- args['Sampler sigma tmin'] = shared.opts.s_tmin if shared.opts.s_tmin != shared.opts.data_labels.get('s_tmin').default else None
- # tome
- token_merging_ratio = p.get_token_merging_ratio()
- token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True) if p.enable_hr else None
- args['ToMe'] = token_merging_ratio if token_merging_ratio != 0 else None
- args['ToMe hires'] = token_merging_ratio_hr if token_merging_ratio_hr != 0 else None
-
- args.update(p.extra_generation_params)
- params_text = ", ".join([k if k == v else f'{k}: {modules.generation_parameters_copypaste.quote(v)}' for k, v in args.items() if v is not None])
- negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
- infotext = f"{all_prompts[index]}{negative_prompt_text}\n{params_text}".strip()
- return infotext
-
-
-def process_images(p: StableDiffusionProcessing) -> Processed:
- debug(f'Process images: {vars(p)}')
- if not hasattr(p.sd_model, 'sd_checkpoint_info'):
- return None
- if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
- p.scripts.before_process(p)
- stored_opts = {}
- for k, v in p.override_settings.copy().items():
- if shared.opts.data.get(k, None) is None and shared.opts.data_labels.get(k, None) is None:
- continue
- orig = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default
- if orig == v or (type(orig) == str and os.path.splitext(orig)[0] == v):
- p.override_settings.pop(k, None)
- for k in p.override_settings.keys():
- stored_opts[k] = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default
- res = None
- try:
- # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
- if p.override_settings.get('sd_model_checkpoint', None) is not None and modules.sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
- shared.log.warning(f"Override not found: checkpoint={p.override_settings.get('sd_model_checkpoint', None)}")
- p.override_settings.pop('sd_model_checkpoint', None)
- modules.sd_models.reload_model_weights()
- if p.override_settings.get('sd_model_refiner', None) is not None and modules.sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_refiner')) is None:
- shared.log.warning(f"Override not found: refiner={p.override_settings.get('sd_model_refiner', None)}")
- p.override_settings.pop('sd_model_refiner', None)
- modules.sd_models.reload_model_weights()
- if p.override_settings.get('sd_vae', None) is not None:
- if p.override_settings.get('sd_vae', None) == 'TAESD':
- p.full_quality = False
- p.override_settings.pop('sd_vae', None)
- if p.override_settings.get('Hires upscaler', None) is not None:
- p.enable_hr = True
- if len(p.override_settings.keys()) > 0:
- shared.log.debug(f'Override: {p.override_settings}')
- for k, v in p.override_settings.items():
- setattr(shared.opts, k, v)
- if k == 'sd_model_checkpoint':
- modules.sd_models.reload_model_weights()
- if k == 'sd_vae':
- modules.sd_vae.reload_vae_weights()
-
- shared.prompt_styles.apply_styles_to_extra(p)
- if not shared.opts.cuda_compile:
- modules.sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
- modules.sd_hijack_freeu.apply_freeu(p, shared.backend == shared.Backend.ORIGINAL)
-
- modules.script_callbacks.before_process_callback(p)
-
- if shared.cmd_opts.profile:
- import cProfile
- profile_python = cProfile.Profile()
- profile_python.enable()
- with context_hypertile_vae(p), context_hypertile_unet(p):
- import torch.profiler # pylint: disable=redefined-outer-name
- activities=[torch.profiler.ProfilerActivity.CPU]
- if torch.cuda.is_available():
- activities.append(torch.profiler.ProfilerActivity.CUDA)
- shared.log.debug(f'Torch profile: activities={activities}')
- if shared.profiler is None:
- shared.profiler = torch.profiler.profile(activities=activities, profile_memory=True, with_modules=True)
- shared.profiler.start()
- shared.profiler.step()
- res = process_images_inner(p)
- errors.profile_torch(shared.profiler, 'Process')
- errors.profile(profile_python, 'Process')
- else:
- with context_hypertile_vae(p), context_hypertile_unet(p):
- res = process_images_inner(p)
-
- finally:
- if not shared.opts.cuda_compile:
- modules.sd_models.apply_token_merging(p.sd_model, 0)
- modules.script_callbacks.after_process_callback(p)
- if p.override_settings_restore_afterwards: # restore opts to original state
- for k, v in stored_opts.items():
- setattr(shared.opts, k, v)
- if k == 'sd_model_checkpoint':
- modules.sd_models.reload_model_weights()
- if k == 'sd_model_refiner':
- modules.sd_models.reload_model_weights()
- if k == 'sd_vae':
- modules.sd_vae.reload_vae_weights()
- return res
-
-
-def validate_sample(tensor):
- if not isinstance(tensor, np.ndarray) and not isinstance(tensor, torch.Tensor):
- return tensor
- if tensor.dtype == torch.bfloat16: # numpy does not support bf16
- tensor = tensor.to(torch.float16)
- if isinstance(tensor, torch.Tensor) and hasattr(tensor, 'detach'):
- sample = tensor.detach().cpu().numpy()
- elif isinstance(tensor, np.ndarray):
- sample = tensor
- else:
- shared.log.warning(f'Unknown sample type: {type(tensor)}')
- sample = 255.0 * np.moveaxis(sample, 0, 2) if shared.backend == shared.Backend.ORIGINAL else 255.0 * sample
- with warnings.catch_warnings(record=True) as w:
- cast = sample.astype(np.uint8)
- if len(w) > 0:
- nans = np.isnan(sample).sum()
- shared.log.error(f'Failed to validate samples: sample={sample.shape} invalid={nans}')
- cast = np.nan_to_num(sample)
- minimum, maximum, mean = np.min(cast), np.max(cast), np.mean(cast)
- cast = cast.astype(np.uint8)
- shared.log.warning(f'Attempted to correct samples: min={minimum:.2f} max={maximum:.2f} mean={mean:.2f}')
- return cast
-
-
-def process_init(p: StableDiffusionProcessing):
- seed = get_fixed_seed(p.seed)
- subseed = get_fixed_seed(p.subseed)
- if type(p.prompt) == list:
- p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
- else:
- p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
- if type(p.negative_prompt) == list:
- p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
- else:
- p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
- if type(seed) == list:
- p.all_seeds = seed
- else:
- p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
- if type(subseed) == list:
- p.all_subseeds = subseed
- else:
- p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
-
-
-def process_images_inner(p: StableDiffusionProcessing) -> Processed:
- """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
-
- if type(p.prompt) == list:
- assert len(p.prompt) > 0
- else:
- assert p.prompt is not None
-
- if shared.backend == shared.Backend.ORIGINAL:
- modules.sd_hijack.model_hijack.apply_circular(p.tiling)
- modules.sd_hijack.model_hijack.clear_comments()
- comments = {}
- infotexts = []
- output_images = []
- cached_uc = [None, None]
- cached_c = [None, None]
-
- process_init(p)
- if os.path.exists(shared.opts.embeddings_dir) and not p.do_not_reload_embeddings and shared.backend == shared.Backend.ORIGINAL:
- modules.sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=False)
- if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
- p.scripts.process(p)
-
-
- def get_conds_with_caching(function, required_prompts, steps, cache):
- if cache[0] is not None and (required_prompts, steps) == cache[0]:
- return cache[1]
- with devices.autocast():
- cache[1] = function(shared.sd_model, required_prompts, steps)
- cache[0] = (required_prompts, steps)
- return cache[1]
-
- def infotext(_inxex=0): # dummy function overriden if there are iterations
- return ''
-
- ema_scope_context = p.sd_model.ema_scope if shared.backend == shared.Backend.ORIGINAL else nullcontext
- shared.state.job_count = p.n_iter
- with devices.inference_context(), ema_scope_context():
- t0 = time.time()
- with devices.autocast():
- p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
- extra_network_data = None
- debug(f'Processing inner: args={vars(p)}')
- for n in range(p.n_iter):
- p.iteration = n
- if shared.state.skipped:
- shared.log.debug(f'Process skipped: {n}/{p.n_iter}')
- shared.state.skipped = False
- continue
- if shared.state.interrupted:
- shared.log.debug(f'Process interrupted: {n}/{p.n_iter}')
- break
- p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
- p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
- p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
- p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
- if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
- p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
- if len(p.prompts) == 0:
- break
- p.prompts, extra_network_data = modules.extra_networks.parse_prompts(p.prompts)
- if not p.disable_extra_networks:
- with devices.autocast():
- modules.extra_networks.activate(p, extra_network_data)
- if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
- p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
- step_multiplier = 1
- sampler_config = modules.sd_samplers.find_sampler_config(p.sampler_name)
- step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
-
- if shared.backend == shared.Backend.ORIGINAL:
- uc = get_conds_with_caching(modules.prompt_parser.get_learned_conditioning, p.negative_prompts, p.steps * step_multiplier, cached_uc)
- c = get_conds_with_caching(modules.prompt_parser.get_multicond_learned_conditioning, p.prompts, p.steps * step_multiplier, cached_c)
- if len(modules.sd_hijack.model_hijack.comments) > 0:
- for comment in modules.sd_hijack.model_hijack.comments:
- comments[comment] = 1
- with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
- x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae), p.full_quality)[0].cpu() for i in range(samples_ddim.size(0))]
- try:
- for x in x_samples_ddim:
- devices.test_for_nans(x, "vae")
- except devices.NansException as e:
- if not shared.opts.no_half and not shared.opts.no_half_vae and shared.cmd_opts.rollback_vae:
- shared.log.warning('Tensor with all NaNs was produced in VAE')
- devices.dtype_vae = torch.bfloat16
- vae_file, vae_source = modules.sd_vae.resolve_vae(p.sd_model.sd_model_checkpoint)
- modules.sd_vae.load_vae(p.sd_model, vae_file, vae_source)
- x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae), p.full_quality)[0].cpu() for i in range(samples_ddim.size(0))]
- for x in x_samples_ddim:
- devices.test_for_nans(x, "vae")
- else:
- raise e
- x_samples_ddim = torch.stack(x_samples_ddim).float()
- x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
- del samples_ddim
-
- elif shared.backend == shared.Backend.DIFFUSERS:
- from modules.processing_diffusers import process_diffusers
- x_samples_ddim = process_diffusers(p)
- else:
- raise ValueError(f"Unknown backend {shared.backend}")
-
- if not shared.opts.keep_incomplete and shared.state.interrupted:
- x_samples_ddim = []
-
- if shared.cmd_opts.lowvram or shared.cmd_opts.medvram and shared.backend == shared.Backend.ORIGINAL:
- modules.lowvram.send_everything_to_cpu()
- devices.torch_gc()
- if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
- p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
- if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
- p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
- p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
- batch_params = modules.scripts.PostprocessBatchListArgs(list(x_samples_ddim))
- p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
- x_samples_ddim = batch_params.images
-
- def infotext(index): # pylint: disable=function-redefined # noqa: F811
- return create_infotext(p, p.prompts, p.seeds, p.subseeds, index=index, all_negative_prompts=p.negative_prompts)
-
- for i, x_sample in enumerate(x_samples_ddim):
- p.batch_index = i
- if type(x_sample) == Image.Image:
- image = x_sample
- x_sample = np.array(x_sample)
- else:
- x_sample = validate_sample(x_sample)
- image = Image.fromarray(x_sample)
- if p.restore_faces:
- if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_face_restoration:
- orig = p.restore_faces
- p.restore_faces = False
- info = infotext(i)
- p.restore_faces = orig
- images.save_image(Image.fromarray(x_sample), path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-face-restore")
- p.ops.append('face')
- x_sample = modules.face_restoration.restore_faces(x_sample)
- image = Image.fromarray(x_sample)
- if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
- pp = modules.scripts.PostprocessImageArgs(image)
- p.scripts.postprocess_image(p, pp)
- image = pp.image
- if p.color_corrections is not None and i < len(p.color_corrections):
- if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_color_correction:
- orig = p.color_corrections
- p.color_corrections = None
- info = infotext(i)
- p.color_corrections = orig
- image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
- images.save_image(image_without_cc, path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-color-correct")
- p.ops.append('color')
- image = apply_color_correction(p.color_corrections[i], image)
- image = apply_overlay(image, p.paste_to, i, p.overlay_images)
- text = infotext(i)
- infotexts.append(text)
- image.info["parameters"] = text
- output_images.append(image)
- if shared.opts.samples_save and not p.do_not_save_samples:
- images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p) # main save image
- if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([shared.opts.save_mask, shared.opts.save_mask_composite, shared.opts.return_mask, shared.opts.return_mask_composite]):
- image_mask = p.mask_for_overlay.convert('RGB')
- image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(3, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
- if shared.opts.save_mask:
- images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p, suffix="-mask")
- if shared.opts.save_mask_composite:
- images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p, suffix="-mask-composite")
- if shared.opts.return_mask:
- output_images.append(image_mask)
- if shared.opts.return_mask_composite:
- output_images.append(image_mask_composite)
- del x_samples_ddim
- devices.torch_gc()
-
- t1 = time.time()
- shared.log.info(f'Processed: images={len(output_images)} time={t1 - t0:.2f} its={(p.steps * len(output_images)) / (t1 - t0):.2f} memory={modules.memstats.memory_stats()}')
-
- p.color_corrections = None
- index_of_first_image = 0
- if (shared.opts.return_grid or shared.opts.grid_save) and not p.do_not_save_grid and len(output_images) > 1:
- if images.check_grid_size(output_images):
- grid = images.image_grid(output_images, p.batch_size)
- if shared.opts.return_grid:
- text = infotext(-1)
- infotexts.insert(0, text)
- grid.info["parameters"] = text
- output_images.insert(0, grid)
- index_of_first_image = 1
- if shared.opts.grid_save:
- images.save_image(grid, p.outpath_grids, "", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=infotext(-1), p=p, grid=True, suffix="-grid") # main save grid
-
- if not p.disable_extra_networks:
- modules.extra_networks.deactivate(p, extra_network_data)
-
- res = Processed(
- p,
- images_list=output_images,
- seed=p.all_seeds[0],
- info=infotext(0),
- comments="\n".join(comments),
- subseed=p.all_subseeds[0],
- index_of_first_image=index_of_first_image,
- infotexts=infotexts,
- )
- if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner) and not (shared.state.interrupted or shared.state.skipped):
- p.scripts.postprocess(p, res)
- return res
-
-
-def old_hires_fix_first_pass_dimensions(width, height):
- """old algorithm for auto-calculating first pass size"""
- desired_pixel_count = 512 * 512
- actual_pixel_count = width * height
- scale = math.sqrt(desired_pixel_count / actual_pixel_count)
- width = math.ceil(scale * width / 64) * 64
- height = math.ceil(scale * height / 64) * 64
- return width, height
-
-
-class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-
- def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_force: bool = False, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
-
- super().__init__(**kwargs)
- if devices.backend == "ipex" and os.environ.get('DISABLE_IPEX_1024_WA', None) is None:
- width_curse = bool(hr_resize_x == 1024 and self.height * (hr_resize_x / self.width) == 1024)
- height_curse = bool(hr_resize_y == 1024 and self.width * (hr_resize_y / self.height) == 1024)
- if (width_curse != height_curse) or (height_curse and width_curse):
- if width_curse:
- hr_resize_x = 1080
- if height_curse:
- hr_resize_y = 1080
- if self.width * hr_scale == 1024 and self.height * hr_scale == 1024:
- hr_scale = 1080 / self.width
- if firstphase_width * hr_scale == 1024 and firstphase_height * hr_scale == 1024:
- hr_scale = 1080 / firstphase_width
- self.enable_hr = enable_hr
- self.denoising_strength = denoising_strength
- self.hr_scale = hr_scale
- self.hr_upscaler = hr_upscaler
- self.hr_force = hr_force
- self.hr_second_pass_steps = hr_second_pass_steps
- self.hr_resize_x = hr_resize_x
- self.hr_resize_y = hr_resize_y
- self.hr_upscale_to_x = hr_resize_x
- self.hr_upscale_to_y = hr_resize_y
- if firstphase_width != 0 or firstphase_height != 0:
- self.hr_upscale_to_x = self.width
- self.hr_upscale_to_y = self.height
- self.width = firstphase_width
- self.height = firstphase_height
- self.truncate_x = 0
- self.truncate_y = 0
- self.applied_old_hires_behavior_to = None
- self.refiner_steps = refiner_steps
- self.refiner_start = refiner_start
- self.refiner_prompt = refiner_prompt
- self.refiner_negative = refiner_negative
- self.sampler = None
- self.scripts = None
- self.script_args = []
-
- def init(self, all_prompts, all_seeds, all_subseeds):
- if shared.backend == shared.Backend.DIFFUSERS:
- shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE)
- self.width = self.width or 512
- self.height = self.height or 512
-
- def init_hr(self):
- if self.hr_resize_x == 0 and self.hr_resize_y == 0:
- self.hr_upscale_to_x = int(self.width * self.hr_scale)
- self.hr_upscale_to_y = int(self.height * self.hr_scale)
- else:
- if self.hr_resize_y == 0:
- self.hr_upscale_to_x = self.hr_resize_x
- self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
- elif self.hr_resize_x == 0:
- self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
- self.hr_upscale_to_y = self.hr_resize_y
- else:
- target_w = self.hr_resize_x
- target_h = self.hr_resize_y
- src_ratio = self.width / self.height
- dst_ratio = self.hr_resize_x / self.hr_resize_y
- if src_ratio < dst_ratio:
- self.hr_upscale_to_x = self.hr_resize_x
- self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
- else:
- self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
- self.hr_upscale_to_y = self.hr_resize_y
- self.truncate_x = (self.hr_upscale_to_x - target_w) // 8
- self.truncate_y = (self.hr_upscale_to_y - target_h) // 8
- # special case: the user has chosen to do nothing
- if (self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height) or self.hr_upscaler is None or self.hr_upscaler == 'None':
- self.is_hr_pass = False
- return
- self.is_hr_pass = True
- hypertile_set(self, hr=True)
- shared.state.job_count = 2 * self.n_iter
- shared.log.debug(f'Init hires: upscaler="{self.hr_upscaler}" sampler="{self.hr_sampler_name}" resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')
-
- def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
-
- latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "None")
- if latent_scale_mode is not None:
- self.hr_force = False # no need to force anything
- if self.enable_hr and (latent_scale_mode is None or self.hr_force):
- if len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) == 0:
- shared.log.warning(f"Cannot find upscaler for hires: {self.hr_upscaler}")
- self.enable_hr = False
-
- self.ops.append('txt2img')
- hypertile_set(self)
- self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model)
- if hasattr(self.sampler, "initialize"):
- self.sampler.initialize(self)
- x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
- samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
- shared.state.nextjob()
- if not self.enable_hr or shared.state.interrupted or shared.state.skipped:
- return samples
-
- self.init_hr()
- if self.is_hr_pass:
- prev_job = shared.state.job
- target_width = self.hr_upscale_to_x
- target_height = self.hr_upscale_to_y
- decoded_samples = None
- if shared.opts.save and shared.opts.save_images_before_highres_fix and not self.do_not_save_samples:
- decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality)
- decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
- for i, x_sample in enumerate(decoded_samples):
- x_sample = validate_sample(x_sample)
- image = Image.fromarray(x_sample)
- bak_extra_generation_params, bak_restore_faces = self.extra_generation_params, self.restore_faces
- self.extra_generation_params = {}
- self.restore_faces = False
- info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=i)
- self.extra_generation_params, self.restore_faces = bak_extra_generation_params, bak_restore_faces
- images.save_image(image, self.outpath_samples, "", seeds[i], prompts[i], shared.opts.samples_format, info=info, suffix="-before-hires")
- if latent_scale_mode is None or self.hr_force: # non-latent upscaling
- shared.state.job = 'upscale'
- if decoded_samples is None:
- decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality)
- decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
- batch_images = []
- for _i, x_sample in enumerate(decoded_samples):
- x_sample = validate_sample(x_sample)
- image = Image.fromarray(x_sample)
- image = images.resize_image(1, image, target_width, target_height, upscaler_name=self.hr_upscaler)
- image = np.array(image).astype(np.float32) / 255.0
- image = np.moveaxis(image, 2, 0)
- batch_images.append(image)
- resized_samples = torch.from_numpy(np.array(batch_images))
- resized_samples = resized_samples.to(device=shared.device, dtype=devices.dtype_vae)
- resized_samples = 2.0 * resized_samples - 1.0
- if shared.opts.sd_vae_sliced_encode and len(decoded_samples) > 1:
- samples = torch.stack([self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(torch.unsqueeze(resized_sample, 0)))[0] for resized_sample in resized_samples])
- else:
- # TODO add TEASD support
- samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(resized_samples))
- image_conditioning = self.img2img_image_conditioning(resized_samples, samples)
- else:
- samples = torch.nn.functional.interpolate(samples, size=(target_height // 8, target_width // 8), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
- if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
- image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality), samples)
- else:
- image_conditioning = self.txt2img_image_conditioning(samples.to(dtype=devices.dtype_vae))
- if self.hr_sampler_name == "PLMS":
- self.hr_sampler_name = 'UniPC'
- if self.hr_force or latent_scale_mode is not None:
- shared.state.job = 'hires'
- if self.denoising_strength > 0:
- self.ops.append('hires')
- devices.torch_gc() # GC now before running the next img2img to prevent running out of memory
- self.sampler = modules.sd_samplers.create_sampler(self.hr_sampler_name or self.sampler_name, self.sd_model)
- if hasattr(self.sampler, "initialize"):
- self.sampler.initialize(self)
- samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
- noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
- modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
- hypertile_set(self, hr=True)
- samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
- modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
- else:
- self.ops.append('upscale')
- x = None
- self.is_hr_pass = False
- shared.state.job = prev_job
- shared.state.nextjob()
-
- return samples
-
-
-class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-
- def __init__(self, init_images: list = None, resize_mode: int = 0, resize_name: str = 'None', denoising_strength: float = 0.3, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, scale_by: float = 1, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
- super().__init__(**kwargs)
- self.init_images = init_images
- self.resize_mode: int = resize_mode
- self.resize_name: str = resize_name
- self.denoising_strength: float = denoising_strength
- self.image_cfg_scale: float = image_cfg_scale
- self.init_latent = None
- self.image_mask = mask
- self.latent_mask = None
- self.mask_for_overlay = None
- self.mask_blur_x = mask_blur # a1111 compatibility item
- self.mask_blur_y = mask_blur # a1111 compatibility item
- self.mask_blur = mask_blur
- self.inpainting_fill = inpainting_fill
- self.inpaint_full_res = inpaint_full_res
- self.inpaint_full_res_padding = inpaint_full_res_padding
- self.inpainting_mask_invert = inpainting_mask_invert
- self.initial_noise_multiplier = shared.opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
- self.mask = None
- self.nmask = None
- self.image_conditioning = None
- self.refiner_steps = refiner_steps
- self.refiner_start = refiner_start
- self.refiner_prompt = refiner_prompt
- self.refiner_negative = refiner_negative
- self.enable_hr = None
- self.is_batch = False
- self.scale_by = scale_by
- self.sampler = None
- self.scripts = None
- self.script_args = []
-
- def init(self, all_prompts, all_seeds, all_subseeds):
- if shared.backend == shared.Backend.DIFFUSERS and self.image_mask is not None and not self.is_control:
- shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING)
- elif shared.backend == shared.Backend.DIFFUSERS and self.image_mask is None and not self.is_control:
- shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
-
- if self.sampler_name == "PLMS":
- self.sampler_name = 'UniPC'
- self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model)
- if hasattr(self.sampler, "initialize"):
- self.sampler.initialize(self)
-
- if self.image_mask is not None:
- self.ops.append('inpaint')
- else:
- self.ops.append('img2img')
- crop_region = None
-
- if self.image_mask is not None:
- if type(self.image_mask) == list:
- self.image_mask = self.image_mask[0]
- self.image_mask = create_binary_mask(self.image_mask)
- if self.inpainting_mask_invert:
- self.image_mask = ImageOps.invert(self.image_mask)
- if self.mask_blur > 0:
- np_mask = np.array(self.image_mask)
- kernel_size = 2 * int(2.5 * self.mask_blur + 0.5) + 1
- np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur)
- np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur)
- self.image_mask = Image.fromarray(np_mask)
- if self.inpaint_full_res:
- self.mask_for_overlay = self.image_mask
- mask = self.image_mask.convert('L')
- crop_region = modules.masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
- crop_region = modules.masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
- x1, y1, x2, y2 = crop_region
- mask = mask.crop(crop_region)
- self.image_mask = images.resize_image(2, mask, self.width, self.height)
- self.paste_to = (x1, y1, x2-x1, y2-y1)
- else:
- self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
- np_mask = np.array(self.image_mask)
- np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
- self.mask_for_overlay = Image.fromarray(np_mask)
- self.overlay_images = []
-
- latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
-
- add_color_corrections = shared.opts.img2img_color_correction and self.color_corrections is None
- if add_color_corrections:
- self.color_corrections = []
- processed = []
- if getattr(self, 'init_images', None) is None:
- return
- if not isinstance(self.init_images, list):
- self.init_images = [self.init_images]
- for img in self.init_images:
- if img is None:
- shared.log.warning(f"Skipping empty image: images={self.init_images}")
- continue
- self.init_img_hash = hashlib.sha256(img.tobytes()).hexdigest()[0:8] # pylint: disable=attribute-defined-outside-init
- self.init_img_width = img.width # pylint: disable=attribute-defined-outside-init
- self.init_img_height = img.height # pylint: disable=attribute-defined-outside-init
- if shared.opts.save_init_img:
- images.save_image(img, path=shared.opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, suffix="-init-image")
- image = images.flatten(img, shared.opts.img2img_background_color)
- if crop_region is None and self.resize_mode != 4 and self.resize_mode > 0:
- if image.width != self.width or image.height != self.height:
- image = images.resize_image(self.resize_mode, image, self.width, self.height, self.resize_name)
- self.width = image.width
- self.height = image.height
- if self.image_mask is not None:
- try:
- image_masked = Image.new('RGBa', (image.width, image.height))
- image_to_paste = image.convert("RGBA").convert("RGBa")
- image_to_mask = ImageOps.invert(self.mask_for_overlay.convert('L')) if self.mask_for_overlay is not None else None
- image_to_mask = image_to_mask.resize((image.width, image.height), Image.Resampling.BILINEAR) if image_to_mask is not None else None
- image_masked.paste(image_to_paste, mask=image_to_mask)
- self.overlay_images.append(image_masked.convert('RGBA'))
- except Exception as e:
- shared.log.error(f"Failed to apply mask to image: {e}")
- if crop_region is not None: # crop_region is not None if we are doing inpaint full res
- image = image.crop(crop_region)
- if image.width != self.width or image.height != self.height:
- image = images.resize_image(3, image, self.width, self.height, self.resize_name)
- if self.image_mask is not None and self.inpainting_fill != 1:
- image = modules.masking.fill(image, latent_mask)
- if add_color_corrections:
- self.color_corrections.append(setup_color_correction(image))
- processed.append(image)
- self.init_images = processed
- self.batch_size = len(self.init_images)
- if self.overlay_images is not None:
- self.overlay_images = self.overlay_images * self.batch_size
- if self.color_corrections is not None and len(self.color_corrections) == 1:
- self.color_corrections = self.color_corrections * self.batch_size
- if shared.backend == shared.Backend.DIFFUSERS:
- return # we've already set self.init_images and self.mask and we dont need any more processing
-
- self.init_images = [np.moveaxis((np.array(image).astype(np.float32) / 255.0), 2, 0) for image in self.init_images]
- if len(self.init_images) == 1:
- batch_images = np.expand_dims(self.init_images[0], axis=0).repeat(self.batch_size, axis=0)
- elif len(self.init_images) <= self.batch_size:
- batch_images = np.array(self.init_images)
- image = torch.from_numpy(batch_images)
- image = 2. * image - 1.
- image = image.to(device=shared.device, dtype=devices.dtype_vae)
- self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
- if self.resize_mode == 4:
- self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // 8, self.width // 8), mode="bilinear")
- if self.image_mask is not None:
- init_mask = latent_mask
- latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
- latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
- latmask = latmask[0]
- latmask = np.tile(latmask[None], (4, 1, 1))
- latmask = np.around(latmask)
- self.mask = torch.asarray(1.0 - latmask).to(device=shared.device, dtype=self.sd_model.dtype)
- self.nmask = torch.asarray(latmask).to(device=shared.device, dtype=self.sd_model.dtype)
- if self.inpainting_fill == 2:
- self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
- elif self.inpainting_fill == 3:
- self.init_latent = self.init_latent * self.mask
- self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
-
- def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
- hypertile_set(self)
- x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
- x *= self.initial_noise_multiplier
- samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
- if self.mask is not None:
- samples = samples * self.nmask + self.init_latent * self.mask
- del x
- devices.torch_gc()
- shared.state.nextjob()
- return samples
-
- def get_token_merging_ratio(self, for_hr=False):
- return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and shared.opts.token_merging_ratio) or shared.opts.token_merging_ratio_img2img or shared.opts.token_merging_ratio
+import os
+import json
+import math
+import time
+import hashlib
+import random
+import warnings
+from contextlib import nullcontext
+from typing import Any, Dict, List
+from dataclasses import dataclass, field
+import torch
+import numpy as np
+import cv2
+from PIL import Image, ImageOps
+from skimage import exposure
+from einops import repeat, rearrange
+from blendmodes.blend import blendLayers, BlendType
+from installer import git_commit
+from modules import shared, devices, errors
+import modules.memstats
+import modules.lowvram
+import modules.masking
+import modules.paths
+import modules.scripts
+import modules.script_callbacks
+import modules.prompt_parser
+import modules.extra_networks
+import modules.face_restoration
+import modules.images as images
+import modules.styles
+import modules.sd_hijack_freeu
+import modules.sd_samplers
+import modules.sd_samplers_common
+import modules.sd_models
+import modules.sd_vae
+import modules.sd_vae_approx
+import modules.taesd.sd_vae_taesd
+import modules.generation_parameters_copypaste
+from modules.sd_hijack_hypertile import context_hypertile_vae, context_hypertile_unet, hypertile_set
+
+
+if shared.backend == shared.Backend.ORIGINAL:
+ import modules.sd_hijack
+
+opt_C = 4
+opt_f = 8
+debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
+debug('Trace: PROCESS')
+
+
+def setup_color_correction(image):
+ debug("Calibrating color correction")
+ correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
+ return correction_target
+
+
+def apply_color_correction(correction, original_image):
+ shared.log.debug(f"Applying color correction: correction={correction.shape} image={original_image}")
+ np_image = np.asarray(original_image)
+ np_recolor = cv2.cvtColor(np_image, cv2.COLOR_RGB2LAB)
+ np_match = exposure.match_histograms(np_recolor, correction, channel_axis=2)
+ np_output = cv2.cvtColor(np_match, cv2.COLOR_LAB2RGB)
+ image = Image.fromarray(np_output.astype("uint8"))
+ image = blendLayers(image, original_image, BlendType.LUMINOSITY)
+ return image
+
+
+def apply_overlay(image: Image, paste_loc, index, overlays):
+ debug(f'Apply overlay: image={image} loc={paste_loc} index={index} overlays={overlays}')
+ if overlays is None or index >= len(overlays):
+ return image
+ overlay = overlays[index]
+ if paste_loc is not None:
+ x, y, w, h = paste_loc
+ if image.width != w or image.height != h or x != 0 or y != 0:
+ base_image = Image.new('RGBA', (overlay.width, overlay.height))
+ image = images.resize_image(2, image, w, h)
+ base_image.paste(image, (x, y))
+ image = base_image
+ image = image.convert('RGBA')
+ image.alpha_composite(overlay)
+ image = image.convert('RGB')
+ return image
+
+
+def create_binary_mask(image):
+ if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
+ image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+ else:
+ image = image.convert('L')
+ return image
+
+
+def images_tensor_to_samples(image, approximation=None, model=None): # pylint: disable=unused-argument
+ if model is None:
+ model = shared.sd_model
+ model.first_stage_model.to(devices.dtype_vae)
+ image = image.to(shared.device, dtype=devices.dtype_vae)
+ image = image * 2 - 1
+ if len(image) > 1:
+ x_latent = torch.stack([
+ model.get_first_stage_encoding(model.encode_first_stage(torch.unsqueeze(img, 0)))[0]
+ for img in image
+ ])
+ else:
+ x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+ return x_latent
+
+
+def txt2img_image_conditioning(sd_model, x, width, height):
+ if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
+ # The "masked-image" in this case will just be all zeros since the entire image is masked.
+ image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+ image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+ # Add the fake full 1s mask to the first dimension.
+ image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) # pylint: disable=not-callable
+ image_conditioning = image_conditioning.to(x.dtype)
+ return image_conditioning
+ elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
+ return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
+ else:
+ # Dummy zero conditioning if we're not using inpainting or unclip models.
+ # Still takes up a bit of memory, but no encoder call.
+ # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+ return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+
+def get_sampler_name(sampler_index: int, img: bool = False) -> str:
+ if len(modules.sd_samplers.samplers) > sampler_index:
+ sampler_name = modules.sd_samplers.samplers[sampler_index].name
+ else:
+ sampler_name = "UniPC"
+ shared.log.warning(f'Sampler not found: index={sampler_index} available={[s.name for s in modules.sd_samplers.samplers]} fallback={sampler_name}')
+ if img and sampler_name == "PLMS":
+ sampler_name = "UniPC"
+ shared.log.warning(f'Sampler not compatible: name=PLMS fallback={sampler_name}')
+ return sampler_name
+
+
+@dataclass(repr=False)
+class StableDiffusionProcessing:
+ """
+ The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
+ """
+ def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, hr_sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, image_cfg_scale: float = None, clip_skip: int = 1, width: int = 512, height: int = 512, full_quality: bool = True, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, diffusers_guidance_rescale: float = 0.7, sag_scale: float = 0.0, resize_mode: int = 0, resize_name: str = 'None', scale_by: float = 0, selected_scale_tab: int = 0, hdr_clamp: bool = False, hdr_boundary: float = 4.0, hdr_threshold: float = 3.5, hdr_center: bool = False, hdr_channel_shift: float = 0.8, hdr_full_shift: float = 0.8, hdr_maximize: bool = False, hdr_max_center: float = 0.6, hdr_max_boundry: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): # pylint: disable=unused-argument
+ self.outpath_samples: str = outpath_samples
+ self.outpath_grids: str = outpath_grids
+ self.prompt: str = prompt
+ self.prompt_for_display: str = None
+ self.negative_prompt: str = (negative_prompt or "")
+ self.styles: list = styles or []
+ self.seed: int = seed
+ self.subseed: int = subseed
+ self.subseed_strength: float = subseed_strength
+ self.seed_resize_from_h: int = seed_resize_from_h
+ self.seed_resize_from_w: int = seed_resize_from_w
+ self.sampler_name: str = sampler_name
+ self.hr_sampler_name: str = hr_sampler_name
+ self.batch_size: int = batch_size
+ self.n_iter: int = n_iter
+ self.steps: int = steps
+ self.hr_second_pass_steps = 0
+ self.cfg_scale: float = cfg_scale
+ self.scale_by: float = scale_by
+ self.image_cfg_scale = image_cfg_scale
+ self.diffusers_guidance_rescale = diffusers_guidance_rescale
+ self.sag_scale = sag_scale
+ if devices.backend == "ipex" and width == 1024 and height == 1024 and not torch.xpu.has_fp64_dtype() and os.environ.get('DISABLE_IPEX_1024_WA', None) is None:
+ width = 1080
+ height = 1080
+ self.width: int = width
+ self.height: int = height
+ self.full_quality: bool = full_quality
+ self.restore_faces: bool = restore_faces
+ self.tiling: bool = tiling
+ self.do_not_save_samples: bool = do_not_save_samples
+ self.do_not_save_grid: bool = do_not_save_grid
+ self.extra_generation_params: dict = extra_generation_params or {}
+ self.overlay_images = overlay_images
+ self.eta = eta
+ self.do_not_reload_embeddings = do_not_reload_embeddings
+ self.paste_to = None
+ self.color_corrections = None
+ self.denoising_strength: float = denoising_strength
+ self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
+ self.override_settings_restore_afterwards = override_settings_restore_afterwards
+ self.is_using_inpainting_conditioning = False
+ self.disable_extra_networks = False
+ self.token_merging_ratio = 0
+ self.token_merging_ratio_hr = 0
+ # self.scripts = modules.scripts.ScriptRunner() # set via property
+ # self.script_args = script_args or [] # set via property
+ self.per_script_args = {}
+ self.all_prompts = None
+ self.all_negative_prompts = None
+ self.all_seeds = None
+ self.all_subseeds = None
+ self.clip_skip = clip_skip
+ self.iteration = 0
+ self.is_control = False
+ self.is_hr_pass = False
+ self.is_refiner_pass = False
+ self.hr_force = False
+ self.enable_hr = None
+ self.hr_scale = None
+ self.hr_upscaler = None
+ self.hr_resize_x = 0
+ self.hr_resize_y = 0
+ self.hr_upscale_to_x = 0
+ self.hr_upscale_to_y = 0
+ self.truncate_x = 0
+ self.truncate_y = 0
+ self.applied_old_hires_behavior_to = None
+ self.refiner_steps = 5
+ self.refiner_start = 0
+ self.refiner_prompt = ''
+ self.refiner_negative = ''
+ self.ops = []
+ self.resize_mode: int = resize_mode
+ self.resize_name: str = resize_name
+ self.ddim_discretize = shared.opts.ddim_discretize
+ self.s_min_uncond = shared.opts.s_min_uncond
+ self.s_churn = shared.opts.s_churn
+ self.s_noise = shared.opts.s_noise
+ self.s_min = shared.opts.s_min
+ self.s_max = shared.opts.s_max
+ self.s_tmin = shared.opts.s_tmin
+ self.s_tmax = float('inf') # not representable as a standard ui option
+ shared.opts.data['clip_skip'] = clip_skip
+ self.task_args = {}
+ # a1111 compatibility items
+ self.refiner_switch_at = 0
+ self.hr_prompt = ''
+ self.all_hr_prompts = []
+ self.hr_negative_prompt = ''
+ self.all_hr_negative_prompts = []
+ self.comments = {}
+ self.is_api = False
+ self.scripts_value: modules.scripts.ScriptRunner = field(default=None, init=False)
+ self.script_args_value: list = field(default=None, init=False)
+ self.scripts_setup_complete: bool = field(default=False, init=False)
+ # hdr
+ self.hdr_clamp = hdr_clamp
+ self.hdr_boundary = hdr_boundary
+ self.hdr_threshold = hdr_threshold
+ self.hdr_center = hdr_center
+ self.hdr_channel_shift = hdr_channel_shift
+ self.hdr_full_shift = hdr_full_shift
+ self.hdr_maximize = hdr_maximize
+ self.hdr_max_center = hdr_max_center
+ self.hdr_max_boundry = hdr_max_boundry
+ self.scheduled_prompt: bool = False
+ self.prompt_embeds = []
+ self.positive_pooleds = []
+ self.negative_embeds = []
+ self.negative_pooleds = []
+
+
+ @property
+ def sd_model(self):
+ return shared.sd_model
+
+ @property
+ def scripts(self):
+ return self.scripts_value
+
+ @scripts.setter
+ def scripts(self, value):
+ self.scripts_value = value
+ if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+ self.setup_scripts()
+
+ @property
+ def script_args(self):
+ return self.script_args_value
+
+ @script_args.setter
+ def script_args(self, value):
+ self.script_args_value = value
+ if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+ self.setup_scripts()
+
+ def setup_scripts(self):
+ self.scripts_setup_complete = True
+ self.scripts.setup_scrips(self, is_ui=not self.is_api)
+
+ def comment(self, text):
+ self.comments[text] = 1
+
+ def txt2img_image_conditioning(self, x, width=None, height=None):
+ self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
+ return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
+
+ def depth2img_image_conditioning(self, source_image):
+ # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
+ from ldm.data.util import AddMiDaS
+ transformer = AddMiDaS(model_type="dpt_hybrid")
+ transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
+ midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
+ midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
+ conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+ conditioning = torch.nn.functional.interpolate(
+ self.sd_model.depth_model(midas_in),
+ size=conditioning_image.shape[2:],
+ mode="bicubic",
+ align_corners=False,
+ )
+ (depth_min, depth_max) = torch.aminmax(conditioning)
+ conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
+ return conditioning
+
+ def edit_image_conditioning(self, source_image):
+ conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
+ return conditioning_image
+
+ def unclip_image_conditioning(self, source_image):
+ c_adm = self.sd_model.embedder(source_image)
+ if self.sd_model.noise_augmentor is not None:
+ noise_level = 0
+ c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
+ c_adm = torch.cat((c_adm, noise_level_emb), 1)
+ return c_adm
+
+ def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
+ self.is_using_inpainting_conditioning = True
+ # Handle the different mask inputs
+ if image_mask is not None:
+ if torch.is_tensor(image_mask):
+ conditioning_mask = image_mask
+ else:
+ conditioning_mask = np.array(image_mask.convert("L"))
+ conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+ conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+ # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+ conditioning_mask = torch.round(conditioning_mask)
+ else:
+ conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
+ # Create another latent image, this time with a masked version of the original input.
+ # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+ conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
+ conditioning_image = torch.lerp(
+ source_image,
+ source_image * (1.0 - conditioning_mask),
+ getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+ )
+ # Encode the new masked image using first stage of network.
+ conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+ # Create the concatenated conditioning tensor to be fed to `c_concat`
+ conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
+ conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+ image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+ image_conditioning = image_conditioning.to(device=shared.device, dtype=source_image.dtype)
+ return image_conditioning
+
+ def diffusers_image_conditioning(self, _source_image, latent_image, _image_mask=None):
+ # shared.log.warning('Diffusers not implemented: img2img_image_conditioning')
+ return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
+ def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
+ source_image = devices.cond_cast_float(source_image)
+ # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
+ # identify itself with a field common to all models. The conditioning_key is also hybrid.
+ if shared.backend == shared.Backend.DIFFUSERS:
+ return self.diffusers_image_conditioning(source_image, latent_image, image_mask)
+ if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
+ return self.depth2img_image_conditioning(source_image)
+ if hasattr(self.sd_model, 'cond_stage_key') and self.sd_model.cond_stage_key == "edit":
+ return self.edit_image_conditioning(source_image)
+ if hasattr(self.sampler, 'conditioning_key') and self.sampler.conditioning_key in {'hybrid', 'concat'}:
+ return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+ if hasattr(self.sampler, 'conditioning_key') and self.sampler.conditioning_key == "crossattn-adm":
+ return self.unclip_image_conditioning(source_image)
+ # Dummy zero conditioning if we're not using inpainting or depth model.
+ return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
+ def init(self, all_prompts, all_seeds, all_subseeds):
+ pass
+
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+ raise NotImplementedError
+
+ def close(self):
+ self.sampler = None # pylint: disable=attribute-defined-outside-init
+
+ def get_token_merging_ratio(self, for_hr=False):
+ if for_hr:
+ return self.token_merging_ratio_hr or shared.opts.token_merging_ratio_hr or self.token_merging_ratio or shared.opts.token_merging_ratio
+ return self.token_merging_ratio or shared.opts.token_merging_ratio
+
+
+class Processed:
+ def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
+ self.images = images_list
+ self.prompt = p.prompt
+ self.negative_prompt = p.negative_prompt
+ self.seed = seed
+ self.subseed = subseed
+ self.subseed_strength = p.subseed_strength
+ self.info = info
+ self.comments = comments
+ self.width = p.width if hasattr(p, 'width') else (self.images[0].width if len(self.images) > 0 else 0)
+ self.height = p.height if hasattr(p, 'height') else (self.images[0].height if len(self.images) > 0 else 0)
+ self.sampler_name = p.sampler_name
+ self.cfg_scale = p.cfg_scale
+ self.image_cfg_scale = p.image_cfg_scale
+ self.steps = p.steps
+ self.batch_size = p.batch_size
+ self.restore_faces = p.restore_faces
+ self.face_restoration_model = shared.opts.face_restoration_model if p.restore_faces else None
+ self.sd_model_hash = getattr(shared.sd_model, 'sd_model_hash', '')
+ self.seed_resize_from_w = p.seed_resize_from_w
+ self.seed_resize_from_h = p.seed_resize_from_h
+ self.denoising_strength = p.denoising_strength
+ self.extra_generation_params = p.extra_generation_params
+ self.index_of_first_image = index_of_first_image
+ self.styles = p.styles
+ self.job_timestamp = shared.state.job_timestamp
+ self.clip_skip = p.clip_skip
+ self.eta = p.eta
+ self.ddim_discretize = p.ddim_discretize
+ self.s_churn = p.s_churn
+ self.s_tmin = p.s_tmin
+ self.s_tmax = p.s_tmax
+ self.s_noise = p.s_noise
+ self.s_min_uncond = p.s_min_uncond
+ self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
+ self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
+ self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
+ self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+ self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
+ self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
+ self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
+ self.all_seeds = all_seeds or p.all_seeds or [self.seed]
+ self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
+ self.token_merging_ratio = p.token_merging_ratio
+ self.token_merging_ratio_hr = p.token_merging_ratio_hr
+ self.infotexts = infotexts or [info]
+
+ def js(self):
+ obj = {
+ "prompt": self.all_prompts[0],
+ "all_prompts": self.all_prompts,
+ "negative_prompt": self.all_negative_prompts[0],
+ "all_negative_prompts": self.all_negative_prompts,
+ "seed": self.seed,
+ "all_seeds": self.all_seeds,
+ "subseed": self.subseed,
+ "all_subseeds": self.all_subseeds,
+ "subseed_strength": self.subseed_strength,
+ "width": self.width,
+ "height": self.height,
+ "sampler_name": self.sampler_name,
+ "cfg_scale": self.cfg_scale,
+ "steps": self.steps,
+ "batch_size": self.batch_size,
+ "restore_faces": self.restore_faces,
+ "face_restoration_model": self.face_restoration_model,
+ "sd_model_hash": self.sd_model_hash,
+ "seed_resize_from_w": self.seed_resize_from_w,
+ "seed_resize_from_h": self.seed_resize_from_h,
+ "denoising_strength": self.denoising_strength,
+ "extra_generation_params": self.extra_generation_params,
+ "index_of_first_image": self.index_of_first_image,
+ "infotexts": self.infotexts,
+ "styles": self.styles,
+ "job_timestamp": self.job_timestamp,
+ "clip_skip": self.clip_skip,
+ # "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
+ }
+ return json.dumps(obj)
+
+ def infotext(self, p: StableDiffusionProcessing, index):
+ return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
+
+ def get_token_merging_ratio(self, for_hr=False):
+ return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
+
+
+def slerp(val, low, high): # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
+ low_norm = low/torch.norm(low, dim=1, keepdim=True)
+ high_norm = high/torch.norm(high, dim=1, keepdim=True)
+ dot = (low_norm*high_norm).sum(1)
+
+ if dot.mean() > 0.9995:
+ return low * val + high * (1 - val)
+
+ omega = torch.acos(dot)
+ so = torch.sin(omega)
+ res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+ return res
+
+
+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+ eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
+ xs = []
+ # if we have multiple seeds, this means we are working with batch size>1; this then
+ # enables the generation of additional tensors with noise that the sampler will use during its processing.
+ # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
+ # produce the same images as with two batches [100], [101].
+ if p is not None and p.sampler is not None and (len(seeds) > 1 and shared.opts.enable_batch_seeds or eta_noise_seed_delta > 0):
+ sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
+ else:
+ sampler_noises = None
+ for i, seed in enumerate(seeds):
+ noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
+ subnoise = None
+ if subseeds is not None:
+ subseed = 0 if i >= len(subseeds) else subseeds[i]
+ subnoise = devices.randn(subseed, noise_shape)
+ # randn results depend on device; gpu and cpu get different results for same seed;
+ # the way I see it, it's better to do this on CPU, so that everyone gets same result;
+ # but the original script had it like this, so I do not dare change it for now because
+ # it will break everyone's seeds.
+ noise = devices.randn(seed, noise_shape)
+ if subnoise is not None:
+ noise = slerp(subseed_strength, noise, subnoise)
+ if noise_shape != shape:
+ x = devices.randn(seed, shape)
+ dx = (shape[2] - noise_shape[2]) // 2
+ dy = (shape[1] - noise_shape[1]) // 2
+ w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
+ h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
+ tx = 0 if dx < 0 else dx
+ ty = 0 if dy < 0 else dy
+ dx = max(-dx, 0)
+ dy = max(-dy, 0)
+ x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
+ noise = x
+ if sampler_noises is not None:
+ cnt = p.sampler.number_of_needed_noises(p)
+ if eta_noise_seed_delta > 0:
+ torch.manual_seed(seed + eta_noise_seed_delta)
+ for j in range(cnt):
+ sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
+ xs.append(noise)
+ if sampler_noises is not None:
+ p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
+ x = torch.stack(xs).to(shared.device)
+ return x
+
+
+def decode_first_stage(model, x, full_quality=True):
+ if not shared.opts.keep_incomplete and (shared.state.skipped or shared.state.interrupted):
+ shared.log.debug(f'Decode VAE: skipped={shared.state.skipped} interrupted={shared.state.interrupted}')
+ x_sample = torch.zeros((len(x), 3, x.shape[2] * 8, x.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
+ return x_sample
+ prev_job = shared.state.job
+ shared.state.job = 'vae'
+ with devices.autocast(disable = x.dtype==devices.dtype_vae):
+ try:
+ if full_quality:
+ if hasattr(model, 'decode_first_stage'):
+ x_sample = model.decode_first_stage(x)
+ elif hasattr(model, 'vae'):
+ x_sample = model.vae(x)
+ else:
+ x_sample = x
+ shared.log.error('Decode VAE unknown model')
+ else:
+ x_sample = torch.zeros((len(x), 3, x.shape[2] * 8, x.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
+ for i in range(len(x_sample)):
+ x_sample[i] = modules.taesd.sd_vae_taesd.decode(x[i])
+ except Exception as e:
+ x_sample = x
+ shared.log.error(f'Decode VAE: {e}')
+ shared.state.job = prev_job
+ return x_sample
+
+
+def get_fixed_seed(seed):
+ if seed is None or seed == '' or seed == -1:
+ return int(random.randrange(4294967294))
+ return seed
+
+
+def fix_seed(p):
+ p.seed = get_fixed_seed(p.seed)
+ p.subseed = get_fixed_seed(p.subseed)
+
+
+def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=None, all_subseeds=None, comments=None, iteration=0, position_in_batch=0, index=None, all_negative_prompts=None):
+ if not hasattr(shared.sd_model, 'sd_checkpoint_info'):
+ return ''
+ if index is None:
+ index = position_in_batch + iteration * p.batch_size
+ if all_prompts is None:
+ all_prompts = p.all_prompts or [p.prompt]
+ if all_negative_prompts is None:
+ all_negative_prompts = p.all_negative_prompts or [p.negative_prompt]
+ if all_seeds is None:
+ all_seeds = p.all_seeds or [p.seed]
+ if all_subseeds is None:
+ all_subseeds = p.all_subseeds or [p.subseed]
+ while len(all_prompts) <= index:
+ all_prompts.append(all_prompts[-1])
+ while len(all_seeds) <= index:
+ all_seeds.append(all_seeds[-1])
+ while len(all_subseeds) <= index:
+ all_subseeds.append(all_subseeds[-1])
+ while len(all_negative_prompts) <= index:
+ all_negative_prompts.append(all_negative_prompts[-1])
+ comment = ', '.join(comments) if comments is not None and type(comments) is list else None
+ ops = list(set(p.ops))
+ ops.reverse()
+ args = {
+ # basic
+ "Steps": p.steps,
+ "Seed": all_seeds[index],
+ "Sampler": p.sampler_name,
+ "CFG scale": p.cfg_scale,
+ "Size": f"{p.width}x{p.height}" if hasattr(p, 'width') and hasattr(p, 'height') else None,
+ "Batch": f'{p.n_iter}x{p.batch_size}' if p.n_iter > 1 or p.batch_size > 1 else None,
+ "Index": f'{p.iteration + 1}x{index + 1}' if (p.n_iter > 1 or p.batch_size > 1) and index >= 0 else None,
+ "Parser": shared.opts.prompt_attention,
+ "Model": None if (not shared.opts.add_model_name_to_info) or (not shared.sd_model.sd_checkpoint_info.model_name) else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', ''),
+ "Model hash": getattr(p, 'sd_model_hash', None if (not shared.opts.add_model_hash_to_info) or (not shared.sd_model.sd_model_hash) else shared.sd_model.sd_model_hash),
+ "VAE": (None if not shared.opts.add_model_name_to_info or modules.sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(modules.sd_vae.loaded_vae_file))[0]) if p.full_quality else 'TAESD',
+ "Seed resize from": None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}",
+ "Clip skip": p.clip_skip if p.clip_skip > 1 else None,
+ "Prompt2": p.refiner_prompt if len(p.refiner_prompt) > 0 else None,
+ "Negative2": p.refiner_negative if len(p.refiner_negative) > 0 else None,
+ "Styles": "; ".join(p.styles) if p.styles is not None and len(p.styles) > 0 else None,
+ "Tiling": p.tiling if p.tiling else None,
+ # sdnext
+ "Backend": 'Diffusers' if shared.backend == shared.Backend.DIFFUSERS else 'Original',
+ "App": 'SD.Next',
+ "Version": git_commit,
+ "Comment": comment,
+ "Operations": '; '.join(ops).replace('"', '') if len(p.ops) > 0 else 'none',
+ }
+ if 'txt2img' in p.ops:
+ pass
+ if shared.backend == shared.Backend.ORIGINAL:
+ args["Variation seed"] = None if p.subseed_strength == 0 else all_subseeds[index],
+ args["Variation strength"] = None if p.subseed_strength == 0 else p.subseed_strength,
+ if 'hires' in p.ops or 'upscale' in p.ops:
+ args["Second pass"] = p.enable_hr
+ args["Hires force"] = p.hr_force
+ args["Hires steps"] = p.hr_second_pass_steps
+ args["Hires upscaler"] = p.hr_upscaler
+ args["Hires upscale"] = p.hr_scale
+ args["Hires resize"] = f"{p.hr_resize_x}x{p.hr_resize_y}"
+ args["Hires size"] = f"{p.hr_upscale_to_x}x{p.hr_upscale_to_y}"
+ args["Denoising strength"] = p.denoising_strength
+ args["Hires sampler"] = p.hr_sampler_name
+ args["Image CFG scale"] = p.image_cfg_scale
+ args["CFG rescale"] = p.diffusers_guidance_rescale
+ if 'refine' in p.ops:
+ args["Second pass"] = p.enable_hr
+ args["Refiner"] = None if (not shared.opts.add_model_name_to_info) or (not shared.sd_refiner) or (not shared.sd_refiner.sd_checkpoint_info.model_name) else shared.sd_refiner.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')
+ args['Image CFG scale'] = p.image_cfg_scale
+ args['Refiner steps'] = p.refiner_steps
+ args['Refiner start'] = p.refiner_start
+ args["Hires steps"] = p.hr_second_pass_steps
+ args["Hires sampler"] = p.hr_sampler_name
+ args["CFG rescale"] = p.diffusers_guidance_rescale
+ if 'img2img' in p.ops or 'inpaint' in p.ops:
+ args["Init image size"] = f"{getattr(p, 'init_img_width', 0)}x{getattr(p, 'init_img_height', 0)}"
+ args["Init image hash"] = getattr(p, 'init_img_hash', None)
+ args["Mask weight"] = getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None
+ args['Resize scale'] = getattr(p, 'scale_by', None)
+ args["Mask blur"] = p.mask_blur if getattr(p, 'mask', None) is not None and getattr(p, 'mask_blur', 0) > 0 else None
+ args["Denoising strength"] = getattr(p, 'denoising_strength', None)
+ if args["Size"] is None:
+ args["Size"] = args["Init image size"]
+ # lookup by index
+ if getattr(p, 'resize_mode', None) is not None:
+ args['Resize mode'] = shared.resize_modes[p.resize_mode]
+ if 'face' in p.ops:
+ args["Face restoration"] = shared.opts.face_restoration_model
+ if 'color' in p.ops:
+ args["Color correction"] = True
+ # embeddings
+ if hasattr(modules.sd_hijack.model_hijack, 'embedding_db') and len(modules.sd_hijack.model_hijack.embedding_db.embeddings_used) > 0: # this is for original hijaacked models only, diffusers are handled separately
+ args["Embeddings"] = ', '.join(modules.sd_hijack.model_hijack.embedding_db.embeddings_used)
+ # samplers
+ args["Sampler ENSD"] = shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and modules.sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) else None
+ args["Sampler ENSM"] = p.initial_noise_multiplier if getattr(p, 'initial_noise_multiplier', 1.0) != 1.0 else None
+ args['Sampler order'] = shared.opts.schedulers_solver_order if shared.opts.schedulers_solver_order != shared.opts.data_labels.get('schedulers_solver_order').default else None
+ if shared.backend == shared.Backend.DIFFUSERS:
+ args['Sampler beta schedule'] = shared.opts.schedulers_beta_schedule if shared.opts.schedulers_beta_schedule != shared.opts.data_labels.get('schedulers_beta_schedule').default else None
+ args['Sampler beta start'] = shared.opts.schedulers_beta_start if shared.opts.schedulers_beta_start != shared.opts.data_labels.get('schedulers_beta_start').default else None
+ args['Sampler beta end'] = shared.opts.schedulers_beta_end if shared.opts.schedulers_beta_end != shared.opts.data_labels.get('schedulers_beta_end').default else None
+ args['Sampler DPM solver'] = shared.opts.schedulers_dpm_solver if shared.opts.schedulers_dpm_solver != shared.opts.data_labels.get('schedulers_dpm_solver').default else None
+ if shared.backend == shared.Backend.ORIGINAL:
+ args['Sampler brownian'] = shared.opts.schedulers_brownian_noise if shared.opts.schedulers_brownian_noise != shared.opts.data_labels.get('schedulers_brownian_noise').default else None
+ args['Sampler discard'] = shared.opts.schedulers_discard_penultimate if shared.opts.schedulers_discard_penultimate != shared.opts.data_labels.get('schedulers_discard_penultimate').default else None
+ args['Sampler dyn threshold'] = shared.opts.schedulers_use_thresholding if shared.opts.schedulers_use_thresholding != shared.opts.data_labels.get('schedulers_use_thresholding').default else None
+ args['Sampler karras'] = shared.opts.schedulers_use_karras if shared.opts.schedulers_use_karras != shared.opts.data_labels.get('schedulers_use_karras').default else None
+ args['Sampler low order'] = shared.opts.schedulers_use_loworder if shared.opts.schedulers_use_loworder != shared.opts.data_labels.get('schedulers_use_loworder').default else None
+ args['Sampler quantization'] = shared.opts.enable_quantization if shared.opts.enable_quantization != shared.opts.data_labels.get('enable_quantization').default else None
+ args['Sampler sigma'] = shared.opts.schedulers_sigma if shared.opts.schedulers_sigma != shared.opts.data_labels.get('schedulers_sigma').default else None
+ args['Sampler sigma min'] = shared.opts.s_min if shared.opts.s_min != shared.opts.data_labels.get('s_min').default else None
+ args['Sampler sigma max'] = shared.opts.s_max if shared.opts.s_max != shared.opts.data_labels.get('s_max').default else None
+ args['Sampler sigma churn'] = shared.opts.s_churn if shared.opts.s_churn != shared.opts.data_labels.get('s_churn').default else None
+ args['Sampler sigma uncond'] = shared.opts.s_churn if shared.opts.s_churn != shared.opts.data_labels.get('s_churn').default else None
+ args['Sampler sigma noise'] = shared.opts.s_noise if shared.opts.s_noise != shared.opts.data_labels.get('s_noise').default else None
+ args['Sampler sigma tmin'] = shared.opts.s_tmin if shared.opts.s_tmin != shared.opts.data_labels.get('s_tmin').default else None
+ # tome
+ token_merging_ratio = p.get_token_merging_ratio()
+ token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True) if p.enable_hr else None
+ args['ToMe'] = token_merging_ratio if token_merging_ratio != 0 else None
+ args['ToMe hires'] = token_merging_ratio_hr if token_merging_ratio_hr != 0 else None
+
+ args.update(p.extra_generation_params)
+ params_text = ", ".join([k if k == v else f'{k}: {modules.generation_parameters_copypaste.quote(v)}' for k, v in args.items() if v is not None])
+ negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
+ infotext = f"{all_prompts[index]}{negative_prompt_text}\n{params_text}".strip()
+ return infotext
+
+
+def process_images(p: StableDiffusionProcessing) -> Processed:
+ debug(f'Process images: {vars(p)}')
+ if not hasattr(p.sd_model, 'sd_checkpoint_info'):
+ return None
+ if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
+ p.scripts.before_process(p)
+ stored_opts = {}
+ for k, v in p.override_settings.copy().items():
+ if shared.opts.data.get(k, None) is None and shared.opts.data_labels.get(k, None) is None:
+ continue
+ orig = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default
+ if orig == v or (type(orig) == str and os.path.splitext(orig)[0] == v):
+ p.override_settings.pop(k, None)
+ for k in p.override_settings.keys():
+ stored_opts[k] = shared.opts.data.get(k, None) or shared.opts.data_labels[k].default
+ res = None
+ try:
+ # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
+ if p.override_settings.get('sd_model_checkpoint', None) is not None and modules.sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
+ shared.log.warning(f"Override not found: checkpoint={p.override_settings.get('sd_model_checkpoint', None)}")
+ p.override_settings.pop('sd_model_checkpoint', None)
+ modules.sd_models.reload_model_weights()
+ if p.override_settings.get('sd_model_refiner', None) is not None and modules.sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_refiner')) is None:
+ shared.log.warning(f"Override not found: refiner={p.override_settings.get('sd_model_refiner', None)}")
+ p.override_settings.pop('sd_model_refiner', None)
+ modules.sd_models.reload_model_weights()
+ if p.override_settings.get('sd_vae', None) is not None:
+ if p.override_settings.get('sd_vae', None) == 'TAESD':
+ p.full_quality = False
+ p.override_settings.pop('sd_vae', None)
+ if p.override_settings.get('Hires upscaler', None) is not None:
+ p.enable_hr = True
+ if len(p.override_settings.keys()) > 0:
+ shared.log.debug(f'Override: {p.override_settings}')
+ for k, v in p.override_settings.items():
+ setattr(shared.opts, k, v)
+ if k == 'sd_model_checkpoint':
+ modules.sd_models.reload_model_weights()
+ if k == 'sd_vae':
+ modules.sd_vae.reload_vae_weights()
+
+ shared.prompt_styles.apply_styles_to_extra(p)
+ if not shared.opts.cuda_compile:
+ modules.sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
+ modules.sd_hijack_freeu.apply_freeu(p, shared.backend == shared.Backend.ORIGINAL)
+
+ modules.script_callbacks.before_process_callback(p)
+
+ if shared.cmd_opts.profile:
+ import cProfile
+ profile_python = cProfile.Profile()
+ profile_python.enable()
+ with context_hypertile_vae(p), context_hypertile_unet(p):
+ import torch.profiler # pylint: disable=redefined-outer-name
+ activities=[torch.profiler.ProfilerActivity.CPU]
+ if torch.cuda.is_available():
+ activities.append(torch.profiler.ProfilerActivity.CUDA)
+ shared.log.debug(f'Torch profile: activities={activities}')
+ if shared.profiler is None:
+ shared.profiler = torch.profiler.profile(activities=activities, profile_memory=True, with_modules=True)
+ shared.profiler.start()
+ shared.profiler.step()
+ res = process_images_inner(p)
+ errors.profile_torch(shared.profiler, 'Process')
+ errors.profile(profile_python, 'Process')
+ else:
+ with context_hypertile_vae(p), context_hypertile_unet(p):
+ res = process_images_inner(p)
+
+ finally:
+ if not shared.opts.cuda_compile:
+ modules.sd_models.apply_token_merging(p.sd_model, 0)
+ modules.script_callbacks.after_process_callback(p)
+ if p.override_settings_restore_afterwards: # restore opts to original state
+ for k, v in stored_opts.items():
+ setattr(shared.opts, k, v)
+ if k == 'sd_model_checkpoint':
+ modules.sd_models.reload_model_weights()
+ if k == 'sd_model_refiner':
+ modules.sd_models.reload_model_weights()
+ if k == 'sd_vae':
+ modules.sd_vae.reload_vae_weights()
+ return res
+
+
+def validate_sample(tensor):
+ if not isinstance(tensor, np.ndarray) and not isinstance(tensor, torch.Tensor):
+ return tensor
+ if tensor.dtype == torch.bfloat16: # numpy does not support bf16
+ tensor = tensor.to(torch.float16)
+ if isinstance(tensor, torch.Tensor) and hasattr(tensor, 'detach'):
+ sample = tensor.detach().cpu().numpy()
+ elif isinstance(tensor, np.ndarray):
+ sample = tensor
+ else:
+ shared.log.warning(f'Unknown sample type: {type(tensor)}')
+ sample = 255.0 * np.moveaxis(sample, 0, 2) if shared.backend == shared.Backend.ORIGINAL else 255.0 * sample
+ with warnings.catch_warnings(record=True) as w:
+ cast = sample.astype(np.uint8)
+ if len(w) > 0:
+ nans = np.isnan(sample).sum()
+ shared.log.error(f'Failed to validate samples: sample={sample.shape} invalid={nans}')
+ cast = np.nan_to_num(sample)
+ minimum, maximum, mean = np.min(cast), np.max(cast), np.mean(cast)
+ cast = cast.astype(np.uint8)
+ shared.log.warning(f'Attempted to correct samples: min={minimum:.2f} max={maximum:.2f} mean={mean:.2f}')
+ return cast
+
+
+def process_init(p: StableDiffusionProcessing):
+ seed = get_fixed_seed(p.seed)
+ subseed = get_fixed_seed(p.subseed)
+ if type(p.prompt) == list:
+ p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
+ else:
+ p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
+ if type(p.negative_prompt) == list:
+ p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
+ else:
+ p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
+ if type(seed) == list:
+ p.all_seeds = seed
+ else:
+ p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
+ if type(subseed) == list:
+ p.all_subseeds = subseed
+ else:
+ p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
+
+
+def process_images_inner(p: StableDiffusionProcessing) -> Processed:
+ """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
+
+ if type(p.prompt) == list:
+ assert len(p.prompt) > 0
+ else:
+ assert p.prompt is not None
+
+ if shared.backend == shared.Backend.ORIGINAL:
+ modules.sd_hijack.model_hijack.apply_circular(p.tiling)
+ modules.sd_hijack.model_hijack.clear_comments()
+ comments = {}
+ infotexts = []
+ output_images = []
+ cached_uc = [None, None]
+ cached_c = [None, None]
+
+ process_init(p)
+ if os.path.exists(shared.opts.embeddings_dir) and not p.do_not_reload_embeddings and shared.backend == shared.Backend.ORIGINAL:
+ modules.sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=False)
+ if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
+ p.scripts.process(p)
+
+
+ def get_conds_with_caching(function, required_prompts, steps, cache):
+ if cache[0] is not None and (required_prompts, steps) == cache[0]:
+ return cache[1]
+ with devices.autocast():
+ cache[1] = function(shared.sd_model, required_prompts, steps)
+ cache[0] = (required_prompts, steps)
+ return cache[1]
+
+ def infotext(_inxex=0): # dummy function overriden if there are iterations
+ return ''
+
+ ema_scope_context = p.sd_model.ema_scope if shared.backend == shared.Backend.ORIGINAL else nullcontext
+ shared.state.job_count = p.n_iter
+ with devices.inference_context(), ema_scope_context():
+ t0 = time.time()
+ with devices.autocast():
+ p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
+ extra_network_data = None
+ debug(f'Processing inner: args={vars(p)}')
+ for n in range(p.n_iter):
+ p.iteration = n
+ if shared.state.skipped:
+ shared.log.debug(f'Process skipped: {n}/{p.n_iter}')
+ shared.state.skipped = False
+ continue
+ if shared.state.interrupted:
+ shared.log.debug(f'Process interrupted: {n}/{p.n_iter}')
+ break
+ p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+ p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+ if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
+ p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
+ if len(p.prompts) == 0:
+ break
+ p.prompts, extra_network_data = modules.extra_networks.parse_prompts(p.prompts)
+ if not p.disable_extra_networks:
+ with devices.autocast():
+ modules.extra_networks.activate(p, extra_network_data)
+ if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
+ p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
+ step_multiplier = 1
+ sampler_config = modules.sd_samplers.find_sampler_config(p.sampler_name)
+ step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
+
+ if shared.backend == shared.Backend.ORIGINAL:
+ uc = get_conds_with_caching(modules.prompt_parser.get_learned_conditioning, p.negative_prompts, p.steps * step_multiplier, cached_uc)
+ c = get_conds_with_caching(modules.prompt_parser.get_multicond_learned_conditioning, p.prompts, p.steps * step_multiplier, cached_c)
+ if len(modules.sd_hijack.model_hijack.comments) > 0:
+ for comment in modules.sd_hijack.model_hijack.comments:
+ comments[comment] = 1
+ with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
+ samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
+ x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae), p.full_quality)[0].cpu() for i in range(samples_ddim.size(0))]
+ try:
+ for x in x_samples_ddim:
+ devices.test_for_nans(x, "vae")
+ except devices.NansException as e:
+ if not shared.opts.no_half and not shared.opts.no_half_vae and shared.cmd_opts.rollback_vae:
+ shared.log.warning('Tensor with all NaNs was produced in VAE')
+ devices.dtype_vae = torch.bfloat16
+ vae_file, vae_source = modules.sd_vae.resolve_vae(p.sd_model.sd_model_checkpoint)
+ modules.sd_vae.load_vae(p.sd_model, vae_file, vae_source)
+ x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae), p.full_quality)[0].cpu() for i in range(samples_ddim.size(0))]
+ for x in x_samples_ddim:
+ devices.test_for_nans(x, "vae")
+ else:
+ raise e
+ x_samples_ddim = torch.stack(x_samples_ddim).float()
+ x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+ del samples_ddim
+
+ elif shared.backend == shared.Backend.DIFFUSERS:
+ from modules.processing_diffusers import process_diffusers
+ x_samples_ddim = process_diffusers(p)
+ else:
+ raise ValueError(f"Unknown backend {shared.backend}")
+
+ if not shared.opts.keep_incomplete and shared.state.interrupted:
+ x_samples_ddim = []
+
+ if shared.cmd_opts.lowvram or shared.cmd_opts.medvram and shared.backend == shared.Backend.ORIGINAL:
+ modules.lowvram.send_everything_to_cpu()
+ devices.torch_gc()
+ if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
+ p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
+ if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
+ p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ batch_params = modules.scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+ p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+ x_samples_ddim = batch_params.images
+
+ def infotext(index): # pylint: disable=function-redefined # noqa: F811
+ return create_infotext(p, p.prompts, p.seeds, p.subseeds, index=index, all_negative_prompts=p.negative_prompts)
+
+ for i, x_sample in enumerate(x_samples_ddim):
+ p.batch_index = i
+ if type(x_sample) == Image.Image:
+ image = x_sample
+ x_sample = np.array(x_sample)
+ else:
+ x_sample = validate_sample(x_sample)
+ image = Image.fromarray(x_sample)
+ if p.restore_faces:
+ if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_face_restoration:
+ orig = p.restore_faces
+ p.restore_faces = False
+ info = infotext(i)
+ p.restore_faces = orig
+ images.save_image(Image.fromarray(x_sample), path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-face-restore")
+ p.ops.append('face')
+ x_sample = modules.face_restoration.restore_faces(x_sample)
+ image = Image.fromarray(x_sample)
+ if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner):
+ pp = modules.scripts.PostprocessImageArgs(image)
+ p.scripts.postprocess_image(p, pp)
+ image = pp.image
+ if p.color_corrections is not None and i < len(p.color_corrections):
+ if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_color_correction:
+ orig = p.color_corrections
+ p.color_corrections = None
+ info = infotext(i)
+ p.color_corrections = orig
+ image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
+ images.save_image(image_without_cc, path=p.outpath_samples, basename="", seed=p.seeds[i], prompt=p.prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix="-before-color-correct")
+ p.ops.append('color')
+ image = apply_color_correction(p.color_corrections[i], image)
+ image = apply_overlay(image, p.paste_to, i, p.overlay_images)
+ text = infotext(i)
+ infotexts.append(text)
+ image.info["parameters"] = text
+ output_images.append(image)
+ if shared.opts.samples_save and not p.do_not_save_samples:
+ images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p) # main save image
+ if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([shared.opts.save_mask, shared.opts.save_mask_composite, shared.opts.return_mask, shared.opts.return_mask_composite]):
+ image_mask = p.mask_for_overlay.convert('RGB')
+ image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(3, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
+ if shared.opts.save_mask:
+ images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p, suffix="-mask")
+ if shared.opts.save_mask_composite:
+ images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], shared.opts.samples_format, info=text, p=p, suffix="-mask-composite")
+ if shared.opts.return_mask:
+ output_images.append(image_mask)
+ if shared.opts.return_mask_composite:
+ output_images.append(image_mask_composite)
+ del x_samples_ddim
+ devices.torch_gc()
+
+ t1 = time.time()
+ shared.log.info(f'Processed: images={len(output_images)} time={t1 - t0:.2f} its={(p.steps * len(output_images)) / (t1 - t0):.2f} memory={modules.memstats.memory_stats()}')
+
+ p.color_corrections = None
+ index_of_first_image = 0
+ if (shared.opts.return_grid or shared.opts.grid_save) and not p.do_not_save_grid and len(output_images) > 1:
+ if images.check_grid_size(output_images):
+ grid = images.image_grid(output_images, p.batch_size)
+ if shared.opts.return_grid:
+ text = infotext(-1)
+ infotexts.insert(0, text)
+ grid.info["parameters"] = text
+ output_images.insert(0, grid)
+ index_of_first_image = 1
+ if shared.opts.grid_save:
+ images.save_image(grid, p.outpath_grids, "", p.all_seeds[0], p.all_prompts[0], shared.opts.grid_format, info=infotext(-1), p=p, grid=True, suffix="-grid") # main save grid
+
+ if not p.disable_extra_networks:
+ modules.extra_networks.deactivate(p, extra_network_data)
+
+ res = Processed(
+ p,
+ images_list=output_images,
+ seed=p.all_seeds[0],
+ info=infotext(0),
+ comments="\n".join(comments),
+ subseed=p.all_subseeds[0],
+ index_of_first_image=index_of_first_image,
+ infotexts=infotexts,
+ )
+ if p.scripts is not None and isinstance(p.scripts, modules.scripts.ScriptRunner) and not (shared.state.interrupted or shared.state.skipped):
+ p.scripts.postprocess(p, res)
+ return res
+
+
+def old_hires_fix_first_pass_dimensions(width, height):
+ """old algorithm for auto-calculating first pass size"""
+ desired_pixel_count = 512 * 512
+ actual_pixel_count = width * height
+ scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+ width = math.ceil(scale * width / 64) * 64
+ height = math.ceil(scale * height / 64) * 64
+ return width, height
+
+
+class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
+
+ def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_force: bool = False, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
+
+ super().__init__(**kwargs)
+ if devices.backend == "ipex" and not torch.xpu.has_fp64_dtype() and os.environ.get('DISABLE_IPEX_1024_WA', None) is None:
+ width_curse = bool(hr_resize_x == 1024 and self.height * (hr_resize_x / self.width) == 1024)
+ height_curse = bool(hr_resize_y == 1024 and self.width * (hr_resize_y / self.height) == 1024)
+ if (width_curse != height_curse) or (height_curse and width_curse):
+ if width_curse:
+ hr_resize_x = 1080
+ if height_curse:
+ hr_resize_y = 1080
+ if self.width * hr_scale == 1024 and self.height * hr_scale == 1024:
+ hr_scale = 1080 / self.width
+ if firstphase_width * hr_scale == 1024 and firstphase_height * hr_scale == 1024:
+ hr_scale = 1080 / firstphase_width
+ self.enable_hr = enable_hr
+ self.denoising_strength = denoising_strength
+ self.hr_scale = hr_scale
+ self.hr_upscaler = hr_upscaler
+ self.hr_force = hr_force
+ self.hr_second_pass_steps = hr_second_pass_steps
+ self.hr_resize_x = hr_resize_x
+ self.hr_resize_y = hr_resize_y
+ self.hr_upscale_to_x = hr_resize_x
+ self.hr_upscale_to_y = hr_resize_y
+ if firstphase_width != 0 or firstphase_height != 0:
+ self.hr_upscale_to_x = self.width
+ self.hr_upscale_to_y = self.height
+ self.width = firstphase_width
+ self.height = firstphase_height
+ self.truncate_x = 0
+ self.truncate_y = 0
+ self.applied_old_hires_behavior_to = None
+ self.refiner_steps = refiner_steps
+ self.refiner_start = refiner_start
+ self.refiner_prompt = refiner_prompt
+ self.refiner_negative = refiner_negative
+ self.sampler = None
+ self.scripts = None
+ self.script_args = []
+
+ def init(self, all_prompts, all_seeds, all_subseeds):
+ if shared.backend == shared.Backend.DIFFUSERS:
+ shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE)
+ self.width = self.width or 512
+ self.height = self.height or 512
+
+ def init_hr(self):
+ if self.hr_resize_x == 0 and self.hr_resize_y == 0:
+ self.hr_upscale_to_x = int(self.width * self.hr_scale)
+ self.hr_upscale_to_y = int(self.height * self.hr_scale)
+ else:
+ if self.hr_resize_y == 0:
+ self.hr_upscale_to_x = self.hr_resize_x
+ self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+ elif self.hr_resize_x == 0:
+ self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+ self.hr_upscale_to_y = self.hr_resize_y
+ else:
+ target_w = self.hr_resize_x
+ target_h = self.hr_resize_y
+ src_ratio = self.width / self.height
+ dst_ratio = self.hr_resize_x / self.hr_resize_y
+ if src_ratio < dst_ratio:
+ self.hr_upscale_to_x = self.hr_resize_x
+ self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+ else:
+ self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+ self.hr_upscale_to_y = self.hr_resize_y
+ self.truncate_x = (self.hr_upscale_to_x - target_w) // 8
+ self.truncate_y = (self.hr_upscale_to_y - target_h) // 8
+ # special case: the user has chosen to do nothing
+ if (self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height) or self.hr_upscaler is None or self.hr_upscaler == 'None':
+ self.is_hr_pass = False
+ return
+ self.is_hr_pass = True
+ hypertile_set(self, hr=True)
+ shared.state.job_count = 2 * self.n_iter
+ shared.log.debug(f'Init hires: upscaler="{self.hr_upscaler}" sampler="{self.hr_sampler_name}" resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')
+
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+
+ latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "None")
+ if latent_scale_mode is not None:
+ self.hr_force = False # no need to force anything
+ if self.enable_hr and (latent_scale_mode is None or self.hr_force):
+ if len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) == 0:
+ shared.log.warning(f"Cannot find upscaler for hires: {self.hr_upscaler}")
+ self.enable_hr = False
+
+ self.ops.append('txt2img')
+ hypertile_set(self)
+ self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model)
+ if hasattr(self.sampler, "initialize"):
+ self.sampler.initialize(self)
+ x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+ samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+ shared.state.nextjob()
+ if not self.enable_hr or shared.state.interrupted or shared.state.skipped:
+ return samples
+
+ self.init_hr()
+ if self.is_hr_pass:
+ prev_job = shared.state.job
+ target_width = self.hr_upscale_to_x
+ target_height = self.hr_upscale_to_y
+ decoded_samples = None
+ if shared.opts.save and shared.opts.save_images_before_highres_fix and not self.do_not_save_samples:
+ decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality)
+ decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
+ for i, x_sample in enumerate(decoded_samples):
+ x_sample = validate_sample(x_sample)
+ image = Image.fromarray(x_sample)
+ bak_extra_generation_params, bak_restore_faces = self.extra_generation_params, self.restore_faces
+ self.extra_generation_params = {}
+ self.restore_faces = False
+ info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=i)
+ self.extra_generation_params, self.restore_faces = bak_extra_generation_params, bak_restore_faces
+ images.save_image(image, self.outpath_samples, "", seeds[i], prompts[i], shared.opts.samples_format, info=info, suffix="-before-hires")
+ if latent_scale_mode is None or self.hr_force: # non-latent upscaling
+ shared.state.job = 'upscale'
+ if decoded_samples is None:
+ decoded_samples = decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality)
+ decoded_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
+ batch_images = []
+ for _i, x_sample in enumerate(decoded_samples):
+ x_sample = validate_sample(x_sample)
+ image = Image.fromarray(x_sample)
+ image = images.resize_image(1, image, target_width, target_height, upscaler_name=self.hr_upscaler)
+ image = np.array(image).astype(np.float32) / 255.0
+ image = np.moveaxis(image, 2, 0)
+ batch_images.append(image)
+ resized_samples = torch.from_numpy(np.array(batch_images))
+ resized_samples = resized_samples.to(device=shared.device, dtype=devices.dtype_vae)
+ resized_samples = 2.0 * resized_samples - 1.0
+ if shared.opts.sd_vae_sliced_encode and len(decoded_samples) > 1:
+ samples = torch.stack([self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(torch.unsqueeze(resized_sample, 0)))[0] for resized_sample in resized_samples])
+ else:
+ # TODO add TEASD support
+ samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(resized_samples))
+ image_conditioning = self.img2img_image_conditioning(resized_samples, samples)
+ else:
+ samples = torch.nn.functional.interpolate(samples, size=(target_height // 8, target_width // 8), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
+ if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
+ image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality), samples)
+ else:
+ image_conditioning = self.txt2img_image_conditioning(samples.to(dtype=devices.dtype_vae))
+ if self.hr_sampler_name == "PLMS":
+ self.hr_sampler_name = 'UniPC'
+ if self.hr_force or latent_scale_mode is not None:
+ shared.state.job = 'hires'
+ if self.denoising_strength > 0:
+ self.ops.append('hires')
+ devices.torch_gc() # GC now before running the next img2img to prevent running out of memory
+ self.sampler = modules.sd_samplers.create_sampler(self.hr_sampler_name or self.sampler_name, self.sd_model)
+ if hasattr(self.sampler, "initialize"):
+ self.sampler.initialize(self)
+ samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
+ noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
+ modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
+ hypertile_set(self, hr=True)
+ samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+ modules.sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
+ else:
+ self.ops.append('upscale')
+ x = None
+ self.is_hr_pass = False
+ shared.state.job = prev_job
+ shared.state.nextjob()
+
+ return samples
+
+
+class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
+
+ def __init__(self, init_images: list = None, resize_mode: int = 0, resize_name: str = 'None', denoising_strength: float = 0.3, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, scale_by: float = 1, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
+ super().__init__(**kwargs)
+ self.init_images = init_images
+ self.resize_mode: int = resize_mode
+ self.resize_name: str = resize_name
+ self.denoising_strength: float = denoising_strength
+ self.image_cfg_scale: float = image_cfg_scale
+ self.init_latent = None
+ self.image_mask = mask
+ self.latent_mask = None
+ self.mask_for_overlay = None
+ self.mask_blur_x = mask_blur # a1111 compatibility item
+ self.mask_blur_y = mask_blur # a1111 compatibility item
+ self.mask_blur = mask_blur
+ self.inpainting_fill = inpainting_fill
+ self.inpaint_full_res = inpaint_full_res
+ self.inpaint_full_res_padding = inpaint_full_res_padding
+ self.inpainting_mask_invert = inpainting_mask_invert
+ self.initial_noise_multiplier = shared.opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
+ self.mask = None
+ self.nmask = None
+ self.image_conditioning = None
+ self.refiner_steps = refiner_steps
+ self.refiner_start = refiner_start
+ self.refiner_prompt = refiner_prompt
+ self.refiner_negative = refiner_negative
+ self.enable_hr = None
+ self.is_batch = False
+ self.scale_by = scale_by
+ self.sampler = None
+ self.scripts = None
+ self.script_args = []
+
+ def init(self, all_prompts, all_seeds, all_subseeds):
+ if shared.backend == shared.Backend.DIFFUSERS and self.image_mask is not None and not self.is_control:
+ shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING)
+ elif shared.backend == shared.Backend.DIFFUSERS and self.image_mask is None and not self.is_control:
+ shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
+
+ if self.sampler_name == "PLMS":
+ self.sampler_name = 'UniPC'
+ self.sampler = modules.sd_samplers.create_sampler(self.sampler_name, self.sd_model)
+ if hasattr(self.sampler, "initialize"):
+ self.sampler.initialize(self)
+
+ if self.image_mask is not None:
+ self.ops.append('inpaint')
+ else:
+ self.ops.append('img2img')
+ crop_region = None
+
+ if self.image_mask is not None:
+ if type(self.image_mask) == list:
+ self.image_mask = self.image_mask[0]
+ if shared.backend == shared.Backend.ORIGINAL:
+ self.image_mask = create_binary_mask(self.image_mask)
+ if self.inpainting_mask_invert:
+ self.image_mask = ImageOps.invert(self.image_mask)
+ if self.mask_blur > 0:
+ np_mask = np.array(self.image_mask)
+ kernel_size = 2 * int(2.5 * self.mask_blur + 0.5) + 1
+ np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur)
+ np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur)
+ self.image_mask = Image.fromarray(np_mask)
+ else:
+ self.image_mask = modules.masking.run_mask(input_image=self.init_images, input_mask=self.image_mask, return_type='grayscale', mask_blur=self.mask_blur, mask_padding=self.inpaint_full_res_padding, segment_enable=False)
+ if self.inpaint_full_res:
+ self.mask_for_overlay = self.image_mask
+ mask = self.image_mask.convert('L')
+ crop_region = modules.masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
+ crop_region = modules.masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
+ x1, y1, x2, y2 = crop_region
+ mask = mask.crop(crop_region)
+ self.image_mask = images.resize_image(2, mask, self.width, self.height)
+ self.paste_to = (x1, y1, x2-x1, y2-y1)
+ else:
+ self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
+ np_mask = np.array(self.image_mask)
+ np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
+ self.mask_for_overlay = Image.fromarray(np_mask)
+ self.overlay_images = []
+
+ latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
+
+ add_color_corrections = shared.opts.img2img_color_correction and self.color_corrections is None
+ if add_color_corrections:
+ self.color_corrections = []
+ processed = []
+ if getattr(self, 'init_images', None) is None:
+ return
+ if not isinstance(self.init_images, list):
+ self.init_images = [self.init_images]
+ for img in self.init_images:
+ if img is None:
+ shared.log.warning(f"Skipping empty image: images={self.init_images}")
+ continue
+ self.init_img_hash = hashlib.sha256(img.tobytes()).hexdigest()[0:8] # pylint: disable=attribute-defined-outside-init
+ self.init_img_width = img.width # pylint: disable=attribute-defined-outside-init
+ self.init_img_height = img.height # pylint: disable=attribute-defined-outside-init
+ if shared.opts.save_init_img:
+ images.save_image(img, path=shared.opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, suffix="-init-image")
+ image = images.flatten(img, shared.opts.img2img_background_color)
+ if crop_region is None and self.resize_mode != 4 and self.resize_mode > 0:
+ if image.width != self.width or image.height != self.height:
+ image = images.resize_image(self.resize_mode, image, self.width, self.height, self.resize_name)
+ self.width = image.width
+ self.height = image.height
+ if self.image_mask is not None:
+ try:
+ image_masked = Image.new('RGBa', (image.width, image.height))
+ image_to_paste = image.convert("RGBA").convert("RGBa")
+ image_to_mask = ImageOps.invert(self.mask_for_overlay.convert('L')) if self.mask_for_overlay is not None else None
+ image_to_mask = image_to_mask.resize((image.width, image.height), Image.Resampling.BILINEAR) if image_to_mask is not None else None
+ image_masked.paste(image_to_paste, mask=image_to_mask)
+ self.overlay_images.append(image_masked.convert('RGBA'))
+ except Exception as e:
+ shared.log.error(f"Failed to apply mask to image: {e}")
+ if crop_region is not None: # crop_region is not None if we are doing inpaint full res
+ image = image.crop(crop_region)
+ if image.width != self.width or image.height != self.height:
+ image = images.resize_image(3, image, self.width, self.height, self.resize_name)
+ if self.image_mask is not None and self.inpainting_fill != 1:
+ image = modules.masking.fill(image, latent_mask)
+ if add_color_corrections:
+ self.color_corrections.append(setup_color_correction(image))
+ processed.append(image)
+ self.init_images = processed
+ self.batch_size = len(self.init_images)
+ if self.overlay_images is not None:
+ self.overlay_images = self.overlay_images * self.batch_size
+ if self.color_corrections is not None and len(self.color_corrections) == 1:
+ self.color_corrections = self.color_corrections * self.batch_size
+ if shared.backend == shared.Backend.DIFFUSERS:
+ return # we've already set self.init_images and self.mask and we dont need any more processing
+
+ self.init_images = [np.moveaxis((np.array(image).astype(np.float32) / 255.0), 2, 0) for image in self.init_images]
+ if len(self.init_images) == 1:
+ batch_images = np.expand_dims(self.init_images[0], axis=0).repeat(self.batch_size, axis=0)
+ elif len(self.init_images) <= self.batch_size:
+ batch_images = np.array(self.init_images)
+ image = torch.from_numpy(batch_images)
+ image = 2. * image - 1.
+ image = image.to(device=shared.device, dtype=devices.dtype_vae)
+ self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
+ if self.resize_mode == 4:
+ self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // 8, self.width // 8), mode="bilinear")
+ if self.image_mask is not None:
+ init_mask = latent_mask
+ latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
+ latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
+ latmask = latmask[0]
+ latmask = np.tile(latmask[None], (4, 1, 1))
+ latmask = np.around(latmask)
+ self.mask = torch.asarray(1.0 - latmask).to(device=shared.device, dtype=self.sd_model.dtype)
+ self.nmask = torch.asarray(latmask).to(device=shared.device, dtype=self.sd_model.dtype)
+ if self.inpainting_fill == 2:
+ self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+ elif self.inpainting_fill == 3:
+ self.init_latent = self.init_latent * self.mask
+ self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
+
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+ hypertile_set(self)
+ x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+ x *= self.initial_noise_multiplier
+ samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
+ if self.mask is not None:
+ samples = samples * self.nmask + self.init_latent * self.mask
+ del x
+ devices.torch_gc()
+ shared.state.nextjob()
+ return samples
+
+ def get_token_merging_ratio(self, for_hr=False):
+ return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and shared.opts.token_merging_ratio) or shared.opts.token_merging_ratio_img2img or shared.opts.token_merging_ratio
diff --git a/modules/processing_correction.py b/modules/processing_correction.py
index 4d4bd8f60..ff038c024 100644
--- a/modules/processing_correction.py
+++ b/modules/processing_correction.py
@@ -63,7 +63,7 @@ def correction(p, timestep, latent):
p.extra_generation_params["HDR center"] = f'{p.hdr_channel_shift}/{p.hdr_full_shift}'
latent = center_tensor(latent, channel_shift=p.hdr_channel_shift, full_shift=p.hdr_full_shift)
if timestep > 1 and timestep < 100 and p.hdr_maximize:
- p.extra_generation_params["HDR max"] = f'{p.hdr_max_center}/p.hdr_max_boundry'
+ p.extra_generation_params["HDR max"] = f'{p.hdr_max_center}/{p.hdr_max_boundry}'
latent = center_tensor(latent, channel_shift=p.hdr_max_center, full_shift=1.0)
latent = maximize_tensor(latent, boundary=p.hdr_max_boundry)
return latent
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 81391c33e..0fe7e3a87 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -17,6 +17,7 @@ import modules.prompt_parser_diffusers as prompt_parser_diffusers
from modules.sd_hijack_hypertile import hypertile_set
from modules.processing_correction import correction_callback
from modules.processing_vae import vae_encode, vae_decode
+from modules.masking import run_mask
debug = shared.log.trace if os.environ.get('SD_DIFFUSERS_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -104,14 +105,12 @@ def process_diffusers(p: StableDiffusionProcessing):
if kwargs.get('latents', None) is None:
return kwargs
kwargs = correction_callback(p, timestep, kwargs)
- if p.scheduled_prompt and hasattr(kwargs, 'prompt_embeds') and hasattr(kwargs, 'negative_prompt_embeds'):
+ if p.scheduled_prompt and 'prompt_embeds' in kwargs and 'negative_prompt_embeds' in kwargs:
try:
i = (step + 1) % len(p.prompt_embeds)
- kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
- kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
+ kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].expand(kwargs["prompt_embeds"].shape)
j = (step + 1) % len(p.negative_embeds)
- kwargs["negative_prompt_embeds"] = p.negative_embeds[j][0:1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
- kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
+ kwargs["negative_prompt_embeds"] = p.negative_embeds[j][0:1].expand(kwargs["negative_prompt_embeds"].shape)
except Exception as e:
shared.log.debug(f"Callback: {e}")
shared.state.current_latent = kwargs['latents']
@@ -169,9 +168,10 @@ def process_diffusers(p: StableDiffusionProcessing):
elif (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INPAINTING or is_img2img_model) and len(getattr(p, 'init_images' ,[])) > 0:
p.ops.append('inpaint')
if p.task_args.get('mask_image', None) is not None: # provided as override by a module
- p.mask = shared.sd_model.mask_processor.blur(p.task_args['mask_image'], blur_factor=p.mask_blur) if p.mask_blur > 0 else p.task_args['mask_image']
+ # p.mask = shared.sd_model.mask_processor.blur(p.task_args['mask_image'], blur_factor=p.mask_blur) if p.mask_blur > 0 else p.task_args['mask_image']
+ p.mask = run_mask(input_image=p.init_images, input_mask=p.task_args['mask_image'], return_type='grayscale')
elif getattr(p, 'image_mask', None) is not None: # standard
- p.mask = p.image_mask
+ p.mask = run_mask(input_image=p.init_images, input_mask=p.image_mask, return_type='grayscale')
elif getattr(p, 'mask', None) is not None: # backward compatibility
pass
else: # fallback
@@ -217,8 +217,7 @@ def process_diffusers(p: StableDiffusionProcessing):
parser = 'Fixed attention'
if shared.opts.prompt_attention != 'Fixed attention' and 'StableDiffusion' in model.__class__.__name__:
try:
- prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, kwargs.get("num_inference_steps", 1), 0, kwargs.pop("clip_skip", None))
- # prompt_embed, pooled, negative_embed, negative_pooled = , , , ,
+ prompt_parser_diffusers.encode_prompts(model, p, prompts, negative_prompts, kwargs.get("num_inference_steps", 1), kwargs.pop("clip_skip", None))
parser = shared.opts.prompt_attention
except Exception as e:
shared.log.error(f'Prompt parser encode: {e}')
diff --git a/modules/progress.py b/modules/progress.py
index 7da590a7c..cc6704d99 100644
--- a/modules/progress.py
+++ b/modules/progress.py
@@ -1,93 +1,93 @@
-import base64
-import io
-import time
-from pydantic import BaseModel, Field # pylint: disable=no-name-in-module
-import modules.shared as shared
-
-
-current_task = None
-pending_tasks = {}
-finished_tasks = []
-recorded_results = []
-recorded_results_limit = 2
-
-
-def start_task(id_task):
- global current_task # pylint: disable=global-statement
- current_task = id_task
- pending_tasks.pop(id_task, None)
-
-
-def record_results(id_task, res):
- recorded_results.append((id_task, res))
- if len(recorded_results) > recorded_results_limit:
- recorded_results.pop(0)
-
-
-def finish_task(id_task):
- global current_task # pylint: disable=global-statement
- if current_task == id_task:
- current_task = None
- finished_tasks.append(id_task)
- if len(finished_tasks) > 16:
- finished_tasks.pop(0)
-
-
-def add_task_to_queue(id_job):
- pending_tasks[id_job] = time.time()
-
-
-class ProgressRequest(BaseModel):
- id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for")
- id_live_preview: int = Field(default=-1, title="Live preview image ID", description="id of last received last preview image")
-
-
-class InternalProgressResponse(BaseModel):
- job: str = Field(default=None, title="Job name", description="Internal job name")
- active: bool = Field(title="Whether the task is being worked on right now")
- queued: bool = Field(title="Whether the task is in queue")
- paused: bool = Field(title="Whether the task is paused")
- completed: bool = Field(title="Whether the task has already finished")
- progress: float = Field(default=None, title="Progress", description="The progress with a range of 0 to 1")
- eta: float = Field(default=None, title="ETA in secs")
- live_preview: str = Field(default=None, title="Live preview image", description="Current live preview; a data: uri")
- id_live_preview: int = Field(default=None, title="Live preview image ID", description="Send this together with next request to prevent receiving same image")
- textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
-
-
-def progressapi(req: ProgressRequest):
- active = req.id_task == current_task
- queued = req.id_task in pending_tasks
- completed = req.id_task in finished_tasks
- paused = shared.state.paused
- if not active:
- return InternalProgressResponse(job=shared.state.job, active=active, queued=queued, paused=paused, completed=completed, id_live_preview=-1, textinfo="Queued..." if queued else "Waiting...")
- if shared.state.job_no > shared.state.job_count:
- shared.state.job_count = shared.state.job_no
- batch_x = max(shared.state.job_no, 0)
- batch_y = max(shared.state.job_count, 1)
- step_x = max(shared.state.sampling_step, 0)
- step_y = max(shared.state.sampling_steps, 1)
- current = step_y * batch_x + step_x
- total = step_y * batch_y
- progress = min(1, abs(current / total) if total > 0 else 0)
- elapsed = time.time() - shared.state.time_start
- predicted = elapsed / progress if progress > 0 else None
- eta = predicted - elapsed if predicted is not None else None
- # shared.log.debug(f'Progress: step={step_x}:{step_y} batch={batch_x}:{batch_y} current={current} total={total} progress={progress} elapsed={elapsed} eta={eta}')
-
- id_live_preview = req.id_live_preview
- live_preview = None
- shared.state.set_current_image()
- if shared.opts.live_previews_enable and (shared.state.id_live_preview != req.id_live_preview) and (shared.state.current_image is not None):
- buffered = io.BytesIO()
- shared.state.current_image.save(buffered, format='jpeg')
- live_preview = f'data:image/jpeg;base64,{base64.b64encode(buffered.getvalue()).decode("ascii")}'
- id_live_preview = shared.state.id_live_preview
-
- res = InternalProgressResponse(job=shared.state.job, active=active, queued=queued, paused=paused, completed=completed, progress=progress, eta=eta, live_preview=live_preview, id_live_preview=id_live_preview, textinfo=shared.state.textinfo)
- return res
-
-
-def setup_progress_api(app):
- return app.add_api_route("/internal/progress", progressapi, methods=["POST"], response_model=InternalProgressResponse)
+import base64
+import io
+import time
+from pydantic import BaseModel, Field # pylint: disable=no-name-in-module
+import modules.shared as shared
+
+
+current_task = None
+pending_tasks = {}
+finished_tasks = []
+recorded_results = []
+recorded_results_limit = 2
+
+
+def start_task(id_task):
+ global current_task # pylint: disable=global-statement
+ current_task = id_task
+ pending_tasks.pop(id_task, None)
+
+
+def record_results(id_task, res):
+ recorded_results.append((id_task, res))
+ if len(recorded_results) > recorded_results_limit:
+ recorded_results.pop(0)
+
+
+def finish_task(id_task):
+ global current_task # pylint: disable=global-statement
+ if current_task == id_task:
+ current_task = None
+ finished_tasks.append(id_task)
+ if len(finished_tasks) > 16:
+ finished_tasks.pop(0)
+
+
+def add_task_to_queue(id_job):
+ pending_tasks[id_job] = time.time()
+
+
+class ProgressRequest(BaseModel):
+ id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for")
+ id_live_preview: int = Field(default=-1, title="Live preview image ID", description="id of last received last preview image")
+
+
+class InternalProgressResponse(BaseModel):
+ job: str = Field(default=None, title="Job name", description="Internal job name")
+ active: bool = Field(title="Whether the task is being worked on right now")
+ queued: bool = Field(title="Whether the task is in queue")
+ paused: bool = Field(title="Whether the task is paused")
+ completed: bool = Field(title="Whether the task has already finished")
+ progress: float = Field(default=None, title="Progress", description="The progress with a range of 0 to 1")
+ eta: float = Field(default=None, title="ETA in secs")
+ live_preview: str = Field(default=None, title="Live preview image", description="Current live preview; a data: uri")
+ id_live_preview: int = Field(default=None, title="Live preview image ID", description="Send this together with next request to prevent receiving same image")
+ textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
+
+
+def progressapi(req: ProgressRequest):
+ active = req.id_task == current_task
+ queued = req.id_task in pending_tasks
+ completed = req.id_task in finished_tasks
+ paused = shared.state.paused
+ if not active:
+ return InternalProgressResponse(job=shared.state.job, active=active, queued=queued, paused=paused, completed=completed, id_live_preview=-1, textinfo="Queued..." if queued else "Waiting...")
+ if shared.state.job_no > shared.state.job_count:
+ shared.state.job_count = shared.state.job_no
+ batch_x = max(shared.state.job_no, 0)
+ batch_y = max(shared.state.job_count, 1)
+ step_x = max(shared.state.sampling_step, 0)
+ step_y = max(shared.state.sampling_steps, 1)
+ current = step_y * batch_x + step_x
+ total = step_y * batch_y
+ progress = min(1, abs(current / total) if total > 0 else 0)
+ elapsed = time.time() - shared.state.time_start
+ predicted = elapsed / progress if progress > 0 else None
+ eta = predicted - elapsed if predicted is not None else None
+ # shared.log.debug(f'Progress: step={step_x}:{step_y} batch={batch_x}:{batch_y} current={current} total={total} progress={progress} elapsed={elapsed} eta={eta}')
+
+ id_live_preview = req.id_live_preview
+ live_preview = None
+ shared.state.set_current_image()
+ if shared.opts.live_previews_enable and (shared.state.id_live_preview != req.id_live_preview) and (shared.state.current_image is not None):
+ buffered = io.BytesIO()
+ shared.state.current_image.save(buffered, format='jpeg')
+ live_preview = f'data:image/jpeg;base64,{base64.b64encode(buffered.getvalue()).decode("ascii")}'
+ id_live_preview = shared.state.id_live_preview
+
+ res = InternalProgressResponse(job=shared.state.job, active=active, queued=queued, paused=paused, completed=completed, progress=progress, eta=eta, live_preview=live_preview, id_live_preview=id_live_preview, textinfo=shared.state.textinfo)
+ return res
+
+
+def setup_progress_api(app):
+ return app.add_api_route("/internal/progress", progressapi, methods=["POST"], response_model=InternalProgressResponse)
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index f3c6b30c6..10cf91f3e 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,401 +1,401 @@
-# pylint: disable=anomalous-backslash-in-string
-
-"""
-import os
-import sys
-from rich import print
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
-"""
-
-import os
-import re
-from collections import namedtuple
-from typing import List
-import lark
-import torch
-from compel import Compel
-from modules.shared import opts, log, backend, Backend
-
-# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
-# will be represented with prompt_schedule like this (assuming steps=100):
-# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
-# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
-# [60, 'fantasy landscape with a lake and an oak in foreground in background masterful']
-# [75, 'fantasy landscape with a lake and an oak in background masterful']
-# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
-
-round_bracket_multiplier = 1.1
-square_bracket_multiplier = 1.0 / 1.1
-re_AND = re.compile(r"\bAND\b")
-# re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
-re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
-ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-schedule_parser = lark.Lark(r"""
-!start: (prompt | /[][():]/+)*
-prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
-!emphasized: "(" prompt ")"
- | "(" prompt ":" prompt ")"
- | "[" prompt "]"
-scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
-alternate: "[" prompt ("|" prompt)+ "]"
-WHITESPACE: /\s+/
-plain: /([^\\\[\]():|]|\\.)+/
-%import common.SIGNED_NUMBER -> NUMBER
-""")
-re_clean = re.compile(r"^\W+", re.S)
-re_whitespace = re.compile(r"\s+", re.S)
-re_break = re.compile(r"\s*\bBREAK\b|##\s*", re.S)
-re_attention_v2 = re.compile(r"""
-\(|\[|\\\(|\\\[|\\|\\\\|
-:([+-]?[.\d]+)|
-\)|\]|\\\)|\\\]|
-[^\(\)\[\]:]+|
-:
-""", re.X)
-re_attention_v1 = re.compile(r"""
-\\\(|
-\\\)|
-\\\[|
-\\]|
-\\\\|
-\\|
-\(|
-\[|
-:([+-]?[.\d]+)\)|
-\)|
-]|
-[^\\()\[\]:]+|
-:
-""", re.X)
-
-
-debug_output = os.environ.get('SD_PROMPT_DEBUG', None)
-debug = log.trace if debug_output is not None else lambda *args, **kwargs: None
-debug('Trace: PROMPT')
-
-
-def get_learned_conditioning_prompt_schedules(prompts, steps):
- """
- >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
- >>> g("test")
- [[10, 'test']]
- >>> g("a [b:3]")
- [[3, 'a '], [10, 'a b']]
- >>> g("a [b: 3]")
- [[3, 'a '], [10, 'a b']]
- >>> g("a [[[b]]:2]")
- [[2, 'a '], [10, 'a [[b]]']]
- >>> g("[(a:2):3]")
- [[3, ''], [10, '(a:2)']]
- >>> g("a [b : c : 1] d")
- [[1, 'a b d'], [10, 'a c d']]
- >>> g("a[b:[c:d:2]:1]e")
- [[1, 'abe'], [2, 'ace'], [10, 'ade']]
- >>> g("a [unbalanced")
- [[10, 'a [unbalanced']]
- >>> g("a [b:.5] c")
- [[5, 'a c'], [10, 'a b c']]
- >>> g("a [{b|d{:.5] c") # not handling this right now
- [[5, 'a c'], [10, 'a {b|d{ c']]
- >>> g("((a][:b:c [d:3]")
- [[3, '((a][:b:c '], [10, '((a][:b:c d']]
- >>> g("[a|(b:1.1)]")
- [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
- """
-
- def collect_steps(steps, tree):
- res = [steps]
- class CollectSteps(lark.Visitor):
- def scheduled(self, tree):
- tree.children[-1] = float(tree.children[-1])
- if tree.children[-1] < 1:
- tree.children[-1] *= steps
- tree.children[-1] = min(steps, int(tree.children[-1]))
- res.append(tree.children[-1])
- def alternate(self, tree): # pylint: disable=unused-argument
- res.extend(range(1, steps+1))
- CollectSteps().visit(tree)
- return sorted(set(res))
-
- def at_step(step, tree):
- class AtStep(lark.Transformer):
- def scheduled(self, args):
- before, after, _, when = args
- yield before or () if step <= when else after
- def alternate(self, args):
- yield next(args[(step - 1)%len(args)]) # pylint: disable=stop-iteration-return
- def start(self, args):
- def flatten(x):
- if type(x) == str:
- yield x
- else:
- for gen in x:
- yield from flatten(gen)
- return ''.join(flatten(args))
- def plain(self, args):
- yield args[0].value
- def __default__(self, data, children, meta):
- for child in children:
- yield child
- return AtStep().transform(tree)
-
- def get_schedule(prompt):
- try:
- tree = schedule_parser.parse(prompt)
- except lark.exceptions.LarkError:
- return [[steps, prompt]]
- return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
-
- promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
- return [promptdict[prompt] for prompt in prompts]
-
-
-def get_learned_conditioning(model, prompts, steps):
- """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
- and the sampling step at which this condition is to be replaced by the next one.
- Input:
- (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
- Output:
- [
- [ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0')) ],
- [ ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')),
- ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0')),
- ]
- ]
- """
- res = []
- prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
- cache = {}
- for prompt, prompt_schedule in zip(prompts, prompt_schedules):
- debug(f'Prompt schedule: {prompt_schedule}')
- cached = cache.get(prompt, None)
- if cached is not None:
- res.append(cached)
- continue
- texts = [x[1] for x in prompt_schedule]
- conds = model.get_learned_conditioning(texts)
- cond_schedule = []
- for i, (end_at_step, _text) in enumerate(prompt_schedule):
- cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
- cache[prompt] = cond_schedule
- res.append(cond_schedule)
- return res
-
-
-def get_multicond_prompt_list(prompts):
- res_indexes = []
- prompt_flat_list = []
- prompt_indexes = {}
- for prompt in prompts:
- subprompts = re_AND.split(prompt)
- indexes = []
- for subprompt in subprompts:
- match = re_weight.search(subprompt)
- text, weight = match.groups() if match is not None else (subprompt, 1.0)
- weight = float(weight) if weight is not None else 1.0
- index = prompt_indexes.get(text, None)
- if index is None:
- index = len(prompt_flat_list)
- prompt_flat_list.append(text)
- prompt_indexes[text] = index
- indexes.append((index, weight))
- res_indexes.append(indexes)
- return res_indexes, prompt_flat_list, prompt_indexes
-
-
-class ComposableScheduledPromptConditioning:
- def __init__(self, schedules, weight=1.0):
- self.schedules: List[ScheduledPromptConditioning] = schedules
- self.weight: float = weight
-
-
-class MulticondLearnedConditioning:
- def __init__(self, shape, batch):
- self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
- self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
-
-
-def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
- """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
- For each prompt, the list is obtained by splitting the prompt using the AND separator.
- https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
- """
- res_indexes, prompt_flat_list, _prompt_indexes = get_multicond_prompt_list(prompts)
- learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
- res = []
- for indexes in res_indexes:
- res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
- return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
-
-
-def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
- param = c[0][0].cond
- res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
- for i, cond_schedule in enumerate(c):
- target_index = 0
- for current, (end_at, _cond) in enumerate(cond_schedule):
- if current_step <= end_at:
- target_index = current
- break
- res[i] = cond_schedule[target_index].cond
- return res
-
-
-def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
- param = c.batch[0][0].schedules[0].cond
- tensors = []
- conds_list = []
- for composable_prompts in c.batch:
- conds_for_batch = []
- for composable_prompt in composable_prompts:
- target_index = 0
- for current, entry in enumerate(composable_prompt.schedules):
- if current_step <= entry.end_at_step:
- target_index = current
- break
- conds_for_batch.append((len(tensors), composable_prompt.weight))
- tensors.append(composable_prompt.schedules[target_index].cond)
- conds_list.append(conds_for_batch)
- # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes and won't be able to torch.stack them. So this fixes that.
- token_count = max([x.shape[0] for x in tensors])
- for i in range(len(tensors)):
- if tensors[i].shape[0] != token_count:
- last_vector = tensors[i][-1:]
- last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
- tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
- return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
-
-
-def parse_prompt_attention(text):
- """
- Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
- Accepted tokens are:
- (abc) - increases attention to abc by a multiplier of 1.1
- (abc:3.12) - increases attention to abc by a multiplier of 3.12
- [abc] - decreases attention to abc by a multiplier of 1.1
- \( - literal character '('
- \[ - literal character '['
- \) - literal character ')'
- \] - literal character ']'
- \\ - literal character '\'
- anything else - just text
- >>> parse_prompt_attention('normal text')
- [['normal text', 1.0]]
- >>> parse_prompt_attention('an (important) word')
- [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
- >>> parse_prompt_attention('(unbalanced')
- [['unbalanced', 1.1]]
- >>> parse_prompt_attention('\(literal\]')
- [['(literal]', 1.0]]
- >>> parse_prompt_attention('(unnecessary)(parens)')
- [['unnecessaryparens', 1.1]]
- >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
- [['a ', 1.0],
- ['house', 1.5730000000000004],
- [' ', 1.1],
- ['on', 1.0],
- [' a ', 1.1],
- ['hill', 0.55],
- [', sun, ', 1.1],
- ['sky', 1.4641000000000006],
- ['.', 1.1]]
- """
- res = []
- round_brackets = []
- square_brackets = []
- if opts.prompt_attention == 'Fixed attention':
- res = [[text, 1.0]]
- debug(f'Prompt: parser={opts.prompt_attention} {res}')
- return res
- elif opts.prompt_attention == 'Compel parser':
- conjunction = Compel.parse_prompt_string(text)
- if conjunction is None or conjunction.prompts is None or conjunction.prompts is None or len(conjunction.prompts[0].children) == 0:
- return [["", 1.0]]
- res = []
- for frag in conjunction.prompts[0].children:
- res.append([frag.text, frag.weight])
- debug(f'Prompt: parser={opts.prompt_attention} {res}')
- return res
- elif opts.prompt_attention == 'A1111 parser':
- re_attention = re_attention_v1
- whitespace = ''
- else:
- re_attention = re_attention_v1
- if backend == Backend.DIFFUSERS:
- text = text.replace('\n', ' BREAK ')
- else:
- text = text.replace('\n', ' ')
- whitespace = ' '
-
- def multiply_range(start_position, multiplier):
- try:
- for p in range(start_position, len(res)):
- res[p][1] *= multiplier
- except Exception as e:
- log(f'Prompt parser: {e}')
-
- for m in re_attention.finditer(text):
- try:
- section = m.group(0)
- weight = m.group(1)
- if section.startswith('\\'):
- res.append([section[1:], 1.0])
- elif section == '(':
- round_brackets.append(len(res))
- elif section == '[':
- square_brackets.append(len(res))
- elif weight is not None and len(round_brackets) > 0:
- multiply_range(round_brackets.pop(), float(weight))
- elif section == ')' and len(round_brackets) > 0:
- multiply_range(round_brackets.pop(), round_bracket_multiplier)
- elif section == ']' and len(square_brackets) > 0:
- multiply_range(square_brackets.pop(), square_bracket_multiplier)
- else:
- parts = re.split(re_break, section)
- for i, part in enumerate(parts):
- if i > 0:
- res.append(["BREAK", -1])
- if opts.prompt_attention == 'Full parser':
- part = re_clean.sub("", part)
- part = re_whitespace.sub(" ", part).strip()
- if len(part) == 0:
- continue
- res.append([part, 1.0])
- except Exception as e:
- log.error(f'Prompt parser: section="{text[m.start():m.end()]}" position={m.start()}:{m.end()} text="{text}" error={e}')
- for pos in round_brackets:
- multiply_range(pos, round_bracket_multiplier)
- for pos in square_brackets:
- multiply_range(pos, square_bracket_multiplier)
- if len(res) == 0:
- res = [["", 1.0]]
- # merge runs of identical weights
- i = 0
- while i + 1 < len(res):
- if res[i][1] == res[i + 1][1]:
- res[i][0] += whitespace + res[i + 1][0]
- res.pop(i + 1)
- else:
- i += 1
- debug(f'Prompt: parser={opts.prompt_attention} {res}')
- return res
-
-if __name__ == "__main__":
- input_text = '[black] [[grey]] (white) ((gray)) ((orange:1.1) yellow) ((purple) and [dark] red:1.1) [mouse:0.2] [(cat:1.1):0.5]'
- print(f'Prompt: {input_text}')
- all_schedules = get_learned_conditioning_prompt_schedules([input_text], 100)[0]
- print('Schedules', all_schedules)
- for schedule in all_schedules:
- print('Schedule', schedule[0])
- opts.data['prompt_attention'] = 'Fixed attention'
- output_list = parse_prompt_attention(schedule[1])
- print(' Fixed:', output_list)
- opts.data['prompt_attention'] = 'Compel parser'
- output_list = parse_prompt_attention(schedule[1])
- print(' Compel:', output_list)
- opts.data['prompt_attention'] = 'A1111 parser'
- output_list = parse_prompt_attention(schedule[1])
- print(' A1111:', output_list)
- opts.data['prompt_attention'] = 'Full parser'
- output_list = parse_prompt_attention(schedule[1])
- print(' Full :', output_list)
+# pylint: disable=anomalous-backslash-in-string
+
+"""
+import os
+import sys
+from rich import print
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+"""
+
+import os
+import re
+from collections import namedtuple
+from typing import List
+import lark
+import torch
+from compel import Compel
+from modules.shared import opts, log, backend, Backend
+
+# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
+# will be represented with prompt_schedule like this (assuming steps=100):
+# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
+# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
+# [60, 'fantasy landscape with a lake and an oak in foreground in background masterful']
+# [75, 'fantasy landscape with a lake and an oak in background masterful']
+# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
+
+round_bracket_multiplier = 1.1
+square_bracket_multiplier = 1.0 / 1.1
+re_AND = re.compile(r"\bAND\b")
+# re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
+schedule_parser = lark.Lark(r"""
+!start: (prompt | /[][():]/+)*
+prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
+!emphasized: "(" prompt ")"
+ | "(" prompt ":" prompt ")"
+ | "[" prompt "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+alternate: "[" prompt ("|" prompt)+ "]"
+WHITESPACE: /\s+/
+plain: /([^\\\[\]():|]|\\.)+/
+%import common.SIGNED_NUMBER -> NUMBER
+""")
+re_clean = re.compile(r"^\W+", re.S)
+re_whitespace = re.compile(r"\s+", re.S)
+re_break = re.compile(r"\s*\bBREAK\b|##\s*", re.S)
+re_attention_v2 = re.compile(r"""
+\(|\[|\\\(|\\\[|\\|\\\\|
+:([+-]?[.\d]+)|
+\)|\]|\\\)|\\\]|
+[^\(\)\[\]:]+|
+:
+""", re.X)
+re_attention_v1 = re.compile(r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:([+-]?[.\d]+)\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""", re.X)
+
+
+debug_output = os.environ.get('SD_PROMPT_DEBUG', None)
+debug = log.trace if debug_output is not None else lambda *args, **kwargs: None
+debug('Trace: PROMPT')
+
+
+def get_learned_conditioning_prompt_schedules(prompts, steps):
+ """
+ >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
+ >>> g("test")
+ [[10, 'test']]
+ >>> g("a [b:3]")
+ [[3, 'a '], [10, 'a b']]
+ >>> g("a [b: 3]")
+ [[3, 'a '], [10, 'a b']]
+ >>> g("a [[[b]]:2]")
+ [[2, 'a '], [10, 'a [[b]]']]
+ >>> g("[(a:2):3]")
+ [[3, ''], [10, '(a:2)']]
+ >>> g("a [b : c : 1] d")
+ [[1, 'a b d'], [10, 'a c d']]
+ >>> g("a[b:[c:d:2]:1]e")
+ [[1, 'abe'], [2, 'ace'], [10, 'ade']]
+ >>> g("a [unbalanced")
+ [[10, 'a [unbalanced']]
+ >>> g("a [b:.5] c")
+ [[5, 'a c'], [10, 'a b c']]
+ >>> g("a [{b|d{:.5] c") # not handling this right now
+ [[5, 'a c'], [10, 'a {b|d{ c']]
+ >>> g("((a][:b:c [d:3]")
+ [[3, '((a][:b:c '], [10, '((a][:b:c d']]
+ >>> g("[a|(b:1.1)]")
+ [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
+ """
+
+ def collect_steps(steps, tree):
+ res = [steps]
+ class CollectSteps(lark.Visitor):
+ def scheduled(self, tree):
+ tree.children[-1] = float(tree.children[-1])
+ if tree.children[-1] < 1:
+ tree.children[-1] *= steps
+ tree.children[-1] = min(steps, int(tree.children[-1]))
+ res.append(tree.children[-1])
+ def alternate(self, tree): # pylint: disable=unused-argument
+ res.extend(range(1, steps+1))
+ CollectSteps().visit(tree)
+ return sorted(set(res))
+
+ def at_step(step, tree):
+ class AtStep(lark.Transformer):
+ def scheduled(self, args):
+ before, after, _, when = args
+ yield before or () if step <= when else after
+ def alternate(self, args):
+ yield next(args[(step - 1)%len(args)]) # pylint: disable=stop-iteration-return
+ def start(self, args):
+ def flatten(x):
+ if type(x) == str:
+ yield x
+ else:
+ for gen in x:
+ yield from flatten(gen)
+ return ''.join(flatten(args))
+ def plain(self, args):
+ yield args[0].value
+ def __default__(self, data, children, meta):
+ for child in children:
+ yield child
+ return AtStep().transform(tree)
+
+ def get_schedule(prompt):
+ try:
+ tree = schedule_parser.parse(prompt)
+ except lark.exceptions.LarkError:
+ return [[steps, prompt]]
+ return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
+
+ promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
+ return [promptdict[prompt] for prompt in prompts]
+
+
+def get_learned_conditioning(model, prompts, steps):
+ """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
+ and the sampling step at which this condition is to be replaced by the next one.
+ Input:
+ (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
+ Output:
+ [
+ [ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0')) ],
+ [ ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')),
+ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0')),
+ ]
+ ]
+ """
+ res = []
+ prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
+ cache = {}
+ for prompt, prompt_schedule in zip(prompts, prompt_schedules):
+ debug(f'Prompt schedule: {prompt_schedule}')
+ cached = cache.get(prompt, None)
+ if cached is not None:
+ res.append(cached)
+ continue
+ texts = [x[1] for x in prompt_schedule]
+ conds = model.get_learned_conditioning(texts)
+ cond_schedule = []
+ for i, (end_at_step, _text) in enumerate(prompt_schedule):
+ cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
+ cache[prompt] = cond_schedule
+ res.append(cond_schedule)
+ return res
+
+
+def get_multicond_prompt_list(prompts):
+ res_indexes = []
+ prompt_flat_list = []
+ prompt_indexes = {}
+ for prompt in prompts:
+ subprompts = re_AND.split(prompt)
+ indexes = []
+ for subprompt in subprompts:
+ match = re_weight.search(subprompt)
+ text, weight = match.groups() if match is not None else (subprompt, 1.0)
+ weight = float(weight) if weight is not None else 1.0
+ index = prompt_indexes.get(text, None)
+ if index is None:
+ index = len(prompt_flat_list)
+ prompt_flat_list.append(text)
+ prompt_indexes[text] = index
+ indexes.append((index, weight))
+ res_indexes.append(indexes)
+ return res_indexes, prompt_flat_list, prompt_indexes
+
+
+class ComposableScheduledPromptConditioning:
+ def __init__(self, schedules, weight=1.0):
+ self.schedules: List[ScheduledPromptConditioning] = schedules
+ self.weight: float = weight
+
+
+class MulticondLearnedConditioning:
+ def __init__(self, shape, batch):
+ self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
+ self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
+
+
+def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
+ """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
+ For each prompt, the list is obtained by splitting the prompt using the AND separator.
+ https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
+ """
+ res_indexes, prompt_flat_list, _prompt_indexes = get_multicond_prompt_list(prompts)
+ learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
+ res = []
+ for indexes in res_indexes:
+ res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
+ return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
+
+
+def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
+ param = c[0][0].cond
+ res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+ for i, cond_schedule in enumerate(c):
+ target_index = 0
+ for current, (end_at, _cond) in enumerate(cond_schedule):
+ if current_step <= end_at:
+ target_index = current
+ break
+ res[i] = cond_schedule[target_index].cond
+ return res
+
+
+def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
+ param = c.batch[0][0].schedules[0].cond
+ tensors = []
+ conds_list = []
+ for composable_prompts in c.batch:
+ conds_for_batch = []
+ for composable_prompt in composable_prompts:
+ target_index = 0
+ for current, entry in enumerate(composable_prompt.schedules):
+ if current_step <= entry.end_at_step:
+ target_index = current
+ break
+ conds_for_batch.append((len(tensors), composable_prompt.weight))
+ tensors.append(composable_prompt.schedules[target_index].cond)
+ conds_list.append(conds_for_batch)
+ # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes and won't be able to torch.stack them. So this fixes that.
+ token_count = max([x.shape[0] for x in tensors])
+ for i in range(len(tensors)):
+ if tensors[i].shape[0] != token_count:
+ last_vector = tensors[i][-1:]
+ last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
+ tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+ return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
+
+
+def parse_prompt_attention(text):
+ """
+ Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+ Accepted tokens are:
+ (abc) - increases attention to abc by a multiplier of 1.1
+ (abc:3.12) - increases attention to abc by a multiplier of 3.12
+ [abc] - decreases attention to abc by a multiplier of 1.1
+ \( - literal character '('
+ \[ - literal character '['
+ \) - literal character ')'
+ \] - literal character ']'
+ \\ - literal character '\'
+ anything else - just text
+ >>> parse_prompt_attention('normal text')
+ [['normal text', 1.0]]
+ >>> parse_prompt_attention('an (important) word')
+ [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+ >>> parse_prompt_attention('(unbalanced')
+ [['unbalanced', 1.1]]
+ >>> parse_prompt_attention('\(literal\]')
+ [['(literal]', 1.0]]
+ >>> parse_prompt_attention('(unnecessary)(parens)')
+ [['unnecessaryparens', 1.1]]
+ >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+ [['a ', 1.0],
+ ['house', 1.5730000000000004],
+ [' ', 1.1],
+ ['on', 1.0],
+ [' a ', 1.1],
+ ['hill', 0.55],
+ [', sun, ', 1.1],
+ ['sky', 1.4641000000000006],
+ ['.', 1.1]]
+ """
+ res = []
+ round_brackets = []
+ square_brackets = []
+ if opts.prompt_attention == 'Fixed attention':
+ res = [[text, 1.0]]
+ debug(f'Prompt: parser={opts.prompt_attention} {res}')
+ return res
+ elif opts.prompt_attention == 'Compel parser':
+ conjunction = Compel.parse_prompt_string(text)
+ if conjunction is None or conjunction.prompts is None or conjunction.prompts is None or len(conjunction.prompts[0].children) == 0:
+ return [["", 1.0]]
+ res = []
+ for frag in conjunction.prompts[0].children:
+ res.append([frag.text, frag.weight])
+ debug(f'Prompt: parser={opts.prompt_attention} {res}')
+ return res
+ elif opts.prompt_attention == 'A1111 parser':
+ re_attention = re_attention_v1
+ whitespace = ''
+ else:
+ re_attention = re_attention_v1
+ if backend == Backend.DIFFUSERS:
+ text = text.replace('\n', ' BREAK ')
+ else:
+ text = text.replace('\n', ' ')
+ whitespace = ' '
+
+ def multiply_range(start_position, multiplier):
+ try:
+ for p in range(start_position, len(res)):
+ res[p][1] *= multiplier
+ except Exception as e:
+ log(f'Prompt parser: {e}')
+
+ for m in re_attention.finditer(text):
+ try:
+ section = m.group(0)
+ weight = m.group(1)
+ if section.startswith('\\'):
+ res.append([section[1:], 1.0])
+ elif section == '(':
+ round_brackets.append(len(res))
+ elif section == '[':
+ square_brackets.append(len(res))
+ elif weight is not None and len(round_brackets) > 0:
+ multiply_range(round_brackets.pop(), float(weight))
+ elif section == ')' and len(round_brackets) > 0:
+ multiply_range(round_brackets.pop(), round_bracket_multiplier)
+ elif section == ']' and len(square_brackets) > 0:
+ multiply_range(square_brackets.pop(), square_bracket_multiplier)
+ else:
+ parts = re.split(re_break, section)
+ for i, part in enumerate(parts):
+ if i > 0:
+ res.append(["BREAK", -1])
+ if opts.prompt_attention == 'Full parser':
+ part = re_clean.sub("", part)
+ part = re_whitespace.sub(" ", part).strip()
+ if len(part) == 0:
+ continue
+ res.append([part, 1.0])
+ except Exception as e:
+ log.error(f'Prompt parser: section="{text[m.start():m.end()]}" position={m.start()}:{m.end()} text="{text}" error={e}')
+ for pos in round_brackets:
+ multiply_range(pos, round_bracket_multiplier)
+ for pos in square_brackets:
+ multiply_range(pos, square_bracket_multiplier)
+ if len(res) == 0:
+ res = [["", 1.0]]
+ # merge runs of identical weights
+ i = 0
+ while i + 1 < len(res):
+ if res[i][1] == res[i + 1][1]:
+ res[i][0] += whitespace + res[i + 1][0]
+ res.pop(i + 1)
+ else:
+ i += 1
+ debug(f'Prompt: parser={opts.prompt_attention} {res}')
+ return res
+
+if __name__ == "__main__":
+ input_text = '[black] [[grey]] (white) ((gray)) ((orange:1.1) yellow) ((purple) and [dark] red:1.1) [mouse:0.2] [(cat:1.1):0.5]'
+ print(f'Prompt: {input_text}')
+ all_schedules = get_learned_conditioning_prompt_schedules([input_text], 100)[0]
+ print('Schedules', all_schedules)
+ for schedule in all_schedules:
+ print('Schedule', schedule[0])
+ opts.data['prompt_attention'] = 'Fixed attention'
+ output_list = parse_prompt_attention(schedule[1])
+ print(' Fixed:', output_list)
+ opts.data['prompt_attention'] = 'Compel parser'
+ output_list = parse_prompt_attention(schedule[1])
+ print(' Compel:', output_list)
+ opts.data['prompt_attention'] = 'A1111 parser'
+ output_list = parse_prompt_attention(schedule[1])
+ print(' A1111:', output_list)
+ opts.data['prompt_attention'] = 'Full parser'
+ output_list = parse_prompt_attention(schedule[1])
+ print(' Full :', output_list)
diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 6ec9df66c..d30f95fb7 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -1,223 +1,239 @@
-import os
-import time
-import typing
-import torch
-from compel import ReturnedEmbeddingsType
-from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
-from modules import shared, prompt_parser, devices
-
-debug = shared.log.trace if os.environ.get('SD_PROMPT_DEBUG', None) is not None else lambda *args, **kwargs: None
-debug('Trace: PROMPT')
-CLIP_SKIP_MAPPING = {
- None: ReturnedEmbeddingsType.LAST_HIDDEN_STATES_NORMALIZED,
- 1: ReturnedEmbeddingsType.LAST_HIDDEN_STATES_NORMALIZED,
- 2: ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NORMALIZED,
-}
-
-
-# from https://github.com/damian0815/compel/blob/main/src/compel/diffusers_textual_inversion_manager.py
-class DiffusersTextualInversionManager(BaseTextualInversionManager):
- def __init__(self, pipe, tokenizer):
- self.pipe = pipe
- self.tokenizer = tokenizer
- if hasattr(self.pipe, 'embedding_db'):
- self.pipe.embedding_db.embeddings_used.clear()
-
- # from https://github.com/huggingface/diffusers/blob/705c592ea98ba4e288d837b9cba2767623c78603/src/diffusers/loaders.py#L599
- def maybe_convert_prompt(self, prompt: typing.Union[str, typing.List[str]], tokenizer="PreTrainedTokenizer"):
- prompts = [prompt] if not isinstance(prompt, typing.List) else prompt
- prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]
- if not isinstance(prompt, typing.List):
- return prompts[0]
- return prompts
-
- def _maybe_convert_prompt(self, prompt: str, tokenizer="PreTrainedTokenizer"):
- tokens = tokenizer.tokenize(prompt)
- unique_tokens = set(tokens)
- for token in unique_tokens:
- if token in tokenizer.added_tokens_encoder:
- if hasattr(self.pipe, 'embedding_db'):
- self.pipe.embedding_db.embeddings_used.append(token)
- replacement = token
- i = 1
- while f"{token}_{i}" in tokenizer.added_tokens_encoder:
- replacement += f" {token}_{i}"
- i += 1
- prompt = prompt.replace(token, replacement)
- if hasattr(self.pipe, 'embedding_db'):
- self.pipe.embedding_db.embeddings_used = list(set(self.pipe.embedding_db.embeddings_used))
- debug(f'Prompt: convert={prompt}')
- return prompt
-
- def expand_textual_inversion_token_ids_if_necessary(self, token_ids: typing.List[int]) -> typing.List[int]:
- if len(token_ids) == 0:
- return token_ids
- prompt = self.pipe.tokenizer.decode(token_ids)
- prompt = self.maybe_convert_prompt(prompt, self.pipe.tokenizer)
- debug(f'Prompt: expand={prompt}')
- return self.pipe.tokenizer.encode(prompt, add_special_tokens=False)
-
-
-def get_prompt_schedule(p, prompt, steps): # pylint: disable=unused-argument
- t0 = time.time()
- temp = []
- schedule = prompt_parser.get_learned_conditioning_prompt_schedules([prompt], steps)[0]
- if all(x == schedule[0] for x in schedule):
- return [prompt], False
- for chunk in schedule:
- for s in range(steps):
- if len(temp) < s + 1 <= chunk[0]:
- temp.append(chunk[1])
- debug(f'Prompt: schedule={temp} time={time.time() - t0}')
- return temp, len(schedule) > 1
-
-
-def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int, step: int = 1, clip_skip: typing.Optional[int] = None): # pylint: disable=unused-argument
- if 'StableDiffusion' not in pipe.__class__.__name__ and 'DemoFusion':
- shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
- return None, None, None, None
- else:
- t0 = time.time()
- positive_schedule, scheduled = get_prompt_schedule(p, prompts[0], steps)
- negative_schedule, neg_scheduled = get_prompt_schedule(p, negative_prompts[0], steps)
- p.scheduled_prompt = scheduled or neg_scheduled
-
- p.prompt_embeds = []
- p.positive_pooleds = []
- p.negative_embeds = []
- p.negative_pooleds = []
-
- cache = {}
- for i in range(max(len(positive_schedule), len(negative_schedule))):
- cached = cache.get(positive_schedule[i % len(positive_schedule)] + negative_schedule[i % len(negative_schedule)], None)
- if cached is not None:
- prompt_embed, positive_pooled, negative_embed, negative_pooled = cached
- else:
- prompt_embed, positive_pooled, negative_embed, negative_pooled = get_weighted_text_embeddings(pipe,
- positive_schedule[i % len(positive_schedule)],
- negative_schedule[i % len(negative_schedule)],
- clip_skip)
- if prompt_embed is not None:
- p.prompt_embeds.append(torch.cat([prompt_embed] * len(prompts), dim=0))
- if negative_embed is not None:
- p.negative_embeds.append(torch.cat([negative_embed] * len(negative_prompts), dim=0))
- if positive_pooled is not None and shared.sd_model_type == "sdxl":
- p.positive_pooleds.append(torch.cat([positive_pooled] * len(prompts), dim=0))
- if negative_pooled is not None and shared.sd_model_type == "sdxl":
- p.negative_pooleds.append(torch.cat([negative_pooled] * len(negative_prompts), dim=0))
- debug(f"Prompt Parser: Elapsed Time {time.time() - t0}")
- return
-
-
-def get_prompts_with_weights(prompt: str):
- manager = DiffusersTextualInversionManager(shared.sd_model, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
- prompt = manager.maybe_convert_prompt(prompt, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
- texts_and_weights = prompt_parser.parse_prompt_attention(prompt)
- texts = [t for t, w in texts_and_weights]
- text_weights = [w for t, w in texts_and_weights]
- debug(f'Prompt: weights={texts_and_weights}')
- return texts, text_weights
-
-
-def prepare_embedding_providers(pipe, clip_skip):
- device = pipe.device if str(pipe.device) != 'meta' else devices.device
- embeddings_providers = []
- if 'XL' in pipe.__class__.__name__:
- embedding_type = ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED
- else:
- if clip_skip > 2:
- shared.log.warning(f"Prompt parser unsupported: clip_skip={clip_skip}")
- clip_skip = 2
- embedding_type = CLIP_SKIP_MAPPING[clip_skip]
- if getattr(pipe, "tokenizer", None) is not None and getattr(pipe, "text_encoder", None) is not None:
- embedding = EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate=False, returned_embeddings_type=embedding_type, device=device)
- embeddings_providers.append(embedding)
- if getattr(pipe, "tokenizer_2", None) is not None and getattr(pipe, "text_encoder_2", None) is not None:
- embedding = EmbeddingsProvider(tokenizer=pipe.tokenizer_2, text_encoder=pipe.text_encoder_2, truncate=False, returned_embeddings_type=embedding_type, device=device)
- embeddings_providers.append(embedding)
- return embeddings_providers
-
-
-def pad_to_same_length(pipe, embeds):
- device = pipe.device if str(pipe.device) != 'meta' else devices.device
- try: # SDXL
- empty_embed = pipe.encode_prompt("")
- except Exception: # SD1.5
- empty_embed = pipe.encode_prompt("", device, 1, False)
- empty_batched = torch.cat([empty_embed[0].to(embeds[0].device)] * embeds[0].shape[0])
- max_token_count = max([embed.shape[1] for embed in embeds])
- for i, embed in enumerate(embeds):
- while embed.shape[1] < max_token_count:
- embed = torch.cat([embed, empty_batched], dim=1)
- embeds[i] = embed
- return embeds
-
-
-def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", clip_skip: int = None):
- device = pipe.device if str(pipe.device) != 'meta' else devices.device
- prompt_2 = prompt.split("TE2:")[-1]
- neg_prompt_2 = neg_prompt.split("TE2:")[-1]
- prompt = prompt.split("TE2:")[0]
- neg_prompt = neg_prompt.split("TE2:")[0]
-
- ps = [get_prompts_with_weights(p) for p in [prompt, prompt_2]]
- positives = [t for t, w in ps]
- positive_weights = [w for t, w in ps]
- ns = [get_prompts_with_weights(p) for p in [neg_prompt, neg_prompt_2]]
- negatives = [t for t, w in ns]
- negative_weights = [w for t, w in ns]
- if getattr(pipe, "tokenizer_2", None) is not None and getattr(pipe, "tokenizer", None) is None:
- positives.pop(0)
- positive_weights.pop(0)
- negatives.pop(0)
- negative_weights.pop(0)
-
- embedding_providers = prepare_embedding_providers(pipe, clip_skip)
- prompt_embeds = []
- negative_prompt_embeds = []
- pooled_prompt_embeds = None
- negative_pooled_prompt_embeds = None
- for i in range(len(embedding_providers)):
- # add BREAK keyword that splits the prompt into multiple fragments
- text = positives[i]
- weights = positive_weights[i]
- text.append('BREAK')
- weights.append(-1)
- provider_embed = []
- while 'BREAK' in text:
- pos = text.index('BREAK')
- debug(f'Prompt: section="{text[:pos]}" len={len(text[:pos])} weights={weights[:pos]}')
- if len(text[:pos]) > 0:
- embed, ptokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[text[:pos]], fragment_weights_batch=[weights[:pos]], device=device, should_return_tokens=True)
- provider_embed.append(embed)
- text = text[pos + 1:]
- weights = weights[pos + 1:]
- prompt_embeds.append(torch.cat(provider_embed, dim=1))
- # negative prompt has no keywords
- embed, ntokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[negatives[i]], fragment_weights_batch=[negative_weights[i]], device=device, should_return_tokens=True)
- negative_prompt_embeds.append(embed)
-
- if prompt_embeds[-1].shape[-1] > 768:
- if shared.opts.diffusers_pooled == "weighted":
- pooled_prompt_embeds = prompt_embeds[-1][
- torch.arange(prompt_embeds[-1].shape[0], device=device),
- (ptokens.to(dtype=torch.int, device=device) == 49407)
- .int()
- .argmax(dim=-1),
- ]
- negative_pooled_prompt_embeds = negative_prompt_embeds[-1][
- torch.arange(negative_prompt_embeds[-1].shape[0], device=device),
- (ntokens.to(dtype=torch.int, device=device) == 49407)
- .int()
- .argmax(dim=-1),
- ]
- else:
- pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[prompt_2], device=device) if prompt_embeds[-1].shape[-1] > 768 else None
- negative_pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[neg_prompt_2], device=device) if negative_prompt_embeds[-1].shape[-1] > 768 else None
-
- prompt_embeds = torch.cat(prompt_embeds, dim=-1) if len(prompt_embeds) > 1 else prompt_embeds[0]
- negative_prompt_embeds = torch.cat(negative_prompt_embeds, dim=-1) if len(negative_prompt_embeds) > 1 else negative_prompt_embeds[0]
- debug(f'Prompt: shape={prompt_embeds.shape} negative={negative_prompt_embeds.shape}')
- if prompt_embeds.shape[1] != negative_prompt_embeds.shape[1]:
- [prompt_embeds, negative_prompt_embeds] = pad_to_same_length(pipe, [prompt_embeds, negative_prompt_embeds])
- return prompt_embeds, pooled_prompt_embeds, negative_prompt_embeds, negative_pooled_prompt_embeds
+import os
+import time
+import typing
+import torch
+from compel import ReturnedEmbeddingsType
+from compel.embeddings_provider import BaseTextualInversionManager, EmbeddingsProvider
+from transformers import PreTrainedTokenizer
+from modules import shared, prompt_parser, devices
+
+debug = shared.log.trace if os.environ.get('SD_PROMPT_DEBUG', None) is not None else lambda *args, **kwargs: None
+debug('Trace: PROMPT')
+
+CLIP_SKIP_MAPPING = {
+ None: ReturnedEmbeddingsType.LAST_HIDDEN_STATES_NORMALIZED,
+ 1: ReturnedEmbeddingsType.LAST_HIDDEN_STATES_NORMALIZED,
+ 2: ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NORMALIZED,
+}
+
+
+# from https://github.com/damian0815/compel/blob/main/src/compel/diffusers_textual_inversion_manager.py
+class DiffusersTextualInversionManager(BaseTextualInversionManager):
+ def __init__(self, pipe, tokenizer):
+ self.pipe = pipe
+ self.tokenizer = tokenizer
+ if hasattr(self.pipe, 'embedding_db'):
+ self.pipe.embedding_db.embeddings_used.clear()
+
+ # code from
+ # https://github.com/huggingface/diffusers/blob/705c592ea98ba4e288d837b9cba2767623c78603/src/diffusers/loaders.py
+ def maybe_convert_prompt(self, prompt: typing.Union[str, typing.List[str]], tokenizer: PreTrainedTokenizer):
+ prompts = [prompt] if not isinstance(prompt, typing.List) else prompt
+ prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]
+ if not isinstance(prompt, typing.List):
+ return prompts[0]
+ return prompts
+
+ def _maybe_convert_prompt(self, prompt: str, tokenizer: PreTrainedTokenizer):
+ tokens = tokenizer.tokenize(prompt)
+ unique_tokens = set(tokens)
+ for token in unique_tokens:
+ if token in tokenizer.added_tokens_encoder:
+ if hasattr(self.pipe, 'embedding_db'):
+ self.pipe.embedding_db.embeddings_used.append(token)
+ replacement = token
+ i = 1
+ while f"{token}_{i}" in tokenizer.added_tokens_encoder:
+ replacement += f" {token}_{i}"
+ i += 1
+ prompt = prompt.replace(token, replacement)
+ if hasattr(self.pipe, 'embedding_db'):
+ self.pipe.embedding_db.embeddings_used = list(set(self.pipe.embedding_db.embeddings_used))
+ debug(f'Prompt: convert={prompt}')
+ return prompt
+
+ def expand_textual_inversion_token_ids_if_necessary(self, token_ids: typing.List[int]) -> typing.List[int]:
+ if len(token_ids) == 0:
+ return token_ids
+ prompt = self.pipe.tokenizer.decode(token_ids)
+ prompt = self.maybe_convert_prompt(prompt, self.pipe.tokenizer)
+ debug(f'Prompt: expand={prompt}')
+ return self.pipe.tokenizer.encode(prompt, add_special_tokens=False)
+
+
+def get_prompt_schedule(prompt, steps):
+ t0 = time.time()
+ temp = []
+ schedule = prompt_parser.get_learned_conditioning_prompt_schedules([prompt], steps)[0]
+ if all(x == schedule[0] for x in schedule):
+ return [schedule[0][1]], False
+ for chunk in schedule:
+ for s in range(steps):
+ if len(temp) < s + 1 <= chunk[0]:
+ temp.append(chunk[1])
+ debug(f'Prompt: schedule={temp} time={time.time() - t0}')
+ return temp, len(schedule) > 1
+
+
+def encode_prompts(pipe, p, prompts: list, negative_prompts: list, steps: int,
+ clip_skip: typing.Optional[int] = None):
+ if 'StableDiffusion' not in pipe.__class__.__name__ and 'DemoFusion':
+ shared.log.warning(f"Prompt parser not supported: {pipe.__class__.__name__}")
+ return None, None, None, None
+ else:
+ t0 = time.time()
+ positive_schedule, scheduled = get_prompt_schedule(prompts[0], steps)
+ negative_schedule, neg_scheduled = get_prompt_schedule(negative_prompts[0], steps)
+ p.scheduled_prompt = scheduled or neg_scheduled
+ p.prompt_embeds = []
+ p.positive_pooleds = []
+ p.negative_embeds = []
+ p.negative_pooleds = []
+
+ cache = {}
+ for i in range(max(len(positive_schedule), len(negative_schedule))):
+ positive_prompt = positive_schedule[i % len(positive_schedule)]
+ negative_prompt = negative_schedule[i % len(negative_schedule)]
+ results = cache.get(positive_prompt + negative_prompt, None)
+
+ if results is None:
+ results = get_weighted_text_embeddings(pipe, positive_prompt, negative_prompt, clip_skip)
+ cache[positive_prompt + negative_prompt] = results
+
+ prompt_embed, positive_pooled, negative_embed, negative_pooled = results
+ if prompt_embed is not None:
+ p.prompt_embeds.append(torch.cat([prompt_embed] * len(prompts), dim=0))
+ if negative_embed is not None:
+ p.negative_embeds.append(torch.cat([negative_embed] * len(negative_prompts), dim=0))
+ if positive_pooled is not None:
+ p.positive_pooleds.append(torch.cat([positive_pooled] * len(prompts), dim=0))
+ if negative_pooled is not None:
+ p.negative_pooleds.append(torch.cat([negative_pooled] * len(negative_prompts), dim=0))
+ debug(f"Prompt Parser: Elapsed Time {time.time() - t0}")
+ return
+
+
+def get_prompts_with_weights(prompt: str):
+ manager = DiffusersTextualInversionManager(shared.sd_model,
+ shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
+ prompt = manager.maybe_convert_prompt(prompt, shared.sd_model.tokenizer or shared.sd_model.tokenizer_2)
+ texts_and_weights = prompt_parser.parse_prompt_attention(prompt)
+ texts, text_weights = zip(*texts_and_weights)
+ debug(f'Prompt: weights={texts_and_weights}')
+ return texts, text_weights
+
+
+def prepare_embedding_providers(pipe, clip_skip):
+ device = pipe.device if str(pipe.device) != 'meta' else devices.device
+ embeddings_providers = []
+ if 'XL' in pipe.__class__.__name__:
+ embedding_type = ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED
+ else:
+ if clip_skip > 2:
+ shared.log.warning(f"Prompt parser unsupported: clip_skip={clip_skip}")
+ clip_skip = 2
+ embedding_type = CLIP_SKIP_MAPPING[clip_skip]
+ if hasattr(pipe, "tokenizer") and hasattr(pipe, "text_encoder"):
+ provider = EmbeddingsProvider(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate=False,
+ returned_embeddings_type=embedding_type, device=device)
+ embeddings_providers.append(provider)
+ if hasattr(pipe, "tokenizer_2") and getattr(pipe, "text_encoder_2"):
+ provider = EmbeddingsProvider(tokenizer=pipe.tokenizer_2, text_encoder=pipe.text_encoder_2, truncate=False,
+ returned_embeddings_type=embedding_type, device=device)
+ embeddings_providers.append(provider)
+ return embeddings_providers
+
+
+def pad_to_same_length(pipe, embeds):
+ device = pipe.device if str(pipe.device) != 'meta' else devices.device
+ try: # SDXL
+ empty_embed = pipe.encode_prompt("")
+ except TypeError: # SD1.5
+ empty_embed = pipe.encode_prompt("", device, 1, False)
+ max_token_count = max([embed.shape[1] for embed in embeds])
+ repeats = max_token_count - min([embed.shape[1] for embed in embeds])
+ empty_batched = empty_embed[0].to(embeds[0].device).repeat(embeds[0].shape[0], repeats // empty_embed[0].shape[1], 1)
+ for i, embed in enumerate(embeds):
+ if embed.shape[1] < max_token_count:
+ embed = torch.cat([embed, empty_batched], dim=1)
+ embeds[i] = embed
+ return embeds
+
+
+def get_weighted_text_embeddings(pipe, prompt: str = "", neg_prompt: str = "", clip_skip: int = None):
+ device = pipe.device if str(pipe.device) != 'meta' else devices.device
+ prompt_2 = prompt.split("TE2:")[-1]
+ neg_prompt_2 = neg_prompt.split("TE2:")[-1]
+ prompt = prompt.split("TE2:")[0]
+ neg_prompt = neg_prompt.split("TE2:")[0]
+
+ ps = [get_prompts_with_weights(p) for p in [prompt, prompt_2]]
+ positives, positive_weights = zip(*ps)
+ ns = [get_prompts_with_weights(p) for p in [neg_prompt, neg_prompt_2]]
+ negatives, negative_weights = zip(*ns)
+ if hasattr(pipe, "tokenizer_2") and not hasattr(pipe, "tokenizer"):
+ positives.pop(0)
+ positive_weights.pop(0)
+ negatives.pop(0)
+ negative_weights.pop(0)
+
+ embedding_providers = prepare_embedding_providers(pipe, clip_skip)
+ prompt_embeds = []
+ negative_prompt_embeds = []
+ pooled_prompt_embeds = None
+ negative_pooled_prompt_embeds = None
+ for i in range(len(embedding_providers)):
+ # add BREAK keyword that splits the prompt into multiple fragments
+ text = list(positives[i])
+ weights = list(positive_weights[i])
+ text.append('BREAK')
+ weights.append(-1)
+ provider_embed = []
+ while 'BREAK' in text:
+ pos = text.index('BREAK')
+ debug(f'Prompt: section="{text[:pos]}" len={len(text[:pos])} weights={weights[:pos]}')
+ if len(text[:pos]) > 0:
+ embed, ptokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(
+ text_batch=[text[:pos]], fragment_weights_batch=[weights[:pos]], device=device,
+ should_return_tokens=True)
+ provider_embed.append(embed)
+ text = text[pos + 1:]
+ weights = weights[pos + 1:]
+ prompt_embeds.append(torch.cat(provider_embed, dim=1))
+ debug(f'Prompt: positive unpadded shape = {prompt_embeds[0].shape}')
+ # negative prompt has no keywords
+ embed, ntokens = embedding_providers[i].get_embeddings_for_weighted_prompt_fragments(text_batch=[negatives[i]],
+ fragment_weights_batch=[
+ negative_weights[i]],
+ device=device,
+ should_return_tokens=True)
+ negative_prompt_embeds.append(embed)
+
+ if prompt_embeds[-1].shape[-1] > 768:
+ if shared.opts.diffusers_pooled == "weighted":
+ pooled_prompt_embeds = prompt_embeds[-1][
+ torch.arange(prompt_embeds[-1].shape[0], device=device),
+ (ptokens.to(dtype=torch.int, device=device) == 49407)
+ .int()
+ .argmax(dim=-1),
+ ]
+ negative_pooled_prompt_embeds = negative_prompt_embeds[-1][
+ torch.arange(negative_prompt_embeds[-1].shape[0], device=device),
+ (ntokens.to(dtype=torch.int, device=device) == 49407)
+ .int()
+ .argmax(dim=-1),
+ ]
+ else:
+ pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[prompt_2], device=device) if \
+ prompt_embeds[-1].shape[-1] > 768 else None
+ negative_pooled_prompt_embeds = embedding_providers[-1].get_pooled_embeddings(texts=[neg_prompt_2],
+ device=device) if \
+ negative_prompt_embeds[-1].shape[-1] > 768 else None
+
+ prompt_embeds = torch.cat(prompt_embeds, dim=-1) if len(prompt_embeds) > 1 else prompt_embeds[0]
+ negative_prompt_embeds = torch.cat(negative_prompt_embeds, dim=-1) if len(negative_prompt_embeds) > 1 else \
+ negative_prompt_embeds[0]
+ debug(f'Prompt: shape={prompt_embeds.shape} negative={negative_prompt_embeds.shape}')
+ if prompt_embeds.shape[1] != negative_prompt_embeds.shape[1]:
+ [prompt_embeds, negative_prompt_embeds] = pad_to_same_length(pipe, [prompt_embeds, negative_prompt_embeds])
+ return prompt_embeds, pooled_prompt_embeds, negative_prompt_embeds, negative_pooled_prompt_embeds
diff --git a/modules/rife/model_ifnet.py b/modules/rife/model_ifnet.py
index ec667425b..eb6fbed07 100644
--- a/modules/rife/model_ifnet.py
+++ b/modules/rife/model_ifnet.py
@@ -1,134 +1,134 @@
-import os
-import sys
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-sys.path.append(os.path.dirname(__file__))
-from warplayer import warp # pylint: disable=wrong-import-position
-
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-
-def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
- return nn.Sequential(
- nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
- padding=padding, dilation=dilation, bias=True),
- nn.LeakyReLU(0.2, True)
- )
-
-def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
- return nn.Sequential(
- nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
- padding=padding, dilation=dilation, bias=False),
- nn.BatchNorm2d(out_planes),
- nn.LeakyReLU(0.2, True)
- )
-
-class ResConv(nn.Module):
- def __init__(self, c, dilation=1):
- super(ResConv, self).__init__()
- self.conv = nn.Conv2d(c, c, 3, 1, dilation, dilation=dilation, groups=1\
-)
- self.beta = nn.Parameter(torch.ones((1, c, 1, 1)), requires_grad=True)
- self.relu = nn.LeakyReLU(0.2, True)
-
- def forward(self, x):
- return self.relu(self.conv(x) * self.beta + x)
-
-class IFBlock(nn.Module):
- def __init__(self, in_planes, c=64):
- super(IFBlock, self).__init__()
- self.conv0 = nn.Sequential(
- conv(in_planes, c//2, 3, 2, 1),
- conv(c//2, c, 3, 2, 1),
- )
- self.convblock = nn.Sequential(
- ResConv(c),
- ResConv(c),
- ResConv(c),
- ResConv(c),
- ResConv(c),
- ResConv(c),
- ResConv(c),
- ResConv(c),
- )
- self.lastconv = nn.Sequential(
- nn.ConvTranspose2d(c, 4*6, 4, 2, 1),
- nn.PixelShuffle(2)
- )
-
- def forward(self, x, flow=None, scale=1):
- x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False)
- if flow is not None:
- flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
- x = torch.cat((x, flow), 1)
- feat = self.conv0(x)
- feat = self.convblock(feat)
- tmp = self.lastconv(feat)
- tmp = F.interpolate(tmp, scale_factor=scale, mode="bilinear", align_corners=False)
- flow = tmp[:, :4] * scale
- mask = tmp[:, 4:5]
- return flow, mask
-
-class IFNet(nn.Module):
- def __init__(self):
- super(IFNet, self).__init__()
- self.block0 = IFBlock(7, c=192)
- self.block1 = IFBlock(8+4, c=128)
- self.block2 = IFBlock(8+4, c=96)
- self.block3 = IFBlock(8+4, c=64)
- # self.contextnet = Contextnet()
- # self.unet = Unet()
-
- def forward( self, x, timestep=0.5, scale_list=[8, 4, 2, 1], training=False, fastmode=True, ensemble=False): # pylint: disable=dangerous-default-value # noqa: B006
- if training is False:
- channel = x.shape[1] // 2
- img0 = x[:, :channel]
- img1 = x[:, channel:]
- if not torch.is_tensor(timestep):
- timestep = (x[:, :1].clone() * 0 + 1) * timestep
- else:
- timestep = timestep.repeat(1, 1, img0.shape[2], img0.shape[3])
- flow_list = []
- merged = []
- mask_list = []
- warped_img0 = img0
- warped_img1 = img1
- flow = None
- mask = None
- # loss_cons = 0
- block = [self.block0, self.block1, self.block2, self.block3]
- for i in range(4):
- if flow is None:
- flow, mask = block[i](torch.cat((img0[:, :3], img1[:, :3], timestep), 1), None, scale=scale_list[i])
- if ensemble:
- f1, m1 = block[i](torch.cat((img1[:, :3], img0[:, :3], 1-timestep), 1), None, scale=scale_list[i])
- flow = (flow + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
- mask = (mask + (-m1)) / 2
- else:
- f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], timestep, mask), 1), flow, scale=scale_list[i])
- if ensemble:
- f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], 1-timestep, -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i]) # pylint: disable=invalid-unary-operand-type
- f0 = (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
- m0 = (m0 + (-m1)) / 2
- flow = flow + f0
- mask = mask + m0
- mask_list.append(mask)
- flow_list.append(flow)
- warped_img0 = warp(img0, flow[:, :2])
- warped_img1 = warp(img1, flow[:, 2:4])
- merged.append((warped_img0, warped_img1))
- mask_list[3] = torch.sigmoid(mask_list[3])
- merged[3] = merged[3][0] * mask_list[3] + merged[3][1] * (1 - mask_list[3])
- if not fastmode:
- print('contextnet is removed')
- '''
- c0 = self.contextnet(img0, flow[:, :2])
- c1 = self.contextnet(img1, flow[:, 2:4])
- tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
- res = tmp[:, :3] * 2 - 1
- merged[3] = torch.clamp(merged[3] + res, 0, 1)
- '''
- return flow_list, mask_list[3], merged
+import os
+import sys
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.path.append(os.path.dirname(__file__))
+from warplayer import warp # pylint: disable=wrong-import-position
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+ return nn.Sequential(
+ nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+ padding=padding, dilation=dilation, bias=True),
+ nn.LeakyReLU(0.2, True)
+ )
+
+def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+ return nn.Sequential(
+ nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+ padding=padding, dilation=dilation, bias=False),
+ nn.BatchNorm2d(out_planes),
+ nn.LeakyReLU(0.2, True)
+ )
+
+class ResConv(nn.Module):
+ def __init__(self, c, dilation=1):
+ super(ResConv, self).__init__()
+ self.conv = nn.Conv2d(c, c, 3, 1, dilation, dilation=dilation, groups=1\
+)
+ self.beta = nn.Parameter(torch.ones((1, c, 1, 1)), requires_grad=True)
+ self.relu = nn.LeakyReLU(0.2, True)
+
+ def forward(self, x):
+ return self.relu(self.conv(x) * self.beta + x)
+
+class IFBlock(nn.Module):
+ def __init__(self, in_planes, c=64):
+ super(IFBlock, self).__init__()
+ self.conv0 = nn.Sequential(
+ conv(in_planes, c//2, 3, 2, 1),
+ conv(c//2, c, 3, 2, 1),
+ )
+ self.convblock = nn.Sequential(
+ ResConv(c),
+ ResConv(c),
+ ResConv(c),
+ ResConv(c),
+ ResConv(c),
+ ResConv(c),
+ ResConv(c),
+ ResConv(c),
+ )
+ self.lastconv = nn.Sequential(
+ nn.ConvTranspose2d(c, 4*6, 4, 2, 1),
+ nn.PixelShuffle(2)
+ )
+
+ def forward(self, x, flow=None, scale=1):
+ x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False)
+ if flow is not None:
+ flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
+ x = torch.cat((x, flow), 1)
+ feat = self.conv0(x)
+ feat = self.convblock(feat)
+ tmp = self.lastconv(feat)
+ tmp = F.interpolate(tmp, scale_factor=scale, mode="bilinear", align_corners=False)
+ flow = tmp[:, :4] * scale
+ mask = tmp[:, 4:5]
+ return flow, mask
+
+class IFNet(nn.Module):
+ def __init__(self):
+ super(IFNet, self).__init__()
+ self.block0 = IFBlock(7, c=192)
+ self.block1 = IFBlock(8+4, c=128)
+ self.block2 = IFBlock(8+4, c=96)
+ self.block3 = IFBlock(8+4, c=64)
+ # self.contextnet = Contextnet()
+ # self.unet = Unet()
+
+ def forward( self, x, timestep=0.5, scale_list=[8, 4, 2, 1], training=False, fastmode=True, ensemble=False): # pylint: disable=dangerous-default-value # noqa: B006
+ if training is False:
+ channel = x.shape[1] // 2
+ img0 = x[:, :channel]
+ img1 = x[:, channel:]
+ if not torch.is_tensor(timestep):
+ timestep = (x[:, :1].clone() * 0 + 1) * timestep
+ else:
+ timestep = timestep.repeat(1, 1, img0.shape[2], img0.shape[3])
+ flow_list = []
+ merged = []
+ mask_list = []
+ warped_img0 = img0
+ warped_img1 = img1
+ flow = None
+ mask = None
+ # loss_cons = 0
+ block = [self.block0, self.block1, self.block2, self.block3]
+ for i in range(4):
+ if flow is None:
+ flow, mask = block[i](torch.cat((img0[:, :3], img1[:, :3], timestep), 1), None, scale=scale_list[i])
+ if ensemble:
+ f1, m1 = block[i](torch.cat((img1[:, :3], img0[:, :3], 1-timestep), 1), None, scale=scale_list[i])
+ flow = (flow + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
+ mask = (mask + (-m1)) / 2
+ else:
+ f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], timestep, mask), 1), flow, scale=scale_list[i])
+ if ensemble:
+ f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], 1-timestep, -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i]) # pylint: disable=invalid-unary-operand-type
+ f0 = (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
+ m0 = (m0 + (-m1)) / 2
+ flow = flow + f0
+ mask = mask + m0
+ mask_list.append(mask)
+ flow_list.append(flow)
+ warped_img0 = warp(img0, flow[:, :2])
+ warped_img1 = warp(img1, flow[:, 2:4])
+ merged.append((warped_img0, warped_img1))
+ mask_list[3] = torch.sigmoid(mask_list[3])
+ merged[3] = merged[3][0] * mask_list[3] + merged[3][1] * (1 - mask_list[3])
+ if not fastmode:
+ print('contextnet is removed')
+ '''
+ c0 = self.contextnet(img0, flow[:, :2])
+ c1 = self.contextnet(img1, flow[:, 2:4])
+ tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
+ res = tmp[:, :3] * 2 - 1
+ merged[3] = torch.clamp(merged[3] + res, 0, 1)
+ '''
+ return flow_list, mask_list[3], merged
diff --git a/modules/rife/refine.py b/modules/rife/refine.py
index a66488345..5d77582cc 100644
--- a/modules/rife/refine.py
+++ b/modules/rife/refine.py
@@ -1,90 +1,90 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from modules.rife.warplayer import warp
-
-
-c = 16
-
-
-def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
- return nn.Sequential(
- nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True),
- nn.LeakyReLU(0.2, True)
- )
-
-
-def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
- return nn.Sequential(
- nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True),
- )
-
-
-def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1): # pylint: disable=unused-argument
- return nn.Sequential(
- torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True),
- nn.LeakyReLU(0.2, True)
- )
-
-
-class Conv2(nn.Module):
- def __init__(self, in_planes, out_planes, stride=2):
- super(Conv2, self).__init__()
- self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
- self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.conv2(x)
- return x
-
-
-class Contextnet(nn.Module):
- def __init__(self):
- super(Contextnet, self).__init__()
- self.conv1 = Conv2(3, c)
- self.conv2 = Conv2(c, 2*c)
- self.conv3 = Conv2(2*c, 4*c)
- self.conv4 = Conv2(4*c, 8*c)
-
- def forward(self, x, flow):
- x = self.conv1(x)
- flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
- f1 = warp(x, flow)
- x = self.conv2(x)
- flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
- f2 = warp(x, flow)
- x = self.conv3(x)
- flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
- f3 = warp(x, flow)
- x = self.conv4(x)
- flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
- f4 = warp(x, flow)
- return [f1, f2, f3, f4]
-
-
-class Unet(nn.Module):
- def __init__(self):
- super(Unet, self).__init__()
- self.down0 = Conv2(17, 2*c)
- self.down1 = Conv2(4*c, 4*c)
- self.down2 = Conv2(8*c, 8*c)
- self.down3 = Conv2(16*c, 16*c)
- self.up0 = deconv(32*c, 8*c)
- self.up1 = deconv(16*c, 4*c)
- self.up2 = deconv(8*c, 2*c)
- self.up3 = deconv(4*c, c)
- self.conv = nn.Conv2d(c, 3, 3, 1, 1)
-
- def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
- s0 = self.down0(
- torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
- s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
- s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
- s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
- x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
- x = self.up1(torch.cat((x, s2), 1))
- x = self.up2(torch.cat((x, s1), 1))
- x = self.up3(torch.cat((x, s0), 1))
- x = self.conv(x)
- return torch.sigmoid(x)
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from modules.rife.warplayer import warp
+
+
+c = 16
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+ return nn.Sequential(
+ nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True),
+ nn.LeakyReLU(0.2, True)
+ )
+
+
+def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+ return nn.Sequential(
+ nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True),
+ )
+
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1): # pylint: disable=unused-argument
+ return nn.Sequential(
+ torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True),
+ nn.LeakyReLU(0.2, True)
+ )
+
+
+class Conv2(nn.Module):
+ def __init__(self, in_planes, out_planes, stride=2):
+ super(Conv2, self).__init__()
+ self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+ self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = self.conv2(x)
+ return x
+
+
+class Contextnet(nn.Module):
+ def __init__(self):
+ super(Contextnet, self).__init__()
+ self.conv1 = Conv2(3, c)
+ self.conv2 = Conv2(c, 2*c)
+ self.conv3 = Conv2(2*c, 4*c)
+ self.conv4 = Conv2(4*c, 8*c)
+
+ def forward(self, x, flow):
+ x = self.conv1(x)
+ flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+ f1 = warp(x, flow)
+ x = self.conv2(x)
+ flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+ f2 = warp(x, flow)
+ x = self.conv3(x)
+ flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+ f3 = warp(x, flow)
+ x = self.conv4(x)
+ flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+ f4 = warp(x, flow)
+ return [f1, f2, f3, f4]
+
+
+class Unet(nn.Module):
+ def __init__(self):
+ super(Unet, self).__init__()
+ self.down0 = Conv2(17, 2*c)
+ self.down1 = Conv2(4*c, 4*c)
+ self.down2 = Conv2(8*c, 8*c)
+ self.down3 = Conv2(16*c, 16*c)
+ self.up0 = deconv(32*c, 8*c)
+ self.up1 = deconv(16*c, 4*c)
+ self.up2 = deconv(8*c, 2*c)
+ self.up3 = deconv(4*c, c)
+ self.conv = nn.Conv2d(c, 3, 3, 1, 1)
+
+ def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
+ s0 = self.down0(
+ torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
+ s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
+ s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
+ s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
+ x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
+ x = self.up1(torch.cat((x, s2), 1))
+ x = self.up2(torch.cat((x, s1), 1))
+ x = self.up3(torch.cat((x, s0), 1))
+ x = self.conv(x)
+ return torch.sigmoid(x)
diff --git a/modules/safe.py b/modules/safe.py
index b3b98897e..21b8c08b2 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -1,179 +1,179 @@
-# this code is adapted from the script contributed by anon from /h/
-
-import pickle
-import collections
-import zipfile
-import re
-
-import torch
-import numpy as np
-import _codecs
-
-# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
-TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage # pylint: disable=protected-access
-
-
-def encode(*args):
- out = _codecs.encode(*args)
- return out
-
-
-class RestrictedUnpickler(pickle.Unpickler):
- extra_handler = None
-
- def persistent_load(self, saved_id):
- assert saved_id[0] == 'storage'
- try:
- return TypedStorage(_internal=True)
- except TypeError:
- return TypedStorage() # PyTorch before 2.0 does not have the _internal argument
-
- def find_class(self, module, name):
- if self.extra_handler is not None:
- res = self.extra_handler(module, name)
- if res is not None:
- return res
-
- if module == 'collections' and name == 'OrderedDict':
- return getattr(collections, name)
- if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:
- return getattr(torch._utils, name) # pylint: disable=protected-access
- if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32', 'BFloat16Storage']:
- return getattr(torch, name)
- if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
- return getattr(torch.nn.modules.container, name)
- if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']:
- return getattr(np.core.multiarray, name)
- if module == 'numpy' and name in ['dtype', 'ndarray']:
- return getattr(np, name)
- if module == '_codecs' and name == 'encode':
- return encode
- if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
- import pytorch_lightning.callbacks
- return pytorch_lightning.callbacks.model_checkpoint
- if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
- import pytorch_lightning.callbacks.model_checkpoint
- return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
- if module == "__builtin__" and name == 'set':
- return set
-
- # Forbid everything else.
- raise Exception(f"global '{module}/{name}' is forbidden") # pylint: disable=broad-exception-raised
-
-
-# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/'
-allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$")
-data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
-
-def check_zip_filenames(filename, names):
- for name in names:
- if allowed_zip_names_re.match(name):
- continue
-
- raise Exception(f"bad file inside {filename}: {name}") # pylint: disable=broad-exception-raised
-
-
-def check_pt(filename, extra_handler):
- try:
-
- # new pytorch format is a zip file
- with zipfile.ZipFile(filename) as z:
- check_zip_filenames(filename, z.namelist())
-
- # find filename of data.pkl in zip file: '/data.pkl'
- data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
- if len(data_pkl_filenames) == 0:
- raise Exception(f"data.pkl not found in {filename}") # pylint: disable=broad-exception-raised
- if len(data_pkl_filenames) > 1:
- raise Exception(f"Multiple data.pkl found in {filename}") # pylint: disable=broad-exception-raised
- with z.open(data_pkl_filenames[0]) as file:
- unpickler = RestrictedUnpickler(file)
- unpickler.extra_handler = extra_handler
- unpickler.load()
-
- except zipfile.BadZipfile:
-
- # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
- with open(filename, "rb") as file:
- unpickler = RestrictedUnpickler(file)
- unpickler.extra_handler = extra_handler
- for _i in range(5):
- unpickler.load()
-
-
-def load(filename, *args, **kwargs):
- return load_with_extra(filename, *args, extra_handler=global_extra_handler, **kwargs)
-
-
-def load_with_extra(filename, extra_handler=None, *args, **kwargs): # pylint: disable=keyword-arg-before-vararg
- """
- this function is intended to be used by extensions that want to load models with
- some extra classes in them that the usual unpickler would find suspicious.
-
- Use the extra_handler argument to specify a function that takes module and field name as text,
- and returns that field's value:
-
- ```python
- def extra(module, name):
- if module == 'collections' and name == 'OrderedDict':
- return collections.OrderedDict
-
- return None
-
- safe.load_with_extra('model.pt', extra_handler=extra)
- ```
-
- The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is
- definitely unsafe.
- """
-
- from modules import shared, errors
-
- try:
- if not shared.cmd_opts.disable_safe_unpickle:
- check_pt(filename, extra_handler)
- except Exception as e:
- errors.display(e, f'verifying pickled file {filename}')
- return None
-
- return unsafe_torch_load(filename, *args, **kwargs)
-
-
-class Extra:
- """
- A class for temporarily setting the global handler for when you can't explicitly call load_with_extra
- (because it's not your code making the torch.load call). The intended use is like this:
-
-```
-import torch
-from modules import safe
-
-def handler(module, name):
- if module == 'torch' and name in ['float64', 'float16']:
- return getattr(torch, name)
-
- return None
-
-with safe.Extra(handler):
- x = torch.load('model.pt')
-```
- """
-
- def __init__(self, handler):
- self.handler = handler
-
- def __enter__(self):
- global global_extra_handler # pylint: disable=global-statement
-
- assert global_extra_handler is None, 'already inside an Extra() block'
- global_extra_handler = self.handler
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- global global_extra_handler # pylint: disable=global-statement
-
- global_extra_handler = None
-
-
-unsafe_torch_load = torch.load
-torch.load = load
-global_extra_handler = None
+# this code is adapted from the script contributed by anon from /h/
+
+import pickle
+import collections
+import zipfile
+import re
+
+import torch
+import numpy as np
+import _codecs
+
+# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
+TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage # pylint: disable=protected-access
+
+
+def encode(*args):
+ out = _codecs.encode(*args)
+ return out
+
+
+class RestrictedUnpickler(pickle.Unpickler):
+ extra_handler = None
+
+ def persistent_load(self, saved_id):
+ assert saved_id[0] == 'storage'
+ try:
+ return TypedStorage(_internal=True)
+ except TypeError:
+ return TypedStorage() # PyTorch before 2.0 does not have the _internal argument
+
+ def find_class(self, module, name):
+ if self.extra_handler is not None:
+ res = self.extra_handler(module, name)
+ if res is not None:
+ return res
+
+ if module == 'collections' and name == 'OrderedDict':
+ return getattr(collections, name)
+ if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:
+ return getattr(torch._utils, name) # pylint: disable=protected-access
+ if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32', 'BFloat16Storage']:
+ return getattr(torch, name)
+ if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
+ return getattr(torch.nn.modules.container, name)
+ if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']:
+ return getattr(np.core.multiarray, name)
+ if module == 'numpy' and name in ['dtype', 'ndarray']:
+ return getattr(np, name)
+ if module == '_codecs' and name == 'encode':
+ return encode
+ if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
+ import pytorch_lightning.callbacks
+ return pytorch_lightning.callbacks.model_checkpoint
+ if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
+ import pytorch_lightning.callbacks.model_checkpoint
+ return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
+ if module == "__builtin__" and name == 'set':
+ return set
+
+ # Forbid everything else.
+ raise Exception(f"global '{module}/{name}' is forbidden") # pylint: disable=broad-exception-raised
+
+
+# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/'
+allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$")
+data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
+
+def check_zip_filenames(filename, names):
+ for name in names:
+ if allowed_zip_names_re.match(name):
+ continue
+
+ raise Exception(f"bad file inside {filename}: {name}") # pylint: disable=broad-exception-raised
+
+
+def check_pt(filename, extra_handler):
+ try:
+
+ # new pytorch format is a zip file
+ with zipfile.ZipFile(filename) as z:
+ check_zip_filenames(filename, z.namelist())
+
+ # find filename of data.pkl in zip file: '/data.pkl'
+ data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
+ if len(data_pkl_filenames) == 0:
+ raise Exception(f"data.pkl not found in {filename}") # pylint: disable=broad-exception-raised
+ if len(data_pkl_filenames) > 1:
+ raise Exception(f"Multiple data.pkl found in {filename}") # pylint: disable=broad-exception-raised
+ with z.open(data_pkl_filenames[0]) as file:
+ unpickler = RestrictedUnpickler(file)
+ unpickler.extra_handler = extra_handler
+ unpickler.load()
+
+ except zipfile.BadZipfile:
+
+ # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+ with open(filename, "rb") as file:
+ unpickler = RestrictedUnpickler(file)
+ unpickler.extra_handler = extra_handler
+ for _i in range(5):
+ unpickler.load()
+
+
+def load(filename, *args, **kwargs):
+ return load_with_extra(filename, *args, extra_handler=global_extra_handler, **kwargs)
+
+
+def load_with_extra(filename, extra_handler=None, *args, **kwargs): # pylint: disable=keyword-arg-before-vararg
+ """
+ this function is intended to be used by extensions that want to load models with
+ some extra classes in them that the usual unpickler would find suspicious.
+
+ Use the extra_handler argument to specify a function that takes module and field name as text,
+ and returns that field's value:
+
+ ```python
+ def extra(module, name):
+ if module == 'collections' and name == 'OrderedDict':
+ return collections.OrderedDict
+
+ return None
+
+ safe.load_with_extra('model.pt', extra_handler=extra)
+ ```
+
+ The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is
+ definitely unsafe.
+ """
+
+ from modules import shared, errors
+
+ try:
+ if not shared.cmd_opts.disable_safe_unpickle:
+ check_pt(filename, extra_handler)
+ except Exception as e:
+ errors.display(e, f'verifying pickled file {filename}')
+ return None
+
+ return unsafe_torch_load(filename, *args, **kwargs)
+
+
+class Extra:
+ """
+ A class for temporarily setting the global handler for when you can't explicitly call load_with_extra
+ (because it's not your code making the torch.load call). The intended use is like this:
+
+```
+import torch
+from modules import safe
+
+def handler(module, name):
+ if module == 'torch' and name in ['float64', 'float16']:
+ return getattr(torch, name)
+
+ return None
+
+with safe.Extra(handler):
+ x = torch.load('model.pt')
+```
+ """
+
+ def __init__(self, handler):
+ self.handler = handler
+
+ def __enter__(self):
+ global global_extra_handler # pylint: disable=global-statement
+
+ assert global_extra_handler is None, 'already inside an Extra() block'
+ global_extra_handler = self.handler
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ global global_extra_handler # pylint: disable=global-statement
+
+ global_extra_handler = None
+
+
+unsafe_torch_load = torch.load
+torch.load = load
+global_extra_handler = None
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6454db2f2..dd5fca68b 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,507 +1,535 @@
-import os
-import sys
-import time
-from collections import namedtuple
-from typing import Optional, Dict, Any
-from fastapi import FastAPI
-from gradio import Blocks
-import modules.errors as errors
-
-
-def report_exception(e, c, job):
- errors.display(e, f'executing callback: {c.script} {job}')
-
-
-class ImageSaveParams:
- def __init__(self, image, p, filename, pnginfo):
- self.image = image
- """the PIL image itself"""
-
- self.p = p
- """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
-
- self.filename = filename
- """name of file that the image would be saved to"""
-
- self.pnginfo = pnginfo
- """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
-
-
-class CFGDenoiserParams:
- def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.image_cond = image_cond
- """Conditioning image"""
-
- self.sigma = sigma
- """Current sigma noise step value"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
- self.text_cond = text_cond
- """ Encoder hidden states of text conditioning from prompt"""
-
- self.text_uncond = text_uncond
- """ Encoder hidden states of text conditioning from negative prompt"""
-
-
-class CFGDenoisedParams:
- def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
- self.inner_model = inner_model
- """Inner model reference used for denoising"""
-
-
-class AfterCFGCallbackParams:
- def __init__(self, x, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class UiTrainTabParams:
- def __init__(self, txt2img_preview_params):
- self.txt2img_preview_params = txt2img_preview_params
-
-
-class ImageGridLoopParams:
- def __init__(self, imgs, cols, rows):
- self.imgs = imgs
- self.cols = cols
- self.rows = rows
-
-
-ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-callback_map = dict(
- callbacks_app_started=[],
- callbacks_before_process=[],
- callbacks_after_process=[],
- callbacks_model_loaded=[],
- callbacks_ui_tabs=[],
- callbacks_ui_train_tabs=[],
- callbacks_ui_settings=[],
- callbacks_before_image_saved=[],
- callbacks_image_saved=[],
- callbacks_image_save_btn=[],
- callbacks_cfg_denoiser=[],
- callbacks_cfg_denoised=[],
- callbacks_cfg_after_cfg=[],
- callbacks_before_component=[],
- callbacks_after_component=[],
- callbacks_image_grid=[],
- callbacks_infotext_pasted=[],
- callbacks_script_unloaded=[],
- callbacks_before_ui=[],
- callbacks_on_reload=[],
-)
-
-timers = {}
-def timer(t0: float, script, callback: str):
- t1 = time.time()
- k = f'{os.path.basename(script)}:{callback}'
- if k not in timers:
- timers[k] = 0
- timers[k] += t1 - t0
-
-
-def print_timers():
- for k, v in timers.items():
- if v > 0.05:
- errors.log.debug(f'Script: time={v:.2f} {k}')
-
-
-def clear_callbacks():
- for callback_list in callback_map.values():
- callback_list.clear()
-
-
-def app_started_callback(demo: Optional[Blocks], app: FastAPI):
- for c in callback_map['callbacks_app_started']:
- try:
- t0 = time.time()
- c.callback(demo, app)
- timer(t0, c.script, 'app_started')
- except Exception as e:
- report_exception(e, c, 'app_started_callback')
-
-
-def before_process_callback(p):
- for c in callback_map['callbacks_before_process']:
- try:
- t0 = time.time()
- c.callback(p)
- timer(t0, c.script, 'before_process')
- except Exception as e:
- report_exception(e, c, 'before_process_callback')
-
-
-def after_process_callback(p):
- for c in callback_map['callbacks_after_process']:
- try:
- t0 = time.time()
- c.callback(p)
- timer(t0, c.script, 'after_process')
- except Exception as e:
- report_exception(e, c, 'after_process_callback')
-
-
-def app_reload_callback():
- for c in callback_map['callbacks_on_reload']:
- try:
- t0 = time.time()
- c.callback()
- timer(t0, c.script, 'on_reload')
- except Exception as e:
- report_exception(e, c, 'callbacks_on_reload')
-
-
-def model_loaded_callback(sd_model):
- for c in callback_map['callbacks_model_loaded']:
- try:
- t0 = time.time()
- c.callback(sd_model)
- timer(t0, c.script, 'model_loaded')
- except Exception as e:
- report_exception(e, c, 'model_loaded_callback')
-
-
-def ui_tabs_callback():
- res = []
- for c in callback_map['callbacks_ui_tabs']:
- try:
- t0 = time.time()
- res += c.callback() or []
- timer(t0, c.script, 'ui_tabs')
- except Exception as e:
- report_exception(e, c, 'ui_tabs_callback')
- return res
-
-
-def ui_train_tabs_callback(params: UiTrainTabParams):
- for c in callback_map['callbacks_ui_train_tabs']:
- try:
- t0 = time.time()
- c.callback(params)
- timer(t0, c.script, 'ui_train_tabs')
- except Exception as e:
- report_exception(e, c, 'callbacks_ui_train_tabs')
-
-
-def ui_settings_callback():
- for c in callback_map['callbacks_ui_settings']:
- try:
- t0 = time.time()
- c.callback()
- timer(t0, c.script, 'ui_settings')
- except Exception as e:
- report_exception(e, c, 'ui_settings_callback')
-
-
-def before_image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_before_image_saved']:
- try:
- t0 = time.time()
- c.callback(params)
- timer(t0, c.script, 'before_image_saved')
- except Exception as e:
- report_exception(e, c, 'before_image_saved_callback')
-
-
-def image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_image_saved']:
- try:
- t0 = time.time()
- c.callback(params)
- timer(t0, c.script, 'image_saved')
- except Exception as e:
- report_exception(e, c, 'image_saved_callback')
-
-
-def image_save_btn_callback(filename: str):
- for c in callback_map['callbacks_image_save_btn']:
- try:
- t0 = time.time()
- c.callback(filename)
- timer(t0, c.script, 'image_save_btn')
- except Exception as e:
- report_exception(e, c, 'image_save_btn_callback')
-
-
-def cfg_denoiser_callback(params: CFGDenoiserParams):
- for c in callback_map['callbacks_cfg_denoiser']:
- try:
- t0 = time.time()
- c.callback(params)
- timer(t0, c.script, 'cfg_denoiser')
- except Exception as e:
- report_exception(e, c, 'cfg_denoiser_callback')
-
-
-def cfg_denoised_callback(params: CFGDenoisedParams):
- for c in callback_map['callbacks_cfg_denoised']:
- try:
- t0 = time.time()
- c.callback(params)
- timer(t0, c.script, 'cfg_denoised')
- except Exception as e:
- report_exception(e, c, 'cfg_denoised_callback')
-
-
-def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
- for c in callback_map['callbacks_cfg_after_cfg']:
- try:
- t0 = time.time()
- c.callback(params)
- timer(t0, c.script, 'cfg_after_cfg')
- except Exception as e:
- report_exception(e, c, 'cfg_after_cfg_callback')
-
-
-def before_component_callback(component, **kwargs):
- for c in callback_map['callbacks_before_component']:
- try:
- t0 = time.time()
- c.callback(component, **kwargs)
- timer(t0, c.script, 'before_component')
- except Exception as e:
- report_exception(e, c, 'before_component_callback')
-
-
-def after_component_callback(component, **kwargs):
- for c in callback_map['callbacks_after_component']:
- try:
- t0 = time.time()
- c.callback(component, **kwargs)
- timer(t0, c.script, 'after_component')
- except Exception as e:
- report_exception(e, c, 'after_component_callback')
-
-
-def image_grid_callback(params: ImageGridLoopParams):
- for c in callback_map['callbacks_image_grid']:
- try:
- t0 = time.time()
- c.callback(params)
- timer(t0, c.script, 'image_grid')
- except Exception as e:
- report_exception(e, c, 'image_grid')
-
-
-def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
- for c in callback_map['callbacks_infotext_pasted']:
- try:
- t0 = time.time()
- c.callback(infotext, params)
- timer(t0, c.script, 'infotext_pasted')
- except Exception as e:
- report_exception(e, c, 'infotext_pasted')
-
-
-def script_unloaded_callback():
- for c in reversed(callback_map['callbacks_script_unloaded']):
- try:
- t0 = time.time()
- c.callback()
- timer(t0, c.script, 'script_unloaded')
- except Exception as e:
- report_exception(e, c, 'script_unloaded')
-
-
-def before_ui_callback():
- for c in reversed(callback_map['callbacks_before_ui']):
- try:
- t0 = time.time()
- c.callback()
- timer(t0, c.script, 'before_ui')
- except Exception as e:
- report_exception(e, c, 'before_ui')
-
-
-def add_callback(callbacks, fun):
- # stack = [x for x in inspect.stack(0) if x.filename != __file__]
- # filename = stack[0].filename if len(stack) > 0 else 'unknown file'
- filename = sys._getframe().f_back.f_back.f_code.co_filename # pylint: disable=protected-access
- callbacks.append(ScriptCallback(filename, fun))
-
-
-def remove_current_script_callbacks():
- # stack = [x for x in inspect.stack() if x.filename != __file__]
- # filename = stack[0].filename if len(stack) > 0 else 'unknown file'
- # if filename == 'unknown file':
- # return
- filename = sys._getframe().f_back.f_back.f_code.co_filename # pylint: disable=protected-access
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
- callback_list.remove(callback_to_remove)
-
-
-def remove_callbacks_for_function(callback_func):
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
- callback_list.remove(callback_to_remove)
-
-
-def on_app_started(callback):
- """register a function to be called when the webui started, the gradio `Block` component and
- fastapi `FastAPI` object are passed as the arguments"""
- add_callback(callback_map['callbacks_app_started'], callback)
-
-
-def on_before_process(callback):
- """register a function to be called just before processing starts"""
- add_callback(callback_map['callbacks_before_process'], callback)
-
-
-def on_after_process(callback):
- """register a function to be called just after processing ends"""
- add_callback(callback_map['callbacks_after_process'], callback)
-
-
-def on_before_reload(callback):
- """register a function to be called just before the server reloads."""
- add_callback(callback_map['callbacks_on_reload'], callback)
-
-
-def on_model_loaded(callback):
- """register a function to be called when the stable diffusion model is created; the model is
- passed as an argument; this function is also called when the script is reloaded. """
- add_callback(callback_map['callbacks_model_loaded'], callback)
-
-
-def on_ui_tabs(callback):
- """register a function to be called when the UI is creating new tabs.
- The function must either return a None, which means no new tabs to be added, or a list, where
- each element is a tuple:
- (gradio_component, title, elem_id)
-
- gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
- title is tab text displayed to user in the UI
- elem_id is HTML id for the tab
- """
- add_callback(callback_map['callbacks_ui_tabs'], callback)
-
-
-def on_ui_train_tabs(callback):
- """register a function to be called when the UI is creating new tabs for the train tab.
- Create your new tabs with gr.Tab.
- """
- add_callback(callback_map['callbacks_ui_train_tabs'], callback)
-
-
-def on_ui_settings(callback):
- """register a function to be called before UI settings are populated; add your settings
- by using shared.opts.add_option(shared.OptionInfo(...)) """
- add_callback(callback_map['callbacks_ui_settings'], callback)
-
-
-def on_before_image_saved(callback):
- """register a function to be called before an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
- """
- add_callback(callback_map['callbacks_before_image_saved'], callback)
-
-
-def on_image_saved(callback):
- """register a function to be called after an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
- """
- add_callback(callback_map['callbacks_image_saved'], callback)
-
-
-def on_image_save_btn(callback):
- """register a function to be called after an image save button is pressed.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
- """
- add_callback(callback_map['callbacks_image_save_btn'], callback)
-
-
-def on_cfg_denoiser(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoiser'], callback)
-
-
-def on_cfg_denoised(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoised'], callback)
-
-
-def on_cfg_after_cfg(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations are completed.
- The callback is called with one argument:
- - params: AfterCFGCallbackParams - parameters to be passed to the script for post-processing after cfg calculation.
- """
- add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
-
-
-def on_before_component(callback):
- """register a function to be called before a component is created.
- The callback is called with arguments:
- - component - gradio component that is about to be created.
- - **kwargs - args to gradio.components.IOComponent.__init__ function
-
- Use elem_id/label fields of kwargs to figure out which component it is.
- This can be useful to inject your own components somewhere in the middle of vanilla UI.
- """
- add_callback(callback_map['callbacks_before_component'], callback)
-
-
-def on_after_component(callback):
- """register a function to be called after a component is created. See on_before_component for more."""
- add_callback(callback_map['callbacks_after_component'], callback)
-
-
-def on_image_grid(callback):
- """register a function to be called before making an image grid.
- The callback is called with one argument:
- - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
- """
- add_callback(callback_map['callbacks_image_grid'], callback)
-
-
-def on_infotext_pasted(callback):
- """register a function to be called before applying an infotext.
- The callback is called with two arguments:
- - infotext: str - raw infotext.
- - result: Dict[str, any] - parsed infotext parameters.
- """
- add_callback(callback_map['callbacks_infotext_pasted'], callback)
-
-
-def on_script_unloaded(callback):
- """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
- the script did should be reverted here"""
-
- add_callback(callback_map['callbacks_script_unloaded'], callback)
-
-
-def on_before_ui(callback):
- """register a function to be called before the UI is created."""
- add_callback(callback_map['callbacks_before_ui'], callback)
+import os
+import sys
+import time
+from collections import namedtuple
+from typing import Optional, Dict, Any
+from fastapi import FastAPI
+from gradio import Blocks
+import modules.errors as errors
+
+
+def report_exception(e, c, job):
+ errors.display(e, f'executing callback: {c.script} {job}')
+
+
+class ImageSaveParams:
+ def __init__(self, image, p, filename, pnginfo):
+ self.image = image
+ """the PIL image itself"""
+
+ self.p = p
+ """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
+
+ self.filename = filename
+ """name of file that the image would be saved to"""
+
+ self.pnginfo = pnginfo
+ """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
+
+
+class ExtraNoiseParams:
+ def __init__(self, noise, x, xi):
+ self.noise = noise
+ """Random noise generated by the seed"""
+
+ self.x = x
+ """Latent representation of the image"""
+
+ self.xi = xi
+ """Noisy latent representation of the image"""
+
+
+class CFGDenoiserParams:
+ def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
+ self.x = x
+ """Latent image representation in the process of being denoised"""
+
+ self.image_cond = image_cond
+ """Conditioning image"""
+
+ self.sigma = sigma
+ """Current sigma noise step value"""
+
+ self.sampling_step = sampling_step
+ """Current Sampling step number"""
+
+ self.total_sampling_steps = total_sampling_steps
+ """Total number of sampling steps planned"""
+
+ self.text_cond = text_cond
+ """ Encoder hidden states of text conditioning from prompt"""
+
+ self.text_uncond = text_uncond
+ """ Encoder hidden states of text conditioning from negative prompt"""
+
+
+class CFGDenoisedParams:
+ def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
+ self.x = x
+ """Latent image representation in the process of being denoised"""
+
+ self.sampling_step = sampling_step
+ """Current Sampling step number"""
+
+ self.total_sampling_steps = total_sampling_steps
+ """Total number of sampling steps planned"""
+
+ self.inner_model = inner_model
+ """Inner model reference used for denoising"""
+
+
+class AfterCFGCallbackParams:
+ def __init__(self, x, sampling_step, total_sampling_steps):
+ self.x = x
+ """Latent image representation in the process of being denoised"""
+
+ self.sampling_step = sampling_step
+ """Current Sampling step number"""
+
+ self.total_sampling_steps = total_sampling_steps
+ """Total number of sampling steps planned"""
+
+
+class UiTrainTabParams:
+ def __init__(self, txt2img_preview_params):
+ self.txt2img_preview_params = txt2img_preview_params
+
+
+class ImageGridLoopParams:
+ def __init__(self, imgs, cols, rows):
+ self.imgs = imgs
+ self.cols = cols
+ self.rows = rows
+
+
+ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
+callback_map = dict(
+ callbacks_app_started=[],
+ callbacks_before_process=[],
+ callbacks_after_process=[],
+ callbacks_model_loaded=[],
+ callbacks_ui_tabs=[],
+ callbacks_ui_train_tabs=[],
+ callbacks_ui_settings=[],
+ callbacks_before_image_saved=[],
+ callbacks_image_saved=[],
+ callbacks_image_save_btn=[],
+ callbacks_cfg_denoiser=[],
+ callbacks_cfg_denoised=[],
+ callbacks_cfg_after_cfg=[],
+ callbacks_before_component=[],
+ callbacks_after_component=[],
+ callbacks_image_grid=[],
+ callbacks_infotext_pasted=[],
+ callbacks_script_unloaded=[],
+ callbacks_before_ui=[],
+ callbacks_on_reload=[],
+)
+
+timers = {}
+def timer(t0: float, script, callback: str):
+ t1 = time.time()
+ k = f'{os.path.basename(script)}:{callback}'
+ if k not in timers:
+ timers[k] = 0
+ timers[k] += t1 - t0
+
+
+def print_timers():
+ for k, v in timers.items():
+ if v > 0.05:
+ errors.log.debug(f'Script: time={v:.2f} {k}')
+
+
+def clear_callbacks():
+ for callback_list in callback_map.values():
+ callback_list.clear()
+
+
+def app_started_callback(demo: Optional[Blocks], app: FastAPI):
+ for c in callback_map['callbacks_app_started']:
+ try:
+ t0 = time.time()
+ c.callback(demo, app)
+ timer(t0, c.script, 'app_started')
+ except Exception as e:
+ report_exception(e, c, 'app_started_callback')
+
+
+def before_process_callback(p):
+ for c in callback_map['callbacks_before_process']:
+ try:
+ t0 = time.time()
+ c.callback(p)
+ timer(t0, c.script, 'before_process')
+ except Exception as e:
+ report_exception(e, c, 'before_process_callback')
+
+
+def after_process_callback(p):
+ for c in callback_map['callbacks_after_process']:
+ try:
+ t0 = time.time()
+ c.callback(p)
+ timer(t0, c.script, 'after_process')
+ except Exception as e:
+ report_exception(e, c, 'after_process_callback')
+
+
+def app_reload_callback():
+ for c in callback_map['callbacks_on_reload']:
+ try:
+ t0 = time.time()
+ c.callback()
+ timer(t0, c.script, 'on_reload')
+ except Exception as e:
+ report_exception(e, c, 'callbacks_on_reload')
+
+
+def model_loaded_callback(sd_model):
+ for c in callback_map['callbacks_model_loaded']:
+ try:
+ t0 = time.time()
+ c.callback(sd_model)
+ timer(t0, c.script, 'model_loaded')
+ except Exception as e:
+ report_exception(e, c, 'model_loaded_callback')
+
+
+def ui_tabs_callback():
+ res = []
+ for c in callback_map['callbacks_ui_tabs']:
+ try:
+ t0 = time.time()
+ res += c.callback() or []
+ timer(t0, c.script, 'ui_tabs')
+ except Exception as e:
+ report_exception(e, c, 'ui_tabs_callback')
+ return res
+
+
+def ui_train_tabs_callback(params: UiTrainTabParams):
+ for c in callback_map['callbacks_ui_train_tabs']:
+ try:
+ t0 = time.time()
+ c.callback(params)
+ timer(t0, c.script, 'ui_train_tabs')
+ except Exception as e:
+ report_exception(e, c, 'callbacks_ui_train_tabs')
+
+
+def ui_settings_callback():
+ for c in callback_map['callbacks_ui_settings']:
+ try:
+ t0 = time.time()
+ c.callback()
+ timer(t0, c.script, 'ui_settings')
+ except Exception as e:
+ report_exception(e, c, 'ui_settings_callback')
+
+
+def before_image_saved_callback(params: ImageSaveParams):
+ for c in callback_map['callbacks_before_image_saved']:
+ try:
+ t0 = time.time()
+ c.callback(params)
+ timer(t0, c.script, 'before_image_saved')
+ except Exception as e:
+ report_exception(e, c, 'before_image_saved_callback')
+
+
+def image_saved_callback(params: ImageSaveParams):
+ for c in callback_map['callbacks_image_saved']:
+ try:
+ t0 = time.time()
+ c.callback(params)
+ timer(t0, c.script, 'image_saved')
+ except Exception as e:
+ report_exception(e, c, 'image_saved_callback')
+
+
+def image_save_btn_callback(filename: str):
+ for c in callback_map['callbacks_image_save_btn']:
+ try:
+ t0 = time.time()
+ c.callback(filename)
+ timer(t0, c.script, 'image_save_btn')
+ except Exception as e:
+ report_exception(e, c, 'image_save_btn_callback')
+
+
+def extra_noise_callback(params: ExtraNoiseParams):
+ for c in callback_map['callbacks_extra_noise']:
+ try:
+ c.callback(params)
+ except Exception as e:
+ report_exception(e, c, 'callbacks_extra_noise')
+
+
+def cfg_denoiser_callback(params: CFGDenoiserParams):
+ for c in callback_map['callbacks_cfg_denoiser']:
+ try:
+ t0 = time.time()
+ c.callback(params)
+ timer(t0, c.script, 'cfg_denoiser')
+ except Exception as e:
+ report_exception(e, c, 'cfg_denoiser_callback')
+
+
+def cfg_denoised_callback(params: CFGDenoisedParams):
+ for c in callback_map['callbacks_cfg_denoised']:
+ try:
+ t0 = time.time()
+ c.callback(params)
+ timer(t0, c.script, 'cfg_denoised')
+ except Exception as e:
+ report_exception(e, c, 'cfg_denoised_callback')
+
+
+def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
+ for c in callback_map['callbacks_cfg_after_cfg']:
+ try:
+ t0 = time.time()
+ c.callback(params)
+ timer(t0, c.script, 'cfg_after_cfg')
+ except Exception as e:
+ report_exception(e, c, 'cfg_after_cfg_callback')
+
+
+def before_component_callback(component, **kwargs):
+ for c in callback_map['callbacks_before_component']:
+ try:
+ t0 = time.time()
+ c.callback(component, **kwargs)
+ timer(t0, c.script, 'before_component')
+ except Exception as e:
+ report_exception(e, c, 'before_component_callback')
+
+
+def after_component_callback(component, **kwargs):
+ for c in callback_map['callbacks_after_component']:
+ try:
+ t0 = time.time()
+ c.callback(component, **kwargs)
+ timer(t0, c.script, 'after_component')
+ except Exception as e:
+ report_exception(e, c, 'after_component_callback')
+
+
+def image_grid_callback(params: ImageGridLoopParams):
+ for c in callback_map['callbacks_image_grid']:
+ try:
+ t0 = time.time()
+ c.callback(params)
+ timer(t0, c.script, 'image_grid')
+ except Exception as e:
+ report_exception(e, c, 'image_grid')
+
+
+def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
+ for c in callback_map['callbacks_infotext_pasted']:
+ try:
+ t0 = time.time()
+ c.callback(infotext, params)
+ timer(t0, c.script, 'infotext_pasted')
+ except Exception as e:
+ report_exception(e, c, 'infotext_pasted')
+
+
+def script_unloaded_callback():
+ for c in reversed(callback_map['callbacks_script_unloaded']):
+ try:
+ t0 = time.time()
+ c.callback()
+ timer(t0, c.script, 'script_unloaded')
+ except Exception as e:
+ report_exception(e, c, 'script_unloaded')
+
+
+def before_ui_callback():
+ for c in reversed(callback_map['callbacks_before_ui']):
+ try:
+ t0 = time.time()
+ c.callback()
+ timer(t0, c.script, 'before_ui')
+ except Exception as e:
+ report_exception(e, c, 'before_ui')
+
+
+def add_callback(callbacks, fun):
+ # stack = [x for x in inspect.stack(0) if x.filename != __file__]
+ # filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+ filename = sys._getframe().f_back.f_back.f_code.co_filename # pylint: disable=protected-access
+ callbacks.append(ScriptCallback(filename, fun))
+
+
+def remove_current_script_callbacks():
+ # stack = [x for x in inspect.stack() if x.filename != __file__]
+ # filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+ # if filename == 'unknown file':
+ # return
+ filename = sys._getframe().f_back.f_back.f_code.co_filename # pylint: disable=protected-access
+ for callback_list in callback_map.values():
+ for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
+ callback_list.remove(callback_to_remove)
+
+
+def remove_callbacks_for_function(callback_func):
+ for callback_list in callback_map.values():
+ for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
+ callback_list.remove(callback_to_remove)
+
+
+def on_app_started(callback):
+ """register a function to be called when the webui started, the gradio `Block` component and
+ fastapi `FastAPI` object are passed as the arguments"""
+ add_callback(callback_map['callbacks_app_started'], callback)
+
+
+def on_before_process(callback):
+ """register a function to be called just before processing starts"""
+ add_callback(callback_map['callbacks_before_process'], callback)
+
+
+def on_after_process(callback):
+ """register a function to be called just after processing ends"""
+ add_callback(callback_map['callbacks_after_process'], callback)
+
+
+def on_before_reload(callback):
+ """register a function to be called just before the server reloads."""
+ add_callback(callback_map['callbacks_on_reload'], callback)
+
+
+def on_model_loaded(callback):
+ """register a function to be called when the stable diffusion model is created; the model is
+ passed as an argument; this function is also called when the script is reloaded. """
+ add_callback(callback_map['callbacks_model_loaded'], callback)
+
+
+def on_ui_tabs(callback):
+ """register a function to be called when the UI is creating new tabs.
+ The function must either return a None, which means no new tabs to be added, or a list, where
+ each element is a tuple:
+ (gradio_component, title, elem_id)
+
+ gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
+ title is tab text displayed to user in the UI
+ elem_id is HTML id for the tab
+ """
+ add_callback(callback_map['callbacks_ui_tabs'], callback)
+
+
+def on_ui_train_tabs(callback):
+ """register a function to be called when the UI is creating new tabs for the train tab.
+ Create your new tabs with gr.Tab.
+ """
+ add_callback(callback_map['callbacks_ui_train_tabs'], callback)
+
+
+def on_ui_settings(callback):
+ """register a function to be called before UI settings are populated; add your settings
+ by using shared.opts.add_option(shared.OptionInfo(...)) """
+ add_callback(callback_map['callbacks_ui_settings'], callback)
+
+
+def on_before_image_saved(callback):
+ """register a function to be called before an image is saved to a file.
+ The callback is called with one argument:
+ - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
+ """
+ add_callback(callback_map['callbacks_before_image_saved'], callback)
+
+
+def on_image_saved(callback):
+ """register a function to be called after an image is saved to a file.
+ The callback is called with one argument:
+ - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
+ """
+ add_callback(callback_map['callbacks_image_saved'], callback)
+
+
+def on_image_save_btn(callback):
+ """register a function to be called after an image save button is pressed.
+ The callback is called with one argument:
+ - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
+ """
+ add_callback(callback_map['callbacks_image_save_btn'], callback)
+
+
+def on_extra_noise(callback):
+ """register a function to be called before adding extra noise in img2img or hires fix;
+ The callback is called with one argument:
+ - params: ExtraNoiseParams - contains noise determined by seed and latent representation of image
+ """
+ add_callback(callback_map['callbacks_extra_noise'], callback)
+
+
+def on_cfg_denoiser(callback):
+ """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+ The callback is called with one argument:
+ - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
+ """
+ add_callback(callback_map['callbacks_cfg_denoiser'], callback)
+
+
+def on_cfg_denoised(callback):
+ """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+ The callback is called with one argument:
+ - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
+ """
+ add_callback(callback_map['callbacks_cfg_denoised'], callback)
+
+
+def on_cfg_after_cfg(callback):
+ """register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations are completed.
+ The callback is called with one argument:
+ - params: AfterCFGCallbackParams - parameters to be passed to the script for post-processing after cfg calculation.
+ """
+ add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
+
+
+def on_before_component(callback):
+ """register a function to be called before a component is created.
+ The callback is called with arguments:
+ - component - gradio component that is about to be created.
+ - **kwargs - args to gradio.components.IOComponent.__init__ function
+
+ Use elem_id/label fields of kwargs to figure out which component it is.
+ This can be useful to inject your own components somewhere in the middle of vanilla UI.
+ """
+ add_callback(callback_map['callbacks_before_component'], callback)
+
+
+def on_after_component(callback):
+ """register a function to be called after a component is created. See on_before_component for more."""
+ add_callback(callback_map['callbacks_after_component'], callback)
+
+
+def on_image_grid(callback):
+ """register a function to be called before making an image grid.
+ The callback is called with one argument:
+ - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
+ """
+ add_callback(callback_map['callbacks_image_grid'], callback)
+
+
+def on_infotext_pasted(callback):
+ """register a function to be called before applying an infotext.
+ The callback is called with two arguments:
+ - infotext: str - raw infotext.
+ - result: Dict[str, any] - parsed infotext parameters.
+ """
+ add_callback(callback_map['callbacks_infotext_pasted'], callback)
+
+
+def on_script_unloaded(callback):
+ """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
+ the script did should be reverted here"""
+
+ add_callback(callback_map['callbacks_script_unloaded'], callback)
+
+
+def on_before_ui(callback):
+ """register a function to be called before the UI is created."""
+ add_callback(callback_map['callbacks_before_ui'], callback)
diff --git a/modules/script_loading.py b/modules/script_loading.py
index 37971eaf1..cfb0eb097 100644
--- a/modules/script_loading.py
+++ b/modules/script_loading.py
@@ -1,56 +1,56 @@
-import io
-import os
-import contextlib
-import importlib.util
-import modules.errors as errors
-from installer import setup_logging, args
-
-
-preloaded = []
-debug = os.environ.get('SD_SCRIPT_DEBUG', None)
-
-
-def load_module(path):
- module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
- module = importlib.util.module_from_spec(module_spec)
- if args.profile:
- import cProfile
- pr = cProfile.Profile()
- pr.enable()
- try:
- if '/sd-extension-' in path: # safe extensions without stdout intercept
- module_spec.loader.exec_module(module)
- else:
- if debug:
- module_spec.loader.exec_module(module)
- stdout = io.StringIO()
- else:
- with contextlib.redirect_stdout(io.StringIO()) as stdout:
- module_spec.loader.exec_module(module)
- setup_logging() # reset since scripts can hijaack logging
- for line in stdout.getvalue().splitlines():
- if len(line) > 0:
- errors.log.info(f"Extension: script='{os.path.relpath(path)}' {line.strip()}")
- except Exception as e:
- errors.display(e, f'Module load: {path}')
- if args.profile:
- errors.profile(pr, f'Scripts: {path}')
- return module
-
-
-def preload_extensions(extensions_dir, parser):
- if not os.path.isdir(extensions_dir):
- return
- for dirname in sorted(os.listdir(extensions_dir)):
- if dirname in preloaded:
- continue
- preloaded.append(dirname)
- preload_script = os.path.join(extensions_dir, dirname, "preload.py")
- if not os.path.isfile(preload_script):
- continue
- try:
- module = load_module(preload_script)
- if hasattr(module, 'preload'):
- module.preload(parser)
- except Exception as e:
- errors.display(e, f'Extension preload: {preload_script}')
+import io
+import os
+import contextlib
+import importlib.util
+import modules.errors as errors
+from installer import setup_logging, args
+
+
+preloaded = []
+debug = os.environ.get('SD_SCRIPT_DEBUG', None)
+
+
+def load_module(path):
+ module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
+ module = importlib.util.module_from_spec(module_spec)
+ if args.profile:
+ import cProfile
+ pr = cProfile.Profile()
+ pr.enable()
+ try:
+ if '/sd-extension-' in path: # safe extensions without stdout intercept
+ module_spec.loader.exec_module(module)
+ else:
+ if debug:
+ module_spec.loader.exec_module(module)
+ stdout = io.StringIO()
+ else:
+ with contextlib.redirect_stdout(io.StringIO()) as stdout:
+ module_spec.loader.exec_module(module)
+ setup_logging() # reset since scripts can hijaack logging
+ for line in stdout.getvalue().splitlines():
+ if len(line) > 0:
+ errors.log.info(f"Extension: script='{os.path.relpath(path)}' {line.strip()}")
+ except Exception as e:
+ errors.display(e, f'Module load: {path}')
+ if args.profile:
+ errors.profile(pr, f'Scripts: {path}')
+ return module
+
+
+def preload_extensions(extensions_dir, parser):
+ if not os.path.isdir(extensions_dir):
+ return
+ for dirname in sorted(os.listdir(extensions_dir)):
+ if dirname in preloaded:
+ continue
+ preloaded.append(dirname)
+ preload_script = os.path.join(extensions_dir, dirname, "preload.py")
+ if not os.path.isfile(preload_script):
+ continue
+ try:
+ module = load_module(preload_script)
+ if hasattr(module, 'preload'):
+ module.preload(parser)
+ except Exception as e:
+ errors.display(e, f'Extension preload: {preload_script}')
diff --git a/modules/scripts.py b/modules/scripts.py
index d6d920cf1..0f308a13f 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,683 +1,683 @@
-import os
-import re
-import sys
-import time
-from collections import namedtuple
-import gradio as gr
-from modules import paths, script_callbacks, extensions, script_loading, scripts_postprocessing, errors, timer
-from installer import log
-
-
-AlwaysVisible = object()
-time_component = {}
-time_setup = {}
-debug = log.trace if os.environ.get('SD_SCRIPT_DEBUG', None) is not None else lambda *args, **kwargs: None
-
-
-class PostprocessImageArgs:
- def __init__(self, image):
- self.image = image
-
-
-class PostprocessBatchListArgs:
- def __init__(self, images):
- self.images = images
-
-
-class Script:
- parent = None
- name = None
- filename = None
- args_from = None
- args_to = None
- alwayson = False
- is_txt2img = False
- is_img2img = False
- api_info = None
- group = None
- infotext_fields = None
- paste_field_names = None
- section = None
-
- def title(self):
- """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
- raise NotImplementedError
-
- def ui(self, is_img2img):
- """this function should create gradio UI elements. See https://gradio.app/docs/#components
- The return value should be an array of all components that are used in processing.
- Values of those returned components will be passed to run() and process() functions.
- """
- pass # pylint: disable=unnecessary-pass
-
- def show(self, is_img2img): # pylint: disable=unused-argument
- """
- is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
- This function should return:
- - False if the script should not be shown in UI at all
- - True if the script should be shown in UI if it's selected in the scripts dropdown
- - script.AlwaysVisible if the script should be shown in UI at all times
- """
- return True
-
- def run(self, p, *args):
- """
- This function is called if the script has been selected in the script dropdown.
- It must do all processing and return the Processed object with results, same as
- one returned by processing.process_images.
- Usually the processing is done by calling the processing.process_images function.
- args contains all values returned by components from ui()
- """
- pass # pylint: disable=unnecessary-pass
-
- def setup(self, p, *args):
- """For AlwaysVisible scripts, this function is called when the processing object is set up, before any processing starts.
- args contains all values returned by components from ui().
- """
- pass # pylint: disable=unnecessary-pass
-
- def before_process(self, p, *args):
- """
- This function is called very early during processing begins for AlwaysVisible scripts.
- You can modify the processing object (p) here, inject hooks, etc.
- args contains all values returned by components from ui()
- """
- pass # pylint: disable=unnecessary-pass
-
- def process(self, p, *args):
- """
- This function is called before processing begins for AlwaysVisible scripts.
- You can modify the processing object (p) here, inject hooks, etc.
- args contains all values returned by components from ui()
- """
- pass # pylint: disable=unnecessary-pass
-
- def before_process_batch(self, p, *args, **kwargs):
- """
- Called before extra networks are parsed from the prompt, so you can add
- new extra network keywords to the prompt with this callback.
- **kwargs will have those items:
- - batch_number - index of current batch, from 0 to number of batches-1
- - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
- - seeds - list of seeds for current batch
- - subseeds - list of subseeds for current batch
- """
- pass # pylint: disable=unnecessary-pass
-
- def process_batch(self, p, *args, **kwargs):
- """
- Same as process(), but called for every batch.
- **kwargs will have those items:
- - batch_number - index of current batch, from 0 to number of batches-1
- - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
- - seeds - list of seeds for current batch
- - subseeds - list of subseeds for current batch
- """
- pass # pylint: disable=unnecessary-pass
-
- def postprocess_batch(self, p, *args, **kwargs):
- """
- Same as process_batch(), but called for every batch after it has been generated.
- **kwargs will have same items as process_batch, and also:
- - batch_number - index of current batch, from 0 to number of batches-1
- - images - torch tensor with all generated images, with values ranging from 0 to 1;
- """
- pass # pylint: disable=unnecessary-pass
-
- def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
- """
- Called for every image after it has been generated.
- """
- pass # pylint: disable=unnecessary-pass
-
- def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):
- """
- Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.
- This is useful when you want to update the entire batch instead of individual images.
- You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
- If the number of images is different from the batch size when returning,
- then the script has the responsibility to also update the following attributes in the processing object (p):
- - p.prompts
- - p.negative_prompts
- - p.seeds
- - p.subseeds
- **kwargs will have same items as process_batch, and also:
- - batch_number - index of current batch, from 0 to number of batches-1
- """
- pass # pylint: disable=unnecessary-pass
-
- def postprocess(self, p, processed, *args):
- """
- This function is called after processing ends for AlwaysVisible scripts.
- args contains all values returned by components from ui()
- """
- pass # pylint: disable=unnecessary-pass
-
- def before_component(self, component, **kwargs):
- """
- Called before a component is created.
- Use elem_id/label fields of kwargs to figure out which component it is.
- This can be useful to inject your own components somewhere in the middle of vanilla UI.
- You can return created components in the ui() function to add them to the list of arguments for your processing functions
- """
- pass # pylint: disable=unnecessary-pass
-
- def after_component(self, component, **kwargs):
- """
- Called after a component is created. Same as above.
- """
- pass # pylint: disable=unnecessary-pass
-
- def describe(self):
- """unused"""
- return ""
-
- def elem_id(self, item_id):
- """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
- title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
- return f'script_{self.parent}_{title}_{item_id}'
-
-
-current_basedir = paths.script_path
-
-
-def basedir():
- """returns the base directory for the current script. For scripts in the main scripts directory,
- this is the main directory (where webui.py resides), and for scripts in extensions directory
- (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)
- """
- return current_basedir
-
-
-ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path", "priority"])
-scripts_data = []
-postprocessing_scripts_data = []
-ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
-
-
-def list_scripts(scriptdirname, extension):
- tmp_list = []
- base = os.path.join(paths.script_path, scriptdirname)
- if os.path.exists(base):
- for filename in sorted(os.listdir(base)):
- tmp_list.append(ScriptFile(paths.script_path, filename, os.path.join(base, filename), '50'))
- for ext in extensions.active():
- tmp_list += ext.list_files(scriptdirname, extension)
- priority_list = []
- for script in tmp_list:
- if os.path.splitext(script.path)[1].lower() == extension and os.path.isfile(script.path):
- if script.basedir == paths.script_path:
- priority = '0'
- elif script.basedir.startswith(os.path.join(paths.script_path, 'scripts')):
- priority = '1'
- elif script.basedir.startswith(os.path.join(paths.script_path, 'extensions-builtin')):
- priority = '2'
- elif script.basedir.startswith(os.path.join(paths.script_path, 'extensions')):
- priority = '3'
- else:
- priority = '9'
- if os.path.isfile(os.path.join(base, "..", ".priority")):
- with open(os.path.join(base, "..", ".priority"), "r", encoding="utf-8") as f:
- priority = priority + str(f.read().strip())
- log.debug(f'Script priority override: ${script.name}:{priority}')
- else:
- priority = priority + script.priority
- priority_list.append(ScriptFile(script.basedir, script.filename, script.path, priority))
- debug(f'Adding script: {script.basedir} {script.filename} {script.path} {priority}')
- priority_sort = sorted(priority_list, key=lambda item: item.priority + item.path.lower(), reverse=False)
- return priority_sort
-
-
-def list_files_with_name(filename):
- res = []
- dirs = [paths.script_path] + [ext.path for ext in extensions.active()]
- for dirpath in dirs:
- if not os.path.isdir(dirpath):
- continue
- path = os.path.join(dirpath, filename)
- if os.path.isfile(path):
- res.append(path)
- return res
-
-
-def load_scripts():
- t = timer.Timer()
- t0 = time.time()
- global current_basedir # pylint: disable=global-statement
- scripts_data.clear()
- postprocessing_scripts_data.clear()
- script_callbacks.clear_callbacks()
- scripts_list = list_scripts("scripts", ".py")
- syspath = sys.path
-
- def register_scripts_from_module(module, scriptfile):
- for script_class in module.__dict__.values():
- if type(script_class) != type:
- continue
- debug(f'Registering script: {scriptfile.path}')
- if issubclass(script_class, Script):
- scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
- elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
- postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
-
- for scriptfile in scripts_list:
- try:
- if scriptfile.basedir != paths.script_path:
- sys.path = [scriptfile.basedir] + sys.path
- current_basedir = scriptfile.basedir
- script_module = script_loading.load_module(scriptfile.path)
- register_scripts_from_module(script_module, scriptfile)
- except Exception as e:
- errors.display(e, f'Load script: {scriptfile.filename}')
- finally:
- current_basedir = paths.script_path
- t.record(os.path.basename(scriptfile.basedir) if scriptfile.basedir != paths.script_path else scriptfile.filename)
- sys.path = syspath
- global scripts_txt2img, scripts_img2img, scripts_control, scripts_postproc # pylint: disable=global-statement
- scripts_txt2img = ScriptRunner()
- scripts_img2img = ScriptRunner()
- scripts_control = ScriptRunner()
- scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
- return t, time.time()-t0
-
-
-def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
- try:
- res = func(*args, **kwargs)
- return res
- except Exception as e:
- errors.display(e, f'Calling script: {filename}/{funcname}')
- return default
-
-
-class ScriptSummary:
- def __init__(self, op):
- self.start = time.time()
- self.update = time.time()
- self.op = op
- self.time = {}
-
- def record(self, script):
- self.update = time.time()
- self.time[script] = round(time.time() - self.update, 2)
-
- def report(self):
- total = sum(self.time.values())
- if total == 0:
- return
- scripts = [f'{k}:{v}' for k, v in self.time.items() if v > 0]
- log.debug(f'Script: op={self.op} total={total} scripts={scripts}')
-
-
-class ScriptRunner:
- def __init__(self):
- self.scripts = []
- self.selectable_scripts = []
- self.alwayson_scripts = []
- self.titles = []
- self.infotext_fields = []
- self.paste_field_names = []
- self.script_load_ctr = 0
- self.is_img2img = False
- self.inputs = [None]
-
- def initialize_scripts(self, is_img2img):
- from modules import scripts_auto_postprocessing
-
- self.scripts.clear()
- self.selectable_scripts.clear()
- self.alwayson_scripts.clear()
- self.titles.clear()
- self.infotext_fields.clear()
- self.paste_field_names.clear()
- self.script_load_ctr = 0
- self.is_img2img = is_img2img
- self.scripts.clear()
- self.alwayson_scripts.clear()
- self.selectable_scripts.clear()
- auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
-
- for script_class, path, _basedir, _script_module in auto_processing_scripts + scripts_data:
- try:
- script = script_class()
- script.filename = path
- script.is_txt2img = not is_img2img
- script.is_img2img = is_img2img
- visibility = script.show(script.is_img2img)
- if visibility == AlwaysVisible:
- self.scripts.append(script)
- self.alwayson_scripts.append(script)
- script.alwayson = True
- elif visibility:
- self.scripts.append(script)
- self.selectable_scripts.append(script)
- except Exception as e:
- log.error(f'Script initialize: {path} {e}')
-
- """
- def create_script_ui(self, script):
- import modules.api.models as api_models
- script.args_from = len(self.inputs)
- script.args_to = len(self.inputs)
- controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
- if controls is None:
- return
- script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
- api_args = []
- for control in controls:
- if not isinstance(control, gr.components.IOComponent):
- log.error(f'Invalid script control: "{script.filename}" control={control}')
- continue
- control.custom_script_source = os.path.basename(script.filename)
- arg_info = api_models.ScriptArg(label=control.label or "")
- for field in ("value", "minimum", "maximum", "step", "choices"):
- v = getattr(control, field, None)
- if v is not None:
- setattr(arg_info, field, v)
- api_args.append(arg_info)
- script.api_info = api_models.ScriptInfo(name=script.name, is_img2img=script.is_img2img, is_alwayson=script.alwayson, args=api_args)
- if script.infotext_fields is not None:
- self.infotext_fields += script.infotext_fields
- if script.paste_field_names is not None:
- self.paste_field_names += script.paste_field_names
- self.inputs += controls
- script.args_to = len(self.inputs)
-
- def setup_ui_for_section(self, section, scriptlist=None):
- if scriptlist is None:
- scriptlist = self.alwayson_scripts
- for script in scriptlist:
- if script.alwayson and script.section != section:
- continue
- if script.create_group:
- with gr.Group(visible=script.alwayson) as group:
- self.create_script_ui(script)
- script.group = group
- else:
- self.create_script_ui(script)
- """
-
- def prepare_ui(self):
- self.inputs = [None]
-
- def setup_ui(self, parent='unknown', accordion=True):
- import modules.api.models as api_models
- self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
- inputs = []
- inputs_alwayson = [True]
-
- def create_script_ui(script: Script, inputs, inputs_alwayson):
- script.parent = parent
- script.args_from = len(inputs)
- script.args_to = len(inputs)
- controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
- if controls is None:
- return
- script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
- api_args = []
- for control in controls:
- debug(f'Script control: parent={script.parent} script="{script.name}" label="{control.label}" type={control} id={control.elem_id}')
- if not isinstance(control, gr.components.IOComponent):
- log.error(f'Invalid script control: "{script.filename}" control={control}')
- continue
- control.custom_script_source = os.path.basename(script.filename)
- arg_info = api_models.ScriptArg(label=control.label or "")
- for field in ("value", "minimum", "maximum", "step", "choices"):
- v = getattr(control, field, None)
- if v is not None:
- setattr(arg_info, field, v)
- api_args.append(arg_info)
-
- script.api_info = api_models.ScriptInfo(
- name=script.name,
- is_img2img=script.is_img2img,
- is_alwayson=script.alwayson,
- args=api_args,
- )
- if script.infotext_fields is not None:
- self.infotext_fields += script.infotext_fields
- if script.paste_field_names is not None:
- self.paste_field_names += script.paste_field_names
- inputs += controls
- inputs_alwayson += [script.alwayson for _ in controls]
- script.args_to = len(inputs)
-
- dropdown = gr.Dropdown(label="Script", elem_id=f'{parent}_script_list', choices=["None"] + self.titles, value="None", type="index")
- inputs.insert(0, dropdown)
- for script in self.selectable_scripts:
- with gr.Group(visible=False) as group:
- t0 = time.time()
- create_script_ui(script, inputs, inputs_alwayson)
- time_setup[script.title()] = time_setup.get(script.title(), 0) + (time.time()-t0)
- script.group = group
-
- def select_script(script_index):
- selected_script = self.selectable_scripts[script_index - 1] if script_index > 0 else None
- return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
-
- def init_field(title):
- if title == 'None': # called when an initial value is set from ui-config.json to show script's UI components
- return
- script_index = self.titles.index(title)
- self.selectable_scripts[script_index].group.visible = True
-
- dropdown.init_field = init_field
- dropdown.change(fn=select_script, inputs=[dropdown], outputs=[script.group for script in self.selectable_scripts])
-
- def onload_script_visibility(params):
- title = params.get('Script', None)
- if title:
- title_index = self.titles.index(title)
- visibility = title_index == self.script_load_ctr
- self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
- return gr.update(visible=visibility)
- else:
- return gr.update(visible=False)
-
- with gr.Accordion(label="Extensions", elem_id=f'{parent}_script_alwayson') if accordion else gr.Group():
- for script in self.alwayson_scripts:
- t0 = time.time()
- with gr.Group(elem_id=f'{parent}_script_{script.title().lower().replace(" ", "_")}', elem_classes=['extension-script']) as group:
- create_script_ui(script, inputs, inputs_alwayson)
- script.group = group
- time_setup[script.title()] = time_setup.get(script.title(), 0) + (time.time()-t0)
-
- self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
- self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
- return inputs
-
- def run(self, p, *args):
- s = ScriptSummary('run')
- script_index = args[0]
- if script_index == 0:
- return None
- script = self.selectable_scripts[script_index-1]
- if script is None:
- return None
- parsed = p.per_script_args.get(script.title(), args[script.args_from:script.args_to])
- processed = script.run(p, *parsed)
- s.record(script.title())
- s.report()
- return processed
-
- def before_process(self, p, **kwargs):
- s = ScriptSummary('before-process')
- for script in self.alwayson_scripts:
- try:
- script_args = p.script_args[script.args_from:script.args_to]
- script.before_process(p, *script_args, **kwargs)
- except Exception as e:
- errors.display(e, f"Error running before process: {script.filename}")
- s.record(script.title())
- s.report()
-
- def process(self, p, **kwargs):
- s = ScriptSummary('process')
- for script in self.alwayson_scripts:
- try:
- args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
- script.process(p, *args, **kwargs)
- except Exception as e:
- errors.display(e, f'Running script process: {script.filename}')
- s.record(script.title())
- s.report()
-
- def before_process_batch(self, p, **kwargs):
- s = ScriptSummary('before-process-batch')
- for script in self.alwayson_scripts:
- try:
- args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
- script.before_process_batch(p, *args, **kwargs)
- except Exception as e:
- errors.display(e, f'Running script before process batch: {script.filename}')
- s.record(script.title())
- s.report()
-
- def process_batch(self, p, **kwargs):
- s = ScriptSummary('process-batch')
- for script in self.alwayson_scripts:
- try:
- args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
- script.process_batch(p, *args, **kwargs)
- except Exception as e:
- errors.display(e, f'Running script process batch: {script.filename}')
- s.record(script.title())
- s.report()
-
- def postprocess(self, p, processed):
- s = ScriptSummary('postprocess')
- for script in self.alwayson_scripts:
- try:
- args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
- script.postprocess(p, processed, *args)
- except Exception as e:
- errors.display(e, f'Running script postprocess: {script.filename}')
- s.record(script.title())
- s.report()
-
- def postprocess_batch(self, p, images, **kwargs):
- s = ScriptSummary('postprocess-batch')
- for script in self.alwayson_scripts:
- try:
- args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
- script.postprocess_batch(p, *args, images=images, **kwargs)
- except Exception as e:
- errors.display(e, f'Running script before postprocess batch: {script.filename}')
- s.record(script.title())
- s.report()
-
- def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):
- s = ScriptSummary('postprocess-batch-list')
- for script in self.alwayson_scripts:
- try:
- args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
- script.postprocess_batch_list(p, pp, *args, **kwargs)
- except Exception as e:
- errors.display(e, f'Running script before postprocess batch list: {script.filename}')
- s.record(script.title())
- s.report()
-
- def postprocess_image(self, p, pp: PostprocessImageArgs):
- s = ScriptSummary('postprocess-image')
- for script in self.alwayson_scripts:
- try:
- args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
- script.postprocess_image(p, pp, *args)
- except Exception as e:
- errors.display(e, f'Running script postprocess image: {script.filename}')
- s.record(script.title())
- s.report()
-
- def before_component(self, component, **kwargs):
- s = ScriptSummary('before-component')
- for script in self.scripts:
- try:
- script.before_component(component, **kwargs)
- except Exception as e:
- errors.display(e, f'Running script before component: {script.filename}')
- s.record(script.title())
- s.report()
-
- def after_component(self, component, **kwargs):
- s = ScriptSummary('after-component')
- for script in self.scripts:
- try:
- script.after_component(component, **kwargs)
- except Exception as e:
- errors.display(e, f'Running script after component: {script.filename}')
- s.record(script.title())
- s.report()
-
- def reload_sources(self, cache):
- s = ScriptSummary('reload-sources')
- for si, script in list(enumerate(self.scripts)):
- args_from = script.args_from
- args_to = script.args_to
- filename = script.filename
- module = cache.get(filename, None)
- if module is None:
- module = script_loading.load_module(script.filename)
- cache[filename] = module
- for script_class in module.__dict__.values():
- if type(script_class) == type and issubclass(script_class, Script):
- self.scripts[si] = script_class()
- self.scripts[si].filename = filename
- self.scripts[si].args_from = args_from
- self.scripts[si].args_to = args_to
- s.record(script.title())
- s.report()
-
-
-scripts_txt2img: ScriptRunner = None
-scripts_img2img: ScriptRunner = None
-scripts_control: ScriptRunner = None
-scripts_current: ScriptRunner = None
-scripts_postproc: scripts_postprocessing.ScriptPostprocessingRunner = None
-reload_scripts = load_scripts # compatibility alias
-
-
-def reload_script_body_only():
- cache = {}
- scripts_txt2img.reload_sources(cache)
- scripts_img2img.reload_sources(cache)
- scripts_control.reload_sources(cache)
-
-
-def add_classes_to_gradio_component(comp):
- """
- this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
- """
- elem_classes = []
- if hasattr(comp, "elem_classes"):
- elem_classes = comp.elem_classes
- if elem_classes is None:
- elem_classes = []
- comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
- if getattr(comp, 'multiselect', False):
- comp.elem_classes.append('multiselect')
-
-
-def IOComponent_init(self, *args, **kwargs):
- if scripts_current is not None:
- scripts_current.before_component(self, **kwargs)
- script_callbacks.before_component_callback(self, **kwargs)
- res = original_IOComponent_init(self, *args, **kwargs) # pylint: disable=assignment-from-no-return
- add_classes_to_gradio_component(self)
- script_callbacks.after_component_callback(self, **kwargs)
- if scripts_current is not None:
- scripts_current.after_component(self, **kwargs)
- return res
-
-
-original_IOComponent_init = gr.components.IOComponent.__init__
-gr.components.IOComponent.__init__ = IOComponent_init
-
-
-def BlockContext_init(self, *args, **kwargs):
- res = original_BlockContext_init(self, *args, **kwargs) # pylint: disable=assignment-from-no-return
- add_classes_to_gradio_component(self)
- return res
-
-
-original_BlockContext_init = gr.blocks.BlockContext.__init__
-gr.blocks.BlockContext.__init__ = BlockContext_init
+import os
+import re
+import sys
+import time
+from collections import namedtuple
+import gradio as gr
+from modules import paths, script_callbacks, extensions, script_loading, scripts_postprocessing, errors, timer
+from installer import log
+
+
+AlwaysVisible = object()
+time_component = {}
+time_setup = {}
+debug = log.trace if os.environ.get('SD_SCRIPT_DEBUG', None) is not None else lambda *args, **kwargs: None
+
+
+class PostprocessImageArgs:
+ def __init__(self, image):
+ self.image = image
+
+
+class PostprocessBatchListArgs:
+ def __init__(self, images):
+ self.images = images
+
+
+class Script:
+ parent = None
+ name = None
+ filename = None
+ args_from = None
+ args_to = None
+ alwayson = False
+ is_txt2img = False
+ is_img2img = False
+ api_info = None
+ group = None
+ infotext_fields = None
+ paste_field_names = None
+ section = None
+
+ def title(self):
+ """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
+ raise NotImplementedError
+
+ def ui(self, is_img2img):
+ """this function should create gradio UI elements. See https://gradio.app/docs/#components
+ The return value should be an array of all components that are used in processing.
+ Values of those returned components will be passed to run() and process() functions.
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def show(self, is_img2img): # pylint: disable=unused-argument
+ """
+ is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
+ This function should return:
+ - False if the script should not be shown in UI at all
+ - True if the script should be shown in UI if it's selected in the scripts dropdown
+ - script.AlwaysVisible if the script should be shown in UI at all times
+ """
+ return True
+
+ def run(self, p, *args):
+ """
+ This function is called if the script has been selected in the script dropdown.
+ It must do all processing and return the Processed object with results, same as
+ one returned by processing.process_images.
+ Usually the processing is done by calling the processing.process_images function.
+ args contains all values returned by components from ui()
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def setup(self, p, *args):
+ """For AlwaysVisible scripts, this function is called when the processing object is set up, before any processing starts.
+ args contains all values returned by components from ui().
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def before_process(self, p, *args):
+ """
+ This function is called very early during processing begins for AlwaysVisible scripts.
+ You can modify the processing object (p) here, inject hooks, etc.
+ args contains all values returned by components from ui()
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def process(self, p, *args):
+ """
+ This function is called before processing begins for AlwaysVisible scripts.
+ You can modify the processing object (p) here, inject hooks, etc.
+ args contains all values returned by components from ui()
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def before_process_batch(self, p, *args, **kwargs):
+ """
+ Called before extra networks are parsed from the prompt, so you can add
+ new extra network keywords to the prompt with this callback.
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def process_batch(self, p, *args, **kwargs):
+ """
+ Same as process(), but called for every batch.
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def postprocess_batch(self, p, *args, **kwargs):
+ """
+ Same as process_batch(), but called for every batch after it has been generated.
+ **kwargs will have same items as process_batch, and also:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - images - torch tensor with all generated images, with values ranging from 0 to 1;
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
+ """
+ Called for every image after it has been generated.
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):
+ """
+ Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.
+ This is useful when you want to update the entire batch instead of individual images.
+ You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
+ If the number of images is different from the batch size when returning,
+ then the script has the responsibility to also update the following attributes in the processing object (p):
+ - p.prompts
+ - p.negative_prompts
+ - p.seeds
+ - p.subseeds
+ **kwargs will have same items as process_batch, and also:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def postprocess(self, p, processed, *args):
+ """
+ This function is called after processing ends for AlwaysVisible scripts.
+ args contains all values returned by components from ui()
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def before_component(self, component, **kwargs):
+ """
+ Called before a component is created.
+ Use elem_id/label fields of kwargs to figure out which component it is.
+ This can be useful to inject your own components somewhere in the middle of vanilla UI.
+ You can return created components in the ui() function to add them to the list of arguments for your processing functions
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def after_component(self, component, **kwargs):
+ """
+ Called after a component is created. Same as above.
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def describe(self):
+ """unused"""
+ return ""
+
+ def elem_id(self, item_id):
+ """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
+ title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
+ return f'script_{self.parent}_{title}_{item_id}'
+
+
+current_basedir = paths.script_path
+
+
+def basedir():
+ """returns the base directory for the current script. For scripts in the main scripts directory,
+ this is the main directory (where webui.py resides), and for scripts in extensions directory
+ (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)
+ """
+ return current_basedir
+
+
+ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path", "priority"])
+scripts_data = []
+postprocessing_scripts_data = []
+ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
+
+
+def list_scripts(scriptdirname, extension):
+ tmp_list = []
+ base = os.path.join(paths.script_path, scriptdirname)
+ if os.path.exists(base):
+ for filename in sorted(os.listdir(base)):
+ tmp_list.append(ScriptFile(paths.script_path, filename, os.path.join(base, filename), '50'))
+ for ext in extensions.active():
+ tmp_list += ext.list_files(scriptdirname, extension)
+ priority_list = []
+ for script in tmp_list:
+ if os.path.splitext(script.path)[1].lower() == extension and os.path.isfile(script.path):
+ if script.basedir == paths.script_path:
+ priority = '0'
+ elif script.basedir.startswith(os.path.join(paths.script_path, 'scripts')):
+ priority = '1'
+ elif script.basedir.startswith(os.path.join(paths.script_path, 'extensions-builtin')):
+ priority = '2'
+ elif script.basedir.startswith(os.path.join(paths.script_path, 'extensions')):
+ priority = '3'
+ else:
+ priority = '9'
+ if os.path.isfile(os.path.join(base, "..", ".priority")):
+ with open(os.path.join(base, "..", ".priority"), "r", encoding="utf-8") as f:
+ priority = priority + str(f.read().strip())
+ log.debug(f'Script priority override: ${script.name}:{priority}')
+ else:
+ priority = priority + script.priority
+ priority_list.append(ScriptFile(script.basedir, script.filename, script.path, priority))
+ debug(f'Adding script: {script.basedir} {script.filename} {script.path} {priority}')
+ priority_sort = sorted(priority_list, key=lambda item: item.priority + item.path.lower(), reverse=False)
+ return priority_sort
+
+
+def list_files_with_name(filename):
+ res = []
+ dirs = [paths.script_path] + [ext.path for ext in extensions.active()]
+ for dirpath in dirs:
+ if not os.path.isdir(dirpath):
+ continue
+ path = os.path.join(dirpath, filename)
+ if os.path.isfile(path):
+ res.append(path)
+ return res
+
+
+def load_scripts():
+ t = timer.Timer()
+ t0 = time.time()
+ global current_basedir # pylint: disable=global-statement
+ scripts_data.clear()
+ postprocessing_scripts_data.clear()
+ script_callbacks.clear_callbacks()
+ scripts_list = list_scripts("scripts", ".py")
+ syspath = sys.path
+
+ def register_scripts_from_module(module, scriptfile):
+ for script_class in module.__dict__.values():
+ if type(script_class) != type:
+ continue
+ debug(f'Registering script: {scriptfile.path}')
+ if issubclass(script_class, Script):
+ scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
+ elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
+ postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
+
+ for scriptfile in scripts_list:
+ try:
+ if scriptfile.basedir != paths.script_path:
+ sys.path = [scriptfile.basedir] + sys.path
+ current_basedir = scriptfile.basedir
+ script_module = script_loading.load_module(scriptfile.path)
+ register_scripts_from_module(script_module, scriptfile)
+ except Exception as e:
+ errors.display(e, f'Load script: {scriptfile.filename}')
+ finally:
+ current_basedir = paths.script_path
+ t.record(os.path.basename(scriptfile.basedir) if scriptfile.basedir != paths.script_path else scriptfile.filename)
+ sys.path = syspath
+ global scripts_txt2img, scripts_img2img, scripts_control, scripts_postproc # pylint: disable=global-statement
+ scripts_txt2img = ScriptRunner()
+ scripts_img2img = ScriptRunner()
+ scripts_control = ScriptRunner()
+ scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
+ return t, time.time()-t0
+
+
+def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
+ try:
+ res = func(*args, **kwargs)
+ return res
+ except Exception as e:
+ errors.display(e, f'Calling script: {filename}/{funcname}')
+ return default
+
+
+class ScriptSummary:
+ def __init__(self, op):
+ self.start = time.time()
+ self.update = time.time()
+ self.op = op
+ self.time = {}
+
+ def record(self, script):
+ self.update = time.time()
+ self.time[script] = round(time.time() - self.update, 2)
+
+ def report(self):
+ total = sum(self.time.values())
+ if total == 0:
+ return
+ scripts = [f'{k}:{v}' for k, v in self.time.items() if v > 0]
+ log.debug(f'Script: op={self.op} total={total} scripts={scripts}')
+
+
+class ScriptRunner:
+ def __init__(self):
+ self.scripts = []
+ self.selectable_scripts = []
+ self.alwayson_scripts = []
+ self.titles = []
+ self.infotext_fields = []
+ self.paste_field_names = []
+ self.script_load_ctr = 0
+ self.is_img2img = False
+ self.inputs = [None]
+
+ def initialize_scripts(self, is_img2img):
+ from modules import scripts_auto_postprocessing
+
+ self.scripts.clear()
+ self.selectable_scripts.clear()
+ self.alwayson_scripts.clear()
+ self.titles.clear()
+ self.infotext_fields.clear()
+ self.paste_field_names.clear()
+ self.script_load_ctr = 0
+ self.is_img2img = is_img2img
+ self.scripts.clear()
+ self.alwayson_scripts.clear()
+ self.selectable_scripts.clear()
+ auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
+
+ for script_class, path, _basedir, _script_module in auto_processing_scripts + scripts_data:
+ try:
+ script = script_class()
+ script.filename = path
+ script.is_txt2img = not is_img2img
+ script.is_img2img = is_img2img
+ visibility = script.show(script.is_img2img)
+ if visibility == AlwaysVisible:
+ self.scripts.append(script)
+ self.alwayson_scripts.append(script)
+ script.alwayson = True
+ elif visibility:
+ self.scripts.append(script)
+ self.selectable_scripts.append(script)
+ except Exception as e:
+ log.error(f'Script initialize: {path} {e}')
+
+ """
+ def create_script_ui(self, script):
+ import modules.api.models as api_models
+ script.args_from = len(self.inputs)
+ script.args_to = len(self.inputs)
+ controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
+ if controls is None:
+ return
+ script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
+ api_args = []
+ for control in controls:
+ if not isinstance(control, gr.components.IOComponent):
+ log.error(f'Invalid script control: "{script.filename}" control={control}')
+ continue
+ control.custom_script_source = os.path.basename(script.filename)
+ arg_info = api_models.ScriptArg(label=control.label or "")
+ for field in ("value", "minimum", "maximum", "step", "choices"):
+ v = getattr(control, field, None)
+ if v is not None:
+ setattr(arg_info, field, v)
+ api_args.append(arg_info)
+ script.api_info = api_models.ScriptInfo(name=script.name, is_img2img=script.is_img2img, is_alwayson=script.alwayson, args=api_args)
+ if script.infotext_fields is not None:
+ self.infotext_fields += script.infotext_fields
+ if script.paste_field_names is not None:
+ self.paste_field_names += script.paste_field_names
+ self.inputs += controls
+ script.args_to = len(self.inputs)
+
+ def setup_ui_for_section(self, section, scriptlist=None):
+ if scriptlist is None:
+ scriptlist = self.alwayson_scripts
+ for script in scriptlist:
+ if script.alwayson and script.section != section:
+ continue
+ if script.create_group:
+ with gr.Group(visible=script.alwayson) as group:
+ self.create_script_ui(script)
+ script.group = group
+ else:
+ self.create_script_ui(script)
+ """
+
+ def prepare_ui(self):
+ self.inputs = [None]
+
+ def setup_ui(self, parent='unknown', accordion=True):
+ import modules.api.models as api_models
+ self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
+ inputs = []
+ inputs_alwayson = [True]
+
+ def create_script_ui(script: Script, inputs, inputs_alwayson):
+ script.parent = parent
+ script.args_from = len(inputs)
+ script.args_to = len(inputs)
+ controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
+ if controls is None:
+ return
+ script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
+ api_args = []
+ for control in controls:
+ debug(f'Script control: parent={script.parent} script="{script.name}" label="{control.label}" type={control} id={control.elem_id}')
+ if not isinstance(control, gr.components.IOComponent):
+ log.error(f'Invalid script control: "{script.filename}" control={control}')
+ continue
+ control.custom_script_source = os.path.basename(script.filename)
+ arg_info = api_models.ScriptArg(label=control.label or "")
+ for field in ("value", "minimum", "maximum", "step", "choices"):
+ v = getattr(control, field, None)
+ if v is not None:
+ setattr(arg_info, field, v)
+ api_args.append(arg_info)
+
+ script.api_info = api_models.ScriptInfo(
+ name=script.name,
+ is_img2img=script.is_img2img,
+ is_alwayson=script.alwayson,
+ args=api_args,
+ )
+ if script.infotext_fields is not None:
+ self.infotext_fields += script.infotext_fields
+ if script.paste_field_names is not None:
+ self.paste_field_names += script.paste_field_names
+ inputs += controls
+ inputs_alwayson += [script.alwayson for _ in controls]
+ script.args_to = len(inputs)
+
+ dropdown = gr.Dropdown(label="Script", elem_id=f'{parent}_script_list', choices=["None"] + self.titles, value="None", type="index")
+ inputs.insert(0, dropdown)
+ for script in self.selectable_scripts:
+ with gr.Group(visible=False) as group:
+ t0 = time.time()
+ create_script_ui(script, inputs, inputs_alwayson)
+ time_setup[script.title()] = time_setup.get(script.title(), 0) + (time.time()-t0)
+ script.group = group
+
+ def select_script(script_index):
+ selected_script = self.selectable_scripts[script_index - 1] if script_index > 0 else None
+ return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
+
+ def init_field(title):
+ if title == 'None': # called when an initial value is set from ui-config.json to show script's UI components
+ return
+ script_index = self.titles.index(title)
+ self.selectable_scripts[script_index].group.visible = True
+
+ dropdown.init_field = init_field
+ dropdown.change(fn=select_script, inputs=[dropdown], outputs=[script.group for script in self.selectable_scripts])
+
+ def onload_script_visibility(params):
+ title = params.get('Script', None)
+ if title:
+ title_index = self.titles.index(title)
+ visibility = title_index == self.script_load_ctr
+ self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
+ return gr.update(visible=visibility)
+ else:
+ return gr.update(visible=False)
+
+ with gr.Accordion(label="Extensions", elem_id=f'{parent}_script_alwayson') if accordion else gr.Group():
+ for script in self.alwayson_scripts:
+ t0 = time.time()
+ with gr.Group(elem_id=f'{parent}_script_{script.title().lower().replace(" ", "_")}', elem_classes=['extension-script']) as group:
+ create_script_ui(script, inputs, inputs_alwayson)
+ script.group = group
+ time_setup[script.title()] = time_setup.get(script.title(), 0) + (time.time()-t0)
+
+ self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
+ self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
+ return inputs
+
+ def run(self, p, *args):
+ s = ScriptSummary('run')
+ script_index = args[0]
+ if script_index == 0:
+ return None
+ script = self.selectable_scripts[script_index-1]
+ if script is None:
+ return None
+ parsed = p.per_script_args.get(script.title(), args[script.args_from:script.args_to])
+ processed = script.run(p, *parsed)
+ s.record(script.title())
+ s.report()
+ return processed
+
+ def before_process(self, p, **kwargs):
+ s = ScriptSummary('before-process')
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.before_process(p, *script_args, **kwargs)
+ except Exception as e:
+ errors.display(e, f"Error running before process: {script.filename}")
+ s.record(script.title())
+ s.report()
+
+ def process(self, p, **kwargs):
+ s = ScriptSummary('process')
+ for script in self.alwayson_scripts:
+ try:
+ args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
+ script.process(p, *args, **kwargs)
+ except Exception as e:
+ errors.display(e, f'Running script process: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def before_process_batch(self, p, **kwargs):
+ s = ScriptSummary('before-process-batch')
+ for script in self.alwayson_scripts:
+ try:
+ args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
+ script.before_process_batch(p, *args, **kwargs)
+ except Exception as e:
+ errors.display(e, f'Running script before process batch: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def process_batch(self, p, **kwargs):
+ s = ScriptSummary('process-batch')
+ for script in self.alwayson_scripts:
+ try:
+ args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
+ script.process_batch(p, *args, **kwargs)
+ except Exception as e:
+ errors.display(e, f'Running script process batch: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def postprocess(self, p, processed):
+ s = ScriptSummary('postprocess')
+ for script in self.alwayson_scripts:
+ try:
+ args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
+ script.postprocess(p, processed, *args)
+ except Exception as e:
+ errors.display(e, f'Running script postprocess: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def postprocess_batch(self, p, images, **kwargs):
+ s = ScriptSummary('postprocess-batch')
+ for script in self.alwayson_scripts:
+ try:
+ args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
+ script.postprocess_batch(p, *args, images=images, **kwargs)
+ except Exception as e:
+ errors.display(e, f'Running script before postprocess batch: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):
+ s = ScriptSummary('postprocess-batch-list')
+ for script in self.alwayson_scripts:
+ try:
+ args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
+ script.postprocess_batch_list(p, pp, *args, **kwargs)
+ except Exception as e:
+ errors.display(e, f'Running script before postprocess batch list: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def postprocess_image(self, p, pp: PostprocessImageArgs):
+ s = ScriptSummary('postprocess-image')
+ for script in self.alwayson_scripts:
+ try:
+ args = p.per_script_args.get(script.title(), p.script_args[script.args_from:script.args_to])
+ script.postprocess_image(p, pp, *args)
+ except Exception as e:
+ errors.display(e, f'Running script postprocess image: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def before_component(self, component, **kwargs):
+ s = ScriptSummary('before-component')
+ for script in self.scripts:
+ try:
+ script.before_component(component, **kwargs)
+ except Exception as e:
+ errors.display(e, f'Running script before component: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def after_component(self, component, **kwargs):
+ s = ScriptSummary('after-component')
+ for script in self.scripts:
+ try:
+ script.after_component(component, **kwargs)
+ except Exception as e:
+ errors.display(e, f'Running script after component: {script.filename}')
+ s.record(script.title())
+ s.report()
+
+ def reload_sources(self, cache):
+ s = ScriptSummary('reload-sources')
+ for si, script in list(enumerate(self.scripts)):
+ args_from = script.args_from
+ args_to = script.args_to
+ filename = script.filename
+ module = cache.get(filename, None)
+ if module is None:
+ module = script_loading.load_module(script.filename)
+ cache[filename] = module
+ for script_class in module.__dict__.values():
+ if type(script_class) == type and issubclass(script_class, Script):
+ self.scripts[si] = script_class()
+ self.scripts[si].filename = filename
+ self.scripts[si].args_from = args_from
+ self.scripts[si].args_to = args_to
+ s.record(script.title())
+ s.report()
+
+
+scripts_txt2img: ScriptRunner = None
+scripts_img2img: ScriptRunner = None
+scripts_control: ScriptRunner = None
+scripts_current: ScriptRunner = None
+scripts_postproc: scripts_postprocessing.ScriptPostprocessingRunner = None
+reload_scripts = load_scripts # compatibility alias
+
+
+def reload_script_body_only():
+ cache = {}
+ scripts_txt2img.reload_sources(cache)
+ scripts_img2img.reload_sources(cache)
+ scripts_control.reload_sources(cache)
+
+
+def add_classes_to_gradio_component(comp):
+ """
+ this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+ """
+ elem_classes = []
+ if hasattr(comp, "elem_classes"):
+ elem_classes = comp.elem_classes
+ if elem_classes is None:
+ elem_classes = []
+ comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
+ if getattr(comp, 'multiselect', False):
+ comp.elem_classes.append('multiselect')
+
+
+def IOComponent_init(self, *args, **kwargs):
+ if scripts_current is not None:
+ scripts_current.before_component(self, **kwargs)
+ script_callbacks.before_component_callback(self, **kwargs)
+ res = original_IOComponent_init(self, *args, **kwargs) # pylint: disable=assignment-from-no-return
+ add_classes_to_gradio_component(self)
+ script_callbacks.after_component_callback(self, **kwargs)
+ if scripts_current is not None:
+ scripts_current.after_component(self, **kwargs)
+ return res
+
+
+original_IOComponent_init = gr.components.IOComponent.__init__
+gr.components.IOComponent.__init__ = IOComponent_init
+
+
+def BlockContext_init(self, *args, **kwargs):
+ res = original_BlockContext_init(self, *args, **kwargs) # pylint: disable=assignment-from-no-return
+ add_classes_to_gradio_component(self)
+ return res
+
+
+original_BlockContext_init = gr.blocks.BlockContext.__init__
+gr.blocks.BlockContext.__init__ = BlockContext_init
diff --git a/modules/scripts_auto_postprocessing.py b/modules/scripts_auto_postprocessing.py
index dbb04ec9a..51d948a21 100644
--- a/modules/scripts_auto_postprocessing.py
+++ b/modules/scripts_auto_postprocessing.py
@@ -1,36 +1,36 @@
-from modules import scripts, scripts_postprocessing, shared
-
-
-class ScriptPostprocessingForMainUI(scripts.Script):
- def __init__(self, script_postproc):
- self.script: scripts_postprocessing.ScriptPostprocessing = script_postproc
- self.postprocessing_controls = None
-
- def title(self):
- return self.script.name
-
- def show(self, is_img2img): # pylint: disable=unused-argument
- return scripts.AlwaysVisible
-
- def ui(self, is_img2img): # pylint: disable=unused-argument
- self.postprocessing_controls = self.script.ui()
- return self.postprocessing_controls.values()
-
- def postprocess_image(self, p, script_pp, *args): # pylint: disable=arguments-differ
- args_dict = dict(zip(self.postprocessing_controls, args))
- pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
- pp.info = {}
- self.script.process(pp, **args_dict)
- p.extra_generation_params.update(pp.info)
- script_pp.image = pp.image
-
-
-def create_auto_preprocessing_script_data():
- res = []
- for name in shared.opts.postprocessing_enable_in_main_ui:
- script = next(iter([x for x in scripts.postprocessing_scripts_data if x.script_class.name == name]), None)
- if script is None:
- continue
- constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class()) # pylint: disable=unnecessary-lambda-assignment
- res.append(scripts.ScriptClassData(script_class=constructor, path=script.path, basedir=script.basedir, module=script.module))
- return res
+from modules import scripts, scripts_postprocessing, shared
+
+
+class ScriptPostprocessingForMainUI(scripts.Script):
+ def __init__(self, script_postproc):
+ self.script: scripts_postprocessing.ScriptPostprocessing = script_postproc
+ self.postprocessing_controls = None
+
+ def title(self):
+ return self.script.name
+
+ def show(self, is_img2img): # pylint: disable=unused-argument
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img): # pylint: disable=unused-argument
+ self.postprocessing_controls = self.script.ui()
+ return self.postprocessing_controls.values()
+
+ def postprocess_image(self, p, script_pp, *args): # pylint: disable=arguments-differ
+ args_dict = dict(zip(self.postprocessing_controls, args))
+ pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
+ pp.info = {}
+ self.script.process(pp, **args_dict)
+ p.extra_generation_params.update(pp.info)
+ script_pp.image = pp.image
+
+
+def create_auto_preprocessing_script_data():
+ res = []
+ for name in shared.opts.postprocessing_enable_in_main_ui:
+ script = next(iter([x for x in scripts.postprocessing_scripts_data if x.script_class.name == name]), None)
+ if script is None:
+ continue
+ constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class()) # pylint: disable=unnecessary-lambda-assignment
+ res.append(scripts.ScriptClassData(script_class=constructor, path=script.path, basedir=script.basedir, module=script.module))
+ return res
diff --git a/modules/scripts_postprocessing.py b/modules/scripts_postprocessing.py
index f4825da17..3bb92b4b5 100644
--- a/modules/scripts_postprocessing.py
+++ b/modules/scripts_postprocessing.py
@@ -1,134 +1,134 @@
-import os
-import gradio as gr
-from modules import errors, shared
-
-
-class PostprocessedImage:
- def __init__(self, image):
- self.image = image
- self.info = {}
-
-
-class ScriptPostprocessing:
- filename = None
- controls = None
- args_from = None
- args_to = None
- order = 1000 # scripts will be ordred by this value in postprocessing UI
- name = None # this function should return the title of the script
- group = None # A gr.Group component that has all script's UI inside it
-
- def ui(self):
- """
- This function should create gradio UI elements. See https://gradio.app/docs/#components
- The return value should be a dictionary that maps parameter names to components used in processing.
- Values of those components will be passed to process() function.
- """
- pass # pylint: disable=unnecessary-pass
-
- def process(self, pp: PostprocessedImage, **args):
- """
- This function is called to postprocess the image.
- args contains a dictionary with all values returned by components from ui()
- """
- pass # pylint: disable=unnecessary-pass
-
- def image_changed(self):
- pass
-
-
-def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
- try:
- res = func(*args, **kwargs)
- return res
- except Exception as e:
- errors.display(e, f"calling {filename}/{funcname}")
-
- return default
-
-
-class ScriptPostprocessingRunner:
- def __init__(self):
- self.scripts = None
- self.ui_created = False
-
- def initialize_scripts(self, scripts_data):
- self.scripts = []
- for script_class, path, _basedir, _script_module in scripts_data:
- script: ScriptPostprocessing = script_class()
- script.filename = path
- if script.name == "Simple Upscale":
- continue
- self.scripts.append(script)
-
- def create_script_ui(self, script, inputs):
- script.args_from = len(inputs)
- script.args_to = len(inputs)
- script.controls = wrap_call(script.ui, script.filename, "ui")
- for control in script.controls.values():
- control.custom_script_source = os.path.basename(script.filename)
- inputs += list(script.controls.values())
- script.args_to = len(inputs)
-
- def scripts_in_preferred_order(self):
- if self.scripts is None:
- import modules.scripts
- self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
- scripts_order = shared.opts.postprocessing_operation_order
-
- def script_score(name):
- for i, possible_match in enumerate(scripts_order):
- if possible_match == name:
- return i
- return len(self.scripts)
-
- script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(self.scripts)}
- return sorted(self.scripts, key=lambda x: script_scores[x.name])
-
- def setup_ui(self):
- inputs = []
- for script in self.scripts_in_preferred_order():
- with gr.Accordion(label=script.name, open=False, elem_classes=['postprocess']) as group:
- self.create_script_ui(script, inputs)
- script.group = group
- self.ui_created = True
- return inputs
-
- def run(self, pp: PostprocessedImage, args):
- for script in self.scripts_in_preferred_order():
- shared.state.job = script.name
- script_args = args[script.args_from:script.args_to]
- process_args = {}
- for (name, _component), value in zip(script.controls.items(), script_args):
- process_args[name] = value
- shared.log.debug(f'Process: script={script.name} args={process_args}')
- script.process(pp, **process_args)
-
- def create_args_for_run(self, scripts_args):
- if not self.ui_created:
- with gr.Blocks(analytics_enabled=False):
- self.setup_ui()
- scripts = self.scripts_in_preferred_order()
- args = [None] * max([x.args_to for x in scripts])
- for script in scripts:
- script_args_dict = scripts_args.get(script.name, None)
- if script_args_dict is not None:
- for i, name in enumerate(script.controls):
- args[script.args_from + i] = script_args_dict.get(name, None)
- return args
-
- def image_changed(self):
- for script in self.scripts_in_preferred_order():
- script.image_changed()
-
- def postprocess(self, filenames, args):
- for script in self.scripts_in_preferred_order():
- if not hasattr(script, 'postprocess'):
- continue
- shared.state.job = script.name
- script_args = args[script.args_from:script.args_to]
- process_args = {}
- for (name, _component), value in zip(script.controls.items(), script_args):
- process_args[name] = value
- shared.log.debug(f'Postprocess: script={script.name} args={process_args}')
- script.postprocess(filenames, **process_args)
+import os
+import gradio as gr
+from modules import errors, shared
+
+
+class PostprocessedImage:
+ def __init__(self, image):
+ self.image = image
+ self.info = {}
+
+
+class ScriptPostprocessing:
+ filename = None
+ controls = None
+ args_from = None
+ args_to = None
+ order = 1000 # scripts will be ordred by this value in postprocessing UI
+ name = None # this function should return the title of the script
+ group = None # A gr.Group component that has all script's UI inside it
+
+ def ui(self):
+ """
+ This function should create gradio UI elements. See https://gradio.app/docs/#components
+ The return value should be a dictionary that maps parameter names to components used in processing.
+ Values of those components will be passed to process() function.
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def process(self, pp: PostprocessedImage, **args):
+ """
+ This function is called to postprocess the image.
+ args contains a dictionary with all values returned by components from ui()
+ """
+ pass # pylint: disable=unnecessary-pass
+
+ def image_changed(self):
+ pass
+
+
+def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
+ try:
+ res = func(*args, **kwargs)
+ return res
+ except Exception as e:
+ errors.display(e, f"calling {filename}/{funcname}")
+
+ return default
+
+
+class ScriptPostprocessingRunner:
+ def __init__(self):
+ self.scripts = None
+ self.ui_created = False
+
+ def initialize_scripts(self, scripts_data):
+ self.scripts = []
+ for script_class, path, _basedir, _script_module in scripts_data:
+ script: ScriptPostprocessing = script_class()
+ script.filename = path
+ if script.name == "Simple Upscale":
+ continue
+ self.scripts.append(script)
+
+ def create_script_ui(self, script, inputs):
+ script.args_from = len(inputs)
+ script.args_to = len(inputs)
+ script.controls = wrap_call(script.ui, script.filename, "ui")
+ for control in script.controls.values():
+ control.custom_script_source = os.path.basename(script.filename)
+ inputs += list(script.controls.values())
+ script.args_to = len(inputs)
+
+ def scripts_in_preferred_order(self):
+ if self.scripts is None:
+ import modules.scripts
+ self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
+ scripts_order = shared.opts.postprocessing_operation_order
+
+ def script_score(name):
+ for i, possible_match in enumerate(scripts_order):
+ if possible_match == name:
+ return i
+ return len(self.scripts)
+
+ script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(self.scripts)}
+ return sorted(self.scripts, key=lambda x: script_scores[x.name])
+
+ def setup_ui(self):
+ inputs = []
+ for script in self.scripts_in_preferred_order():
+ with gr.Accordion(label=script.name, open=False, elem_classes=['postprocess']) as group:
+ self.create_script_ui(script, inputs)
+ script.group = group
+ self.ui_created = True
+ return inputs
+
+ def run(self, pp: PostprocessedImage, args):
+ for script in self.scripts_in_preferred_order():
+ shared.state.job = script.name
+ script_args = args[script.args_from:script.args_to]
+ process_args = {}
+ for (name, _component), value in zip(script.controls.items(), script_args):
+ process_args[name] = value
+ shared.log.debug(f'Process: script={script.name} args={process_args}')
+ script.process(pp, **process_args)
+
+ def create_args_for_run(self, scripts_args):
+ if not self.ui_created:
+ with gr.Blocks(analytics_enabled=False):
+ self.setup_ui()
+ scripts = self.scripts_in_preferred_order()
+ args = [None] * max([x.args_to for x in scripts])
+ for script in scripts:
+ script_args_dict = scripts_args.get(script.name, None)
+ if script_args_dict is not None:
+ for i, name in enumerate(script.controls):
+ args[script.args_from + i] = script_args_dict.get(name, None)
+ return args
+
+ def image_changed(self):
+ for script in self.scripts_in_preferred_order():
+ script.image_changed()
+
+ def postprocess(self, filenames, args):
+ for script in self.scripts_in_preferred_order():
+ if not hasattr(script, 'postprocess'):
+ continue
+ shared.state.job = script.name
+ script_args = args[script.args_from:script.args_to]
+ process_args = {}
+ for (name, _component), value in zip(script.controls.items(), script_args):
+ process_args[name] = value
+ shared.log.debug(f'Postprocess: script={script.name} args={process_args}')
+ script.postprocess(filenames, **process_args)
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index c30525c30..e9ac1be92 100644
--- a/modules/sd_disable_initialization.py
+++ b/modules/sd_disable_initialization.py
@@ -1,92 +1,92 @@
-import ldm.modules.encoders.modules
-import open_clip
-import torch
-import transformers.utils.hub
-
-
-class DisableInitialization:
- """
- When an object of this class enters a `with` block, it starts:
- - preventing torch's layer initialization functions from working
- - changes CLIP and OpenCLIP to not download model weights
- - changes CLIP to not make requests to check if there is a new version of a file you already have
-
- When it leaves the block, it reverts everything to how it was before.
-
- Use it like this:
- ```
- with DisableInitialization():
- do_things()
- ```
- """
-
- def __init__(self, disable_clip=True):
- self.replaced = []
- self.disable_clip = disable_clip
-
- def replace(self, obj, field, func):
- original = getattr(obj, field, None)
- if original is None:
- return None
-
- self.replaced.append((obj, field, original))
- setattr(obj, field, func)
-
- return original
-
- def __enter__(self):
- def do_nothing(*args, **kwargs): # pylint: disable=unused-argument
- pass
-
- def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs): # pylint: disable=unused-argument
- return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
-
- def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
- res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
- res.name_or_path = pretrained_model_name_or_path
- return res
-
- def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
- args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
- return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
-
- def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
-
- # this file is always 404, prevent making request
- if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
- return None
-
- try:
- res = original(url, *args, local_files_only=True, **kwargs)
- if res is None:
- res = original(url, *args, local_files_only=False, **kwargs)
- return res
- except Exception:
- return original(url, *args, local_files_only=False, **kwargs)
-
- def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs): # pylint: disable=unused-argument
- return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
-
- def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs): # pylint: disable=unused-argument
- return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
-
- def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs): # pylint: disable=unused-argument
- return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
-
- self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
- self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
- self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
-
- if self.disable_clip:
- self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained) # pylint: disable=attribute-defined-outside-init
- self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained) # pylint: disable=attribute-defined-outside-init
- self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model) # pylint: disable=attribute-defined-outside-init
- self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file) # pylint: disable=attribute-defined-outside-init
- self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file) # pylint: disable=attribute-defined-outside-init
- self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache) # pylint: disable=attribute-defined-outside-init
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- for obj, field, original in self.replaced:
- setattr(obj, field, original)
-
- self.replaced.clear()
+import ldm.modules.encoders.modules
+import open_clip
+import torch
+import transformers.utils.hub
+
+
+class DisableInitialization:
+ """
+ When an object of this class enters a `with` block, it starts:
+ - preventing torch's layer initialization functions from working
+ - changes CLIP and OpenCLIP to not download model weights
+ - changes CLIP to not make requests to check if there is a new version of a file you already have
+
+ When it leaves the block, it reverts everything to how it was before.
+
+ Use it like this:
+ ```
+ with DisableInitialization():
+ do_things()
+ ```
+ """
+
+ def __init__(self, disable_clip=True):
+ self.replaced = []
+ self.disable_clip = disable_clip
+
+ def replace(self, obj, field, func):
+ original = getattr(obj, field, None)
+ if original is None:
+ return None
+
+ self.replaced.append((obj, field, original))
+ setattr(obj, field, func)
+
+ return original
+
+ def __enter__(self):
+ def do_nothing(*args, **kwargs): # pylint: disable=unused-argument
+ pass
+
+ def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs): # pylint: disable=unused-argument
+ return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
+
+ def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
+ res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
+ res.name_or_path = pretrained_model_name_or_path
+ return res
+
+ def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
+ args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
+ return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
+
+ def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
+
+ # this file is always 404, prevent making request
+ if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
+ return None
+
+ try:
+ res = original(url, *args, local_files_only=True, **kwargs)
+ if res is None:
+ res = original(url, *args, local_files_only=False, **kwargs)
+ return res
+ except Exception:
+ return original(url, *args, local_files_only=False, **kwargs)
+
+ def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs): # pylint: disable=unused-argument
+ return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
+
+ def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs): # pylint: disable=unused-argument
+ return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
+
+ def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs): # pylint: disable=unused-argument
+ return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
+
+ self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
+ self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
+ self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
+
+ if self.disable_clip:
+ self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained) # pylint: disable=attribute-defined-outside-init
+ self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained) # pylint: disable=attribute-defined-outside-init
+ self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model) # pylint: disable=attribute-defined-outside-init
+ self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file) # pylint: disable=attribute-defined-outside-init
+ self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file) # pylint: disable=attribute-defined-outside-init
+ self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache) # pylint: disable=attribute-defined-outside-init
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ for obj, field, original in self.replaced:
+ setattr(obj, field, original)
+
+ self.replaced.clear()
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a1975b0c0..1e4f6adaf 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -1,307 +1,313 @@
-from types import MethodType, SimpleNamespace
-import torch
-from torch.nn.functional import silu
-import ldm.modules.attention
-import ldm.modules.distributions.distributions
-import ldm.modules.diffusionmodules.model
-import ldm.modules.diffusionmodules.openaimodel
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-import ldm.modules.encoders.modules
-
-import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared
-from modules.hypernetworks import hypernetwork
-from modules.shared import opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
-
-attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
-diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
-diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
-
-# new memory efficient cross attention blocks do not support hypernets and we already
-# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
-ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
-ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
-
-# silence new console spam from SD2
-ldm.modules.attention.print = lambda *args: None
-ldm.modules.diffusionmodules.model.print = lambda *args: None
-
-current_optimizer = SimpleNamespace(**{ "name": "none" })
-
-def apply_optimizations():
- undo_optimizations()
- ldm.modules.diffusionmodules.model.nonlinearity = silu
- ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
- optimization_method = None
- can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
- if devices.device == torch.device("cpu"):
- if opts.cross_attention_optimization == "Scaled-Dot-Product":
- shared.log.warning("Cross-attention: Scaled dot product is not available on CPU")
- can_use_sdp = False
- if opts.cross_attention_optimization == "xFormers":
- shared.log.warning("Cross-attention: xFormers is not available on CPU")
- shared.xformers_available = False
-
- shared.log.info(f"Cross-attention: optimization={opts.cross_attention_optimization} options={opts.cross_attention_options}")
- if opts.cross_attention_optimization == "Disabled":
- optimization_method = 'none'
- if can_use_sdp and opts.cross_attention_optimization == "Scaled-Dot-Product" and 'SDP disable memory attention' in opts.cross_attention_options:
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
- optimization_method = 'sdp-no-mem'
- elif can_use_sdp and opts.cross_attention_optimization == "Scaled-Dot-Product":
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
- optimization_method = 'sdp'
- if shared.xformers_available and opts.cross_attention_optimization == "xFormers":
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
- optimization_method = 'xformers'
- if opts.cross_attention_optimization == "Sub-quadratic":
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward
- optimization_method = 'sub-quadratic'
- if opts.cross_attention_optimization == "Split attention":
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
- optimization_method = 'v1'
- if opts.cross_attention_optimization == "InvokeAI's":
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
- optimization_method = 'invokeai'
- if opts.cross_attention_optimization == "Doggettx's":
- ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
- ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
- optimization_method = 'doggettx'
- current_optimizer.name = optimization_method
- return optimization_method
-
-
-def undo_optimizations():
- ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
- ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
- ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
-
-
-def fix_checkpoint():
- """checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want
- checkpoints to be added when not training (there's a warning)"""
- pass # pylint: disable=unnecessary-pass
-
-
-def weighted_loss(sd_model, pred, target, mean=True):
- #Calculate the weight normally, but ignore the mean
- loss = sd_model._old_get_loss(pred, target, mean=False) # pylint: disable=protected-access
-
- #Check if we have weights available
- weight = getattr(sd_model, '_custom_loss_weight', None)
- if weight is not None:
- loss *= weight
-
- #Return the loss, as mean if specified
- return loss.mean() if mean else loss
-
-def weighted_forward(sd_model, x, c, w, *args, **kwargs):
- try:
- #Temporarily append weights to a place accessible during loss calc
- sd_model._custom_loss_weight = w # pylint: disable=protected-access
-
- #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
- #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
- if not hasattr(sd_model, '_old_get_loss'):
- sd_model._old_get_loss = sd_model.get_loss # pylint: disable=protected-access
- sd_model.get_loss = MethodType(weighted_loss, sd_model)
-
- #Run the standard forward function, but with the patched 'get_loss'
- return sd_model.forward(x, c, *args, **kwargs)
- finally:
- try:
- #Delete temporary weights if appended
- del sd_model._custom_loss_weight
- except AttributeError:
- pass
-
- #If we have an old loss function, reset the loss function to the original one
- if hasattr(sd_model, '_old_get_loss'):
- sd_model.get_loss = sd_model._old_get_loss # pylint: disable=protected-access
- del sd_model._old_get_loss
-
-def apply_weighted_forward(sd_model):
- #Add new function 'weighted_forward' that can be called to calc weighted loss
- sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
-
-def undo_weighted_forward(sd_model):
- try:
- del sd_model.weighted_forward
- except AttributeError:
- pass
-
-
-class StableDiffusionModelHijack:
- fixes = None
- comments = []
- layers = None
- circular_enabled = False
- clip = None
- optimization_method = None
-
- embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
-
- def __init__(self):
- self.embedding_db.add_embedding_dir(opts.embeddings_dir)
-
- def hijack(self, m):
- if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
- model_embeddings = m.cond_stage_model.roberta.embeddings
- model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
- m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
-
- elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
- model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
- model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
- m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-
- elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
- m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
- m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-
- apply_weighted_forward(m)
- if m.cond_stage_key == "edit":
- sd_hijack_unet.hijack_ddpm_edit()
-
- if opts.ipex_optimize and shared.backend == shared.Backend.ORIGINAL:
- try:
- import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
- m.model.training = False
- m.model = ipex.optimize(m.model, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
- shared.log.info("Applied IPEX Optimize.")
- except Exception as err:
- shared.log.warning(f"IPEX Optimize not supported: {err}")
-
- if (opts.cuda_compile or opts.cuda_compile_vae or opts.cuda_compile_upscaler) and shared.opts.cuda_compile_backend != 'none' and shared.backend == shared.Backend.ORIGINAL:
- try:
- import logging
- shared.log.info(f"Compiling pipeline={m.model.__class__.__name__} mode={opts.cuda_compile_backend}")
- import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
- log_level = logging.WARNING if opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
- if hasattr(torch, '_logging'):
- torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
- torch._dynamo.config.verbose = opts.cuda_compile_verbose # pylint: disable=protected-access
- torch._dynamo.config.suppress_errors = opts.cuda_compile_errors # pylint: disable=protected-access
- torch.backends.cudnn.benchmark = True
- if opts.cuda_compile_backend == 'hidet':
- import hidet # pylint: disable=import-error
- hidet.torch.dynamo_config.use_tensor_core(True)
- hidet.torch.dynamo_config.search_space(2)
- m.model = torch.compile(m.model, mode=opts.cuda_compile_mode, backend=opts.cuda_compile_backend, fullgraph=opts.cuda_compile_fullgraph, dynamic=False)
- shared.log.info("Model complilation done.")
- except Exception as err:
- shared.log.warning(f"Model compile not supported: {err}")
- finally:
- from installer import setup_logging
- setup_logging()
-
- self.optimization_method = apply_optimizations()
- self.clip = m.cond_stage_model
-
- def flatten(el):
- flattened = [flatten(children) for children in el.children()]
- res = [el]
- for c in flattened:
- res += c
- return res
-
- self.layers = flatten(m)
-
- def undo_hijack(self, m):
- if not hasattr(m, 'cond_stage_model'):
- return # not ldm model
- if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
- m.cond_stage_model = m.cond_stage_model.wrapped
- elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
- m.cond_stage_model = m.cond_stage_model.wrapped
- model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
- if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
- model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
- elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords:
- m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
- m.cond_stage_model = m.cond_stage_model.wrapped
- undo_optimizations()
- undo_weighted_forward(m)
- self.apply_circular(False)
- self.layers = None
- self.clip = None
-
- def apply_circular(self, enable):
- if self.circular_enabled == enable:
- return
- self.circular_enabled = enable
- for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
- layer.padding_mode = 'circular' if enable else 'zeros'
-
- def clear_comments(self):
- self.comments = []
-
- def get_prompt_lengths(self, text):
- if self.clip is None:
- return 0, 0
- _, token_count = self.clip.process_texts([text])
- return token_count, self.clip.get_target_prompt_token_count(token_count)
-
-
-class EmbeddingsWithFixes(torch.nn.Module):
- def __init__(self, wrapped, embeddings):
- super().__init__()
- self.wrapped = wrapped
- self.embeddings = embeddings
-
- def forward(self, input_ids):
- batch_fixes = self.embeddings.fixes
- self.embeddings.fixes = None
-
- inputs_embeds = self.wrapped(input_ids)
-
- if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
- return inputs_embeds
-
- vecs = []
- for fixes, tensor in zip(batch_fixes, inputs_embeds):
- for offset, embedding in fixes:
- emb = devices.cond_cast_unet(embedding.vec)
- emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
- tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
-
- vecs.append(tensor)
-
- return torch.stack(vecs)
-
-
-def add_circular_option_to_conv_2d():
- conv2d_constructor = torch.nn.Conv2d.__init__
-
- def conv2d_constructor_circular(self, *args, **kwargs):
- return conv2d_constructor(self, *args, padding_mode='circular', **kwargs)
-
- torch.nn.Conv2d.__init__ = conv2d_constructor_circular
-
-
-model_hijack = StableDiffusionModelHijack()
-
-
-def register_buffer(self, name, attr):
- """
- Fix register buffer bug for Mac OS.
- """
-
- if type(attr) == torch.Tensor:
- if attr.device != devices.device:
- attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
-
- setattr(self, name, attr)
-
-
-ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
-ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
-
-# Ensure samping from Guassian for DDPM follows types
-ldm.modules.distributions.distributions.DiagonalGaussianDistribution.sample = lambda self: self.mean.to(self.parameters.dtype) + self.std.to(self.parameters.dtype) * torch.randn(self.mean.shape, dtype=self.parameters.dtype).to(device=self.parameters.device)
+from types import MethodType, SimpleNamespace
+import io
+import contextlib
+import torch
+from torch.nn.functional import silu
+
+from modules import shared
+shared.log.debug('Importing LDM')
+stdout = io.StringIO()
+with contextlib.redirect_stdout(stdout):
+ import ldm.modules.attention
+ import ldm.modules.distributions.distributions
+ import ldm.modules.diffusionmodules.model
+ import ldm.modules.diffusionmodules.openaimodel
+ import ldm.models.diffusion.ddim
+ import ldm.models.diffusion.plms
+ import ldm.modules.encoders.modules
+
+import modules.textual_inversion.textual_inversion
+from modules import devices, sd_hijack_optimizations
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
+from modules.hypernetworks import hypernetwork
+
+attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
+diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
+
+# new memory efficient cross attention blocks do not support hypernets and we already
+# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
+ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
+ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+
+# silence new console spam from SD2
+ldm.modules.attention.print = lambda *args: None
+ldm.modules.diffusionmodules.model.print = lambda *args: None
+
+current_optimizer = SimpleNamespace(**{ "name": "none" })
+
+def apply_optimizations():
+ undo_optimizations()
+ ldm.modules.diffusionmodules.model.nonlinearity = silu
+ ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+ optimization_method = None
+ can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
+ if devices.device == torch.device("cpu"):
+ if shared.opts.cross_attention_optimization == "Scaled-Dot-Product":
+ shared.log.warning("Cross-attention: Scaled dot product is not available on CPU")
+ can_use_sdp = False
+ if shared.opts.cross_attention_optimization == "xFormers":
+ shared.log.warning("Cross-attention: xFormers is not available on CPU")
+ shared.xformers_available = False
+
+ shared.log.info(f"Cross-attention: optimization={shared.opts.cross_attention_optimization} options={shared.opts.cross_attention_options}")
+ if shared.opts.cross_attention_optimization == "Disabled":
+ optimization_method = 'none'
+ if can_use_sdp and shared.opts.cross_attention_optimization == "Scaled-Dot-Product" and 'SDP disable memory attention' in shared.opts.cross_attention_options:
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
+ optimization_method = 'sdp-no-mem'
+ elif can_use_sdp and shared.opts.cross_attention_optimization == "Scaled-Dot-Product":
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
+ optimization_method = 'sdp'
+ if shared.xformers_available and shared.opts.cross_attention_optimization == "xFormers":
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+ optimization_method = 'xformers'
+ if shared.opts.cross_attention_optimization == "Sub-quadratic":
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward
+ optimization_method = 'sub-quadratic'
+ if shared.opts.cross_attention_optimization == "Split attention":
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+ optimization_method = 'v1'
+ if shared.opts.cross_attention_optimization == "InvokeAI's":
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
+ optimization_method = 'invokeai'
+ if shared.opts.cross_attention_optimization == "Doggettx's":
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+ optimization_method = 'doggettx'
+ current_optimizer.name = optimization_method
+ return optimization_method
+
+
+def undo_optimizations():
+ ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+ ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+
+
+def fix_checkpoint():
+ """checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want
+ checkpoints to be added when not training (there's a warning)"""
+ pass # pylint: disable=unnecessary-pass
+
+
+def weighted_loss(sd_model, pred, target, mean=True):
+ #Calculate the weight normally, but ignore the mean
+ loss = sd_model._old_get_loss(pred, target, mean=False) # pylint: disable=protected-access
+
+ #Check if we have weights available
+ weight = getattr(sd_model, '_custom_loss_weight', None)
+ if weight is not None:
+ loss *= weight
+
+ #Return the loss, as mean if specified
+ return loss.mean() if mean else loss
+
+def weighted_forward(sd_model, x, c, w, *args, **kwargs):
+ try:
+ #Temporarily append weights to a place accessible during loss calc
+ sd_model._custom_loss_weight = w # pylint: disable=protected-access
+
+ #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
+ #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
+ if not hasattr(sd_model, '_old_get_loss'):
+ sd_model._old_get_loss = sd_model.get_loss # pylint: disable=protected-access
+ sd_model.get_loss = MethodType(weighted_loss, sd_model)
+
+ #Run the standard forward function, but with the patched 'get_loss'
+ return sd_model.forward(x, c, *args, **kwargs)
+ finally:
+ try:
+ #Delete temporary weights if appended
+ del sd_model._custom_loss_weight
+ except AttributeError:
+ pass
+
+ #If we have an old loss function, reset the loss function to the original one
+ if hasattr(sd_model, '_old_get_loss'):
+ sd_model.get_loss = sd_model._old_get_loss # pylint: disable=protected-access
+ del sd_model._old_get_loss
+
+def apply_weighted_forward(sd_model):
+ #Add new function 'weighted_forward' that can be called to calc weighted loss
+ sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
+
+def undo_weighted_forward(sd_model):
+ try:
+ del sd_model.weighted_forward
+ except AttributeError:
+ pass
+
+
+class StableDiffusionModelHijack:
+ fixes = None
+ comments = []
+ layers = None
+ circular_enabled = False
+ clip = None
+ optimization_method = None
+
+ embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
+
+ def __init__(self):
+ self.embedding_db.add_embedding_dir(shared.opts.embeddings_dir)
+
+ def hijack(self, m):
+ if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+ model_embeddings = m.cond_stage_model.roberta.embeddings
+ model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
+ m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
+
+ elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
+ model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+ model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+ m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+
+ elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
+ m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
+ m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+
+ apply_weighted_forward(m)
+ if m.cond_stage_key == "edit":
+ sd_hijack_unet.hijack_ddpm_edit()
+
+ if "Model" in shared.opts.ipex_optimize and shared.backend == shared.Backend.ORIGINAL:
+ try:
+ import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
+ m.model.training = False
+ m.model = ipex.optimize(m.model, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
+ shared.log.info("Applied IPEX Optimize.")
+ except Exception as err:
+ shared.log.warning(f"IPEX Optimize not supported: {err}")
+
+ if "Model" in shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none' and shared.backend == shared.Backend.ORIGINAL:
+ try:
+ import logging
+ shared.log.info(f"Compiling pipeline={m.model.__class__.__name__} mode={shared.opts.cuda_compile_backend}")
+ import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
+ log_level = logging.WARNING if shared.opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
+ if hasattr(torch, '_logging'):
+ torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
+ torch._dynamo.config.verbose = shared.opts.cuda_compile_verbose # pylint: disable=protected-access
+ torch._dynamo.config.suppress_errors = shared.opts.cuda_compile_errors # pylint: disable=protected-access
+ torch.backends.cudnn.benchmark = True
+ if shared.opts.cuda_compile_backend == 'hidet':
+ import hidet # pylint: disable=import-error
+ hidet.torch.dynamo_config.use_tensor_core(True)
+ hidet.torch.dynamo_config.search_space(2)
+ m.model = torch.compile(m.model, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph, dynamic=False)
+ shared.log.info("Model complilation done.")
+ except Exception as err:
+ shared.log.warning(f"Model compile not supported: {err}")
+ finally:
+ from installer import setup_logging
+ setup_logging()
+
+ self.optimization_method = apply_optimizations()
+ self.clip = m.cond_stage_model
+
+ def flatten(el):
+ flattened = [flatten(children) for children in el.children()]
+ res = [el]
+ for c in flattened:
+ res += c
+ return res
+
+ self.layers = flatten(m)
+
+ def undo_hijack(self, m):
+ if not hasattr(m, 'cond_stage_model'):
+ return # not ldm model
+ if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+ m.cond_stage_model = m.cond_stage_model.wrapped
+ elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
+ m.cond_stage_model = m.cond_stage_model.wrapped
+ model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+ if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
+ model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+ elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords:
+ m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
+ m.cond_stage_model = m.cond_stage_model.wrapped
+ undo_optimizations()
+ undo_weighted_forward(m)
+ self.apply_circular(False)
+ self.layers = None
+ self.clip = None
+
+ def apply_circular(self, enable):
+ if self.circular_enabled == enable:
+ return
+ self.circular_enabled = enable
+ for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
+ layer.padding_mode = 'circular' if enable else 'zeros'
+
+ def clear_comments(self):
+ self.comments = []
+
+ def get_prompt_lengths(self, text):
+ if self.clip is None:
+ return 0, 0
+ _, token_count = self.clip.process_texts([text])
+ return token_count, self.clip.get_target_prompt_token_count(token_count)
+
+
+class EmbeddingsWithFixes(torch.nn.Module):
+ def __init__(self, wrapped, embeddings):
+ super().__init__()
+ self.wrapped = wrapped
+ self.embeddings = embeddings
+
+ def forward(self, input_ids):
+ batch_fixes = self.embeddings.fixes
+ self.embeddings.fixes = None
+
+ inputs_embeds = self.wrapped(input_ids)
+
+ if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
+ return inputs_embeds
+
+ vecs = []
+ for fixes, tensor in zip(batch_fixes, inputs_embeds):
+ for offset, embedding in fixes:
+ emb = devices.cond_cast_unet(embedding.vec)
+ emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
+ tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
+
+ vecs.append(tensor)
+
+ return torch.stack(vecs)
+
+
+def add_circular_option_to_conv_2d():
+ conv2d_constructor = torch.nn.Conv2d.__init__
+
+ def conv2d_constructor_circular(self, *args, **kwargs):
+ return conv2d_constructor(self, *args, padding_mode='circular', **kwargs)
+
+ torch.nn.Conv2d.__init__ = conv2d_constructor_circular
+
+
+model_hijack = StableDiffusionModelHijack()
+
+
+def register_buffer(self, name, attr):
+ """
+ Fix register buffer bug for Mac OS.
+ """
+
+ if type(attr) == torch.Tensor:
+ if attr.device != devices.device:
+ attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
+
+ setattr(self, name, attr)
+
+
+ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
+ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
+
+# Ensure samping from Guassian for DDPM follows types
+ldm.modules.distributions.distributions.DiagonalGaussianDistribution.sample = lambda self: self.mean.to(self.parameters.dtype) + self.std.to(self.parameters.dtype) * torch.randn(self.mean.shape, dtype=self.parameters.dtype).to(device=self.parameters.device)
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 14510a689..c8fe52885 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -1,256 +1,256 @@
-import math
-from collections import namedtuple
-import torch
-from modules import prompt_parser, devices, sd_hijack
-from modules.shared import opts
-
-
-class PromptChunk:
- """
- This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
- If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
- Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
- so just 75 tokens from prompt.
- """
- def __init__(self):
- self.tokens = []
- self.multipliers = []
- self.fixes = []
-
-
-PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
-"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
-chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
-are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
-
-
-class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
- """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
- have unlimited prompt length and assign weights to tokens in prompt.
- """
- def __init__(self, wrapped, hijack):
- super().__init__()
- self.wrapped = wrapped
- """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
- depending on model."""
- self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
- self.chunk_length = 75
-
- def empty_chunk(self):
- """creates an empty PromptChunk and returns it"""
- chunk = PromptChunk()
- chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
- chunk.multipliers = [1.0] * (self.chunk_length + 2)
- return chunk
-
- def get_target_prompt_token_count(self, token_count):
- """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
- return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
-
- def tokenize(self, texts):
- """Converts a batch of texts into a batch of token ids"""
- raise NotImplementedError
-
- def encode_with_transformers(self, tokens):
- """
- converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;
- All python lists with tokens are assumed to have same length, usually 77.
- if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
- model - can be 768 and 1024.
- Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
- """
- raise NotImplementedError
-
- def encode_embedding_init_text(self, init_text, nvpt):
- """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
- transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""
- raise NotImplementedError
-
- def tokenize_line(self, line):
- """
- this transforms a single prompt into a list of PromptChunk objects - as many as needed to
- represent the prompt.
- Returns the list and the total number of tokens in the prompt.
- """
- parsed = prompt_parser.parse_prompt_attention(line)
- tokenized = self.tokenize([text for text, _ in parsed])
- chunks = []
- chunk = PromptChunk()
- token_count = 0
- last_comma = -1
-
- def next_chunk(is_last=False):
- """puts current chunk into the list of results and produces the next one - empty;
- if is_last is true, tokens tokens at the end won't add to token_count"""
- nonlocal token_count
- nonlocal last_comma
- nonlocal chunk
- if is_last:
- token_count += len(chunk.tokens)
- else:
- token_count += self.chunk_length
- to_add = self.chunk_length - len(chunk.tokens)
- if to_add > 0:
- chunk.tokens += [self.id_end] * to_add
- chunk.multipliers += [1.0] * to_add
- chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
- chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
- last_comma = -1
- chunks.append(chunk)
- chunk = PromptChunk()
-
- for tokens, (text, weight) in zip(tokenized, parsed):
- if text == 'BREAK' and weight == -1:
- next_chunk()
- continue
- position = 0
- while position < len(tokens):
- token = tokens[position]
- if token == self.comma_token:
- last_comma = len(chunk.tokens)
- # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
- # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
- elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
- break_location = last_comma + 1
- reloc_tokens = chunk.tokens[break_location:]
- reloc_mults = chunk.multipliers[break_location:]
- chunk.tokens = chunk.tokens[:break_location]
- chunk.multipliers = chunk.multipliers[:break_location]
- next_chunk()
- chunk.tokens = reloc_tokens
- chunk.multipliers = reloc_mults
- if len(chunk.tokens) == self.chunk_length:
- next_chunk()
- embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position)
- if embedding is None:
- chunk.tokens.append(token)
- chunk.multipliers.append(weight)
- position += 1
- continue
- emb_len = int(embedding.vec.shape[0])
- if len(chunk.tokens) + emb_len > self.chunk_length:
- next_chunk()
- chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
- chunk.tokens += [0] * emb_len
- chunk.multipliers += [weight] * emb_len
- position += embedding_length_in_tokens
- if len(chunk.tokens) > 0 or len(chunks) == 0:
- next_chunk(is_last=True)
- return chunks, token_count
-
- def process_texts(self, texts):
- """
- Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
- length, in tokens, of all texts.
- """
- token_count = 0
- cache = {}
- batch_chunks = []
- for line in texts:
- if line in cache:
- chunks = cache[line]
- else:
- chunks, current_token_count = self.tokenize_line(line)
- token_count = max(current_token_count, token_count)
- cache[line] = chunks
- batch_chunks.append(chunks)
- return batch_chunks, token_count
-
- def forward(self, texts):
- """
- Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
- Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
- be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
- An example shape returned by this function can be: (2, 77, 768).
- Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
- is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
- """
- batch_chunks, _token_count = self.process_texts(texts)
- used_embeddings = {}
- chunk_count = max([len(x) for x in batch_chunks])
- zs = []
- for i in range(chunk_count):
- batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
- tokens = [x.tokens for x in batch_chunk]
- multipliers = [x.multipliers for x in batch_chunk]
- self.hijack.fixes = [x.fixes for x in batch_chunk]
- for fixes in self.hijack.fixes:
- for _position, embedding in fixes:
- used_embeddings[embedding.name] = embedding
- z = self.process_tokens(tokens, multipliers)
- zs.append(z)
- self.hijack.embedding_db.embeddings_used = list(used_embeddings)
- return torch.hstack(zs)
-
- def process_tokens(self, remade_batch_tokens, batch_multipliers):
- """
- sends one single prompt chunk to be encoded by transformers neural network.
- remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
- there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
- Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
- corresponds to one token.
- """
- tokens = torch.asarray(remade_batch_tokens).to(devices.device)
- # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
- if self.id_end != self.id_pad:
- for batch_pos in range(len(remade_batch_tokens)):
- index = remade_batch_tokens[batch_pos].index(self.id_end)
- tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
-
- z = self.encode_with_transformers(tokens)
- # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
- batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
- if opts.prompt_mean_norm:
- original_mean = z.mean()
- z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
- new_mean = z.mean()
- z = z * (original_mean / new_mean)
- else:
- z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
- return z
-
-
-class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
- def __init__(self, wrapped, hijack):
- super().__init__(wrapped, hijack)
- self.tokenizer = wrapped.tokenizer
- vocab = self.tokenizer.get_vocab()
- self.comma_token = vocab.get(',', None)
- self.token_mults = {}
- tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
- for text, ident in tokens_with_parens:
- mult = 1.0
- for c in text:
- if c == '[':
- mult /= 1.1
- if c == ']':
- mult *= 1.1
- if c == '(':
- mult *= 1.1
- if c == ')':
- mult /= 1.1
- if mult != 1.0:
- self.token_mults[ident] = mult
- self.id_start = self.wrapped.tokenizer.bos_token_id
- self.id_end = self.wrapped.tokenizer.eos_token_id
- self.id_pad = self.id_end
-
- def tokenize(self, texts):
- tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
- return tokenized
-
- def encode_with_transformers(self, tokens):
- clip_skip = opts.data['clip_skip'] or 1
- outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-clip_skip)
- if clip_skip > 1:
- z = outputs.hidden_states[-clip_skip]
- z = self.wrapped.transformer.text_model.final_layer_norm(z)
- else:
- z = outputs.last_hidden_state
- return z
-
- def encode_embedding_init_text(self, init_text, nvpt):
- embedding_layer = self.wrapped.transformer.text_model.embeddings
- ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
- embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
- return embedded
+import math
+from collections import namedtuple
+import torch
+from modules import prompt_parser, devices, sd_hijack
+from modules.shared import opts
+
+
+class PromptChunk:
+ """
+ This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
+ If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
+ Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
+ so just 75 tokens from prompt.
+ """
+ def __init__(self):
+ self.tokens = []
+ self.multipliers = []
+ self.fixes = []
+
+
+PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
+"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
+chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
+are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
+
+
+class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
+ """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
+ have unlimited prompt length and assign weights to tokens in prompt.
+ """
+ def __init__(self, wrapped, hijack):
+ super().__init__()
+ self.wrapped = wrapped
+ """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
+ depending on model."""
+ self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
+ self.chunk_length = 75
+
+ def empty_chunk(self):
+ """creates an empty PromptChunk and returns it"""
+ chunk = PromptChunk()
+ chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
+ chunk.multipliers = [1.0] * (self.chunk_length + 2)
+ return chunk
+
+ def get_target_prompt_token_count(self, token_count):
+ """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
+ return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
+
+ def tokenize(self, texts):
+ """Converts a batch of texts into a batch of token ids"""
+ raise NotImplementedError
+
+ def encode_with_transformers(self, tokens):
+ """
+ converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;
+ All python lists with tokens are assumed to have same length, usually 77.
+ if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
+ model - can be 768 and 1024.
+ Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
+ """
+ raise NotImplementedError
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
+ transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""
+ raise NotImplementedError
+
+ def tokenize_line(self, line):
+ """
+ this transforms a single prompt into a list of PromptChunk objects - as many as needed to
+ represent the prompt.
+ Returns the list and the total number of tokens in the prompt.
+ """
+ parsed = prompt_parser.parse_prompt_attention(line)
+ tokenized = self.tokenize([text for text, _ in parsed])
+ chunks = []
+ chunk = PromptChunk()
+ token_count = 0
+ last_comma = -1
+
+ def next_chunk(is_last=False):
+ """puts current chunk into the list of results and produces the next one - empty;
+ if is_last is true, tokens tokens at the end won't add to token_count"""
+ nonlocal token_count
+ nonlocal last_comma
+ nonlocal chunk
+ if is_last:
+ token_count += len(chunk.tokens)
+ else:
+ token_count += self.chunk_length
+ to_add = self.chunk_length - len(chunk.tokens)
+ if to_add > 0:
+ chunk.tokens += [self.id_end] * to_add
+ chunk.multipliers += [1.0] * to_add
+ chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
+ chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
+ last_comma = -1
+ chunks.append(chunk)
+ chunk = PromptChunk()
+
+ for tokens, (text, weight) in zip(tokenized, parsed):
+ if text == 'BREAK' and weight == -1:
+ next_chunk()
+ continue
+ position = 0
+ while position < len(tokens):
+ token = tokens[position]
+ if token == self.comma_token:
+ last_comma = len(chunk.tokens)
+ # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
+ # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
+ elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
+ break_location = last_comma + 1
+ reloc_tokens = chunk.tokens[break_location:]
+ reloc_mults = chunk.multipliers[break_location:]
+ chunk.tokens = chunk.tokens[:break_location]
+ chunk.multipliers = chunk.multipliers[:break_location]
+ next_chunk()
+ chunk.tokens = reloc_tokens
+ chunk.multipliers = reloc_mults
+ if len(chunk.tokens) == self.chunk_length:
+ next_chunk()
+ embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position)
+ if embedding is None:
+ chunk.tokens.append(token)
+ chunk.multipliers.append(weight)
+ position += 1
+ continue
+ emb_len = int(embedding.vec.shape[0])
+ if len(chunk.tokens) + emb_len > self.chunk_length:
+ next_chunk()
+ chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
+ chunk.tokens += [0] * emb_len
+ chunk.multipliers += [weight] * emb_len
+ position += embedding_length_in_tokens
+ if len(chunk.tokens) > 0 or len(chunks) == 0:
+ next_chunk(is_last=True)
+ return chunks, token_count
+
+ def process_texts(self, texts):
+ """
+ Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
+ length, in tokens, of all texts.
+ """
+ token_count = 0
+ cache = {}
+ batch_chunks = []
+ for line in texts:
+ if line in cache:
+ chunks = cache[line]
+ else:
+ chunks, current_token_count = self.tokenize_line(line)
+ token_count = max(current_token_count, token_count)
+ cache[line] = chunks
+ batch_chunks.append(chunks)
+ return batch_chunks, token_count
+
+ def forward(self, texts):
+ """
+ Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
+ Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
+ be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
+ An example shape returned by this function can be: (2, 77, 768).
+ Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
+ is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
+ """
+ batch_chunks, _token_count = self.process_texts(texts)
+ used_embeddings = {}
+ chunk_count = max([len(x) for x in batch_chunks])
+ zs = []
+ for i in range(chunk_count):
+ batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
+ tokens = [x.tokens for x in batch_chunk]
+ multipliers = [x.multipliers for x in batch_chunk]
+ self.hijack.fixes = [x.fixes for x in batch_chunk]
+ for fixes in self.hijack.fixes:
+ for _position, embedding in fixes:
+ used_embeddings[embedding.name] = embedding
+ z = self.process_tokens(tokens, multipliers)
+ zs.append(z)
+ self.hijack.embedding_db.embeddings_used = list(used_embeddings)
+ return torch.hstack(zs)
+
+ def process_tokens(self, remade_batch_tokens, batch_multipliers):
+ """
+ sends one single prompt chunk to be encoded by transformers neural network.
+ remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
+ there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
+ Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
+ corresponds to one token.
+ """
+ tokens = torch.asarray(remade_batch_tokens).to(devices.device)
+ # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
+ if self.id_end != self.id_pad:
+ for batch_pos in range(len(remade_batch_tokens)):
+ index = remade_batch_tokens[batch_pos].index(self.id_end)
+ tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
+
+ z = self.encode_with_transformers(tokens)
+ # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
+ batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
+ if opts.prompt_mean_norm:
+ original_mean = z.mean()
+ z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+ new_mean = z.mean()
+ z = z * (original_mean / new_mean)
+ else:
+ z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+ return z
+
+
+class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+ self.tokenizer = wrapped.tokenizer
+ vocab = self.tokenizer.get_vocab()
+ self.comma_token = vocab.get(',', None)
+ self.token_mults = {}
+ tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
+ for text, ident in tokens_with_parens:
+ mult = 1.0
+ for c in text:
+ if c == '[':
+ mult /= 1.1
+ if c == ']':
+ mult *= 1.1
+ if c == '(':
+ mult *= 1.1
+ if c == ')':
+ mult /= 1.1
+ if mult != 1.0:
+ self.token_mults[ident] = mult
+ self.id_start = self.wrapped.tokenizer.bos_token_id
+ self.id_end = self.wrapped.tokenizer.eos_token_id
+ self.id_pad = self.id_end
+
+ def tokenize(self, texts):
+ tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
+ return tokenized
+
+ def encode_with_transformers(self, tokens):
+ clip_skip = opts.data['clip_skip'] or 1
+ outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-clip_skip)
+ if clip_skip > 1:
+ z = outputs.hidden_states[-clip_skip]
+ z = self.wrapped.transformer.text_model.final_layer_norm(z)
+ else:
+ z = outputs.last_hidden_state
+ return z
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ embedding_layer = self.wrapped.transformer.text_model.embeddings
+ ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+ embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
+ return embedded
diff --git a/modules/sd_hijack_clip_old.py b/modules/sd_hijack_clip_old.py
index 21af997e9..ba6c38754 100644
--- a/modules/sd_hijack_clip_old.py
+++ b/modules/sd_hijack_clip_old.py
@@ -1,82 +1,82 @@
-from modules import sd_hijack_clip
-from modules import shared
-
-
-def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
- id_start = self.id_start
- id_end = self.id_end
- maxlen = self.wrapped.max_length # you get to stay at 77
- used_custom_terms = []
- remade_batch_tokens = []
- hijack_comments = []
- hijack_fixes = []
- token_count = 0
-
- cache = {}
- batch_tokens = self.tokenize(texts)
- batch_multipliers = []
- for tokens in batch_tokens:
- tuple_tokens = tuple(tokens)
-
- if tuple_tokens in cache:
- remade_tokens, fixes, multipliers = cache[tuple_tokens]
- else:
- fixes = []
- remade_tokens = []
- multipliers = []
- mult = 1.0
-
- i = 0
- while i < len(tokens):
- token = tokens[i]
-
- embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
-
- mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None
- if mult_change is not None:
- mult *= mult_change
- i += 1
- elif embedding is None:
- remade_tokens.append(token)
- multipliers.append(mult)
- i += 1
- else:
- emb_len = int(embedding.vec.shape[0])
- fixes.append((len(remade_tokens), embedding))
- remade_tokens += [0] * emb_len
- multipliers += [mult] * emb_len
- used_custom_terms.append((embedding.name, embedding.checksum()))
- i += embedding_length_in_tokens
-
- if len(remade_tokens) > maxlen - 2:
- vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
- ovf = remade_tokens[maxlen - 2:]
- overflowing_words = [vocab.get(int(x), "") for x in ovf]
- overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
- hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
- token_count = len(remade_tokens)
- remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
- remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
- cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
-
- multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
- multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
-
- remade_batch_tokens.append(remade_tokens)
- hijack_fixes.append(fixes)
- batch_multipliers.append(multipliers)
- return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
-
-def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
- batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, _token_count = process_text_old(self, texts)
-
- self.hijack.comments += hijack_comments
-
- if len(used_custom_terms) > 0:
- embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
- self.hijack.comments.append(f"Used embeddings: {embedding_names}")
-
- self.hijack.fixes = hijack_fixes
- return self.process_tokens(remade_batch_tokens, batch_multipliers)
+from modules import sd_hijack_clip
+from modules import shared
+
+
+def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
+ id_start = self.id_start
+ id_end = self.id_end
+ maxlen = self.wrapped.max_length # you get to stay at 77
+ used_custom_terms = []
+ remade_batch_tokens = []
+ hijack_comments = []
+ hijack_fixes = []
+ token_count = 0
+
+ cache = {}
+ batch_tokens = self.tokenize(texts)
+ batch_multipliers = []
+ for tokens in batch_tokens:
+ tuple_tokens = tuple(tokens)
+
+ if tuple_tokens in cache:
+ remade_tokens, fixes, multipliers = cache[tuple_tokens]
+ else:
+ fixes = []
+ remade_tokens = []
+ multipliers = []
+ mult = 1.0
+
+ i = 0
+ while i < len(tokens):
+ token = tokens[i]
+
+ embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+
+ mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None
+ if mult_change is not None:
+ mult *= mult_change
+ i += 1
+ elif embedding is None:
+ remade_tokens.append(token)
+ multipliers.append(mult)
+ i += 1
+ else:
+ emb_len = int(embedding.vec.shape[0])
+ fixes.append((len(remade_tokens), embedding))
+ remade_tokens += [0] * emb_len
+ multipliers += [mult] * emb_len
+ used_custom_terms.append((embedding.name, embedding.checksum()))
+ i += embedding_length_in_tokens
+
+ if len(remade_tokens) > maxlen - 2:
+ vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
+ ovf = remade_tokens[maxlen - 2:]
+ overflowing_words = [vocab.get(int(x), "") for x in ovf]
+ overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
+ hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
+ token_count = len(remade_tokens)
+ remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
+ remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+ cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
+
+ multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
+ multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+
+ remade_batch_tokens.append(remade_tokens)
+ hijack_fixes.append(fixes)
+ batch_multipliers.append(multipliers)
+ return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
+
+
+def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
+ batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, _token_count = process_text_old(self, texts)
+
+ self.hijack.comments += hijack_comments
+
+ if len(used_custom_terms) > 0:
+ embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
+ self.hijack.comments.append(f"Used embeddings: {embedding_names}")
+
+ self.hijack.fixes = hijack_fixes
+ return self.process_tokens(remade_batch_tokens, batch_multipliers)
diff --git a/modules/sd_hijack_freeu.py b/modules/sd_hijack_freeu.py
index 430c764dc..a1e560485 100644
--- a/modules/sd_hijack_freeu.py
+++ b/modules/sd_hijack_freeu.py
@@ -2,7 +2,6 @@ import math
import functools
import torch
from modules import shared
-from modules.sd_hijack_unet import th
# based on
# official params are b1,b2,s1,s2
@@ -129,6 +128,7 @@ def ratio_to_region(width: float, offset: float, n: int):
def apply_freeu(p, backend_original):
+ from modules.sd_hijack_unet import th
global state_enabled # pylint: disable=global-statement
global cat_original # pylint: disable=global-statement
if backend_original:
diff --git a/modules/sd_hijack_open_clip.py b/modules/sd_hijack_open_clip.py
index 0ff673490..3c06ed54d 100644
--- a/modules/sd_hijack_open_clip.py
+++ b/modules/sd_hijack_open_clip.py
@@ -1,29 +1,29 @@
-import open_clip.tokenizer
-import torch
-
-from modules import sd_hijack_clip, devices
-
-tokenizer = open_clip.tokenizer._tokenizer # pylint: disable=protected-access
-
-
-class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
- def __init__(self, wrapped, hijack):
- super().__init__(wrapped, hijack)
- self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ','][0]
- self.id_start = tokenizer.encoder[""]
- self.id_end = tokenizer.encoder[""]
- self.id_pad = 0
-
- def tokenize(self, texts):
- tokenized = [tokenizer.encode(text) for text in texts]
- return tokenized
-
- def encode_with_transformers(self, tokens):
- z = self.wrapped.encode_with_transformer(tokens)
- return z
-
- def encode_embedding_init_text(self, init_text, nvpt): # pylint: disable=unused-argument
- ids = tokenizer.encode(init_text)
- ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
- embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
- return embedded
+import open_clip.tokenizer
+import torch
+
+from modules import sd_hijack_clip, devices
+
+tokenizer = open_clip.tokenizer._tokenizer # pylint: disable=protected-access
+
+
+class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+ self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ','][0]
+ self.id_start = tokenizer.encoder[""]
+ self.id_end = tokenizer.encoder[""]
+ self.id_pad = 0
+
+ def tokenize(self, texts):
+ tokenized = [tokenizer.encode(text) for text in texts]
+ return tokenized
+
+ def encode_with_transformers(self, tokens):
+ z = self.wrapped.encode_with_transformer(tokens)
+ return z
+
+ def encode_embedding_init_text(self, init_text, nvpt): # pylint: disable=unused-argument
+ ids = tokenizer.encode(init_text)
+ ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
+ embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
+ return embedded
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 9a3d0d237..60a0d3f22 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,520 +1,520 @@
-from __future__ import annotations
-import sys
-import math
-import psutil
-
-import torch
-from torch import einsum
-
-from ldm.util import default
-from einops import rearrange
-
-from modules import shared, errors, devices
-from modules.hypernetworks import hypernetwork
-
-from .sub_quadratic_attention import efficient_dot_product_attention # pylint: disable=relative-beyond-top-level
-
-
-if shared.opts.cross_attention_optimization == "xFormers":
- try:
- import xformers.ops # pylint: disable=import-error
- shared.xformers_available = True
- except Exception:
- pass
-else:
- if sys.modules.get("xformers", None) is not None:
- shared.log.debug('Unloading xFormers')
- sys.modules["xformers"] = None
- sys.modules["xformers.ops"] = None
-
-
-def get_available_vram():
- if shared.device.type == 'cuda' or shared.device.type == 'xpu':
- try:
- stats = torch.cuda.memory_stats(shared.device)
- mem_active = stats['active_bytes.all.current']
- mem_reserved = stats['reserved_bytes.all.current']
- mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
- mem_free_torch = mem_reserved - mem_active
- mem_free_total = mem_free_cuda + mem_free_torch
- except Exception:
- mem_free_total = 1024 * 1024 * 1024
- return mem_free_total
- elif shared.device.type == 'privateuseone':
- return torch.dml.mem_get_info(shared.device)[0]
- else:
- return psutil.virtual_memory().available
-
-
-# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
-def split_cross_attention_forward_v1(self, x, context=None, mask=None): # pylint: disable=unused-argument
- h = self.heads
-
- q_in = self.to_q(x)
- context = default(context, x)
-
- context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
- k_in = self.to_k(context_k)
- v_in = self.to_v(context_v)
- del context, context_k, context_v, x
-
- q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
- del q_in, k_in, v_in
-
- dtype = q.dtype
- if shared.opts.upcast_attn:
- q, k, v = q.float(), k.float(), v.float()
-
- with devices.without_autocast(disable=not shared.opts.upcast_attn):
- r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
- for i in range(0, q.shape[0], 2):
- end = i + 2
- s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
- s1 *= self.scale
- s2 = s1.softmax(dim=-1)
- del s1
- r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
- del s2
- del q, k, v
-
- r1 = r1.to(dtype)
-
- r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
- del r1
-
- return self.to_out(r2)
-
-
-# taken from https://github.com/Doggettx/stable-diffusion and modified
-def split_cross_attention_forward(self, x, context=None, mask=None): # pylint: disable=unused-argument
- h = self.heads
- q_in = self.to_q(x)
- context = default(context, x)
-
- context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
- k_in = self.to_k(context_k)
- v_in = self.to_v(context_v)
-
- dtype = q_in.dtype
- if shared.opts.upcast_attn:
- q_in, k_in, v_in = q_in.float(), k_in.float(), v_in if v_in.device.type == 'mps' else v_in.float()
-
- with devices.without_autocast(disable=not shared.opts.upcast_attn):
- k_in = k_in * self.scale
- del context, x
- q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
- del q_in, k_in, v_in
- r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
- mem_free_total = get_available_vram()
- gb = 1024 ** 3
- tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
- modifier = 3 if q.element_size() == 2 else 2.5
- mem_required = tensor_size * modifier
- steps = 1
- if mem_required > mem_free_total:
- steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
- if steps > 64:
- max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
- raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
- f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
- slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
- for i in range(0, q.shape[1], slice_size):
- end = i + slice_size
- s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
- s2 = s1.softmax(dim=-1, dtype=q.dtype)
- del s1
- r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
- del s2
- del q, k, v
- r1 = r1.to(dtype)
- r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
- del r1
-
- return self.to_out(r2)
-
-
-# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
-mem_total_gb = psutil.virtual_memory().total // (1 << 30)
-
-def einsum_op_compvis(q, k, v):
- s = einsum('b i d, b j d -> b i j', q, k)
- s = s.softmax(dim=-1, dtype=s.dtype)
- return einsum('b i j, b j d -> b i d', s, v)
-
-def einsum_op_slice_0(q, k, v, slice_size):
- r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
- for i in range(0, q.shape[0], slice_size):
- end = i + slice_size
- r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
- return r
-
-def einsum_op_slice_1(q, k, v, slice_size):
- r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
- for i in range(0, q.shape[1], slice_size):
- end = i + slice_size
- r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
- return r
-
-def einsum_op_mps_v1(q, k, v):
- if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
- return einsum_op_compvis(q, k, v)
- else:
- slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
- if slice_size % 4096 == 0:
- slice_size -= 1
- return einsum_op_slice_1(q, k, v, slice_size)
-
-def einsum_op_mps_v2(q, k, v):
- if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
- return einsum_op_compvis(q, k, v)
- else:
- return einsum_op_slice_0(q, k, v, 1)
-
-def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
- size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
- if size_mb <= max_tensor_mb:
- return einsum_op_compvis(q, k, v)
- div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
- if div <= q.shape[0]:
- return einsum_op_slice_0(q, k, v, q.shape[0] // div)
- return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
-
-def einsum_op_cuda(q, k, v):
- try:
- stats = torch.cuda.memory_stats(q.device)
- mem_active = stats['active_bytes.all.current']
- mem_reserved = stats['reserved_bytes.all.current']
- mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
- mem_free_torch = mem_reserved - mem_active
- mem_free_total = mem_free_cuda + mem_free_torch
- except Exception:
- mem_free_total = 1024 * 1024 * 1024
- # Divide factor of safety as there's copying and fragmentation
- return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
-
-def einsum_op_dml(q, k, v):
- mem_free, mem_total = torch.dml.mem_get_info(q.device)
- mem_active = mem_total - mem_free
- mem_reserved = mem_total * 0.7
- return einsum_op_tensor_mem(q, k, v, (mem_reserved - mem_active) if mem_reserved > mem_active else 1)
-
-def einsum_op(q, k, v):
- if q.device.type == 'cuda' or q.device.type == 'xpu':
- return einsum_op_cuda(q, k, v)
-
- if q.device.type == 'mps':
- if mem_total_gb >= 32 and q.shape[0] % 32 != 0 and q.shape[0] * q.shape[1] < 2**18:
- return einsum_op_mps_v1(q, k, v)
- return einsum_op_mps_v2(q, k, v)
-
- if q.device.type == 'privateuseone':
- return einsum_op_dml(q, k, v)
-
- # Smaller slices are faster due to L2/L3/SLC caches.
- # Tested on i7 with 8MB L3 cache.
- return einsum_op_tensor_mem(q, k, v, 32)
-
-def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): # pylint: disable=unused-argument
- h = self.heads
-
- q = self.to_q(x)
- context = default(context, x)
-
- context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
- k = self.to_k(context_k)
- v = self.to_v(context_v)
- del context, context_k, context_v, x
-
- dtype = q.dtype
- if shared.opts.upcast_attn:
- q, k, v = q.float(), k.float(), v if v.device.type == 'mps' else v.float()
-
- with devices.without_autocast(disable=not shared.opts.upcast_attn):
- k = k * self.scale
- q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
- r = einsum_op(q, k, v)
- r = r.to(dtype)
- return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
-
-# -- End of code from https://github.com/invoke-ai/InvokeAI --
-
-
-# Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
-# The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface
-def sub_quad_attention_forward(self, x, context=None, mask=None):
- assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."
-
- h = self.heads
-
- q = self.to_q(x)
- context = default(context, x)
-
- context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
- k = self.to_k(context_k)
- v = self.to_v(context_v)
- del context, context_k, context_v, x
-
- q = q.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
- k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
- v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
-
- if q.device.type == 'mps':
- q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
-
- dtype = q.dtype
- if shared.opts.upcast_attn:
- q, k = q.float(), k.float()
-
- x = sub_quad_attention(q, k, v, q_chunk_size=shared.opts.sub_quad_q_chunk_size, kv_chunk_size=shared.opts.sub_quad_kv_chunk_size, chunk_threshold=shared.opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
-
- x = x.to(dtype)
-
- x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)
-
- out_proj, dropout = self.to_out
- x = out_proj(x)
- x = dropout(x)
-
- return x
-
-def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold=None, use_checkpoint=True):
- bytes_per_token = torch.finfo(q.dtype).bits//8
- batch_x_heads, q_tokens, _ = q.shape
- _, k_tokens, _ = k.shape
- qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
-
- if chunk_threshold is None:
- chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
- elif chunk_threshold == 0:
- chunk_threshold_bytes = None
- else:
- chunk_threshold_bytes = int(0.01 * chunk_threshold * get_available_vram())
-
- if kv_chunk_size_min is None and chunk_threshold_bytes is not None:
- kv_chunk_size_min = chunk_threshold_bytes // (batch_x_heads * bytes_per_token * (k.shape[2] + v.shape[2]))
- elif kv_chunk_size_min == 0:
- kv_chunk_size_min = None
-
- if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
- # the big matmul fits into our memory limit; do everything in 1 chunk,
- # i.e. send it down the unchunked fast-path
- kv_chunk_size = k_tokens
-
- with devices.without_autocast(disable=q.dtype == v.dtype):
- return efficient_dot_product_attention(
- q,
- k,
- v,
- query_chunk_size=q_chunk_size,
- kv_chunk_size=kv_chunk_size,
- kv_chunk_size_min = kv_chunk_size_min,
- use_checkpoint=use_checkpoint,
- )
-
-
-def get_xformers_flash_attention_op(q, k, v):
- if 'xFormers enable flash Attention' not in shared.opts.cross_attention_options:
- return None
-
- try:
- flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp # pylint: disable=used-before-assignment
- fw, _bw = flash_attention_op
- if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
- return flash_attention_op
- except Exception as e:
- errors.display_once(e, "enabling flash attention")
-
- return None
-
-
-def xformers_attention_forward(self, x, context=None, mask=None): # pylint: disable=unused-argument
- h = self.heads
- q_in = self.to_q(x)
- context = default(context, x)
-
- context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
- k_in = self.to_k(context_k)
- v_in = self.to_v(context_v)
-
- q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
- del q_in, k_in, v_in
-
- dtype = q.dtype
- if shared.opts.upcast_attn:
- q, k, v = q.float(), k.float(), v.float()
-
- out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
-
- out = out.to(dtype)
-
- out = rearrange(out, 'b n h d -> b n (h d)', h=h)
- return self.to_out(out)
-
-# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
-# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
-def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
- batch_size, sequence_length, inner_dim = x.shape
-
- if mask is not None:
- mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
- mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
-
- h = self.heads
- q_in = self.to_q(x)
- context = default(context, x)
-
- context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
- k_in = self.to_k(context_k)
- v_in = self.to_v(context_v)
-
- head_dim = inner_dim // h
- q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
- k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
- v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
- del q_in, k_in, v_in
- dtype = q.dtype
- if shared.opts.upcast_attn:
- q, k, v = q.float(), k.float(), v.float()
-
- # the output of sdp = (batch, num_heads, seq_len, head_dim)
- hidden_states = torch.nn.functional.scaled_dot_product_attention(
- q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
- )
-
- hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
- hidden_states = hidden_states.to(dtype)
-
- # linear proj
- hidden_states = self.to_out[0](hidden_states)
- # dropout
- hidden_states = self.to_out[1](hidden_states)
- return hidden_states
-
-def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None):
- with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
- return scaled_dot_product_attention_forward(self, x, context, mask)
-
-def cross_attention_attnblock_forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q1 = self.q(h_)
- k1 = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- b, c, h, w = q1.shape
-
- q2 = q1.reshape(b, c, h*w)
- del q1
-
- q = q2.permute(0, 2, 1) # b,hw,c
- del q2
-
- k = k1.reshape(b, c, h*w) # b,c,hw
- del k1
-
- h_ = torch.zeros_like(k, device=q.device)
-
- mem_free_total = get_available_vram()
-
- tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
- mem_required = tensor_size * 2.5
- steps = 1
-
- if mem_required > mem_free_total:
- steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-
- slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
- for i in range(0, q.shape[1], slice_size):
- end = i + slice_size
-
- w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
- w2 = w1 * (int(c)**(-0.5))
- del w1
- w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
- del w2
-
- # attend to values
- v1 = v.reshape(b, c, h*w)
- w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
- del w3
-
- h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
- del v1, w4
-
- h2 = h_.reshape(b, c, h, w)
- del h_
-
- h3 = self.proj_out(h2)
- del h2
-
- h3 += x
-
- return h3
-
-def xformers_attnblock_forward(self, x):
- try:
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
- b, c, h, w = q.shape # pylint: disable=unused-variable
- q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
- dtype = q.dtype
- if shared.opts.upcast_attn:
- q, k = q.float(), k.float()
- q = q.contiguous()
- k = k.contiguous()
- v = v.contiguous()
- out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
- out = out.to(dtype)
- out = rearrange(out, 'b (h w) c -> b c h w', h=h)
- out = self.proj_out(out)
- return x + out
- except NotImplementedError:
- return cross_attention_attnblock_forward(self, x)
-
-def sdp_attnblock_forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
- b, c, h, w = q.shape # pylint: disable=unused-variable
-
- # SDP optimization kenels are built for operations with multiple attention heads.
- # Four dimensional tensors are required for mem_efficient and flash attention to work.
- # We add an attention head dimension `a` to allow these kernels to be used.
- q, k, v = (rearrange(t, '(b a) c h w -> b a (h w) c', a=1) for t in (q, k, v))
- dtype = q.dtype
- if shared.opts.upcast_attn:
- q, k, v = q.float(), k.float(), v.float()
- q = q.contiguous()
- k = k.contiguous()
- v = v.contiguous()
- out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
- out = out.to(dtype)
- out = rearrange(out, 'b a (h w) c -> (b a) c h w', h=h) # remove the one attention head dimension `a`
- out = self.proj_out(out)
- return x + out
-
-def sdp_no_mem_attnblock_forward(self, x):
- with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
- return sdp_attnblock_forward(self, x)
-
-def sub_quad_attnblock_forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
- b, c, h, w = q.shape # pylint: disable=unused-variable
- q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
- q = q.contiguous()
- k = k.contiguous()
- v = v.contiguous()
- out = sub_quad_attention(q, k, v, q_chunk_size=shared.opts.sub_quad_q_chunk_size, kv_chunk_size=shared.opts.sub_quad_kv_chunk_size, chunk_threshold=shared.opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
- out = rearrange(out, 'b (h w) c -> b c h w', h=h)
- out = self.proj_out(out)
- return x + out
+from __future__ import annotations
+import sys
+import math
+import psutil
+
+import torch
+from torch import einsum
+
+from ldm.util import default
+from einops import rearrange
+
+from modules import shared, errors, devices
+from modules.hypernetworks import hypernetwork
+
+from .sub_quadratic_attention import efficient_dot_product_attention # pylint: disable=relative-beyond-top-level
+
+
+if shared.opts.cross_attention_optimization == "xFormers":
+ try:
+ import xformers.ops # pylint: disable=import-error
+ shared.xformers_available = True
+ except Exception:
+ pass
+else:
+ if sys.modules.get("xformers", None) is not None:
+ shared.log.debug('Unloading xFormers')
+ sys.modules["xformers"] = None
+ sys.modules["xformers.ops"] = None
+
+
+def get_available_vram():
+ if shared.device.type == 'cuda' or shared.device.type == 'xpu':
+ try:
+ stats = torch.cuda.memory_stats(shared.device)
+ mem_active = stats['active_bytes.all.current']
+ mem_reserved = stats['reserved_bytes.all.current']
+ mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+ mem_free_torch = mem_reserved - mem_active
+ mem_free_total = mem_free_cuda + mem_free_torch
+ except Exception:
+ mem_free_total = 1024 * 1024 * 1024
+ return mem_free_total
+ elif shared.device.type == 'privateuseone':
+ return torch.dml.mem_get_info(shared.device)[0]
+ else:
+ return psutil.virtual_memory().available
+
+
+# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
+def split_cross_attention_forward_v1(self, x, context=None, mask=None): # pylint: disable=unused-argument
+ h = self.heads
+
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+ del context, context_k, context_v, x
+
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
+ del q_in, k_in, v_in
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+
+ with devices.without_autocast(disable=not shared.opts.upcast_attn):
+ r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+ for i in range(0, q.shape[0], 2):
+ end = i + 2
+ s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
+ s1 *= self.scale
+ s2 = s1.softmax(dim=-1)
+ del s1
+ r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
+ del s2
+ del q, k, v
+
+ r1 = r1.to(dtype)
+
+ r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+ del r1
+
+ return self.to_out(r2)
+
+
+# taken from https://github.com/Doggettx/stable-diffusion and modified
+def split_cross_attention_forward(self, x, context=None, mask=None): # pylint: disable=unused-argument
+ h = self.heads
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+
+ dtype = q_in.dtype
+ if shared.opts.upcast_attn:
+ q_in, k_in, v_in = q_in.float(), k_in.float(), v_in if v_in.device.type == 'mps' else v_in.float()
+
+ with devices.without_autocast(disable=not shared.opts.upcast_attn):
+ k_in = k_in * self.scale
+ del context, x
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
+ del q_in, k_in, v_in
+ r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+ mem_free_total = get_available_vram()
+ gb = 1024 ** 3
+ tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
+ modifier = 3 if q.element_size() == 2 else 2.5
+ mem_required = tensor_size * modifier
+ steps = 1
+ if mem_required > mem_free_total:
+ steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+ if steps > 64:
+ max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+ raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+ f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
+ slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+ for i in range(0, q.shape[1], slice_size):
+ end = i + slice_size
+ s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
+ s2 = s1.softmax(dim=-1, dtype=q.dtype)
+ del s1
+ r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+ del s2
+ del q, k, v
+ r1 = r1.to(dtype)
+ r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+ del r1
+
+ return self.to_out(r2)
+
+
+# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
+mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+
+def einsum_op_compvis(q, k, v):
+ s = einsum('b i d, b j d -> b i j', q, k)
+ s = s.softmax(dim=-1, dtype=s.dtype)
+ return einsum('b i j, b j d -> b i d', s, v)
+
+def einsum_op_slice_0(q, k, v, slice_size):
+ r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+ for i in range(0, q.shape[0], slice_size):
+ end = i + slice_size
+ r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
+ return r
+
+def einsum_op_slice_1(q, k, v, slice_size):
+ r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+ for i in range(0, q.shape[1], slice_size):
+ end = i + slice_size
+ r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
+ return r
+
+def einsum_op_mps_v1(q, k, v):
+ if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
+ return einsum_op_compvis(q, k, v)
+ else:
+ slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+ if slice_size % 4096 == 0:
+ slice_size -= 1
+ return einsum_op_slice_1(q, k, v, slice_size)
+
+def einsum_op_mps_v2(q, k, v):
+ if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
+ return einsum_op_compvis(q, k, v)
+ else:
+ return einsum_op_slice_0(q, k, v, 1)
+
+def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
+ size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
+ if size_mb <= max_tensor_mb:
+ return einsum_op_compvis(q, k, v)
+ div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
+ if div <= q.shape[0]:
+ return einsum_op_slice_0(q, k, v, q.shape[0] // div)
+ return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
+
+def einsum_op_cuda(q, k, v):
+ try:
+ stats = torch.cuda.memory_stats(q.device)
+ mem_active = stats['active_bytes.all.current']
+ mem_reserved = stats['reserved_bytes.all.current']
+ mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
+ mem_free_torch = mem_reserved - mem_active
+ mem_free_total = mem_free_cuda + mem_free_torch
+ except Exception:
+ mem_free_total = 1024 * 1024 * 1024
+ # Divide factor of safety as there's copying and fragmentation
+ return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+
+def einsum_op_dml(q, k, v):
+ mem_free, mem_total = torch.dml.mem_get_info(q.device)
+ mem_active = mem_total - mem_free
+ mem_reserved = mem_total * 0.7
+ return einsum_op_tensor_mem(q, k, v, (mem_reserved - mem_active) if mem_reserved > mem_active else 1)
+
+def einsum_op(q, k, v):
+ if q.device.type == 'cuda' or q.device.type == 'xpu':
+ return einsum_op_cuda(q, k, v)
+
+ if q.device.type == 'mps':
+ if mem_total_gb >= 32 and q.shape[0] % 32 != 0 and q.shape[0] * q.shape[1] < 2**18:
+ return einsum_op_mps_v1(q, k, v)
+ return einsum_op_mps_v2(q, k, v)
+
+ if q.device.type == 'privateuseone':
+ return einsum_op_dml(q, k, v)
+
+ # Smaller slices are faster due to L2/L3/SLC caches.
+ # Tested on i7 with 8MB L3 cache.
+ return einsum_op_tensor_mem(q, k, v, 32)
+
+def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): # pylint: disable=unused-argument
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k = self.to_k(context_k)
+ v = self.to_v(context_v)
+ del context, context_k, context_v, x
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v if v.device.type == 'mps' else v.float()
+
+ with devices.without_autocast(disable=not shared.opts.upcast_attn):
+ k = k * self.scale
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
+ r = einsum_op(q, k, v)
+ r = r.to(dtype)
+ return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
+
+# -- End of code from https://github.com/invoke-ai/InvokeAI --
+
+
+# Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
+# The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface
+def sub_quad_attention_forward(self, x, context=None, mask=None):
+ assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."
+
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k = self.to_k(context_k)
+ v = self.to_v(context_v)
+ del context, context_k, context_v, x
+
+ q = q.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+ k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+ v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+
+ if q.device.type == 'mps':
+ q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k = q.float(), k.float()
+
+ x = sub_quad_attention(q, k, v, q_chunk_size=shared.opts.sub_quad_q_chunk_size, kv_chunk_size=shared.opts.sub_quad_kv_chunk_size, chunk_threshold=shared.opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
+
+ x = x.to(dtype)
+
+ x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)
+
+ out_proj, dropout = self.to_out
+ x = out_proj(x)
+ x = dropout(x)
+
+ return x
+
+def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold=None, use_checkpoint=True):
+ bytes_per_token = torch.finfo(q.dtype).bits//8
+ batch_x_heads, q_tokens, _ = q.shape
+ _, k_tokens, _ = k.shape
+ qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
+
+ if chunk_threshold is None:
+ chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
+ elif chunk_threshold == 0:
+ chunk_threshold_bytes = None
+ else:
+ chunk_threshold_bytes = int(0.01 * chunk_threshold * get_available_vram())
+
+ if kv_chunk_size_min is None and chunk_threshold_bytes is not None:
+ kv_chunk_size_min = chunk_threshold_bytes // (batch_x_heads * bytes_per_token * (k.shape[2] + v.shape[2]))
+ elif kv_chunk_size_min == 0:
+ kv_chunk_size_min = None
+
+ if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
+ # the big matmul fits into our memory limit; do everything in 1 chunk,
+ # i.e. send it down the unchunked fast-path
+ kv_chunk_size = k_tokens
+
+ with devices.without_autocast(disable=q.dtype == v.dtype):
+ return efficient_dot_product_attention(
+ q,
+ k,
+ v,
+ query_chunk_size=q_chunk_size,
+ kv_chunk_size=kv_chunk_size,
+ kv_chunk_size_min = kv_chunk_size_min,
+ use_checkpoint=use_checkpoint,
+ )
+
+
+def get_xformers_flash_attention_op(q, k, v):
+ if 'xFormers enable flash Attention' not in shared.opts.cross_attention_options:
+ return None
+
+ try:
+ flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp # pylint: disable=used-before-assignment
+ fw, _bw = flash_attention_op
+ if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
+ return flash_attention_op
+ except Exception as e:
+ errors.display_once(e, "enabling flash attention")
+
+ return None
+
+
+def xformers_attention_forward(self, x, context=None, mask=None): # pylint: disable=unused-argument
+ h = self.heads
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+
+ q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
+ del q_in, k_in, v_in
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
+
+ out = out.to(dtype)
+
+ out = rearrange(out, 'b n h d -> b n (h d)', h=h)
+ return self.to_out(out)
+
+# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
+# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
+def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
+ batch_size, sequence_length, inner_dim = x.shape
+
+ if mask is not None:
+ mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
+ mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
+
+ h = self.heads
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+
+ head_dim = inner_dim // h
+ q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+ k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+ v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+ del q_in, k_in, v_in
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+
+ # the output of sdp = (batch, num_heads, seq_len, head_dim)
+ hidden_states = torch.nn.functional.scaled_dot_product_attention(
+ q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
+ )
+
+ hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
+ hidden_states = hidden_states.to(dtype)
+
+ # linear proj
+ hidden_states = self.to_out[0](hidden_states)
+ # dropout
+ hidden_states = self.to_out[1](hidden_states)
+ return hidden_states
+
+def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None):
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+ return scaled_dot_product_attention_forward(self, x, context, mask)
+
+def cross_attention_attnblock_forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q1 = self.q(h_)
+ k1 = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b, c, h, w = q1.shape
+
+ q2 = q1.reshape(b, c, h*w)
+ del q1
+
+ q = q2.permute(0, 2, 1) # b,hw,c
+ del q2
+
+ k = k1.reshape(b, c, h*w) # b,c,hw
+ del k1
+
+ h_ = torch.zeros_like(k, device=q.device)
+
+ mem_free_total = get_available_vram()
+
+ tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+ mem_required = tensor_size * 2.5
+ steps = 1
+
+ if mem_required > mem_free_total:
+ steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+ slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+ for i in range(0, q.shape[1], slice_size):
+ end = i + slice_size
+
+ w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+ w2 = w1 * (int(c)**(-0.5))
+ del w1
+ w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
+ del w2
+
+ # attend to values
+ v1 = v.reshape(b, c, h*w)
+ w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
+ del w3
+
+ h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+ del v1, w4
+
+ h2 = h_.reshape(b, c, h, w)
+ del h_
+
+ h3 = self.proj_out(h2)
+ del h2
+
+ h3 += x
+
+ return h3
+
+def xformers_attnblock_forward(self, x):
+ try:
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape # pylint: disable=unused-variable
+ q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k = q.float(), k.float()
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
+ out = out.to(dtype)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+ out = self.proj_out(out)
+ return x + out
+ except NotImplementedError:
+ return cross_attention_attnblock_forward(self, x)
+
+def sdp_attnblock_forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape # pylint: disable=unused-variable
+
+ # SDP optimization kenels are built for operations with multiple attention heads.
+ # Four dimensional tensors are required for mem_efficient and flash attention to work.
+ # We add an attention head dimension `a` to allow these kernels to be used.
+ q, k, v = (rearrange(t, '(b a) c h w -> b a (h w) c', a=1) for t in (q, k, v))
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
+ out = out.to(dtype)
+ out = rearrange(out, 'b a (h w) c -> (b a) c h w', h=h) # remove the one attention head dimension `a`
+ out = self.proj_out(out)
+ return x + out
+
+def sdp_no_mem_attnblock_forward(self, x):
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+ return sdp_attnblock_forward(self, x)
+
+def sub_quad_attnblock_forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape # pylint: disable=unused-variable
+ q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = sub_quad_attention(q, k, v, q_chunk_size=shared.opts.sub_quad_q_chunk_size, kv_chunk_size=shared.opts.sub_quad_kv_chunk_size, chunk_threshold=shared.opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+ out = self.proj_out(out)
+ return x + out
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index ea8773147..d8d356071 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -1,79 +1,79 @@
-import torch
-from packaging import version
-
-from modules import devices
-from modules.sd_hijack_utils import CondFunc
-
-
-class TorchHijackForUnet:
- """
- This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
- this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
- """
-
- def __getattr__(self, item):
- if item == 'cat':
- return self.cat
-
- if hasattr(torch, item):
- return getattr(torch, item)
-
- raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
- def cat(self, tensors, *args, **kwargs):
- if len(tensors) == 2:
- a, b = tensors
- if a.shape[-2:] != b.shape[-2:]:
- a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
-
- tensors = (a, b)
-
- return torch.cat(tensors, *args, **kwargs)
-
-
-th = TorchHijackForUnet()
-
-
-# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
-def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
-
- if isinstance(cond, dict):
- for y in cond.keys():
- cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
-
- with devices.autocast():
- return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
-
-
-class GELUHijack(torch.nn.GELU, torch.nn.Module):
- def __init__(self, *args, **kwargs): # pylint: disable=super-init-not-called
- torch.nn.GELU.__init__(self, *args, **kwargs)
- def forward(self, input): # pylint: disable=redefined-builtin
- if devices.unet_needs_upcast:
- return torch.nn.GELU.forward(self.float(), input.float()).to(devices.dtype_unet)
- else:
- return torch.nn.GELU.forward(self, input)
-
-
-ddpm_edit_hijack = None
-def hijack_ddpm_edit():
- global ddpm_edit_hijack # pylint: disable=global-statement
- if not ddpm_edit_hijack:
- CondFunc('modules.hijack.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
- CondFunc('modules.hijack.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
- ddpm_edit_hijack = CondFunc('modules.hijack.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
-
-
-unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast # pylint: disable=unnecessary-lambda-assignment
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
-CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
-if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
- CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
- CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
- CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
-
-first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16 # pylint: disable=unnecessary-lambda-assignment
-first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs) # pylint: disable=unnecessary-lambda-assignment
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
+import torch
+from packaging import version
+
+from modules import devices
+from modules.sd_hijack_utils import CondFunc
+
+
+class TorchHijackForUnet:
+ """
+ This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
+ this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
+ """
+
+ def __getattr__(self, item):
+ if item == 'cat':
+ return self.cat
+
+ if hasattr(torch, item):
+ return getattr(torch, item)
+
+ raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+ def cat(self, tensors, *args, **kwargs):
+ if len(tensors) == 2:
+ a, b = tensors
+ if a.shape[-2:] != b.shape[-2:]:
+ a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
+
+ tensors = (a, b)
+
+ return torch.cat(tensors, *args, **kwargs)
+
+
+th = TorchHijackForUnet()
+
+
+# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
+def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
+
+ if isinstance(cond, dict):
+ for y in cond.keys():
+ cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+
+ with devices.autocast():
+ return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
+
+
+class GELUHijack(torch.nn.GELU, torch.nn.Module):
+ def __init__(self, *args, **kwargs): # pylint: disable=super-init-not-called
+ torch.nn.GELU.__init__(self, *args, **kwargs)
+ def forward(self, input): # pylint: disable=redefined-builtin
+ if devices.unet_needs_upcast:
+ return torch.nn.GELU.forward(self.float(), input.float()).to(devices.dtype_unet)
+ else:
+ return torch.nn.GELU.forward(self, input)
+
+
+ddpm_edit_hijack = None
+def hijack_ddpm_edit():
+ global ddpm_edit_hijack # pylint: disable=global-statement
+ if not ddpm_edit_hijack:
+ CondFunc('modules.hijack.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
+ CondFunc('modules.hijack.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
+ ddpm_edit_hijack = CondFunc('modules.hijack.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+
+
+unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast # pylint: disable=unnecessary-lambda-assignment
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
+if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
+ CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
+ CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
+ CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
+
+first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16 # pylint: disable=unnecessary-lambda-assignment
+first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs) # pylint: disable=unnecessary-lambda-assignment
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
diff --git a/modules/sd_hijack_utils.py b/modules/sd_hijack_utils.py
index d281be635..179ebc78e 100644
--- a/modules/sd_hijack_utils.py
+++ b/modules/sd_hijack_utils.py
@@ -1,28 +1,28 @@
-import importlib
-
-class CondFunc:
- def __new__(cls, orig_func, sub_func, cond_func):
- self = super(CondFunc, cls).__new__(cls)
- if isinstance(orig_func, str):
- func_path = orig_func.split('.')
- for i in range(len(func_path)-1, -1, -1):
- try:
- resolved_obj = importlib.import_module('.'.join(func_path[:i]))
- break
- except ImportError:
- pass
- for attr_name in func_path[i:-1]:
- resolved_obj = getattr(resolved_obj, attr_name)
- orig_func = getattr(resolved_obj, func_path[-1])
- setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
- self.__init__(orig_func, sub_func, cond_func)
- return lambda *args, **kwargs: self(*args, **kwargs)
- def __init__(self, orig_func, sub_func, cond_func):
- self.__orig_func = orig_func
- self.__sub_func = sub_func
- self.__cond_func = cond_func
- def __call__(self, *args, **kwargs):
- if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
- return self.__sub_func(self.__orig_func, *args, **kwargs)
- else:
- return self.__orig_func(*args, **kwargs)
+import importlib
+
+class CondFunc:
+ def __new__(cls, orig_func, sub_func, cond_func):
+ self = super(CondFunc, cls).__new__(cls)
+ if isinstance(orig_func, str):
+ func_path = orig_func.split('.')
+ for i in range(len(func_path)-1, -1, -1):
+ try:
+ resolved_obj = importlib.import_module('.'.join(func_path[:i]))
+ break
+ except ImportError:
+ pass
+ for attr_name in func_path[i:-1]:
+ resolved_obj = getattr(resolved_obj, attr_name)
+ orig_func = getattr(resolved_obj, func_path[-1])
+ setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
+ self.__init__(orig_func, sub_func, cond_func)
+ return lambda *args, **kwargs: self(*args, **kwargs)
+ def __init__(self, orig_func, sub_func, cond_func):
+ self.__orig_func = orig_func
+ self.__sub_func = sub_func
+ self.__cond_func = cond_func
+ def __call__(self, *args, **kwargs):
+ if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
+ return self.__sub_func(self.__orig_func, *args, **kwargs)
+ else:
+ return self.__orig_func(*args, **kwargs)
diff --git a/modules/sd_hijack_xlmr.py b/modules/sd_hijack_xlmr.py
index 28528329b..2aa646aea 100644
--- a/modules/sd_hijack_xlmr.py
+++ b/modules/sd_hijack_xlmr.py
@@ -1,32 +1,32 @@
-import torch
-
-from modules import sd_hijack_clip, devices
-
-
-class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
- def __init__(self, wrapped, hijack):
- super().__init__(wrapped, hijack)
-
- self.id_start = wrapped.config.bos_token_id
- self.id_end = wrapped.config.eos_token_id
- self.id_pad = wrapped.config.pad_token_id
-
- self.comma_token = self.tokenizer.get_vocab().get(',', None) # alt diffusion doesn't have bits for comma
-
- def encode_with_transformers(self, tokens):
- # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
- # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
- # layer to work with - you have to use the last
-
- attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
- features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
- z = features['projection_state']
-
- return z
-
- def encode_embedding_init_text(self, init_text, nvpt):
- embedding_layer = self.wrapped.roberta.embeddings
- ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
- embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
-
- return embedded
+import torch
+
+from modules import sd_hijack_clip, devices
+
+
+class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+
+ self.id_start = wrapped.config.bos_token_id
+ self.id_end = wrapped.config.eos_token_id
+ self.id_pad = wrapped.config.pad_token_id
+
+ self.comma_token = self.tokenizer.get_vocab().get(',', None) # alt diffusion doesn't have bits for comma
+
+ def encode_with_transformers(self, tokens):
+ # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
+ # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
+ # layer to work with - you have to use the last
+
+ attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
+ features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
+ z = features['projection_state']
+
+ return z
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ embedding_layer = self.wrapped.roberta.embeddings
+ ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+ embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+
+ return embedded
diff --git a/modules/sd_models.py b/modules/sd_models.py
index a04ff0b0f..3132d1884 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -18,9 +18,8 @@ import diffusers
from omegaconf import OmegaConf
import tomesd
from transformers import logging as transformers_logging
-import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
-from modules import paths, shared, shared_items, shared_state, modelloader, devices, script_callbacks, sd_vae, errors, hashes, sd_models_config, sd_models_compile, sd_hijack_inpainting
+from modules import paths, shared, shared_items, shared_state, modelloader, devices, script_callbacks, sd_vae, errors, hashes, sd_models_config, sd_models_compile
from modules.timer import Timer
from modules.memstats import memory_stats
from modules.paths import models_path, script_path
@@ -123,7 +122,8 @@ def setup_model():
if not os.path.exists(model_path):
os.makedirs(model_path, exist_ok=True)
list_models()
- enable_midas_autodownload()
+ if shared.backend == shared.Backend.ORIGINAL:
+ enable_midas_autodownload()
def checkpoint_tiles(use_short=False): # pylint: disable=unused-argument
@@ -162,27 +162,8 @@ def list_models():
elif shared.cmd_opts.ckpt != shared.default_sd_model_file and shared.cmd_opts.ckpt is not None:
shared.log.warning(f"Checkpoint not found: {shared.cmd_opts.ckpt}")
shared.log.info(f'Available models: path="{shared.opts.ckpt_dir}" items={len(checkpoints_list)} time={time.time()-t0:.2f}')
-
checkpoints_list = dict(sorted(checkpoints_list.items(), key=lambda cp: cp[1].filename))
- """
- if len(checkpoints_list) == 0:
- if not shared.cmd_opts.no_download:
- key = input('Download the default model? (y/N) ')
- if key.lower().startswith('y'):
- if shared.backend == shared.Backend.ORIGINAL:
- model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
- shared.opts.data['sd_model_checkpoint'] = "v1-5-pruned-emaonly.safetensors"
- model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
- else:
- default_model_id = "runwayml/stable-diffusion-v1-5"
- modelloader.download_diffusers_model(default_model_id, shared.opts.diffusers_dir)
- model_list = modelloader.load_diffusers_models(model_path=os.path.join(models_path, 'Diffusers'), command_path=shared.opts.diffusers_dir)
- for filename in sorted(model_list, key=str.lower):
- checkpoint_info = CheckpointInfo(filename)
- if checkpoint_info.name is not None:
- checkpoint_info.register()
- """
def update_model_hashes():
txt = []
@@ -251,10 +232,12 @@ def select_checkpoint(op='model'):
if checkpoint_info is not None:
shared.log.info(f'Select: {op}="{checkpoint_info.title if checkpoint_info is not None else None}"')
return checkpoint_info
- if len(checkpoints_list) == 0 and not shared.cmd_opts.no_download:
+ if len(checkpoints_list) == 0:
shared.log.warning("Cannot generate without a checkpoint")
shared.log.info("Set system paths to use existing folders in a different location")
- shared.log.info("Or use --ckpt to force using existing checkpoint")
+ shared.log.info(" or use --models_dir to specify base folder with all models")
+ shared.log.info(" or use --ckpt_dir to specify folder with models")
+ shared.log.info(" or use --ckpt to force using existing model")
return None
checkpoint_info = next(iter(checkpoints_list.values()))
if model_checkpoint is not None:
@@ -418,8 +401,15 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
return None
if checkpoint_info in checkpoints_loaded:
shared.log.info("Model weights loading: from cache")
+ checkpoints_loaded.move_to_end(checkpoint_info, last=True) # FIFO -> LRU cache
return checkpoints_loaded[checkpoint_info]
res = read_state_dict(checkpoint_info.filename)
+ if shared.opts.sd_checkpoint_cache > 0 and shared.backend == shared.Backend.ORIGINAL:
+ # cache newly loaded model
+ checkpoints_loaded[checkpoint_info] = res
+ # clean up cache if limit is reached
+ while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+ checkpoints_loaded.popitem(last=False)
timer.record("load")
return res
@@ -440,9 +430,6 @@ def load_model_weights(model: torch.nn.Module, checkpoint_info: CheckpointInfo,
return False
del state_dict
timer.record("apply")
- if shared.opts.sd_checkpoint_cache > 0:
- # cache newly loaded model
- checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
if shared.opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
timer.record("channels")
@@ -464,9 +451,6 @@ def load_model_weights(model: torch.nn.Module, checkpoint_info: CheckpointInfo,
else:
model.model.diffusion_model.to(devices.dtype_unet)
model.first_stage_model.to(devices.dtype_vae)
- # clean up cache if limit is reached
- while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
- checkpoints_loaded.popitem(last=False)
model.sd_model_hash = checkpoint_info.calculate_shorthash()
model.sd_model_checkpoint = checkpoint_info.filename
model.sd_checkpoint_info = checkpoint_info
@@ -492,29 +476,30 @@ def enable_midas_autodownload():
This function applies a wrapper to download the model to the correct
location automatically.
"""
+ import ldm.modules.midas.api
midas_path = os.path.join(paths.models_path, 'midas')
- for k, v in midas.api.ISL_PATHS.items():
+ for k, v in ldm.modules.midas.api.ISL_PATHS.items():
file_name = os.path.basename(v)
- midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
+ ldm.modules.midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
midas_urls = {
"dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
"dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
"midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
"midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
}
- midas.api.load_model_inner = midas.api.load_model
+ ldm.modules.midas.api.load_model_inner = ldm.modules.midas.api.load_model
def load_model_wrapper(model_type):
- path = midas.api.ISL_PATHS[model_type]
+ path = ldm.modules.midas.api.ISL_PATHS[model_type]
if not os.path.exists(path):
if not os.path.exists(midas_path):
mkdir(midas_path)
shared.log.info(f"Downloading midas model weights for {model_type} to {path}")
request.urlretrieve(midas_urls[model_type], path)
shared.log.info(f"{model_type} downloaded")
- return midas.api.load_model_inner(model_type)
+ return ldm.modules.midas.api.load_model_inner(model_type)
- midas.api.load_model = load_model_wrapper
+ ldm.modules.midas.api.load_model = load_model_wrapper
def repair_config(sd_config):
@@ -537,13 +522,10 @@ sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_w
def change_backend():
- shared.log.info(f'Backend changed: {shared.backend}')
+ shared.log.info(f'Backend changed: from={shared.backend} to={shared.opts.sd_backend}')
shared.log.warning('Full server restart required to apply all changes')
- if shared.backend == shared.Backend.ORIGINAL:
- change_from = shared.Backend.DIFFUSERS
- else:
- change_from = shared.Backend.ORIGINAL
- unload_model_weights(change_from=change_from)
+ unload_model_weights()
+ shared.backend = shared.Backend.ORIGINAL if shared.opts.sd_backend == 'original' else shared.Backend.DIFFUSERS
checkpoints_loaded.clear()
from modules.sd_samplers import list_samplers
list_samplers(shared.backend)
@@ -865,6 +847,9 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
}
if hasattr(pipeline, 'from_single_file'):
diffusers_load_config['use_safetensors'] = True
+ if shared.opts.disable_accelerate:
+ from diffusers.utils import import_utils
+ import_utils._accelerate_available = False # pylint: disable=protected-access
sd_model = pipeline.from_single_file(checkpoint_info.path, **diffusers_load_config)
if sd_model is not None and hasattr(sd_model, 'unet') and hasattr(sd_model.unet, 'config') and 'inpainting' in checkpoint_info.path.lower():
shared.log.debug('Model patch: type=inpaint')
@@ -898,7 +883,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
set_diffuser_options(sd_model, vae, op)
base_sent_to_cpu=False
- if (shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none') or shared.opts.ipex_optimize:
+ if (shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none') or shared.opts.ipex_optimize or shared.opts.nncf_compress_weights:
if op == 'refiner' and not getattr(sd_model, 'has_accelerate', False):
gpu_vram = memory_stats().get('gpu', {})
free_vram = gpu_vram.get('total', 0) - gpu_vram.get('used', 0)
@@ -1108,7 +1093,10 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None,
current_checkpoint_info = model_data.sd_refiner.sd_checkpoint_info
unload_model_weights(op=op)
- sd_hijack_inpainting.do_inpainting_hijack()
+ if shared.backend == shared.Backend.ORIGINAL:
+ from modules import sd_hijack_inpainting
+ sd_hijack_inpainting.do_inpainting_hijack()
+
devices.set_cuda_params()
if already_loaded_state_dict is not None:
state_dict = already_loaded_state_dict
@@ -1221,7 +1209,7 @@ def reload_model_weights(sd_model=None, info=None, reuse_dict=False, op='model')
unload_model_weights(op=op)
sd_model = None
timer = Timer()
- state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+ state_dict = get_checkpoint_state_dict(checkpoint_info, timer) if shared.backend == shared.Backend.ORIGINAL else None # TODO Revist after Diffusers enables state_dict loading
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
timer.record("config")
if sd_model is None or checkpoint_config != getattr(sd_model, 'used_config', None):
@@ -1283,13 +1271,14 @@ def disable_offload(sd_model):
remove_hook_from_module(model, recurse=True)
-def unload_model_weights(op='model', change_from='none'):
+def unload_model_weights(op='model'):
if shared.compiled_model_state is not None:
shared.compiled_model_state.compiled_cache.clear()
shared.compiled_model_state.partitioned_modules.clear()
+ shared.compiled_model_state = None
if op == 'model' or op == 'dict':
if model_data.sd_model:
- if (shared.backend == shared.Backend.ORIGINAL and change_from != shared.Backend.DIFFUSERS) or change_from == shared.Backend.ORIGINAL:
+ if shared.backend == shared.Backend.ORIGINAL:
from modules import sd_hijack
model_data.sd_model.to(devices.cpu)
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
@@ -1300,7 +1289,7 @@ def unload_model_weights(op='model', change_from='none'):
shared.log.debug(f'Unload weights {op}: {memory_stats()}')
else:
if model_data.sd_refiner:
- if (shared.backend == shared.Backend.ORIGINAL and change_from != shared.Backend.DIFFUSERS) or change_from == shared.Backend.ORIGINAL:
+ if shared.backend == shared.Backend.ORIGINAL:
from modules import sd_hijack
model_data.sd_model.to(devices.cpu)
sd_hijack.model_hijack.undo_hijack(model_data.sd_refiner)
diff --git a/modules/sd_models_compile.py b/modules/sd_models_compile.py
index 9b6d3242e..8fc9faa5c 100644
--- a/modules/sd_models_compile.py
+++ b/modules/sd_models_compile.py
@@ -8,7 +8,8 @@ from installer import setup_logging
#Used by OpenVINO, can be used with TensorRT or Olive
class CompiledModelState:
def __init__(self):
- self.model_str = ""
+ self.is_compiled = False
+ self.model_hash_str = ""
self.first_pass = True
self.first_pass_refiner = True
self.first_pass_vae = True
@@ -18,28 +19,41 @@ class CompiledModelState:
self.partition_id = 0
self.cn_model = []
self.lora_model = []
- self.lora_compile = False
self.compiled_cache = {}
self.partitioned_modules = {}
- self.subgraph_type = []
- self.compile_dont_use_4bit = False
def ipex_optimize(sd_model):
try:
t0 = time.time()
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
- if hasattr(sd_model, 'unet'):
- sd_model.unet.training = False
- sd_model.unet = ipex.optimize(sd_model.unet, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
- else:
- shared.log.warning('IPEX Optimize enabled but model has no Unet')
- if hasattr(sd_model, 'vae'):
- sd_model.vae.training = False
- sd_model.vae = ipex.optimize(sd_model.vae, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
- if hasattr(sd_model, 'movq'):
- sd_model.movq.training = False
- sd_model.movq = ipex.optimize(sd_model.movq, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
+ if "Model" in shared.opts.ipex_optimize:
+ if hasattr(sd_model, 'unet'):
+ sd_model.unet.training = False
+ sd_model.unet = ipex.optimize(sd_model.unet, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
+ elif hasattr(sd_model, 'transformer'):
+ sd_model.transformer.training = False
+ sd_model.transformer = ipex.optimize(sd_model.transformer, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
+ else:
+ shared.log.warning('IPEX Optimize enabled but model has no Unet or Transformer')
+ if "VAE" in shared.opts.ipex_optimize:
+ if hasattr(sd_model, 'vae'):
+ sd_model.vae.training = False
+ sd_model.vae = ipex.optimize(sd_model.vae, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
+ elif hasattr(sd_model, 'movq'):
+ sd_model.movq.training = False
+ sd_model.movq = ipex.optimize(sd_model.movq, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
+ else:
+ shared.log.warning('Compress VAE Weights enabled but model has no VAE')
+ if "Text Encoder" in shared.opts.ipex_optimize:
+ if hasattr(sd_model, 'text_encoder'):
+ sd_model.text_encoder.training = False
+ sd_model.text_encoder = ipex.optimize(sd_model.text_encoder, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
+ if hasattr(sd_model, 'text_encoder_2'):
+ sd_model.text_encoder_2.training = False
+ sd_model.text_encoder_2 = ipex.optimize(sd_model.text_encoder_2, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
+ else:
+ shared.log.warning('IPEX Optimize Text Encoder Weights enabled but model has no Text Encoder')
t1 = time.time()
shared.log.info(f"IPEX Optimize: time={t1-t0:.2f}")
return sd_model
@@ -50,15 +64,30 @@ def nncf_compress_weights(sd_model):
try:
t0 = time.time()
import nncf
- if hasattr(sd_model, 'unet'):
- sd_model.unet = nncf.compress_weights(sd_model.unet)
- else:
- shared.log.warning('Compress Weights enabled but model has no Unet')
- if shared.opts.nncf_compress_vae_weights:
- if hasattr(sd_model, 'vae'):
+ shared.compiled_model_state = CompiledModelState()
+ shared.compiled_model_state.is_compiled = True
+
+ if "Model" in shared.opts.nncf_compress_weights:
+ if hasattr(sd_model, 'unet') and hasattr(sd_model.unet, 'config'):
+ sd_model.unet = nncf.compress_weights(sd_model.unet)
+ elif hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'config'):
+ sd_model.transformer = nncf.compress_weights(sd_model.transformer)
+ else:
+ shared.log.warning('Compress Weights enabled but model has no Unet or Transformer')
+ if "VAE" in shared.opts.nncf_compress_weights:
+ if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'decode'):
sd_model.vae = nncf.compress_weights(sd_model.vae)
- if hasattr(sd_model, 'movq'):
+ elif hasattr(sd_model, 'movq') and hasattr(sd_model.movq, 'decode'):
sd_model.movq = nncf.compress_weights(sd_model.movq)
+ else:
+ shared.log.warning('Compress VAE Weights enabled but model has no VAE')
+ if "Text Encoder" in shared.opts.nncf_compress_weights:
+ if hasattr(sd_model, 'text_encoder') and hasattr(sd_model.text_encoder, 'config'):
+ sd_model.text_encoder = nncf.compress_weights(sd_model.text_encoder)
+ if hasattr(sd_model, 'text_encoder_2') and hasattr(sd_model.text_encoder_2, 'config'):
+ sd_model.text_encoder_2 = nncf.compress_weights(sd_model.text_encoder_2)
+ else:
+ shared.log.warning('Compress VAE Text Encoder Weights enabled but model has no Text Encoder')
t1 = time.time()
shared.log.info(f"Compress Weights: time={t1-t0:.2f}")
return sd_model
@@ -70,16 +99,11 @@ def optimize_openvino():
try:
from modules.intel.openvino import openvino_fx # pylint: disable=unused-import
torch._dynamo.eval_frame.check_if_dynamo_supported = lambda: True # pylint: disable=protected-access
- if shared.compiled_model_state is None:
- shared.compiled_model_state = CompiledModelState()
- else:
+ if shared.compiled_model_state is not None:
shared.compiled_model_state.compiled_cache.clear()
shared.compiled_model_state.partitioned_modules.clear()
- backup_lora_model = []
- if shared.compiled_model_state.lora_compile:
- backup_lora_model = shared.compiled_model_state.lora_model
- shared.compiled_model_state = CompiledModelState()
- shared.compiled_model_state.lora_model = backup_lora_model
+ shared.compiled_model_state = CompiledModelState()
+ shared.compiled_model_state.is_compiled = True
shared.compiled_model_state.first_pass = True if not shared.opts.cuda_compile_precompile else False
shared.compiled_model_state.first_pass_vae = True if not shared.opts.cuda_compile_precompile else False
shared.compiled_model_state.first_pass_refiner = True if not shared.opts.cuda_compile_precompile else False
@@ -149,22 +173,24 @@ def compile_torch(sd_model):
shared.log.error(f"Torch inductor config error: {e}")
t0 = time.time()
- if shared.opts.cuda_compile:
+ if "Model" in shared.opts.cuda_compile:
if hasattr(sd_model, 'unet') and hasattr(sd_model.unet, 'config'):
sd_model.unet = torch.compile(sd_model.unet, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
+ elif hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'config'):
+ sd_model.transformer = torch.compile(sd_model.transformer, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
else:
- shared.log.warning('Model compile enabled but model has no Unet')
- if shared.opts.cuda_compile_vae:
+ shared.log.warning('Model compile enabled but model has no Unet or Transformer')
+ if "VAE" in shared.opts.cuda_compile:
if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'decode'):
sd_model.vae.decode = torch.compile(sd_model.vae.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
elif hasattr(sd_model, 'movq') and hasattr(sd_model.movq, 'decode'):
sd_model.movq.decode = torch.compile(sd_model.movq.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
else:
shared.log.warning('Model compile enabled but model has no VAE')
- if shared.opts.cuda_compile_text_encoder:
- if hasattr(sd_model, 'text_encoder'):
+ if "Text Encoder" in shared.opts.cuda_compile:
+ if hasattr(sd_model, 'text_encoder') and hasattr(sd_model.text_encoder, 'config'):
sd_model.text_encoder = torch.compile(sd_model.text_encoder, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
- if hasattr(sd_model, 'text_encoder_2'):
+ if hasattr(sd_model, 'text_encoder_2') and hasattr(sd_model.text_encoder_2, 'config'):
sd_model.text_encoder_2 = torch.compile(sd_model.text_encoder_2, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
else:
shared.log.warning('Text Encoder compile enabled but model has no Text Encoder')
@@ -183,12 +209,12 @@ def compile_diffusers(sd_model):
sd_model = ipex_optimize(sd_model)
if shared.opts.nncf_compress_weights and not (shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx"):
sd_model = nncf_compress_weights(sd_model)
- if not (shared.opts.cuda_compile or shared.opts.cuda_compile_vae or shared.opts.cuda_compile_upscaler):
+ if not shared.opts.cuda_compile:
return sd_model
if shared.opts.cuda_compile_backend == 'none':
shared.log.warning('Model compile enabled but no backend specified')
return sd_model
- shared.log.info(f"Model compile: pipeline={sd_model.__class__.__name__} mode={shared.opts.cuda_compile_mode} backend={shared.opts.cuda_compile_backend} fullgraph={shared.opts.cuda_compile_fullgraph} unet={shared.opts.cuda_compile} vae={shared.opts.cuda_compile_vae} upscaler={shared.opts.cuda_compile_upscaler}")
+ shared.log.info(f"Model compile: pipeline={sd_model.__class__.__name__} mode={shared.opts.cuda_compile_mode} backend={shared.opts.cuda_compile_backend} fullgraph={shared.opts.cuda_compile_fullgraph} compile={shared.opts.cuda_compile}")
if shared.opts.cuda_compile_backend == 'stable-fast':
sd_model = compile_stablefast(sd_model)
else:
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py
index 3df76caa5..50b8659d8 100644
--- a/modules/sd_models_config.py
+++ b/modules/sd_models_config.py
@@ -1,113 +1,114 @@
-import os
-
-import torch
-
-from modules import paths, sd_disable_initialization, devices
-
-sd_repo_configs_path = 'configs'
-config_default = paths.sd_default_config
-config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference-512-base.yaml")
-config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-768-v.yaml")
-config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
-config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
-config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
-config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
-config_inpainting = os.path.join(paths.sd_configs_path, "v1-inpainting-inference.yaml")
-config_instruct_pix2pix = os.path.join(paths.sd_configs_path, "instruct-pix2pix.yaml")
-config_alt_diffusion = os.path.join(paths.sd_configs_path, "alt-diffusion-inference.yaml")
-
-
-def is_using_v_parameterization_for_sd2(state_dict):
- """
- Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
- """
- import ldm.modules.diffusionmodules.openaimodel
-
- device = devices.cpu
- with sd_disable_initialization.DisableInitialization():
- unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
- use_checkpoint=True,
- use_fp16=False,
- image_size=32,
- in_channels=4,
- out_channels=4,
- model_channels=320,
- attention_resolutions=[4, 2, 1],
- num_res_blocks=2,
- channel_mult=[1, 2, 4, 4],
- num_head_channels=64,
- use_spatial_transformer=True,
- use_linear_in_transformer=True,
- transformer_depth=1,
- context_dim=1024,
- legacy=False
- )
- unet.eval()
-
- with devices.inference_context():
- unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
- unet.load_state_dict(unet_sd, strict=True)
- unet.to(device=device, dtype=torch.float)
-
- test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
- x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
-
- out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
-
- return out < -1
-
-
-def guess_model_config_from_state_dict(sd, _filename):
- if sd is None:
- return None
- sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
- diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
- sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
-
- if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
- return config_depth_model
- elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
- return config_unclip
- elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
- return config_unopenclip
-
- if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
- if diffusion_model_input.shape[1] == 9:
- return config_sd2_inpainting
- elif is_using_v_parameterization_for_sd2(sd):
- return config_sd2v
- else:
- return config_sd2
-
- if diffusion_model_input is not None:
- if diffusion_model_input.shape[1] == 9:
- return config_inpainting
- if diffusion_model_input.shape[1] == 8:
- return config_instruct_pix2pix
-
- if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
- return config_alt_diffusion
-
- return config_default
-
-
-def find_checkpoint_config(state_dict, info):
- if info is None:
- return guess_model_config_from_state_dict(state_dict, "")
-
- config = find_checkpoint_config_near_filename(info)
- if config is not None:
- return config
-
- return guess_model_config_from_state_dict(state_dict, info.filename)
-
-
-def find_checkpoint_config_near_filename(info):
- if info is None:
- return None
-
- config = f"{os.path.splitext(info.filename)[0]}.yaml"
- if os.path.exists(config):
- return config
-
- return None
+import os
+
+import torch
+
+from modules import paths, devices
+
+sd_repo_configs_path = 'configs'
+config_default = paths.sd_default_config
+config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference-512-base.yaml")
+config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-768-v.yaml")
+config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
+config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
+config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
+config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
+config_inpainting = os.path.join(paths.sd_configs_path, "v1-inpainting-inference.yaml")
+config_instruct_pix2pix = os.path.join(paths.sd_configs_path, "instruct-pix2pix.yaml")
+config_alt_diffusion = os.path.join(paths.sd_configs_path, "alt-diffusion-inference.yaml")
+
+
+def is_using_v_parameterization_for_sd2(state_dict):
+ """
+ Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
+ """
+ from modules import sd_disable_initialization
+ import ldm.modules.diffusionmodules.openaimodel
+
+ device = devices.cpu
+ with sd_disable_initialization.DisableInitialization():
+ unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
+ use_checkpoint=True,
+ use_fp16=False,
+ image_size=32,
+ in_channels=4,
+ out_channels=4,
+ model_channels=320,
+ attention_resolutions=[4, 2, 1],
+ num_res_blocks=2,
+ channel_mult=[1, 2, 4, 4],
+ num_head_channels=64,
+ use_spatial_transformer=True,
+ use_linear_in_transformer=True,
+ transformer_depth=1,
+ context_dim=1024,
+ legacy=False
+ )
+ unet.eval()
+
+ with devices.inference_context():
+ unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
+ unet.load_state_dict(unet_sd, strict=True)
+ unet.to(device=device, dtype=torch.float)
+
+ test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
+ x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
+
+ out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
+
+ return out < -1
+
+
+def guess_model_config_from_state_dict(sd, _filename):
+ if sd is None:
+ return None
+ sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
+ diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+ sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
+
+ if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
+ return config_depth_model
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
+ return config_unclip
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
+ return config_unopenclip
+
+ if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
+ if diffusion_model_input.shape[1] == 9:
+ return config_sd2_inpainting
+ elif is_using_v_parameterization_for_sd2(sd):
+ return config_sd2v
+ else:
+ return config_sd2
+
+ if diffusion_model_input is not None:
+ if diffusion_model_input.shape[1] == 9:
+ return config_inpainting
+ if diffusion_model_input.shape[1] == 8:
+ return config_instruct_pix2pix
+
+ if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
+ return config_alt_diffusion
+
+ return config_default
+
+
+def find_checkpoint_config(state_dict, info):
+ if info is None:
+ return guess_model_config_from_state_dict(state_dict, "")
+
+ config = find_checkpoint_config_near_filename(info)
+ if config is not None:
+ return config
+
+ return guess_model_config_from_state_dict(state_dict, info.filename)
+
+
+def find_checkpoint_config_near_filename(info):
+ if info is None:
+ return None
+
+ config = f"{os.path.splitext(info.filename)[0]}.yaml"
+ if os.path.exists(config):
+ return config
+
+ return None
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 891d83eea..806a32e00 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -1,83 +1,84 @@
-import os
-from modules import sd_samplers_compvis, sd_samplers_kdiffusion, sd_samplers_diffusers, shared
-from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # pylint: disable=unused-import
-
-
-debug = shared.log.trace if os.environ.get('SD_SAMPLER_DEBUG', None) is not None else lambda *args, **kwargs: None
-debug('Trace: SAMPLER')
-all_samplers = []
-all_samplers = []
-all_samplers_map = {}
-samplers = all_samplers
-samplers_for_img2img = all_samplers
-samplers_map = {}
-
-
-def list_samplers(backend_name = shared.backend):
- global all_samplers # pylint: disable=global-statement
- global all_samplers_map # pylint: disable=global-statement
- global samplers # pylint: disable=global-statement
- global samplers_for_img2img # pylint: disable=global-statement
- global samplers_map # pylint: disable=global-statement
- if backend_name == shared.Backend.ORIGINAL:
- all_samplers = [*sd_samplers_compvis.samplers_data_compvis, *sd_samplers_kdiffusion.samplers_data_k_diffusion]
- else:
- all_samplers = [*sd_samplers_diffusers.samplers_data_diffusers]
- all_samplers_map = {x.name: x for x in all_samplers}
- samplers = all_samplers
- samplers_for_img2img = all_samplers
- samplers_map = {}
- # shared.log.debug(f'Available samplers: {[x.name for x in all_samplers]}')
-
-
-def find_sampler_config(name):
- if name is not None and name != 'None':
- config = all_samplers_map.get(name, None)
- else:
- config = all_samplers[0]
- return config
-
-
-def visible_sampler_names():
- visible_samplers = [x for x in all_samplers if x.name in shared.opts.show_samplers] if len(shared.opts.show_samplers) > 0 else all_samplers
- return visible_samplers
-
-
-def create_sampler(name, model):
- if name == 'Default' and hasattr(model, 'scheduler'):
- config = {k: v for k, v in model.scheduler.config.items() if not k.startswith('_')}
- shared.log.debug(f'Sampler default {type(model.scheduler).__name__}: {config}')
- return model.scheduler
- config = find_sampler_config(name)
- if config is None:
- shared.log.error(f'Attempting to use unknown sampler: {name}')
- config = all_samplers[0]
- if shared.backend == shared.Backend.ORIGINAL:
- sampler = config.constructor(model)
- sampler.config = config
- sampler.initialize(p=None)
- sampler.name = name
- shared.log.debug(f'Sampler: sampler="{sampler.name}" config={sampler.config.options}')
- return sampler
- elif shared.backend == shared.Backend.DIFFUSERS:
- sampler = config.constructor(model)
- if not hasattr(model, 'scheduler_config'):
- model.scheduler_config = sampler.sampler.config.copy()
- model.scheduler = sampler.sampler
- shared.log.debug(f'Sampler: sampler="{sampler.name}" config={sampler.config}')
- return sampler.sampler
- else:
- return None
-
-
-def set_samplers():
- global samplers # pylint: disable=global-statement
- global samplers_for_img2img # pylint: disable=global-statement
- samplers = visible_sampler_names()
- # samplers_for_img2img = [x for x in samplers if x.name != "PLMS"]
- samplers_for_img2img = samplers
- samplers_map.clear()
- for sampler in all_samplers:
- samplers_map[sampler.name.lower()] = sampler.name
- for alias in sampler.aliases:
- samplers_map[alias.lower()] = sampler.name
+import os
+from modules import shared
+from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # pylint: disable=unused-import
+
+debug = shared.log.trace if os.environ.get('SD_SAMPLER_DEBUG', None) is not None else lambda *args, **kwargs: None
+debug('Trace: SAMPLER')
+all_samplers = []
+all_samplers = []
+all_samplers_map = {}
+samplers = all_samplers
+samplers_for_img2img = all_samplers
+samplers_map = {}
+
+
+def list_samplers(backend_name = shared.backend):
+ global all_samplers # pylint: disable=global-statement
+ global all_samplers_map # pylint: disable=global-statement
+ global samplers # pylint: disable=global-statement
+ global samplers_for_img2img # pylint: disable=global-statement
+ global samplers_map # pylint: disable=global-statement
+ if backend_name == shared.Backend.ORIGINAL:
+ from modules import sd_samplers_compvis, sd_samplers_kdiffusion
+ all_samplers = [*sd_samplers_compvis.samplers_data_compvis, *sd_samplers_kdiffusion.samplers_data_k_diffusion]
+ else:
+ from modules import sd_samplers_diffusers
+ all_samplers = [*sd_samplers_diffusers.samplers_data_diffusers]
+ all_samplers_map = {x.name: x for x in all_samplers}
+ samplers = all_samplers
+ samplers_for_img2img = all_samplers
+ samplers_map = {}
+ # shared.log.debug(f'Available samplers: {[x.name for x in all_samplers]}')
+
+
+def find_sampler_config(name):
+ if name is not None and name != 'None':
+ config = all_samplers_map.get(name, None)
+ else:
+ config = all_samplers[0]
+ return config
+
+
+def visible_sampler_names():
+ visible_samplers = [x for x in all_samplers if x.name in shared.opts.show_samplers] if len(shared.opts.show_samplers) > 0 else all_samplers
+ return visible_samplers
+
+
+def create_sampler(name, model):
+ if name == 'Default' and hasattr(model, 'scheduler'):
+ config = {k: v for k, v in model.scheduler.config.items() if not k.startswith('_')}
+ shared.log.debug(f'Sampler default {type(model.scheduler).__name__}: {config}')
+ return model.scheduler
+ config = find_sampler_config(name)
+ if config is None:
+ shared.log.error(f'Attempting to use unknown sampler: {name}')
+ config = all_samplers[0]
+ if shared.backend == shared.Backend.ORIGINAL:
+ sampler = config.constructor(model)
+ sampler.config = config
+ sampler.initialize(p=None)
+ sampler.name = name
+ shared.log.debug(f'Sampler: sampler="{sampler.name}" config={sampler.config.options}')
+ return sampler
+ elif shared.backend == shared.Backend.DIFFUSERS:
+ sampler = config.constructor(model)
+ if not hasattr(model, 'scheduler_config'):
+ model.scheduler_config = sampler.sampler.config.copy()
+ model.scheduler = sampler.sampler
+ shared.log.debug(f'Sampler: sampler="{sampler.name}" config={sampler.config}')
+ return sampler.sampler
+ else:
+ return None
+
+
+def set_samplers():
+ global samplers # pylint: disable=global-statement
+ global samplers_for_img2img # pylint: disable=global-statement
+ samplers = visible_sampler_names()
+ # samplers_for_img2img = [x for x in samplers if x.name != "PLMS"]
+ samplers_for_img2img = samplers
+ samplers_map.clear()
+ for sampler in all_samplers:
+ samplers_map[sampler.name.lower()] = sampler.name
+ for alias in sampler.aliases:
+ samplers_map[alias.lower()] = sampler.name
diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py
index 8de26b8f3..6685a4d38 100644
--- a/modules/sd_samplers_cfg_denoiser.py
+++ b/modules/sd_samplers_cfg_denoiser.py
@@ -1,233 +1,233 @@
-# TODO a1111 compatibility module
-# TODO cfg_denoiser implementation missing
-
-import torch
-from modules import prompt_parser, devices, sd_samplers_common
-
-from modules.shared import opts, state
-import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
-from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
-
-
-def catenate_conds(conds):
- if not isinstance(conds[0], dict):
- return torch.cat(conds)
-
- return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
-
-
-def subscript_cond(cond, a, b):
- if not isinstance(cond, dict):
- return cond[a:b]
-
- return {key: vec[a:b] for key, vec in cond.items()}
-
-
-def pad_cond(tensor, repeats, empty):
- if not isinstance(tensor, dict):
- return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
-
- tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
- return tensor
-
-
-class CFGDenoiser(torch.nn.Module):
- """
- Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
- that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
- instead of one. Originally, the second prompt is just an empty string, but we use non-empty
- negative prompt.
- """
-
- def __init__(self, sampler):
- super().__init__()
- self.model_wrap = None
- self.mask = None
- self.nmask = None
- self.init_latent = None
- self.steps = None
- """number of steps as specified by user in UI"""
-
- self.total_steps = None
- """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
-
- self.step = 0
- self.image_cfg_scale = None
- self.padded_cond_uncond = False
- self.sampler = sampler
- self.model_wrap = None
- self.p = None
- self.mask_before_denoising = False
-
- @property
- def inner_model(self):
- raise NotImplementedError
-
- def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
- denoised_uncond = x_out[-uncond.shape[0]:]
- denoised = torch.clone(denoised_uncond)
-
- for i, conds in enumerate(conds_list):
- for cond_index, weight in conds:
- denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
- return denoised
-
- def combine_denoised_for_edit_model(self, x_out, cond_scale):
- out_cond, out_img_cond, out_uncond = x_out.chunk(3)
- denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
-
- return denoised
-
- def get_pred_x0(self, x_in, x_out, sigma): # pylint: disable=unused-argument
- return x_out
-
- def update_inner_model(self):
- self.model_wrap = None
-
- c, uc = self.p.get_conds()
- self.sampler.sampler_extra_args['cond'] = c
- self.sampler.sampler_extra_args['uncond'] = uc
-
- def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
- if state.interrupted or state.skipped:
- raise sd_samplers_common.InterruptedException
-
- # TODO cfg_scale implementation missing
- # if sd_samplers_common.apply_refiner(self):
- # cond = self.sampler.sampler_extra_args['cond']
- # uncond = self.sampler.sampler_extra_args['uncond']
-
- # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
- # so is_edit_model is set to False to support AND composition.
- is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
-
- conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
- uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
- assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
-
- if self.mask_before_denoising and self.mask is not None:
- x = self.init_latent * self.mask + self.nmask * x
-
- batch_size = len(conds_list)
- repeats = [len(conds_list[i]) for i in range(batch_size)]
-
- if shared.sd_model.model.conditioning_key == "crossattn-adm":
- image_uncond = torch.zeros_like(image_cond)
- make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm} # pylint: disable=unnecessary-lambda-assignment
- else:
- image_uncond = image_cond
- if isinstance(uncond, dict):
- make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]} # pylint: disable=unnecessary-lambda-assignment
- else:
- make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]} # pylint: disable=unnecessary-lambda-assignment
-
- if not is_edit_model:
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
- else:
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
-
- denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
- cfg_denoiser_callback(denoiser_params)
- x_in = denoiser_params.x
- image_cond_in = denoiser_params.image_cond
- sigma_in = denoiser_params.sigma
- tensor = denoiser_params.text_cond
- uncond = denoiser_params.text_uncond
- skip_uncond = False
-
- # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
- if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
- skip_uncond = True
- x_in = x_in[:-batch_size]
- sigma_in = sigma_in[:-batch_size]
-
- self.padded_cond_uncond = False
- if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
- empty = shared.sd_model.cond_stage_model_empty_prompt
- num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
- if num_repeats < 0:
- tensor = pad_cond(tensor, -num_repeats, empty)
- self.padded_cond_uncond = True
- elif num_repeats > 0:
- uncond = pad_cond(uncond, num_repeats, empty)
- self.padded_cond_uncond = True
-
- if tensor.shape[1] == uncond.shape[1] or skip_uncond:
- if is_edit_model:
- cond_in = catenate_conds([tensor, uncond, uncond])
- elif skip_uncond:
- cond_in = tensor
- else:
- cond_in = catenate_conds([tensor, uncond])
-
- if shared.opts.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
- else:
- x_out = torch.zeros_like(x_in)
- for batch_offset in range(0, x_out.shape[0], batch_size):
- a = batch_offset
- b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
- else:
- x_out = torch.zeros_like(x_in)
- batch_size = batch_size*2 if shared.opts.batch_cond_uncond else batch_size
- for batch_offset in range(0, tensor.shape[0], batch_size):
- a = batch_offset
- b = min(a + batch_size, tensor.shape[0])
-
- if not is_edit_model:
- c_crossattn = subscript_cond(tensor, a, b)
- else:
- c_crossattn = torch.cat([tensor[a:b]], uncond)
-
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
-
- if not skip_uncond:
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
-
- denoised_image_indexes = [x[0][0] for x in conds_list]
- if skip_uncond:
- fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
- x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
-
- denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
- cfg_denoised_callback(denoised_params)
-
- devices.test_for_nans(x_out, "unet")
-
- if is_edit_model:
- denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
- elif skip_uncond:
- denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
- else:
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-
- if not self.mask_before_denoising and self.mask is not None:
- denoised = self.init_latent * self.mask + self.nmask * denoised
-
- self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
-
- if opts.live_preview_content == "Prompt":
- preview = self.sampler.last_latent
- elif opts.live_preview_content == "Negative prompt":
- preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
- else:
- preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
-
- sd_samplers_common.store_latent(preview)
-
- after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
- cfg_after_cfg_callback(after_cfg_callback_params)
- denoised = after_cfg_callback_params.x
-
- self.step += 1
- return denoised
+# TODO a1111 compatibility module
+# TODO cfg_denoiser implementation missing
+
+import torch
+from modules import prompt_parser, devices, sd_samplers_common
+
+from modules.shared import opts, state
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
+
+
+def catenate_conds(conds):
+ if not isinstance(conds[0], dict):
+ return torch.cat(conds)
+
+ return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+
+
+def subscript_cond(cond, a, b):
+ if not isinstance(cond, dict):
+ return cond[a:b]
+
+ return {key: vec[a:b] for key, vec in cond.items()}
+
+
+def pad_cond(tensor, repeats, empty):
+ if not isinstance(tensor, dict):
+ return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+ tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+ return tensor
+
+
+class CFGDenoiser(torch.nn.Module):
+ """
+ Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+ that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+ instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+ negative prompt.
+ """
+
+ def __init__(self, sampler):
+ super().__init__()
+ self.model_wrap = None
+ self.mask = None
+ self.nmask = None
+ self.init_latent = None
+ self.steps = None
+ """number of steps as specified by user in UI"""
+
+ self.total_steps = None
+ """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
+
+ self.step = 0
+ self.image_cfg_scale = None
+ self.padded_cond_uncond = False
+ self.sampler = sampler
+ self.model_wrap = None
+ self.p = None
+ self.mask_before_denoising = False
+
+ @property
+ def inner_model(self):
+ raise NotImplementedError
+
+ def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+ denoised_uncond = x_out[-uncond.shape[0]:]
+ denoised = torch.clone(denoised_uncond)
+
+ for i, conds in enumerate(conds_list):
+ for cond_index, weight in conds:
+ denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+ return denoised
+
+ def combine_denoised_for_edit_model(self, x_out, cond_scale):
+ out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+ denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+
+ return denoised
+
+ def get_pred_x0(self, x_in, x_out, sigma): # pylint: disable=unused-argument
+ return x_out
+
+ def update_inner_model(self):
+ self.model_wrap = None
+
+ c, uc = self.p.get_conds()
+ self.sampler.sampler_extra_args['cond'] = c
+ self.sampler.sampler_extra_args['uncond'] = uc
+
+ def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
+ if state.interrupted or state.skipped:
+ raise sd_samplers_common.InterruptedException
+
+ # TODO cfg_scale implementation missing
+ # if sd_samplers_common.apply_refiner(self):
+ # cond = self.sampler.sampler_extra_args['cond']
+ # uncond = self.sampler.sampler_extra_args['uncond']
+
+ # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+ # so is_edit_model is set to False to support AND composition.
+ is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
+
+ conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+ uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+ assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+
+ if self.mask_before_denoising and self.mask is not None:
+ x = self.init_latent * self.mask + self.nmask * x
+
+ batch_size = len(conds_list)
+ repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+ if shared.sd_model.model.conditioning_key == "crossattn-adm":
+ image_uncond = torch.zeros_like(image_cond)
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm} # pylint: disable=unnecessary-lambda-assignment
+ else:
+ image_uncond = image_cond
+ if isinstance(uncond, dict):
+ make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]} # pylint: disable=unnecessary-lambda-assignment
+ else:
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]} # pylint: disable=unnecessary-lambda-assignment
+
+ if not is_edit_model:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
+ else:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
+
+ denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
+ cfg_denoiser_callback(denoiser_params)
+ x_in = denoiser_params.x
+ image_cond_in = denoiser_params.image_cond
+ sigma_in = denoiser_params.sigma
+ tensor = denoiser_params.text_cond
+ uncond = denoiser_params.text_uncond
+ skip_uncond = False
+
+ # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+ if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+ skip_uncond = True
+ x_in = x_in[:-batch_size]
+ sigma_in = sigma_in[:-batch_size]
+
+ self.padded_cond_uncond = False
+ if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+ empty = shared.sd_model.cond_stage_model_empty_prompt
+ num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+ if num_repeats < 0:
+ tensor = pad_cond(tensor, -num_repeats, empty)
+ self.padded_cond_uncond = True
+ elif num_repeats > 0:
+ uncond = pad_cond(uncond, num_repeats, empty)
+ self.padded_cond_uncond = True
+
+ if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+ if is_edit_model:
+ cond_in = catenate_conds([tensor, uncond, uncond])
+ elif skip_uncond:
+ cond_in = tensor
+ else:
+ cond_in = catenate_conds([tensor, uncond])
+
+ if shared.opts.batch_cond_uncond:
+ x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
+ else:
+ x_out = torch.zeros_like(x_in)
+ for batch_offset in range(0, x_out.shape[0], batch_size):
+ a = batch_offset
+ b = a + batch_size
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
+ else:
+ x_out = torch.zeros_like(x_in)
+ batch_size = batch_size*2 if shared.opts.batch_cond_uncond else batch_size
+ for batch_offset in range(0, tensor.shape[0], batch_size):
+ a = batch_offset
+ b = min(a + batch_size, tensor.shape[0])
+
+ if not is_edit_model:
+ c_crossattn = subscript_cond(tensor, a, b)
+ else:
+ c_crossattn = torch.cat([tensor[a:b]], uncond)
+
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
+
+ if not skip_uncond:
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
+
+ denoised_image_indexes = [x[0][0] for x in conds_list]
+ if skip_uncond:
+ fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+ x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+
+ denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
+ cfg_denoised_callback(denoised_params)
+
+ devices.test_for_nans(x_out, "unet")
+
+ if is_edit_model:
+ denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+ elif skip_uncond:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+ else:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+ if not self.mask_before_denoising and self.mask is not None:
+ denoised = self.init_latent * self.mask + self.nmask * denoised
+
+ self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
+
+ if opts.live_preview_content == "Prompt":
+ preview = self.sampler.last_latent
+ elif opts.live_preview_content == "Negative prompt":
+ preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
+ else:
+ preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
+
+ sd_samplers_common.store_latent(preview)
+
+ after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+ cfg_after_cfg_callback(after_cfg_callback_params)
+ denoised = after_cfg_callback_params.x
+
+ self.step += 1
+ return denoised
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index e749aa378..86c090324 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -1,128 +1,128 @@
-from collections import namedtuple
-import torch
-import torchvision.transforms as T
-from PIL import Image
-from modules import devices, processing, images, sd_vae_approx, sd_samplers, shared
-import modules.taesd.sd_vae_taesd as sd_vae_taesd
-
-
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
-approximation_indexes = { "Simple": 0, "Approximate": 1, "TAESD": 2, "Full VAE": 3 }
-warned = False
-
-
-def warn_once(message):
- global warned # pylint: disable=global-statement
- if not warned:
- shared.log.warning(message)
- warned = True
-
-
-def setup_img2img_steps(p, steps=None):
- if shared.opts.img2img_fix_steps or steps is not None:
- requested_steps = (steps or p.steps)
- steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
- t_enc = requested_steps - 1
- else:
- steps = p.steps
- t_enc = int(min(p.denoising_strength, 0.999) * steps)
-
- return steps, t_enc
-
-
-def single_sample_to_image(sample, approximation=None):
- if approximation is None:
- approximation = approximation_indexes.get(shared.opts.show_progress_type, None)
- if approximation is None:
- warn_once('Unknown decode type, please reset preview method')
- approximation = 0
-
- # normal sample is [4,64,64]
- if sample.dtype == torch.bfloat16:
- sample = sample.to(torch.float16)
- if len(sample.shape) > 4: # likely unknown video latent (e.g. svd)
- return Image.new(mode="RGB", size=(512, 512))
- if len(sample.shape) == 4 and sample.shape[0]: # likely animatediff latent
- sample = sample.permute(1, 0, 2, 3)[0]
- if approximation == 0: # Simple
- x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
- elif approximation == 1: # Approximate
- x_sample = sd_vae_approx.nn_approximation(sample) * 0.5 + 0.5
- if shared.sd_model_type == "sdxl":
- x_sample = x_sample[[2,1,0], :, :] # BGR to RGB
- elif approximation == 2: # TAESD
- x_sample = sd_vae_taesd.decode(sample)
- x_sample = (1.0 + x_sample) / 2.0 # preview requires smaller range
- elif approximation == 3: # Full VAE
- x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
- else:
- warn_once(f"Unknown latent decode type: {approximation}")
- return Image.new(mode="RGB", size=(512, 512))
-
- try:
- if x_sample.dtype == torch.bfloat16:
- x_sample.to(torch.float16)
- transform = T.ToPILImage()
- image = transform(x_sample)
- except Exception as e:
- warn_once(f'Live preview: {e}')
- image = Image.new(mode="RGB", size=(512, 512))
- return image
-
-
-def sample_to_image(samples, index=0, approximation=None):
- return single_sample_to_image(samples[index], approximation)
-
-
-def samples_to_image_grid(samples, approximation=None):
- return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
-
-
-def images_tensor_to_samples(image, approximation=None, model=None):
- '''image[0, 1] -> latent'''
- if approximation is None:
- approximation = approximation_indexes.get(shared.opts.show_progress_type, 0)
- if approximation == 2:
- image = image.to(devices.device, devices.dtype)
- x_latent = sd_vae_taesd.encode(image)
- else:
- if model is None:
- model = shared.sd_model
- model.first_stage_model.to(devices.dtype_vae)
- image = image.to(shared.device, dtype=devices.dtype_vae)
- image = image * 2 - 1
- if len(image) > 1:
- image_latents = [model.get_first_stage_encoding(model.encode_first_stage(torch.unsqueeze(img, 0)))[0] for img in image]
- x_latent = torch.stack(image_latents)
- else:
- x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
- return x_latent
-
-
-def store_latent(decoded):
- shared.state.current_latent = decoded
- if shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % shared.opts.show_progress_every_n_steps == 0:
- if not shared.parallel_processing_allowed:
- image = sample_to_image(decoded)
- shared.state.assign_current_image(image)
-
-
-def is_sampler_using_eta_noise_seed_delta(p):
- """returns whether sampler from config will use eta noise seed delta for image creation"""
- sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
- eta = 0
- if hasattr(p, "eta"):
- eta = p.eta
- if not hasattr(p.sampler, "eta"):
- return False
- if eta is None and p.sampler is not None:
- eta = p.sampler.eta
- if eta is None and sampler_config is not None:
- eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
- if eta == 0:
- return False
- return True
-
-
-class InterruptedException(BaseException):
- pass
+from collections import namedtuple
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from modules import devices, processing, images, sd_vae_approx, sd_samplers, shared
+import modules.taesd.sd_vae_taesd as sd_vae_taesd
+
+
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+approximation_indexes = { "Simple": 0, "Approximate": 1, "TAESD": 2, "Full VAE": 3 }
+warned = False
+
+
+def warn_once(message):
+ global warned # pylint: disable=global-statement
+ if not warned:
+ shared.log.warning(message)
+ warned = True
+
+
+def setup_img2img_steps(p, steps=None):
+ if shared.opts.img2img_fix_steps or steps is not None:
+ requested_steps = (steps or p.steps)
+ steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
+ t_enc = requested_steps - 1
+ else:
+ steps = p.steps
+ t_enc = int(min(p.denoising_strength, 0.999) * steps)
+
+ return steps, t_enc
+
+
+def single_sample_to_image(sample, approximation=None):
+ if approximation is None:
+ approximation = approximation_indexes.get(shared.opts.show_progress_type, None)
+ if approximation is None:
+ warn_once('Unknown decode type, please reset preview method')
+ approximation = 0
+
+ # normal sample is [4,64,64]
+ if sample.dtype == torch.bfloat16:
+ sample = sample.to(torch.float16)
+ if len(sample.shape) > 4: # likely unknown video latent (e.g. svd)
+ return Image.new(mode="RGB", size=(512, 512))
+ if len(sample.shape) == 4 and sample.shape[0]: # likely animatediff latent
+ sample = sample.permute(1, 0, 2, 3)[0]
+ if approximation == 0: # Simple
+ x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
+ elif approximation == 1: # Approximate
+ x_sample = sd_vae_approx.nn_approximation(sample) * 0.5 + 0.5
+ if shared.sd_model_type == "sdxl":
+ x_sample = x_sample[[2,1,0], :, :] # BGR to RGB
+ elif approximation == 2: # TAESD
+ x_sample = sd_vae_taesd.decode(sample)
+ x_sample = (1.0 + x_sample) / 2.0 # preview requires smaller range
+ elif approximation == 3: # Full VAE
+ x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
+ else:
+ warn_once(f"Unknown latent decode type: {approximation}")
+ return Image.new(mode="RGB", size=(512, 512))
+
+ try:
+ if x_sample.dtype == torch.bfloat16:
+ x_sample.to(torch.float16)
+ transform = T.ToPILImage()
+ image = transform(x_sample)
+ except Exception as e:
+ warn_once(f'Live preview: {e}')
+ image = Image.new(mode="RGB", size=(512, 512))
+ return image
+
+
+def sample_to_image(samples, index=0, approximation=None):
+ return single_sample_to_image(samples[index], approximation)
+
+
+def samples_to_image_grid(samples, approximation=None):
+ return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
+
+
+def images_tensor_to_samples(image, approximation=None, model=None):
+ '''image[0, 1] -> latent'''
+ if approximation is None:
+ approximation = approximation_indexes.get(shared.opts.show_progress_type, 0)
+ if approximation == 2:
+ image = image.to(devices.device, devices.dtype)
+ x_latent = sd_vae_taesd.encode(image)
+ else:
+ if model is None:
+ model = shared.sd_model
+ model.first_stage_model.to(devices.dtype_vae)
+ image = image.to(shared.device, dtype=devices.dtype_vae)
+ image = image * 2 - 1
+ if len(image) > 1:
+ image_latents = [model.get_first_stage_encoding(model.encode_first_stage(torch.unsqueeze(img, 0)))[0] for img in image]
+ x_latent = torch.stack(image_latents)
+ else:
+ x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+ return x_latent
+
+
+def store_latent(decoded):
+ shared.state.current_latent = decoded
+ if shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % shared.opts.show_progress_every_n_steps == 0:
+ if not shared.parallel_processing_allowed:
+ image = sample_to_image(decoded)
+ shared.state.assign_current_image(image)
+
+
+def is_sampler_using_eta_noise_seed_delta(p):
+ """returns whether sampler from config will use eta noise seed delta for image creation"""
+ sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
+ eta = 0
+ if hasattr(p, "eta"):
+ eta = p.eta
+ if not hasattr(p.sampler, "eta"):
+ return False
+ if eta is None and p.sampler is not None:
+ eta = p.sampler.eta
+ if eta is None and sampler_config is not None:
+ eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
+ if eta == 0:
+ return False
+ return True
+
+
+class InterruptedException(BaseException):
+ pass
diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py
index c5ec87a1c..6e56dfd75 100644
--- a/modules/sd_samplers_compvis.py
+++ b/modules/sd_samplers_compvis.py
@@ -1,215 +1,215 @@
-import math
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-import numpy as np
-import torch
-from modules import sd_samplers_common, prompt_parser, shared
-import modules.unipc
-
-
-samplers_data_compvis = [
- sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.unipc.UniPCSampler, model), [], {}),
- sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True}),
- sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
-]
-
-
-class VanillaStableDiffusionSampler:
- def __init__(self, constructor, sd_model):
- self.sampler = constructor(sd_model)
- self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')
- self.is_plms = hasattr(self.sampler, 'p_sample_plms')
- self.is_unipc = isinstance(self.sampler, modules.unipc.UniPCSampler)
- self.orig_p_sample_ddim = None
- if self.is_plms:
- self.orig_p_sample_ddim = self.sampler.p_sample_plms
- elif self.is_ddim:
- self.orig_p_sample_ddim = self.sampler.p_sample_ddim
- self.mask = None
- self.nmask = None
- self.init_latent = None
- self.sampler_noises = None
- self.step = 0
- self.stop_at = None
- self.eta = None
- self.config = None
- self.last_latent = None
- self.conditioning_key = sd_model.model.conditioning_key
-
- def number_of_needed_noises(self, p): # pylint: disable=unused-argument
- return 0
-
- def launch_sampling(self, steps, func):
- shared.state.sampling_steps = steps
- shared.state.sampling_step = 0
- try:
- return func()
- except sd_samplers_common.InterruptedException:
- return self.last_latent
-
- def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
- x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
- res = self.orig_p_sample_ddim(x_dec, cond, ts, *args, unconditional_conditioning=unconditional_conditioning, **kwargs)
- x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
- return res
-
- def before_sample(self, x, ts, cond, unconditional_conditioning):
- if shared.state.interrupted or shared.state.skipped:
- raise sd_samplers_common.InterruptedException
- if shared.state.paused:
- shared.log.debug('Sampling paused')
- while shared.state.paused:
- if shared.state.interrupted or shared.state.skipped:
- raise sd_samplers_common.InterruptedException
- import time
- time.sleep(0.1)
-
- if self.stop_at is not None and self.step > self.stop_at:
- raise sd_samplers_common.InterruptedException
-
- # Have to unwrap the inpainting conditioning here to perform pre-processing
- image_conditioning = None
- uc_image_conditioning = None
- if isinstance(cond, dict):
- if self.conditioning_key == "crossattn-adm":
- image_conditioning = cond["c_adm"]
- uc_image_conditioning = unconditional_conditioning["c_adm"]
- else:
- image_conditioning = cond["c_concat"][0]
- cond = cond["c_crossattn"][0]
- unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
-
- conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
- unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
-
- assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers'
- cond = tensor
-
- # for DDIM, shapes must match, we can't just process cond and uncond independently;
- # filling unconditional_conditioning with repeats of the last vector to match length is
- # not 100% correct but should work well enough
- if unconditional_conditioning.shape[1] < cond.shape[1]:
- last_vector = unconditional_conditioning[:, -1:]
- last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
- unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
- elif unconditional_conditioning.shape[1] > cond.shape[1]:
- unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
-
- if self.mask is not None:
- encode_fn = self.sampler.model.q_sample
- if self.is_unipc:
- encode_fn = self.sampler.stochastic_encode
- img_orig = encode_fn(self.init_latent, ts)
- x = img_orig * self.mask + self.nmask * x
-
- # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
- # Note that they need to be lists because it just concatenates them later.
- if image_conditioning is not None:
- if self.conditioning_key == "crossattn-adm":
- cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
- unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
- else:
- cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
- return x, ts, cond, unconditional_conditioning
-
- def update_step(self, last_latent):
- self.last_latent = self.init_latent * self.mask + self.nmask * last_latent if self.mask is not None else last_latent
- sd_samplers_common.store_latent(self.last_latent)
- self.step += 1
- shared.state.sampling_step = self.step
-
- def after_sample(self, x, ts, cond, uncond, res):
- if not self.is_unipc:
- self.update_step(res[1])
- return x, ts, cond, uncond, res
-
- def unipc_after_update(self, x, model_x): # pylint: disable=unused-argument
- self.update_step(x)
-
- def initialize(self, p):
- if p is not None:
- if self.is_ddim:
- self.eta = p.eta if p.eta is not None else shared.opts.scheduler_eta
- else:
- self.eta = 0.0
- if self.eta != 0.0:
- p.extra_generation_params["Sampler Eta"] = self.eta
- if self.is_unipc:
- keys = [
- ('Solver order', 'schedulers_solver_order'),
- ('Sampler low order', 'schedulers_use_loworder'),
- ('UniPC variant', 'uni_pc_variant'),
- ('UniPC skip type', 'uni_pc_skip_type'),
- ]
- for name, key in keys:
- v = getattr(shared.opts, key)
- if v != shared.opts.get_default(key):
- p.extra_generation_params[name] = v
- for fieldname in ['p_sample_ddim', 'p_sample_plms']:
- if hasattr(self.sampler, fieldname):
- setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
- if self.is_unipc:
- self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))
-
- self.mask = p.mask if hasattr(p, 'mask') else None
- self.nmask = p.nmask if hasattr(p, 'nmask') else None
-
-
- def adjust_steps_if_invalid(self, p, num_steps):
- if ((self.config.name == 'DDIM') and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS') or (self.config.name == 'UniPC'):
- if self.config.name == 'UniPC' and num_steps < shared.opts.schedulers_solver_order:
- num_steps = shared.opts.schedulers_solver_order
- valid_step = 999 / (1000 // num_steps)
- if valid_step == math.floor(valid_step):
- return min(int(valid_step) + 1, num_steps)
-
- return num_steps
-
- def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
- steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
- steps = self.adjust_steps_if_invalid(p, steps)
- self.initialize(p)
-
- self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
- x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
-
- self.init_latent = x
- self.last_latent = x
- self.step = 0
-
- # Wrap the conditioning models with additional image conditioning for inpainting model
- if image_conditioning is not None:
- if self.conditioning_key == "crossattn-adm":
- conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
- unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
- else:
- conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
- samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
-
- return samples
-
- def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
- self.initialize(p)
-
- self.init_latent = None
- self.last_latent = x
- self.step = 0
-
- steps = self.adjust_steps_if_invalid(p, steps or p.steps)
-
- # Wrap the conditioning models with additional image conditioning for inpainting model
- # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
- if image_conditioning is not None:
- if self.conditioning_key == "crossattn-adm":
- conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
- unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
- else:
- conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
- unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
-
- samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
-
- return samples_ddim
+import math
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+import numpy as np
+import torch
+from modules import sd_samplers_common, prompt_parser, shared
+import modules.unipc
+
+
+samplers_data_compvis = [
+ sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.unipc.UniPCSampler, model), [], {}),
+ sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True}),
+ sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
+]
+
+
+class VanillaStableDiffusionSampler:
+ def __init__(self, constructor, sd_model):
+ self.sampler = constructor(sd_model)
+ self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')
+ self.is_plms = hasattr(self.sampler, 'p_sample_plms')
+ self.is_unipc = isinstance(self.sampler, modules.unipc.UniPCSampler)
+ self.orig_p_sample_ddim = None
+ if self.is_plms:
+ self.orig_p_sample_ddim = self.sampler.p_sample_plms
+ elif self.is_ddim:
+ self.orig_p_sample_ddim = self.sampler.p_sample_ddim
+ self.mask = None
+ self.nmask = None
+ self.init_latent = None
+ self.sampler_noises = None
+ self.step = 0
+ self.stop_at = None
+ self.eta = None
+ self.config = None
+ self.last_latent = None
+ self.conditioning_key = sd_model.model.conditioning_key
+
+ def number_of_needed_noises(self, p): # pylint: disable=unused-argument
+ return 0
+
+ def launch_sampling(self, steps, func):
+ shared.state.sampling_steps = steps
+ shared.state.sampling_step = 0
+ try:
+ return func()
+ except sd_samplers_common.InterruptedException:
+ return self.last_latent
+
+ def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+ x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
+ res = self.orig_p_sample_ddim(x_dec, cond, ts, *args, unconditional_conditioning=unconditional_conditioning, **kwargs)
+ x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
+ return res
+
+ def before_sample(self, x, ts, cond, unconditional_conditioning):
+ if shared.state.interrupted or shared.state.skipped:
+ raise sd_samplers_common.InterruptedException
+ if shared.state.paused:
+ shared.log.debug('Sampling paused')
+ while shared.state.paused:
+ if shared.state.interrupted or shared.state.skipped:
+ raise sd_samplers_common.InterruptedException
+ import time
+ time.sleep(0.1)
+
+ if self.stop_at is not None and self.step > self.stop_at:
+ raise sd_samplers_common.InterruptedException
+
+ # Have to unwrap the inpainting conditioning here to perform pre-processing
+ image_conditioning = None
+ uc_image_conditioning = None
+ if isinstance(cond, dict):
+ if self.conditioning_key == "crossattn-adm":
+ image_conditioning = cond["c_adm"]
+ uc_image_conditioning = unconditional_conditioning["c_adm"]
+ else:
+ image_conditioning = cond["c_concat"][0]
+ cond = cond["c_crossattn"][0]
+ unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
+
+ conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+ unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
+
+ assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers'
+ cond = tensor
+
+ # for DDIM, shapes must match, we can't just process cond and uncond independently;
+ # filling unconditional_conditioning with repeats of the last vector to match length is
+ # not 100% correct but should work well enough
+ if unconditional_conditioning.shape[1] < cond.shape[1]:
+ last_vector = unconditional_conditioning[:, -1:]
+ last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
+ unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
+ elif unconditional_conditioning.shape[1] > cond.shape[1]:
+ unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
+
+ if self.mask is not None:
+ encode_fn = self.sampler.model.q_sample
+ if self.is_unipc:
+ encode_fn = self.sampler.stochastic_encode
+ img_orig = encode_fn(self.init_latent, ts)
+ x = img_orig * self.mask + self.nmask * x
+
+ # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
+ # Note that they need to be lists because it just concatenates them later.
+ if image_conditioning is not None:
+ if self.conditioning_key == "crossattn-adm":
+ cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
+ else:
+ cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+ return x, ts, cond, unconditional_conditioning
+
+ def update_step(self, last_latent):
+ self.last_latent = self.init_latent * self.mask + self.nmask * last_latent if self.mask is not None else last_latent
+ sd_samplers_common.store_latent(self.last_latent)
+ self.step += 1
+ shared.state.sampling_step = self.step
+
+ def after_sample(self, x, ts, cond, uncond, res):
+ if not self.is_unipc:
+ self.update_step(res[1])
+ return x, ts, cond, uncond, res
+
+ def unipc_after_update(self, x, model_x): # pylint: disable=unused-argument
+ self.update_step(x)
+
+ def initialize(self, p):
+ if p is not None:
+ if self.is_ddim:
+ self.eta = p.eta if p.eta is not None else shared.opts.scheduler_eta
+ else:
+ self.eta = 0.0
+ if self.eta != 0.0:
+ p.extra_generation_params["Sampler Eta"] = self.eta
+ if self.is_unipc:
+ keys = [
+ ('Solver order', 'schedulers_solver_order'),
+ ('Sampler low order', 'schedulers_use_loworder'),
+ ('UniPC variant', 'uni_pc_variant'),
+ ('UniPC skip type', 'uni_pc_skip_type'),
+ ]
+ for name, key in keys:
+ v = getattr(shared.opts, key)
+ if v != shared.opts.get_default(key):
+ p.extra_generation_params[name] = v
+ for fieldname in ['p_sample_ddim', 'p_sample_plms']:
+ if hasattr(self.sampler, fieldname):
+ setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
+ if self.is_unipc:
+ self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))
+
+ self.mask = p.mask if hasattr(p, 'mask') else None
+ self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
+
+ def adjust_steps_if_invalid(self, p, num_steps):
+ if ((self.config.name == 'DDIM') and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS') or (self.config.name == 'UniPC'):
+ if self.config.name == 'UniPC' and num_steps < shared.opts.schedulers_solver_order:
+ num_steps = shared.opts.schedulers_solver_order
+ valid_step = 999 / (1000 // num_steps)
+ if valid_step == math.floor(valid_step):
+ return min(int(valid_step) + 1, num_steps)
+
+ return num_steps
+
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+ steps = self.adjust_steps_if_invalid(p, steps)
+ self.initialize(p)
+
+ self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
+ x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
+
+ self.init_latent = x
+ self.last_latent = x
+ self.step = 0
+
+ # Wrap the conditioning models with additional image conditioning for inpainting model
+ if image_conditioning is not None:
+ if self.conditioning_key == "crossattn-adm":
+ conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
+ else:
+ conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+ samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
+
+ return samples
+
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ self.initialize(p)
+
+ self.init_latent = None
+ self.last_latent = x
+ self.step = 0
+
+ steps = self.adjust_steps_if_invalid(p, steps or p.steps)
+
+ # Wrap the conditioning models with additional image conditioning for inpainting model
+ # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
+ if image_conditioning is not None:
+ if self.conditioning_key == "crossattn-adm":
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
+ else:
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
+
+ samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+
+ return samples_ddim
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 3210edb09..c26f0753e 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -1,385 +1,393 @@
-import sys
-import time
-import inspect
-from collections import deque
-import torch
-from modules import prompt_parser
-from modules import devices
-from modules import sd_samplers_common
-import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
-from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
-
-
-# deal with k-diffusion imports
-k_sampling = None
-try:
- import k_diffusion # pylint: disable=wrong-import-order
- k_sampling = k_diffusion.sampling
-except ImportError:
- pass
-try:
- if k_sampling is None:
- import importlib
- k_diffusion = importlib.import_module('modules.k-diffusion.k_diffusion')
- k_sampling = k_diffusion.sampling
-except Exception:
- pass
-if k_sampling is None:
- shared.log.info(f'Path search: {sys.path}')
- shared.log.error("Module not found: k-diffusion")
- sys.exit(1)
-
-
-samplers_k_diffusion = [
- ('Euler', 'sample_euler', ['k_euler'], {"scheduler": "default"}),
- ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"scheduler": "default", "brownian_noise": False}),
- ('Heun', 'sample_heun', ['k_heun'], {"scheduler": "default"}),
- ('LMS', 'sample_lms', ['k_lms'], {"scheduler": "default"}),
- ('DPM Adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"scheduler": "default", "brownian_noise": False}),
- ('DPM Fast', 'sample_dpm_fast', ['k_dpm_fast'], {"scheduler": "default", "brownian_noise": False}),
- ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True, "scheduler": "default", "brownian_noise": False}),
- ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "second_order": True, "scheduler": "default", "brownian_noise": False}),
- ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {"scheduler": "default", "brownian_noise": False}),
- ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde'], {'discard_next_to_last_sigma': True, "scheduler": "default", "brownian_noise": False}),
- ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"solver_type": "heun", "scheduler": "default", "brownian_noise": False}),
- ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"second_order": True, "scheduler": "default", "brownian_noise": False}),
- ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "scheduler": "default", "brownian_noise": False}),
- ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "scheduler": "default", "brownian_noise": False}),
-]
-
-samplers_data_k_diffusion = [
- sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
- for label, funcname, aliases, options in samplers_k_diffusion
- if hasattr(k_sampling, funcname)
-]
-
-sampler_extra_params = {
- 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
- 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
- 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-}
-
-
-class CFGDenoiser(torch.nn.Module):
- """
- Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
- that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
- instead of one. Originally, the second prompt is just an empty string, but we use non-empty
- negative prompt.
- """
- def __init__(self, model):
- super().__init__()
- self.inner_model = model
- self.mask = None
- self.nmask = None
- self.init_latent = None
- self.step = 0
- self.image_cfg_scale = None
-
- def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
- denoised_uncond = x_out[-uncond.shape[0]:]
- denoised = torch.clone(denoised_uncond)
- for i, conds in enumerate(conds_list):
- for cond_index, weight in conds:
- denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
- return denoised
-
- def combine_denoised_for_edit_model(self, x_out, cond_scale):
- out_cond, out_img_cond, out_uncond = x_out.chunk(3)
- denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
- return denoised
-
- def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
- if shared.state.interrupted or shared.state.skipped:
- raise sd_samplers_common.InterruptedException
- if shared.state.paused:
- shared.log.debug('Sampling paused')
- while shared.state.paused:
- if shared.state.interrupted or shared.state.skipped:
- raise sd_samplers_common.InterruptedException
- time.sleep(0.1)
- # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
- # so is_edit_model is set to False to support AND composition.
- is_edit_model = (shared.sd_model is not None) and hasattr(shared.sd_model, 'cond_stage_key') and (shared.sd_model.cond_stage_key == "edit") and (self.image_cfg_scale is not None) and (self.image_cfg_scale != 1.0)
- conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
- uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
- assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
- batch_size = len(conds_list)
- repeats = [len(conds_list[i]) for i in range(batch_size)]
- if shared.sd_model.model.conditioning_key == "crossattn-adm":
- image_uncond = torch.zeros_like(image_cond)
- make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} # pylint: disable=C3001
- else:
- image_uncond = image_cond
- make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} # pylint: disable=C3001
- if not is_edit_model:
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
- else:
- x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
- sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
- denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, shared.state.sampling_step, shared.state.sampling_steps, tensor, uncond)
- cfg_denoiser_callback(denoiser_params)
- x_in = denoiser_params.x
- image_cond_in = denoiser_params.image_cond
- sigma_in = denoiser_params.sigma
- tensor = denoiser_params.text_cond
- uncond = denoiser_params.text_uncond
- skip_uncond = False
- # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
- if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
- skip_uncond = True
- x_in = x_in[:-batch_size]
- sigma_in = sigma_in[:-batch_size]
-
- if tensor.shape[1] == uncond.shape[1] or skip_uncond:
- if is_edit_model:
- cond_in = torch.cat([tensor, uncond, uncond])
- elif skip_uncond:
- cond_in = tensor
- else:
- cond_in = torch.cat([tensor, uncond])
- """
- adjusted_cond_scale = cond_scale # Adjusted cond_scale for uncond
- last_uncond_steps = max(0, state.sampling_steps - 2) # Determine the last two steps before uncond stops
- if self.step >= last_uncond_steps: # Check if we're in the last two steps before uncond stops
- adjusted_cond_scale *= 1.5 # Apply uncond with 150% cond_scale
- else:
- if (self.step - last_uncond_steps) % 3 == 0: # Check if it's one of every three steps after uncond stops
- adjusted_cond_scale *= 1.5 # Apply uncond with 150% cond_scale
- """
- if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
- else:
- x_out = torch.zeros_like(x_in)
- for batch_offset in range(0, x_out.shape[0], batch_size):
- a = batch_offset
- b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
- else:
- x_out = torch.zeros_like(x_in)
- batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
- for batch_offset in range(0, tensor.shape[0], batch_size):
- a = batch_offset
- b = min(a + batch_size, tensor.shape[0])
- if not is_edit_model:
- c_crossattn = [tensor[a:b]]
- else:
- c_crossattn = torch.cat([tensor[a:b]], uncond)
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
- if not skip_uncond:
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
- denoised_image_indexes = [x[0][0] for x in conds_list]
- if skip_uncond:
- fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
- x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
- denoised_params = CFGDenoisedParams(x_out, shared.state.sampling_step, shared.state.sampling_steps, self.inner_model)
- cfg_denoised_callback(denoised_params)
- devices.test_for_nans(x_out, "unet")
- if shared.opts.live_preview_content == "Prompt":
- sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
- elif shared.opts.live_preview_content == "Negative prompt":
- sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
- if is_edit_model:
- denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
- elif skip_uncond:
- denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
- else:
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
- if self.mask is not None:
- if devices.backend == "directml":
- self.init_latent = self.init_latent.float()
- denoised = self.init_latent * self.mask + self.nmask * denoised
- self.init_latent = self.init_latent.half()
- else:
- denoised = self.init_latent * self.mask + self.nmask * denoised
- after_cfg_callback_params = AfterCFGCallbackParams(denoised, shared.state.sampling_step, shared.state.sampling_steps)
- cfg_after_cfg_callback(after_cfg_callback_params)
- denoised = after_cfg_callback_params.x
- self.step += 1
- return denoised
-
-
-class TorchHijack:
- def __init__(self, sampler_noises):
- # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
- # implementation.
- self.sampler_noises = deque(sampler_noises)
-
- def __getattr__(self, item):
- if item == 'randn_like':
- return self.randn_like
- if hasattr(torch, item):
- return getattr(torch, item)
- raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
- def randn_like(self, x):
- if self.sampler_noises:
- noise = self.sampler_noises.popleft()
- if noise.shape == x.shape:
- return noise
- if x.device.type == 'mps':
- return torch.randn_like(x, device=devices.cpu).to(x.device)
- else:
- return torch.randn_like(x)
-
-
-class KDiffusionSampler:
- def __init__(self, funcname, sd_model):
- denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
- self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
- self.funcname = funcname
- self.func = getattr(k_sampling, self.funcname)
- self.extra_params = sampler_extra_params.get(funcname, [])
- self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
- self.sampler_noises = None
- self.stop_at = None
- self.eta = None
- self.config = None # set by the function calling the constructor
- self.last_latent = None
- self.s_min_uncond = None
- self.conditioning_key = sd_model.model.conditioning_key
-
- def callback_state(self, d):
- step = d['i']
- latent = d["denoised"]
- if shared.opts.live_preview_content == "Combined":
- sd_samplers_common.store_latent(latent)
- self.last_latent = latent
- if self.stop_at is not None and step > self.stop_at:
- raise sd_samplers_common.InterruptedException
- shared.state.sampling_step = step
-
- def launch_sampling(self, steps, func):
- shared.state.sampling_steps = steps
- shared.state.sampling_step = 0
- try:
- return func()
- except sd_samplers_common.InterruptedException:
- return self.last_latent
-
- def number_of_needed_noises(self, p):
- return p.steps
-
- def initialize(self, p):
- if self.config.options.get('brownian_noise', None) is not None:
- self.config.options['brownian_noise'] = shared.opts.data.get('schedulers_brownian_noise', False)
- if self.config.options.get('scheduler', None) is not None:
- self.config.options['scheduler'] = shared.opts.data.get('schedulers_sigma', None)
- if p is None:
- return {}
- self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
- self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
- self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
- self.eta = p.eta if p.eta is not None else shared.opts.scheduler_eta
- self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
- k_sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
- extra_params_kwargs = {}
- for param_name in self.extra_params:
- if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
- extra_params_kwargs[param_name] = getattr(p, param_name)
- if 'eta' in inspect.signature(self.func).parameters:
- if self.eta != 1.0:
- p.extra_generation_params["Sampler Eta"] = self.eta
- extra_params_kwargs['eta'] = self.eta
- return extra_params_kwargs
-
- def get_sigmas(self, p, steps): # pylint: disable=unused-argument
- discard_next_to_last_sigma = shared.opts.data.get('schedulers_discard_penultimate', True) if self.config.options.get('discard_next_to_last_sigma', None) is not None else False
- steps += 1 if discard_next_to_last_sigma else 0
- if self.config.options.get('scheduler', None) == 'default' or self.config.options.get('scheduler', None) is None:
- sigmas = self.model_wrap.get_sigmas(steps)
- elif self.config.options.get('scheduler', None) == 'karras':
- sigma_min = p.s_min if p.s_min > 0 else self.model_wrap.sigmas[0].item()
- sigma_max = p.s_max if p.s_max > 0 else self.model_wrap.sigmas[-1].item()
- sigmas = k_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
- elif self.config.options.get('scheduler', None) == 'exponential':
- sigma_min = p.s_min if p.s_min > 0 else self.model_wrap.sigmas[0].item()
- sigma_max = p.s_max if p.s_max > 0 else self.model_wrap.sigmas[-1].item()
- sigmas = k_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
- elif self.config.options.get('scheduler', None) == 'polyexponential':
- sigma_min = p.s_min if p.s_min > 0 else self.model_wrap.sigmas[0].item()
- sigma_max = p.s_max if p.s_max > 0 else self.model_wrap.sigmas[-1].item()
- sigmas = k_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
- elif self.config.options.get('scheduler', None) == 'vp':
- sigmas = k_sampling.get_sigmas_vp(n=steps, device=shared.device)
- if discard_next_to_last_sigma:
- sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
- return sigmas
-
- def create_noise_sampler(self, x, sigmas, p):
- """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
- if shared.opts.no_dpmpp_sde_batch_determinism:
- return None
- positive_sigmas = sigmas[sigmas > 0]
- if positive_sigmas.numel() > 0:
- sigma_min = positive_sigmas.min(dim=0)[0]
- else:
- sigma_min = 0
- sigma_max = sigmas.max()
- current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
- return k_sampling.BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
-
- def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
- steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
- sigmas = self.get_sigmas(p, steps)
- sigma_sched = sigmas[steps - t_enc - 1:]
- xi = x + noise * sigma_sched[0]
- extra_params_kwargs = self.initialize(p)
- parameters = inspect.signature(self.func).parameters
- if 'sigma_min' in parameters:
- ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
- extra_params_kwargs['sigma_min'] = sigma_sched[-2]
- if 'sigma_max' in parameters:
- extra_params_kwargs['sigma_max'] = sigma_sched[0]
- if 'n' in parameters:
- extra_params_kwargs['n'] = len(sigma_sched) - 1
- if 'sigma_sched' in parameters:
- extra_params_kwargs['sigma_sched'] = sigma_sched
- if 'sigmas' in parameters:
- extra_params_kwargs['sigmas'] = sigma_sched
- if self.config.options.get('brownian_noise', False) and 'noise_sampler' in parameters:
- noise_sampler = self.create_noise_sampler(x, sigmas, p)
- extra_params_kwargs['noise_sampler'] = noise_sampler
- self.model_wrap_cfg.init_latent = x
- self.last_latent = x
- extra_args = {
- 'cond': conditioning,
- 'image_cond': image_conditioning,
- 'uncond': unconditional_conditioning,
- 'cond_scale': p.cfg_scale,
- 's_min_uncond': self.s_min_uncond
- }
- samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- samples = samples.type(devices.dtype)
- return samples
-
- def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
- steps = steps or p.steps
- sigmas = self.get_sigmas(p, steps)
- x = x * sigmas[0]
- extra_params_kwargs = self.initialize(p)
- parameters = inspect.signature(self.func).parameters
- if 'sigma_min' in parameters:
- extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
- extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
- if 'n' in parameters:
- extra_params_kwargs['n'] = steps
- else:
- extra_params_kwargs['sigmas'] = sigmas
- if self.config.options.get('brownian_noise', False) and 'noise_sampler' in parameters:
- noise_sampler = self.create_noise_sampler(x, sigmas, p)
- extra_params_kwargs['noise_sampler'] = noise_sampler
- self.last_latent = x
- samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
- 'cond': conditioning,
- 'image_cond': image_conditioning,
- 'uncond': unconditional_conditioning,
- 'cond_scale': p.cfg_scale,
- 's_min_uncond': self.s_min_uncond
- }, disable=False, callback=self.callback_state, **extra_params_kwargs))
- return samples
+import sys
+import time
+import inspect
+from collections import deque
+import torch
+from modules import prompt_parser
+from modules import devices
+from modules import sd_samplers_common
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
+from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
+
+
+# deal with k-diffusion imports
+k_sampling = None
+try:
+ import k_diffusion # pylint: disable=wrong-import-order
+ k_sampling = k_diffusion.sampling
+except ImportError:
+ pass
+try:
+ if k_sampling is None:
+ import importlib
+ k_diffusion = importlib.import_module('modules.k-diffusion.k_diffusion')
+ k_sampling = k_diffusion.sampling
+except Exception:
+ pass
+if k_sampling is None:
+ shared.log.info(f'Path search: {sys.path}')
+ shared.log.error("Module not found: k-diffusion")
+ sys.exit(1)
+
+
+samplers_k_diffusion = [
+ ('Euler', 'sample_euler', ['k_euler'], {"scheduler": "default"}),
+ ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"scheduler": "default", "brownian_noise": False}),
+ ('Heun', 'sample_heun', ['k_heun'], {"scheduler": "default"}),
+ ('LMS', 'sample_lms', ['k_lms'], {"scheduler": "default"}),
+ ('DPM Adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"scheduler": "default", "brownian_noise": False}),
+ ('DPM Fast', 'sample_dpm_fast', ['k_dpm_fast'], {"scheduler": "default", "brownian_noise": False}),
+ ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True, "scheduler": "default", "brownian_noise": False}),
+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "second_order": True, "scheduler": "default", "brownian_noise": False}),
+ ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {"scheduler": "default", "brownian_noise": False}),
+ ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde'], {'discard_next_to_last_sigma': True, "scheduler": "default", "brownian_noise": False}),
+ ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"solver_type": "heun", "scheduler": "default", "brownian_noise": False}),
+ ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"second_order": True, "scheduler": "default", "brownian_noise": False}),
+ ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "scheduler": "default", "brownian_noise": False}),
+ ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "scheduler": "default", "brownian_noise": False}),
+]
+
+samplers_data_k_diffusion = [
+ sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+ for label, funcname, aliases, options in samplers_k_diffusion
+ if hasattr(k_sampling, funcname)
+]
+
+sampler_extra_params = {
+ 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+}
+
+
+class CFGDenoiser(torch.nn.Module):
+ """
+ Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+ that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+ instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+ negative prompt.
+ """
+ def __init__(self, model):
+ super().__init__()
+ self.inner_model = model
+ self.mask = None
+ self.nmask = None
+ self.init_latent = None
+ self.step = 0
+ self.image_cfg_scale = None
+
+ def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+ denoised_uncond = x_out[-uncond.shape[0]:]
+ denoised = torch.clone(denoised_uncond)
+ for i, conds in enumerate(conds_list):
+ for cond_index, weight in conds:
+ denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+ return denoised
+
+ def combine_denoised_for_edit_model(self, x_out, cond_scale):
+ out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+ denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+ return denoised
+
+ def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
+ if shared.state.interrupted or shared.state.skipped:
+ raise sd_samplers_common.InterruptedException
+ if shared.state.paused:
+ shared.log.debug('Sampling paused')
+ while shared.state.paused:
+ if shared.state.interrupted or shared.state.skipped:
+ raise sd_samplers_common.InterruptedException
+ time.sleep(0.1)
+ # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+ # so is_edit_model is set to False to support AND composition.
+ is_edit_model = (shared.sd_model is not None) and hasattr(shared.sd_model, 'cond_stage_key') and (shared.sd_model.cond_stage_key == "edit") and (self.image_cfg_scale is not None) and (self.image_cfg_scale != 1.0)
+ conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+ uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+ assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+ batch_size = len(conds_list)
+ repeats = [len(conds_list[i]) for i in range(batch_size)]
+ if shared.sd_model.model.conditioning_key == "crossattn-adm":
+ image_uncond = torch.zeros_like(image_cond)
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} # pylint: disable=C3001
+ else:
+ image_uncond = image_cond
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} # pylint: disable=C3001
+ if not is_edit_model:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
+ else:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
+ denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, shared.state.sampling_step, shared.state.sampling_steps, tensor, uncond)
+ cfg_denoiser_callback(denoiser_params)
+ x_in = denoiser_params.x
+ image_cond_in = denoiser_params.image_cond
+ sigma_in = denoiser_params.sigma
+ tensor = denoiser_params.text_cond
+ uncond = denoiser_params.text_uncond
+ skip_uncond = False
+ # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+ if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+ skip_uncond = True
+ x_in = x_in[:-batch_size]
+ sigma_in = sigma_in[:-batch_size]
+
+ if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+ if is_edit_model:
+ cond_in = torch.cat([tensor, uncond, uncond])
+ elif skip_uncond:
+ cond_in = tensor
+ else:
+ cond_in = torch.cat([tensor, uncond])
+ """
+ adjusted_cond_scale = cond_scale # Adjusted cond_scale for uncond
+ last_uncond_steps = max(0, state.sampling_steps - 2) # Determine the last two steps before uncond stops
+ if self.step >= last_uncond_steps: # Check if we're in the last two steps before uncond stops
+ adjusted_cond_scale *= 1.5 # Apply uncond with 150% cond_scale
+ else:
+ if (self.step - last_uncond_steps) % 3 == 0: # Check if it's one of every three steps after uncond stops
+ adjusted_cond_scale *= 1.5 # Apply uncond with 150% cond_scale
+ """
+ if shared.batch_cond_uncond:
+ x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
+ else:
+ x_out = torch.zeros_like(x_in)
+ for batch_offset in range(0, x_out.shape[0], batch_size):
+ a = batch_offset
+ b = a + batch_size
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
+ else:
+ x_out = torch.zeros_like(x_in)
+ batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+ for batch_offset in range(0, tensor.shape[0], batch_size):
+ a = batch_offset
+ b = min(a + batch_size, tensor.shape[0])
+ if not is_edit_model:
+ c_crossattn = [tensor[a:b]]
+ else:
+ c_crossattn = torch.cat([tensor[a:b]], uncond)
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
+ if not skip_uncond:
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+ denoised_image_indexes = [x[0][0] for x in conds_list]
+ if skip_uncond:
+ fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+ x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+ denoised_params = CFGDenoisedParams(x_out, shared.state.sampling_step, shared.state.sampling_steps, self.inner_model)
+ cfg_denoised_callback(denoised_params)
+ devices.test_for_nans(x_out, "unet")
+ if shared.opts.live_preview_content == "Prompt":
+ sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
+ elif shared.opts.live_preview_content == "Negative prompt":
+ sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
+ if is_edit_model:
+ denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+ elif skip_uncond:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+ else:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+ if self.mask is not None:
+ if devices.backend == "directml":
+ self.init_latent = self.init_latent.float()
+ denoised = self.init_latent * self.mask + self.nmask * denoised
+ self.init_latent = self.init_latent.half()
+ else:
+ denoised = self.init_latent * self.mask + self.nmask * denoised
+ after_cfg_callback_params = AfterCFGCallbackParams(denoised, shared.state.sampling_step, shared.state.sampling_steps)
+ cfg_after_cfg_callback(after_cfg_callback_params)
+ denoised = after_cfg_callback_params.x
+ self.step += 1
+ return denoised
+
+
+class TorchHijack:
+ def __init__(self, sampler_noises):
+ # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+ # implementation.
+ self.sampler_noises = deque(sampler_noises)
+
+ def __getattr__(self, item):
+ if item == 'randn_like':
+ return self.randn_like
+ if hasattr(torch, item):
+ return getattr(torch, item)
+ raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+ def randn_like(self, x):
+ if self.sampler_noises:
+ noise = self.sampler_noises.popleft()
+ if noise.shape == x.shape:
+ return noise
+ if x.device.type == 'mps':
+ return torch.randn_like(x, device=devices.cpu).to(x.device)
+ else:
+ return torch.randn_like(x)
+
+
+class KDiffusionSampler:
+ def __init__(self, funcname, sd_model):
+ denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+ self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
+ self.funcname = funcname
+ self.func = getattr(k_sampling, self.funcname)
+ self.extra_params = sampler_extra_params.get(funcname, [])
+ self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+ self.sampler_noises = None
+ self.stop_at = None
+ self.eta = None
+ self.config = None # set by the function calling the constructor
+ self.last_latent = None
+ self.s_min_uncond = None
+ self.conditioning_key = sd_model.model.conditioning_key
+
+ def callback_state(self, d):
+ step = d['i']
+ latent = d["denoised"]
+ if shared.opts.live_preview_content == "Combined":
+ sd_samplers_common.store_latent(latent)
+ self.last_latent = latent
+ if self.stop_at is not None and step > self.stop_at:
+ raise sd_samplers_common.InterruptedException
+ shared.state.sampling_step = step
+
+ def launch_sampling(self, steps, func):
+ shared.state.sampling_steps = steps
+ shared.state.sampling_step = 0
+ try:
+ return func()
+ except sd_samplers_common.InterruptedException:
+ return self.last_latent
+
+ def number_of_needed_noises(self, p):
+ return p.steps
+
+ def initialize(self, p):
+ if self.config.options.get('brownian_noise', None) is not None:
+ self.config.options['brownian_noise'] = shared.opts.data.get('schedulers_brownian_noise', False)
+ if self.config.options.get('scheduler', None) is not None:
+ self.config.options['scheduler'] = shared.opts.data.get('schedulers_sigma', None)
+ if p is None:
+ return {}
+ self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+ self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+ self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+ self.eta = p.eta if p.eta is not None else shared.opts.scheduler_eta
+ self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
+ k_sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
+ extra_params_kwargs = {}
+ for param_name in self.extra_params:
+ if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+ extra_params_kwargs[param_name] = getattr(p, param_name)
+ if 'eta' in inspect.signature(self.func).parameters:
+ if self.eta != 1.0:
+ p.extra_generation_params["Sampler Eta"] = self.eta
+ extra_params_kwargs['eta'] = self.eta
+ return extra_params_kwargs
+
+ def get_sigmas(self, p, steps): # pylint: disable=unused-argument
+ discard_next_to_last_sigma = shared.opts.data.get('schedulers_discard_penultimate', True) if self.config.options.get('discard_next_to_last_sigma', None) is not None else False
+ steps += 1 if discard_next_to_last_sigma else 0
+ if self.config.options.get('scheduler', None) == 'default' or self.config.options.get('scheduler', None) is None:
+ sigmas = self.model_wrap.get_sigmas(steps)
+ elif self.config.options.get('scheduler', None) == 'karras':
+ sigma_min = p.s_min if p.s_min > 0 else self.model_wrap.sigmas[0].item()
+ sigma_max = p.s_max if p.s_max > 0 else self.model_wrap.sigmas[-1].item()
+ sigmas = k_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+ elif self.config.options.get('scheduler', None) == 'exponential':
+ sigma_min = p.s_min if p.s_min > 0 else self.model_wrap.sigmas[0].item()
+ sigma_max = p.s_max if p.s_max > 0 else self.model_wrap.sigmas[-1].item()
+ sigmas = k_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+ elif self.config.options.get('scheduler', None) == 'polyexponential':
+ sigma_min = p.s_min if p.s_min > 0 else self.model_wrap.sigmas[0].item()
+ sigma_max = p.s_max if p.s_max > 0 else self.model_wrap.sigmas[-1].item()
+ sigmas = k_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+ elif self.config.options.get('scheduler', None) == 'vp':
+ sigmas = k_sampling.get_sigmas_vp(n=steps, device=shared.device)
+ if discard_next_to_last_sigma:
+ sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+ return sigmas
+
+ def create_noise_sampler(self, x, sigmas, p):
+ """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
+ if shared.opts.no_dpmpp_sde_batch_determinism:
+ return None
+ positive_sigmas = sigmas[sigmas > 0]
+ if positive_sigmas.numel() > 0:
+ sigma_min = positive_sigmas.min(dim=0)[0]
+ else:
+ sigma_min = 0
+ sigma_max = sigmas.max()
+ current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
+ return k_sampling.BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+ sigmas = self.get_sigmas(p, steps)
+ sigma_sched = sigmas[steps - t_enc - 1:]
+ xi = x + noise * sigma_sched[0]
+ if shared.opts.img2img_extra_noise > 0:
+ p.extra_generation_params["Extra noise"] = shared.opts.img2img_extra_noise
+ extra_noise_params = ExtraNoiseParams(noise, x, xi)
+ extra_noise_callback(extra_noise_params)
+ noise = extra_noise_params.noise
+ xi += noise * shared.opts.img2img_extra_noise
+
+ extra_params_kwargs = self.initialize(p)
+ parameters = inspect.signature(self.func).parameters
+ if 'sigma_min' in parameters:
+ ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+ extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+ if 'sigma_max' in parameters:
+ extra_params_kwargs['sigma_max'] = sigma_sched[0]
+ if 'n' in parameters:
+ extra_params_kwargs['n'] = len(sigma_sched) - 1
+ if 'sigma_sched' in parameters:
+ extra_params_kwargs['sigma_sched'] = sigma_sched
+ if 'sigmas' in parameters:
+ extra_params_kwargs['sigmas'] = sigma_sched
+ if self.config.options.get('brownian_noise', False) and 'noise_sampler' in parameters:
+ noise_sampler = self.create_noise_sampler(x, sigmas, p)
+ extra_params_kwargs['noise_sampler'] = noise_sampler
+ self.model_wrap_cfg.init_latent = x
+ self.last_latent = x
+ extra_args = {
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': p.cfg_scale,
+ 's_min_uncond': self.s_min_uncond
+ }
+ samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ samples = samples.type(devices.dtype)
+ return samples
+
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps = steps or p.steps
+ sigmas = self.get_sigmas(p, steps)
+ x = x * sigmas[0]
+ extra_params_kwargs = self.initialize(p)
+ parameters = inspect.signature(self.func).parameters
+ if 'sigma_min' in parameters:
+ extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+ extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
+ if 'n' in parameters:
+ extra_params_kwargs['n'] = steps
+ else:
+ extra_params_kwargs['sigmas'] = sigmas
+ if self.config.options.get('brownian_noise', False) and 'noise_sampler' in parameters:
+ noise_sampler = self.create_noise_sampler(x, sigmas, p)
+ extra_params_kwargs['noise_sampler'] = noise_sampler
+ self.last_latent = x
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': p.cfg_scale,
+ 's_min_uncond': self.s_min_uncond
+ }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+ return samples
diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py
index a15c76a4a..03716ee08 100644
--- a/modules/sd_samplers_timesteps_impl.py
+++ b/modules/sd_samplers_timesteps_impl.py
@@ -1,139 +1,139 @@
-# TODO a1111 compatibility module
-
-import torch
-import tqdm
-import k_diffusion.sampling
-import numpy as np
-
-from modules import shared
-from modules.unipc import uni_pc
-
-
-@torch.no_grad()
-def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
- alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
- alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32) # pylint: disable=not-callable
- sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
- sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
-
- extra_args = {} if extra_args is None else extra_args
- s_in = x.new_ones((x.shape[0]))
- s_x = x.new_ones((x.shape[0], 1, 1, 1))
- for i in tqdm.trange(len(timesteps) - 1, disable=disable):
- index = len(timesteps) - 1 - i
-
- e_t = model(x, timesteps[index].item() * s_in, **extra_args)
-
- a_t = alphas[index].item() * s_x
- a_prev = alphas_prev[index].item() * s_x
- sigma_t = sigmas[index].item() * s_x
- sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
-
- pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
- dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
- noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
- x = a_prev.sqrt() * pred_x0 + dir_xt + noise
-
- if callback is not None:
- callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
-
- return x
-
-
-@torch.no_grad()
-def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
- alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
- alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32) # pylint: disable=not-callable
- sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
-
- extra_args = {} if extra_args is None else extra_args
- s_in = x.new_ones([x.shape[0]])
- s_x = x.new_ones((x.shape[0], 1, 1, 1))
- old_eps = []
-
- def get_x_prev_and_pred_x0(e_t, index):
- # select parameters corresponding to the currently considered timestep
- a_t = alphas[index].item() * s_x
- a_prev = alphas_prev[index].item() * s_x
- sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
-
- # current prediction for x_0
- pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-
- # direction pointing to x_t
- dir_xt = (1. - a_prev).sqrt() * e_t
- x_prev = a_prev.sqrt() * pred_x0 + dir_xt
- return x_prev, pred_x0
-
- for i in tqdm.trange(len(timesteps) - 1, disable=disable):
- index = len(timesteps) - 1 - i
- ts = timesteps[index].item() * s_in
- t_next = timesteps[max(index - 1, 0)].item() * s_in
-
- e_t = model(x, ts, **extra_args)
-
- if len(old_eps) == 0:
- # Pseudo Improved Euler (2nd order)
- x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
- e_t_next = model(x_prev, t_next, **extra_args)
- e_t_prime = (e_t + e_t_next) / 2
- elif len(old_eps) == 1:
- # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
- e_t_prime = (3 * e_t - old_eps[-1]) / 2
- elif len(old_eps) == 2:
- # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
- e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
- else:
- # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
- e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
- x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
- old_eps.append(e_t)
- if len(old_eps) >= 4:
- old_eps.pop(0)
-
- x = x_prev
-
- if callback is not None:
- callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
-
- return x
-
-
-class UniPCCFG(uni_pc.UniPC):
- def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):
- super().__init__(None, *args, **kwargs)
-
- def after_update(x, model_x):
- callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})
- self.index += 1
-
- self.cfg_model = cfg_model
- self.extra_args = extra_args
- self.callback = callback
- self.index = 0
- self.after_update = after_update
-
- def get_model_input_time(self, t_continuous):
- return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.
-
- def model(self, x, t):
- t_input = self.get_model_input_time(t)
-
- res = self.cfg_model(x, t_input, **self.extra_args)
-
- return res
-
-
-def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False): # pylint: disable=unused-argument
- alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
-
- ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
- t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None # this is likely off by a bit - if someone wants to fix it please by all means
- unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)
- x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
-
- return x
+# TODO a1111 compatibility module
+
+import torch
+import tqdm
+import k_diffusion.sampling
+import numpy as np
+
+from modules import shared
+from modules.unipc import uni_pc
+
+
+@torch.no_grad()
+def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+ alphas = alphas_cumprod[timesteps]
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32) # pylint: disable=not-callable
+ sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+ sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
+
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones((x.shape[0]))
+ s_x = x.new_ones((x.shape[0], 1, 1, 1))
+ for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+ index = len(timesteps) - 1 - i
+
+ e_t = model(x, timesteps[index].item() * s_in, **extra_args)
+
+ a_t = alphas[index].item() * s_x
+ a_prev = alphas_prev[index].item() * s_x
+ sigma_t = sigmas[index].item() * s_x
+ sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
+
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
+ noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
+ x = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+ return x
+
+
+@torch.no_grad()
+def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+ alphas = alphas_cumprod[timesteps]
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32) # pylint: disable=not-callable
+ sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ s_x = x.new_ones((x.shape[0], 1, 1, 1))
+ old_eps = []
+
+ def get_x_prev_and_pred_x0(e_t, index):
+ # select parameters corresponding to the currently considered timestep
+ a_t = alphas[index].item() * s_x
+ a_prev = alphas_prev[index].item() * s_x
+ sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
+
+ # current prediction for x_0
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev).sqrt() * e_t
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt
+ return x_prev, pred_x0
+
+ for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+ index = len(timesteps) - 1 - i
+ ts = timesteps[index].item() * s_in
+ t_next = timesteps[max(index - 1, 0)].item() * s_in
+
+ e_t = model(x, ts, **extra_args)
+
+ if len(old_eps) == 0:
+ # Pseudo Improved Euler (2nd order)
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+ e_t_next = model(x_prev, t_next, **extra_args)
+ e_t_prime = (e_t + e_t_next) / 2
+ elif len(old_eps) == 1:
+ # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (3 * e_t - old_eps[-1]) / 2
+ elif len(old_eps) == 2:
+ # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+ else:
+ # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+ old_eps.append(e_t)
+ if len(old_eps) >= 4:
+ old_eps.pop(0)
+
+ x = x_prev
+
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+ return x
+
+
+class UniPCCFG(uni_pc.UniPC):
+ def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):
+ super().__init__(None, *args, **kwargs)
+
+ def after_update(x, model_x):
+ callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})
+ self.index += 1
+
+ self.cfg_model = cfg_model
+ self.extra_args = extra_args
+ self.callback = callback
+ self.index = 0
+ self.after_update = after_update
+
+ def get_model_input_time(self, t_continuous):
+ return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.
+
+ def model(self, x, t):
+ t_input = self.get_model_input_time(t)
+
+ res = self.cfg_model(x, t_input, **self.extra_args)
+
+ return res
+
+
+def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False): # pylint: disable=unused-argument
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+
+ ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+ t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None # this is likely off by a bit - if someone wants to fix it please by all means
+ unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)
+ x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+
+ return x
diff --git a/modules/sd_vae_approx.py b/modules/sd_vae_approx.py
index 14a67fca1..0b6db41bc 100644
--- a/modules/sd_vae_approx.py
+++ b/modules/sd_vae_approx.py
@@ -1,80 +1,80 @@
-import os
-import torch
-from torch import nn
-from modules import devices, paths, shared
-
-
-sd_vae_approx_model = None
-
-
-class VAEApprox(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv1 = nn.Conv2d(4, 8, (7, 7))
- self.conv2 = nn.Conv2d(8, 16, (5, 5))
- self.conv3 = nn.Conv2d(16, 32, (3, 3))
- self.conv4 = nn.Conv2d(32, 64, (3, 3))
- self.conv5 = nn.Conv2d(64, 32, (3, 3))
- self.conv6 = nn.Conv2d(32, 16, (3, 3))
- self.conv7 = nn.Conv2d(16, 8, (3, 3))
- self.conv8 = nn.Conv2d(8, 3, (3, 3))
-
- def forward(self, x):
- extra = 11
- try:
- x = nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2))
- x = nn.functional.pad(x, (extra, extra, extra, extra)) # pylint: disable=not-callable
- for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8, ]:
- x = layer(x)
- x = nn.functional.leaky_relu(x, 0.1)
- except Exception:
- pass
- return x
-
-
-def nn_approximation(sample): # Approximate NN
- global sd_vae_approx_model # pylint: disable=global-statement
- if sd_vae_approx_model is None:
- model_path = os.path.join(paths.models_path, "VAE-approx", "model.pt")
- sd_vae_approx_model = VAEApprox()
- if not os.path.exists(model_path):
- model_path = os.path.join(paths.script_path, "models", "VAE-approx", "model.pt")
- approx_weights = torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None)
- sd_vae_approx_model.load_state_dict(approx_weights)
- sd_vae_approx_model.eval()
- sd_vae_approx_model.to(devices.device, sample.dtype)
- shared.log.debug(f'Load VAE decode approximate: model="{model_path}"')
- try:
- in_sample = sample.to(devices.device).unsqueeze(0)
- x_sample = sd_vae_approx_model(in_sample)
- x_sample = x_sample[0]
- return x_sample
- except Exception as e:
- shared.log.error(f'Decode approximate: {e}')
- return sample
-
-
-def cheap_approximation(sample): # Approximate simple
- # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
- if shared.sd_model_type == "sdxl":
- simple_weights = torch.tensor([
- [0.4543,-0.2868, 0.1566,-0.4748],
- [0.5008, 0.0952, 0.2155,-0.3268],
- [0.5294, 0.1625,-0.0624,-0.3793]
- ]).reshape(3, 4, 1, 1)
- simple_bias = torch.tensor([0.1375, 0.0144, -0.0675])
- else:
- simple_weights = torch.tensor([
- [0.298, 0.187,-0.158,-0.184],
- [0.207, 0.286, 0.189,-0.271],
- [0.208, 0.173, 0.264,-0.473],
- ]).reshape(3, 4, 1, 1)
- simple_bias = None
- try:
- weights = simple_weights.to(sample.device, sample.dtype)
- bias = simple_bias.to(sample.device, sample.dtype) if simple_bias is not None else None
- x_sample = nn.functional.conv2d(sample, weights, bias) # pylint: disable=not-callable
- return x_sample
- except Exception as e:
- shared.log.error(f'Decode simple: {e}')
- return sample
+import os
+import torch
+from torch import nn
+from modules import devices, paths, shared
+
+
+sd_vae_approx_model = None
+
+
+class VAEApprox(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.conv1 = nn.Conv2d(4, 8, (7, 7))
+ self.conv2 = nn.Conv2d(8, 16, (5, 5))
+ self.conv3 = nn.Conv2d(16, 32, (3, 3))
+ self.conv4 = nn.Conv2d(32, 64, (3, 3))
+ self.conv5 = nn.Conv2d(64, 32, (3, 3))
+ self.conv6 = nn.Conv2d(32, 16, (3, 3))
+ self.conv7 = nn.Conv2d(16, 8, (3, 3))
+ self.conv8 = nn.Conv2d(8, 3, (3, 3))
+
+ def forward(self, x):
+ extra = 11
+ try:
+ x = nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2))
+ x = nn.functional.pad(x, (extra, extra, extra, extra)) # pylint: disable=not-callable
+ for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8, ]:
+ x = layer(x)
+ x = nn.functional.leaky_relu(x, 0.1)
+ except Exception:
+ pass
+ return x
+
+
+def nn_approximation(sample): # Approximate NN
+ global sd_vae_approx_model # pylint: disable=global-statement
+ if sd_vae_approx_model is None:
+ model_path = os.path.join(paths.models_path, "VAE-approx", "model.pt")
+ sd_vae_approx_model = VAEApprox()
+ if not os.path.exists(model_path):
+ model_path = os.path.join(paths.script_path, "models", "VAE-approx", "model.pt")
+ approx_weights = torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None)
+ sd_vae_approx_model.load_state_dict(approx_weights)
+ sd_vae_approx_model.eval()
+ sd_vae_approx_model.to(devices.device, sample.dtype)
+ shared.log.debug(f'Load VAE decode approximate: model="{model_path}"')
+ try:
+ in_sample = sample.to(devices.device).unsqueeze(0)
+ x_sample = sd_vae_approx_model(in_sample)
+ x_sample = x_sample[0]
+ return x_sample
+ except Exception as e:
+ shared.log.error(f'Decode approximate: {e}')
+ return sample
+
+
+def cheap_approximation(sample): # Approximate simple
+ # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
+ if shared.sd_model_type == "sdxl":
+ simple_weights = torch.tensor([
+ [0.4543,-0.2868, 0.1566,-0.4748],
+ [0.5008, 0.0952, 0.2155,-0.3268],
+ [0.5294, 0.1625,-0.0624,-0.3793]
+ ]).reshape(3, 4, 1, 1)
+ simple_bias = torch.tensor([0.1375, 0.0144, -0.0675])
+ else:
+ simple_weights = torch.tensor([
+ [0.298, 0.187,-0.158,-0.184],
+ [0.207, 0.286, 0.189,-0.271],
+ [0.208, 0.173, 0.264,-0.473],
+ ]).reshape(3, 4, 1, 1)
+ simple_bias = None
+ try:
+ weights = simple_weights.to(sample.device, sample.dtype)
+ bias = simple_bias.to(sample.device, sample.dtype) if simple_bias is not None else None
+ x_sample = nn.functional.conv2d(sample, weights, bias) # pylint: disable=not-callable
+ return x_sample
+ except Exception as e:
+ shared.log.error(f'Decode simple: {e}')
+ return sample
diff --git a/modules/shared.py b/modules/shared.py
index 287b57370..1e5b86ac5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -1,975 +1,977 @@
-import io
-import os
-import sys
-import time
-import json
-import contextlib
-from types import SimpleNamespace
-from urllib.parse import urlparse
-from enum import Enum
-import requests
-import gradio as gr
-import fasteners
-import orjson
-import diffusers
-from rich.console import Console
-from modules import errors, shared_items, shared_state, cmd_args, ui_components, theme
-from modules.paths import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # pylint: disable=W0611
-from modules.dml import memory_providers, default_memory_provider, directml_do_hijack
-import modules.interrogate
-import modules.memmon
-import modules.styles
-import modules.devices as devices # pylint: disable=R0402
-import modules.paths as paths
-from installer import print_dict
-from installer import log as central_logger # pylint: disable=E0611
-
-
-errors.install([gr])
-demo: gr.Blocks = None
-log = central_logger
-progress_print_out = sys.stdout
-parser = cmd_args.parser
-url = 'https://github.com/vladmandic/automatic'
-cmd_opts, _ = parser.parse_known_args()
-hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
-xformers_available = False
-clip_model = None
-interrogator = modules.interrogate.InterrogateModels("interrogate")
-sd_upscalers = []
-face_restorers = []
-tab_names = []
-extra_networks = []
-options_templates = {}
-hypernetworks = {}
-loaded_hypernetworks = []
-settings_components = None
-latent_upscale_default_mode = "None"
-latent_upscale_modes = {
- "Latent Nearest": {"mode": "nearest", "antialias": False},
- "Latent Nearest-exact": {"mode": "nearest-exact", "antialias": False},
- "Latent Area": {"mode": "area", "antialias": False},
- "Latent Bilinear": {"mode": "bilinear", "antialias": False},
- "Latent Bicubic": {"mode": "bicubic", "antialias": False},
- "Latent Bilinear antialias": {"mode": "bilinear", "antialias": True},
- "Latent Bicubic antialias": {"mode": "bicubic", "antialias": True},
- # "Latent Linear": {"mode": "linear", "antialias": False}, # not supported for latents with channels=4
- # "Latent Trilinear": {"mode": "trilinear", "antialias": False}, # not supported for latents with channels=4
-}
-restricted_opts = {
- "samples_filename_pattern",
- "directories_filename_pattern",
- "outdir_samples",
- "outdir_txt2img_samples",
- "outdir_img2img_samples",
- "outdir_extras_samples",
- "outdir_grids",
- "outdir_txt2img_grids",
- "outdir_save",
- "outdir_init_images"
-}
-resize_modes = ["None", "Fixed", "Crop", "Fill", "Latent"]
-compatibility_opts = ['clip_skip', 'uni_pc_lower_order_final', 'uni_pc_order']
-console = Console(log_time=True, log_time_format='%H:%M:%S-%f')
-dir_timestamps = {}
-dir_cache = {}
-
-
-class Backend(Enum):
- ORIGINAL = 1
- DIFFUSERS = 2
-
-
-state = shared_state.State()
-if not hasattr(cmd_opts, "use_openvino"):
- cmd_opts.use_openvino = False
-if cmd_opts.use_openvino:
- backend = Backend.DIFFUSERS
- cmd_opts.backend = 'diffusers'
-else:
- backend = Backend.DIFFUSERS if (cmd_opts.backend is not None) and (cmd_opts.backend.lower() == 'diffusers') else Backend.ORIGINAL # initial since we don't have opts loaded yet
-
-
-class OptionInfo:
- def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, folder=None, submit=None, comment_before='', comment_after=''):
- self.default = default
- self.label = label
- self.component = component
- self.component_args = component_args
- self.onchange = onchange
- self.section = section
- self.refresh = refresh
- self.folder = folder
- self.comment_before = comment_before # HTML text that will be added after label in UI
- self.comment_after = comment_after # HTML text that will be added before label in UI
- self.submit = submit
-
- def link(self, label, uri):
- self.comment_before += f"[{label} ]"
- return self
-
- def js(self, label, js_func):
- self.comment_before += f"[{label} ]"
- return self
-
- def info(self, info):
- self.comment_after += f"({info}) "
- return self
-
- def html(self, info):
- self.comment_after += f"{info} "
- return self
-
- def needs_restart(self):
- self.comment_after += " (requires restart) "
- return self
-
-
-def options_section(section_identifier, options_dict):
- for v in options_dict.values():
- v.section = section_identifier
- return options_dict
-
-
-def list_checkpoint_tiles():
- import modules.sd_models # pylint: disable=W0621
- return modules.sd_models.checkpoint_tiles()
-
-default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.ckpt"
-
-
-def is_url(string):
- parsed_url = urlparse(string)
- return all([parsed_url.scheme, parsed_url.netloc])
-
-
-def reload_hypernetworks():
- from modules.hypernetworks import hypernetwork
- global hypernetworks # pylint: disable=W0603
- hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir)
-
-
-def refresh_checkpoints():
- import modules.sd_models # pylint: disable=W0621
- return modules.sd_models.list_models()
-
-
-def refresh_vaes():
- import modules.sd_vae # pylint: disable=W0621
- modules.sd_vae.refresh_vae_list()
-
-
-def refresh_upscalers():
- import modules.modelloader # pylint: disable=W0621
- modules.modelloader.load_upscalers()
-
-
-def list_samplers():
- import modules.sd_samplers # pylint: disable=W0621
- modules.sd_samplers.set_samplers()
- return modules.sd_samplers.all_samplers
-
-
-def temp_disable_extensions():
- disable_safe = ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris', 'sd-webui-agent-scheduler', 'clip-interrogator-ext', 'stable-diffusion-webui-rembg', 'sd-extension-chainner', 'stable-diffusion-webui-images-browser']
- disable_diffusers = ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris', 'sd-webui-animatediff']
- disable_original = []
- disabled = []
- if cmd_opts.safe:
- for ext in disable_safe:
- if ext not in opts.disabled_extensions:
- disabled.append(ext)
- if backend == Backend.DIFFUSERS:
- for ext in disable_diffusers:
- if ext not in opts.disabled_extensions:
- disabled.append(ext)
- if backend == Backend.ORIGINAL:
- for ext in disable_original:
- if ext not in opts.disabled_extensions:
- disabled.append(ext)
- cmd_opts.controlnet_loglevel = 'WARNING'
- return disabled
-
-
-def readfile(filename, silent=False, lock=False):
- data = {}
- lock_file = None
- locked = False
- try:
- # if not os.path.exists(filename):
- # return {}
- t0 = time.time()
- if lock:
- lock_file = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
- locked = lock_file.acquire_read_lock(blocking=True, timeout=3)
- with open(filename, "rb") as file:
- b = file.read()
- data = orjson.loads(b) # pylint: disable=no-member
- # if type(data) is str:
- # data = json.loads(data)
- t1 = time.time()
- if not silent:
- log.debug(f'Read: file="{filename}" json={len(data)} bytes={os.path.getsize(filename)} time={t1-t0:.3f}')
- except Exception as e:
- if not silent:
- log.error(f'Reading failed: {filename} {e}')
- finally:
- if lock_file is not None:
- lock_file.release_read_lock()
- if locked and os.path.exists(f"{filename}.lock"):
- os.remove(f"{filename}.lock")
- return data
-
-
-def writefile(data, filename, mode='w', silent=False, atomic=False):
- lock = None
- locked = False
- import tempfile
-
-
- def default(obj):
- log.error(f"Saving: {filename} not a valid object: {obj}")
- return str(obj)
-
- try:
- t0 = time.time()
- # skipkeys=True, ensure_ascii=True, check_circular=True, allow_nan=True
- if type(data) == dict:
- output = json.dumps(data, indent=2, default=default)
- elif type(data) == list:
- output = json.dumps(data, indent=2, default=default)
- elif isinstance(data, object):
- simple = {}
- for k in data.__dict__:
- if data.__dict__[k] is not None:
- simple[k] = data.__dict__[k]
- output = json.dumps(simple, indent=2, default=default)
- else:
- raise ValueError('not a valid object')
- lock = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
- locked = lock.acquire_write_lock(blocking=True, timeout=3)
- if atomic:
- with tempfile.NamedTemporaryFile(mode=mode, encoding="utf8", delete=False, dir=os.path.dirname(filename)) as f:
- f.write(output)
- f.flush()
- os.fsync(f.fileno())
- os.replace(f.name, filename)
- else:
- with open(filename, mode=mode, encoding="utf8") as file:
- file.write(output)
- t1 = time.time()
- if not silent:
- log.debug(f'Save: file="{filename}" json={len(data)} bytes={len(output)} time={t1-t0:.3f}')
- except Exception as e:
- log.error(f'Saving failed: {filename} {e}')
- errors.display(e, 'Saving failed')
- finally:
- if lock is not None:
- lock.release_read_lock()
- if locked and os.path.exists(f"{filename}.lock"):
- os.remove(f"{filename}.lock")
-
-
-if devices.backend == "cpu":
- cross_attention_optimization_default = "Doggettx's"
-elif devices.backend == "mps":
- cross_attention_optimization_default = "Doggettx's"
-elif devices.backend == "ipex":
- cross_attention_optimization_default = "Scaled-Dot-Product"
-elif devices.backend == "directml":
- cross_attention_optimization_default = "Sub-quadratic"
-elif devices.backend == "rocm":
- cross_attention_optimization_default = "Sub-quadratic"
-else: # cuda
- cross_attention_optimization_default ="Scaled-Dot-Product"
-
-
-options_templates.update(options_section(('sd', "Execution & Models"), {
- "sd_backend": OptionInfo("original" if not cmd_opts.use_openvino else "diffusers", "Execution backend", gr.Radio, {"choices": ["original", "diffusers"] }),
- "sd_checkpoint_autoload": OptionInfo(True, "Model autoload on server start"),
- "sd_model_checkpoint": OptionInfo(default_checkpoint, "Base model", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
- "sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
- "sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
- "sd_model_dict": OptionInfo('None', "Use baseline data from a different model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
- "stream_load": OptionInfo(False, "Load models using stream loading method"),
- "model_reuse_dict": OptionInfo(False, "When loading models attempt to reuse previous model dictionary", gr.Checkbox, {"visible": False}),
- "prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, {"choices": ["Full parser", "Compel parser", "A1111 parser", "Fixed attention"] }),
- "prompt_mean_norm": OptionInfo(True, "Prompt attention mean normalization"),
- "comma_padding_backtrack": OptionInfo(20, "Prompt padding for long prompts", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
- "sd_checkpoint_cache": OptionInfo(0, "Number of cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
- "sd_vae_checkpoint_cache": OptionInfo(0, "Number of cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
- "sd_disable_ckpt": OptionInfo(False, "Disallow usage of models in ckpt format"),
-}))
-
-options_templates.update(options_section(('cuda', "Compute Settings"), {
- "math_sep": OptionInfo("Execution precision ", "", gr.HTML),
- "precision": OptionInfo("Autocast", "Precision type", gr.Radio, {"choices": ["Autocast", "Full"]}),
- "cuda_dtype": OptionInfo("FP32" if sys.platform == "darwin" or cmd_opts.use_openvino else "BF16" if devices.backend == "ipex" else "FP16", "Device precision type", gr.Radio, {"choices": ["FP32", "FP16", "BF16"]}),
- "no_half": OptionInfo(False if not cmd_opts.use_openvino else True, "Use full precision for model (--no-half)", None, None, None),
- "no_half_vae": OptionInfo(False if not cmd_opts.use_openvino else True, "Use full precision for VAE (--no-half-vae)"),
- "upcast_sampling": OptionInfo(False if sys.platform != "darwin" else True, "Enable upcast sampling"),
- "upcast_attn": OptionInfo(False, "Enable upcast cross attention layer"),
- "cuda_cast_unet": OptionInfo(False, "Use fixed UNet precision"),
- "disable_nan_check": OptionInfo(True, "Disable NaN check in produced images/latent spaces", gr.Checkbox, {"visible": False}),
- "rollback_vae": OptionInfo(False, "Attempt VAE roll back when produced NaN values"),
-
- "cross_attention_sep": OptionInfo("Cross-attention ", "", gr.HTML),
- "cross_attention_optimization": OptionInfo(cross_attention_optimization_default, "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
- "cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
- "sub_quad_sep": OptionInfo("Sub-quadratic options ", "", gr.HTML),
- "sub_quad_q_chunk_size": OptionInfo(512, "cross-attention query chunk size", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
- "sub_quad_kv_chunk_size": OptionInfo(512, "cross-attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
- "sub_quad_chunk_threshold": OptionInfo(80, "cross-attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
-
- "other_sep": OptionInfo("Execution precision ", "", gr.HTML),
- "opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
- "cudnn_benchmark": OptionInfo(False, "Enable full-depth cuDNN benchmark feature"),
- "diffusers_fuse_projections": OptionInfo(False, "Enable fused projections"),
- "torch_gc_threshold": OptionInfo(80, "Memory usage threshold before running Torch GC", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
-
- "cuda_compile_sep": OptionInfo("Model Compile ", "", gr.HTML),
- "cuda_compile": OptionInfo(False if not cmd_opts.use_openvino else True, "Compile UNet"),
- "cuda_compile_vae": OptionInfo(False if not cmd_opts.use_openvino else True, "Compile VAE"),
- "cuda_compile_text_encoder": OptionInfo(False, "Compile Text Encoder"),
- "cuda_compile_upscaler": OptionInfo(False if not cmd_opts.use_openvino else True, "Compile Upscaler"),
- "cuda_compile_backend": OptionInfo("none" if not cmd_opts.use_openvino else "openvino_fx", "Model compile backend", gr.Radio, {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex', 'openvino_fx', 'stable-fast']}),
- "cuda_compile_mode": OptionInfo("default", "Model compile mode", gr.Radio, {"choices": ['default', 'reduce-overhead', 'max-autotune', 'max-autotune-no-cudagraphs']}),
- "cuda_compile_fullgraph": OptionInfo(False, "Model compile fullgraph"),
- "cuda_compile_precompile": OptionInfo(False, "Model compile precompile"),
- "cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
- "cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
- "diffusers_quantization": OptionInfo(False, "Enable dynamic quantization with torchao"),
- "nncf_compress_weights": OptionInfo(False, "Compress Model weights with NNCF"),
- "nncf_compress_vae_weights": OptionInfo(False, "Compress VAE weights with NNCF"),
-
- "ipex_sep": OptionInfo("IPEX, DirectML and OpenVINO ", "", gr.HTML),
- "ipex_optimize": OptionInfo(False if not devices.backend == "ipex" else True, "Enable IPEX Optimize for Intel GPUs"),
- "ipex_optimize_upscaler": OptionInfo(False if not devices.backend == "ipex" else True, "Enable IPEX Optimize for Intel GPUs with Upscalers"),
- "directml_memory_provider": OptionInfo(default_memory_provider, 'DirectML memory stats provider', gr.Radio, {"choices": memory_providers}),
- "directml_catch_nan": OptionInfo(False, "DirectML retry specific operation when NaN is produced if possible. (makes generation slower)"),
- "openvino_disable_model_caching": OptionInfo(False, "OpenVINO disable model caching"),
- "openvino_hetero_gpu": OptionInfo(False, "OpenVINO use Hetero Device for single inference with multiple devices"),
- "openvino_remove_cpu_from_hetero": OptionInfo(False, "OpenVINO remove CPU from Hetero Device"),
- "openvino_remove_igpu_from_hetero": OptionInfo(False, "OpenVINO remove iGPU from Hetero Device"),
- "nncf_compress_weights_mode": OptionInfo("INT8", "OpenVINO compress mode for NNCF (CPU Only)", gr.Radio, {"choices": ['INT8', 'INT4_SYM', 'INT4_ASYM', 'NF4']}),
- "nncf_compress_weights_raito": OptionInfo(1.0, "OpenVINO compress ratio for NNCF with 4-bit modes", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-}))
-
-options_templates.update(options_section(('advanced', "Inference Settings"), {
- "token_merging_sep": OptionInfo("Token merging ", "", gr.HTML),
- "token_merging_ratio": OptionInfo(0.0, "Token merging ratio (txt2img)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
- "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio (img2img)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
- "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio (hires)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
-
- "freeu_sep": OptionInfo("FreeU ", "", gr.HTML),
- "freeu_enabled": OptionInfo(False, "FreeU enabled"),
- "freeu_b1": OptionInfo(1.2, "1st stage backbone factor", gr.Slider, {"minimum": 1.0, "maximum": 2.0, "step": 0.01}),
- "freeu_b2": OptionInfo(1.4, "2nd stage backbone factor", gr.Slider, {"minimum": 1.0, "maximum": 2.0, "step": 0.01}),
- "freeu_s1": OptionInfo(0.9, "1st stage skip factor", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "freeu_s2": OptionInfo(0.2, "2nd stage skip factor", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-
- "hypertile_sep": OptionInfo("HyperTile ", "", gr.HTML),
- "hypertile_unet_enabled": OptionInfo(False, "HyperTile for UNet enabled"),
- "hypertile_unet_tile": OptionInfo(256, "HyperTile for UNet tile size", gr.Slider, {"minimum": 0, "maximum": 1024, "step": 8}),
- "hypertile_vae_enabled": OptionInfo(False, "HyperTile for VAE enabled", gr.Checkbox),
- "hypertile_vae_tile": OptionInfo(128, "HyperTile for VAE tile size", gr.Slider, {"minimum": 0, "maximum": 1024, "step": 8}),
-
- "inference_other_sep": OptionInfo("Other ", "", gr.HTML),
- "batch_frame_mode": OptionInfo(False, "Process multiple images in batch in parallel"),
- "inference_mode": OptionInfo("no-grad", "Torch inference mode", gr.Radio, {"choices": ["no-grad", "inference-mode", "none"]}),
- "sd_vae_sliced_encode": OptionInfo(False, "VAE sliced encode"),
-}))
-
-options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
- "diffusers_pipeline": OptionInfo('Autodetect', 'Diffusers pipeline', gr.Dropdown, lambda: {"choices": list(shared_items.get_pipelines()) }),
- "diffusers_move_base": OptionInfo(True, "Move base model to CPU when using refiner"),
- "diffusers_move_unet": OptionInfo(True, "Move base model to CPU when using VAE"),
- "diffusers_move_refiner": OptionInfo(True, "Move refiner model to CPU when not in use"),
- "diffusers_extract_ema": OptionInfo(True, "Use model EMA weights when possible"),
- "diffusers_generator_device": OptionInfo("GPU", "Generator device", gr.Radio, {"choices": ["GPU", "CPU", "Unset"]}),
- "diffusers_model_cpu_offload": OptionInfo(False, "Enable model CPU offload (--medvram)"),
- "diffusers_seq_cpu_offload": OptionInfo(False, "Enable sequential CPU offload (--lowvram)"),
- "diffusers_vae_upcast": OptionInfo("default", "VAE upcasting", gr.Radio, {"choices": ['default', 'true', 'false']}),
- "diffusers_vae_slicing": OptionInfo(True, "Enable VAE slicing"),
- "diffusers_vae_tiling": OptionInfo(True if not cmd_opts.use_openvino else False, "Enable VAE tiling"),
- "diffusers_attention_slicing": OptionInfo(False, "Enable attention slicing"),
- "diffusers_model_load_variant": OptionInfo("default", "Diffusers model loading variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
- "diffusers_vae_load_variant": OptionInfo("default", "Diffusers VAE loading variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
- "custom_diffusers_pipeline": OptionInfo('', 'Load custom Diffusers pipeline'),
- "diffusers_eval": OptionInfo(True, "Force model eval"),
- "diffusers_force_zeros": OptionInfo(True, "Force zeros for prompts when empty"),
- "diffusers_aesthetics_score": OptionInfo(False, "Require aesthetics score"),
- "diffusers_pooled": OptionInfo("default", "Diffusers SDXL pooled embeds (experimental)", gr.Radio, {"choices": ['default', 'weighted']}),
-}))
-
-options_templates.update(options_section(('system-paths', "System Paths"), {
- "models_paths_sep_options": OptionInfo("Models paths ", "", gr.HTML),
- "models_dir": OptionInfo('models', "Base path where all models are stored", folder=True),
- "ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Folder with stable diffusion models", folder=True),
- "diffusers_dir": OptionInfo(os.path.join(paths.models_path, 'Diffusers'), "Folder with Hugggingface models", folder=True),
- "hfcache_dir": OptionInfo(os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'hub'), "Folder for Hugggingface cache", folder=True),
- "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Folder with VAE files", folder=True),
- "sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
- "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Folder with LoRA network(s)", folder=True),
- "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", gr.Text, {"visible": False}),
- "styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "File or Folder with user-defined styles", folder=True),
- "embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Folder with textual inversion embeddings", folder=True),
- "hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Folder with Hypernetwork models", folder=True),
- "control_dir": OptionInfo(os.path.join(paths.models_path, 'control'), "Folder with Control models", folder=True),
- "codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Folder with codeformer models", folder=True),
- "gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Folder with GFPGAN models", folder=True),
- "esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Folder with ESRGAN models", folder=True),
- "bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Folder with BSRGAN models", folder=True),
- "realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Folder with RealESRGAN models", folder=True),
- "scunet_models_path": OptionInfo(os.path.join(paths.models_path, 'SCUNet'), "Folder with SCUNet models", folder=True),
- "swinir_models_path": OptionInfo(os.path.join(paths.models_path, 'SwinIR'), "Folder with SwinIR models", folder=True),
- "ldsr_models_path": OptionInfo(os.path.join(paths.models_path, 'LDSR'), "Folder with LDSR models", folder=True),
- "clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Folder with CLIP models", folder=True),
-
- "other_paths_sep_options": OptionInfo("Other paths ", "", gr.HTML),
- "openvino_cache_path": OptionInfo('cache', "Directory for OpenVINO cache", folder=True),
- "temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default", folder=True),
- "clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"),
-}))
-
-options_templates.update(options_section(('saving-images', "Image Options"), {
- "keep_incomplete": OptionInfo(True, "Keep incomplete images"),
- "samples_save": OptionInfo(True, "Always save all generated images"),
- "samples_format": OptionInfo('jpg', 'File format for generated images', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
- "jpeg_quality": OptionInfo(90, "Quality for saved images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
- "img_max_size_mp": OptionInfo(250, "Maximum image size (MP)", gr.Slider, {"minimum": 100, "maximum": 2000, "step": 1}),
- "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
- "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
- "samples_save_zip": OptionInfo(True, "Create zip archive when downloading multiple images"),
-
- "image_sep_metadata": OptionInfo("Metadata/Logging ", "", gr.HTML),
- "image_metadata": OptionInfo(True, "Include metadata in saved images"),
- "save_txt": OptionInfo(False, "Create info file for each every image"),
- "save_log_fn": OptionInfo("", "Create JSON log file for each saved image", component_args=hide_dirs),
- "image_watermark_enabled": OptionInfo(False, "Include watermark in saved images"),
- "image_watermark": OptionInfo('', "Image watermark string"),
- "image_sep_grid": OptionInfo("Grid Options ", "", gr.HTML),
- "grid_save": OptionInfo(True, "Always save all generated image grids"),
- "grid_format": OptionInfo('jpg', 'File format for grids', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
- "n_rows": OptionInfo(-1, "Grid row count", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
- "grid_background": OptionInfo("#000000", "Grid background color", ui_components.FormColorPicker, {}),
- "font": OptionInfo("", "Font file"),
- "font_color": OptionInfo("#FFFFFF", "Font color", ui_components.FormColorPicker, {}),
-
- "save_sep_options": OptionInfo("Intermediate Image Saving ", "", gr.HTML),
- "save_init_img": OptionInfo(False, "Save copy of img2img init images"),
- "save_images_before_highres_fix": OptionInfo(False, "Save copy of image before applying hires"),
- "save_images_before_refiner": OptionInfo(False, "Save copy of image before running refiner"),
- "save_images_before_face_restoration": OptionInfo(False, "Save copy of image before doing face restoration"),
- "save_images_before_color_correction": OptionInfo(False, "Save copy of image before applying color correction"),
- "save_mask": OptionInfo(False, "Save copy of the inpainting greyscale mask"),
- "save_mask_composite": OptionInfo(False, "Save copy of inpainting masked composite"),
-}))
-
-options_templates.update(options_section(('saving-paths', "Image Naming & Paths"), {
- "saving_sep_images": OptionInfo("Save options ", "", gr.HTML),
- "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
- "use_original_name_batch": OptionInfo(True, "Use original name during batch process"),
- "use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler as suffix", gr.Checkbox, {"visible": False}),
- "samples_filename_pattern": OptionInfo("[seq]-[model_name]-[prompt_words]", "Images filename pattern", component_args=hide_dirs),
- "directories_max_prompt_words": OptionInfo(8, "Max words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 99, "step": 1, **hide_dirs}),
- "use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button", gr.Checkbox, {"visible": False}),
- "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
- "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
-
- "outdir_sep_dirs": OptionInfo("Directories ", "", gr.HTML),
- "outdir_samples": OptionInfo("", "Output directory for images", component_args=hide_dirs, folder=True),
- "outdir_txt2img_samples": OptionInfo("outputs/text", 'Directory for text generate', component_args=hide_dirs, folder=True),
- "outdir_img2img_samples": OptionInfo("outputs/image", 'Directory for image generate', component_args=hide_dirs, folder=True),
- "outdir_control_samples": OptionInfo("outputs/control", 'Directory for control generate', component_args=hide_dirs, folder=True),
- "outdir_extras_samples": OptionInfo("outputs/extras", 'Directory for processed images', component_args=hide_dirs, folder=True),
- "outdir_save": OptionInfo("outputs/save", "Directory for manually saved images", component_args=hide_dirs, folder=True),
- "outdir_video": OptionInfo("outputs/video", "Directory for videos", component_args=hide_dirs, folder=True),
- "outdir_init_images": OptionInfo("outputs/init-images", "Directory for init images", component_args=hide_dirs, folder=True),
-
- "outdir_sep_grids": OptionInfo("Grids ", "", gr.HTML),
- "grid_extended_filename": OptionInfo(True, "Add extended info (seed, prompt) to filename when saving grid", gr.Checkbox, {"visible": False}),
- "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory", gr.Checkbox, {"visible": False}),
- "outdir_grids": OptionInfo("", "Output directory for grids", component_args=hide_dirs, folder=True),
- "outdir_txt2img_grids": OptionInfo("outputs/grids", 'Output directory for txt2img grids', component_args=hide_dirs, folder=True),
- "outdir_img2img_grids": OptionInfo("outputs/grids", 'Output directory for img2img grids', component_args=hide_dirs, folder=True),
- "outdir_control_grids": OptionInfo("outputs/grids", 'Output directory for control grids', component_args=hide_dirs, folder=True),
-}))
-
-options_templates.update(options_section(('ui', "User Interface"), {
- "motd": OptionInfo(True, "Show MOTD"),
- "gradio_theme": OptionInfo("black-teal", "UI theme", gr.Dropdown, lambda: {"choices": theme.list_themes()}, refresh=theme.refresh_themes),
- "theme_style": OptionInfo("Auto", "Theme mode", gr.Radio, {"choices": ["Auto", "Dark", "Light"]}),
- "font_size": OptionInfo(16, "Font size", gr.Slider, {"minimum": 8, "maximum": 32, "step": 1, "visible": True}),
- "tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"], "visible": False}),
- "gallery_height": OptionInfo("", "Gallery height", gr.Textbox),
- "compact_view": OptionInfo(False, "Compact view"),
- "return_grid": OptionInfo(True, "Show grid in results"),
- "return_mask": OptionInfo(False, "Inpainting include greyscale mask in results"),
- "return_mask_composite": OptionInfo(False, "Inpainting include masked composite in results"),
- "disable_weights_auto_swap": OptionInfo(True, "Do not change selected model when reading generation parameters"),
- "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
- "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
- "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
- "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing ", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
- "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }), # pylint: disable=anomalous-backslash-in-string
- "quicksettings_list": OptionInfo(["sd_model_checkpoint"] if backend == Backend.ORIGINAL else ["sd_model_checkpoint", "sd_model_refiner"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
- "ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
-}))
-
-options_templates.update(options_section(('live-preview', "Live Previews"), {
- "show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
- "live_previews_enable": OptionInfo(True, "Show live previews of the created image", gr.Checkbox, {"visible": False}),
- "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid", gr.Checkbox, {"visible": False}),
- "notification_audio_enable": OptionInfo(False, "Play a sound when images are finished generating"),
- "notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound", component_args=hide_dirs, folder=True),
- "show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
- "show_progress_type": OptionInfo("Approximate", "Live preview method", gr.Radio, {"choices": ["Simple", "Approximate", "TAESD", "Full VAE"]}),
- "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"], "visible": False}),
- "live_preview_refresh_period": OptionInfo(500, "Progress update period", gr.Slider, {"minimum": 0, "maximum": 5000, "step": 25}),
- "logmonitor_show": OptionInfo(True, "Show log view"),
- "logmonitor_refresh_period": OptionInfo(5000, "Log view update period", gr.Slider, {"minimum": 0, "maximum": 30000, "step": 25}),
-}))
-
-options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
- "show_samplers": OptionInfo([], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
- 'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
- "scheduler_eta": OptionInfo(1.0, "Noise multiplier (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "schedulers_solver_order": OptionInfo(2, "Solver order (where applicable)", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}),
-
- # managed from ui.py for backend original
- "schedulers_brownian_noise": OptionInfo(True, "Use Brownian noise", gr.Checkbox, {"visible": False}),
- "schedulers_discard_penultimate": OptionInfo(True, "Discard penultimate sigma", gr.Checkbox, {"visible": False}),
- "schedulers_sigma": OptionInfo("default", "Sigma algorithm", gr.Radio, {"choices": ['default', 'karras', 'exponential', 'polyexponential'], "visible": False}),
- "schedulers_use_karras": OptionInfo(True, "Use Karras sigmas", gr.Checkbox, {"visible": False}),
- "schedulers_use_thresholding": OptionInfo(False, "Use dynamic thresholding", gr.Checkbox, {"visible": False}),
- "schedulers_use_loworder": OptionInfo(True, "Use simplified solvers in final steps", gr.Checkbox, {"visible": False}),
- "schedulers_prediction_type": OptionInfo("default", "Override model prediction type", gr.Radio, {"choices": ['default', 'epsilon', 'sample', 'v_prediction']}),
-
- # managed from ui.py for backend diffusers
- "schedulers_sep_diffusers": OptionInfo("Diffusers specific config ", "", gr.HTML),
- "schedulers_dpm_solver": OptionInfo("sde-dpmsolver++", "DPM solver algorithm", gr.Radio, {"choices": ['dpmsolver', 'dpmsolver++', 'sde-dpmsolver', 'sde-dpmsolver++']}),
- "schedulers_beta_schedule": OptionInfo("default", "Beta schedule", gr.Radio, {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2']}),
- 'schedulers_beta_start': OptionInfo(0, "Beta start", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001}),
- 'schedulers_beta_end': OptionInfo(0, "Beta end", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001}),
- 'schedulers_timesteps_range': OptionInfo(1000, "Timesteps range", gr.Slider, {"minimum": 250, "maximum": 4000, "step": 1}),
- "schedulers_rescale_betas": OptionInfo(False, "Rescale betas with zero terminal SNR", gr.Checkbox),
-
- # managed from ui.py for backend original k-diffusion
- "schedulers_sep_kdiffusers": OptionInfo("K-Diffusion specific config ", "", gr.HTML),
- "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
- "enable_quantization": OptionInfo(True, "Enable quantization for sharper and cleaner results"),
- 's_churn': OptionInfo(0.0, "Sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 's_min_uncond': OptionInfo(0.0, "Sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
- 's_tmin': OptionInfo(0.0, "Sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 's_noise': OptionInfo(1.0, "Sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 's_min': OptionInfo(0.0, "Sigma min", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 's_max': OptionInfo(0.0, "Sigma max", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 1.0}),
- "schedulers_sep_compvis": OptionInfo("CompVis specific config ", "", gr.HTML),
- 'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
- 'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
- "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
- # TODO pad_cond_uncond implementation missing for original backend
- "pad_cond_uncond": OptionInfo(True, "Pad prompt and negative prompt to be same length", gr.Checkbox, {"visible": False}),
- # TODO batch_cond-uncond implementation missing for original backend
- "batch_cond_uncond": OptionInfo(True, "Do conditional and unconditional denoising in one batch", gr.Checkbox, {"visible": False}),
-}))
-
-options_templates.update(options_section(('postprocessing', "Postprocessing"), {
- 'postprocessing_enable_in_main_ui': OptionInfo([], "Enable additional postprocessing operations", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
- 'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
-
- "postprocessing_sep_img2img": OptionInfo("Img2Img & Inpainting ", "", gr.HTML),
- "img2img_color_correction": OptionInfo(False, "Apply color correction"),
- "img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified", gr.Checkbox, { "visible": False }),
- "img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", ui_components.FormColorPicker, {}),
- "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
- "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
-
- "postprocessing_sep_face_restoration": OptionInfo("Face Restoration ", "", gr.HTML),
- "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
- "code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
- "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
-
- "postprocessing_sep_upscalers": OptionInfo("Upscaling ", "", gr.HTML),
- "upscaler_unload": OptionInfo(False, "Unload upscaler after processing"),
- "upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers], "visible": False}, refresh=refresh_upscalers),
- "upscaler_tile_size": OptionInfo(192, "Upscaler tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
- "upscaler_tile_overlap": OptionInfo(8, "Upscaler tile overlap", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
-}))
-
-options_templates.update(options_section(('training', "Training"), {
- "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible"),
- "pin_memory": OptionInfo(True, "Pin training dataset to memory"),
- "save_optimizer_state": OptionInfo(False, "Save resumable optimizer state when training"),
- "save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file on training start"),
- "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
- "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
- "embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train', 'templates'), "Embeddings train templates directory", folder=True),
- "training_image_repeats_per_epoch": OptionInfo(1, "Image repeats per epoch", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
- "training_write_csv_every": OptionInfo(0, "Save loss CSV file every n steps"),
- "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging"),
- "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard"),
- "training_tensorboard_flush_every": OptionInfo(120, "Tensorboard flush period"),
-}))
-
-options_templates.update(options_section(('interrogate', "Interrogate"), {
- "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
- "interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results"),
- "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
- "interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
- "interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
- "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file", gr.Slider, { "visible": False }),
- "interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "Interrogate: skip categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
- "interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
- "deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"),
- "deepbooru_use_spaces": OptionInfo(False, "Use spaces for tags in deepbooru"),
- "deepbooru_escape": OptionInfo(True, "Escape brackets in deepbooru"),
- "deepbooru_filter_tags": OptionInfo("", "Filter out tags from deepbooru output"),
-}))
-
-options_templates.update(options_section(('extra_networks', "Extra Networks"), {
- "extra_networks_sep1": OptionInfo("Extra networks UI ", "", gr.HTML),
- "extra_networks": OptionInfo(["All"], "Extra networks", ui_components.DropdownMulti, lambda: {"choices": ['All'] + [en.title for en in extra_networks]}),
- "extra_networks_view": OptionInfo("gallery", "UI view", gr.Radio, {"choices": ["gallery", "list"]}),
- "extra_networks_card_cover": OptionInfo("sidebar", "UI position", gr.Radio, {"choices": ["cover", "inline", "sidebar"]}),
- "extra_networks_height": OptionInfo(53, "UI height (%)", gr.Slider, {"minimum": 10, "maximum": 100, "step": 1}),
- "extra_networks_sidebar_width": OptionInfo(35, "UI sidebar width (%)", gr.Slider, {"minimum": 10, "maximum": 80, "step": 1}),
- "extra_networks_card_size": OptionInfo(160, "UI card size (px)", gr.Slider, {"minimum": 20, "maximum": 2000, "step": 1}),
- "extra_networks_card_square": OptionInfo(True, "UI disable variable aspect ratio"),
- "extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
- "extra_networks_sep2": OptionInfo("Extra networks general ", "", gr.HTML),
- "extra_network_skip_indexing": OptionInfo(False, "Build info on first access", gr.Checkbox),
- "extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "extra_networks_sep3": OptionInfo("Extra networks settings ", "", gr.HTML),
- "extra_networks_styles": OptionInfo(True, "Show built-in styles"),
- "lora_preferred_name": OptionInfo("filename", "LoRA preffered name", gr.Radio, {"choices": ["filename", "alias"]}),
- "lora_add_hashes_to_infotext": OptionInfo(True, "LoRA add hash info"),
- "lora_force_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA use alternative loading method"),
- "lora_fuse_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA use merge when using alternative method"),
- "lora_in_memory_limit": OptionInfo(0, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
- "lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
- "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
-}))
-
-options_templates.update(options_section((None, "Hidden options"), {
- "disabled_extensions": OptionInfo([], "Disable these extensions"),
- "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "user", "all"]}),
- "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
-}))
-
-options_templates.update()
-
-
-class Options:
- data = None
- data_labels = options_templates
- filename = None
- typemap = {int: float}
-
- def __init__(self):
- self.data = {k: v.default for k, v in self.data_labels.items()}
-
- def __setattr__(self, key, value): # pylint: disable=inconsistent-return-statements
- if self.data is not None:
- if key in self.data or key in self.data_labels:
- if cmd_opts.freeze:
- log.warning(f'Settings are frozen: {key}')
- return
- if cmd_opts.hide_ui_dir_config and key in restricted_opts:
- log.warning(f'Settings key is restricted: {key}')
- return
- self.data[key] = value
- return
- return super(Options, self).__setattr__(key, value) # pylint: disable=super-with-arguments
-
- def __getattr__(self, item):
- if self.data is not None:
- if item in self.data:
- return self.data[item]
- if item in self.data_labels:
- return self.data_labels[item].default
- return super(Options, self).__getattribute__(item) # pylint: disable=super-with-arguments
-
- def set(self, key, value):
- """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
- oldval = self.data.get(key, None)
- if oldval is None:
- oldval = self.data_labels[key].default
- if oldval == value:
- return False
- try:
- setattr(self, key, value)
- except RuntimeError:
- return False
- if self.data_labels[key].onchange is not None:
- try:
- self.data_labels[key].onchange()
- except Exception as e:
- log.error(f'Error in onchange callback: {key} {value} {e}')
- setattr(self, key, oldval)
- return False
- return True
-
- def get_default(self, key):
- """returns the default value for the key"""
- data_label = self.data_labels.get(key)
- return data_label.default if data_label is not None else None
-
- def save(self, filename=None, silent=False):
- if filename is None:
- filename = self.filename
- if cmd_opts.freeze:
- log.warning(f'Settings saving is disabled: {filename}')
- return
- try:
- # output = json.dumps(self.data, indent=2)
- diff = {}
- unused_settings = []
- for k, v in self.data.items():
- if k in self.data_labels:
- if type(v) is list:
- diff[k] = v
- if self.data_labels[k].default != v:
- diff[k] = v
- else:
- if k not in compatibility_opts:
- unused_settings.append(k)
- diff[k] = v
- writefile(diff, filename, silent=silent)
- if len(unused_settings) > 0:
- log.debug(f"Unused settings: {unused_settings}")
- except Exception as e:
- log.error(f'Saving settings failed: {filename} {e}')
-
- def same_type(self, x, y):
- if x is None or y is None:
- return True
- type_x = self.typemap.get(type(x), type(x))
- type_y = self.typemap.get(type(y), type(y))
- return type_x == type_y
-
- def load(self, filename=None):
- if filename is None:
- filename = self.filename
- if not os.path.isfile(filename):
- log.debug(f'Created default config: {filename}')
- self.save(filename)
- return
- self.data = readfile(filename, lock=True)
- if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
- self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
- unknown_settings = []
- for k, v in self.data.items():
- info = self.data_labels.get(k, None)
- if info is not None and not self.same_type(info.default, v):
- log.error(f"Error: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})")
- if info is None and k not in compatibility_opts:
- unknown_settings.append(k)
- if len(unknown_settings) > 0:
- log.debug(f"Unknown settings: {unknown_settings}")
-
- def onchange(self, key, func, call=True):
- item = self.data_labels.get(key)
- item.onchange = func
- if call:
- func()
-
- def dumpjson(self):
- d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
- metadata = {
- k: {
- "is_stored": k in self.data and self.data[k] != self.data_labels[k].default, # pylint: disable=unnecessary-dict-index-lookup
- "tab_name": v.section[0]
- } for k, v in self.data_labels.items()
- }
- return json.dumps({"values": d, "metadata": metadata})
-
- def add_option(self, key, info):
- self.data_labels[key] = info
-
- def reorder(self):
- """reorder settings so that all items related to section always go together"""
- section_ids = {}
- settings_items = self.data_labels.items()
- for _k, item in settings_items:
- if item.section not in section_ids:
- section_ids[item.section] = len(section_ids)
- self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
-
- def cast_value(self, key, value):
- """casts an arbitrary to the same type as this setting's value with key
- Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
- """
- if value is None:
- return None
- default_value = self.data_labels[key].default
- if default_value is None:
- default_value = getattr(self, key, None)
- if default_value is None:
- return None
- expected_type = type(default_value)
- if expected_type == bool and value == "False":
- value = False
- elif expected_type == type(value):
- pass
- else:
- value = expected_type(value)
- return value
-
-profiler = None
-opts = Options()
-config_filename = cmd_opts.config
-opts.load(config_filename)
-cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
-if cmd_opts.backend is None:
- backend = Backend.DIFFUSERS if cmd_opts.use_openvino or opts.data.get('sd_backend', 'original') == 'diffusers' else Backend.ORIGINAL
-else:
- backend = Backend.DIFFUSERS if cmd_opts.use_openvino or cmd_opts.backend.lower() == 'diffusers' else Backend.ORIGINAL
-opts.data['sd_backend'] = 'diffusers' if backend == Backend.DIFFUSERS else 'original'
-if cmd_opts.use_xformers:
- opts.data['cross_attention_optimization'] = 'xFormers'
-opts.data['uni_pc_lower_order_final'] = opts.schedulers_use_loworder # compatibility
-opts.data['uni_pc_order'] = opts.schedulers_solver_order # compatibility
-log.info(f'Engine: backend={backend} compute={devices.backend} mode={devices.inference_context.__name__} device={devices.get_optimal_device_name()} cross-optimization="{opts.cross_attention_optimization}"')
-log.info(f'Device: {print_dict(devices.get_gpu_info())}')
-
-prompt_styles = modules.styles.StyleDatabase(opts)
-cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
-device = devices.device
-batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
-parallel_processing_allowed = not cmd_opts.lowvram
-mem_mon = modules.memmon.MemUsageMonitor("MemMon", devices.device)
-max_workers = 4
-if devices.backend == "directml":
- directml_do_hijack()
-
-class TotalTQDM: # compatibility with previous global-tqdm
- # import tqdm
- def __init__(self):
- pass
- def reset(self):
- pass
- def update(self):
- pass
- def updateTotal(self, new_total):
- pass
- def clear(self):
- pass
-total_tqdm = TotalTQDM()
-
-
-def restart_server(restart=True):
- if demo is None:
- return
- log.warning('Server shutdown requested')
- try:
- sys.tracebacklimit = 0
- stdout = io.StringIO()
- stderr = io.StringIO()
- with contextlib.redirect_stdout(stdout), contextlib.redirect_stdout(stderr):
- demo.server.wants_restart = restart
- demo.server.should_exit = True
- demo.server.force_exit = True
- demo.close(verbose=False)
- demo.server.close()
- demo.fns = []
- time.sleep(1)
- sys.tracebacklimit = 100
- # os._exit(0)
- except (Exception, BaseException) as e:
- log.error(f'Server shutdown error: {e}')
- if restart:
- log.info('Server will restart')
-
-
-def restore_defaults(restart=True):
- if os.path.exists(cmd_opts.config):
- log.info('Restoring server defaults')
- os.remove(cmd_opts.config)
- restart_server(restart)
-
-
-def listdir(path):
- if not os.path.exists(path):
- return []
- mtime = os.path.getmtime(path)
- if path in dir_timestamps and mtime == dir_timestamps[path]:
- return dir_cache[path]
- else:
- dir_cache[path] = [os.path.join(path, f) for f in os.listdir(path)]
- dir_timestamps[path] = mtime
- return dir_cache[path]
-
-
-def walk_files(path, allowed_extensions=None):
- if not os.path.exists(path):
- return
- if allowed_extensions is not None:
- allowed_extensions = set(allowed_extensions)
- for root, _dirs, files in os.walk(path, followlinks=True):
- for filename in files:
- if allowed_extensions is not None:
- _, ext = os.path.splitext(filename)
- if ext not in allowed_extensions:
- continue
- yield os.path.join(root, filename)
-
-
-def html_path(filename):
- return os.path.join(paths.script_path, "html", filename)
-
-
-def html(filename):
- path = html_path(filename)
- if os.path.exists(path):
- with open(path, encoding="utf8") as file:
- return file.read()
- return ""
-
-
-def get_version():
- version = None
- if version is None:
- try:
- import subprocess
- res = subprocess.run('git log --pretty=format:"%h %ad" -1 --date=short', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
- ver = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ' '
- githash, updated = ver.split(' ')
- res = subprocess.run('git remote get-url origin', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
- origin = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
- res = subprocess.run('git rev-parse --abbrev-ref HEAD', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
- branch = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
- version = {
- 'app': 'sd.next',
- 'updated': updated,
- 'hash': githash,
- 'url': origin.replace('\n', '') + '/tree/' + branch.replace('\n', '')
- }
- except Exception:
- version = { 'app': 'sd.next' }
- return version
-
-
-def req(url_addr, headers = None, **kwargs):
- if headers is None:
- headers = { 'Content-type': 'application/json' }
- try:
- res = requests.get(url_addr, timeout=30, headers=headers, verify=False, allow_redirects=True, **kwargs)
- except Exception as e:
- log.error(f'HTTP request error: url={url_addr} {e}')
- res = { 'status_code': 500, 'text': f'HTTP request error: url={url_addr} {e}' }
- res = SimpleNamespace(**res)
- return res
-
-
-sd_model: diffusers.DiffusionPipeline = None # dummy and overwritten by class
-sd_refiner: diffusers.DiffusionPipeline = None # dummy and overwritten by class
-sd_model_type: str = '' # dummy and overwritten by class
-sd_refiner_type: str = '' # dummy and overwritten by class
-compiled_model_state = None
-
-from modules.modeldata import Shared # pylint: disable=ungrouped-imports
-sys.modules[__name__].__class__ = Shared
+import io
+import os
+import sys
+import time
+import json
+import contextlib
+from types import SimpleNamespace
+from urllib.parse import urlparse
+from enum import Enum
+import requests
+import gradio as gr
+import fasteners
+import orjson
+import diffusers
+from rich.console import Console
+from modules import errors, shared_items, shared_state, cmd_args, ui_components, theme
+from modules.paths import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # pylint: disable=W0611
+from modules.dml import memory_providers, default_memory_provider, directml_do_hijack
+import modules.interrogate
+import modules.memmon
+import modules.styles
+import modules.devices as devices # pylint: disable=R0402
+import modules.paths as paths
+from installer import print_dict
+from installer import log as central_logger # pylint: disable=E0611
+
+
+errors.install([gr])
+demo: gr.Blocks = None
+log = central_logger
+progress_print_out = sys.stdout
+parser = cmd_args.parser
+url = 'https://github.com/vladmandic/automatic'
+cmd_opts, _ = parser.parse_known_args()
+hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
+xformers_available = False
+clip_model = None
+interrogator = modules.interrogate.InterrogateModels("interrogate")
+sd_upscalers = []
+face_restorers = []
+tab_names = []
+extra_networks = []
+options_templates = {}
+hypernetworks = {}
+loaded_hypernetworks = []
+settings_components = None
+latent_upscale_default_mode = "None"
+latent_upscale_modes = {
+ "Latent Nearest": {"mode": "nearest", "antialias": False},
+ "Latent Nearest-exact": {"mode": "nearest-exact", "antialias": False},
+ "Latent Area": {"mode": "area", "antialias": False},
+ "Latent Bilinear": {"mode": "bilinear", "antialias": False},
+ "Latent Bicubic": {"mode": "bicubic", "antialias": False},
+ "Latent Bilinear antialias": {"mode": "bilinear", "antialias": True},
+ "Latent Bicubic antialias": {"mode": "bicubic", "antialias": True},
+ # "Latent Linear": {"mode": "linear", "antialias": False}, # not supported for latents with channels=4
+ # "Latent Trilinear": {"mode": "trilinear", "antialias": False}, # not supported for latents with channels=4
+}
+restricted_opts = {
+ "samples_filename_pattern",
+ "directories_filename_pattern",
+ "outdir_samples",
+ "outdir_txt2img_samples",
+ "outdir_img2img_samples",
+ "outdir_extras_samples",
+ "outdir_grids",
+ "outdir_txt2img_grids",
+ "outdir_save",
+ "outdir_init_images"
+}
+resize_modes = ["None", "Fixed", "Crop", "Fill", "Latent"]
+compatibility_opts = ['clip_skip', 'uni_pc_lower_order_final', 'uni_pc_order']
+console = Console(log_time=True, log_time_format='%H:%M:%S-%f')
+dir_timestamps = {}
+dir_cache = {}
+
+
+class Backend(Enum):
+ ORIGINAL = 1
+ DIFFUSERS = 2
+
+
+state = shared_state.State()
+if not hasattr(cmd_opts, "use_openvino"):
+ cmd_opts.use_openvino = False
+
+
+def readfile(filename, silent=False, lock=False):
+ data = {}
+ lock_file = None
+ locked = False
+ try:
+ # if not os.path.exists(filename):
+ # return {}
+ t0 = time.time()
+ if lock:
+ lock_file = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
+ locked = lock_file.acquire_read_lock(blocking=True, timeout=3)
+ with open(filename, "rb") as file:
+ b = file.read()
+ data = orjson.loads(b) # pylint: disable=no-member
+ # if type(data) is str:
+ # data = json.loads(data)
+ t1 = time.time()
+ if not silent:
+ log.debug(f'Read: file="{filename}" json={len(data)} bytes={os.path.getsize(filename)} time={t1-t0:.3f}')
+ except Exception as e:
+ if not silent:
+ log.error(f'Reading failed: {filename} {e}')
+ finally:
+ if lock_file is not None:
+ lock_file.release_read_lock()
+ if locked and os.path.exists(f"{filename}.lock"):
+ os.remove(f"{filename}.lock")
+ return data
+
+
+def writefile(data, filename, mode='w', silent=False, atomic=False):
+ lock = None
+ locked = False
+ import tempfile
+
+
+ def default(obj):
+ log.error(f"Saving: {filename} not a valid object: {obj}")
+ return str(obj)
+
+ try:
+ t0 = time.time()
+ # skipkeys=True, ensure_ascii=True, check_circular=True, allow_nan=True
+ if type(data) == dict:
+ output = json.dumps(data, indent=2, default=default)
+ elif type(data) == list:
+ output = json.dumps(data, indent=2, default=default)
+ elif isinstance(data, object):
+ simple = {}
+ for k in data.__dict__:
+ if data.__dict__[k] is not None:
+ simple[k] = data.__dict__[k]
+ output = json.dumps(simple, indent=2, default=default)
+ else:
+ raise ValueError('not a valid object')
+ lock = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
+ locked = lock.acquire_write_lock(blocking=True, timeout=3)
+ if atomic:
+ with tempfile.NamedTemporaryFile(mode=mode, encoding="utf8", delete=False, dir=os.path.dirname(filename)) as f:
+ f.write(output)
+ f.flush()
+ os.fsync(f.fileno())
+ os.replace(f.name, filename)
+ else:
+ with open(filename, mode=mode, encoding="utf8") as file:
+ file.write(output)
+ t1 = time.time()
+ if not silent:
+ log.debug(f'Save: file="{filename}" json={len(data)} bytes={len(output)} time={t1-t0:.3f}')
+ except Exception as e:
+ log.error(f'Saving failed: {filename} {e}')
+ errors.display(e, 'Saving failed')
+ finally:
+ if lock is not None:
+ lock.release_read_lock()
+ if locked and os.path.exists(f"{filename}.lock"):
+ os.remove(f"{filename}.lock")
+
+
+# early select backend
+default_backend = 'original'
+early_opts = readfile(cmd_opts.config, silent=True)
+early_backend = early_opts.get('sd_backend', default_backend)
+backend = Backend.DIFFUSERS if early_backend.lower() == 'diffusers' else Backend.ORIGINAL
+if cmd_opts.backend is not None: # override with args
+ backend = Backend.DIFFUSERS if cmd_opts.backend.lower() == 'diffusers' else Backend.ORIGINAL
+if cmd_opts.use_openvino: # override for openvino
+ backend = Backend.DIFFUSERS
+ from modules.intel.openvino import get_device_list as get_openvino_device_list # pylint: disable=ungrouped-imports
+
+
+class OptionInfo:
+ def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, folder=None, submit=None, comment_before='', comment_after=''):
+ self.default = default
+ self.label = label
+ self.component = component
+ self.component_args = component_args
+ self.onchange = onchange
+ self.section = section
+ self.refresh = refresh
+ self.folder = folder
+ self.comment_before = comment_before # HTML text that will be added after label in UI
+ self.comment_after = comment_after # HTML text that will be added before label in UI
+ self.submit = submit
+
+ def link(self, label, uri):
+ self.comment_before += f"[{label} ]"
+ return self
+
+ def js(self, label, js_func):
+ self.comment_before += f"[{label} ]"
+ return self
+
+ def info(self, info):
+ self.comment_after += f"({info}) "
+ return self
+
+ def html(self, info):
+ self.comment_after += f"{info} "
+ return self
+
+ def needs_restart(self):
+ self.comment_after += " (requires restart) "
+ return self
+
+
+def options_section(section_identifier, options_dict):
+ for v in options_dict.values():
+ v.section = section_identifier
+ return options_dict
+
+
+def list_checkpoint_tiles():
+ import modules.sd_models # pylint: disable=W0621
+ return modules.sd_models.checkpoint_tiles()
+
+default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.ckpt"
+
+
+def is_url(string):
+ parsed_url = urlparse(string)
+ return all([parsed_url.scheme, parsed_url.netloc])
+
+
+def reload_hypernetworks():
+ from modules.hypernetworks import hypernetwork
+ global hypernetworks # pylint: disable=W0603
+ hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir)
+
+
+def refresh_checkpoints():
+ import modules.sd_models # pylint: disable=W0621
+ return modules.sd_models.list_models()
+
+
+def refresh_vaes():
+ import modules.sd_vae # pylint: disable=W0621
+ modules.sd_vae.refresh_vae_list()
+
+
+def refresh_upscalers():
+ import modules.modelloader # pylint: disable=W0621
+ modules.modelloader.load_upscalers()
+
+
+def list_samplers():
+ import modules.sd_samplers # pylint: disable=W0621
+ modules.sd_samplers.set_samplers()
+ return modules.sd_samplers.all_samplers
+
+
+def temp_disable_extensions():
+ disable_safe = ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris', 'sd-webui-agent-scheduler', 'clip-interrogator-ext', 'stable-diffusion-webui-rembg', 'sd-extension-chainner', 'stable-diffusion-webui-images-browser']
+ disable_diffusers = ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris', 'sd-webui-animatediff']
+ disable_original = []
+ disabled = []
+ if cmd_opts.safe:
+ for ext in disable_safe:
+ if ext not in opts.disabled_extensions:
+ disabled.append(ext)
+ if backend == Backend.DIFFUSERS:
+ for ext in disable_diffusers:
+ if ext not in opts.disabled_extensions:
+ disabled.append(ext)
+ if backend == Backend.ORIGINAL:
+ for ext in disable_original:
+ if ext not in opts.disabled_extensions:
+ disabled.append(ext)
+ cmd_opts.controlnet_loglevel = 'WARNING'
+ return disabled
+
+
+if devices.backend == "cpu":
+ cross_attention_optimization_default = "Doggettx's"
+elif devices.backend == "mps":
+ cross_attention_optimization_default = "Doggettx's"
+elif devices.backend == "ipex":
+ cross_attention_optimization_default = "Scaled-Dot-Product"
+elif devices.backend == "directml":
+ cross_attention_optimization_default = "Sub-quadratic"
+elif devices.backend == "rocm":
+ cross_attention_optimization_default = "Sub-quadratic"
+else: # cuda
+ cross_attention_optimization_default ="Scaled-Dot-Product"
+
+
+options_templates.update(options_section(('sd', "Execution & Models"), {
+ "sd_backend": OptionInfo(default_backend, "Execution backend", gr.Radio, {"choices": ["original", "diffusers"] }),
+ "sd_model_checkpoint": OptionInfo(default_checkpoint, "Base model", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
+ "sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
+ "sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
+ "sd_checkpoint_autoload": OptionInfo(True, "Model autoload on start"),
+ "sd_model_dict": OptionInfo('None', "Use separate base dict", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
+ "stream_load": OptionInfo(False, "Load models using stream loading method", gr.Checkbox, {"visible": backend == Backend.ORIGINAL }),
+ "model_reuse_dict": OptionInfo(False, "Reuse loaded model dictionary", gr.Checkbox, {"visible": False}),
+ "prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, {"choices": ["Full parser", "Compel parser", "A1111 parser", "Fixed attention"] }),
+ "prompt_mean_norm": OptionInfo(True, "Prompt attention normalization", gr.Checkbox, {"visible": backend == Backend.ORIGINAL }),
+ "comma_padding_backtrack": OptionInfo(20, "Prompt padding", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1, "visible": backend == Backend.ORIGINAL }),
+ "sd_checkpoint_cache": OptionInfo(0, "Cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": backend == Backend.ORIGINAL }),
+ "sd_vae_checkpoint_cache": OptionInfo(0, "Cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
+ "sd_disable_ckpt": OptionInfo(False, "Disallow models in ckpt format"),
+}))
+
+options_templates.update(options_section(('cuda', "Compute Settings"), {
+ "math_sep": OptionInfo("Execution precision ", "", gr.HTML),
+ "precision": OptionInfo("Autocast", "Precision type", gr.Radio, {"choices": ["Autocast", "Full"]}),
+ "cuda_dtype": OptionInfo("FP32" if sys.platform == "darwin" or cmd_opts.use_openvino else "BF16" if devices.backend == "ipex" else "FP16", "Device precision type", gr.Radio, {"choices": ["FP32", "FP16", "BF16"]}),
+ "no_half": OptionInfo(False if not cmd_opts.use_openvino else True, "Full precision for model (--no-half)", None, None, None),
+ "no_half_vae": OptionInfo(False if not cmd_opts.use_openvino else True, "Full precision for VAE (--no-half-vae)"),
+ "upcast_sampling": OptionInfo(False if sys.platform != "darwin" else True, "Upcast sampling"),
+ "upcast_attn": OptionInfo(False, "Upcast attention layer"),
+ "cuda_cast_unet": OptionInfo(False, "Fixed UNet precision"),
+ "disable_nan_check": OptionInfo(True, "Disable NaN check", gr.Checkbox, {"visible": False}),
+ "rollback_vae": OptionInfo(False, "Attempt VAE roll back for NaN values"),
+
+ "cross_attention_sep": OptionInfo("Attention ", "", gr.HTML),
+ "cross_attention_optimization": OptionInfo(cross_attention_optimization_default, "Attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
+ "cross_attention_options": OptionInfo([], "Attention advanced options", gr.CheckboxGroup, {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
+ "sub_quad_sep": OptionInfo("Sub-quadratic options ", "", gr.HTML),
+ "sub_quad_q_chunk_size": OptionInfo(512, "Attention query chunk size", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
+ "sub_quad_kv_chunk_size": OptionInfo(512, "Attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
+ "sub_quad_chunk_threshold": OptionInfo(80, "Attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
+
+ "other_sep": OptionInfo("Execution precision ", "", gr.HTML),
+ "opt_channelslast": OptionInfo(False, "Use channels last "),
+ "cudnn_benchmark": OptionInfo(False, "Full-depth cuDNN benchmark feature"),
+ "diffusers_fuse_projections": OptionInfo(False, "Fused projections"),
+ "torch_gc_threshold": OptionInfo(80, "Memory usage threshold for GC", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
+
+ "cuda_compile_sep": OptionInfo("Model Compile ", "", gr.HTML),
+ "cuda_compile": OptionInfo([] if not cmd_opts.use_openvino else ["Model", "VAE", "Upscaler"], "Compile Model", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder", "Upscaler"]}),
+ "cuda_compile_backend": OptionInfo("none" if not cmd_opts.use_openvino else "openvino_fx", "Model compile backend", gr.Radio, {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex', 'openvino_fx', 'stable-fast']}),
+ "cuda_compile_mode": OptionInfo("default", "Model compile mode", gr.Radio, {"choices": ['default', 'reduce-overhead', 'max-autotune', 'max-autotune-no-cudagraphs']}),
+ "cuda_compile_fullgraph": OptionInfo(False, "Model compile fullgraph"),
+ "cuda_compile_precompile": OptionInfo(False, "Model compile precompile"),
+ "cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
+ "cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
+ "diffusers_quantization": OptionInfo(False, "Dynamic quantization with TorchAO"),
+ "nncf_compress_weights": OptionInfo([], "Compress Model weights with NNCF", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder"], "visible": backend == Backend.DIFFUSERS}),
+
+ "ipex_sep": OptionInfo("IPEX ", "", gr.HTML, {"visible": devices.backend == "ipex"}),
+ "ipex_optimize": OptionInfo(["Model", "VAE", "Text Encoder", "Upscaler"] if devices.backend == "ipex" else [], "IPEX Optimize for Intel GPUs", gr.CheckboxGroup, {"choices": ["Model", "VAE", "Text Encoder", "Upscaler"], "visible": devices.backend == "ipex"}),
+
+ "directml_sep": OptionInfo("IPEX and DirectML ", "", gr.HTML, {"visible": devices.backend == "directml"}),
+ "directml_memory_provider": OptionInfo(default_memory_provider, 'DirectML memory stats provider', gr.Radio, {"choices": memory_providers, "visible": devices.backend == "directml"}),
+ "directml_catch_nan": OptionInfo(False, "DirectML retry ops for NaN", gr.Checkbox, {"visible": devices.backend == "directml"}),
+
+ "openvino_sep": OptionInfo("OpenVINO ", "", gr.HTML, {"visible": cmd_opts.use_openvino}),
+ "openvino_devices": OptionInfo([], "OpenVINO devices to use", gr.CheckboxGroup, {"choices": get_openvino_device_list() if cmd_opts.use_openvino else [], "visible": cmd_opts.use_openvino}),
+ "nncf_compress_weights_mode": OptionInfo("INT8", "OpenVINO compress mode for NNCF", gr.Radio, {"choices": ['INT8', 'INT4_SYM', 'INT4_ASYM', 'NF4'], "visible": cmd_opts.use_openvino}),
+ "nncf_compress_weights_raito": OptionInfo(1.0, "OpenVINO compress ratio for NNCF", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": cmd_opts.use_openvino}),
+ "openvino_disable_model_caching": OptionInfo(False, "OpenVINO disable model caching", gr.Checkbox, {"visible": cmd_opts.use_openvino}),
+}))
+
+options_templates.update(options_section(('advanced', "Inference Settings"), {
+ "token_merging_sep": OptionInfo("Token merging ", "", gr.HTML),
+ "token_merging_ratio": OptionInfo(0.0, "Token merging ratio for txt2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
+ "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
+ "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for hires", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
+
+ "freeu_sep": OptionInfo("FreeU ", "", gr.HTML),
+ "freeu_enabled": OptionInfo(False, "FreeU"),
+ "freeu_b1": OptionInfo(1.2, "1st stage backbone factor", gr.Slider, {"minimum": 1.0, "maximum": 2.0, "step": 0.01}),
+ "freeu_b2": OptionInfo(1.4, "2nd stage backbone factor", gr.Slider, {"minimum": 1.0, "maximum": 2.0, "step": 0.01}),
+ "freeu_s1": OptionInfo(0.9, "1st stage skip factor", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "freeu_s2": OptionInfo(0.2, "2nd stage skip factor", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+
+ "hypertile_sep": OptionInfo("HyperTile ", "", gr.HTML),
+ "hypertile_unet_enabled": OptionInfo(False, "HyperTile UNet"),
+ "hypertile_unet_tile": OptionInfo(256, "HyperTile UNet tile size", gr.Slider, {"minimum": 0, "maximum": 1024, "step": 8}),
+ "hypertile_vae_enabled": OptionInfo(False, "HyperTile VAE", gr.Checkbox),
+ "hypertile_vae_tile": OptionInfo(128, "HyperTile VAE tile size", gr.Slider, {"minimum": 0, "maximum": 1024, "step": 8}),
+
+ "inference_other_sep": OptionInfo("Other ", "", gr.HTML),
+ "batch_frame_mode": OptionInfo(False, "Parallel process images in batch"),
+ "inference_mode": OptionInfo("no-grad", "Torch inference mode", gr.Radio, {"choices": ["no-grad", "inference-mode", "none"]}),
+ "sd_vae_sliced_encode": OptionInfo(False, "VAE sliced encode"),
+}))
+
+options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
+ "diffusers_pipeline": OptionInfo('Autodetect', 'Diffusers pipeline', gr.Dropdown, lambda: {"choices": list(shared_items.get_pipelines()) }),
+ "diffusers_move_base": OptionInfo(True, "Move base model to CPU when using refiner"),
+ "diffusers_move_unet": OptionInfo(True, "Move base model to CPU when using VAE"),
+ "diffusers_move_refiner": OptionInfo(True, "Move refiner model to CPU when not in use"),
+ "diffusers_extract_ema": OptionInfo(True, "Use model EMA weights when possible"),
+ "diffusers_generator_device": OptionInfo("GPU", "Generator device", gr.Radio, {"choices": ["GPU", "CPU", "Unset"]}),
+ "diffusers_model_cpu_offload": OptionInfo(False, "Model CPU offload (--medvram)"),
+ "diffusers_seq_cpu_offload": OptionInfo(False, "Sequential CPU offload (--lowvram)"),
+ "diffusers_vae_upcast": OptionInfo("default", "VAE upcasting", gr.Radio, {"choices": ['default', 'true', 'false']}),
+ "diffusers_vae_slicing": OptionInfo(True, "VAE slicing"),
+ "diffusers_vae_tiling": OptionInfo(True if not cmd_opts.use_openvino else False, "VAE tiling"),
+ "diffusers_attention_slicing": OptionInfo(False, "Attention slicing"),
+ "diffusers_model_load_variant": OptionInfo("default", "Preferred Model variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
+ "diffusers_vae_load_variant": OptionInfo("default", "Preferred VAE variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
+ "custom_diffusers_pipeline": OptionInfo('', 'Load custom Diffusers pipeline'),
+ "diffusers_eval": OptionInfo(True, "Force model eval"),
+ "disable_accelerate": OptionInfo(False, "Disable accelerate"),
+ "diffusers_force_zeros": OptionInfo(True, "Force zeros for prompts when empty"),
+ "diffusers_aesthetics_score": OptionInfo(False, "Require aesthetics score"),
+ "diffusers_pooled": OptionInfo("default", "Diffusers SDXL pooled embeds", gr.Radio, {"choices": ['default', 'weighted']}),
+}))
+
+options_templates.update(options_section(('system-paths', "System Paths"), {
+ "models_paths_sep_options": OptionInfo("Models paths ", "", gr.HTML),
+ "models_dir": OptionInfo('models', "Base path where all models are stored", folder=True),
+ "ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Folder with stable diffusion models", folder=True),
+ "diffusers_dir": OptionInfo(os.path.join(paths.models_path, 'Diffusers'), "Folder with Hugggingface models", folder=True),
+ "hfcache_dir": OptionInfo(os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'hub'), "Folder for Hugggingface cache", folder=True),
+ "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Folder with VAE files", folder=True),
+ "sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
+ "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Folder with LoRA network(s)", folder=True),
+ "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", gr.Text, {"visible": False}),
+ "styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "File or Folder with user-defined styles", folder=True),
+ "embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Folder with textual inversion embeddings", folder=True),
+ "hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Folder with Hypernetwork models", folder=True),
+ "control_dir": OptionInfo(os.path.join(paths.models_path, 'control'), "Folder with Control models", folder=True),
+ "codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Folder with codeformer models", folder=True),
+ "gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Folder with GFPGAN models", folder=True),
+ "esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Folder with ESRGAN models", folder=True),
+ "bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Folder with BSRGAN models", folder=True),
+ "realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Folder with RealESRGAN models", folder=True),
+ "scunet_models_path": OptionInfo(os.path.join(paths.models_path, 'SCUNet'), "Folder with SCUNet models", folder=True),
+ "swinir_models_path": OptionInfo(os.path.join(paths.models_path, 'SwinIR'), "Folder with SwinIR models", folder=True),
+ "ldsr_models_path": OptionInfo(os.path.join(paths.models_path, 'LDSR'), "Folder with LDSR models", folder=True),
+ "clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Folder with CLIP models", folder=True),
+
+ "other_paths_sep_options": OptionInfo("Other paths ", "", gr.HTML),
+ "openvino_cache_path": OptionInfo('cache', "Directory for OpenVINO cache", folder=True),
+ "temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default", folder=True),
+ "clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"),
+}))
+
+options_templates.update(options_section(('saving-images', "Image Options"), {
+ "keep_incomplete": OptionInfo(True, "Keep incomplete images"),
+ "samples_save": OptionInfo(True, "Save all generated images"),
+ "samples_format": OptionInfo('jpg', 'File format', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
+ "jpeg_quality": OptionInfo(90, "Image quality", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
+ "img_max_size_mp": OptionInfo(250, "Maximum image size (MP)", gr.Slider, {"minimum": 100, "maximum": 2000, "step": 1}),
+ "webp_lossless": OptionInfo(False, "WebP lossless compression"),
+ "save_selected_only": OptionInfo(True, "Save only saves selected image"),
+ "samples_save_zip": OptionInfo(True, "Create ZIP archive"),
+
+ "image_sep_metadata": OptionInfo("Metadata/Logging ", "", gr.HTML),
+ "image_metadata": OptionInfo(True, "Include metadata"),
+ "save_txt": OptionInfo(False, "Create info file per image"),
+ "save_log_fn": OptionInfo("", "Update JSON log file per image", component_args=hide_dirs),
+ "image_watermark_enabled": OptionInfo(False, "Include watermark"),
+ "image_watermark": OptionInfo('', "Watermark string"),
+ "image_sep_grid": OptionInfo("Grid Options ", "", gr.HTML),
+ "grid_save": OptionInfo(True, "Save all generated image grids"),
+ "grid_format": OptionInfo('jpg', 'File format', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
+ "n_rows": OptionInfo(-1, "Row count", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
+ "grid_background": OptionInfo("#000000", "Background color", ui_components.FormColorPicker, {}),
+ "font": OptionInfo("", "Font file"),
+ "font_color": OptionInfo("#FFFFFF", "Font color", ui_components.FormColorPicker, {}),
+
+ "save_sep_options": OptionInfo("Intermediate Image Saving ", "", gr.HTML),
+ "save_init_img": OptionInfo(False, "Save init images"),
+ "save_images_before_highres_fix": OptionInfo(False, "Save image before hires"),
+ "save_images_before_refiner": OptionInfo(False, "Save image before refiner"),
+ "save_images_before_face_restoration": OptionInfo(False, "Save image before face restoration"),
+ "save_images_before_color_correction": OptionInfo(False, "Save image before color correction"),
+ "save_mask": OptionInfo(False, "Save inpainting mask"),
+ "save_mask_composite": OptionInfo(False, "Save inpainting masked composite"),
+}))
+
+options_templates.update(options_section(('saving-paths', "Image Naming & Paths"), {
+ "saving_sep_images": OptionInfo("Save options ", "", gr.HTML),
+ "save_images_add_number": OptionInfo(True, "Numbered filenames", component_args=hide_dirs),
+ "use_original_name_batch": OptionInfo(True, "Batch uses original name"),
+ "use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler as suffix", gr.Checkbox, {"visible": False}),
+ "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
+ "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
+ "samples_filename_pattern": OptionInfo("[seq]-[model_name]-[prompt_words]", "Images filename pattern", component_args=hide_dirs),
+ "directories_max_prompt_words": OptionInfo(8, "Max words per pattern", gr.Slider, {"minimum": 1, "maximum": 99, "step": 1, **hide_dirs}),
+ "use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button", gr.Checkbox, {"visible": False}),
+
+ "outdir_sep_dirs": OptionInfo("Folders ", "", gr.HTML),
+ "outdir_samples": OptionInfo("", "Images folder", component_args=hide_dirs, folder=True),
+ "outdir_txt2img_samples": OptionInfo("outputs/text", 'Folder for text generate', component_args=hide_dirs, folder=True),
+ "outdir_img2img_samples": OptionInfo("outputs/image", 'Folder for image generate', component_args=hide_dirs, folder=True),
+ "outdir_control_samples": OptionInfo("outputs/control", 'Folder for control generate', component_args=hide_dirs, folder=True),
+ "outdir_extras_samples": OptionInfo("outputs/extras", 'Folder for processed images', component_args=hide_dirs, folder=True),
+ "outdir_save": OptionInfo("outputs/save", "Folder for manually saved images", component_args=hide_dirs, folder=True),
+ "outdir_video": OptionInfo("outputs/video", "Folder for videos", component_args=hide_dirs, folder=True),
+ "outdir_init_images": OptionInfo("outputs/init-images", "Folder for init images", component_args=hide_dirs, folder=True),
+
+ "outdir_sep_grids": OptionInfo("Grids ", "", gr.HTML),
+ "grid_extended_filename": OptionInfo(True, "Add extended info to filename when saving grid", gr.Checkbox, {"visible": False}),
+ "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory", gr.Checkbox, {"visible": False}),
+ "outdir_grids": OptionInfo("", "Grids folder", component_args=hide_dirs, folder=True),
+ "outdir_txt2img_grids": OptionInfo("outputs/grids", 'Folder for txt2img grids', component_args=hide_dirs, folder=True),
+ "outdir_img2img_grids": OptionInfo("outputs/grids", 'Folder for img2img grids', component_args=hide_dirs, folder=True),
+ "outdir_control_grids": OptionInfo("outputs/grids", 'Folder for control grids', component_args=hide_dirs, folder=True),
+}))
+
+options_templates.update(options_section(('ui', "User Interface"), {
+ "motd": OptionInfo(True, "Show MOTD"),
+ "gradio_theme": OptionInfo("black-teal", "UI theme", gr.Dropdown, lambda: {"choices": theme.list_themes()}, refresh=theme.refresh_themes),
+ "theme_style": OptionInfo("Auto", "Theme mode", gr.Radio, {"choices": ["Auto", "Dark", "Light"]}),
+ "font_size": OptionInfo(16, "Font size", gr.Slider, {"minimum": 8, "maximum": 32, "step": 1, "visible": True}),
+ "tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"], "visible": False}),
+ "gallery_height": OptionInfo("", "Gallery height", gr.Textbox),
+ "compact_view": OptionInfo(False, "Compact view"),
+ "return_grid": OptionInfo(True, "Show grid in results"),
+ "return_mask": OptionInfo(False, "Inpainting include greyscale mask in results"),
+ "return_mask_composite": OptionInfo(False, "Inpainting include masked composite in results"),
+ "disable_weights_auto_swap": OptionInfo(True, "Do not change selected model when reading generation parameters"),
+ "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
+ "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
+ "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
+ "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing ", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001, "visible": False}),
+ "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }), # pylint: disable=anomalous-backslash-in-string
+ "quicksettings_list": OptionInfo(["sd_model_checkpoint"] if backend == Backend.ORIGINAL else ["sd_model_checkpoint", "sd_model_refiner"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
+ "ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
+}))
+
+options_templates.update(options_section(('live-preview', "Live Previews"), {
+ "show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
+ "live_previews_enable": OptionInfo(True, "Show live previews", gr.Checkbox, {"visible": False}),
+ "show_progress_grid": OptionInfo(True, "Show previews as a grid", gr.Checkbox, {"visible": False}),
+ "notification_audio_enable": OptionInfo(False, "Play a notification upon completion"),
+ "notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound", component_args=hide_dirs, folder=True),
+ "show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+ "show_progress_type": OptionInfo("Approximate", "Live preview method", gr.Radio, {"choices": ["Simple", "Approximate", "TAESD", "Full VAE"]}),
+ "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"], "visible": False}),
+ "live_preview_refresh_period": OptionInfo(500, "Progress update period", gr.Slider, {"minimum": 0, "maximum": 5000, "step": 25}),
+ "logmonitor_show": OptionInfo(True, "Show log view"),
+ "logmonitor_refresh_period": OptionInfo(5000, "Log view update period", gr.Slider, {"minimum": 0, "maximum": 30000, "step": 25}),
+}))
+
+options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
+ "show_samplers": OptionInfo([], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
+ 'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
+ "scheduler_eta": OptionInfo(1.0, "Noise multiplier (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "schedulers_solver_order": OptionInfo(2, "Solver order (where applicable)", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}),
+
+ # managed from ui.py for backend original
+ "schedulers_brownian_noise": OptionInfo(True, "Use Brownian noise", gr.Checkbox, {"visible": False}),
+ "schedulers_discard_penultimate": OptionInfo(True, "Discard penultimate sigma", gr.Checkbox, {"visible": False}),
+ "schedulers_sigma": OptionInfo("default", "Sigma algorithm", gr.Radio, {"choices": ['default', 'karras', 'exponential', 'polyexponential'], "visible": False}),
+ "schedulers_use_karras": OptionInfo(True, "Use Karras sigmas", gr.Checkbox, {"visible": False}),
+ "schedulers_use_thresholding": OptionInfo(False, "Use dynamic thresholding", gr.Checkbox, {"visible": False}),
+ "schedulers_use_loworder": OptionInfo(True, "Use simplified solvers in final steps", gr.Checkbox, {"visible": False}),
+ "schedulers_prediction_type": OptionInfo("default", "Override model prediction type", gr.Radio, {"choices": ['default', 'epsilon', 'sample', 'v_prediction']}),
+
+ # managed from ui.py for backend diffusers
+ "schedulers_sep_diffusers": OptionInfo("Diffusers specific config ", "", gr.HTML),
+ "schedulers_dpm_solver": OptionInfo("sde-dpmsolver++", "DPM solver algorithm", gr.Radio, {"choices": ['dpmsolver', 'dpmsolver++', 'sde-dpmsolver', 'sde-dpmsolver++']}),
+ "schedulers_beta_schedule": OptionInfo("default", "Beta schedule", gr.Radio, {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2']}),
+ 'schedulers_beta_start': OptionInfo(0, "Beta start", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001}),
+ 'schedulers_beta_end': OptionInfo(0, "Beta end", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001}),
+ 'schedulers_timesteps_range': OptionInfo(1000, "Timesteps range", gr.Slider, {"minimum": 250, "maximum": 4000, "step": 1}),
+ "schedulers_rescale_betas": OptionInfo(False, "Rescale betas with zero terminal SNR", gr.Checkbox),
+
+ # managed from ui.py for backend original k-diffusion
+ "schedulers_sep_kdiffusers": OptionInfo("K-Diffusion specific config ", "", gr.HTML),
+ "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching"),
+ "enable_quantization": OptionInfo(True, "Use quantization"),
+ 's_churn': OptionInfo(0.0, "Sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ 's_min_uncond': OptionInfo(0.0, "Sigma negative guidance minimum ", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
+ 's_tmin': OptionInfo(0.0, "Sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ 's_noise': OptionInfo(1.0, "Sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ 's_min': OptionInfo(0.0, "Sigma min", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ 's_max': OptionInfo(0.0, "Sigma max", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 1.0}),
+ "schedulers_sep_compvis": OptionInfo("CompVis specific config ", "", gr.HTML),
+ 'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
+ 'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
+ "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
+ # TODO pad_cond_uncond implementation missing for original backend
+ "pad_cond_uncond": OptionInfo(True, "Pad prompt and negative prompt to be same length", gr.Checkbox, {"visible": False}),
+ # TODO batch_cond-uncond implementation missing for original backend
+ "batch_cond_uncond": OptionInfo(True, "Do conditional and unconditional denoising in one batch", gr.Checkbox, {"visible": False}),
+}))
+
+options_templates.update(options_section(('postprocessing', "Postprocessing"), {
+ 'postprocessing_enable_in_main_ui': OptionInfo([], "Additional postprocessing operations", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+ 'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+
+ "postprocessing_sep_img2img": OptionInfo("Img2Img & Inpainting ", "", gr.HTML),
+ "img2img_color_correction": OptionInfo(False, "Apply color correction"),
+ "img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified", gr.Checkbox, { "visible": False }),
+ "img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", ui_components.FormColorPicker, {}),
+ "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
+ "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
+
+ "postprocessing_sep_face_restoration": OptionInfo("Face Restoration ", "", gr.HTML),
+ "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
+ "code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+ "face_restoration_unload": OptionInfo(False, "Move face restoration model to CPU when complete"),
+
+ "postprocessing_sep_upscalers": OptionInfo("Upscaling ", "", gr.HTML),
+ "upscaler_unload": OptionInfo(False, "Unload upscaler after processing"),
+ "upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers], "visible": False}, refresh=refresh_upscalers),
+ "upscaler_tile_size": OptionInfo(192, "Upscaler tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+ "upscaler_tile_overlap": OptionInfo(8, "Upscaler tile overlap", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
+}))
+
+options_templates.update(options_section(('training', "Training"), {
+ "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training"),
+ "pin_memory": OptionInfo(True, "Pin training dataset to memory"),
+ "save_optimizer_state": OptionInfo(False, "Save resumable optimizer state when training"),
+ "save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file"),
+ "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
+ "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
+ "embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train', 'templates'), "Embeddings train templates directory", folder=True),
+ "training_image_repeats_per_epoch": OptionInfo(1, "Image repeats per epoch", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
+ "training_write_csv_every": OptionInfo(0, "Save loss CSV file every n steps"),
+ "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging"),
+ "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard"),
+ "training_tensorboard_flush_every": OptionInfo(120, "Tensorboard flush period"),
+}))
+
+options_templates.update(options_section(('interrogate', "Interrogate"), {
+ "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
+ "interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results"),
+ "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+ "interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+ "interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+ "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file", gr.Slider, { "visible": False }),
+ "interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "Interrogate: skip categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
+ "interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+ "deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"),
+ "deepbooru_use_spaces": OptionInfo(False, "Use spaces for tags in deepbooru"),
+ "deepbooru_escape": OptionInfo(True, "Escape brackets in deepbooru"),
+ "deepbooru_filter_tags": OptionInfo("", "Filter out tags from deepbooru output"),
+}))
+
+options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+ "extra_networks_sep1": OptionInfo("Extra networks UI ", "", gr.HTML),
+ "extra_networks": OptionInfo(["All"], "Extra networks", ui_components.DropdownMulti, lambda: {"choices": ['All'] + [en.title for en in extra_networks]}),
+ "extra_networks_view": OptionInfo("gallery", "UI view", gr.Radio, {"choices": ["gallery", "list"]}),
+ "extra_networks_card_cover": OptionInfo("sidebar", "UI position", gr.Radio, {"choices": ["cover", "inline", "sidebar"]}),
+ "extra_networks_height": OptionInfo(53, "UI height (%)", gr.Slider, {"minimum": 10, "maximum": 100, "step": 1}),
+ "extra_networks_sidebar_width": OptionInfo(35, "UI sidebar width (%)", gr.Slider, {"minimum": 10, "maximum": 80, "step": 1}),
+ "extra_networks_card_size": OptionInfo(160, "UI card size (px)", gr.Slider, {"minimum": 20, "maximum": 2000, "step": 1}),
+ "extra_networks_card_square": OptionInfo(True, "UI disable variable aspect ratio"),
+ "extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
+ "extra_networks_sep2": OptionInfo("Extra networks general ", "", gr.HTML),
+ "extra_network_skip_indexing": OptionInfo(False, "Build info on first access", gr.Checkbox),
+ "extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "extra_networks_sep3": OptionInfo("Extra networks settings ", "", gr.HTML),
+ "extra_networks_styles": OptionInfo(True, "Show built-in styles"),
+ "lora_preferred_name": OptionInfo("filename", "LoRA preffered name", gr.Radio, {"choices": ["filename", "alias"]}),
+ "lora_add_hashes_to_infotext": OptionInfo(True, "LoRA add hash info"),
+ "lora_force_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA use alternative loading method"),
+ "lora_fuse_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA use merge when using alternative method"),
+ "lora_in_memory_limit": OptionInfo(1, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
+ "lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
+ "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
+}))
+
+options_templates.update(options_section((None, "Hidden options"), {
+ "disabled_extensions": OptionInfo([], "Disable these extensions"),
+ "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "user", "all"]}),
+ "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
+}))
+
+options_templates.update()
+
+
+class Options:
+ data = None
+ data_labels = options_templates
+ filename = None
+ typemap = {int: float}
+
+ def __init__(self):
+ self.data = {k: v.default for k, v in self.data_labels.items()}
+
+ def __setattr__(self, key, value): # pylint: disable=inconsistent-return-statements
+ if self.data is not None:
+ if key in self.data or key in self.data_labels:
+ if cmd_opts.freeze:
+ log.warning(f'Settings are frozen: {key}')
+ return
+ if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+ log.warning(f'Settings key is restricted: {key}')
+ return
+ self.data[key] = value
+ return
+ return super(Options, self).__setattr__(key, value) # pylint: disable=super-with-arguments
+
+ def __getattr__(self, item):
+ if self.data is not None:
+ if item in self.data:
+ return self.data[item]
+ if item in self.data_labels:
+ return self.data_labels[item].default
+ return super(Options, self).__getattribute__(item) # pylint: disable=super-with-arguments
+
+ def set(self, key, value):
+ """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
+ oldval = self.data.get(key, None)
+ if oldval is None:
+ oldval = self.data_labels[key].default
+ if oldval == value:
+ return False
+ try:
+ setattr(self, key, value)
+ except RuntimeError:
+ return False
+ if self.data_labels[key].onchange is not None:
+ try:
+ self.data_labels[key].onchange()
+ except Exception as e:
+ log.error(f'Error in onchange callback: {key} {value} {e}')
+ setattr(self, key, oldval)
+ return False
+ return True
+
+ def get_default(self, key):
+ """returns the default value for the key"""
+ data_label = self.data_labels.get(key)
+ return data_label.default if data_label is not None else None
+
+ def save(self, filename=None, silent=False):
+ if filename is None:
+ filename = self.filename
+ if cmd_opts.freeze:
+ log.warning(f'Settings saving is disabled: {filename}')
+ return
+ try:
+ # output = json.dumps(self.data, indent=2)
+ diff = {}
+ unused_settings = []
+ for k, v in self.data.items():
+ if k in self.data_labels:
+ if type(v) is list:
+ diff[k] = v
+ if self.data_labels[k].default != v:
+ diff[k] = v
+ else:
+ if k not in compatibility_opts:
+ unused_settings.append(k)
+ diff[k] = v
+ writefile(diff, filename, silent=silent)
+ if len(unused_settings) > 0:
+ log.debug(f"Unused settings: {unused_settings}")
+ except Exception as e:
+ log.error(f'Saving settings failed: {filename} {e}')
+
+ def same_type(self, x, y):
+ if x is None or y is None:
+ return True
+ type_x = self.typemap.get(type(x), type(x))
+ type_y = self.typemap.get(type(y), type(y))
+ return type_x == type_y
+
+ def load(self, filename=None):
+ if filename is None:
+ filename = self.filename
+ if not os.path.isfile(filename):
+ log.debug(f'Created default config: {filename}')
+ self.save(filename)
+ return
+ self.data = readfile(filename, lock=True)
+ if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
+ self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
+ unknown_settings = []
+ for k, v in self.data.items():
+ info = self.data_labels.get(k, None)
+ if info is not None and not self.same_type(info.default, v):
+ log.error(f"Error: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})")
+ if info is None and k not in compatibility_opts:
+ unknown_settings.append(k)
+ if len(unknown_settings) > 0:
+ log.debug(f"Unknown settings: {unknown_settings}")
+
+ def onchange(self, key, func, call=True):
+ item = self.data_labels.get(key)
+ item.onchange = func
+ if call:
+ func()
+
+ def dumpjson(self):
+ d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
+ metadata = {
+ k: {
+ "is_stored": k in self.data and self.data[k] != self.data_labels[k].default, # pylint: disable=unnecessary-dict-index-lookup
+ "tab_name": v.section[0]
+ } for k, v in self.data_labels.items()
+ }
+ return json.dumps({"values": d, "metadata": metadata})
+
+ def add_option(self, key, info):
+ self.data_labels[key] = info
+
+ def reorder(self):
+ """reorder settings so that all items related to section always go together"""
+ section_ids = {}
+ settings_items = self.data_labels.items()
+ for _k, item in settings_items:
+ if item.section not in section_ids:
+ section_ids[item.section] = len(section_ids)
+ self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
+
+ def cast_value(self, key, value):
+ """casts an arbitrary to the same type as this setting's value with key
+ Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
+ """
+ if value is None:
+ return None
+ default_value = self.data_labels[key].default
+ if default_value is None:
+ default_value = getattr(self, key, None)
+ if default_value is None:
+ return None
+ expected_type = type(default_value)
+ if expected_type == bool and value == "False":
+ value = False
+ elif expected_type == type(value):
+ pass
+ else:
+ value = expected_type(value)
+ return value
+
+profiler = None
+opts = Options()
+config_filename = cmd_opts.config
+opts.load(config_filename)
+cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
+if cmd_opts.use_xformers:
+ opts.data['cross_attention_optimization'] = 'xFormers'
+opts.data['uni_pc_lower_order_final'] = opts.schedulers_use_loworder # compatibility
+opts.data['uni_pc_order'] = opts.schedulers_solver_order # compatibility
+log.info(f'Engine: backend={backend} compute={devices.backend} device={devices.get_optimal_device_name()} attention="{opts.cross_attention_optimization}" mode={devices.inference_context.__name__}')
+log.info(f'Device: {print_dict(devices.get_gpu_info())}')
+
+prompt_styles = modules.styles.StyleDatabase(opts)
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
+device = devices.device
+batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
+parallel_processing_allowed = not cmd_opts.lowvram
+mem_mon = modules.memmon.MemUsageMonitor("MemMon", devices.device)
+max_workers = 4
+if devices.backend == "directml":
+ directml_do_hijack()
+
+
+class TotalTQDM: # compatibility with previous global-tqdm
+ # import tqdm
+ def __init__(self):
+ pass
+ def reset(self):
+ pass
+ def update(self):
+ pass
+ def updateTotal(self, new_total):
+ pass
+ def clear(self):
+ pass
+total_tqdm = TotalTQDM()
+
+
+def restart_server(restart=True):
+ if demo is None:
+ return
+ log.warning('Server shutdown requested')
+ try:
+ sys.tracebacklimit = 0
+ stdout = io.StringIO()
+ stderr = io.StringIO()
+ with contextlib.redirect_stdout(stdout), contextlib.redirect_stdout(stderr):
+ demo.server.wants_restart = restart
+ demo.server.should_exit = True
+ demo.server.force_exit = True
+ demo.close(verbose=False)
+ demo.server.close()
+ demo.fns = []
+ time.sleep(1)
+ sys.tracebacklimit = 100
+ # os._exit(0)
+ except (Exception, BaseException) as e:
+ log.error(f'Server shutdown error: {e}')
+ if restart:
+ log.info('Server will restart')
+
+
+def restore_defaults(restart=True):
+ if os.path.exists(cmd_opts.config):
+ log.info('Restoring server defaults')
+ os.remove(cmd_opts.config)
+ restart_server(restart)
+
+
+def listdir(path):
+ if not os.path.exists(path):
+ return []
+ mtime = os.path.getmtime(path)
+ if path in dir_timestamps and mtime == dir_timestamps[path]:
+ return dir_cache[path]
+ else:
+ dir_cache[path] = [os.path.join(path, f) for f in os.listdir(path)]
+ dir_timestamps[path] = mtime
+ return dir_cache[path]
+
+
+def walk_files(path, allowed_extensions=None):
+ if not os.path.exists(path):
+ return
+ if allowed_extensions is not None:
+ allowed_extensions = set(allowed_extensions)
+ for root, _dirs, files in os.walk(path, followlinks=True):
+ for filename in files:
+ if allowed_extensions is not None:
+ _, ext = os.path.splitext(filename)
+ if ext not in allowed_extensions:
+ continue
+ yield os.path.join(root, filename)
+
+
+def html_path(filename):
+ return os.path.join(paths.script_path, "html", filename)
+
+
+def html(filename):
+ path = html_path(filename)
+ if os.path.exists(path):
+ with open(path, encoding="utf8") as file:
+ return file.read()
+ return ""
+
+
+def get_version():
+ version = None
+ if version is None:
+ try:
+ import subprocess
+ res = subprocess.run('git log --pretty=format:"%h %ad" -1 --date=short', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
+ ver = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ' '
+ githash, updated = ver.split(' ')
+ res = subprocess.run('git remote get-url origin', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
+ origin = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
+ res = subprocess.run('git rev-parse --abbrev-ref HEAD', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
+ branch = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
+ version = {
+ 'app': 'sd.next',
+ 'updated': updated,
+ 'hash': githash,
+ 'url': origin.replace('\n', '') + '/tree/' + branch.replace('\n', '')
+ }
+ except Exception:
+ version = { 'app': 'sd.next' }
+ return version
+
+
+def req(url_addr, headers = None, **kwargs):
+ if headers is None:
+ headers = { 'Content-type': 'application/json' }
+ try:
+ res = requests.get(url_addr, timeout=30, headers=headers, verify=False, allow_redirects=True, **kwargs)
+ except Exception as e:
+ log.error(f'HTTP request error: url={url_addr} {e}')
+ res = { 'status_code': 500, 'text': f'HTTP request error: url={url_addr} {e}' }
+ res = SimpleNamespace(**res)
+ return res
+
+
+sd_model: diffusers.DiffusionPipeline = None # dummy and overwritten by class
+sd_refiner: diffusers.DiffusionPipeline = None # dummy and overwritten by class
+sd_model_type: str = '' # dummy and overwritten by class
+sd_refiner_type: str = '' # dummy and overwritten by class
+compiled_model_state = None
+
+from modules.modeldata import Shared # pylint: disable=ungrouped-imports
+sys.modules[__name__].__class__ = Shared
diff --git a/modules/shared_items.py b/modules/shared_items.py
index b0d4f1ec4..0796cac43 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -1,55 +1,55 @@
-def postprocessing_scripts():
- import modules.scripts
- return modules.scripts.scripts_postproc.scripts
-
-
-def sd_vae_items():
- import modules.sd_vae
- return ["Automatic", "None"] + list(modules.sd_vae.vae_dict)
-
-
-def refresh_vae_list():
- import modules.sd_vae
- modules.sd_vae.refresh_vae_list()
-
-
-def list_crossattention():
- return [
- "Disabled",
- "xFormers",
- "Scaled-Dot-Product",
- "Doggettx's",
- "InvokeAI's",
- "Sub-quadratic",
- "Split attention"
- ]
-
-def get_pipelines():
- import diffusers
- from installer import log
- pipelines = { # note: not all pipelines can be used manually as they require prior pipeline next to decoder pipeline
- 'Autodetect': None,
- 'Stable Diffusion': getattr(diffusers, 'StableDiffusionPipeline', None),
- 'Stable Diffusion Inpaint': getattr(diffusers, 'StableDiffusionInpaintPipeline', None),
- 'Stable Diffusion Img2Img': getattr(diffusers, 'StableDiffusionImg2ImgPipeline', None),
- 'Stable Diffusion Instruct': getattr(diffusers, 'StableDiffusionInstructPix2PixPipeline', None),
- 'Stable Diffusion Upscale': getattr(diffusers, 'StableDiffusionUpscalePipeline', None),
- 'Stable Diffusion XL': getattr(diffusers, 'StableDiffusionXLPipeline', None),
- 'Stable Diffusion XL Img2Img': getattr(diffusers, 'StableDiffusionXLImg2ImgPipeline', None),
- 'Stable Diffusion XL Inpaint': getattr(diffusers, 'StableDiffusionXLInpaintPipeline', None),
- 'Stable Diffusion XL Instruct': getattr(diffusers, 'StableDiffusionXLInstructPix2PixPipeline', None),
- 'Latent Consistency Model': getattr(diffusers, 'LatentConsistencyModelPipeline', None),
- 'PixArt Alpha': getattr(diffusers, 'PixArtAlphaPipeline', None),
- 'UniDiffuser': getattr(diffusers, 'UniDiffuserPipeline', None),
- 'Wuerstchen': getattr(diffusers, 'WuerstchenCombinedPipeline', None),
- 'Kandinsky 2.1': getattr(diffusers, 'KandinskyPipeline', None),
- 'Kandinsky 2.2': getattr(diffusers, 'KandinskyV22Pipeline', None),
- 'Kandinsky 3': getattr(diffusers, 'Kandinsky3Pipeline', None),
- 'DeepFloyd IF': getattr(diffusers, 'IFPipeline', None),
- 'Custom Diffusers Pipeline': getattr(diffusers, 'DiffusionPipeline', None),
- # Segmind SSD-1B, Segmind Tiny
- }
- for k, v in pipelines.items():
- if k != 'Autodetect' and v is None:
- log.error(f'Not available: pipeline={k} diffusers={diffusers.__version__} path={diffusers.__file__}')
- return pipelines
+def postprocessing_scripts():
+ import modules.scripts
+ return modules.scripts.scripts_postproc.scripts
+
+
+def sd_vae_items():
+ import modules.sd_vae
+ return ["Automatic", "None"] + list(modules.sd_vae.vae_dict)
+
+
+def refresh_vae_list():
+ import modules.sd_vae
+ modules.sd_vae.refresh_vae_list()
+
+
+def list_crossattention():
+ return [
+ "Disabled",
+ "xFormers",
+ "Scaled-Dot-Product",
+ "Doggettx's",
+ "InvokeAI's",
+ "Sub-quadratic",
+ "Split attention"
+ ]
+
+def get_pipelines():
+ import diffusers
+ from installer import log
+ pipelines = { # note: not all pipelines can be used manually as they require prior pipeline next to decoder pipeline
+ 'Autodetect': None,
+ 'Stable Diffusion': getattr(diffusers, 'StableDiffusionPipeline', None),
+ 'Stable Diffusion Inpaint': getattr(diffusers, 'StableDiffusionInpaintPipeline', None),
+ 'Stable Diffusion Img2Img': getattr(diffusers, 'StableDiffusionImg2ImgPipeline', None),
+ 'Stable Diffusion Instruct': getattr(diffusers, 'StableDiffusionInstructPix2PixPipeline', None),
+ 'Stable Diffusion Upscale': getattr(diffusers, 'StableDiffusionUpscalePipeline', None),
+ 'Stable Diffusion XL': getattr(diffusers, 'StableDiffusionXLPipeline', None),
+ 'Stable Diffusion XL Img2Img': getattr(diffusers, 'StableDiffusionXLImg2ImgPipeline', None),
+ 'Stable Diffusion XL Inpaint': getattr(diffusers, 'StableDiffusionXLInpaintPipeline', None),
+ 'Stable Diffusion XL Instruct': getattr(diffusers, 'StableDiffusionXLInstructPix2PixPipeline', None),
+ 'Latent Consistency Model': getattr(diffusers, 'LatentConsistencyModelPipeline', None),
+ 'PixArt Alpha': getattr(diffusers, 'PixArtAlphaPipeline', None),
+ 'UniDiffuser': getattr(diffusers, 'UniDiffuserPipeline', None),
+ 'Wuerstchen': getattr(diffusers, 'WuerstchenCombinedPipeline', None),
+ 'Kandinsky 2.1': getattr(diffusers, 'KandinskyPipeline', None),
+ 'Kandinsky 2.2': getattr(diffusers, 'KandinskyV22Pipeline', None),
+ 'Kandinsky 3': getattr(diffusers, 'Kandinsky3Pipeline', None),
+ 'DeepFloyd IF': getattr(diffusers, 'IFPipeline', None),
+ 'Custom Diffusers Pipeline': getattr(diffusers, 'DiffusionPipeline', None),
+ # Segmind SSD-1B, Segmind Tiny
+ }
+ for k, v in pipelines.items():
+ if k != 'Autodetect' and v is None:
+ log.error(f'Not available: pipeline={k} diffusers={diffusers.__version__} path={diffusers.__file__}')
+ return pipelines
diff --git a/modules/styles.py b/modules/styles.py
index 1f9a6fd52..58cf3e2a5 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -1,237 +1,237 @@
-# We need this so Python doesn't complain about the unknown StableDiffusionProcessing-typehint at runtime
-from __future__ import annotations
-import re
-import os
-import csv
-import json
-import time
-from installer import log
-
-
-class Style():
- def __init__(self, name: str, desc: str = "", prompt: str = "", negative_prompt: str = "", extra: str = "", filename: str = "", preview: str = "", mtime: float = 0):
- self.name = name
- self.description = desc
- self.prompt = prompt
- self.negative_prompt = negative_prompt
- self.extra = extra
- self.filename = filename
- self.preview = preview
- self.mtime = mtime
-
-def merge_prompts(style_prompt: str, prompt: str) -> str:
- if "{prompt}" in style_prompt:
- res = style_prompt.replace("{prompt}", prompt)
- else:
- original_prompt = prompt.strip()
- style_prompt = style_prompt.strip()
- parts = filter(None, (original_prompt, style_prompt))
- if original_prompt.endswith(","):
- res = " ".join(parts)
- else:
- res = ", ".join(parts)
- return res
-
-
-def apply_styles_to_prompt(prompt, styles):
- for style in styles:
- prompt = merge_prompts(style, prompt)
- return prompt
-
-
-def apply_styles_to_extra(p, style: Style):
- if style is None:
- return
- name_map = {
- 'sampler': 'sampler_name',
- }
- from modules.generation_parameters_copypaste import parse_generation_parameters
- extra = parse_generation_parameters(style.extra)
- extra.pop('Prompt', None)
- extra.pop('Negative prompt', None)
- fields = []
- for k, v in extra.items():
- k = k.lower()
- k = k.replace(' ', '_')
- if k in name_map: # rename some fields
- k = name_map[k]
- if hasattr(p, k):
- orig = getattr(p, k)
- if type(orig) != type(v) and orig is not None:
- v = type(orig)(v)
- setattr(p, k, v)
- fields.append(f'{k}={v}')
- log.info(f'Applying style: name="{style.name}" extra={fields}')
-
-
-class StyleDatabase:
- def __init__(self, opts):
- from modules import paths
-
- self.no_style = Style("None")
- self.styles = {}
- self.path = opts.styles_dir
- self.built_in = opts.extra_networks_styles
- if os.path.isfile(opts.styles_dir) or opts.styles_dir.endswith(".csv"):
- legacy_file = opts.styles_dir
- self.load_csv(legacy_file)
- opts.styles_dir = os.path.join(paths.models_path, "styles")
- self.path = opts.styles_dir
- os.makedirs(opts.styles_dir, exist_ok=True)
- self.save_styles(opts.styles_dir, verbose=True)
- log.debug(f'Migrated styles: file={legacy_file} folder={opts.styles_dir}')
- self.reload()
- if not os.path.isdir(opts.styles_dir):
- opts.styles_dir = os.path.join(paths.models_path, "styles")
- self.path = opts.styles_dir
- os.makedirs(opts.styles_dir, exist_ok=True)
-
- def load_style(self, fn, prefix=None):
- with open(fn, 'r', encoding='utf-8') as f:
- new_style = None
- try:
- all_styles = json.load(f)
- if type(all_styles) is dict:
- all_styles = [all_styles]
- for style in all_styles:
- if type(style) is not dict or "name" not in style:
- raise ValueError('cannot parse style')
- basename = os.path.splitext(os.path.basename(fn))[0]
- name = re.sub(r'[\t\r\n]', '', style.get("name", basename)).strip()
- if prefix is not None:
- name = os.path.join(prefix, name)
- else:
- name = os.path.join(os.path.dirname(os.path.relpath(fn, self.path)), name)
- new_style = Style(
- name=name,
- desc=style.get('description', name),
- prompt=style.get("prompt", ""),
- negative_prompt=style.get("negative", ""),
- extra=style.get("extra", ""),
- preview=style.get("preview", None),
- filename=fn,
- mtime=os.path.getmtime(fn),
- )
- self.styles[style["name"]] = new_style
- except Exception as e:
- log.error(f'Failed to load style: file={fn} error={e}')
- return new_style
-
-
- def reload(self):
- t0 = time.time()
- self.styles.clear()
-
- def list_folder(folder):
- import concurrent
- future_items = {}
- with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
- for filename in os.listdir(folder):
- fn = os.path.abspath(os.path.join(folder, filename))
- if os.path.isfile(fn) and fn.lower().endswith(".json"):
- future_items[executor.submit(self.load_style, fn, None)] = fn
- # self.load_style(fn)
- elif os.path.isdir(fn) and not fn.startswith('.'):
- list_folder(fn)
- self.styles = dict(sorted(self.styles.items(), key=lambda style: style[1].filename))
- if self.built_in:
- fn = os.path.join('html', 'art-styles.json')
- future_items[executor.submit(self.load_style, fn, 'built-in')] = fn
- for future in concurrent.futures.as_completed(future_items):
- future.result()
-
- list_folder(self.path)
- t1 = time.time()
- log.debug(f'Load styles: folder="{self.path}" items={len(self.styles.keys())} time={t1-t0:.2f}')
-
- def find_style(self, name):
- found = [style for style in self.styles.values() if style.name == name]
- return found[0] if len(found) > 0 else self.no_style
-
- def get_style_prompts(self, styles):
- if styles is None or not isinstance(styles, list):
- log.error(f'Invalid styles: {styles}')
- return []
- return [self.find_style(x).prompt for x in styles]
-
- def get_negative_style_prompts(self, styles):
- if styles is None or not isinstance(styles, list):
- log.error(f'Invalid styles: {styles}')
- return []
- return [self.find_style(x).negative_prompt for x in styles]
-
- def apply_styles_to_prompt(self, prompt, styles):
- if styles is None or not isinstance(styles, list):
- log.error(f'Invalid styles: {styles}')
- return prompt
- return apply_styles_to_prompt(prompt, [self.find_style(x).prompt for x in styles])
-
- def apply_negative_styles_to_prompt(self, prompt, styles):
- if styles is None or not isinstance(styles, list):
- log.error(f'Invalid styles: {styles}')
- return prompt
- return apply_styles_to_prompt(prompt, [self.find_style(x).negative_prompt for x in styles])
-
- def apply_styles_to_extra(self, p):
- if p.styles is None or not isinstance(p.styles, list):
- log.error(f'Invalid styles: {p.styles}')
- return
- for style in p.styles:
- s = self.find_style(style)
- apply_styles_to_extra(p, s)
-
- def save_styles(self, path, verbose=False):
- for name in list(self.styles):
- style = {
- "name": name,
- "prompt": self.styles[name].prompt,
- "negative": self.styles[name].negative_prompt,
- "extra": "",
- "preview": "",
- }
- keepcharacters = (' ','.','_')
- fn = "".join(c for c in name if c.isalnum() or c in keepcharacters).rstrip()
- fn = os.path.join(path, fn + ".json")
- try:
- with open(fn, 'w', encoding='utf-8') as f:
- json.dump(style, f, indent=2)
- if verbose:
- log.debug(f'Saved style: name={name} file={fn}')
- except Exception as e:
- log.error(f'Failed to save style: name={name} file={path} error={e}')
- count = len(list(self.styles))
- if count > 0:
- log.debug(f'Saved styles: folder="{path}" items={count}')
-
- def load_csv(self, legacy_file):
- if not os.path.isfile(legacy_file):
- return
- with open(legacy_file, "r", encoding="utf-8-sig", newline='') as file:
- reader = csv.DictReader(file, skipinitialspace=True)
- num = 0
- for row in reader:
- try:
- name = row["name"]
- prompt = row["prompt"] if "prompt" in row else row["text"]
- negative = row.get("negative_prompt", "") if "negative_prompt" in row else row.get("negative", "")
- self.styles[name] = Style(name, desc=name, prompt=prompt, negative_prompt=negative, extra="")
- log.debug(f'Migrated style: {self.styles[name].__dict__}')
- num += 1
- except Exception:
- log.error(f'Styles error: file="{legacy_file}" row={row}')
- log.info(f'Load legacy styles: file="{legacy_file}" loaded={num} created={len(list(self.styles))}')
-
- """
- def save_csv(self, path: str) -> None:
- import tempfile
- basedir = os.path.dirname(path)
- if basedir is not None and len(basedir) > 0:
- os.makedirs(basedir, exist_ok=True)
- fd, temp_path = tempfile.mkstemp(".csv")
- with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
- writer = csv.DictWriter(file, fieldnames=Style._fields)
- writer.writeheader()
- writer.writerows(style._asdict() for k, style in self.styles.items())
- log.debug(f'Saved legacy styles: {path} {len(self.styles.keys())}')
- shutil.move(temp_path, path)
- """
+# We need this so Python doesn't complain about the unknown StableDiffusionProcessing-typehint at runtime
+from __future__ import annotations
+import re
+import os
+import csv
+import json
+import time
+from installer import log
+
+
+class Style():
+ def __init__(self, name: str, desc: str = "", prompt: str = "", negative_prompt: str = "", extra: str = "", filename: str = "", preview: str = "", mtime: float = 0):
+ self.name = name
+ self.description = desc
+ self.prompt = prompt
+ self.negative_prompt = negative_prompt
+ self.extra = extra
+ self.filename = filename
+ self.preview = preview
+ self.mtime = mtime
+
+def merge_prompts(style_prompt: str, prompt: str) -> str:
+ if "{prompt}" in style_prompt:
+ res = style_prompt.replace("{prompt}", prompt)
+ else:
+ original_prompt = prompt.strip()
+ style_prompt = style_prompt.strip()
+ parts = filter(None, (original_prompt, style_prompt))
+ if original_prompt.endswith(","):
+ res = " ".join(parts)
+ else:
+ res = ", ".join(parts)
+ return res
+
+
+def apply_styles_to_prompt(prompt, styles):
+ for style in styles:
+ prompt = merge_prompts(style, prompt)
+ return prompt
+
+
+def apply_styles_to_extra(p, style: Style):
+ if style is None:
+ return
+ name_map = {
+ 'sampler': 'sampler_name',
+ }
+ from modules.generation_parameters_copypaste import parse_generation_parameters
+ extra = parse_generation_parameters(style.extra)
+ extra.pop('Prompt', None)
+ extra.pop('Negative prompt', None)
+ fields = []
+ for k, v in extra.items():
+ k = k.lower()
+ k = k.replace(' ', '_')
+ if k in name_map: # rename some fields
+ k = name_map[k]
+ if hasattr(p, k):
+ orig = getattr(p, k)
+ if type(orig) != type(v) and orig is not None:
+ v = type(orig)(v)
+ setattr(p, k, v)
+ fields.append(f'{k}={v}')
+ log.info(f'Applying style: name="{style.name}" extra={fields}')
+
+
+class StyleDatabase:
+ def __init__(self, opts):
+ from modules import paths
+
+ self.no_style = Style("None")
+ self.styles = {}
+ self.path = opts.styles_dir
+ self.built_in = opts.extra_networks_styles
+ if os.path.isfile(opts.styles_dir) or opts.styles_dir.endswith(".csv"):
+ legacy_file = opts.styles_dir
+ self.load_csv(legacy_file)
+ opts.styles_dir = os.path.join(paths.models_path, "styles")
+ self.path = opts.styles_dir
+ os.makedirs(opts.styles_dir, exist_ok=True)
+ self.save_styles(opts.styles_dir, verbose=True)
+ log.debug(f'Migrated styles: file={legacy_file} folder={opts.styles_dir}')
+ self.reload()
+ if not os.path.isdir(opts.styles_dir):
+ opts.styles_dir = os.path.join(paths.models_path, "styles")
+ self.path = opts.styles_dir
+ os.makedirs(opts.styles_dir, exist_ok=True)
+
+ def load_style(self, fn, prefix=None):
+ with open(fn, 'r', encoding='utf-8') as f:
+ new_style = None
+ try:
+ all_styles = json.load(f)
+ if type(all_styles) is dict:
+ all_styles = [all_styles]
+ for style in all_styles:
+ if type(style) is not dict or "name" not in style:
+ raise ValueError('cannot parse style')
+ basename = os.path.splitext(os.path.basename(fn))[0]
+ name = re.sub(r'[\t\r\n]', '', style.get("name", basename)).strip()
+ if prefix is not None:
+ name = os.path.join(prefix, name)
+ else:
+ name = os.path.join(os.path.dirname(os.path.relpath(fn, self.path)), name)
+ new_style = Style(
+ name=name,
+ desc=style.get('description', name),
+ prompt=style.get("prompt", ""),
+ negative_prompt=style.get("negative", ""),
+ extra=style.get("extra", ""),
+ preview=style.get("preview", None),
+ filename=fn,
+ mtime=os.path.getmtime(fn),
+ )
+ self.styles[style["name"]] = new_style
+ except Exception as e:
+ log.error(f'Failed to load style: file={fn} error={e}')
+ return new_style
+
+
+ def reload(self):
+ t0 = time.time()
+ self.styles.clear()
+
+ def list_folder(folder):
+ import concurrent
+ future_items = {}
+ with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
+ for filename in os.listdir(folder):
+ fn = os.path.abspath(os.path.join(folder, filename))
+ if os.path.isfile(fn) and fn.lower().endswith(".json"):
+ future_items[executor.submit(self.load_style, fn, None)] = fn
+ # self.load_style(fn)
+ elif os.path.isdir(fn) and not fn.startswith('.'):
+ list_folder(fn)
+ self.styles = dict(sorted(self.styles.items(), key=lambda style: style[1].filename))
+ if self.built_in:
+ fn = os.path.join('html', 'art-styles.json')
+ future_items[executor.submit(self.load_style, fn, 'built-in')] = fn
+ for future in concurrent.futures.as_completed(future_items):
+ future.result()
+
+ list_folder(self.path)
+ t1 = time.time()
+ log.debug(f'Load styles: folder="{self.path}" items={len(self.styles.keys())} time={t1-t0:.2f}')
+
+ def find_style(self, name):
+ found = [style for style in self.styles.values() if style.name == name]
+ return found[0] if len(found) > 0 else self.no_style
+
+ def get_style_prompts(self, styles):
+ if styles is None or not isinstance(styles, list):
+ log.error(f'Invalid styles: {styles}')
+ return []
+ return [self.find_style(x).prompt for x in styles]
+
+ def get_negative_style_prompts(self, styles):
+ if styles is None or not isinstance(styles, list):
+ log.error(f'Invalid styles: {styles}')
+ return []
+ return [self.find_style(x).negative_prompt for x in styles]
+
+ def apply_styles_to_prompt(self, prompt, styles):
+ if styles is None or not isinstance(styles, list):
+ log.error(f'Invalid styles: {styles}')
+ return prompt
+ return apply_styles_to_prompt(prompt, [self.find_style(x).prompt for x in styles])
+
+ def apply_negative_styles_to_prompt(self, prompt, styles):
+ if styles is None or not isinstance(styles, list):
+ log.error(f'Invalid styles: {styles}')
+ return prompt
+ return apply_styles_to_prompt(prompt, [self.find_style(x).negative_prompt for x in styles])
+
+ def apply_styles_to_extra(self, p):
+ if p.styles is None or not isinstance(p.styles, list):
+ log.error(f'Invalid styles: {p.styles}')
+ return
+ for style in p.styles:
+ s = self.find_style(style)
+ apply_styles_to_extra(p, s)
+
+ def save_styles(self, path, verbose=False):
+ for name in list(self.styles):
+ style = {
+ "name": name,
+ "prompt": self.styles[name].prompt,
+ "negative": self.styles[name].negative_prompt,
+ "extra": "",
+ "preview": "",
+ }
+ keepcharacters = (' ','.','_')
+ fn = "".join(c for c in name if c.isalnum() or c in keepcharacters).rstrip()
+ fn = os.path.join(path, fn + ".json")
+ try:
+ with open(fn, 'w', encoding='utf-8') as f:
+ json.dump(style, f, indent=2)
+ if verbose:
+ log.debug(f'Saved style: name={name} file={fn}')
+ except Exception as e:
+ log.error(f'Failed to save style: name={name} file={path} error={e}')
+ count = len(list(self.styles))
+ if count > 0:
+ log.debug(f'Saved styles: folder="{path}" items={count}')
+
+ def load_csv(self, legacy_file):
+ if not os.path.isfile(legacy_file):
+ return
+ with open(legacy_file, "r", encoding="utf-8-sig", newline='') as file:
+ reader = csv.DictReader(file, skipinitialspace=True)
+ num = 0
+ for row in reader:
+ try:
+ name = row["name"]
+ prompt = row["prompt"] if "prompt" in row else row["text"]
+ negative = row.get("negative_prompt", "") if "negative_prompt" in row else row.get("negative", "")
+ self.styles[name] = Style(name, desc=name, prompt=prompt, negative_prompt=negative, extra="")
+ log.debug(f'Migrated style: {self.styles[name].__dict__}')
+ num += 1
+ except Exception:
+ log.error(f'Styles error: file="{legacy_file}" row={row}')
+ log.info(f'Load legacy styles: file="{legacy_file}" loaded={num} created={len(list(self.styles))}')
+
+ """
+ def save_csv(self, path: str) -> None:
+ import tempfile
+ basedir = os.path.dirname(path)
+ if basedir is not None and len(basedir) > 0:
+ os.makedirs(basedir, exist_ok=True)
+ fd, temp_path = tempfile.mkstemp(".csv")
+ with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
+ writer = csv.DictWriter(file, fieldnames=Style._fields)
+ writer.writeheader()
+ writer.writerows(style._asdict() for k, style in self.styles.items())
+ log.debug(f'Saved legacy styles: {path} {len(self.styles.keys())}')
+ shutil.move(temp_path, path)
+ """
diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 2ccd4a27d..b4bf9828e 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,337 +1,337 @@
-import os
-import cv2
-import requests
-import numpy as np
-from PIL import ImageDraw
-
-GREEN = "#0F0"
-BLUE = "#00F"
-RED = "#F00"
-
-
-def crop_image(im, settings):
- """ Intelligently crop an image to the subject matter """
-
- scale_by = 1
- if is_landscape(im.width, im.height):
- scale_by = settings.crop_height / im.height
- elif is_portrait(im.width, im.height):
- scale_by = settings.crop_width / im.width
- elif is_square(im.width, im.height):
- if is_square(settings.crop_width, settings.crop_height):
- scale_by = settings.crop_width / im.width
- elif is_landscape(settings.crop_width, settings.crop_height):
- scale_by = settings.crop_width / im.width
- elif is_portrait(settings.crop_width, settings.crop_height):
- scale_by = settings.crop_height / im.height
-
- im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
- im_debug = im.copy()
-
- focus = focal_point(im_debug, settings)
-
- # take the focal point and turn it into crop coordinates that try to center over the focal
- # point but then get adjusted back into the frame
- y_half = int(settings.crop_height / 2)
- x_half = int(settings.crop_width / 2)
-
- x1 = focus.x - x_half
- if x1 < 0:
- x1 = 0
- elif x1 + settings.crop_width > im.width:
- x1 = im.width - settings.crop_width
-
- y1 = focus.y - y_half
- if y1 < 0:
- y1 = 0
- elif y1 + settings.crop_height > im.height:
- y1 = im.height - settings.crop_height
-
- x2 = x1 + settings.crop_width
- y2 = y1 + settings.crop_height
-
- crop = [x1, y1, x2, y2]
-
- results = []
-
- results.append(im.crop(tuple(crop)))
-
- if settings.annotate_image:
- d = ImageDraw.Draw(im_debug)
- rect = list(crop)
- rect[2] -= 1
- rect[3] -= 1
- d.rectangle(rect, outline=GREEN)
- results.append(im_debug)
- if settings.destop_view_image:
- im_debug.show()
-
- return results
-
-def focal_point(im, settings):
- corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
- entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
- face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
-
- pois = []
-
- weight_pref_total = 0
- if len(corner_points) > 0:
- weight_pref_total += settings.corner_points_weight
- if len(entropy_points) > 0:
- weight_pref_total += settings.entropy_points_weight
- if len(face_points) > 0:
- weight_pref_total += settings.face_points_weight
-
- corner_centroid = None
- if len(corner_points) > 0:
- corner_centroid = centroid(corner_points)
- corner_centroid.weight = settings.corner_points_weight / weight_pref_total
- pois.append(corner_centroid)
-
- entropy_centroid = None
- if len(entropy_points) > 0:
- entropy_centroid = centroid(entropy_points)
- entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
- pois.append(entropy_centroid)
-
- face_centroid = None
- if len(face_points) > 0:
- face_centroid = centroid(face_points)
- face_centroid.weight = settings.face_points_weight / weight_pref_total
- pois.append(face_centroid)
-
- average_point = poi_average(pois, settings)
-
- if settings.annotate_image:
- d = ImageDraw.Draw(im)
- max_size = min(im.width, im.height) * 0.07
- if corner_centroid is not None:
- color = BLUE
- box = corner_centroid.bounding(max_size * corner_centroid.weight)
- d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
- d.ellipse(box, outline=color)
- if len(corner_points) > 1:
- for f in corner_points:
- d.rectangle(f.bounding(4), outline=color)
- if entropy_centroid is not None:
- color = "#ff0"
- box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
- d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
- d.ellipse(box, outline=color)
- if len(entropy_points) > 1:
- for f in entropy_points:
- d.rectangle(f.bounding(4), outline=color)
- if face_centroid is not None:
- color = RED
- box = face_centroid.bounding(max_size * face_centroid.weight)
- d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
- d.ellipse(box, outline=color)
- if len(face_points) > 1:
- for f in face_points:
- d.rectangle(f.bounding(4), outline=color)
-
- d.ellipse(average_point.bounding(max_size), outline=GREEN)
-
- return average_point
-
-
-def image_face_points(im, settings):
- if settings.dnn_model_path is not None:
- detector = cv2.FaceDetectorYN.create(
- settings.dnn_model_path,
- "",
- (im.width, im.height),
- 0.9, # score threshold
- 0.3, # nms threshold
- 5000 # keep top k before nms
- )
- faces = detector.detect(np.array(im))
- results = []
- if faces[1] is not None:
- for face in faces[1]:
- x = face[0]
- y = face[1]
- w = face[2]
- h = face[3]
- results.append(
- PointOfInterest(
- int(x + (w * 0.5)), # face focus left/right is center
- int(y + (h * 0.33)), # face focus up/down is close to the top of the head
- size = w,
- weight = 1/len(faces[1])
- )
- )
- return results
- else:
- np_im = np.array(im)
- gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-
- tries = [
- [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
- [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
- [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
- [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
- [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
- [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
- [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
- [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
- ]
- for t in tries:
- classifier = cv2.CascadeClassifier(t[0])
- minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
- try:
- faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
- minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
- except Exception:
- continue
-
- if len(faces) > 0:
- rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
- return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
- return []
-
-
-def image_corner_points(im, settings): # pylint: disable=unused-argument
- grayscale = im.convert("L")
-
- # naive attempt at preventing focal points from collecting at watermarks near the bottom
- gd = ImageDraw.Draw(grayscale)
- gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-
- np_im = np.array(grayscale)
-
- points = cv2.goodFeaturesToTrack(
- np_im,
- maxCorners=100,
- qualityLevel=0.04,
- minDistance=min(grayscale.width, grayscale.height)*0.06,
- useHarrisDetector=False,
- )
-
- if points is None:
- return []
-
- focal_points = []
- for point in points:
- x, y = point.ravel()
- focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
-
- return focal_points
-
-
-def image_entropy_points(im, settings):
- landscape = im.height < im.width
- portrait = im.height > im.width
- if landscape:
- move_idx = [0, 2]
- move_max = im.size[0]
- elif portrait:
- move_idx = [1, 3]
- move_max = im.size[1]
- else:
- return []
-
- e_max = 0
- crop_current = [0, 0, settings.crop_width, settings.crop_height]
- crop_best = crop_current
- while crop_current[move_idx[1]] < move_max:
- crop = im.crop(tuple(crop_current))
- e = image_entropy(crop)
-
- if e > e_max:
- e_max = e
- crop_best = list(crop_current)
-
- crop_current[move_idx[0]] += 4
- crop_current[move_idx[1]] += 4
-
- x_mid = int(crop_best[0] + settings.crop_width/2)
- y_mid = int(crop_best[1] + settings.crop_height/2)
-
- return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
-
-
-def image_entropy(im):
- # greyscale image entropy
- # band = np.asarray(im.convert("L"))
- band = np.asarray(im.convert("1"), dtype=np.uint8)
- hist, _ = np.histogram(band, bins=range(0, 256))
- hist = hist[hist > 0]
- return -np.log2(hist / hist.sum()).sum()
-
-def centroid(pois):
- x = [poi.x for poi in pois]
- y = [poi.y for poi in pois]
- return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
-
-
-def poi_average(pois, settings): # pylint: disable=unused-argument
- weight = 0.0
- x = 0.0
- y = 0.0
- for poi in pois:
- weight += poi.weight
- x += poi.x * poi.weight
- y += poi.y * poi.weight
- avg_x = round(weight and x / weight)
- avg_y = round(weight and y / weight)
-
- return PointOfInterest(avg_x, avg_y)
-
-
-def is_landscape(w, h):
- return w > h
-
-
-def is_portrait(w, h):
- return h > w
-
-
-def is_square(w, h):
- return w == h
-
-
-def download_and_cache_models(dirname):
- download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
- model_file_name = 'face_detection_yunet.onnx'
- if not os.path.exists(dirname):
- os.makedirs(dirname, exist_ok=True)
- cache_file = os.path.join(dirname, model_file_name)
- if not os.path.exists(cache_file):
- print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
- response = requests.get(download_url, timeout=60*60*2)
- with open(cache_file, "wb") as f:
- f.write(response.content)
-
- if os.path.exists(cache_file):
- return cache_file
- return None
-
-
-class PointOfInterest:
- def __init__(self, x, y, weight=1.0, size=10):
- self.x = x
- self.y = y
- self.weight = weight
- self.size = size
-
- def bounding(self, size):
- return [
- self.x - size//2,
- self.y - size//2,
- self.x + size//2,
- self.y + size//2
- ]
-
-
-class Settings:
- def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
- self.crop_width = crop_width
- self.crop_height = crop_height
- self.corner_points_weight = corner_points_weight
- self.entropy_points_weight = entropy_points_weight
- self.face_points_weight = face_points_weight
- self.annotate_image = annotate_image
- self.destop_view_image = False
- self.dnn_model_path = dnn_model_path
+import os
+import cv2
+import requests
+import numpy as np
+from PIL import ImageDraw
+
+GREEN = "#0F0"
+BLUE = "#00F"
+RED = "#F00"
+
+
+def crop_image(im, settings):
+ """ Intelligently crop an image to the subject matter """
+
+ scale_by = 1
+ if is_landscape(im.width, im.height):
+ scale_by = settings.crop_height / im.height
+ elif is_portrait(im.width, im.height):
+ scale_by = settings.crop_width / im.width
+ elif is_square(im.width, im.height):
+ if is_square(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_width / im.width
+ elif is_landscape(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_width / im.width
+ elif is_portrait(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_height / im.height
+
+ im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+ im_debug = im.copy()
+
+ focus = focal_point(im_debug, settings)
+
+ # take the focal point and turn it into crop coordinates that try to center over the focal
+ # point but then get adjusted back into the frame
+ y_half = int(settings.crop_height / 2)
+ x_half = int(settings.crop_width / 2)
+
+ x1 = focus.x - x_half
+ if x1 < 0:
+ x1 = 0
+ elif x1 + settings.crop_width > im.width:
+ x1 = im.width - settings.crop_width
+
+ y1 = focus.y - y_half
+ if y1 < 0:
+ y1 = 0
+ elif y1 + settings.crop_height > im.height:
+ y1 = im.height - settings.crop_height
+
+ x2 = x1 + settings.crop_width
+ y2 = y1 + settings.crop_height
+
+ crop = [x1, y1, x2, y2]
+
+ results = []
+
+ results.append(im.crop(tuple(crop)))
+
+ if settings.annotate_image:
+ d = ImageDraw.Draw(im_debug)
+ rect = list(crop)
+ rect[2] -= 1
+ rect[3] -= 1
+ d.rectangle(rect, outline=GREEN)
+ results.append(im_debug)
+ if settings.destop_view_image:
+ im_debug.show()
+
+ return results
+
+def focal_point(im, settings):
+ corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
+ entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
+ face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
+
+ pois = []
+
+ weight_pref_total = 0
+ if len(corner_points) > 0:
+ weight_pref_total += settings.corner_points_weight
+ if len(entropy_points) > 0:
+ weight_pref_total += settings.entropy_points_weight
+ if len(face_points) > 0:
+ weight_pref_total += settings.face_points_weight
+
+ corner_centroid = None
+ if len(corner_points) > 0:
+ corner_centroid = centroid(corner_points)
+ corner_centroid.weight = settings.corner_points_weight / weight_pref_total
+ pois.append(corner_centroid)
+
+ entropy_centroid = None
+ if len(entropy_points) > 0:
+ entropy_centroid = centroid(entropy_points)
+ entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
+ pois.append(entropy_centroid)
+
+ face_centroid = None
+ if len(face_points) > 0:
+ face_centroid = centroid(face_points)
+ face_centroid.weight = settings.face_points_weight / weight_pref_total
+ pois.append(face_centroid)
+
+ average_point = poi_average(pois, settings)
+
+ if settings.annotate_image:
+ d = ImageDraw.Draw(im)
+ max_size = min(im.width, im.height) * 0.07
+ if corner_centroid is not None:
+ color = BLUE
+ box = corner_centroid.bounding(max_size * corner_centroid.weight)
+ d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
+ d.ellipse(box, outline=color)
+ if len(corner_points) > 1:
+ for f in corner_points:
+ d.rectangle(f.bounding(4), outline=color)
+ if entropy_centroid is not None:
+ color = "#ff0"
+ box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
+ d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
+ d.ellipse(box, outline=color)
+ if len(entropy_points) > 1:
+ for f in entropy_points:
+ d.rectangle(f.bounding(4), outline=color)
+ if face_centroid is not None:
+ color = RED
+ box = face_centroid.bounding(max_size * face_centroid.weight)
+ d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
+ d.ellipse(box, outline=color)
+ if len(face_points) > 1:
+ for f in face_points:
+ d.rectangle(f.bounding(4), outline=color)
+
+ d.ellipse(average_point.bounding(max_size), outline=GREEN)
+
+ return average_point
+
+
+def image_face_points(im, settings):
+ if settings.dnn_model_path is not None:
+ detector = cv2.FaceDetectorYN.create(
+ settings.dnn_model_path,
+ "",
+ (im.width, im.height),
+ 0.9, # score threshold
+ 0.3, # nms threshold
+ 5000 # keep top k before nms
+ )
+ faces = detector.detect(np.array(im))
+ results = []
+ if faces[1] is not None:
+ for face in faces[1]:
+ x = face[0]
+ y = face[1]
+ w = face[2]
+ h = face[3]
+ results.append(
+ PointOfInterest(
+ int(x + (w * 0.5)), # face focus left/right is center
+ int(y + (h * 0.33)), # face focus up/down is close to the top of the head
+ size = w,
+ weight = 1/len(faces[1])
+ )
+ )
+ return results
+ else:
+ np_im = np.array(im)
+ gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+
+ tries = [
+ [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+ [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+ ]
+ for t in tries:
+ classifier = cv2.CascadeClassifier(t[0])
+ minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+ try:
+ faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+ minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+ except Exception:
+ continue
+
+ if len(faces) > 0:
+ rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+ return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
+ return []
+
+
+def image_corner_points(im, settings): # pylint: disable=unused-argument
+ grayscale = im.convert("L")
+
+ # naive attempt at preventing focal points from collecting at watermarks near the bottom
+ gd = ImageDraw.Draw(grayscale)
+ gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+ np_im = np.array(grayscale)
+
+ points = cv2.goodFeaturesToTrack(
+ np_im,
+ maxCorners=100,
+ qualityLevel=0.04,
+ minDistance=min(grayscale.width, grayscale.height)*0.06,
+ useHarrisDetector=False,
+ )
+
+ if points is None:
+ return []
+
+ focal_points = []
+ for point in points:
+ x, y = point.ravel()
+ focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
+
+ return focal_points
+
+
+def image_entropy_points(im, settings):
+ landscape = im.height < im.width
+ portrait = im.height > im.width
+ if landscape:
+ move_idx = [0, 2]
+ move_max = im.size[0]
+ elif portrait:
+ move_idx = [1, 3]
+ move_max = im.size[1]
+ else:
+ return []
+
+ e_max = 0
+ crop_current = [0, 0, settings.crop_width, settings.crop_height]
+ crop_best = crop_current
+ while crop_current[move_idx[1]] < move_max:
+ crop = im.crop(tuple(crop_current))
+ e = image_entropy(crop)
+
+ if e > e_max:
+ e_max = e
+ crop_best = list(crop_current)
+
+ crop_current[move_idx[0]] += 4
+ crop_current[move_idx[1]] += 4
+
+ x_mid = int(crop_best[0] + settings.crop_width/2)
+ y_mid = int(crop_best[1] + settings.crop_height/2)
+
+ return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
+
+
+def image_entropy(im):
+ # greyscale image entropy
+ # band = np.asarray(im.convert("L"))
+ band = np.asarray(im.convert("1"), dtype=np.uint8)
+ hist, _ = np.histogram(band, bins=range(0, 256))
+ hist = hist[hist > 0]
+ return -np.log2(hist / hist.sum()).sum()
+
+def centroid(pois):
+ x = [poi.x for poi in pois]
+ y = [poi.y for poi in pois]
+ return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
+
+
+def poi_average(pois, settings): # pylint: disable=unused-argument
+ weight = 0.0
+ x = 0.0
+ y = 0.0
+ for poi in pois:
+ weight += poi.weight
+ x += poi.x * poi.weight
+ y += poi.y * poi.weight
+ avg_x = round(weight and x / weight)
+ avg_y = round(weight and y / weight)
+
+ return PointOfInterest(avg_x, avg_y)
+
+
+def is_landscape(w, h):
+ return w > h
+
+
+def is_portrait(w, h):
+ return h > w
+
+
+def is_square(w, h):
+ return w == h
+
+
+def download_and_cache_models(dirname):
+ download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
+ model_file_name = 'face_detection_yunet.onnx'
+ if not os.path.exists(dirname):
+ os.makedirs(dirname, exist_ok=True)
+ cache_file = os.path.join(dirname, model_file_name)
+ if not os.path.exists(cache_file):
+ print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
+ response = requests.get(download_url, timeout=60*60*2)
+ with open(cache_file, "wb") as f:
+ f.write(response.content)
+
+ if os.path.exists(cache_file):
+ return cache_file
+ return None
+
+
+class PointOfInterest:
+ def __init__(self, x, y, weight=1.0, size=10):
+ self.x = x
+ self.y = y
+ self.weight = weight
+ self.size = size
+
+ def bounding(self, size):
+ return [
+ self.x - size//2,
+ self.y - size//2,
+ self.x + size//2,
+ self.y + size//2
+ ]
+
+
+class Settings:
+ def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
+ self.crop_width = crop_width
+ self.crop_height = crop_height
+ self.corner_points_weight = corner_points_weight
+ self.entropy_points_weight = entropy_points_weight
+ self.face_points_weight = face_points_weight
+ self.annotate_image = annotate_image
+ self.destop_view_image = False
+ self.dnn_model_path = dnn_model_path
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 912736f71..e80106ebe 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -1,232 +1,231 @@
-import os
-import re
-import random
-from collections import defaultdict
-import numpy as np
-import PIL
-import torch
-from PIL import Image
-from torch.utils.data import Dataset, DataLoader, Sampler
-from torchvision import transforms
-import tqdm
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-from modules import devices, shared
-
-re_numbers_at_start = re.compile(r"^[-\d]+\s*")
-
-
-class DatasetEntry:
- def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
- self.filename = filename
- self.filename_text = filename_text
- self.weight = weight
- self.latent_dist = latent_dist
- self.latent_sample = latent_sample
- self.cond = cond
- self.cond_text = cond_text
- self.pixel_values = pixel_values
-
-
-class PersonalizedBase(Dataset):
- def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
- re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
-
- self.placeholder_token = placeholder_token
- self.flip = transforms.RandomHorizontalFlip(p=flip_p)
- self.dataset = []
- with open(template_file, "r", encoding="utf8") as file:
- lines = [x.strip() for x in file.readlines()]
- self.lines = lines
-
- assert data_root, 'dataset directory not specified'
- assert os.path.isdir(data_root), "Dataset directory doesn't exist"
- assert os.listdir(data_root), "Dataset directory is empty"
-
- self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
- self.shuffle_tags = shuffle_tags
- self.tag_drop_out = tag_drop_out
- groups = defaultdict(list)
- shared.log.info(f"TI Training: Preparing dataset: {data_root}")
- for path in tqdm.tqdm(self.image_paths):
- alpha_channel = None
- if shared.state.interrupted:
- raise RuntimeError("interrupted")
- try:
- image = Image.open(path)
- if use_weight and 'A' in image.getbands():
- alpha_channel = image.getchannel('A')
- image = image.convert('RGB')
- if not varsize:
- image = image.resize((width, height), PIL.Image.BICUBIC)
- except Exception:
- continue
-
- text_filename = f"{os.path.splitext(path)[0]}.txt"
- filename = os.path.basename(path)
-
- if os.path.exists(text_filename):
- with open(text_filename, "r", encoding="utf8") as file:
- filename_text = file.read()
- else:
- filename_text = os.path.splitext(filename)[0]
- filename_text = re.sub(re_numbers_at_start, '', filename_text)
- if re_word:
- tokens = re_word.findall(filename_text)
- filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
-
- npimage = np.array(image).astype(np.uint8)
- npimage = (npimage / 127.5 - 1.0).astype(np.float32)
-
- torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
- latent_sample = None
-
- with devices.autocast():
- latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
-
- if latent_sampling_method == "deterministic":
- if isinstance(latent_dist, DiagonalGaussianDistribution):
- latent_dist.std = torch.exp(0 * latent_dist.logvar)
- else:
- latent_sampling_method = "once"
- latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-
- if use_weight and alpha_channel is not None:
- channels, *latent_size = latent_sample.shape
- weight_img = alpha_channel.resize(latent_size)
- npweight = np.array(weight_img).astype(np.float32)
- #Repeat for every channel in the latent sample
- weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
- #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
- weight -= weight.min()
- weight /= weight.mean()
- elif use_weight:
- #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
- weight = torch.ones(latent_sample.shape)
- else:
- weight = None
-
- if latent_sampling_method == "random":
- entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
- else:
- entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
-
- if not (self.tag_drop_out != 0 or self.shuffle_tags):
- entry.cond_text = self.create_text(filename_text)
-
- if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
- with devices.autocast():
- entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
- groups[image.size].append(len(self.dataset))
- self.dataset.append(entry)
- del torchdata
- del latent_dist
- del latent_sample
- del weight
-
- self.length = len(self.dataset)
- self.groups = list(groups.values())
- assert self.length > 0, "No images have been found in the dataset."
- self.batch_size = min(batch_size, self.length)
- self.gradient_step = min(gradient_step, self.length // self.batch_size)
- self.latent_sampling_method = latent_sampling_method
-
- if len(groups) > 1:
- print("Buckets:")
- for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]):
- print(f" {w}x{h}: {len(ids)}")
- print()
-
- def create_text(self, filename_text):
- text = random.choice(self.lines)
- tags = filename_text.split(',')
- if self.tag_drop_out != 0:
- tags = [t for t in tags if random.random() > self.tag_drop_out]
- if self.shuffle_tags:
- random.shuffle(tags)
- text = text.replace("[filewords]", ','.join(tags))
- text = text.replace("[name]", self.placeholder_token)
- return text
-
- def __len__(self):
- return self.length
-
- def __getitem__(self, i):
- entry = self.dataset[i]
- if self.tag_drop_out != 0 or self.shuffle_tags:
- entry.cond_text = self.create_text(entry.filename_text)
- if self.latent_sampling_method == "random":
- entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
- return entry
-
-
-class GroupedBatchSampler(Sampler):
- def __init__(self, data_source: PersonalizedBase, batch_size: int):
- super().__init__(data_source)
-
- n = len(data_source)
- self.groups = data_source.groups
- self.len = n_batch = n // batch_size
- expected = [len(g) / n * n_batch * batch_size for g in data_source.groups]
- self.base = [int(e) // batch_size for e in expected]
- self.n_rand_batches = nrb = n_batch - sum(self.base)
- self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
- self.batch_size = batch_size
-
- def __len__(self):
- return self.len
-
- def __iter__(self):
- b = self.batch_size
-
- for g in self.groups:
- random.shuffle(g)
-
- batches = []
- for g in self.groups:
- batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
- for _ in range(self.n_rand_batches):
- rand_group = random.choices(self.groups, self.probs)[0]
- batches.append(random.choices(rand_group, k=b))
-
- random.shuffle(batches)
-
- yield from batches
-
-
-class PersonalizedDataLoader(DataLoader):
- def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
- super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
- if latent_sampling_method == "random":
- self.collate_fn = collate_wrapper_random
- else:
- self.collate_fn = collate_wrapper
-
-
-class BatchLoader:
- def __init__(self, data):
- self.cond_text = [entry.cond_text for entry in data]
- self.cond = [entry.cond for entry in data]
- self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
- if all(entry.weight is not None for entry in data):
- self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
- else:
- self.weight = None
- #self.emb_index = [entry.emb_index for entry in data]
- #print(self.latent_sample.device)
-
- def pin_memory(self):
- self.latent_sample = self.latent_sample.pin_memory()
- return self
-
-def collate_wrapper(batch):
- return BatchLoader(batch)
-
-class BatchLoaderRandom(BatchLoader):
- def __init__(self, data):
- super().__init__(data)
-
- def pin_memory(self):
- return self
-
-def collate_wrapper_random(batch):
- return BatchLoaderRandom(batch)
+import os
+import re
+import random
+from collections import defaultdict
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import Dataset, DataLoader, Sampler
+from torchvision import transforms
+import tqdm
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+from modules import devices, shared
+
+re_numbers_at_start = re.compile(r"^[-\d]+\s*")
+
+
+class DatasetEntry:
+ def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
+ self.filename = filename
+ self.filename_text = filename_text
+ self.weight = weight
+ self.latent_dist = latent_dist
+ self.latent_sample = latent_sample
+ self.cond = cond
+ self.cond_text = cond_text
+ self.pixel_values = pixel_values
+
+
+class PersonalizedBase(Dataset):
+ def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
+ re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
+
+ self.placeholder_token = placeholder_token
+ self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+ self.dataset = []
+ with open(template_file, "r", encoding="utf8") as file:
+ lines = [x.strip() for x in file.readlines()]
+ self.lines = lines
+
+ assert data_root, 'dataset directory not specified'
+ assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+ assert os.listdir(data_root), "Dataset directory is empty"
+
+ self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+ self.shuffle_tags = shuffle_tags
+ self.tag_drop_out = tag_drop_out
+ groups = defaultdict(list)
+ shared.log.info(f"TI Training: Preparing dataset: {data_root}")
+ for path in tqdm.tqdm(self.image_paths):
+ alpha_channel = None
+ if shared.state.interrupted:
+ raise RuntimeError("interrupted")
+ try:
+ image = Image.open(path)
+ if use_weight and 'A' in image.getbands():
+ alpha_channel = image.getchannel('A')
+ image = image.convert('RGB')
+ if not varsize:
+ image = image.resize((width, height), Image.Resampling.BICUBIC)
+ except Exception:
+ continue
+
+ text_filename = f"{os.path.splitext(path)[0]}.txt"
+ filename = os.path.basename(path)
+
+ if os.path.exists(text_filename):
+ with open(text_filename, "r", encoding="utf8") as file:
+ filename_text = file.read()
+ else:
+ filename_text = os.path.splitext(filename)[0]
+ filename_text = re.sub(re_numbers_at_start, '', filename_text)
+ if re_word:
+ tokens = re_word.findall(filename_text)
+ filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
+
+ npimage = np.array(image).astype(np.uint8)
+ npimage = (npimage / 127.5 - 1.0).astype(np.float32)
+
+ torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
+ latent_sample = None
+
+ with devices.autocast():
+ latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
+
+ if latent_sampling_method == "deterministic":
+ if isinstance(latent_dist, DiagonalGaussianDistribution):
+ latent_dist.std = torch.exp(0 * latent_dist.logvar)
+ else:
+ latent_sampling_method = "once"
+ latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+
+ if use_weight and alpha_channel is not None:
+ channels, *latent_size = latent_sample.shape
+ weight_img = alpha_channel.resize(latent_size)
+ npweight = np.array(weight_img).astype(np.float32)
+ #Repeat for every channel in the latent sample
+ weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
+ #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
+ weight -= weight.min()
+ weight /= weight.mean()
+ elif use_weight:
+ #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
+ weight = torch.ones(latent_sample.shape)
+ else:
+ weight = None
+
+ if latent_sampling_method == "random":
+ entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
+ else:
+ entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
+
+ if not (self.tag_drop_out != 0 or self.shuffle_tags):
+ entry.cond_text = self.create_text(filename_text)
+
+ if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
+ with devices.autocast():
+ entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
+ groups[image.size].append(len(self.dataset))
+ self.dataset.append(entry)
+ del torchdata
+ del latent_dist
+ del latent_sample
+ del weight
+
+ self.length = len(self.dataset)
+ self.groups = list(groups.values())
+ assert self.length > 0, "No images have been found in the dataset."
+ self.batch_size = min(batch_size, self.length)
+ self.gradient_step = min(gradient_step, self.length // self.batch_size)
+ self.latent_sampling_method = latent_sampling_method
+
+ if len(groups) > 1:
+ print("Buckets:")
+ for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]):
+ print(f" {w}x{h}: {len(ids)}")
+ print()
+
+ def create_text(self, filename_text):
+ text = random.choice(self.lines)
+ tags = filename_text.split(',')
+ if self.tag_drop_out != 0:
+ tags = [t for t in tags if random.random() > self.tag_drop_out]
+ if self.shuffle_tags:
+ random.shuffle(tags)
+ text = text.replace("[filewords]", ','.join(tags))
+ text = text.replace("[name]", self.placeholder_token)
+ return text
+
+ def __len__(self):
+ return self.length
+
+ def __getitem__(self, i):
+ entry = self.dataset[i]
+ if self.tag_drop_out != 0 or self.shuffle_tags:
+ entry.cond_text = self.create_text(entry.filename_text)
+ if self.latent_sampling_method == "random":
+ entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
+ return entry
+
+
+class GroupedBatchSampler(Sampler):
+ def __init__(self, data_source: PersonalizedBase, batch_size: int):
+ super().__init__(data_source)
+
+ n = len(data_source)
+ self.groups = data_source.groups
+ self.len = n_batch = n // batch_size
+ expected = [len(g) / n * n_batch * batch_size for g in data_source.groups]
+ self.base = [int(e) // batch_size for e in expected]
+ self.n_rand_batches = nrb = n_batch - sum(self.base)
+ self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
+ self.batch_size = batch_size
+
+ def __len__(self):
+ return self.len
+
+ def __iter__(self):
+ b = self.batch_size
+
+ for g in self.groups:
+ random.shuffle(g)
+
+ batches = []
+ for g in self.groups:
+ batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
+ for _ in range(self.n_rand_batches):
+ rand_group = random.choices(self.groups, self.probs)[0]
+ batches.append(random.choices(rand_group, k=b))
+
+ random.shuffle(batches)
+
+ yield from batches
+
+
+class PersonalizedDataLoader(DataLoader):
+ def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
+ super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
+ if latent_sampling_method == "random":
+ self.collate_fn = collate_wrapper_random
+ else:
+ self.collate_fn = collate_wrapper
+
+
+class BatchLoader:
+ def __init__(self, data):
+ self.cond_text = [entry.cond_text for entry in data]
+ self.cond = [entry.cond for entry in data]
+ self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+ if all(entry.weight is not None for entry in data):
+ self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
+ else:
+ self.weight = None
+ #self.emb_index = [entry.emb_index for entry in data]
+ #print(self.latent_sample.device)
+
+ def pin_memory(self):
+ self.latent_sample = self.latent_sample.pin_memory()
+ return self
+
+def collate_wrapper(batch):
+ return BatchLoader(batch)
+
+class BatchLoaderRandom(BatchLoader):
+ def __init__(self, data):
+ super().__init__(data)
+
+ def pin_memory(self):
+ return self
+
+def collate_wrapper_random(batch):
+ return BatchLoaderRandom(batch)
diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index a59798bb4..a33d3e450 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -1,213 +1,213 @@
-import base64
-import json
-import numpy as np
-import zlib
-from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
-import torch
-from modules.shared import opts
-
-
-class EmbeddingEncoder(json.JSONEncoder):
- def default(self, obj):
- if isinstance(obj, torch.Tensor):
- return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
- return json.JSONEncoder.default(self, obj)
-
-
-class EmbeddingDecoder(json.JSONDecoder):
- def __init__(self, *args, **kwargs):
- json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs)
-
- def object_hook(self, d):
- if 'TORCHTENSOR' in d:
- return torch.from_numpy(np.array(d['TORCHTENSOR']))
- return d
-
-
-def embedding_to_b64(data):
- d = json.dumps(data, cls=EmbeddingEncoder)
- return base64.b64encode(d.encode())
-
-
-def embedding_from_b64(data):
- d = base64.b64decode(data)
- return json.loads(d, cls=EmbeddingDecoder)
-
-
-def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
- while True:
- seed = (a * seed + c) % m
- yield seed % 255
-
-
-def xor_block(block):
- g = lcg()
- randblock = np.array([next(g) for _ in range(np.prod(block.shape))]).astype(np.uint8).reshape(block.shape)
- return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
-
-
-def style_block(block, sequence):
- im = Image.new('RGB', (block.shape[1], block.shape[0]))
- draw = ImageDraw.Draw(im)
- i = 0
- for x in range(-6, im.size[0], 8):
- for yi, y in enumerate(range(-6, im.size[1], 8)):
- offset = 0
- if yi % 2 == 0:
- offset = 4
- shade = sequence[i % len(sequence)]
- i += 1
- draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
-
- fg = np.array(im).astype(np.uint8) & 0xF0
-
- return block ^ fg
-
-
-def insert_image_data_embed(image, data):
- d = 3
- data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
- data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
- data_np_high = data_np_ >> 4
- data_np_low = data_np_ & 0x0F
-
- h = image.size[1]
- next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
- next_size = next_size + ((h*d)-(next_size % (h*d)))
-
- data_np_low = np.resize(data_np_low, next_size)
- data_np_low = data_np_low.reshape((h, -1, d))
-
- data_np_high = np.resize(data_np_high, next_size)
- data_np_high = data_np_high.reshape((h, -1, d))
-
- edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
- edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
-
- data_np_low = style_block(data_np_low, sequence=edge_style)
- data_np_low = xor_block(data_np_low)
- data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
- data_np_high = xor_block(data_np_high)
-
- im_low = Image.fromarray(data_np_low, mode='RGB')
- im_high = Image.fromarray(data_np_high, mode='RGB')
-
- background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
- background.paste(im_low, (0, 0))
- background.paste(image, (im_low.size[0]+1, 0))
- background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
-
- return background
-
-
-def crop_black(img, tol=0):
- mask = (img > tol).all(2)
- mask0, mask1 = mask.any(0), mask.any(1)
- col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
- row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
- return img[row_start:row_end, col_start:col_end]
-
-
-def extract_image_data_embed(image):
- d = 3
- outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
- black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
- if black_cols[0].shape[0] < 2:
- return None
-
- data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
- data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
-
- data_block_lower = xor_block(data_block_lower)
- data_block_upper = xor_block(data_block_upper)
-
- data_block = (data_block_upper << 4) | (data_block_lower)
- data_block = data_block.flatten().tobytes()
-
- data = zlib.decompress(data_block)
- return json.loads(data, cls=EmbeddingDecoder)
-
-
-def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
- from math import cos
- image = srcimage.copy()
- fontsize = 32
- if textfont is None:
- textfont = opts.font or 'javascript/roboto.ttf'
-
- factor = 1.5
- gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
- for y in range(image.size[1]):
- mag = 1-cos(y/image.size[1]*factor)
- mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
- gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
- image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
-
- draw = ImageDraw.Draw(image)
-
- font = ImageFont.truetype(textfont, fontsize)
- padding = 10
-
- _, _, w, h = draw.textbbox((0, 0), title, font=font)
- fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
- font = ImageFont.truetype(textfont, fontsize)
- _, _, w, h = draw.textbbox((0, 0), title, font=font)
- draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
-
- _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)
- fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
- _, _, w, h = draw.textbbox((0, 0), footerMid, font=font)
- fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
- _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
- fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
-
- font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
-
- draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
- draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
- draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
-
- return image
-
-
-if __name__ == '__main__':
-
- testEmbed = Image.open('test_embedding.png')
- data = extract_image_data_embed(testEmbed)
- assert data is not None
-
- data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
- assert data is not None
-
- image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
- cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
-
- test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
-
- embedded_image = insert_image_data_embed(cap_image, test_embed)
-
- retrived_embed = extract_image_data_embed(embedded_image)
-
- assert str(retrived_embed) == str(test_embed)
-
- embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
-
- assert embedded_image == embedded_image2
-
- g = lcg()
- shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
-
- reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177,
- 95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179,
- 160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193,
- 38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28,
- 30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0,
- 41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185,
- 66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82,
- 204, 86, 73, 222, 44, 198, 118, 240, 97]
-
- assert shared_random == reference_random
-
- hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
-
- assert 12731374 == hunna_kay_random_sum
+import base64
+import json
+import zlib
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+import torch
+from modules.shared import opts
+
+
+class EmbeddingEncoder(json.JSONEncoder):
+ def default(self, obj):
+ if isinstance(obj, torch.Tensor):
+ return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
+ return json.JSONEncoder.default(self, obj)
+
+
+class EmbeddingDecoder(json.JSONDecoder):
+ def __init__(self, *args, **kwargs):
+ json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs)
+
+ def object_hook(self, d):
+ if 'TORCHTENSOR' in d:
+ return torch.from_numpy(np.array(d['TORCHTENSOR']))
+ return d
+
+
+def embedding_to_b64(data):
+ d = json.dumps(data, cls=EmbeddingEncoder)
+ return base64.b64encode(d.encode())
+
+
+def embedding_from_b64(data):
+ d = base64.b64decode(data)
+ return json.loads(d, cls=EmbeddingDecoder)
+
+
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+ while True:
+ seed = (a * seed + c) % m
+ yield seed % 255
+
+
+def xor_block(block):
+ g = lcg()
+ randblock = np.array([next(g) for _ in range(np.prod(block.shape))]).astype(np.uint8).reshape(block.shape)
+ return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
+
+
+def style_block(block, sequence):
+ im = Image.new('RGB', (block.shape[1], block.shape[0]))
+ draw = ImageDraw.Draw(im)
+ i = 0
+ for x in range(-6, im.size[0], 8):
+ for yi, y in enumerate(range(-6, im.size[1], 8)):
+ offset = 0
+ if yi % 2 == 0:
+ offset = 4
+ shade = sequence[i % len(sequence)]
+ i += 1
+ draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
+
+ fg = np.array(im).astype(np.uint8) & 0xF0
+
+ return block ^ fg
+
+
+def insert_image_data_embed(image, data):
+ d = 3
+ data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
+ data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
+ data_np_high = data_np_ >> 4
+ data_np_low = data_np_ & 0x0F
+
+ h = image.size[1]
+ next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
+ next_size = next_size + ((h*d)-(next_size % (h*d)))
+
+ data_np_low = np.resize(data_np_low, next_size)
+ data_np_low = data_np_low.reshape((h, -1, d))
+
+ data_np_high = np.resize(data_np_high, next_size)
+ data_np_high = data_np_high.reshape((h, -1, d))
+
+ edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
+ edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
+
+ data_np_low = style_block(data_np_low, sequence=edge_style)
+ data_np_low = xor_block(data_np_low)
+ data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
+ data_np_high = xor_block(data_np_high)
+
+ im_low = Image.fromarray(data_np_low, mode='RGB')
+ im_high = Image.fromarray(data_np_high, mode='RGB')
+
+ background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
+ background.paste(im_low, (0, 0))
+ background.paste(image, (im_low.size[0]+1, 0))
+ background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
+
+ return background
+
+
+def crop_black(img, tol=0):
+ mask = (img > tol).all(2)
+ mask0, mask1 = mask.any(0), mask.any(1)
+ col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
+ row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
+ return img[row_start:row_end, col_start:col_end]
+
+
+def extract_image_data_embed(image):
+ d = 3
+ outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
+ black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
+ if black_cols[0].shape[0] < 2:
+ return None
+
+ data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
+ data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
+
+ data_block_lower = xor_block(data_block_lower)
+ data_block_upper = xor_block(data_block_upper)
+
+ data_block = (data_block_upper << 4) | (data_block_lower)
+ data_block = data_block.flatten().tobytes()
+
+ data = zlib.decompress(data_block)
+ return json.loads(data, cls=EmbeddingDecoder)
+
+
+def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
+ from math import cos
+ image = srcimage.copy()
+ fontsize = 32
+ if textfont is None:
+ textfont = opts.font or 'javascript/notosans-nerdfont-regular.ttf'
+
+ factor = 1.5
+ gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
+ for y in range(image.size[1]):
+ mag = 1-cos(y/image.size[1]*factor)
+ mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+ gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
+ image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
+
+ draw = ImageDraw.Draw(image)
+
+ font = ImageFont.truetype(textfont, fontsize)
+ padding = 10
+
+ _, _, w, _h = draw.textbbox((0, 0), title, font=font)
+ fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
+ font = ImageFont.truetype(textfont, fontsize)
+ _, _, w, _h = draw.textbbox((0, 0), title, font=font)
+ draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
+
+ _, _, w, _h = draw.textbbox((0, 0), footerLeft, font=font)
+ fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+ _, _, w, _h = draw.textbbox((0, 0), footerMid, font=font)
+ fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+ _, _, w, _h = draw.textbbox((0, 0), footerRight, font=font)
+ fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+
+ font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
+
+ draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
+ draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
+ draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
+
+ return image
+
+
+if __name__ == '__main__':
+
+ testEmbed = Image.open('test_embedding.png')
+ test_data = extract_image_data_embed(testEmbed)
+ assert test_data is not None
+
+ test_data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
+ assert test_data is not None
+
+ new_image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
+ cap_image = caption_image_overlay(new_image, 'title', 'footerLeft', 'footerMid', 'footerRight')
+
+ test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
+
+ embedded_image = insert_image_data_embed(cap_image, test_embed)
+
+ retrived_embed = extract_image_data_embed(embedded_image)
+
+ assert str(retrived_embed) == str(test_embed)
+
+ embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
+
+ assert embedded_image == embedded_image2
+
+ g = lcg()
+ shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
+
+ reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177,
+ 95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179,
+ 160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193,
+ 38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28,
+ 30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0,
+ 41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185,
+ 66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82,
+ 204, 86, 73, 222, 44, 198, 118, 240, 97]
+
+ assert shared_random == reference_random
+
+ hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
+
+ assert 12731374 == hunna_kay_random_sum
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index f12a62824..45a2eeb54 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -1,77 +1,77 @@
-class LearnScheduleIterator:
- def __init__(self, learn_rate, max_steps, cur_step=0):
- """
- specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000
- """
-
- pairs = learn_rate.split(',')
- self.rates = []
- self.it = 0
- self.maxit = 0
- try:
- for pair in pairs:
- if not pair.strip():
- continue
- tmp = pair.split(':')
- if len(tmp) == 2:
- step = int(tmp[1])
- if step > cur_step:
- self.rates.append((float(tmp[0]), min(step, max_steps)))
- self.maxit += 1
- if step > max_steps:
- return
- elif step == -1:
- self.rates.append((float(tmp[0]), max_steps))
- self.maxit += 1
- return
- else:
- self.rates.append((float(tmp[0]), max_steps))
- self.maxit += 1
- return
- assert self.rates
- except (ValueError, AssertionError) as e:
- raise RuntimeError('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') from e
-
-
- def __iter__(self):
- return self
-
- def __next__(self):
- if self.it < self.maxit:
- self.it += 1
- return self.rates[self.it - 1]
- else:
- raise StopIteration
-
-
-class LearnRateScheduler:
- def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
- self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
- (self.learn_rate, self.end_step) = next(self.schedules)
- self.verbose = verbose
-
- # if self.verbose:
- # print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
-
- self.finished = False
-
- def step(self, step_number):
- if step_number < self.end_step:
- return False
-
- try:
- (self.learn_rate, self.end_step) = next(self.schedules)
- except StopIteration:
- self.finished = True
- return False
- return True
-
- def apply(self, optimizer, step_number):
- if not self.step(step_number):
- return
-
- # if self.verbose:
- # tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
-
- for pg in optimizer.param_groups:
- pg['lr'] = self.learn_rate
+class LearnScheduleIterator:
+ def __init__(self, learn_rate, max_steps, cur_step=0):
+ """
+ specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000
+ """
+
+ pairs = learn_rate.split(',')
+ self.rates = []
+ self.it = 0
+ self.maxit = 0
+ try:
+ for pair in pairs:
+ if not pair.strip():
+ continue
+ tmp = pair.split(':')
+ if len(tmp) == 2:
+ step = int(tmp[1])
+ if step > cur_step:
+ self.rates.append((float(tmp[0]), min(step, max_steps)))
+ self.maxit += 1
+ if step > max_steps:
+ return
+ elif step == -1:
+ self.rates.append((float(tmp[0]), max_steps))
+ self.maxit += 1
+ return
+ else:
+ self.rates.append((float(tmp[0]), max_steps))
+ self.maxit += 1
+ return
+ assert self.rates
+ except (ValueError, AssertionError) as e:
+ raise RuntimeError('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') from e
+
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+ if self.it < self.maxit:
+ self.it += 1
+ return self.rates[self.it - 1]
+ else:
+ raise StopIteration
+
+
+class LearnRateScheduler:
+ def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
+ self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
+ (self.learn_rate, self.end_step) = next(self.schedules)
+ self.verbose = verbose
+
+ # if self.verbose:
+ # print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+ self.finished = False
+
+ def step(self, step_number):
+ if step_number < self.end_step:
+ return False
+
+ try:
+ (self.learn_rate, self.end_step) = next(self.schedules)
+ except StopIteration:
+ self.finished = True
+ return False
+ return True
+
+ def apply(self, optimizer, step_number):
+ if not self.step(step_number):
+ return
+
+ # if self.verbose:
+ # tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+ for pg in optimizer.param_groups:
+ pg['lr'] = self.learn_rate
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index a2ef4c6e6..6813da77d 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,217 +1,217 @@
-import os
-import math
-from tqdm import tqdm
-from PIL import Image, ImageOps
-from modules import paths, shared, images, deepbooru
-from modules.textual_inversion import autocrop
-
-
-def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size=False, process_keep_channels=False, process_flip=False, process_split=False, process_caption_only=False, process_caption=False, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None): # pylint: disable=unused-argument
- try:
- if process_caption:
- shared.interrogator.load()
-
- if process_caption_deepbooru:
- deepbooru.model.start()
-
- preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_keep_channels, process_flip, process_split, process_caption, process_caption_deepbooru, process_caption_only, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
-
- finally:
-
- if process_caption:
- shared.interrogator.send_blip_to_ram()
-
- if process_caption_deepbooru:
- deepbooru.model.stop()
-
-
-class PreprocessParams:
- src = None
- dstdir = None
- subindex = 0
- flip = False
- process_caption_only = False
- process_caption = False
- process_caption_deepbooru = False
- preprocess_txt_action = None
-
-
-def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None, existing_caption_filename=None):
- caption = ""
- if params.process_caption:
- caption += shared.interrogator.generate_caption(image)
- if params.process_caption_deepbooru:
- if len(caption) > 0:
- caption += ", "
- caption += deepbooru.model.tag_multi(image)
-
- filename_part = params.src
- filename_part = os.path.splitext(filename_part)[0]
- filename_part = os.path.basename(filename_part)
-
- basename = f"{index:05}-{params.subindex}-{filename_part}"
- if not params.process_caption_only:
- image.save(os.path.join(params.dstdir, f"{basename}.png"))
-
- if params.preprocess_txt_action == 'prepend' and existing_caption:
- caption = f"{existing_caption} {caption}"
- elif params.preprocess_txt_action == 'append' and existing_caption:
- caption = f"{caption} {existing_caption}"
- elif params.preprocess_txt_action == 'copy' and existing_caption:
- caption = existing_caption
- caption = caption.strip()
- if len(caption) > 0:
- if params.process_caption_only:
- fn = os.path.join(params.dstdir, f"{filename_part}.txt")
- elif existing_caption_filename is not None:
- fn = existing_caption_filename
- else:
- fn = os.path.join(params.dstdir, f"{basename}.txt")
- with open(fn, "w", encoding="utf8") as file:
- file.write(caption)
-
- params.subindex += 1
-
-
-def save_pic(image, index, params, existing_caption=None, existing_caption_filename=None):
- save_pic_with_caption(image, index, params, existing_caption=existing_caption, existing_caption_filename=existing_caption_filename)
- if params.flip:
- save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption, existing_caption_filename=existing_caption_filename)
-
-
-def split_pic(image, inverse_xy, width, height, overlap_ratio):
- if inverse_xy:
- from_w, from_h = image.height, image.width
- to_w, to_h = height, width
- else:
- from_w, from_h = image.width, image.height
- to_w, to_h = width, height
- h = from_h * to_w // from_w
- if inverse_xy:
- image = image.resize((h, to_w))
- else:
- image = image.resize((to_w, h))
-
- split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
- y_step = (h - to_h) / (split_count - 1)
- for i in range(split_count):
- y = int(y_step * i)
- if inverse_xy:
- splitted = image.crop((y, 0, y + to_h, to_w))
- else:
- splitted = image.crop((0, y, to_w, y + to_h))
- yield splitted
-
-# not using torchvision.transforms.CenterCrop because it doesn't allow float regions
-def center_crop(image: Image, w: int, h: int):
- iw, ih = image.size
- if ih / h < iw / w:
- sw = w * ih / h
- box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
- else:
- sh = h * iw / w
- box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
- return image.resize((w, h), Image.Resampling.LANCZOS, box)
-
-
-def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
- iw, ih = image.size
- err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h)) # pylint: disable=unnecessary-lambda-assignment,unnecessary-direct-lambda-call
- wh = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64)
- if minarea <= w * h <= maxarea and err(w, h) <= threshold),
- key= lambda wh: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1],
- default=None
- )
- return wh and center_crop(image, *wh)
-
-
-def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_keep_channels, process_flip, process_split, process_caption, process_caption_deepbooru, process_caption_only, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold):
-
- width = process_width
- height = process_height
- src = os.path.abspath(process_src)
- dst = os.path.abspath(process_dst)
- split_threshold = max(0.0, min(1.0, split_threshold))
- overlap_ratio = max(0.0, min(0.9, overlap_ratio))
- assert src != dst, 'same directory specified as source and destination'
- os.makedirs(dst, exist_ok=True)
- files = os.listdir(src)
- shared.state.job = "preprocess"
- shared.state.textinfo = "Preprocessing..."
- shared.state.job_count = len(files)
- params = PreprocessParams()
- params.dstdir = dst
- params.flip = process_flip
- params.process_caption_only = process_caption_only
- params.process_caption = process_caption
- params.process_caption_deepbooru = process_caption_deepbooru
- params.preprocess_txt_action = preprocess_txt_action
- pbar = tqdm(files)
- for index, imagefile in enumerate(pbar):
- params.subindex = 0
- filename = os.path.join(src, imagefile)
- try:
- img = Image.open(filename)
- img = ImageOps.exif_transpose(img)
- if not process_keep_channels:
- img = img.convert("RGB")
- except Exception:
- continue
-
- description = f"Preprocessing image {index + 1}/{len(files)}"
- pbar.set_description(description)
- shared.state.textinfo = description
- params.src = filename
- existing_caption = None
- existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
- if os.path.exists(existing_caption_filename):
- with open(existing_caption_filename, 'r', encoding="utf8") as file:
- existing_caption = file.read()
- else:
- existing_caption_filename = None
- if shared.state.interrupted:
- break
- if img.height > img.width:
- ratio = (img.width * height) / (img.height * width)
- inverse_xy = False
- else:
- ratio = (img.height * width) / (img.width * height)
- inverse_xy = True
- process_default_resize = True
- if process_split and ratio < 1.0 and ratio <= split_threshold:
- for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
- save_pic(splitted, index, params, existing_caption=existing_caption, existing_caption_filename=existing_caption_filename)
- process_default_resize = False
- if process_focal_crop and img.height != img.width:
- dnn_model_path = None
- try:
- dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
- except Exception as e:
- print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
- autocrop_settings = autocrop.Settings(
- crop_width = width,
- crop_height = height,
- face_points_weight = process_focal_crop_face_weight,
- entropy_points_weight = process_focal_crop_entropy_weight,
- corner_points_weight = process_focal_crop_edges_weight,
- annotate_image = process_focal_crop_debug,
- dnn_model_path = dnn_model_path,
- )
- for focal in autocrop.crop_image(img, autocrop_settings):
- save_pic(focal, index, params, existing_caption=existing_caption)
- process_default_resize = False
-
- if process_multicrop:
- cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
- if cropped is not None:
- save_pic(cropped, index, params, existing_caption=existing_caption)
- else:
- print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
- process_default_resize = False
- if process_keep_original_size:
- save_pic(img, index, params, existing_caption=existing_caption)
- process_default_resize = False
- if process_default_resize:
- img = images.resize_image(1, img, width, height)
- save_pic(img, index, params, existing_caption=existing_caption)
- shared.state.nextjob()
+import os
+import math
+from tqdm import tqdm
+from PIL import Image, ImageOps
+from modules import paths, shared, images, deepbooru
+from modules.textual_inversion import autocrop
+
+
+def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size=False, process_keep_channels=False, process_flip=False, process_split=False, process_caption_only=False, process_caption=False, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None): # pylint: disable=unused-argument
+ try:
+ if process_caption:
+ shared.interrogator.load()
+
+ if process_caption_deepbooru:
+ deepbooru.model.start()
+
+ preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_keep_channels, process_flip, process_split, process_caption, process_caption_deepbooru, process_caption_only, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
+
+ finally:
+
+ if process_caption:
+ shared.interrogator.send_blip_to_ram()
+
+ if process_caption_deepbooru:
+ deepbooru.model.stop()
+
+
+class PreprocessParams:
+ src = None
+ dstdir = None
+ subindex = 0
+ flip = False
+ process_caption_only = False
+ process_caption = False
+ process_caption_deepbooru = False
+ preprocess_txt_action = None
+
+
+def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None, existing_caption_filename=None):
+ caption = ""
+ if params.process_caption:
+ caption += shared.interrogator.generate_caption(image)
+ if params.process_caption_deepbooru:
+ if len(caption) > 0:
+ caption += ", "
+ caption += deepbooru.model.tag_multi(image)
+
+ filename_part = params.src
+ filename_part = os.path.splitext(filename_part)[0]
+ filename_part = os.path.basename(filename_part)
+
+ basename = f"{index:05}-{params.subindex}-{filename_part}"
+ if not params.process_caption_only:
+ image.save(os.path.join(params.dstdir, f"{basename}.png"))
+
+ if params.preprocess_txt_action == 'prepend' and existing_caption:
+ caption = f"{existing_caption} {caption}"
+ elif params.preprocess_txt_action == 'append' and existing_caption:
+ caption = f"{caption} {existing_caption}"
+ elif params.preprocess_txt_action == 'copy' and existing_caption:
+ caption = existing_caption
+ caption = caption.strip()
+ if len(caption) > 0:
+ if params.process_caption_only:
+ fn = os.path.join(params.dstdir, f"{filename_part}.txt")
+ elif existing_caption_filename is not None:
+ fn = existing_caption_filename
+ else:
+ fn = os.path.join(params.dstdir, f"{basename}.txt")
+ with open(fn, "w", encoding="utf8") as file:
+ file.write(caption)
+
+ params.subindex += 1
+
+
+def save_pic(image, index, params, existing_caption=None, existing_caption_filename=None):
+ save_pic_with_caption(image, index, params, existing_caption=existing_caption, existing_caption_filename=existing_caption_filename)
+ if params.flip:
+ save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption, existing_caption_filename=existing_caption_filename)
+
+
+def split_pic(image, inverse_xy, width, height, overlap_ratio):
+ if inverse_xy:
+ from_w, from_h = image.height, image.width
+ to_w, to_h = height, width
+ else:
+ from_w, from_h = image.width, image.height
+ to_w, to_h = width, height
+ h = from_h * to_w // from_w
+ if inverse_xy:
+ image = image.resize((h, to_w))
+ else:
+ image = image.resize((to_w, h))
+
+ split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
+ y_step = (h - to_h) / (split_count - 1)
+ for i in range(split_count):
+ y = int(y_step * i)
+ if inverse_xy:
+ splitted = image.crop((y, 0, y + to_h, to_w))
+ else:
+ splitted = image.crop((0, y, to_w, y + to_h))
+ yield splitted
+
+# not using torchvision.transforms.CenterCrop because it doesn't allow float regions
+def center_crop(image: Image, w: int, h: int):
+ iw, ih = image.size
+ if ih / h < iw / w:
+ sw = w * ih / h
+ box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
+ else:
+ sh = h * iw / w
+ box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
+ return image.resize((w, h), Image.Resampling.LANCZOS, box)
+
+
+def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
+ iw, ih = image.size
+ err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h)) # pylint: disable=unnecessary-lambda-assignment,unnecessary-direct-lambda-call
+ wh = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64)
+ if minarea <= w * h <= maxarea and err(w, h) <= threshold),
+ key= lambda wh: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1],
+ default=None
+ )
+ return wh and center_crop(image, *wh)
+
+
+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_keep_channels, process_flip, process_split, process_caption, process_caption_deepbooru, process_caption_only, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold):
+
+ width = process_width
+ height = process_height
+ src = os.path.abspath(process_src)
+ dst = os.path.abspath(process_dst)
+ split_threshold = max(0.0, min(1.0, split_threshold))
+ overlap_ratio = max(0.0, min(0.9, overlap_ratio))
+ assert src != dst, 'same directory specified as source and destination'
+ os.makedirs(dst, exist_ok=True)
+ files = os.listdir(src)
+ shared.state.job = "preprocess"
+ shared.state.textinfo = "Preprocessing..."
+ shared.state.job_count = len(files)
+ params = PreprocessParams()
+ params.dstdir = dst
+ params.flip = process_flip
+ params.process_caption_only = process_caption_only
+ params.process_caption = process_caption
+ params.process_caption_deepbooru = process_caption_deepbooru
+ params.preprocess_txt_action = preprocess_txt_action
+ pbar = tqdm(files)
+ for index, imagefile in enumerate(pbar):
+ params.subindex = 0
+ filename = os.path.join(src, imagefile)
+ try:
+ img = Image.open(filename)
+ img = ImageOps.exif_transpose(img)
+ if not process_keep_channels:
+ img = img.convert("RGB")
+ except Exception:
+ continue
+
+ description = f"Preprocessing image {index + 1}/{len(files)}"
+ pbar.set_description(description)
+ shared.state.textinfo = description
+ params.src = filename
+ existing_caption = None
+ existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
+ if os.path.exists(existing_caption_filename):
+ with open(existing_caption_filename, 'r', encoding="utf8") as file:
+ existing_caption = file.read()
+ else:
+ existing_caption_filename = None
+ if shared.state.interrupted:
+ break
+ if img.height > img.width:
+ ratio = (img.width * height) / (img.height * width)
+ inverse_xy = False
+ else:
+ ratio = (img.height * width) / (img.width * height)
+ inverse_xy = True
+ process_default_resize = True
+ if process_split and ratio < 1.0 and ratio <= split_threshold:
+ for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
+ save_pic(splitted, index, params, existing_caption=existing_caption, existing_caption_filename=existing_caption_filename)
+ process_default_resize = False
+ if process_focal_crop and img.height != img.width:
+ dnn_model_path = None
+ try:
+ dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
+ except Exception as e:
+ print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
+ autocrop_settings = autocrop.Settings(
+ crop_width = width,
+ crop_height = height,
+ face_points_weight = process_focal_crop_face_weight,
+ entropy_points_weight = process_focal_crop_entropy_weight,
+ corner_points_weight = process_focal_crop_edges_weight,
+ annotate_image = process_focal_crop_debug,
+ dnn_model_path = dnn_model_path,
+ )
+ for focal in autocrop.crop_image(img, autocrop_settings):
+ save_pic(focal, index, params, existing_caption=existing_caption)
+ process_default_resize = False
+
+ if process_multicrop:
+ cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
+ if cropped is not None:
+ save_pic(cropped, index, params, existing_caption=existing_caption)
+ else:
+ print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
+ process_default_resize = False
+ if process_keep_original_size:
+ save_pic(img, index, params, existing_caption=existing_caption)
+ process_default_resize = False
+ if process_default_resize:
+ img = images.resize_image(1, img, width, height)
+ save_pic(img, index, params, existing_caption=existing_caption)
+ shared.state.nextjob()
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 3da8b643e..a51a550db 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -1,41 +1,20 @@
-import csv
-import html
import os
+import html
+import csv
import time
from collections import namedtuple
-
-import numpy as np
-import safetensors.torch
import torch
-from PIL import Image, PngImagePlugin
-from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
-
+import safetensors.torch
+import numpy as np
+from PIL import Image, PngImagePlugin
+from modules import shared, devices, processing, sd_models, images, errors
import modules.textual_inversion.dataset
-from modules import (
- devices,
- errors,
- images,
- processing,
- sd_hijack,
- sd_hijack_checkpoint,
- sd_models,
- shared,
-)
-from modules.files_cache import (
- directory_files,
- directory_mtime,
- extension_filter,
-)
-from modules.textual_inversion.image_embedding import (
- caption_image_overlay,
- embedding_from_b64,
- embedding_to_b64,
- extract_image_data_embed,
- insert_image_data_embed,
-)
from modules.textual_inversion.learn_schedule import LearnRateScheduler
+from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
from modules.textual_inversion.ti_logging import save_settings_to_file
+from modules.files_cache import directory_files, extension_filter, directory_mtime
+
TextualInversionTemplate = namedtuple("TextualInversionTemplate", ["name", "path"])
textual_inversion_templates = {}
@@ -388,6 +367,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
def tensorboard_setup(log_directory):
+ from torch.utils.tensorboard import SummaryWriter
os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
return SummaryWriter(
log_dir=os.path.join(log_directory, "tensorboard"),
@@ -436,6 +416,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # pylint: disable=unused-argument
+ from modules import sd_hijack, sd_hijack_checkpoint
shared.log.debug(f'train_embedding: embedding_name={embedding_name}|learn_rate={learn_rate}|batch_size={batch_size}|gradient_step={gradient_step}|data_root={data_root}|log_directory={log_directory}|training_width={training_width}|training_height={training_height}|varsize={varsize}|steps={steps}|clip_grad_mode={clip_grad_mode}|clip_grad_value={clip_grad_value}|shuffle_tags={shuffle_tags}|tag_drop_out={tag_drop_out}|latent_sampling_method={latent_sampling_method}|use_weight={use_weight}|create_image_every={create_image_every}|save_embedding_every={save_embedding_every}|template_filename={template_filename}|save_image_with_stored_embedding={save_image_with_stored_embedding}|preview_from_txt2img={preview_from_txt2img}|preview_prompt={preview_prompt}|preview_negative_prompt={preview_negative_prompt}|preview_steps={preview_steps}|preview_sampler_index={preview_sampler_index}|preview_cfg_scale={preview_cfg_scale}|preview_seed={preview_seed}|preview_width={preview_width}|preview_height={preview_height}')
save_embedding_every = save_embedding_every or 0
diff --git a/modules/textual_inversion/ti_logging.py b/modules/textual_inversion/ti_logging.py
index a79696e3f..1116ec91d 100644
--- a/modules/textual_inversion/ti_logging.py
+++ b/modules/textual_inversion/ti_logging.py
@@ -1,22 +1,22 @@
-import datetime
-import json
-import os
-
-saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "latent_sampling_method"}
-saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
-saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
-saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
-saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
-
-
-def save_settings_to_file(log_directory, all_params):
- now = datetime.datetime.now()
- params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")}
- keys = saved_params_all
- if all_params.get('preview_from_txt2img'):
- keys = keys | saved_params_previews
- params.update({k: v for k, v in all_params.items() if k in keys})
- filename = f"settings-{now.strftime('%Y-%m-%d_%H-%M-%S')}.json"
- with open(os.path.join(log_directory, filename), "w", encoding='utf-8') as file:
- print(f'Training settings file: {os.path.join(log_directory, filename)}')
- json.dump(params, file, indent=2)
+import datetime
+import json
+import os
+
+saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "latent_sampling_method"}
+saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
+saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
+saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
+saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
+
+
+def save_settings_to_file(log_directory, all_params):
+ now = datetime.datetime.now()
+ params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")}
+ keys = saved_params_all
+ if all_params.get('preview_from_txt2img'):
+ keys = keys | saved_params_previews
+ params.update({k: v for k, v in all_params.items() if k in keys})
+ filename = f"settings-{now.strftime('%Y-%m-%d_%H-%M-%S')}.json"
+ with open(os.path.join(log_directory, filename), "w", encoding='utf-8') as file:
+ print(f'Training settings file: {os.path.join(log_directory, filename)}')
+ json.dump(params, file, indent=2)
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 76367b772..9c5d7e3c4 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -1,32 +1,35 @@
-import html
-import gradio as gr
-import modules.textual_inversion.textual_inversion
-import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared
-
-
-def create_embedding(name, initialization_text, nvpt, overwrite_old):
- filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)
- sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
- return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
-
-
-def preprocess(*args):
- modules.textual_inversion.preprocess.preprocess(*args)
- return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
-
-
-def train_embedding(*args):
- assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
- apply_optimizations = False
- try:
- if not apply_optimizations:
- sd_hijack.undo_optimizations()
- embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
- res = f"Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps. Embedding saved to {html.escape(filename)}"
- return res, ""
- except Exception:
- raise
- finally:
- if not apply_optimizations:
- sd_hijack.apply_optimizations()
+import html
+import gradio as gr
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
+from modules import shared
+
+
+def create_embedding(name, initialization_text, nvpt, overwrite_old):
+ from modules import sd_hijack
+ filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)
+ sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+ return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
+
+
+def preprocess(*args):
+ modules.textual_inversion.preprocess.preprocess(*args)
+ return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
+
+
+def train_embedding(*args):
+ from modules import sd_hijack
+ assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
+ apply_optimizations = False
+ try:
+ if not apply_optimizations:
+ sd_hijack.undo_optimizations()
+ embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
+ res = f"Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps. Embedding saved to {html.escape(filename)}"
+ return res, ""
+ except Exception as e:
+ shared.log.error(f"Exception in train_embedding: {e}")
+ raise RuntimeError from e
+ finally:
+ if not apply_optimizations:
+ sd_hijack.apply_optimizations()
diff --git a/modules/theme.py b/modules/theme.py
index 35f98fe55..321cafbf3 100644
--- a/modules/theme.py
+++ b/modules/theme.py
@@ -2,10 +2,10 @@ import os
import json
import gradio as gr
import modules.shared
-# from modules.shared import log, opts, req, writefile
gradio_theme = gr.themes.Base()
+# modules.shared.opts.onchange("gradio_theme", reload_gradio_theme)
def list_builtin_themes():
diff --git a/modules/timer.py b/modules/timer.py
index 17ebc559d..da3996f59 100644
--- a/modules/timer.py
+++ b/modules/timer.py
@@ -1,35 +1,35 @@
-import time
-
-
-class Timer:
- def __init__(self):
- self.start = time.time()
- self.records = {}
- self.total = 0
-
- def elapsed(self):
- end = time.time()
- res = end - self.start
- self.start = end
- return res
-
- def record(self, category, extra_time=0):
- e = self.elapsed()
- if category not in self.records:
- self.records[category] = 0
-
- self.records[category] += e + extra_time
- self.total += e + extra_time
-
- def summary(self, min_time=0.05):
- res = f"{self.total:.2f}"
- additions = [x for x in self.records.items() if x[1] >= min_time]
- if not additions:
- return res
- res += " { " + " ".join([f"{category}={time_taken:.2f}" for category, time_taken in additions]) + " }"
- return res
-
- def reset(self):
- self.__init__()
-
-startup = Timer()
+import time
+
+
+class Timer:
+ def __init__(self):
+ self.start = time.time()
+ self.records = {}
+ self.total = 0
+
+ def elapsed(self):
+ end = time.time()
+ res = end - self.start
+ self.start = end
+ return res
+
+ def record(self, category, extra_time=0):
+ e = self.elapsed()
+ if category not in self.records:
+ self.records[category] = 0
+
+ self.records[category] += e + extra_time
+ self.total += e + extra_time
+
+ def summary(self, min_time=0.05):
+ res = f"{self.total:.2f} "
+ additions = [x for x in self.records.items() if x[1] >= min_time]
+ if not additions:
+ return res
+ res += " ".join([f"{category}={time_taken:.2f}" for category, time_taken in additions])
+ return res
+
+ def reset(self):
+ self.__init__()
+
+startup = Timer()
diff --git a/modules/txt2img.py b/modules/txt2img.py
index edb8d87e8..8a52003b8 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -1,94 +1,94 @@
-import os
-import modules.scripts
-from modules import shared, processing
-from modules.generation_parameters_copypaste import create_override_settings_dict
-from modules.ui import plaintext_to_html
-
-
-debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
-debug('Trace: PROCESS')
-
-
-def txt2img(id_task,
- prompt, negative_prompt, prompt_styles,
- steps, sampler_index, hr_sampler_index,
- full_quality, restore_faces, tiling,
- n_iter, batch_size,
- cfg_scale, image_cfg_scale, diffusers_guidance_rescale, sag_scale,
- clip_skip,
- seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
- height, width,
- enable_hr, denoising_strength,
- hr_scale, hr_upscaler, hr_force, hr_second_pass_steps, hr_resize_x, hr_resize_y,
- refiner_steps, refiner_start, refiner_prompt, refiner_negative,
- hdr_clamp, hdr_boundary, hdr_threshold, hdr_center, hdr_channel_shift, hdr_full_shift, hdr_maximize, hdr_max_center, hdr_max_boundry,
- override_settings_texts,
- *args):
-
- debug(f'txt2img: id_task={id_task}|prompt={prompt}|negative={negative_prompt}|styles={prompt_styles}|steps={steps}|sampler_index={sampler_index}|hr_sampler_index={hr_sampler_index}|full_quality={full_quality}|restore_faces={restore_faces}|tiling={tiling}|batch_count={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|clip_skip={clip_skip}|seed={seed}|subseed={subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|height={height}|width={width}|enable_hr={enable_hr}|denoising_strength={denoising_strength}|hr_scale={hr_scale}|hr_upscaler={hr_upscaler}|hr_force={hr_force}|hr_second_pass_steps={hr_second_pass_steps}|hr_resize_x={hr_resize_x}|hr_resize_y={hr_resize_y}|image_cfg_scale={image_cfg_scale}|diffusers_guidance_rescale={diffusers_guidance_rescale}|refiner_steps={refiner_steps}|refiner_start={refiner_start}|refiner_prompt={refiner_prompt}|refiner_negative={refiner_negative}|override_settings={override_settings_texts}')
-
- if shared.sd_model is None:
- shared.log.warning('Model not loaded')
- return [], '', '', 'Error: model not loaded'
-
- override_settings = create_override_settings_dict(override_settings_texts)
- if sampler_index is None:
- sampler_index = 0
- if hr_sampler_index is None:
- hr_sampler_index = 0
-
- p = processing.StableDiffusionProcessingTxt2Img(
- sd_model=shared.sd_model,
- outpath_samples=shared.opts.outdir_samples or shared.opts.outdir_txt2img_samples,
- outpath_grids=shared.opts.outdir_grids or shared.opts.outdir_txt2img_grids,
- prompt=prompt,
- styles=prompt_styles,
- negative_prompt=negative_prompt,
- seed=seed,
- subseed=subseed,
- subseed_strength=subseed_strength,
- seed_resize_from_h=seed_resize_from_h,
- seed_resize_from_w=seed_resize_from_w,
- seed_enable_extras=True,
- sampler_name = processing.get_sampler_name(sampler_index),
- hr_sampler_name = processing.get_sampler_name(hr_sampler_index),
- batch_size=batch_size,
- n_iter=n_iter,
- steps=steps,
- cfg_scale=cfg_scale,
- image_cfg_scale=image_cfg_scale,
- diffusers_guidance_rescale=diffusers_guidance_rescale,
- sag_scale=sag_scale,
- clip_skip=clip_skip,
- width=width,
- height=height,
- full_quality=full_quality,
- restore_faces=restore_faces,
- tiling=tiling,
- enable_hr=enable_hr,
- denoising_strength=denoising_strength,
- hr_scale=hr_scale,
- hr_upscaler=hr_upscaler,
- hr_force=hr_force,
- hr_second_pass_steps=hr_second_pass_steps,
- hr_resize_x=hr_resize_x,
- hr_resize_y=hr_resize_y,
- refiner_steps=refiner_steps,
- refiner_start=refiner_start,
- refiner_prompt=refiner_prompt,
- refiner_negative=refiner_negative,
- hdr_clamp=hdr_clamp, hdr_boundary=hdr_boundary, hdr_threshold=hdr_threshold,
- hdr_center=hdr_center, hdr_channel_shift=hdr_channel_shift, hdr_full_shift=hdr_full_shift,
- hdr_maximize=hdr_maximize, hdr_max_center=hdr_max_center, hdr_max_boundry=hdr_max_boundry,
- override_settings=override_settings,
- )
- p.scripts = modules.scripts.scripts_txt2img
- p.script_args = args
- processed = modules.scripts.scripts_txt2img.run(p, *args)
- if processed is None:
- processed = processing.process_images(p)
- p.close()
- if processed is None:
- return [], '', '', 'Error: processing failed'
- generation_info_js = processed.js() if processed is not None else ''
- return processed.images, generation_info_js, processed.info, plaintext_to_html(processed.comments)
+import os
+import modules.scripts
+from modules import shared, processing
+from modules.generation_parameters_copypaste import create_override_settings_dict
+from modules.ui import plaintext_to_html
+
+
+debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
+debug('Trace: PROCESS')
+
+
+def txt2img(id_task,
+ prompt, negative_prompt, prompt_styles,
+ steps, sampler_index, hr_sampler_index,
+ full_quality, restore_faces, tiling,
+ n_iter, batch_size,
+ cfg_scale, image_cfg_scale, diffusers_guidance_rescale, sag_scale,
+ clip_skip,
+ seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
+ height, width,
+ enable_hr, denoising_strength,
+ hr_scale, hr_upscaler, hr_force, hr_second_pass_steps, hr_resize_x, hr_resize_y,
+ refiner_steps, refiner_start, refiner_prompt, refiner_negative,
+ hdr_clamp, hdr_boundary, hdr_threshold, hdr_center, hdr_channel_shift, hdr_full_shift, hdr_maximize, hdr_max_center, hdr_max_boundry,
+ override_settings_texts,
+ *args):
+
+ debug(f'txt2img: id_task={id_task}|prompt={prompt}|negative={negative_prompt}|styles={prompt_styles}|steps={steps}|sampler_index={sampler_index}|hr_sampler_index={hr_sampler_index}|full_quality={full_quality}|restore_faces={restore_faces}|tiling={tiling}|batch_count={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|clip_skip={clip_skip}|seed={seed}|subseed={subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|height={height}|width={width}|enable_hr={enable_hr}|denoising_strength={denoising_strength}|hr_scale={hr_scale}|hr_upscaler={hr_upscaler}|hr_force={hr_force}|hr_second_pass_steps={hr_second_pass_steps}|hr_resize_x={hr_resize_x}|hr_resize_y={hr_resize_y}|image_cfg_scale={image_cfg_scale}|diffusers_guidance_rescale={diffusers_guidance_rescale}|refiner_steps={refiner_steps}|refiner_start={refiner_start}|refiner_prompt={refiner_prompt}|refiner_negative={refiner_negative}|override_settings={override_settings_texts}')
+
+ if shared.sd_model is None:
+ shared.log.warning('Model not loaded')
+ return [], '', '', 'Error: model not loaded'
+
+ override_settings = create_override_settings_dict(override_settings_texts)
+ if sampler_index is None:
+ sampler_index = 0
+ if hr_sampler_index is None:
+ hr_sampler_index = 0
+
+ p = processing.StableDiffusionProcessingTxt2Img(
+ sd_model=shared.sd_model,
+ outpath_samples=shared.opts.outdir_samples or shared.opts.outdir_txt2img_samples,
+ outpath_grids=shared.opts.outdir_grids or shared.opts.outdir_txt2img_grids,
+ prompt=prompt,
+ styles=prompt_styles,
+ negative_prompt=negative_prompt,
+ seed=seed,
+ subseed=subseed,
+ subseed_strength=subseed_strength,
+ seed_resize_from_h=seed_resize_from_h,
+ seed_resize_from_w=seed_resize_from_w,
+ seed_enable_extras=True,
+ sampler_name = processing.get_sampler_name(sampler_index),
+ hr_sampler_name = processing.get_sampler_name(hr_sampler_index),
+ batch_size=batch_size,
+ n_iter=n_iter,
+ steps=steps,
+ cfg_scale=cfg_scale,
+ image_cfg_scale=image_cfg_scale,
+ diffusers_guidance_rescale=diffusers_guidance_rescale,
+ sag_scale=sag_scale,
+ clip_skip=clip_skip,
+ width=width,
+ height=height,
+ full_quality=full_quality,
+ restore_faces=restore_faces,
+ tiling=tiling,
+ enable_hr=enable_hr,
+ denoising_strength=denoising_strength,
+ hr_scale=hr_scale,
+ hr_upscaler=hr_upscaler,
+ hr_force=hr_force,
+ hr_second_pass_steps=hr_second_pass_steps,
+ hr_resize_x=hr_resize_x,
+ hr_resize_y=hr_resize_y,
+ refiner_steps=refiner_steps,
+ refiner_start=refiner_start,
+ refiner_prompt=refiner_prompt,
+ refiner_negative=refiner_negative,
+ hdr_clamp=hdr_clamp, hdr_boundary=hdr_boundary, hdr_threshold=hdr_threshold,
+ hdr_center=hdr_center, hdr_channel_shift=hdr_channel_shift, hdr_full_shift=hdr_full_shift,
+ hdr_maximize=hdr_maximize, hdr_max_center=hdr_max_center, hdr_max_boundry=hdr_max_boundry,
+ override_settings=override_settings,
+ )
+ p.scripts = modules.scripts.scripts_txt2img
+ p.script_args = args
+ processed = modules.scripts.scripts_txt2img.run(p, *args)
+ if processed is None:
+ processed = processing.process_images(p)
+ p.close()
+ if processed is None:
+ return [], '', '', 'Error: processing failed'
+ generation_info_js = processed.js() if processed is not None else ''
+ return processed.images, generation_info_js, processed.info, plaintext_to_html(processed.comments)
diff --git a/modules/ui.py b/modules/ui.py
index 8780b40bb..7be951525 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,484 +1,486 @@
-import os
-import mimetypes
-import gradio as gr
-import gradio.routes
-import gradio.utils
-from modules.call_queue import wrap_gradio_call
-from modules import timer, gr_hijack, shared, theme, sd_models, script_callbacks, modelloader, ui_common, ui_loadsave, ui_symbols, ui_javascript, generation_parameters_copypaste
-from modules.ui_components import FormRow
-from modules.paths import script_path, data_path # pylint: disable=unused-import
-from modules.dml import directml_override_opts
-import modules.scripts
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-import modules.errors
-
-
-modules.errors.install()
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-log = shared.log
-opts = shared.opts
-cmd_opts = shared.cmd_opts
-ui_system_tabs = None
-switch_values_symbol = ui_symbols.switch
-detect_image_size_symbol = ui_symbols.detect
-paste_symbol = ui_symbols.paste
-clear_prompt_symbol = ui_symbols.clear
-restore_progress_symbol = ui_symbols.apply
-folder_symbol = ui_symbols.folder
-extra_networks_symbol = ui_symbols.networks
-apply_style_symbol = ui_symbols.apply
-save_style_symbol = ui_symbols.save
-paste_function = None
-gr_hijack.init()
-
-
-if not cmd_opts.share and not cmd_opts.listen:
- # fix gradio phoning home
- gradio.utils.version_check = lambda: None
- gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-
-def gr_show(visible=True):
- return {"visible": visible, "__type__": "update"}
-
-
-def create_output_panel(tabname, outdir): # pylint: disable=unused-argument # outdir is used by extensions
- a, b, c, _d, e = ui_common.create_output_panel(tabname)
- return a, b, c, e
-
-
-def plaintext_to_html(text): # may be referenced by extensions
- return ui_common.plaintext_to_html(text)
-
-
-def infotext_to_html(text): # may be referenced by extensions
- return ui_common.infotext_to_html(text)
-
-
-def send_gradio_gallery_to_image(x):
- if len(x) == 0:
- return None
- return generation_parameters_copypaste.image_from_url_text(x[0])
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
- return ui_common.create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id)
-
-
-def connect_clear_prompt(button): # pylint: disable=unused-argument
- pass
-
-
-def setup_progressbar(*args, **kwargs): # pylint: disable=unused-argument
- pass
-
-
-def apply_setting(key, value):
- if value is None:
- return gr.update()
- if shared.cmd_opts.freeze:
- return gr.update()
- # dont allow model to be swapped when model hash exists in prompt
- if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
- return gr.update()
- if key == "sd_model_checkpoint":
- ckpt_info = sd_models.get_closet_checkpoint_match(value)
- if ckpt_info is not None:
- value = ckpt_info.title
- else:
- return gr.update()
- comp_args = opts.data_labels[key].component_args
- if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
- return gr.update()
- valtype = type(opts.data_labels[key].default)
- oldval = opts.data.get(key, None)
- opts.data[key] = valtype(value) if valtype != type(None) else value
- if oldval != value and opts.data_labels[key].onchange is not None:
- opts.data_labels[key].onchange()
- opts.save(shared.config_filename)
- return getattr(opts, key)
-
-
-def get_value_for_setting(key):
- value = getattr(opts, key)
- info = opts.data_labels[key]
- args = info.component_args() if callable(info.component_args) else info.component_args or {}
- args = {k: v for k, v in args.items() if k not in {'precision'}}
- return gr.update(value=value, **args)
-
-
-def ordered_ui_categories():
- return ['dimensions', 'sampler', 'seed', 'denoising', 'cfg', 'checkboxes', 'accordions', 'override_settings', 'scripts'] # a1111 compatibility item, not implemented
-
-
-def create_ui(startup_timer = None):
- if startup_timer is None:
- timer.startup = timer.Timer()
- ui_javascript.reload_javascript()
- generation_parameters_copypaste.reset()
-
- with gr.Blocks(analytics_enabled=False) as txt2img_interface:
- from modules import ui_txt2img
- ui_txt2img.create_ui()
- timer.startup.record("ui-txt2img")
-
- with gr.Blocks(analytics_enabled=False) as img2img_interface:
- from modules import ui_img2img
- ui_img2img.create_ui()
- timer.startup.record("ui-img2img")
-
- modules.scripts.scripts_current = None
-
- with gr.Blocks(analytics_enabled=False) as control_interface:
- if shared.backend == shared.Backend.DIFFUSERS:
- from modules import ui_control
- ui_control.create_ui()
- timer.startup.record("ui-control")
-
- with gr.Blocks(analytics_enabled=False) as extras_interface:
- from modules import ui_postprocessing
- ui_postprocessing.create_ui()
- timer.startup.record("ui-extras")
-
- with gr.Blocks(analytics_enabled=False) as train_interface:
- from modules import ui_train
- ui_train.create_ui()
- timer.startup.record("ui-train")
-
- with gr.Blocks(analytics_enabled=False) as models_interface:
- from modules import ui_models
- ui_models.create_ui()
- timer.startup.record("ui-models")
-
- with gr.Blocks(analytics_enabled=False) as interrogate_interface:
- from modules import ui_interrogate
- ui_interrogate.create_ui()
- timer.startup.record("ui-interrogate")
-
-
- def create_setting_component(key, is_quicksettings=False):
- def fun():
- return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
- info = opts.data_labels[key]
- t = type(info.default)
- args = (info.component_args() if callable(info.component_args) else info.component_args) or {}
- if info.component is not None:
- comp = info.component
- elif t == str:
- comp = gr.Textbox
- elif t == int:
- comp = gr.Number
- elif t == bool:
- comp = gr.Checkbox
- else:
- raise ValueError(f'bad options item type: {t} for key {key}')
- elem_id = f"setting_{key}"
-
- if not is_quicksettings:
- dirtyable_setting = gr.Group(elem_classes="dirtyable", visible=args.get("visible", True))
- dirtyable_setting.__enter__()
- dirty_indicator = gr.Button("", elem_classes="modification-indicator", elem_id="modification_indicator_" + key)
-
- if info.refresh is not None:
- if is_quicksettings:
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **args)
- ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
- else:
- with FormRow():
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **args)
- ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
- elif info.folder is not None:
- with FormRow():
- res = comp(label=info.label, value=fun(), elem_id=elem_id, elem_classes="folder-selector", **args)
- # ui_common.create_browse_button(res, f"folder_{key}")
- else:
- try:
- res = comp(label=info.label, value=fun(), elem_id=elem_id, **args)
- except Exception as e:
- log.error(f'Error creating setting: {key} {e}')
- res = None
-
- if res is not None and not is_quicksettings:
- res.change(fn=None, inputs=res, _js=f'(val) => markIfModified("{key}", val)')
- dirty_indicator.click(fn=lambda: getattr(opts, key), outputs=res, show_progress=False)
- dirtyable_setting.__exit__()
-
- return res
-
- def create_dirty_indicator(key, keys_to_reset, **kwargs):
- def get_opt_values():
- return [getattr(opts, _key) for _key in keys_to_reset]
-
- elements_to_reset = [component_dict[_key] for _key in keys_to_reset if component_dict[_key] is not None]
- indicator = gr.Button("", elem_classes="modification-indicator", elem_id=f"modification_indicator_{key}", **kwargs)
- indicator.click(fn=get_opt_values, outputs=elements_to_reset, show_progress=False)
- return indicator
-
- loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config)
- components = []
- component_dict = {}
- shared.settings_components = component_dict
-
- script_callbacks.ui_settings_callback()
- opts.reorder()
-
- def run_settings(*args):
- changed = []
- for key, value, comp in zip(opts.data_labels.keys(), args, components):
- if comp == dummy_component or value=='dummy':
- continue
- if not opts.same_type(value, opts.data_labels[key].default):
- log.error(f'Setting bad value: {key}={value} expecting={type(opts.data_labels[key].default).__name__}')
- continue
- if opts.set(key, value):
- changed.append(key)
- if cmd_opts.use_directml:
- directml_override_opts()
- if cmd_opts.use_openvino:
- if not shared.opts.cuda_compile:
- shared.log.warning("OpenVINO: Enabling Torch Compile")
- shared.opts.cuda_compile = True
- if shared.opts.cuda_compile_backend != "openvino_fx":
- shared.log.warning("OpenVINO: Setting Torch Compiler backend to OpenVINO FX")
- shared.opts.cuda_compile_backend = "openvino_fx"
- if shared.opts.sd_backend != "diffusers":
- shared.log.warning("OpenVINO: Setting backend to Diffusers")
- shared.opts.sd_backend = "diffusers"
- try:
- if len(changed) > 0:
- opts.save(shared.config_filename)
- log.info(f'Settings: changed={len(changed)} {changed}')
- except RuntimeError:
- log.error(f'Settings failed: change={len(changed)} {changed}')
- return opts.dumpjson(), f'{len(changed)} Settings changed without save: {", ".join(changed)}'
- return opts.dumpjson(), f'{len(changed)} Settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}'
-
- def run_settings_single(value, key):
- if not opts.same_type(value, opts.data_labels[key].default):
- return gr.update(visible=True), opts.dumpjson()
- if not opts.set(key, value):
- return gr.update(value=getattr(opts, key)), opts.dumpjson()
- if cmd_opts.use_directml:
- directml_override_opts()
- opts.save(shared.config_filename)
- log.debug(f'Setting changed: key={key}, value={value}')
- return get_value_for_setting(key), opts.dumpjson()
-
- with gr.Blocks(analytics_enabled=False) as settings_interface:
- with gr.Row(elem_id="system_row"):
- restart_submit = gr.Button(value="Restart server", variant='primary', elem_id="restart_submit")
- shutdown_submit = gr.Button(value="Shutdown server", variant='primary', elem_id="shutdown_submit")
- unload_sd_model = gr.Button(value='Unload checkpoint', variant='primary', elem_id="sett_unload_sd_model")
- reload_sd_model = gr.Button(value='Reload checkpoint', variant='primary', elem_id="sett_reload_sd_model")
-
- with gr.Tabs(elem_id="system") as system_tabs:
- global ui_system_tabs # pylint: disable=global-statement
- ui_system_tabs = system_tabs
- with gr.TabItem("Settings", id="system_settings", elem_id="tab_settings"):
- with gr.Row(elem_id="settings_row"):
- settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
- preview_theme = gr.Button(value="Preview theme", variant='primary', elem_id="settings_preview_theme")
- defaults_submit = gr.Button(value="Restore defaults", variant='primary', elem_id="defaults_submit")
- with gr.Row():
- _settings_search = gr.Text(label="Search", elem_id="settings_search")
-
- result = gr.HTML(elem_id="settings_result")
- quicksettings_names = opts.quicksettings_list
- quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
- quicksettings_list = []
-
- previous_section = []
- tab_item_keys = []
- current_tab = None
- current_row = None
- dummy_component = gr.Textbox(visible=False, value='dummy')
- with gr.Tabs(elem_id="settings"):
- for i, (k, item) in enumerate(opts.data_labels.items()):
- section_must_be_skipped = item.section[0] is None
- if previous_section != item.section and not section_must_be_skipped:
- elem_id, text = item.section
- if current_tab is not None and len(previous_section) > 0:
- create_dirty_indicator(previous_section[0], tab_item_keys)
- tab_item_keys = []
- current_row.__exit__()
- current_tab.__exit__()
- current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
- current_tab.__enter__()
- current_row = gr.Column(variant='compact')
- current_row.__enter__()
- previous_section = item.section
- if k in quicksettings_names and not shared.cmd_opts.freeze:
- quicksettings_list.append((i, k, item))
- components.append(dummy_component)
- elif section_must_be_skipped:
- components.append(dummy_component)
- else:
- component = create_setting_component(k)
- component_dict[k] = component
- tab_item_keys.append(k)
- components.append(component)
- if current_tab is not None and len(previous_section) > 0:
- create_dirty_indicator(previous_section[0], tab_item_keys)
- tab_item_keys = []
- current_row.__exit__()
- current_tab.__exit__()
-
- request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications", visible=False)
- with gr.TabItem("Show all pages", elem_id="settings_show_all_pages"):
- create_dirty_indicator("show_all_pages", [], interactive=False)
-
- with gr.TabItem("User interface", id="system_config", elem_id="tab_config"):
- loadsave.create_ui()
- create_dirty_indicator("tab_defaults", [], interactive=False)
-
- with gr.TabItem("Change log", id="change_log", elem_id="system_tab_changelog"):
- with open('CHANGELOG.md', 'r', encoding='utf-8') as f:
- md = f.read()
- gr.Markdown(md)
-
- with gr.TabItem("Licenses", id="system_licenses", elem_id="system_tab_licenses"):
- gr.HTML(shared.html("licenses.html"), elem_id="licenses", elem_classes="licenses")
- create_dirty_indicator("tab_licenses", [], interactive=False)
-
- def unload_sd_weights():
- modules.sd_models.unload_model_weights(op='model')
- modules.sd_models.unload_model_weights(op='refiner')
-
- def reload_sd_weights():
- modules.sd_models.reload_model_weights()
-
- unload_sd_model.click(fn=unload_sd_weights, inputs=[], outputs=[])
- reload_sd_model.click(fn=reload_sd_weights, inputs=[], outputs=[])
- request_notifications.click(fn=lambda: None, inputs=[], outputs=[], _js='function(){}')
- preview_theme.click(fn=None, _js='previewTheme', inputs=[], outputs=[])
-
- timer.startup.record("ui-settings")
-
- interfaces = []
- interfaces += [(txt2img_interface, "Text", "txt2img")]
- interfaces += [(img2img_interface, "Image", "img2img")]
- interfaces += [(control_interface, "Control", "control")] if control_interface is not None else []
- interfaces += [(extras_interface, "Process", "process")]
- interfaces += [(interrogate_interface, "Interrogate", "interrogate")]
- interfaces += [(train_interface, "Train", "train")]
- interfaces += [(models_interface, "Models", "models")]
- interfaces += script_callbacks.ui_tabs_callback()
- interfaces += [(settings_interface, "System", "system")]
-
- from modules import ui_extensions
- extensions_interface = ui_extensions.create_ui()
- interfaces += [(extensions_interface, "Extensions", "extensions")]
- timer.startup.record("ui-extensions")
-
- shared.tab_names = []
- for _interface, label, _ifid in interfaces:
- shared.tab_names.append(label)
-
- with gr.Blocks(theme=theme.gradio_theme, analytics_enabled=False, title="SD.Next") as ui_app:
- with gr.Row(elem_id="quicksettings", variant="compact"):
- for _i, k, _item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
- component = create_setting_component(k, is_quicksettings=True)
- component_dict[k] = component
-
- generation_parameters_copypaste.connect_paste_params_buttons()
-
- with gr.Tabs(elem_id="tabs") as tabs:
- for interface, label, ifid in interfaces:
- if interface is None:
- continue
- # if label in shared.opts.hidden_tabs or label == '':
- # continue
- with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
- # log.debug(f'UI render: id={ifid}')
- interface.render()
- for interface, _label, ifid in interfaces:
- if interface is None:
- continue
- if ifid in ["extensions", "system"]:
- continue
- loadsave.add_block(interface, ifid)
- loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
- loadsave.setup_ui()
- if opts.notification_audio_enable and os.path.exists(os.path.join(script_path, opts.notification_audio_path)):
- gr.Audio(interactive=False, value=os.path.join(script_path, opts.notification_audio_path), elem_id="audio_notification", visible=False)
-
- text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
- components = [c for c in components if c is not None]
- settings_submit.click(
- fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
- inputs=components,
- outputs=[text_settings, result],
- )
- defaults_submit.click(fn=lambda: shared.restore_defaults(restart=True), _js="restartReload")
- restart_submit.click(fn=lambda: shared.restart_server(restart=True), _js="restartReload")
- shutdown_submit.click(fn=lambda: shared.restart_server(restart=False), _js="restartReload")
-
- for _i, k, _item in quicksettings_list:
- component = component_dict[k]
- info = opts.data_labels[k]
- change_handler = component.release if hasattr(component, 'release') else component.change
- change_handler(
- fn=lambda value, k=k: run_settings_single(value, key=k),
- inputs=[component],
- outputs=[component, text_settings],
- show_progress=info.refresh is not None,
- )
-
- dummy_component = gr.Textbox(visible=False, value='dummy')
- button_set_checkpoint = gr.Button('Change model', elem_id='change_checkpoint', visible=False)
- button_set_checkpoint.click(
- fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
- _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
- inputs=[component_dict['sd_model_checkpoint'], dummy_component],
- outputs=[component_dict['sd_model_checkpoint'], text_settings],
- )
- button_set_refiner = gr.Button('Change refiner', elem_id='change_refiner', visible=False)
- button_set_refiner.click(
- fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
- _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
- inputs=[component_dict['sd_model_refiner'], dummy_component],
- outputs=[component_dict['sd_model_refiner'], text_settings],
- )
- button_set_vae = gr.Button('Change VAE', elem_id='change_vae', visible=False)
- button_set_vae.click(
- fn=lambda value, _: run_settings_single(value, key='sd_vae'),
- _js="function(v){ var res = desiredVAEName; desiredVAEName = ''; return [res || v, null]; }",
- inputs=[component_dict['sd_vae'], dummy_component],
- outputs=[component_dict['sd_vae'], text_settings],
- )
-
- def reference_submit(model):
- if '@' not in model: # diffusers
- loaded = modelloader.load_reference(model)
- return model if loaded else opts.sd_model_checkpoint
- else: # civitai
- model, url = model.split('@')
- loaded = modelloader.load_civitai(model, url)
- return loaded if loaded is not None else opts.sd_model_checkpoint
-
- button_set_reference = gr.Button('Change reference', elem_id='change_reference', visible=False)
- button_set_reference.click(
- fn=reference_submit,
- _js="function(v){ return desiredCheckpointName; }",
- inputs=[component_dict['sd_model_checkpoint']],
- outputs=[component_dict['sd_model_checkpoint']],
- )
- component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
- def get_settings_values():
- return [get_value_for_setting(key) for key in component_keys]
-
- ui_app.load(
- fn=get_settings_values,
- inputs=[],
- outputs=[component_dict[k] for k in component_keys if component_dict[k] is not None],
- queue=False,
- )
-
- timer.startup.record("ui-defaults")
- loadsave.dump_defaults()
- ui_app.ui_loadsave = loadsave
- return ui_app
+import os
+import mimetypes
+import gradio as gr
+import gradio.routes
+import gradio.utils
+from modules.call_queue import wrap_gradio_call
+from modules import timer, gr_hijack, shared, theme, sd_models, script_callbacks, modelloader, ui_common, ui_loadsave, ui_symbols, ui_javascript, generation_parameters_copypaste
+from modules.ui_components import FormRow
+from modules.paths import script_path, data_path # pylint: disable=unused-import
+from modules.dml import directml_override_opts
+import modules.scripts
+import modules.errors
+
+
+modules.errors.install()
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+log = shared.log
+opts = shared.opts
+cmd_opts = shared.cmd_opts
+ui_system_tabs = None
+switch_values_symbol = ui_symbols.switch
+detect_image_size_symbol = ui_symbols.detect
+paste_symbol = ui_symbols.paste
+clear_prompt_symbol = ui_symbols.clear
+restore_progress_symbol = ui_symbols.apply
+folder_symbol = ui_symbols.folder
+extra_networks_symbol = ui_symbols.networks
+apply_style_symbol = ui_symbols.apply
+save_style_symbol = ui_symbols.save
+paste_function = None
+gr_hijack.init()
+
+
+if not cmd_opts.share and not cmd_opts.listen:
+ # fix gradio phoning home
+ gradio.utils.version_check = lambda: None
+ gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+
+def gr_show(visible=True):
+ return {"visible": visible, "__type__": "update"}
+
+
+def create_output_panel(tabname, outdir): # pylint: disable=unused-argument # outdir is used by extensions
+ a, b, c, _d, e = ui_common.create_output_panel(tabname)
+ return a, b, c, e
+
+
+def plaintext_to_html(text): # may be referenced by extensions
+ return ui_common.plaintext_to_html(text)
+
+
+def infotext_to_html(text): # may be referenced by extensions
+ return ui_common.infotext_to_html(text)
+
+
+def send_gradio_gallery_to_image(x):
+ if len(x) == 0:
+ return None
+ return generation_parameters_copypaste.image_from_url_text(x[0])
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+ return ui_common.create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id)
+
+
+def connect_clear_prompt(button): # pylint: disable=unused-argument
+ pass
+
+
+def setup_progressbar(*args, **kwargs): # pylint: disable=unused-argument
+ pass
+
+
+def apply_setting(key, value):
+ if value is None:
+ return gr.update()
+ if shared.cmd_opts.freeze:
+ return gr.update()
+ # dont allow model to be swapped when model hash exists in prompt
+ if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+ return gr.update()
+ if key == "sd_model_checkpoint":
+ ckpt_info = sd_models.get_closet_checkpoint_match(value)
+ if ckpt_info is not None:
+ value = ckpt_info.title
+ else:
+ return gr.update()
+ comp_args = opts.data_labels[key].component_args
+ if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+ return gr.update()
+ valtype = type(opts.data_labels[key].default)
+ oldval = opts.data.get(key, None)
+ opts.data[key] = valtype(value) if valtype != type(None) else value
+ if oldval != value and opts.data_labels[key].onchange is not None:
+ opts.data_labels[key].onchange()
+ opts.save(shared.config_filename)
+ return getattr(opts, key)
+
+
+def get_value_for_setting(key):
+ value = getattr(opts, key)
+ info = opts.data_labels[key]
+ args = info.component_args() if callable(info.component_args) else info.component_args or {}
+ args = {k: v for k, v in args.items() if k not in {'precision'}}
+ return gr.update(value=value, **args)
+
+
+def ordered_ui_categories():
+ return ['dimensions', 'sampler', 'seed', 'denoising', 'cfg', 'checkboxes', 'accordions', 'override_settings', 'scripts'] # a1111 compatibility item, not implemented
+
+
+def create_ui(startup_timer = None):
+ if startup_timer is None:
+ timer.startup = timer.Timer()
+ ui_javascript.reload_javascript()
+ generation_parameters_copypaste.reset()
+
+ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+ from modules import ui_txt2img
+ ui_txt2img.create_ui()
+ timer.startup.record("ui-txt2img")
+
+ with gr.Blocks(analytics_enabled=False) as img2img_interface:
+ from modules import ui_img2img
+ ui_img2img.create_ui()
+ timer.startup.record("ui-img2img")
+
+ modules.scripts.scripts_current = None
+
+ with gr.Blocks(analytics_enabled=False) as control_interface:
+ if shared.backend == shared.Backend.DIFFUSERS:
+ from modules import ui_control
+ ui_control.create_ui()
+ timer.startup.record("ui-control")
+
+ with gr.Blocks(analytics_enabled=False) as extras_interface:
+ from modules import ui_postprocessing
+ ui_postprocessing.create_ui()
+ timer.startup.record("ui-extras")
+
+ with gr.Blocks(analytics_enabled=False) as train_interface:
+ from modules import ui_train
+ ui_train.create_ui()
+ timer.startup.record("ui-train")
+
+ with gr.Blocks(analytics_enabled=False) as models_interface:
+ from modules import ui_models
+ ui_models.create_ui()
+ timer.startup.record("ui-models")
+
+ with gr.Blocks(analytics_enabled=False) as interrogate_interface:
+ from modules import ui_interrogate
+ ui_interrogate.create_ui()
+ timer.startup.record("ui-interrogate")
+
+
+ def create_setting_component(key, is_quicksettings=False):
+ def fun():
+ return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+ info = opts.data_labels[key]
+ t = type(info.default)
+ args = (info.component_args() if callable(info.component_args) else info.component_args) or {}
+ if info.component is not None:
+ comp = info.component
+ elif t == str:
+ comp = gr.Textbox
+ elif t == int:
+ comp = gr.Number
+ elif t == bool:
+ comp = gr.Checkbox
+ else:
+ raise ValueError(f'bad options item type: {t} for key {key}')
+ elem_id = f"setting_{key}"
+
+ if not is_quicksettings:
+ dirtyable_setting = gr.Group(elem_classes="dirtyable", visible=args.get("visible", True))
+ dirtyable_setting.__enter__()
+ dirty_indicator = gr.Button("", elem_classes="modification-indicator", elem_id="modification_indicator_" + key)
+
+ if info.refresh is not None:
+ if is_quicksettings:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **args)
+ ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
+ else:
+ with FormRow():
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **args)
+ ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
+ elif info.folder is not None:
+ with FormRow():
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, elem_classes="folder-selector", **args)
+ # ui_common.create_browse_button(res, f"folder_{key}")
+ else:
+ try:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **args)
+ except Exception as e:
+ log.error(f'Error creating setting: {key} {e}')
+ res = None
+
+ if res is not None and not is_quicksettings:
+ res.change(fn=None, inputs=res, _js=f'(val) => markIfModified("{key}", val)')
+ dirty_indicator.click(fn=lambda: getattr(opts, key), outputs=res, show_progress=False)
+ dirtyable_setting.__exit__()
+
+ return res
+
+ def create_dirty_indicator(key, keys_to_reset, **kwargs):
+ def get_opt_values():
+ return [getattr(opts, _key) for _key in keys_to_reset]
+
+ elements_to_reset = [component_dict[_key] for _key in keys_to_reset if component_dict[_key] is not None]
+ indicator = gr.Button("", elem_classes="modification-indicator", elem_id=f"modification_indicator_{key}", **kwargs)
+ indicator.click(fn=get_opt_values, outputs=elements_to_reset, show_progress=False)
+ return indicator
+
+ loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config)
+ components = []
+ component_dict = {}
+ shared.settings_components = component_dict
+
+ script_callbacks.ui_settings_callback()
+ opts.reorder()
+
+ def run_settings(*args):
+ changed = []
+ for key, value, comp in zip(opts.data_labels.keys(), args, components):
+ if comp == dummy_component or value=='dummy':
+ continue
+ if not opts.same_type(value, opts.data_labels[key].default):
+ log.error(f'Setting bad value: {key}={value} expecting={type(opts.data_labels[key].default).__name__}')
+ continue
+ if opts.set(key, value):
+ changed.append(key)
+ if cmd_opts.use_directml:
+ directml_override_opts()
+ if cmd_opts.use_openvino:
+ if not shared.opts.cuda_compile:
+ shared.log.warning("OpenVINO: Enabling Torch Compile")
+ shared.opts.cuda_compile = True
+ if shared.opts.cuda_compile_backend != "openvino_fx":
+ shared.log.warning("OpenVINO: Setting Torch Compiler backend to OpenVINO FX")
+ shared.opts.cuda_compile_backend = "openvino_fx"
+ if shared.opts.sd_backend != "diffusers":
+ shared.log.warning("OpenVINO: Setting backend to Diffusers")
+ shared.opts.sd_backend = "diffusers"
+ try:
+ if len(changed) > 0:
+ opts.save(shared.config_filename)
+ log.info(f'Settings: changed={len(changed)} {changed}')
+ except RuntimeError:
+ log.error(f'Settings failed: change={len(changed)} {changed}')
+ return opts.dumpjson(), f'{len(changed)} Settings changed without save: {", ".join(changed)}'
+ return opts.dumpjson(), f'{len(changed)} Settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}'
+
+ def run_settings_single(value, key):
+ if not opts.same_type(value, opts.data_labels[key].default):
+ return gr.update(visible=True), opts.dumpjson()
+ if not opts.set(key, value):
+ return gr.update(value=getattr(opts, key)), opts.dumpjson()
+ if cmd_opts.use_directml:
+ directml_override_opts()
+ opts.save(shared.config_filename)
+ log.debug(f'Setting changed: key={key}, value={value}')
+ return get_value_for_setting(key), opts.dumpjson()
+
+ with gr.Blocks(analytics_enabled=False) as settings_interface:
+ with gr.Row(elem_id="system_row"):
+ restart_submit = gr.Button(value="Restart server", variant='primary', elem_id="restart_submit")
+ shutdown_submit = gr.Button(value="Shutdown server", variant='primary', elem_id="shutdown_submit")
+ unload_sd_model = gr.Button(value='Unload checkpoint', variant='primary', elem_id="sett_unload_sd_model")
+ reload_sd_model = gr.Button(value='Reload checkpoint', variant='primary', elem_id="sett_reload_sd_model")
+
+ with gr.Tabs(elem_id="system") as system_tabs:
+ global ui_system_tabs # pylint: disable=global-statement
+ ui_system_tabs = system_tabs
+ with gr.TabItem("Settings", id="system_settings", elem_id="tab_settings"):
+ with gr.Row(elem_id="settings_row"):
+ settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+ preview_theme = gr.Button(value="Preview theme", variant='primary', elem_id="settings_preview_theme")
+ defaults_submit = gr.Button(value="Restore defaults", variant='primary', elem_id="defaults_submit")
+ with gr.Row():
+ _settings_search = gr.Text(label="Search", elem_id="settings_search")
+
+ result = gr.HTML(elem_id="settings_result")
+ quicksettings_names = opts.quicksettings_list
+ quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
+ quicksettings_list = []
+
+ previous_section = []
+ tab_item_keys = []
+ current_tab = None
+ current_row = None
+ dummy_component = gr.Textbox(visible=False, value='dummy')
+ with gr.Tabs(elem_id="settings"):
+ for i, (k, item) in enumerate(opts.data_labels.items()):
+ section_must_be_skipped = item.section[0] is None
+ if previous_section != item.section and not section_must_be_skipped:
+ elem_id, text = item.section
+ if current_tab is not None and len(previous_section) > 0:
+ create_dirty_indicator(previous_section[0], tab_item_keys)
+ tab_item_keys = []
+ current_row.__exit__()
+ current_tab.__exit__()
+ current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
+ current_tab.__enter__()
+ current_row = gr.Column(variant='compact')
+ current_row.__enter__()
+ previous_section = item.section
+ if k in quicksettings_names and not shared.cmd_opts.freeze:
+ quicksettings_list.append((i, k, item))
+ components.append(dummy_component)
+ elif section_must_be_skipped:
+ components.append(dummy_component)
+ else:
+ component = create_setting_component(k)
+ component_dict[k] = component
+ tab_item_keys.append(k)
+ components.append(component)
+ if current_tab is not None and len(previous_section) > 0:
+ create_dirty_indicator(previous_section[0], tab_item_keys)
+ tab_item_keys = []
+ current_row.__exit__()
+ current_tab.__exit__()
+
+ request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications", visible=False)
+ with gr.TabItem("Show all pages", elem_id="settings_show_all_pages"):
+ create_dirty_indicator("show_all_pages", [], interactive=False)
+
+ with gr.TabItem("Update", id="system_update", elem_id="tab_update"):
+ from modules import update
+ update.create_ui()
+
+ with gr.TabItem("User interface", id="system_config", elem_id="tab_config"):
+ loadsave.create_ui()
+ create_dirty_indicator("tab_defaults", [], interactive=False)
+
+ with gr.TabItem("Change log", id="change_log", elem_id="system_tab_changelog"):
+ with open('CHANGELOG.md', 'r', encoding='utf-8') as f:
+ md = f.read()
+ gr.Markdown(md)
+
+ with gr.TabItem("Licenses", id="system_licenses", elem_id="system_tab_licenses"):
+ gr.HTML(shared.html("licenses.html"), elem_id="licenses", elem_classes="licenses")
+ create_dirty_indicator("tab_licenses", [], interactive=False)
+
+ def unload_sd_weights():
+ modules.sd_models.unload_model_weights(op='model')
+ modules.sd_models.unload_model_weights(op='refiner')
+
+ def reload_sd_weights():
+ modules.sd_models.reload_model_weights()
+
+ unload_sd_model.click(fn=unload_sd_weights, inputs=[], outputs=[])
+ reload_sd_model.click(fn=reload_sd_weights, inputs=[], outputs=[])
+ request_notifications.click(fn=lambda: None, inputs=[], outputs=[], _js='function(){}')
+ preview_theme.click(fn=None, _js='previewTheme', inputs=[], outputs=[])
+
+ timer.startup.record("ui-settings")
+
+ interfaces = []
+ interfaces += [(txt2img_interface, "Text", "txt2img")]
+ interfaces += [(img2img_interface, "Image", "img2img")]
+ interfaces += [(control_interface, "Control", "control")] if control_interface is not None else []
+ interfaces += [(extras_interface, "Process", "process")]
+ interfaces += [(interrogate_interface, "Interrogate", "interrogate")]
+ interfaces += [(train_interface, "Train", "train")]
+ interfaces += [(models_interface, "Models", "models")]
+ interfaces += script_callbacks.ui_tabs_callback()
+ interfaces += [(settings_interface, "System", "system")]
+
+ from modules import ui_extensions
+ extensions_interface = ui_extensions.create_ui()
+ interfaces += [(extensions_interface, "Extensions", "extensions")]
+ timer.startup.record("ui-extensions")
+
+ shared.tab_names = []
+ for _interface, label, _ifid in interfaces:
+ shared.tab_names.append(label)
+
+ with gr.Blocks(theme=theme.gradio_theme, analytics_enabled=False, title="SD.Next") as ui_app:
+ with gr.Row(elem_id="quicksettings", variant="compact"):
+ for _i, k, _item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
+ component = create_setting_component(k, is_quicksettings=True)
+ component_dict[k] = component
+
+ generation_parameters_copypaste.connect_paste_params_buttons()
+
+ with gr.Tabs(elem_id="tabs") as tabs:
+ for interface, label, ifid in interfaces:
+ if interface is None:
+ continue
+ # if label in shared.opts.hidden_tabs or label == '':
+ # continue
+ with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
+ # log.debug(f'UI render: id={ifid}')
+ interface.render()
+ for interface, _label, ifid in interfaces:
+ if interface is None:
+ continue
+ if ifid in ["extensions", "system"]:
+ continue
+ loadsave.add_block(interface, ifid)
+ loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+ loadsave.setup_ui()
+ if opts.notification_audio_enable and os.path.exists(os.path.join(script_path, opts.notification_audio_path)):
+ gr.Audio(interactive=False, value=os.path.join(script_path, opts.notification_audio_path), elem_id="audio_notification", visible=False)
+
+ text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+ components = [c for c in components if c is not None]
+ settings_submit.click(
+ fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
+ inputs=components,
+ outputs=[text_settings, result],
+ )
+ defaults_submit.click(fn=lambda: shared.restore_defaults(restart=True), _js="restartReload")
+ restart_submit.click(fn=lambda: shared.restart_server(restart=True), _js="restartReload")
+ shutdown_submit.click(fn=lambda: shared.restart_server(restart=False), _js="restartReload")
+
+ for _i, k, _item in quicksettings_list:
+ component = component_dict[k]
+ info = opts.data_labels[k]
+ change_handler = component.release if hasattr(component, 'release') else component.change
+ change_handler(
+ fn=lambda value, k=k: run_settings_single(value, key=k),
+ inputs=[component],
+ outputs=[component, text_settings],
+ show_progress=info.refresh is not None,
+ )
+
+ dummy_component = gr.Textbox(visible=False, value='dummy')
+ button_set_checkpoint = gr.Button('Change model', elem_id='change_checkpoint', visible=False)
+ button_set_checkpoint.click(
+ fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
+ _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+ inputs=[component_dict['sd_model_checkpoint'], dummy_component],
+ outputs=[component_dict['sd_model_checkpoint'], text_settings],
+ )
+ button_set_refiner = gr.Button('Change refiner', elem_id='change_refiner', visible=False)
+ button_set_refiner.click(
+ fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
+ _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+ inputs=[component_dict['sd_model_refiner'], dummy_component],
+ outputs=[component_dict['sd_model_refiner'], text_settings],
+ )
+ button_set_vae = gr.Button('Change VAE', elem_id='change_vae', visible=False)
+ button_set_vae.click(
+ fn=lambda value, _: run_settings_single(value, key='sd_vae'),
+ _js="function(v){ var res = desiredVAEName; desiredVAEName = ''; return [res || v, null]; }",
+ inputs=[component_dict['sd_vae'], dummy_component],
+ outputs=[component_dict['sd_vae'], text_settings],
+ )
+
+ def reference_submit(model):
+ if '@' not in model: # diffusers
+ loaded = modelloader.load_reference(model)
+ return model if loaded else opts.sd_model_checkpoint
+ else: # civitai
+ model, url = model.split('@')
+ loaded = modelloader.load_civitai(model, url)
+ return loaded if loaded is not None else opts.sd_model_checkpoint
+
+ button_set_reference = gr.Button('Change reference', elem_id='change_reference', visible=False)
+ button_set_reference.click(
+ fn=reference_submit,
+ _js="function(v){ return desiredCheckpointName; }",
+ inputs=[component_dict['sd_model_checkpoint']],
+ outputs=[component_dict['sd_model_checkpoint']],
+ )
+ component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+ def get_settings_values():
+ return [get_value_for_setting(key) for key in component_keys]
+
+ ui_app.load(
+ fn=get_settings_values,
+ inputs=[],
+ outputs=[component_dict[k] for k in component_keys if component_dict[k] is not None],
+ queue=False,
+ )
+
+ timer.startup.record("ui-defaults")
+ loadsave.dump_defaults()
+ ui_app.ui_loadsave = loadsave
+ return ui_app
diff --git a/modules/ui_common.py b/modules/ui_common.py
index 4bd40b50a..84e374f48 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -1,380 +1,380 @@
-import json
-import html
-import os
-import shutil
-import platform
-import subprocess
-from functools import reduce
-import gradio as gr
-from modules import call_queue, shared, prompt_parser
-from modules import generation_parameters_copypaste
-from modules import ui_sections
-from modules.ui_components import FormRow, ToolButton
-import modules.ui_symbols as symbols
-import modules.images
-import modules.script_callbacks
-
-
-folder_symbol = symbols.folder
-debug = shared.log.trace if os.environ.get('SD_PASTE_DEBUG', None) is not None else lambda *args, **kwargs: None
-debug('Trace: PASTE')
-
-
-def gr_show(visible=True):
- return {"visible": visible, "__type__": "update"}
-
-
-def update_generation_info(generation_info, html_info, img_index):
- try:
- generation_info = json.loads(generation_info)
- if img_index < 0 or img_index >= len(generation_info["infotexts"]):
- return html_info, generation_info
- infotext = generation_info["infotexts"][img_index]
- html_info_formatted = infotext_to_html(infotext)
- return html_info, html_info_formatted
- except Exception:
- pass
- return html_info, html_info
-
-
-def plaintext_to_html(text):
- res = '' + "
'
- return res
-
-
-def infotext_to_html(text):
- res = generation_parameters_copypaste.parse_generation_parameters(text)
- prompt = res.get('Prompt', '')
- negative = res.get('Negative prompt', '')
- res.pop('Prompt', None)
- res.pop('Negative prompt', None)
- params = [f'{k}: {v}' for k, v in res.items() if v is not None]
- params = '| '.join(params) if len(params) > 0 else ''
- code = f'''
- Prompt: {html.escape(prompt)}
- Negative: {html.escape(negative)}
- Parameters: {html.escape(params)}
- '''
- return code
-
-
-def delete_files(js_data, images, _html_info, index):
- try:
- data = json.loads(js_data)
- except Exception:
- data = { 'index_of_first_image': 0 }
- start_index = 0
- if index > -1 and shared.opts.save_selected_only and (index >= data['index_of_first_image']):
- images = [images[index]]
- start_index = index
- filenames = []
- filenames = []
- fullfns = []
- for _image_index, filedata in enumerate(images, start_index):
- if 'name' in filedata and os.path.isfile(filedata['name']):
- fullfn = filedata['name']
- filenames.append(os.path.basename(fullfn))
- try:
- os.remove(fullfn)
- base, _ext = os.path.splitext(fullfn)
- desc = f'{base}.txt'
- if os.path.exists(desc):
- os.remove(desc)
- fullfns.append(fullfn)
- shared.log.info(f"Deleting image: {fullfn}")
- except Exception as e:
- shared.log.error(f'Error deleting file: {fullfn} {e}')
- images = [image for image in images if image['name'] not in fullfns]
- return images, plaintext_to_html(f"Deleted: {filenames[0] if len(filenames) > 0 else 'none'}")
-
-
-def save_files(js_data, images, html_info, index):
- os.makedirs(shared.opts.outdir_save, exist_ok=True)
-
- class PObject: # pylint: disable=too-few-public-methods
- def __init__(self, d=None):
- if d is not None:
- for k, v in d.items():
- setattr(self, k, v)
- self.prompt = getattr(self, 'prompt', None) or getattr(self, 'Prompt', None)
- self.all_prompts = getattr(self, 'all_prompts', [self.prompt])
- self.negative_prompt = getattr(self, 'negative_prompt', None)
- self.all_negative_prompt = getattr(self, 'all_negative_prompts', [self.negative_prompt])
- self.seed = getattr(self, 'seed', None) or getattr(self, 'Seed', None)
- self.all_seeds = getattr(self, 'all_seeds', [self.seed])
- self.subseed = getattr(self, 'subseed', None)
- self.all_subseeds = getattr(self, 'all_subseeds', [self.subseed])
- self.width = getattr(self, 'width', None)
- self.height = getattr(self, 'height', None)
- self.index_of_first_image = getattr(self, 'index_of_first_image', 0)
- self.infotexts = getattr(self, 'infotexts', [html_info])
- self.infotext = self.infotexts[0] if len(self.infotexts) > 0 else html_info
- self.outpath_grids = shared.opts.outdir_grids or shared.opts.outdir_txt2img_grids
- try:
- data = json.loads(js_data)
- except Exception:
- data = {}
- p = PObject(data)
- start_index = 0
- if index > -1 and shared.opts.save_selected_only and (index >= p.index_of_first_image): # ensures we are looking at a specific non-grid picture, and we have save_selected_only # pylint: disable=no-member
- images = [images[index]]
- start_index = index
- filenames = []
- fullfns = []
- for image_index, filedata in enumerate(images, start_index):
- is_grid = image_index < p.index_of_first_image # pylint: disable=no-member
- i = 0 if is_grid else (image_index - p.index_of_first_image) # pylint: disable=no-member
- while len(p.all_seeds) <= i:
- p.all_seeds.append(p.seed)
- while len(p.all_prompts) <= i:
- p.all_prompts.append(p.prompt)
- while len(p.infotexts) <= i:
- p.infotexts.append(p.infotext)
- if 'name' in filedata and ('tmp' not in filedata['name']) and os.path.isfile(filedata['name']):
- fullfn = filedata['name']
- filenames.append(os.path.basename(fullfn))
- fullfns.append(fullfn)
- destination = shared.opts.outdir_save
- namegen = modules.images.FilenameGenerator(p, seed=p.all_seeds[i], prompt=p.all_prompts[i], image=None) # pylint: disable=no-member
- dirname = namegen.apply(shared.opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
- destination = os.path.join(destination, dirname)
- destination = namegen.sanitize(destination)
- os.makedirs(destination, exist_ok = True)
- shutil.copy(fullfn, destination)
- shared.log.info(f'Copying image: file="{fullfn}" folder="{destination}"')
- tgt_filename = os.path.join(destination, os.path.basename(fullfn))
- if shared.opts.save_txt:
- try:
- from PIL import Image
- image = Image.open(fullfn)
- info, _ = images.read_info_from_image(image)
- filename_txt = f"{os.path.splitext(tgt_filename)[0]}.txt"
- with open(filename_txt, "w", encoding="utf8") as file:
- file.write(f"{info}\n")
- shared.log.debug(f'Saving: text="{filename_txt}"')
- except Exception as e:
- shared.log.warning(f'Image description save failed: {filename_txt} {e}')
- modules.script_callbacks.image_save_btn_callback(tgt_filename)
- else:
- image = generation_parameters_copypaste.image_from_url_text(filedata)
- info = p.infotexts[i + 1] if len(p.infotexts) > len(p.all_seeds) else p.infotexts[i] # infotexts may be offset by 1 because the first image is the grid
- fullfn, txt_fullfn = modules.images.save_image(image, shared.opts.outdir_save, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], info=info, extension=shared.opts.samples_format, grid=is_grid, p=p)
- if fullfn is None:
- continue
- filename = os.path.relpath(fullfn, shared.opts.outdir_save)
- filenames.append(filename)
- fullfns.append(fullfn)
- if txt_fullfn:
- filenames.append(os.path.basename(txt_fullfn))
- # fullfns.append(txt_fullfn)
- modules.script_callbacks.image_save_btn_callback(filename)
- if shared.opts.samples_save_zip and len(fullfns) > 1:
- zip_filepath = os.path.join(shared.opts.outdir_save, "images.zip")
- from zipfile import ZipFile
- with ZipFile(zip_filepath, "w") as zip_file:
- for i in range(len(fullfns)):
- if os.path.isfile(fullfns[i]):
- with open(fullfns[i], mode="rb") as f:
- zip_file.writestr(filenames[i], f.read())
- fullfns.insert(0, zip_filepath)
- return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0] if len(filenames) > 0 else 'none'}")
-
-
-def open_folder(result_gallery, gallery_index = 0):
- try:
- folder = os.path.dirname(result_gallery[gallery_index]['name'])
- except Exception:
- folder = shared.opts.outdir_samples
- if not os.path.exists(folder):
- shared.log.warning(f'Folder open: folder={folder} does not exist')
- return
- elif not os.path.isdir(folder):
- shared.log.warning(f"Folder open: folder={folder} not a folder")
- return
-
- if not shared.cmd_opts.hide_ui_dir_config:
- path = os.path.normpath(folder)
- if platform.system() == "Windows":
- os.startfile(path) # pylint: disable=no-member
- elif platform.system() == "Darwin":
- subprocess.Popen(["open", path]) # pylint: disable=consider-using-with
- elif "microsoft-standard-WSL2" in platform.uname().release:
- subprocess.Popen(["wsl-open", path]) # pylint: disable=consider-using-with
- else:
- subprocess.Popen(["xdg-open", path]) # pylint: disable=consider-using-with
-
-
-def interrogate_clip(image):
- if image is None:
- shared.log.error("Interrogate: no image selected")
- return gr.update()
- prompt = shared.interrogator.interrogate(image)
- return gr.update() if prompt is None else prompt
-
-
-def interrogate_booru(image):
- if image is None:
- shared.log.error("Interrogate: no image selected")
- return gr.update()
- from modules import deepbooru
- prompt = deepbooru.model.tag(image)
- return gr.update() if prompt is None else prompt
-
-
-def create_output_panel(tabname, preview=True, prompt=None):
- with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
- with gr.Group(elem_id=f"{tabname}_gallery_container"):
- if tabname == "txt2img":
- gr.HTML(value="", elem_id="main_info", visible=False, elem_classes=["main-info"])
- # columns are for <576px, <768px, <992px, <1200px, <1400px, >1400px
- result_gallery = gr.Gallery(value=[], label='Output', show_label=False, show_download_button=True, allow_preview=True, elem_id=f"{tabname}_gallery", container=False, preview=preview, columns=5, object_fit='scale-down', height=shared.opts.gallery_height or None)
- if prompt is not None:
- interrogate_clip_btn, interrogate_booru_btn = ui_sections.create_interrogate_buttons('control')
- interrogate_clip_btn.click(fn=interrogate_clip, inputs=[result_gallery], outputs=[prompt])
- interrogate_booru_btn.click(fn=interrogate_booru, inputs=[result_gallery], outputs=[prompt])
-
-
- with gr.Column(elem_id=f"{tabname}_footer", elem_classes="gallery_footer"):
- dummy_component = gr.Label(visible=False)
- with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
- if not shared.cmd_opts.listen:
- open_folder_button = gr.Button('Show', visible=not shared.cmd_opts.hide_ui_dir_config, elem_id=f'open_folder_{tabname}')
- open_folder_button.click(open_folder, _js="(gallery, dummy) => [gallery, selected_gallery_index()]", inputs=[result_gallery, dummy_component], outputs=[])
- else:
- clip_files = gr.Button('Copy', elem_id=f'open_folder_{tabname}')
- clip_files.click(fn=None, _js='clip_gallery_urls', inputs=[result_gallery], outputs=[])
- save = gr.Button('Save', elem_id=f'save_{tabname}')
- delete = gr.Button('Delete', elem_id=f'delete_{tabname}')
- if shared.backend == shared.Backend.ORIGINAL:
- buttons = generation_parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
- else:
- buttons = generation_parameters_copypaste.create_buttons(["img2img", "inpaint", "control", "extras"])
-
- download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
- with gr.Group():
- html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext", visible=False) # contains raw infotext as returned by wrapped call
- html_info_formatted = gr.HTML(elem_id=f'html_info_formatted_{tabname}', elem_classes="infotext", visible=True) # contains html formatted infotext
- html_info.change(fn=infotext_to_html, inputs=[html_info], outputs=[html_info_formatted], show_progress=False)
- html_log = gr.HTML(elem_id=f'html_log_{tabname}')
- generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
- generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
-
- generation_info_button.click(fn=update_generation_info, _js="(x, y, z) => [x, y, selected_gallery_index()]", show_progress=False, # triggered on gallery change from js
- inputs=[generation_info, html_info, html_info],
- outputs=[html_info, html_info_formatted],
- )
- save.click(fn=call_queue.wrap_gradio_call(save_files), _js="(x, y, z, i) => [x, y, z, selected_gallery_index()]", show_progress=False,
- inputs=[generation_info, result_gallery, html_info, html_info],
- outputs=[download_files, html_log],
- )
- delete.click(fn=call_queue.wrap_gradio_call(delete_files), _js="(x, y, z, i) => [x, y, z, selected_gallery_index()]",
- inputs=[generation_info, result_gallery, html_info, html_info],
- outputs=[result_gallery, html_log],
- )
-
- if tabname == "txt2img":
- paste_field_names = modules.scripts.scripts_txt2img.paste_field_names
- elif tabname == "img2img":
- paste_field_names = modules.scripts.scripts_img2img.paste_field_names
- elif tabname == "control":
- paste_field_names = modules.scripts.scripts_control.paste_field_names
- else:
- paste_field_names = []
- for paste_tabname, paste_button in buttons.items():
- debug(f'Create output panel: button={paste_button} tabname={paste_tabname}')
- bindings = generation_parameters_copypaste.ParamBinding(paste_button=paste_button, tabname=paste_tabname, source_tabname=("txt2img" if tabname == "txt2img" else None), source_image_component=result_gallery, paste_field_names=paste_field_names)
- generation_parameters_copypaste.register_paste_params_button(bindings)
- return result_gallery, generation_info, html_info, html_info_formatted, html_log
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id, visible: bool = True):
- def refresh():
- refresh_method()
- args = refreshed_args() if callable(refreshed_args) else refreshed_args
- for k, v in args.items():
- setattr(refresh_component, k, v)
- return gr.update(**(args or {}))
-
- refresh_button = ToolButton(value=symbols.refresh, elem_id=elem_id, visible=visible)
- refresh_button.click(fn=refresh, inputs=[], outputs=[refresh_component])
- return refresh_button
-
-
-def create_browse_button(browse_component, elem_id):
- def browse(folder):
- # import subprocess
- if folder is not None:
- return gr.update(value = folder)
- return gr.update()
-
- browse_button = ToolButton(value=symbols.folder, elem_id=elem_id)
- browse_button.click(fn=browse, _js="async () => await browseFolder()", inputs=[browse_component], outputs=[browse_component])
- # browse_button.click(fn=browse, inputs=[browse_component], outputs=[browse_component])
- return browse_button
-
-
-def create_override_inputs(tab): # pylint: disable=unused-argument
- with FormRow(elem_id=f"{tab}_override_settings_row"):
- override_settings = gr.Dropdown([], value=None, label="Override settings", visible=False, elem_id=f"{tab}_override_settings", multiselect=True)
- override_settings.change(fn=lambda x: gr.Dropdown.update(visible=len(x) > 0), inputs=[override_settings], outputs=[override_settings])
- return override_settings
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
- """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
- (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
- was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
- def copy_seed(gen_info_string: str, index: int):
- res = -1
- try:
- gen_info = json.loads(gen_info_string)
- shared.log.debug(f'Reuse: info={gen_info}')
- index -= gen_info.get('index_of_first_image', 0)
- index = int(index)
-
- if is_subseed and gen_info.get('subseed_strength', 0) > 0:
- all_subseeds = gen_info.get('all_subseeds', [-1])
- res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
- else:
- all_seeds = gen_info.get('all_seeds', [-1])
- res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
- except json.decoder.JSONDecodeError:
- if gen_info_string != '':
- shared.log.error(f"Error parsing JSON generation info: {gen_info_string}")
- return [res, gr_show(False)]
-
- dummy_component = gr.Number(visible=False, value=0)
- reuse_seed.click(fn=copy_seed, _js="(x, y) => [x, selected_gallery_index()]", show_progress=False, inputs=[generation_info, dummy_component], outputs=[seed, dummy_component])
-
-
-def update_token_counter(text, steps):
- from modules import extra_networks, sd_hijack
- try:
- text, _ = extra_networks.parse_prompt(text)
- _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
- prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
- except Exception:
- prompt_schedules = [[[steps, text]]]
-
- flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
- prompts = [prompt_text for step, prompt_text in flat_prompts]
- if shared.backend == shared.Backend.ORIGINAL:
- token_count, max_length = max([sd_hijack.model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
- elif shared.backend == shared.Backend.DIFFUSERS:
- if shared.sd_model is not None and hasattr(shared.sd_model, 'tokenizer'):
- tokenizer = shared.sd_model.tokenizer
- if tokenizer is None:
- token_count = 0
- max_length = 75
- else:
- has_bos_token = tokenizer.bos_token_id is not None
- has_eos_token = tokenizer.eos_token_id is not None
- ids = [shared.sd_model.tokenizer(prompt) for prompt in prompts]
- if len(ids) > 0 and hasattr(ids[0], 'input_ids'):
- ids = [x.input_ids for x in ids]
- token_count = max([len(x) for x in ids]) - int(has_bos_token) - int(has_eos_token)
- max_length = tokenizer.model_max_length - int(has_bos_token) - int(has_eos_token)
- else:
- token_count = 0
- max_length = 75
- return f"{token_count}/{max_length} "
+import json
+import html
+import os
+import shutil
+import platform
+import subprocess
+from functools import reduce
+import gradio as gr
+from modules import call_queue, shared, prompt_parser
+from modules import generation_parameters_copypaste
+from modules import ui_sections
+from modules.ui_components import FormRow, ToolButton
+import modules.ui_symbols as symbols
+import modules.images
+import modules.script_callbacks
+
+
+folder_symbol = symbols.folder
+debug = shared.log.trace if os.environ.get('SD_PASTE_DEBUG', None) is not None else lambda *args, **kwargs: None
+debug('Trace: PASTE')
+
+
+def gr_show(visible=True):
+ return {"visible": visible, "__type__": "update"}
+
+
+def update_generation_info(generation_info, html_info, img_index):
+ try:
+ generation_info = json.loads(generation_info)
+ if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+ return html_info, generation_info
+ infotext = generation_info["infotexts"][img_index]
+ html_info_formatted = infotext_to_html(infotext)
+ return html_info, html_info_formatted
+ except Exception:
+ pass
+ return html_info, html_info
+
+
+def plaintext_to_html(text):
+ res = '' + "
'
+ return res
+
+
+def infotext_to_html(text):
+ res = generation_parameters_copypaste.parse_generation_parameters(text)
+ prompt = res.get('Prompt', '')
+ negative = res.get('Negative prompt', '')
+ res.pop('Prompt', None)
+ res.pop('Negative prompt', None)
+ params = [f'{k}: {v}' for k, v in res.items() if v is not None]
+ params = '| '.join(params) if len(params) > 0 else ''
+ code = f'''
+ Prompt: {html.escape(prompt)}
+ Negative: {html.escape(negative)}
+ Parameters: {html.escape(params)}
+ '''
+ return code
+
+
+def delete_files(js_data, images, _html_info, index):
+ try:
+ data = json.loads(js_data)
+ except Exception:
+ data = { 'index_of_first_image': 0 }
+ start_index = 0
+ if index > -1 and shared.opts.save_selected_only and (index >= data['index_of_first_image']):
+ images = [images[index]]
+ start_index = index
+ filenames = []
+ filenames = []
+ fullfns = []
+ for _image_index, filedata in enumerate(images, start_index):
+ if 'name' in filedata and os.path.isfile(filedata['name']):
+ fullfn = filedata['name']
+ filenames.append(os.path.basename(fullfn))
+ try:
+ os.remove(fullfn)
+ base, _ext = os.path.splitext(fullfn)
+ desc = f'{base}.txt'
+ if os.path.exists(desc):
+ os.remove(desc)
+ fullfns.append(fullfn)
+ shared.log.info(f"Deleting image: {fullfn}")
+ except Exception as e:
+ shared.log.error(f'Error deleting file: {fullfn} {e}')
+ images = [image for image in images if image['name'] not in fullfns]
+ return images, plaintext_to_html(f"Deleted: {filenames[0] if len(filenames) > 0 else 'none'}")
+
+
+def save_files(js_data, images, html_info, index):
+ os.makedirs(shared.opts.outdir_save, exist_ok=True)
+
+ class PObject: # pylint: disable=too-few-public-methods
+ def __init__(self, d=None):
+ if d is not None:
+ for k, v in d.items():
+ setattr(self, k, v)
+ self.prompt = getattr(self, 'prompt', None) or getattr(self, 'Prompt', None)
+ self.all_prompts = getattr(self, 'all_prompts', [self.prompt])
+ self.negative_prompt = getattr(self, 'negative_prompt', None)
+ self.all_negative_prompt = getattr(self, 'all_negative_prompts', [self.negative_prompt])
+ self.seed = getattr(self, 'seed', None) or getattr(self, 'Seed', None)
+ self.all_seeds = getattr(self, 'all_seeds', [self.seed])
+ self.subseed = getattr(self, 'subseed', None)
+ self.all_subseeds = getattr(self, 'all_subseeds', [self.subseed])
+ self.width = getattr(self, 'width', None)
+ self.height = getattr(self, 'height', None)
+ self.index_of_first_image = getattr(self, 'index_of_first_image', 0)
+ self.infotexts = getattr(self, 'infotexts', [html_info])
+ self.infotext = self.infotexts[0] if len(self.infotexts) > 0 else html_info
+ self.outpath_grids = shared.opts.outdir_grids or shared.opts.outdir_txt2img_grids
+ try:
+ data = json.loads(js_data)
+ except Exception:
+ data = {}
+ p = PObject(data)
+ start_index = 0
+ if index > -1 and shared.opts.save_selected_only and (index >= p.index_of_first_image): # ensures we are looking at a specific non-grid picture, and we have save_selected_only # pylint: disable=no-member
+ images = [images[index]]
+ start_index = index
+ filenames = []
+ fullfns = []
+ for image_index, filedata in enumerate(images, start_index):
+ is_grid = image_index < p.index_of_first_image # pylint: disable=no-member
+ i = 0 if is_grid else (image_index - p.index_of_first_image) # pylint: disable=no-member
+ while len(p.all_seeds) <= i:
+ p.all_seeds.append(p.seed)
+ while len(p.all_prompts) <= i:
+ p.all_prompts.append(p.prompt)
+ while len(p.infotexts) <= i:
+ p.infotexts.append(p.infotext)
+ if 'name' in filedata and ('tmp' not in filedata['name']) and os.path.isfile(filedata['name']):
+ fullfn = filedata['name']
+ filenames.append(os.path.basename(fullfn))
+ fullfns.append(fullfn)
+ destination = shared.opts.outdir_save
+ namegen = modules.images.FilenameGenerator(p, seed=p.all_seeds[i], prompt=p.all_prompts[i], image=None) # pylint: disable=no-member
+ dirname = namegen.apply(shared.opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
+ destination = os.path.join(destination, dirname)
+ destination = namegen.sanitize(destination)
+ os.makedirs(destination, exist_ok = True)
+ shutil.copy(fullfn, destination)
+ shared.log.info(f'Copying image: file="{fullfn}" folder="{destination}"')
+ tgt_filename = os.path.join(destination, os.path.basename(fullfn))
+ if shared.opts.save_txt:
+ try:
+ from PIL import Image
+ image = Image.open(fullfn)
+ info, _ = images.read_info_from_image(image)
+ filename_txt = f"{os.path.splitext(tgt_filename)[0]}.txt"
+ with open(filename_txt, "w", encoding="utf8") as file:
+ file.write(f"{info}\n")
+ shared.log.debug(f'Saving: text="{filename_txt}"')
+ except Exception as e:
+ shared.log.warning(f'Image description save failed: {filename_txt} {e}')
+ modules.script_callbacks.image_save_btn_callback(tgt_filename)
+ else:
+ image = generation_parameters_copypaste.image_from_url_text(filedata)
+ info = p.infotexts[i + 1] if len(p.infotexts) > len(p.all_seeds) else p.infotexts[i] # infotexts may be offset by 1 because the first image is the grid
+ fullfn, txt_fullfn = modules.images.save_image(image, shared.opts.outdir_save, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], info=info, extension=shared.opts.samples_format, grid=is_grid, p=p)
+ if fullfn is None:
+ continue
+ filename = os.path.relpath(fullfn, shared.opts.outdir_save)
+ filenames.append(filename)
+ fullfns.append(fullfn)
+ if txt_fullfn:
+ filenames.append(os.path.basename(txt_fullfn))
+ # fullfns.append(txt_fullfn)
+ modules.script_callbacks.image_save_btn_callback(filename)
+ if shared.opts.samples_save_zip and len(fullfns) > 1:
+ zip_filepath = os.path.join(shared.opts.outdir_save, "images.zip")
+ from zipfile import ZipFile
+ with ZipFile(zip_filepath, "w") as zip_file:
+ for i in range(len(fullfns)):
+ if os.path.isfile(fullfns[i]):
+ with open(fullfns[i], mode="rb") as f:
+ zip_file.writestr(filenames[i], f.read())
+ fullfns.insert(0, zip_filepath)
+ return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0] if len(filenames) > 0 else 'none'}")
+
+
+def open_folder(result_gallery, gallery_index = 0):
+ try:
+ folder = os.path.dirname(result_gallery[gallery_index]['name'])
+ except Exception:
+ folder = shared.opts.outdir_samples
+ if not os.path.exists(folder):
+ shared.log.warning(f'Folder open: folder={folder} does not exist')
+ return
+ elif not os.path.isdir(folder):
+ shared.log.warning(f"Folder open: folder={folder} not a folder")
+ return
+
+ if not shared.cmd_opts.hide_ui_dir_config:
+ path = os.path.normpath(folder)
+ if platform.system() == "Windows":
+ os.startfile(path) # pylint: disable=no-member
+ elif platform.system() == "Darwin":
+ subprocess.Popen(["open", path]) # pylint: disable=consider-using-with
+ elif "microsoft-standard-WSL2" in platform.uname().release:
+ subprocess.Popen(["wsl-open", path]) # pylint: disable=consider-using-with
+ else:
+ subprocess.Popen(["xdg-open", path]) # pylint: disable=consider-using-with
+
+
+def interrogate_clip(image):
+ if image is None:
+ shared.log.error("Interrogate: no image selected")
+ return gr.update()
+ prompt = shared.interrogator.interrogate(image)
+ return gr.update() if prompt is None else prompt
+
+
+def interrogate_booru(image):
+ if image is None:
+ shared.log.error("Interrogate: no image selected")
+ return gr.update()
+ from modules import deepbooru
+ prompt = deepbooru.model.tag(image)
+ return gr.update() if prompt is None else prompt
+
+
+def create_output_panel(tabname, preview=True, prompt=None):
+ with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
+ with gr.Group(elem_id=f"{tabname}_gallery_container"):
+ if tabname == "txt2img":
+ gr.HTML(value="", elem_id="main_info", visible=False, elem_classes=["main-info"])
+ # columns are for <576px, <768px, <992px, <1200px, <1400px, >1400px
+ result_gallery = gr.Gallery(value=[], label='Output', show_label=False, show_download_button=True, allow_preview=True, elem_id=f"{tabname}_gallery", container=False, preview=preview, columns=5, object_fit='scale-down', height=shared.opts.gallery_height or None)
+ if prompt is not None:
+ interrogate_clip_btn, interrogate_booru_btn = ui_sections.create_interrogate_buttons('control')
+ interrogate_clip_btn.click(fn=interrogate_clip, inputs=[result_gallery], outputs=[prompt])
+ interrogate_booru_btn.click(fn=interrogate_booru, inputs=[result_gallery], outputs=[prompt])
+
+
+ with gr.Column(elem_id=f"{tabname}_footer", elem_classes="gallery_footer"):
+ dummy_component = gr.Label(visible=False)
+ with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
+ if not shared.cmd_opts.listen:
+ open_folder_button = gr.Button('Show', visible=not shared.cmd_opts.hide_ui_dir_config, elem_id=f'open_folder_{tabname}')
+ open_folder_button.click(open_folder, _js="(gallery, dummy) => [gallery, selected_gallery_index()]", inputs=[result_gallery, dummy_component], outputs=[])
+ else:
+ clip_files = gr.Button('Copy', elem_id=f'open_folder_{tabname}')
+ clip_files.click(fn=None, _js='clip_gallery_urls', inputs=[result_gallery], outputs=[])
+ save = gr.Button('Save', elem_id=f'save_{tabname}')
+ delete = gr.Button('Delete', elem_id=f'delete_{tabname}')
+ if shared.backend == shared.Backend.ORIGINAL:
+ buttons = generation_parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+ else:
+ buttons = generation_parameters_copypaste.create_buttons(["img2img", "inpaint", "control", "extras"])
+
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
+ with gr.Group():
+ html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext", visible=False) # contains raw infotext as returned by wrapped call
+ html_info_formatted = gr.HTML(elem_id=f'html_info_formatted_{tabname}', elem_classes="infotext", visible=True) # contains html formatted infotext
+ html_info.change(fn=infotext_to_html, inputs=[html_info], outputs=[html_info_formatted], show_progress=False)
+ html_log = gr.HTML(elem_id=f'html_log_{tabname}')
+ generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
+ generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+
+ generation_info_button.click(fn=update_generation_info, _js="(x, y, z) => [x, y, selected_gallery_index()]", show_progress=False, # triggered on gallery change from js
+ inputs=[generation_info, html_info, html_info],
+ outputs=[html_info, html_info_formatted],
+ )
+ save.click(fn=call_queue.wrap_gradio_call(save_files), _js="(x, y, z, i) => [x, y, z, selected_gallery_index()]", show_progress=False,
+ inputs=[generation_info, result_gallery, html_info, html_info],
+ outputs=[download_files, html_log],
+ )
+ delete.click(fn=call_queue.wrap_gradio_call(delete_files), _js="(x, y, z, i) => [x, y, z, selected_gallery_index()]",
+ inputs=[generation_info, result_gallery, html_info, html_info],
+ outputs=[result_gallery, html_log],
+ )
+
+ if tabname == "txt2img":
+ paste_field_names = modules.scripts.scripts_txt2img.paste_field_names
+ elif tabname == "img2img":
+ paste_field_names = modules.scripts.scripts_img2img.paste_field_names
+ elif tabname == "control":
+ paste_field_names = modules.scripts.scripts_control.paste_field_names
+ else:
+ paste_field_names = []
+ for paste_tabname, paste_button in buttons.items():
+ debug(f'Create output panel: button={paste_button} tabname={paste_tabname}')
+ bindings = generation_parameters_copypaste.ParamBinding(paste_button=paste_button, tabname=paste_tabname, source_tabname=("txt2img" if tabname == "txt2img" else None), source_image_component=result_gallery, paste_field_names=paste_field_names)
+ generation_parameters_copypaste.register_paste_params_button(bindings)
+ return result_gallery, generation_info, html_info, html_info_formatted, html_log
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id, visible: bool = True):
+ def refresh():
+ refresh_method()
+ args = refreshed_args() if callable(refreshed_args) else refreshed_args
+ for k, v in args.items():
+ setattr(refresh_component, k, v)
+ return gr.update(**(args or {}))
+
+ refresh_button = ToolButton(value=symbols.refresh, elem_id=elem_id, visible=visible)
+ refresh_button.click(fn=refresh, inputs=[], outputs=[refresh_component])
+ return refresh_button
+
+
+def create_browse_button(browse_component, elem_id):
+ def browse(folder):
+ # import subprocess
+ if folder is not None:
+ return gr.update(value = folder)
+ return gr.update()
+
+ browse_button = ToolButton(value=symbols.folder, elem_id=elem_id)
+ browse_button.click(fn=browse, _js="async () => await browseFolder()", inputs=[browse_component], outputs=[browse_component])
+ # browse_button.click(fn=browse, inputs=[browse_component], outputs=[browse_component])
+ return browse_button
+
+
+def create_override_inputs(tab): # pylint: disable=unused-argument
+ with FormRow(elem_id=f"{tab}_override_settings_row"):
+ override_settings = gr.Dropdown([], value=None, label="Override settings", visible=False, elem_id=f"{tab}_override_settings", multiselect=True)
+ override_settings.change(fn=lambda x: gr.Dropdown.update(visible=len(x) > 0), inputs=[override_settings], outputs=[override_settings])
+ return override_settings
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
+ """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+ (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+ was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+ def copy_seed(gen_info_string: str, index: int):
+ res = -1
+ try:
+ gen_info = json.loads(gen_info_string)
+ shared.log.debug(f'Reuse: info={gen_info}')
+ index -= gen_info.get('index_of_first_image', 0)
+ index = int(index)
+
+ if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+ all_subseeds = gen_info.get('all_subseeds', [-1])
+ res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+ else:
+ all_seeds = gen_info.get('all_seeds', [-1])
+ res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+ except json.decoder.JSONDecodeError:
+ if gen_info_string != '':
+ shared.log.error(f"Error parsing JSON generation info: {gen_info_string}")
+ return [res, gr_show(False)]
+
+ dummy_component = gr.Number(visible=False, value=0)
+ reuse_seed.click(fn=copy_seed, _js="(x, y) => [x, selected_gallery_index()]", show_progress=False, inputs=[generation_info, dummy_component], outputs=[seed, dummy_component])
+
+
+def update_token_counter(text, steps):
+ from modules import extra_networks, sd_hijack
+ try:
+ text, _ = extra_networks.parse_prompt(text)
+ _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+ prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+ except Exception:
+ prompt_schedules = [[[steps, text]]]
+
+ flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+ prompts = [prompt_text for step, prompt_text in flat_prompts]
+ if shared.backend == shared.Backend.ORIGINAL:
+ token_count, max_length = max([sd_hijack.model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
+ elif shared.backend == shared.Backend.DIFFUSERS:
+ if shared.sd_model is not None and hasattr(shared.sd_model, 'tokenizer'):
+ tokenizer = shared.sd_model.tokenizer
+ if tokenizer is None:
+ token_count = 0
+ max_length = 75
+ else:
+ has_bos_token = tokenizer.bos_token_id is not None
+ has_eos_token = tokenizer.eos_token_id is not None
+ ids = [shared.sd_model.tokenizer(prompt) for prompt in prompts]
+ if len(ids) > 0 and hasattr(ids[0], 'input_ids'):
+ ids = [x.input_ids for x in ids]
+ token_count = max([len(x) for x in ids]) - int(has_bos_token) - int(has_eos_token)
+ max_length = tokenizer.model_max_length - int(has_bos_token) - int(has_eos_token)
+ else:
+ token_count = 0
+ max_length = 75
+ return f"{token_count}/{max_length} "
diff --git a/modules/ui_components.py b/modules/ui_components.py
index 0e5862765..f9aed5c6c 100644
--- a/modules/ui_components.py
+++ b/modules/ui_components.py
@@ -1,130 +1,130 @@
-import gradio as gr
-
-
-class FormComponent:
- def get_expected_parent(self):
- return gr.components.Form
-
-
-gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
-
-
-class ToolButton(FormComponent, gr.Button):
- """Small button with single emoji as text, fits inside gradio forms"""
-
- def __init__(self, *args, **kwargs):
- classes = kwargs.pop("elem_classes", [])
- super().__init__(*args, elem_classes=["tool", *classes], **kwargs)
-
- def get_block_name(self):
- return "button"
-
-
-class FormRow(FormComponent, gr.Row):
- """Same as gr.Row but fits inside gradio forms"""
-
- def get_block_name(self):
- return "row"
-
-
-class FormColumn(FormComponent, gr.Column):
- """Same as gr.Column but fits inside gradio forms"""
-
- def get_block_name(self):
- return "column"
-
-
-class FormGroup(FormComponent, gr.Group):
- """Same as gr.Row but fits inside gradio forms"""
-
- def get_block_name(self):
- return "group"
-
-
-class FormHTML(FormComponent, gr.HTML):
- """Same as gr.HTML but fits inside gradio forms"""
-
- def get_block_name(self):
- return "html"
-
-
-class FormColorPicker(FormComponent, gr.ColorPicker):
- """Same as gr.ColorPicker but fits inside gradio forms"""
-
- def get_block_name(self):
- return "colorpicker"
-
-
-class DropdownMulti(FormComponent, gr.Dropdown):
- """Same as gr.Dropdown but always multiselect"""
- def __init__(self, **kwargs):
- super().__init__(multiselect=True, **kwargs)
-
- def get_block_name(self):
- return "dropdown"
-
-
-class DropdownEditable(FormComponent, gr.Dropdown):
- """Same as gr.Dropdown but allows editing value"""
- def __init__(self, **kwargs):
- super().__init__(allow_custom_value=True, **kwargs)
-
- def get_block_name(self):
- return "dropdown"
-
-
-class InputAccordion(gr.Checkbox):
- """A gr.Accordion that can be used as an input - returns True if open, False if closed.
- Actaully just a hidden checkbox, but creates an accordion that follows and is followed by the state of the checkbox.
- """
- global_index = 0
-
- def __init__(self, value, **kwargs):
- self.accordion_id = kwargs.get('elem_id')
- if self.accordion_id is None:
- self.accordion_id = f"input-accordion-{InputAccordion.global_index}"
- InputAccordion.global_index += 1
- kwargs_checkbox = {**kwargs, "elem_id": f"{self.accordion_id}-checkbox", "visible": False}
- super().__init__(value, **kwargs_checkbox)
- self.change(fn=None, _js='function(checked){ inputAccordionChecked("' + self.accordion_id + '", checked); }', inputs=[self])
- kwargs_accordion = {
- **kwargs,
- "elem_id": self.accordion_id,
- "label": kwargs.get('label', 'Accordion'),
- "elem_classes": ['input-accordion'],
- "open": value,
- }
- self.accordion = gr.Accordion(**kwargs_accordion)
-
- def extra(self):
- """Allows you to put something into the label of the accordion.
- Use it like this:
- ```
- with InputAccordion(False, label="Accordion") as acc:
- with acc.extra():
- FormHTML(value="hello", min_width=0)
- ...
- ```
- """
- return gr.Column(elem_id=self.accordion_id + '-extra', elem_classes='input-accordion-extra', min_width=0)
-
- def __enter__(self):
- self.accordion.__enter__()
- return self
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- self.accordion.__exit__(exc_type, exc_val, exc_tb)
-
- def get_block_name(self):
- return "checkbox"
-
-
-class ResizeHandleRow(gr.Row):
- """Same as gr.Row but fits inside gradio forms"""
-
- def __init__(self, **kwargs):
- super().__init__(**kwargs)
- self.elem_classes.append("resize-handle-row")
-
- def get_block_name(self):
- return "row"
+import gradio as gr
+
+
+class FormComponent:
+ def get_expected_parent(self):
+ return gr.components.Form
+
+
+gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
+
+
+class ToolButton(FormComponent, gr.Button):
+ """Small button with single emoji as text, fits inside gradio forms"""
+
+ def __init__(self, *args, **kwargs):
+ classes = kwargs.pop("elem_classes", [])
+ super().__init__(*args, elem_classes=["tool", *classes], **kwargs)
+
+ def get_block_name(self):
+ return "button"
+
+
+class FormRow(FormComponent, gr.Row):
+ """Same as gr.Row but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "row"
+
+
+class FormColumn(FormComponent, gr.Column):
+ """Same as gr.Column but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "column"
+
+
+class FormGroup(FormComponent, gr.Group):
+ """Same as gr.Row but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "group"
+
+
+class FormHTML(FormComponent, gr.HTML):
+ """Same as gr.HTML but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "html"
+
+
+class FormColorPicker(FormComponent, gr.ColorPicker):
+ """Same as gr.ColorPicker but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "colorpicker"
+
+
+class DropdownMulti(FormComponent, gr.Dropdown):
+ """Same as gr.Dropdown but always multiselect"""
+ def __init__(self, **kwargs):
+ super().__init__(multiselect=True, **kwargs)
+
+ def get_block_name(self):
+ return "dropdown"
+
+
+class DropdownEditable(FormComponent, gr.Dropdown):
+ """Same as gr.Dropdown but allows editing value"""
+ def __init__(self, **kwargs):
+ super().__init__(allow_custom_value=True, **kwargs)
+
+ def get_block_name(self):
+ return "dropdown"
+
+
+class InputAccordion(gr.Checkbox):
+ """A gr.Accordion that can be used as an input - returns True if open, False if closed.
+ Actaully just a hidden checkbox, but creates an accordion that follows and is followed by the state of the checkbox.
+ """
+ global_index = 0
+
+ def __init__(self, value, **kwargs):
+ self.accordion_id = kwargs.get('elem_id')
+ if self.accordion_id is None:
+ self.accordion_id = f"input-accordion-{InputAccordion.global_index}"
+ InputAccordion.global_index += 1
+ kwargs_checkbox = {**kwargs, "elem_id": f"{self.accordion_id}-checkbox", "visible": False}
+ super().__init__(value, **kwargs_checkbox)
+ self.change(fn=None, _js='function(checked){ inputAccordionChecked("' + self.accordion_id + '", checked); }', inputs=[self])
+ kwargs_accordion = {
+ **kwargs,
+ "elem_id": self.accordion_id,
+ "label": kwargs.get('label', 'Accordion'),
+ "elem_classes": ['input-accordion'],
+ "open": value,
+ }
+ self.accordion = gr.Accordion(**kwargs_accordion)
+
+ def extra(self):
+ """Allows you to put something into the label of the accordion.
+ Use it like this:
+ ```
+ with InputAccordion(False, label="Accordion") as acc:
+ with acc.extra():
+ FormHTML(value="hello", min_width=0)
+ ...
+ ```
+ """
+ return gr.Column(elem_id=self.accordion_id + '-extra', elem_classes='input-accordion-extra', min_width=0)
+
+ def __enter__(self):
+ self.accordion.__enter__()
+ return self
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ self.accordion.__exit__(exc_type, exc_val, exc_tb)
+
+ def get_block_name(self):
+ return "checkbox"
+
+
+class ResizeHandleRow(gr.Row):
+ """Same as gr.Row but fits inside gradio forms"""
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+ self.elem_classes.append("resize-handle-row")
+
+ def get_block_name(self):
+ return "row"
diff --git a/modules/ui_control.py b/modules/ui_control.py
index 0d94c45d3..4ad193ef6 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -2,7 +2,6 @@ import os
import time
import gradio as gr
import matplotlib.pyplot as plt
-import numpy as np
from PIL import Image
from modules.control import unit
from modules.control import processors # patrickvonplaten controlnet_aux
@@ -12,7 +11,7 @@ from modules.control.units import lite # vislearn ControlNet-XS
from modules.control.units import t2iadapter # TencentARC T2I-Adapter
from modules.control.units import reference # reference pipeline
from scripts import ipadapter # pylint: disable=no-name-in-module
-from modules import errors, shared, progress, sd_samplers, ui_components, ui_symbols, ui_common, ui_sections, generation_parameters_copypaste, call_queue, scripts # pylint: disable=ungrouped-imports
+from modules import errors, shared, progress, sd_samplers, ui_components, ui_symbols, ui_common, ui_sections, generation_parameters_copypaste, call_queue, scripts, masking # pylint: disable=ungrouped-imports
gr_height = 512
@@ -34,6 +33,7 @@ def initialize():
lite.cache_dir = os.path.join(shared.opts.control_dir, 'lite')
t2iadapter.cache_dir = os.path.join(shared.opts.control_dir, 'adapter')
processors.cache_dir = os.path.join(shared.opts.control_dir, 'processor')
+ masking.cache_dir = os.path.join(shared.opts.control_dir, 'segment')
unit.default_device = devices.device
unit.default_dtype = devices.dtype
os.makedirs(shared.opts.control_dir, exist_ok=True)
@@ -42,6 +42,7 @@ def initialize():
os.makedirs(lite.cache_dir, exist_ok=True)
os.makedirs(t2iadapter.cache_dir, exist_ok=True)
os.makedirs(processors.cache_dir, exist_ok=True)
+ os.makedirs(masking.cache_dir, exist_ok=True)
scripts.scripts_current = scripts.scripts_control
scripts.scripts_current.initialize_scripts(is_img2img=True)
@@ -116,35 +117,7 @@ def get_video(filepath: str):
return msg
-def select_mask(image: Image.Image, negative: bool = False):
- if image is None:
- return image
- image_mask = image.convert("L")
- if negative:
- image_mask = image_mask.point(lambda x: 255 if x < 4 else 0)
- else:
- image_mask = image_mask.point(lambda x: 255 if x > 127 else 0)
- return image_mask
-
-
-def expand_mask(image: Image.Image, expand: int = 64):
- import cv2
- if image is None:
- return image
- pil_mask = image.convert("L")
- np_mask = np.array(pil_mask)
- erode, dilate, threshold = 3, 8, 4
- if threshold > 0:
- _thres, np_mask = cv2.threshold(np_mask, threshold, 255, cv2.THRESH_BINARY_INV) # create mask
- if erode > 0:
- np_mask = cv2.erode(np_mask, np.ones((erode, erode), np.uint8), iterations=expand//dilate) # remove noise
- if dilate > 0:
- np_mask = cv2.dilate(np_mask, np.ones((dilate, dilate), np.uint8), iterations=expand//dilate) # expand area
- image_mask = Image.fromarray(np_mask.astype(np.uint8))
- return image_mask
-
-
-def select_input(input_mode, input_image, selected_init, init_type, input_resize, input_inpaint, input_video, input_batch, input_folder, _mask_blur, mask_overlap):
+def select_input(input_mode, input_image, selected_init, init_type, input_resize, input_inpaint, input_video, input_batch, input_folder):
global busy, input_source, input_init, input_mask # pylint: disable=global-statement
busy = True
if input_mode == 'Select':
@@ -172,14 +145,14 @@ def select_input(input_mode, input_image, selected_init, init_type, input_resize
# control inputs
if isinstance(selected_input, Image.Image): # image via upload -> image
if input_mode == 'Outpaint':
- input_mask = expand_mask(image=selected_input, expand=mask_overlap)
+ input_mask = masking.run_mask(input_image=selected_input, input_mask=None, return_type='grayscale')
input_source = [selected_input]
input_type = 'PIL.Image'
shared.log.debug(f'Control input: type={input_type} input={input_source}')
status = f'Control input | Image | Size {selected_input.width}x{selected_input.height} | Mode {selected_input.mode}'
res = [gr.Tabs.update(selected='out-gallery'), status]
elif isinstance(selected_input, dict): # inpaint -> dict image+mask
- input_mask = select_mask(image=selected_input['mask'], negative=False)
+ input_mask = masking.run_mask(input_image=selected_input['image'], input_mask=selected_input['mask'], return_type='grayscale')
selected_input = selected_input['image']
input_source = [selected_input]
input_type = 'PIL.Image'
@@ -217,7 +190,7 @@ def select_input(input_mode, input_image, selected_init, init_type, input_resize
elif init_type == 2: # Separate init image
if isinstance(selected_init, Image.Image): # image via upload -> image
if input_mode == 'Outpaint':
- input_mask = expand_mask(image=selected_init, expand=mask_overlap)
+ input_mask = masking.run_mask(input_image=selected_init, input_mask=None, return_type='grayscale')
input_source = [selected_init]
input_init = [selected_init]
input_type = 'PIL.Image'
@@ -225,7 +198,7 @@ def select_input(input_mode, input_image, selected_init, init_type, input_resize
status = f'Control input | Image | Size {selected_init.width}x{selected_init.height} | Mode {selected_init.mode}'
res = [gr.Tabs.update(selected='out-gallery'), status]
elif isinstance(selected_init, dict): # inpaint -> dict image+mask
- input_mask = select_mask(image=selected_init['mask'])
+ input_mask = masking.run_mask(input_image=selected_init['image'], input_mask=selected_init['mask'], return_type='grayscale')
input_init = selected_init['image']
input_source = [selected_init]
input_type = 'PIL.Image'
@@ -313,9 +286,6 @@ def create_ui(_blocks: gr.Blocks=None):
input_type = gr.Radio(label="Input type", choices=['Control only', 'Init image same as control', 'Separate init image'], value='Control only', type='index', elem_id='control_input_type')
with gr.Row():
denoising_strength = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, label='Denoising strength', value=0.50, elem_id="control_denoising_strength")
- with gr.Row():
- mask_blur = gr.Slider(minimum=0, maximum=100, step=1, label='Blur', value=8, elem_id="control_mask_blur")
- mask_overlap = gr.Slider(minimum=0, maximum=100, step=1, label='Overlap', value=64, elem_id="control_mask_overlap")
with gr.Accordion(open=False, label="Size", elem_id="control_size", elem_classes=["small-accordion"]):
with gr.Tabs():
@@ -329,7 +299,11 @@ def create_ui(_blocks: gr.Blocks=None):
steps, sampler_index = ui_sections.create_sampler_and_steps_selection(sd_samplers.samplers, "control")
batch_count, batch_size = ui_sections.create_batch_inputs('control')
+
seed, _reuse_seed, subseed, _reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = ui_sections.create_seed_inputs('control', reuse_visible=False)
+
+ masking.create_segment_ui()
+
cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, sag_scale, full_quality, restore_faces, tiling, hdr_clamp, hdr_boundary, hdr_threshold, hdr_center, hdr_channel_shift, hdr_full_shift, hdr_maximize, hdr_max_center, hdr_max_boundry = ui_sections.create_advanced_inputs('control')
with gr.Accordion(open=False, label="Video", elem_id="control_video", elem_classes=["small-accordion"]):
@@ -353,7 +327,7 @@ def create_ui(_blocks: gr.Blocks=None):
with gr.Row(variant='compact', elem_id="control_extra_networks", visible=False) as extra_networks_ui:
from modules import timer, ui_extra_networks
extra_networks_ui = ui_extra_networks.create_ui(extra_networks_ui, btn_extra, 'control', skip_indexing=shared.opts.extra_network_skip_indexing)
- timer.startup.record('ui-extra-networks')
+ timer.startup.record('ui-en')
with gr.Row(elem_id='control_status'):
result_txt = gr.HTML(elem_classes=['control-result'], elem_id='control-result')
@@ -366,7 +340,7 @@ def create_ui(_blocks: gr.Blocks=None):
input_mode = gr.Label(value='select', visible=False)
input_image = gr.Image(label="Input", show_label=False, type="pil", source="upload", interactive=True, tool="editor", height=gr_height, visible=True, image_mode='RGB', elem_id='control_input_select')
input_resize = gr.Image(label="Input", show_label=False, type="pil", source="upload", interactive=True, tool="select", height=gr_height, visible=False, image_mode='RGB', elem_id='control_input_resize')
- input_inpaint = gr.Image(label="Input", show_label=False, type="pil", source="upload", interactive=True, tool="sketch", height=gr_height, visible=False, image_mode='RGB', elem_id='control_input_inpaint', brush_radius=64, mask_opacity=0.6)
+ input_inpaint = gr.Image(label="Input", show_label=False, type="pil", source="upload", interactive=True, tool="sketch", height=gr_height, visible=False, image_mode='RGB', elem_id='control_input_inpaint', brush_radius=32, mask_opacity=0.6)
interrogate_clip, interrogate_booru = ui_sections.create_interrogate_buttons('control')
with gr.Row():
input_buttons = [gr.Button('Select', visible=True, interactive=False), gr.Button('Inpaint', visible=True, interactive=True), gr.Button('Outpaint', visible=True, interactive=True)]
@@ -680,7 +654,7 @@ def create_ui(_blocks: gr.Blocks=None):
interrogate_clip.click(fn=ui_common.interrogate_clip, inputs=[input_image], outputs=[prompt])
interrogate_booru.click(fn=ui_common.interrogate_booru, inputs=[input_image], outputs=[prompt])
- select_fields = [input_mode, input_image, init_image, input_type, input_resize, input_inpaint, input_video, input_batch, input_folder, mask_blur, mask_overlap]
+ select_fields = [input_mode, input_image, init_image, input_type, input_resize, input_inpaint, input_video, input_batch, input_folder]
select_output = [output_tabs, result_txt]
select_dict = dict(
fn=select_input,
@@ -707,7 +681,7 @@ def create_ui(_blocks: gr.Blocks=None):
cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, sag_scale, full_quality, restore_faces, tiling, hdr_clamp, hdr_boundary, hdr_threshold, hdr_center, hdr_channel_shift, hdr_full_shift, hdr_maximize, hdr_max_center, hdr_max_boundry,
resize_mode_before, resize_name_before, width_before, height_before, scale_by_before, selected_scale_tab_before,
resize_mode_after, resize_name_after, width_after, height_after, scale_by_after, selected_scale_tab_after,
- denoising_strength, batch_count, batch_size, mask_blur, mask_overlap,
+ denoising_strength, batch_count, batch_size,
video_skip_frames, video_type, video_duration, video_loop, video_pad, video_interpolate,
ip_adapter, ip_scale, ip_image,
]
@@ -732,6 +706,8 @@ def create_ui(_blocks: gr.Blocks=None):
generation_parameters_copypaste.add_paste_fields("control", input_image, paste_fields, override_settings)
bindings = generation_parameters_copypaste.ParamBinding(paste_button=btn_paste, tabname="control", source_text_component=prompt, source_image_component=output_gallery)
generation_parameters_copypaste.register_paste_params_button(bindings)
+ masking.bind_controls(input_inpaint, preview_process)
+
if os.environ.get('SD_CONTROL_DEBUG', None) is not None: # debug only
from modules.control.test import test_processors, test_controlnets, test_adapters, test_xs, test_lite
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 8cf67bc67..10b9acd58 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -1,5 +1,5 @@
+import os
import json
-import os.path
import shutil
import errno
import html
@@ -7,7 +7,6 @@ from datetime import datetime, timedelta
import git
import gradio as gr
from modules import extensions, shared, paths, errors
-from modules.call_queue import wrap_gradio_gpu_call
extensions_index = "https://vladmandic.github.io/sd-data/pages/extensions.json"
@@ -438,7 +437,7 @@ def create_ui():
info = gr.HTML('')
extensions_table = gr.HTML(create_html(search_text.value, sort_column.value))
check.click(
- fn=wrap_gradio_gpu_call(check_updates, extra_outputs=[gr.update()]),
+ fn=modules.ui.wrap_gradio_call(check_updates, extra_outputs=[gr.update()]),
_js="extensions_check",
inputs=[info, extensions_disabled_list, search_text, sort_column],
outputs=[extensions_table, info],
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 20907011a..bc6e16217 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -21,7 +21,6 @@ import modules.ui_symbols as symbols
allowed_dirs = []
-dir_timestamps = {}
refresh_time = 0
extra_pages = shared.extra_networks
debug = shared.log.trace if os.environ.get('SD_EN_DEBUG', None) is not None else lambda *args, **kwargs: None
@@ -149,9 +148,6 @@ class ExtraNetworksPage:
preview = f"./sd_extra_networks/thumb?filename={quoted_filename}&mtime={mtime}"
return preview
- def search_terms_from_path(self, filename):
- return filename.replace('\\', '/')
-
def is_empty(self, folder):
return any(files_cache.list_files(folder, ext_filter=['.ckpt', '.safetensors', '.pt', '.json']))
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py
index c217348c0..612fdb3ee 100644
--- a/modules/ui_extra_networks_checkpoints.py
+++ b/modules/ui_extra_networks_checkpoints.py
@@ -1,84 +1,82 @@
-import os
-import html
-import json
-import concurrent
-from modules import shared, ui_extra_networks, sd_models
-
-
-reference_dir = os.path.join('models', 'Reference')
-
-class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
- def __init__(self):
- super().__init__('Model')
-
- def refresh(self):
- shared.refresh_checkpoints()
-
- def list_reference(self): # pylint: disable=inconsistent-return-statements
- reference_models = shared.readfile(os.path.join('html', 'reference.json'))
- for k, v in reference_models.items():
- if shared.backend != shared.Backend.DIFFUSERS:
- if not v.get('original', False):
- continue
- url = v.get('alt', None) or v['path']
- else:
- url = v['path']
- name = os.path.join(reference_dir, k)
- preview = v.get('preview', v['path'])
- yield {
- "type": 'Model',
- "name": name,
- "title": name,
- "filename": url,
- "search_term": self.search_terms_from_path(name),
- "preview": self.find_preview(os.path.join(reference_dir, preview)),
- "local_preview": self.find_preview_file(os.path.join(reference_dir, preview)),
- "onclick": '"' + html.escape(f"""return selectReference({json.dumps(url)})""") + '"',
- "hash": None,
- "mtime": 0,
- "size": 0,
- "info": {},
- "metadata": {},
- "description": v.get('desc', ''),
- }
-
- def create_item(self, name):
- record = None
- try:
- checkpoint: sd_models.CheckpointInfo = sd_models.checkpoints_list.get(name)
- exists = os.path.exists(checkpoint.filename)
- record = {
- "type": 'Model',
- "name": checkpoint.name,
- "title": checkpoint.title,
- "filename": checkpoint.filename,
- "hash": checkpoint.shorthash,
- "search_term": self.search_terms_from_path(checkpoint.title),
- "preview": self.find_preview(checkpoint.filename),
- "local_preview": f"{os.path.splitext(checkpoint.filename)[0]}.{shared.opts.samples_format}",
- "metadata": checkpoint.metadata,
- "onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
- "mtime": os.path.getmtime(checkpoint.filename) if exists else 0,
- "size": os.path.getsize(checkpoint.filename) if exists else 0,
- }
- record["info"] = self.find_info(checkpoint.filename)
- record["description"] = self.find_description(checkpoint.filename, record["info"])
- except Exception as e:
- shared.log.debug(f"Extra networks error: type=model file={name} {e}")
- return record
-
- def list_items(self):
- with concurrent.futures.ThreadPoolExecutor(max_workers=shared.max_workers) as executor:
- future_items = {executor.submit(self.create_item, cp): cp for cp in list(sd_models.checkpoints_list.copy())}
- for future in concurrent.futures.as_completed(future_items):
- item = future.result()
- if item is not None:
- yield item
- for record in self.list_reference():
- yield record
-
- def allowed_directories_for_previews(self):
- if shared.backend == shared.Backend.DIFFUSERS:
- return [v for v in [shared.opts.ckpt_dir, shared.opts.diffusers_dir, reference_dir] if v is not None]
- else:
- return [v for v in [shared.opts.ckpt_dir, reference_dir, sd_models.model_path] if v is not None]
+import os
+import html
+import json
+import concurrent
+from modules import shared, ui_extra_networks, sd_models
+
+
+reference_dir = os.path.join('models', 'Reference')
+
+class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Model')
+
+ def refresh(self):
+ shared.refresh_checkpoints()
+
+ def list_reference(self): # pylint: disable=inconsistent-return-statements
+ reference_models = shared.readfile(os.path.join('html', 'reference.json'))
+ for k, v in reference_models.items():
+ if shared.backend != shared.Backend.DIFFUSERS:
+ if not v.get('original', False):
+ continue
+ url = v.get('alt', None) or v['path']
+ else:
+ url = v['path']
+ name = os.path.join(reference_dir, k)
+ preview = v.get('preview', v['path'])
+ yield {
+ "type": 'Model',
+ "name": name,
+ "title": name,
+ "filename": url,
+ "preview": self.find_preview(os.path.join(reference_dir, preview)),
+ "local_preview": self.find_preview_file(os.path.join(reference_dir, preview)),
+ "onclick": '"' + html.escape(f"""return selectReference({json.dumps(url)})""") + '"',
+ "hash": None,
+ "mtime": 0,
+ "size": 0,
+ "info": {},
+ "metadata": {},
+ "description": v.get('desc', ''),
+ }
+
+ def create_item(self, name):
+ record = None
+ try:
+ checkpoint: sd_models.CheckpointInfo = sd_models.checkpoints_list.get(name)
+ exists = os.path.exists(checkpoint.filename)
+ record = {
+ "type": 'Model',
+ "name": checkpoint.name,
+ "title": checkpoint.title,
+ "filename": checkpoint.filename,
+ "hash": checkpoint.shorthash,
+ "preview": self.find_preview(checkpoint.filename),
+ "local_preview": f"{os.path.splitext(checkpoint.filename)[0]}.{shared.opts.samples_format}",
+ "metadata": checkpoint.metadata,
+ "onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
+ "mtime": os.path.getmtime(checkpoint.filename) if exists else 0,
+ "size": os.path.getsize(checkpoint.filename) if exists else 0,
+ }
+ record["info"] = self.find_info(checkpoint.filename)
+ record["description"] = self.find_description(checkpoint.filename, record["info"])
+ except Exception as e:
+ shared.log.debug(f"Extra networks error: type=model file={name} {e}")
+ return record
+
+ def list_items(self):
+ with concurrent.futures.ThreadPoolExecutor(max_workers=shared.max_workers) as executor:
+ future_items = {executor.submit(self.create_item, cp): cp for cp in list(sd_models.checkpoints_list.copy())}
+ for future in concurrent.futures.as_completed(future_items):
+ item = future.result()
+ if item is not None:
+ yield item
+ for record in self.list_reference():
+ yield record
+
+ def allowed_directories_for_previews(self):
+ if shared.backend == shared.Backend.DIFFUSERS:
+ return [v for v in [shared.opts.ckpt_dir, shared.opts.diffusers_dir, reference_dir] if v is not None]
+ else:
+ return [v for v in [shared.opts.ckpt_dir, reference_dir, sd_models.model_path] if v is not None]
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py
index 28189dad4..caf861938 100644
--- a/modules/ui_extra_networks_hypernets.py
+++ b/modules/ui_extra_networks_hypernets.py
@@ -1,34 +1,33 @@
-import json
-import os
-from modules import shared, ui_extra_networks
-
-
-class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
- def __init__(self):
- super().__init__('Hypernetwork')
-
- def refresh(self):
- shared.reload_hypernetworks()
-
- def list_items(self):
- for name, path in shared.hypernetworks.items():
- try:
- name = os.path.relpath(os.path.splitext(path)[0], shared.opts.hypernetwork_dir)
- yield {
- "type": 'Hypernetwork',
- "name": name,
- "filename": path,
- "preview": self.find_preview(path),
- "description": self.find_description(path),
- "info": self.find_info(path),
- "search_term": self.search_terms_from_path(name),
- "prompt": json.dumps(f""),
- "local_preview": f"{os.path.splitext(path)[0]}.{shared.opts.samples_format}",
- "mtime": os.path.getmtime(path),
- "size": os.path.getsize(path),
- }
- except Exception as e:
- shared.log.debug(f"Extra networks error: type=hypernetwork file={path} {e}")
-
- def allowed_directories_for_previews(self):
- return [shared.opts.hypernetwork_dir]
+import json
+import os
+from modules import shared, ui_extra_networks
+
+
+class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Hypernetwork')
+
+ def refresh(self):
+ shared.reload_hypernetworks()
+
+ def list_items(self):
+ for name, path in shared.hypernetworks.items():
+ try:
+ name = os.path.relpath(os.path.splitext(path)[0], shared.opts.hypernetwork_dir)
+ yield {
+ "type": 'Hypernetwork',
+ "name": name,
+ "filename": path,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
+ "info": self.find_info(path),
+ "prompt": json.dumps(f""),
+ "local_preview": f"{os.path.splitext(path)[0]}.{shared.opts.samples_format}",
+ "mtime": os.path.getmtime(path),
+ "size": os.path.getsize(path),
+ }
+ except Exception as e:
+ shared.log.debug(f"Extra networks error: type=hypernetwork file={path} {e}")
+
+ def allowed_directories_for_previews(self):
+ return [shared.opts.hypernetwork_dir]
diff --git a/modules/ui_extra_networks_styles.py b/modules/ui_extra_networks_styles.py
index 9f46850e9..d7532adc5 100644
--- a/modules/ui_extra_networks_styles.py
+++ b/modules/ui_extra_networks_styles.py
@@ -1,137 +1,135 @@
-import os
-import html
-import json
-import concurrent
-from modules import shared, extra_networks, ui_extra_networks, styles
-
-
-class ExtraNetworksPageStyles(ui_extra_networks.ExtraNetworksPage):
- def __init__(self):
- super().__init__('Style')
-
- def refresh(self):
- shared.prompt_styles.reload()
-
- def parse_desc(self, desc):
- lines = desc.strip().split("\n")
- params = { 'name': '', 'description': '', 'prompt': '', 'negative': '', 'extra': ''}
- found = ''
- for line in lines:
- line = line.strip()
- if line.lower().startswith('name:'):
- found = 'name'
- params['name'] = line[5:].strip()
- elif line.lower().startswith('description:'):
- found = 'description'
- params['description'] = line[12:].strip()
- elif line.lower().startswith('prompt:'):
- found = 'prompt'
- params['prompt'] = line[7:].strip()
- elif line.lower().startswith('negative:'):
- found = 'negative'
- params['negative'] = line[9:].strip()
- elif line.lower().startswith('extra:'):
- found = 'extra'
- params['extra'] = line[6:].strip()
- elif found != '':
- params[found] += '\n' + line
- if params['name'] == '':
- return None
- if params['description'] == '':
- params['description'] = params['name']
- return params
-
- def create_style(self, params):
- from modules.images import FilenameGenerator
- from hashlib import sha256
- namegen = FilenameGenerator(p=None, seed=None, prompt=params.get('Prompt', ''), image=None, grid=False)
- name = namegen.prompt_words()
- sha = sha256(json.dumps(name).encode()).hexdigest()[0:8]
- fn = os.path.join(shared.opts.styles_dir, sha + '.json')
- item = {
- "type": 'Style',
- "name": name,
- "title": name,
- "filename": fn,
- "search_term": f'{self.search_terms_from_path(fn)} {params.get("Prompt", "")}',
- "preview": self.find_preview(name),
- "description": '',
- "prompt": params.get('Prompt', ''),
- "negative": params.get('Negative prompt', ''),
- "extra": '',
- "local_preview": f"{name}.{shared.opts.samples_format}",
- }
- return item
-
- def create_item(self, k):
- item = None
- try:
- style = shared.prompt_styles.styles.get(k)
- fn = os.path.splitext(getattr(style, 'filename', ''))[0]
- name = getattr(style, 'name', '')
- if name == '':
- return item
- txt = f'Prompt: {getattr(style, "prompt", "")}'
- if len(getattr(style, 'negative_prompt', '')) > 0:
- txt += f'\nNegative: {style.negative_prompt}'
- item = {
- "type": 'Style',
- "name": name,
- "title": k,
- "filename": style.filename,
- "search_term": f'{self.search_terms_from_path(name)} {txt}',
- "preview": style.preview if getattr(style, 'preview', None) is not None and style.preview.startswith('data:') else self.find_preview(fn),
- "description": style.description if getattr(style, 'description', None) is not None and len(style.description) > 0 else txt,
- "prompt": getattr(style, 'prompt', ''),
- "negative": getattr(style, 'negative_prompt', ''),
- "extra": getattr(style, 'extra', ''),
- "local_preview": f"{fn}.{shared.opts.samples_format}",
- "onclick": '"' + html.escape(f"""return selectStyle({json.dumps(name)})""") + '"',
- "mtime": getattr(style, 'mtime', 0),
- "size": os.path.getsize(style.filename),
- }
- except Exception as e:
- shared.log.debug(f"Extra networks error: type=style file={k} {e}")
- return item
-
- def list_items(self):
- with concurrent.futures.ThreadPoolExecutor(max_workers=shared.max_workers) as executor:
- future_items = {executor.submit(self.create_item, style): style for style in list(shared.prompt_styles.styles)}
- for future in concurrent.futures.as_completed(future_items):
- item = future.result()
- if item is not None:
- yield item
-
- def allowed_directories_for_previews(self):
- return [v for v in [shared.opts.styles_dir] if v is not None] + ['html']
-
-
-class ExtraNetworkStyles(extra_networks.ExtraNetwork):
- def __init__(self):
- super().__init__('style')
- self.indexes = {}
-
- def activate(self, p, params_list):
- for param in params_list:
- if len(param.items) > 0:
- style = None
- search = param.items[0]
- # style = shared.prompt_styles.find_style(param.items[0])
- match = [s for s in shared.prompt_styles.styles.values() if s.name == search]
- if len(match) > 0:
- style = match[0]
- else:
- match = [s for s in shared.prompt_styles.styles.values() if s.name.startswith(search)]
- if len(match) > 0:
- i = self.indexes.get(search, 0)
- self.indexes[search] = (i + 1) % len(match)
- style = match[self.indexes[search]]
- if style is not None:
- p.styles.append(style.name)
- p.prompts = [styles.merge_prompts(style.prompt, prompt) for prompt in p.prompts]
- p.negative_prompts = [styles.merge_prompts(style.negative_prompt, prompt) for prompt in p.negative_prompts]
- styles.apply_styles_to_extra(p, style)
-
-
- def deactivate(self, p):
- pass
+import os
+import html
+import json
+import concurrent
+from modules import shared, extra_networks, ui_extra_networks, styles
+
+
+class ExtraNetworksPageStyles(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Style')
+
+ def refresh(self):
+ shared.prompt_styles.reload()
+
+ def parse_desc(self, desc):
+ lines = desc.strip().split("\n")
+ params = { 'name': '', 'description': '', 'prompt': '', 'negative': '', 'extra': ''}
+ found = ''
+ for line in lines:
+ line = line.strip()
+ if line.lower().startswith('name:'):
+ found = 'name'
+ params['name'] = line[5:].strip()
+ elif line.lower().startswith('description:'):
+ found = 'description'
+ params['description'] = line[12:].strip()
+ elif line.lower().startswith('prompt:'):
+ found = 'prompt'
+ params['prompt'] = line[7:].strip()
+ elif line.lower().startswith('negative:'):
+ found = 'negative'
+ params['negative'] = line[9:].strip()
+ elif line.lower().startswith('extra:'):
+ found = 'extra'
+ params['extra'] = line[6:].strip()
+ elif found != '':
+ params[found] += '\n' + line
+ if params['name'] == '':
+ return None
+ if params['description'] == '':
+ params['description'] = params['name']
+ return params
+
+ def create_style(self, params):
+ from modules.images import FilenameGenerator
+ from hashlib import sha256
+ namegen = FilenameGenerator(p=None, seed=None, prompt=params.get('Prompt', ''), image=None, grid=False)
+ name = namegen.prompt_words()
+ sha = sha256(json.dumps(name).encode()).hexdigest()[0:8]
+ fn = os.path.join(shared.opts.styles_dir, sha + '.json')
+ item = {
+ "type": 'Style',
+ "name": name,
+ "title": name,
+ "filename": fn,
+ "preview": self.find_preview(name),
+ "description": '',
+ "prompt": params.get('Prompt', ''),
+ "negative": params.get('Negative prompt', ''),
+ "extra": '',
+ "local_preview": f"{name}.{shared.opts.samples_format}",
+ }
+ return item
+
+ def create_item(self, k):
+ item = None
+ try:
+ style = shared.prompt_styles.styles.get(k)
+ fn = os.path.splitext(getattr(style, 'filename', ''))[0]
+ name = getattr(style, 'name', '')
+ if name == '':
+ return item
+ txt = f'Prompt: {getattr(style, "prompt", "")}'
+ if len(getattr(style, 'negative_prompt', '')) > 0:
+ txt += f'\nNegative: {style.negative_prompt}'
+ item = {
+ "type": 'Style',
+ "name": name,
+ "title": k,
+ "filename": style.filename,
+ "preview": style.preview if getattr(style, 'preview', None) is not None and style.preview.startswith('data:') else self.find_preview(fn),
+ "description": style.description if getattr(style, 'description', None) is not None and len(style.description) > 0 else txt,
+ "prompt": getattr(style, 'prompt', ''),
+ "negative": getattr(style, 'negative_prompt', ''),
+ "extra": getattr(style, 'extra', ''),
+ "local_preview": f"{fn}.{shared.opts.samples_format}",
+ "onclick": '"' + html.escape(f"""return selectStyle({json.dumps(name)})""") + '"',
+ "mtime": getattr(style, 'mtime', 0),
+ "size": os.path.getsize(style.filename),
+ }
+ except Exception as e:
+ shared.log.debug(f"Extra networks error: type=style file={k} {e}")
+ return item
+
+ def list_items(self):
+ with concurrent.futures.ThreadPoolExecutor(max_workers=shared.max_workers) as executor:
+ future_items = {executor.submit(self.create_item, style): style for style in list(shared.prompt_styles.styles)}
+ for future in concurrent.futures.as_completed(future_items):
+ item = future.result()
+ if item is not None:
+ yield item
+
+ def allowed_directories_for_previews(self):
+ return [v for v in [shared.opts.styles_dir] if v is not None] + ['html']
+
+
+class ExtraNetworkStyles(extra_networks.ExtraNetwork):
+ def __init__(self):
+ super().__init__('style')
+ self.indexes = {}
+
+ def activate(self, p, params_list):
+ for param in params_list:
+ if len(param.items) > 0:
+ style = None
+ search = param.items[0]
+ # style = shared.prompt_styles.find_style(param.items[0])
+ match = [s for s in shared.prompt_styles.styles.values() if s.name == search]
+ if len(match) > 0:
+ style = match[0]
+ else:
+ match = [s for s in shared.prompt_styles.styles.values() if s.name.startswith(search)]
+ if len(match) > 0:
+ i = self.indexes.get(search, 0)
+ self.indexes[search] = (i + 1) % len(match)
+ style = match[self.indexes[search]]
+ if style is not None:
+ p.styles.append(style.name)
+ p.prompts = [styles.merge_prompts(style.prompt, prompt) for prompt in p.prompts]
+ p.negative_prompts = [styles.merge_prompts(style.negative_prompt, prompt) for prompt in p.negative_prompts]
+ styles.apply_styles_to_extra(p, style)
+
+
+ def deactivate(self, p):
+ pass
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py
index 730cbd502..4eadfd34f 100644
--- a/modules/ui_extra_networks_textual_inversion.py
+++ b/modules/ui_extra_networks_textual_inversion.py
@@ -32,7 +32,6 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
"name": name,
"filename": embedding.filename,
"preview": self.find_preview(embedding.filename),
- "search_term": self.search_terms_from_path(name),
"prompt": json.dumps(f" {os.path.splitext(embedding.name)[0]}"),
"local_preview": f"{path}.{shared.opts.samples_format}",
"tags": tags,
diff --git a/modules/ui_extra_networks_vae.py b/modules/ui_extra_networks_vae.py
index e05920a6b..a4a212fdf 100644
--- a/modules/ui_extra_networks_vae.py
+++ b/modules/ui_extra_networks_vae.py
@@ -1,38 +1,37 @@
-import html
-import json
-import os
-from modules import shared, ui_extra_networks, sd_vae, hashes
-
-
-class ExtraNetworksPageVAEs(ui_extra_networks.ExtraNetworksPage):
- def __init__(self):
- super().__init__('VAE')
-
- def refresh(self):
- shared.refresh_vaes()
-
- def list_items(self):
- for name, filename in sd_vae.vae_dict.items():
- try:
- record = {
- "type": 'VAE',
- "name": name,
- "title": name,
- "filename": filename,
- "hash": hashes.sha256_from_cache(filename, f"vae/{filename}"),
- "search_term": self.search_terms_from_path(filename),
- "preview": self.find_preview(filename),
- "local_preview": f"{os.path.splitext(filename)[0]}.{shared.opts.samples_format}",
- "metadata": {},
- "onclick": '"' + html.escape(f"""return selectVAE({json.dumps(name)})""") + '"',
- "mtime": os.path.getmtime(filename),
- "size": os.path.getsize(filename),
- }
- record["info"] = self.find_info(filename)
- record["description"] = self.find_description(filename, record["info"])
- yield record
- except Exception as e:
- shared.log.debug(f"Extra networks error: type=vae file={filename} {e}")
-
- def allowed_directories_for_previews(self):
- return [v for v in [shared.opts.vae_dir] if v is not None]
+import html
+import json
+import os
+from modules import shared, ui_extra_networks, sd_vae, hashes
+
+
+class ExtraNetworksPageVAEs(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('VAE')
+
+ def refresh(self):
+ shared.refresh_vaes()
+
+ def list_items(self):
+ for name, filename in sd_vae.vae_dict.items():
+ try:
+ record = {
+ "type": 'VAE',
+ "name": name,
+ "title": name,
+ "filename": filename,
+ "hash": hashes.sha256_from_cache(filename, f"vae/{filename}"),
+ "preview": self.find_preview(filename),
+ "local_preview": f"{os.path.splitext(filename)[0]}.{shared.opts.samples_format}",
+ "metadata": {},
+ "onclick": '"' + html.escape(f"""return selectVAE({json.dumps(name)})""") + '"',
+ "mtime": os.path.getmtime(filename),
+ "size": os.path.getsize(filename),
+ }
+ record["info"] = self.find_info(filename)
+ record["description"] = self.find_description(filename, record["info"])
+ yield record
+ except Exception as e:
+ shared.log.debug(f"Extra networks error: type=vae file={filename} {e}")
+
+ def allowed_directories_for_previews(self):
+ return [v for v in [shared.opts.vae_dir] if v is not None]
diff --git a/modules/ui_img2img.py b/modules/ui_img2img.py
index 109f7606f..708b35769 100644
--- a/modules/ui_img2img.py
+++ b/modules/ui_img2img.py
@@ -3,7 +3,7 @@ from PIL import Image
import gradio as gr
import numpy as np
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call
-from modules import shared, ui_common, ui_sections, generation_parameters_copypaste
+from modules import timer, shared, ui_common, ui_sections, generation_parameters_copypaste
from modules.ui_components import FormRow, FormGroup
@@ -45,6 +45,7 @@ def create_ui():
with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks_ui:
from modules import ui_extra_networks
extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks_ui, img2img_extra_networks_button, 'img2img', skip_indexing=shared.opts.extra_network_skip_indexing)
+ timer.startup.record('ui-en')
with FormRow(elem_id="img2img_interface", equal_height=False):
with gr.Column(variant='compact', elem_id="img2img_settings"):
@@ -135,18 +136,13 @@ def create_ui():
# with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
with gr.Accordion(open=True, label="Mask", elem_classes=["small-accordion"], elem_id="img2img_mask_group") as inpaint_controls:
with FormRow():
- mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
- mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+ mask_blur = gr.Slider(label='Blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+ inpaint_full_res_padding = gr.Slider(label='Padding', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+ mask_alpha = gr.Slider(label="Alpha", minimum=0.0, maximum=1.0, step=0.05, value=1.0, elem_id="img2img_mask_alpha")
with FormRow():
- with gr.Column():
- inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
- with gr.Column():
- inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'noise', 'nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
- with FormRow():
- with gr.Column():
- inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
- with gr.Column():
- inpaint_full_res_padding = gr.Slider(label='Masked padding', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+ inpainting_mask_invert = gr.Radio(label='Mode', choices=['masked', 'inverse'], value='masked', type="index", elem_id="img2img_mask_mode")
+ inpaint_full_res = gr.Radio(label="Inpaint area", choices=["full", "masked"], type="index", value="full", elem_id="img2img_inpaint_full_res")
+ inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'noise', 'nothing'], value='original', type="index", elem_id="img2img_inpainting_fill", visible=shared.backend == shared.Backend.ORIGINAL)
def select_img2img_tab(tab):
return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3)
diff --git a/modules/ui_interrogate.py b/modules/ui_interrogate.py
index 98fbe9a2f..f6972d22b 100644
--- a/modules/ui_interrogate.py
+++ b/modules/ui_interrogate.py
@@ -2,15 +2,12 @@ import os
import base64
from io import BytesIO
import gradio as gr
-import open_clip
import torch
from PIL import Image
from pydantic import BaseModel, Field # pylint: disable=no-name-in-module
-from fastapi import FastAPI
from fastapi.exceptions import HTTPException
-from clip_interrogator import Config, Interrogator
import modules.generation_parameters_copypaste as parameters_copypaste
-from modules import devices, lowvram, shared, paths
+from modules import devices, lowvram, shared, paths, ui_common
ci = None
@@ -33,6 +30,7 @@ class BatchWriter:
def load(clip_model_name):
+ from clip_interrogator import Config, Interrogator
global ci # pylint: disable=global-statement
if ci is None:
config = Config(device=devices.get_optimal_device(), cache_path=os.path.join(paths.models_path, 'Interrogator'), clip_model_name=clip_model_name, quiet=True)
@@ -109,6 +107,7 @@ def image_to_prompt(image, mode, clip_model_name):
def get_models():
+ import open_clip
return ['/'.join(x) for x in open_clip.list_pretrained()]
@@ -213,7 +212,9 @@ def create_ui():
batch_btn = gr.Button("Interrogate", variant='primary')
with gr.Column():
with gr.Row():
- clip_model = gr.Dropdown(get_models(), value='ViT-L-14/openai', label='CLIP Model')
+ # clip_model = gr.Dropdown(get_models(), value='ViT-L-14/openai', label='CLIP Model')
+ clip_model = gr.Dropdown([], value='ViT-L-14/openai', label='CLIP Model')
+ ui_common.create_refresh_button(clip_model, get_models, lambda: {"choices": get_models()}, 'refresh_interrogate_models')
with gr.Row():
mode = gr.Radio(['best', 'fast', 'classic', 'caption', 'negative'], label='Mode', value='best')
interrogate_btn.click(image_to_prompt, inputs=[image, mode, clip_model], outputs=prompt)
@@ -232,7 +233,7 @@ def decode_base64_to_image(encoding):
raise HTTPException(status_code=500, detail="Invalid encoded image") from e
-def mount_interrogator_api(_: gr.Blocks, app: FastAPI): # TODO redesign interrogator api
+def mount_interrogator_api(_: gr.Blocks, app): # TODO redesign interrogator api
class InterrogatorAnalyzeRequest(BaseModel):
image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
@@ -243,6 +244,7 @@ def mount_interrogator_api(_: gr.Blocks, app: FastAPI): # TODO redesign interrog
@app.get("/interrogator/models")
async def api_get_models():
+ import open_clip
return ["/".join(x) for x in open_clip.list_pretrained()]
@app.post("/interrogator/prompt")
diff --git a/modules/ui_javascript.py b/modules/ui_javascript.py
index 4c4fd75dc..1e62318e4 100644
--- a/modules/ui_javascript.py
+++ b/modules/ui_javascript.py
@@ -5,6 +5,7 @@ from modules import shared, theme
from modules.paths import script_path, data_path
import modules.scripts
+
def webpath(fn):
if fn.startswith(script_path):
web_path = os.path.relpath(fn, script_path).replace('\\', '/')
diff --git a/modules/ui_loadsave.py b/modules/ui_loadsave.py
index fbcf57523..69d79f879 100644
--- a/modules/ui_loadsave.py
+++ b/modules/ui_loadsave.py
@@ -250,7 +250,7 @@ class UiLoadsave:
with gr.Row(elem_id="config_row"):
self.ui_defaults_apply = gr.Button(value='Set new defaults', elem_id="ui_defaults_apply", variant="primary")
self.ui_defaults_submenu = gr.Button(value='Set menu states', elem_id="ui_submenu_apply", variant="primary")
- self.ui_defaults_restore = gr.Button(value='Restore system defaults', elem_id="ui_defaults_restore", variant="primary")
+ self.ui_defaults_restore = gr.Button(value='Restore defaults', elem_id="ui_defaults_restore", variant="primary")
self.ui_defaults_view = gr.Button(value='Refresh changes', elem_id="ui_defaults_view", variant="secondary")
self.ui_defaults_review = gr.HTML("", elem_id="ui_defaults_review")
diff --git a/modules/ui_models.py b/modules/ui_models.py
index ab9870c05..ddccb19ba 100644
--- a/modules/ui_models.py
+++ b/modules/ui_models.py
@@ -1,4 +1,5 @@
import os
+import time
import json
import inspect
from datetime import datetime
@@ -7,7 +8,7 @@ from modules import sd_models, sd_vae, extras
from modules.ui_components import FormRow, ToolButton
from modules.ui_common import create_refresh_button
from modules.call_queue import wrap_gradio_gpu_call
-from modules.shared import opts, log, req, readfile
+from modules.shared import opts, log, req, readfile, max_workers
import modules.errors
import modules.hashes
from modules.merging import merge_methods
@@ -502,11 +503,50 @@ def create_ui():
list_models()
return res
- def civit_search_metadata(civit_previews_rehash, title):
+ def atomic_civit_search_metadata(item, res, rehash):
+ from modules.modelloader import download_civit_preview, download_civit_meta
+ meta = os.path.splitext(item['filename'])[0] + '.json'
+ has_meta = os.path.isfile(meta) and os.stat(meta).st_size > 0
+ if ('card-no-preview.png' in item['preview'] or not has_meta) and os.path.isfile(item['filename']):
+ sha = item.get('hash', None)
+ found = False
+ if sha is not None and len(sha) > 0:
+ r = req(f'https://civitai.com/api/v1/model-versions/by-hash/{sha}')
+ log.debug(f'CivitAI search: name="{item["name"]}" hash={sha} status={r.status_code}')
+ if r.status_code == 200:
+ d = r.json()
+ res.append(download_civit_meta(item['filename'], d['modelId']))
+ if d.get('images') is not None:
+ for i in d['images']:
+ preview_url = i['url']
+ img_res = download_civit_preview(item['filename'], preview_url)
+ res.append(img_res)
+ if 'error' not in img_res:
+ found = True
+ break
+ if not found and rehash and os.stat(item['filename']).st_size < (1024 * 1024 * 1024):
+ sha = modules.hashes.calculate_sha256(item['filename'], quiet=True)[:10]
+ r = req(f'https://civitai.com/api/v1/model-versions/by-hash/{sha}')
+ log.debug(f'CivitAI search: name="{item["name"]}" hash={sha} status={r.status_code}')
+ if r.status_code == 200:
+ d = r.json()
+ res.append(download_civit_meta(item['filename'], d['modelId']))
+ if d.get('images') is not None:
+ for i in d['images']:
+ preview_url = i['url']
+ img_res = download_civit_preview(item['filename'], preview_url)
+ res.append(img_res)
+ if 'error' not in img_res:
+ found = True
+ break
+
+ def civit_search_metadata(rehash, title):
log.debug(f'CivitAI search metadata: {title if type(title) == str else "all"}')
from modules.ui_extra_networks import get_pages
- from modules.modelloader import download_civit_preview, download_civit_meta
res = []
+ i = 0
+ t0 = time.time()
+ candidates = []
for page in get_pages():
if type(title) == str:
if page.title != title:
@@ -514,40 +554,15 @@ def create_ui():
if page.name == 'style':
continue
for item in page.list_items():
- meta = os.path.splitext(item['filename'])[0] + '.json'
- has_meta = os.path.isfile(meta) and os.stat(meta).st_size > 0
- if ('card-no-preview.png' in item['preview'] or not has_meta) and os.path.isfile(item['filename']):
- sha = item.get('hash', None)
- found = False
- if sha is not None and len(sha) > 0:
- r = req(f'https://civitai.com/api/v1/model-versions/by-hash/{sha}')
- log.debug(f'CivitAI search: name="{item["name"]}" hash={sha} status={r.status_code}')
- if r.status_code == 200:
- d = r.json()
- res.append(download_civit_meta(item['filename'], d['modelId']))
- if d.get('images') is not None:
- for i in d['images']:
- preview_url = i['url']
- img_res = download_civit_preview(item['filename'], preview_url)
- res.append(img_res)
- if 'error' not in img_res:
- found = True
- break
- if not found and civit_previews_rehash and os.stat(item['filename']).st_size < (1024 * 1024 * 1024):
- sha = modules.hashes.calculate_sha256(item['filename'], quiet=True)[:10]
- r = req(f'https://civitai.com/api/v1/model-versions/by-hash/{sha}')
- log.debug(f'CivitAI search: name="{item["name"]}" hash={sha} status={r.status_code}')
- if r.status_code == 200:
- d = r.json()
- res.append(download_civit_meta(item['filename'], d['modelId']))
- if d.get('images') is not None:
- for i in d['images']:
- preview_url = i['url']
- img_res = download_civit_preview(item['filename'], preview_url)
- res.append(img_res)
- if 'error' not in img_res:
- found = True
- break
+ i += 1
+ candidates.append(item)
+ # atomic_civit_search_metadata(item, res, rehash)
+ import concurrent
+ with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+ for fn in candidates:
+ executor.submit(atomic_civit_search_metadata, fn, res, rehash)
+ t1 = time.time()
+ log.debug(f'CivitAI search metadata: items={i} time={t1-t0:.2f}')
txt = 'Select existing embedding to continue training or create a new one ')
- with FormRow():
- with gr.Column():
- with gr.Row():
- ti_name = gr.Dropdown(label='Select embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
- create_refresh_button(ti_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
- with gr.Column():
- ti_new_name = gr.Textbox(label="Create emebedding")
- ti_init_text = gr.Textbox(label="Initialization text", value="*")
- ti_vectors = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
- ti_overwrite = gr.Checkbox(value=False, label="Overwrite Old Embedding")
- with gr.Row():
- ti_create = gr.Button(value="Create embedding", variant='secondary')
-
- with gr.Box():
- gr.HTML('Training parameters ')
- ti_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+ gr.HTML('Select existing embedding to continue training or create a new one ')
with FormRow():
- ti_clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
- ti_clip_grad_value = gr.Number(label="Gradient clip value", value=0.1)
- ti_batch_size = gr.Number(label='Batch size', value=1, precision=0)
- ti_gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
- ti_steps = gr.Number(label='Max steps', value=1000, precision=0)
+ with gr.Column():
+ with gr.Row():
+ ti_name = gr.Dropdown(label='Select embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+ create_refresh_button(ti_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+ with gr.Column():
+ ti_new_name = gr.Textbox(label="Create emebedding")
+ ti_init_text = gr.Textbox(label="Initialization text", value="*")
+ ti_vectors = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+ ti_overwrite = gr.Checkbox(value=False, label="Overwrite Old Embedding")
+ with gr.Row():
+ ti_create = gr.Button(value="Create embedding", variant='secondary')
- with gr.Box():
- gr.HTML('Training images ')
- ti_dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
- with FormRow():
- ti_varsize = gr.Checkbox(label="Do not resize images", value=False)
- ti_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
- ti_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
- ti_use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False)
+ with gr.Box():
+ gr.HTML('Training parameters ')
+ ti_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+ with FormRow():
+ ti_clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+ ti_clip_grad_value = gr.Number(label="Gradient clip value", value=0.1)
+ ti_batch_size = gr.Number(label='Batch size', value=1, precision=0)
+ ti_gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
+ ti_steps = gr.Number(label='Max steps', value=1000, precision=0)
- with gr.Box():
- gr.HTML('Dataset processing ')
- with FormRow():
- ti_template = gr.Dropdown(label='Prompt template', value="style_filewords.txt", choices=get_textual_inversion_template_names())
- create_refresh_button(ti_template, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
- ti_shuffle = gr.Checkbox(label="Shuffle tags", value=False)
- ti_tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts", value=0)
- ti_latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
+ with gr.Box():
+ gr.HTML('Training images ')
+ ti_dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+ with FormRow():
+ ti_varsize = gr.Checkbox(label="Do not resize images", value=False)
+ ti_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+ ti_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
+ ti_use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False)
- with gr.Box():
- gr.HTML('Training outputs ')
- with FormRow():
- ti_create_every = gr.Number(label='Create interim images', value=500, precision=0)
- ti_save_every = gr.Number(label='Create interim embeddings', value=500, precision=0)
- ti_save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
- ti_preview_from_txt2img = gr.Checkbox(label='Use current settings for previews', value=False)
- ti_log_directory = gr.Textbox(label='Log directory', placeholder="Defaults to train/log/embedding", value="")
+ with gr.Box():
+ gr.HTML('Dataset processing ')
+ with FormRow():
+ ti_template = gr.Dropdown(label='Prompt template', value="style_filewords.txt", choices=get_textual_inversion_template_names())
+ create_refresh_button(ti_template, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+ ti_shuffle = gr.Checkbox(label="Shuffle tags", value=False)
+ ti_tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts", value=0)
+ ti_latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
- ti_stop.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
+ with gr.Box():
+ gr.HTML('Training outputs ')
+ with FormRow():
+ ti_create_every = gr.Number(label='Create interim images', value=500, precision=0)
+ ti_save_every = gr.Number(label='Create interim embeddings', value=500, precision=0)
+ ti_save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+ ti_preview_from_txt2img = gr.Checkbox(label='Use current settings for previews', value=False)
+ ti_log_directory = gr.Textbox(label='Log directory', placeholder="Defaults to train/log/embedding", value="")
- ti_create.click(
- fn=modules.textual_inversion.ui.create_embedding,
- inputs=[
- ti_new_name,
- ti_init_text,
- ti_vectors,
- ti_overwrite,
- ],
- outputs=[
- ti_name,
- train_output,
- train_outcome,
- ]
- )
+ ti_stop.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
- ti_train.click(
- fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
- _js="startTrainMonitor",
- inputs=[
- dummy_component,
- ti_name,
- ti_learn_rate,
- ti_batch_size,
- ti_gradient_step,
- ti_dataset_directory,
- ti_log_directory,
- ti_width,
- ti_height,
- ti_varsize,
- ti_steps,
- ti_clip_grad_mode,
- ti_clip_grad_value,
- ti_shuffle,
- ti_tag_drop_out,
- ti_latent_sampling_method,
- ti_use_weight,
- ti_create_every,
- ti_save_every,
- ti_template,
- ti_save_image_with_stored_embedding,
- ti_preview_from_txt2img,
- *txt2img_preview_params,
- ],
- outputs=[
- train_output,
- train_outcome,
- ]
- )
+ ti_create.click(
+ fn=modules.textual_inversion.ui.create_embedding,
+ inputs=[
+ ti_new_name,
+ ti_init_text,
+ ti_vectors,
+ ti_overwrite,
+ ],
+ outputs=[
+ ti_name,
+ train_output,
+ train_outcome,
+ ]
+ )
+
+ ti_train.click(
+ fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+ _js="startTrainMonitor",
+ inputs=[
+ dummy_component,
+ ti_name,
+ ti_learn_rate,
+ ti_batch_size,
+ ti_gradient_step,
+ ti_dataset_directory,
+ ti_log_directory,
+ ti_width,
+ ti_height,
+ ti_varsize,
+ ti_steps,
+ ti_clip_grad_mode,
+ ti_clip_grad_value,
+ ti_shuffle,
+ ti_tag_drop_out,
+ ti_latent_sampling_method,
+ ti_use_weight,
+ ti_create_every,
+ ti_save_every,
+ ti_template,
+ ti_save_image_with_stored_embedding,
+ ti_preview_from_txt2img,
+ *txt2img_preview_params,
+ ],
+ outputs=[
+ train_output,
+ train_outcome,
+ ]
+ )
### train hypernetwork tab
- with gr.Tab(label="Train hypernetwork", id="train_hypernetwork_tab") as tab_hn:
- tab_hn.select(fn=lambda: train_tab_change('hn'), inputs=[], outputs=[action_pp, action_ti, action_hn])
- gr.HTML('Select existing hypernetwork to continue training or create a new one ')
- with FormRow():
- with gr.Column():
+ if shared.backend == shared.Backend.ORIGINAL:
+ from modules import sd_hijack
+ with gr.Tab(label="Train hypernetwork", id="train_hypernetwork_tab") as tab_hn:
+ tab_hn.select(fn=lambda: train_tab_change('hn'), inputs=[], outputs=[action_pp, action_ti, action_hn])
+ gr.HTML('Select existing hypernetwork to continue training or create a new one ')
+ with FormRow():
+ with gr.Column():
+ with FormRow():
+ hn_name = gr.Dropdown(label='Hypernetwork', choices=sorted(shared.hypernetworks))
+ create_refresh_button(hn_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
+ with gr.Column():
+ hn_new_name = gr.Textbox(label="Name")
+ hn_new_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
+ hn_new_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
+ with gr.Row():
+ hn_new_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
+ hn_new_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+ hn_new_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+ hn_new_use_dropout = gr.Checkbox(label="Use dropout")
+ hn_new_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
+ hn_overwrite = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
+ with gr.Row():
+ hn_create = gr.Button(value="Create hypernetwork", variant='secondary')
+
+ with gr.Box():
+ gr.HTML('Training parameters ')
+ hn_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
with FormRow():
- hn_name = gr.Dropdown(label='Hypernetwork', choices=sorted(shared.hypernetworks))
- create_refresh_button(hn_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
- with gr.Column():
- hn_new_name = gr.Textbox(label="Name")
- hn_new_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
- hn_new_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
- with gr.Row():
- hn_new_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
- hn_new_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
- hn_new_add_layer_norm = gr.Checkbox(label="Add layer normalization")
- hn_new_use_dropout = gr.Checkbox(label="Use dropout")
- hn_new_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
- hn_overwrite = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
- with gr.Row():
- hn_create = gr.Button(value="Create hypernetwork", variant='secondary')
+ hn_clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+ hn_clip_grad_value = gr.Number(label="Gradient clip value", value=0.1)
+ hn_batch_size = gr.Number(label='Batch size', value=1, precision=0)
+ hn_gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
+ hn_steps = gr.Number(label='Max steps', value=1000, precision=0)
- with gr.Box():
- gr.HTML('Training parameters ')
- hn_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
- with FormRow():
- hn_clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
- hn_clip_grad_value = gr.Number(label="Gradient clip value", value=0.1)
- hn_batch_size = gr.Number(label='Batch size', value=1, precision=0)
- hn_gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
- hn_steps = gr.Number(label='Max steps', value=1000, precision=0)
+ with gr.Box():
+ gr.HTML('Training images ')
+ hn_dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+ with FormRow():
+ hn_varsize = gr.Checkbox(label="Do not resize images", value=False)
+ hn_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+ hn_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
+ hn_use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False)
- with gr.Box():
- gr.HTML('Training images ')
- hn_dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
- with FormRow():
- hn_varsize = gr.Checkbox(label="Do not resize images", value=False)
- hn_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
- hn_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
- hn_use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False)
+ with gr.Box():
+ gr.HTML('Dataset processing ')
+ with FormRow():
+ hn_template = gr.Dropdown(label='Prompt template', value="style_filewords.txt", choices=get_textual_inversion_template_names())
+ create_refresh_button(hn_template, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+ hn_shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
+ hn_tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts", value=0)
+ hn_latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
- with gr.Box():
- gr.HTML('Dataset processing ')
- with FormRow():
- hn_template = gr.Dropdown(label='Prompt template', value="style_filewords.txt", choices=get_textual_inversion_template_names())
- create_refresh_button(hn_template, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
- hn_shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
- hn_tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts", value=0)
- hn_latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
+ with gr.Box():
+ gr.HTML('Training outputs ')
+ with FormRow():
+ hn_create_every = gr.Number(label='Create interim images', value=500, precision=0)
+ hn_save_every = gr.Number(label='Create interim hypernetworks', value=500, precision=0)
+ hn_preview_from_txt2img = gr.Checkbox(label='Use current settings for previews', value=False)
+ hn_log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value=f"{os.path.join('cmd_opts.data_dir', 'train/log/embeddings')}")
- with gr.Box():
- gr.HTML('Training outputs ')
- with FormRow():
- hn_create_every = gr.Number(label='Create interim images', value=500, precision=0)
- hn_save_every = gr.Number(label='Create interim hypernetworks', value=500, precision=0)
- hn_preview_from_txt2img = gr.Checkbox(label='Use current settings for previews', value=False)
- hn_log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value=f"{os.path.join('cmd_opts.data_dir', 'train/log/embeddings')}")
+ hn_stop.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
- hn_stop.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[])
+ hn_create.click(
+ fn=modules.hypernetworks.ui.create_hypernetwork,
+ inputs=[
+ hn_new_name,
+ hn_new_sizes,
+ hn_overwrite,
+ hn_new_layer_structure,
+ hn_new_activation_func,
+ hn_new_initialization_option,
+ hn_new_add_layer_norm,
+ hn_new_use_dropout,
+ hn_new_dropout_structure
+ ],
+ outputs=[
+ hn_name,
+ train_output,
+ train_outcome,
+ ]
+ )
- hn_create.click(
- fn=modules.hypernetworks.ui.create_hypernetwork,
- inputs=[
- hn_new_name,
- hn_new_sizes,
- hn_overwrite,
- hn_new_layer_structure,
- hn_new_activation_func,
- hn_new_initialization_option,
- hn_new_add_layer_norm,
- hn_new_use_dropout,
- hn_new_dropout_structure
- ],
- outputs=[
- hn_name,
- train_output,
- train_outcome,
- ]
- )
-
- hn_train.click(
- fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
- _js="startTrainMonitor",
- inputs=[
- dummy_component,
- hn_name,
- hn_learn_rate,
- hn_batch_size,
- hn_gradient_step,
- hn_dataset_directory,
- hn_log_directory,
- hn_width,
- hn_height,
- hn_varsize,
- hn_steps,
- hn_clip_grad_mode,
- hn_clip_grad_value,
- hn_shuffle_tags,
- hn_tag_drop_out,
- hn_latent_sampling_method,
- hn_use_weight,
- hn_create_every,
- hn_save_every,
- hn_template,
- hn_preview_from_txt2img,
- *txt2img_preview_params,
- ],
- outputs=[
- train_output,
- train_outcome,
- ]
- )
+ hn_train.click(
+ fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+ _js="startTrainMonitor",
+ inputs=[
+ dummy_component,
+ hn_name,
+ hn_learn_rate,
+ hn_batch_size,
+ hn_gradient_step,
+ hn_dataset_directory,
+ hn_log_directory,
+ hn_width,
+ hn_height,
+ hn_varsize,
+ hn_steps,
+ hn_clip_grad_mode,
+ hn_clip_grad_value,
+ hn_shuffle_tags,
+ hn_tag_drop_out,
+ hn_latent_sampling_method,
+ hn_use_weight,
+ hn_create_every,
+ hn_save_every,
+ hn_template,
+ hn_preview_from_txt2img,
+ *txt2img_preview_params,
+ ],
+ outputs=[
+ train_output,
+ train_outcome,
+ ]
+ )
params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
script_callbacks.ui_train_tabs_callback(params)
diff --git a/modules/ui_txt2img.py b/modules/ui_txt2img.py
index d03010fcf..e2d3e3a66 100644
--- a/modules/ui_txt2img.py
+++ b/modules/ui_txt2img.py
@@ -29,7 +29,7 @@ def create_ui():
with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks_ui:
from modules import ui_extra_networks
extra_networks_ui = ui_extra_networks.create_ui(extra_networks_ui, txt2img_extra_networks_button, 'txt2img', skip_indexing=shared.opts.extra_network_skip_indexing)
- timer.startup.record('ui-extra-networks')
+ timer.startup.record('ui-en')
with gr.Row(elem_id="txt2img_interface", equal_height=False):
with gr.Column(variant='compact', elem_id="txt2img_settings"):
diff --git a/modules/update.py b/modules/update.py
new file mode 100644
index 000000000..88c6ea9e1
--- /dev/null
+++ b/modules/update.py
@@ -0,0 +1,97 @@
+from types import SimpleNamespace
+import gradio as gr
+import installer as i
+
+
+version = SimpleNamespace(**{
+ 'url': '',
+ 'branch': '',
+ 'current': '0000-00-00',
+ 'chash': '0000000',
+ 'latest': '0000-00-00',
+ 'lhash': '0000000',
+})
+
+
+def get_version():
+ # try:
+ origin = i.git('remote get-url origin')
+ origin = origin.splitlines()[0]
+ version.branch = i.git('rev-parse --abbrev-ref HEAD')
+ version.branch = version.branch.splitlines()[0]
+ version.url = origin + '/tree/' + version.branch
+
+ ver = i.git('log --pretty=format:"%h %ad" -1 --date=short')
+ ver = ver.splitlines()[0]
+ version.chash, version.current = ver.split(' ')
+
+ ver = i.git(f'log origin/{version.branch} --pretty=format:"%h %ad" -1 --date=short')
+ ver = ver.splitlines()[0]
+ version.lhash, version.latest = ver.split(' ')
+
+ # except Exception as e:
+ # i.log.error(f'Version check failed: {e}')
+ i.log.info(f'Version: {vars(version)}')
+ html = f'''
+
+ Current branch: {version.branch}
+ Current version: {version.current} hash {version.chash}
+ Latest version: {version.latest} hash {version.lhash}
+ '''
+ return html
+
+
+def apply_update(update_rebase, update_submodules, update_extensions):
+ html = [
+ 'Updating...',
+ f'Core rebase: {update_rebase} | Submodules: {update_submodules} | Extensions: {update_extensions}',
+ f'Current version: {version.current} hash {version.chash}
',
+ ]
+ get_version()
+ phash = version.chash
+ try:
+ if update_rebase:
+ i.git('add .')
+ i.git('stash')
+ res = i.update('.', current_branch=True, rebase=update_rebase)
+ html.append(res.replace('\n', 'Update successful! ')
+ else:
+ html.append('No changes ')
+ return ' 
-