Merge branch 'master' into directml

.gitignore

@@ -32,6 +32,8 @@ venv
 /models/**/*
 /interrogate/**/*
 /train/log/**/*
+/textual_inversion/**/*
+/detected_maps/**/*
 /tmp
 /log
 /cert

TODO.md

@@ -8,6 +8,7 @@ Stuff to be fixed...
 - Run VAE with hires at 1280
 - Transformers version
 - Move Restart Server from WebUI to Launch and reload modules
+- gr.Dropdowns with None selected
 
 ## Features
 

javascript/progressbar.js

@@ -77,7 +77,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd = null, o
         setTitle("")
         if (divProgress) parentProgressbar.removeChild(divProgress)
         if (parentGallery) parentGallery.removeChild(livePreview)
-        atEnd()
+        if (atEnd) atEnd()
     }
 
     var fun = function(id_task, id_live_preview){

javascript/textualInversion.js

@@ -5,6 +5,7 @@ function start_training_textual_inversion(){
     gradioApp().querySelector('#ti_error').innerHTML=''
     var id = randomId()
     const onProgress = (progress) => gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo;
+    // requestProgress(id_task, progressbarContainer, gallery, atEnd = null, onProgress = null, once = false) {
     requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), null, onProgress, false)
     var res = args_to_array(arguments)
     res[0] = id

modules/cmd_args.py

@@ -67,12 +67,16 @@ def compatibility_args(opts, args):
     parser.add_argument("--disable-nan-check", help=argparse.SUPPRESS, default=opts.disable_nan_check)
     parser.add_argument("--token-merging", help=argparse.SUPPRESS, default=opts.token_merging)
     parser.add_argument("--rollback-vae", help=argparse.SUPPRESS, default=opts.rollback_vae)
-
     parser.add_argument("--no-half", help=argparse.SUPPRESS, default=opts.no_half)
     parser.add_argument("--no-half-vae", help=argparse.SUPPRESS, default=opts.no_half_vae)
     parser.add_argument("--precision", help=argparse.SUPPRESS, default=opts.precision)
     parser.add_argument("--api", help=argparse.SUPPRESS, default=True)
 
+    opts.use_old_emphasis_implementation = False
+    opts.use_old_karras_scheduler_sigmas = False
+    opts.no_dpmpp_sde_batch_determinism = False
+    opts.use_old_hires_fix_width_height = False
+
     parser.add_argument("--lora-dir", help=argparse.SUPPRESS, default=opts.lora_dir)
     args = parser.parse_args()
     if 'lyco_dir' in args:

modules/extras.py

@@ -5,12 +5,11 @@ import shutil
 
 import torch
 import tqdm
-
-from modules import shared, images, sd_models, sd_vae, sd_models_config
-from modules.ui_common import plaintext_to_html
 import gradio as gr
 import safetensors.torch
 
+from modules import shared, images, sd_models, sd_vae, sd_models_config
+
 
 def run_pnginfo(image):
     if image is None:
@@ -130,14 +129,14 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     result_is_instruct_pix2pix_model = False
 
     if theta_func2:
-        shared.state.textinfo = f"Loading B"
+        shared.state.textinfo = "Loading B"
         print(f"Loading {secondary_model_info.filename}...")
         theta_1 = sd_models.read_state_dict(secondary_model_info.filename)
     else:
         theta_1 = None
 
     if theta_func1:
-        shared.state.textinfo = f"Loading C"
+        shared.state.textinfo = "Loading C"
         print(f"Loading {tertiary_model_info.filename}...")
         theta_2 = sd_models.read_state_dict(tertiary_model_info.filename)
 
@@ -193,7 +192,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
                     result_is_inpainting_model = True
             else:
                 theta_0[key] = theta_func2(a, b, multiplier)
-            
+
             theta_0[key] = to_half(theta_0[key], save_as_half)
 
         shared.state.sampling_step += 1

modules/images.py

@@ -634,7 +634,7 @@ def read_info_from_image(image):
         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']: # remove unwanted tags
+    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":

modules/processing.py

@@ -474,6 +474,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not 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(':', '')),
+        "VAE": (None if not opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0]),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "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}"),
@@ -490,7 +491,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Token merging stride x": None if opts.token_merging_stride_x == 2 else opts.token_merging_stride_x,
         "Token merging stride y": None if opts.token_merging_stride_y == 2 else opts.token_merging_stride_y
     }
-
     generation_params.update(p.extra_generation_params)
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])

modules/textual_inversion/textual_inversion.py

@@ -1,23 +1,19 @@
 import os
-from collections import namedtuple
-
-import torch
-import tqdm
 import html
 import csv
+from collections import namedtuple
+import torch
+import tqdm
 import safetensors.torch
-
+from rich import print # pylint: disable=redefined-builtin
 import numpy as np
 from PIL import Image, PngImagePlugin
 from torch.utils.tensorboard import SummaryWriter
-
 from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors
 import modules.textual_inversion.dataset
 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.logging import save_settings_to_file
-from rich import print # pylint: disable=redefined-builtin
 
 TextualInversionTemplate = namedtuple("TextualInversionTemplate", ["name", "path"])
 textual_inversion_templates = {}
@@ -26,7 +22,7 @@ textual_inversion_templates = {}
 def list_textual_inversion_templates():
     textual_inversion_templates.clear()
 
-    for root, dirs, fns in os.walk(shared.opts.embeddings_templates_dir):
+    for root, _dirs, fns in os.walk(shared.opts.embeddings_templates_dir):
         for fn in fns:
             path = os.path.join(root, fn)
 
@@ -198,7 +194,7 @@ class EmbeddingDatabase:
         if not os.path.isdir(embdir.path):
             return
 
-        for root, dirs, fns in os.walk(embdir.path, followlinks=True):
+        for root, _dirs, fns in os.walk(embdir.path, followlinks=True):
             for fn in fns:
                 try:
                     fullfn = os.path.join(root, fn)
@@ -214,7 +210,7 @@ class EmbeddingDatabase:
     def load_textual_inversion_embeddings(self, force_reload=False):
         if not force_reload:
             need_reload = False
-            for path, embdir in self.embedding_dirs.items():
+            for _path, embdir in self.embedding_dirs.items():
                 if embdir.has_changed():
                     need_reload = True
                     break
@@ -227,7 +223,7 @@ class EmbeddingDatabase:
         self.skipped_embeddings.clear()
         self.expected_shape = self.get_expected_shape()
 
-        for path, embdir in self.embedding_dirs.items():
+        for _path, embdir in self.embedding_dirs.items():
             self.load_from_dir(embdir)
             embdir.update()
 
@@ -270,13 +266,13 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     # Remove illegal characters from name.
     name = "".join( x for x in name if (x.isalnum() or x in "._- "))
     fn = os.path.join(shared.opts.embeddings_dir, f"{name}.pt")
-    if not overwrite_old:
-        assert not os.path.exists(fn), f"file {fn} already exists"
-
-    embedding = Embedding(vec, name)
-    embedding.step = 0
-    embedding.save(fn)
-
+    if not overwrite_old and os.path.exists(fn):
+        print(f"Embedding already exists: {fn}")
+    else:
+        embedding = Embedding(vec, name)
+        embedding.step = 0
+        embedding.save(fn)
+        print(f'Created embedding: {fn} vectors {num_vectors_per_token} init {init_text}')
     return fn
 
 
@@ -288,7 +284,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         return
     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
 
-    with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
+    with open(os.path.join(log_directory, filename), "a+", newline='', encoding='utf-8') as fout:
         csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
 
         if write_csv_header:
@@ -460,7 +456,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     try:
         sd_hijack_checkpoint.add()
 
-        for i in range((steps-initial_step) * gradient_step):
+        for _i in range((steps-initial_step) * gradient_step):
             if scheduler.finished:
                 break
             if shared.state.interrupted:
@@ -574,7 +570,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                     if image is not None:
                         shared.state.assign_current_image(image)
 
-                        last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                        last_saved_image, _last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                         last_saved_image += f", prompt: {preview_text}"
 
                         if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
@@ -587,18 +583,17 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                         info = PngImagePlugin.PngInfo()
                         data = torch.load(last_saved_file)
                         info.add_text("sd-ti-embedding", embedding_to_b64(data))
-
-                        title = "<{}>".format(data.get('name', '???'))
+                        title = f"<{data.get('name', '???')}>"
 
                         try:
                             vectorSize = list(data['string_to_param'].values())[0].shape[0]
-                        except Exception as e:
+                        except Exception:
                             vectorSize = '?'
 
                         checkpoint = sd_models.select_checkpoint()
                         footer_left = checkpoint.model_name
-                        footer_mid = '[{}]'.format(checkpoint.shorthash)
-                        footer_right = '{}v {}s'.format(vectorSize, steps_done)
+                        footer_mid = f'[{checkpoint.shorthash}]'
+                        footer_right = f'{vectorSize}v {steps_done}s'
 
                         captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                         captioned_image = insert_image_data_embed(captioned_image, data)
@@ -606,7 +601,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                         captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
                         embedding_yet_to_be_embedded = False
 
-                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                    last_saved_image, _last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                     last_saved_image += f", prompt: {preview_text}"
 
                 shared.state.job_no = embedding.step
@@ -624,7 +619,6 @@ Last saved image: {html.escape(last_saved_image)}<br/>
         save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
     except Exception as e:
         errors.display(e, 'embedding train')
-        pass
     finally:
         pbar.leave = False
         pbar.close()

modules/textual_inversion/ui.py

@@ -1,7 +1,5 @@
 import html
-
 import gradio as gr
-
 import modules.textual_inversion.textual_inversion
 import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared
@@ -9,9 +7,7 @@ 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}", ""
 
 
@@ -42,4 +38,3 @@ Embedding saved to {html.escape(filename)}
     finally:
         if not apply_optimizations:
             sd_hijack.apply_optimizations()
-

modules/ui.py

@@ -1127,7 +1127,7 @@ def create_ui():
                         gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
 
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="train/log/embeddings", elem_id="train_log_directory")
 
                     with FormRow():
                         template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
@@ -1136,7 +1136,7 @@ def create_ui():
                     training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
                     training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
                     varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
-                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+                    steps = gr.Number(label='Max steps', value=1000, precision=0, elem_id="train_steps")
 
                     with FormRow():
                         create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
@@ -1740,32 +1740,3 @@ def reload_javascript():
 
 if not hasattr(shared, 'GradioTemplateResponseOriginal'):
     shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-
-
-def versions_html():
-    import torch
-    import launch
-
-    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
-    commit = launch.commit_hash()
-    short_commit = commit[0:8]
-
-    if shared.xformers_available:
-        import xformers
-        xformers_version = xformers.__version__
-    else:
-        xformers_version = "N/A"
-
-    return f"""
-python: <span title="{sys.version}">{python_version}</span>
- • 
-torch: {getattr(torch, '__long_version__',torch.__version__)}
- • 
-xformers: {xformers_version}
- • 
-gradio: {gr.__version__}
- • 
-commit: <a href="https://github.com/vladmandic/automatic/commit/{commit}">{short_commit}</a>
- • 
-checkpoint: <a id="sd_checkpoint_hash">N/A</a>
-"""

modules/ui_extra_networks_hypernets.py

@@ -1,6 +1,5 @@
 import json
 import os
-
 from modules import shared, ui_extra_networks
 
 
@@ -14,7 +13,6 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
     def list_items(self):
         for name, path in shared.hypernetworks.items():
             path, ext = os.path.splitext(path)
-
             yield {
                 "name": name,
                 "filename": path,

modules/ui_extra_networks_textual_inversion.py

@@ -25,12 +25,12 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
         for embedding in embeddings:
             path, _ext = os.path.splitext(embedding.filename)
             yield {
-                "name": embedding.name,
+                "name": os.path.splitext(embedding.name)[0],
                 "filename": embedding.filename,
                 "preview": self.find_preview(path),
                 "description": self.find_description(path),
                 "search_term": self.search_terms_from_path(embedding.filename),
-                "prompt": json.dumps(embedding.name),
+                "prompt": json.dumps(os.path.splitext(embedding.name)[0]),
                 "local_preview": f"{path}.preview.{shared.opts.samples_format}",
             }
 

modules/ui_postprocessing.py

@@ -32,17 +32,24 @@ def create_ui():
         with gr.Column():
             result_images, html_info_x, html_info, _html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
             html_info = gr.HTML(elem_id="pnginfo_html_info")
-            generation_info = gr.Textbox(elem_id="pnginfo_generation_info", label="Parameters")
+            generation_info = gr.Textbox(elem_id="pnginfo_generation_info", label="Parameters", visible=False)
+            generation_info_pretty = gr.Textbox(elem_id="pnginfo_generation_info_pretty", label="Parameters")
+
             gr.HTML('Full metadata')
             html2_info = gr.HTML(elem_id="pnginfo_html2_info")
 
         for tabname, button in buttons.items():
             parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=extras_image))
 
+    def pretty_geninfo(generation_info):
+        return generation_info.replace(', ', '\n')
+
     tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
     tab_batch.select(fn=lambda: 1, inputs=[], outputs=[tab_index])
     tab_batch_dir.select(fn=lambda: 2, inputs=[], outputs=[tab_index])
 
+    generation_info.change(fn=pretty_geninfo, inputs=[generation_info], outputs=[generation_info_pretty])
+
     extras_image.change(
         fn=wrap_gradio_call(run_pnginfo),
         inputs=[extras_image],

setup.py

@@ -137,12 +137,18 @@ def update(folder):
     branch = git('branch', folder)
     if 'main' in branch:
         git('checkout main', folder)
+        branch = 'main'
     elif 'master' in branch:
         git('checkout master', folder)
+        branch = 'master'
     else:
         log.warning(f'Unknown branch for: {folder}')
-    git('pull --autostash --rebase', folder)
-    branch = git('branch', folder)
+        branch = None
+    if branch is None:
+        git('pull --autostash --rebase', folder)
+    else:
+        git(f'pull origin {branch} --autostash --rebase', folder)
+    # branch = git('branch', folder)
 
 
 # clone git repository
@@ -175,6 +181,9 @@ def check_python():
     if shutil.which(git_cmd) is None:
         log.error('Git not found')
         exit(1)
+    else:
+        git_version = git('--version', folder=None, ignore=False)
+        log.debug(f'Git {git_version.replace("git version", "").strip()}')
 
 
 # check torch version
@@ -325,6 +334,7 @@ def install_extensions():
 def install_submodules():
     log.info('Installing submodules')
     txt = git('submodule')
+    log.debug(f'Submodules list: {txt}')
     if 'no submodule mapping found' in txt:
         log.warning('Attempting repository recover')
         git('add .')
@@ -334,7 +344,7 @@ def install_submodules():
         git('reset --hard origin/master')
         git('checkout master')
         log.info('Continuing setup')
-    git('submodule --quiet update --init --recursive')
+    txt = git('submodule --quiet update --init --recursive')
     if not args.noupdate:
         log.info('Updating submodules')
         submodules = git('submodule').splitlines()