Merge pull request #24 from AUTOMATIC1111/master

sync branches

extensions-builtin/Lora/lora.py

@@ -57,6 +57,7 @@ class LoraUpDownModule:
     def __init__(self):
         self.up = None
         self.down = None
+        self.alpha = None
 
 
 def assign_lora_names_to_compvis_modules(sd_model):
@@ -92,6 +93,15 @@ def load_lora(name, filename):
             keys_failed_to_match.append(key_diffusers)
             continue
 
+        lora_module = lora.modules.get(key, None)
+        if lora_module is None:
+            lora_module = LoraUpDownModule()
+            lora.modules[key] = lora_module
+
+        if lora_key == "alpha":
+            lora_module.alpha = weight.item()
+            continue
+
         if type(sd_module) == torch.nn.Linear:
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
         elif type(sd_module) == torch.nn.Conv2d:
@@ -104,17 +114,12 @@ def load_lora(name, filename):
 
         module.to(device=devices.device, dtype=devices.dtype)
 
-        lora_module = lora.modules.get(key, None)
-        if lora_module is None:
-            lora_module = LoraUpDownModule()
-            lora.modules[key] = lora_module
-
         if lora_key == "lora_up.weight":
             lora_module.up = module
         elif lora_key == "lora_down.weight":
             lora_module.down = module
         else:
-            assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight or lora_down.weight'
+            assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
 
     if len(keys_failed_to_match) > 0:
         print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
@@ -161,7 +166,7 @@ def lora_forward(module, input, res):
     for lora in loaded_loras:
         module = lora.modules.get(lora_layer_name, None)
         if module is not None:
-            res = res + module.up(module.down(input)) * lora.multiplier
+            res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
 
     return res
 

html/extra-networks-card.html

@@ -1,8 +1,8 @@
-<div class='card' {preview_html} onclick='return cardClicked({tabname}, {prompt}, {allow_negative_prompt})'>
+<div class='card' {preview_html} onclick={card_clicked}>
 	<div class='actions'>
 		<div class='additional'>
 			<ul>
-				<a href="#" title="replace preview image with currently selected in gallery" onclick='return saveCardPreview(event, {tabname}, {local_preview})'>replace preview</a>
+				<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
 			</ul>
 		</div>
 		<span class='name'>{name}</span>

html/image-update.svg

@@ -0,0 +1,7 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+  <filter id='shadow' color-interpolation-filters="sRGB">
+    <feDropShadow flood-color="black" dx="0" dy="0" flood-opacity="0.9" stdDeviation="0.5"/>
+    <feDropShadow flood-color="black" dx="0" dy="0" flood-opacity="0.9" stdDeviation="0.5"/>
+  </filter>
+  <path style="filter:url(#shadow);" fill="#FFFFFF" d="M13.18 19C13.35 19.72 13.64 20.39 14.03 21H5C3.9 21 3 20.11 3 19V5C3 3.9 3.9 3 5 3H19C20.11 3 21 3.9 21 5V11.18C20.5 11.07 20 11 19.5 11C19.33 11 19.17 11 19 11.03V5H5V19H13.18M11.21 15.83L9.25 13.47L6.5 17H13.03C13.14 15.54 13.73 14.22 14.64 13.19L13.96 12.29L11.21 15.83M19 13.5V12L16.75 14.25L19 16.5V15C20.38 15 21.5 16.12 21.5 17.5C21.5 17.9 21.41 18.28 21.24 18.62L22.33 19.71C22.75 19.08 23 18.32 23 17.5C23 15.29 21.21 13.5 19 13.5M19 20C17.62 20 16.5 18.88 16.5 17.5C16.5 17.1 16.59 16.72 16.76 16.38L15.67 15.29C15.25 15.92 15 16.68 15 17.5C15 19.71 16.79 21.5 19 21.5V23L21.25 20.75L19 18.5V20Z" />
+</svg>

javascript/ui.js

@@ -104,11 +104,6 @@ function create_tab_index_args(tabId, args){
     return res
 }
 
-function get_extras_tab_index(){
-    const [,,...args] = [...arguments]
-    return [get_tab_index('mode_extras'), get_tab_index('extras_resize_mode'), ...args]
-}
-
 function get_img2img_tab_index() {
     let res = args_to_array(arguments)
     res.splice(-2)

launch.py

@@ -182,7 +182,7 @@ def run_extensions_installers(settings_file):
 def prepare_environment():
     global skip_install
 
-    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
+    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 
@@ -190,8 +190,6 @@ def prepare_environment():
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
 
-    xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
-
     stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
     taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
     k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
@@ -213,6 +211,7 @@ def prepare_environment():
     sys.argv, _ = extract_arg(sys.argv, '-f')
     sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
     sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
+    sys.argv, reinstall_torch = extract_arg(sys.argv, '--reinstall-torch')
     sys.argv, update_check = extract_arg(sys.argv, '--update-check')
     sys.argv, run_tests, test_dir = extract_opt(sys.argv, '--tests')
     sys.argv, skip_install = extract_arg(sys.argv, '--skip-install')
@@ -224,7 +223,7 @@ def prepare_environment():
     # print(f"Python {sys.version}")
     # print(f"Commit hash: {commit}")
     
-    if not is_installed("torch") or not is_installed("torchvision"):
+    if reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
 
     if not skip_torch_cuda_test:
@@ -242,7 +241,7 @@ def prepare_environment():
     if (not is_installed("xformers") or reinstall_xformers) and xformers:
         if platform.system() == "Windows":
             if platform.python_version().startswith("3.10"):
-                run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
+                run_pip(f"install -U -I --no-deps xformers==0.0.16rc425", "xformers")
             else:
                 print("Installation of xformers is not supported in this version of Python.")
                 print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")

modules/api/api.py

@@ -11,10 +11,9 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.extras import run_extras
 from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
@@ -23,6 +22,8 @@ from modules.sd_models import checkpoints_list, find_checkpoint_config
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
 from typing import List
+import piexif
+import piexif.helper
 
 def upscaler_to_index(name: str):
     try:
@@ -45,10 +46,8 @@ def validate_sampler_name(name):
 
 def setUpscalers(req: dict):
     reqDict = vars(req)
-    reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
-    reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
-    reqDict.pop('upscaler_1')
-    reqDict.pop('upscaler_2')
+    reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
+    reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
     return reqDict
 
 def decode_base64_to_image(encoding):
@@ -59,18 +58,30 @@ def decode_base64_to_image(encoding):
 def encode_pil_to_base64(image):
     with io.BytesIO() as output_bytes:
 
-        # Copy any text-only metadata
-        use_metadata = False
-        metadata = PngImagePlugin.PngInfo()
-        for key, value in image.info.items():
-            if isinstance(key, str) and isinstance(value, str):
-                metadata.add_text(key, value)
-                use_metadata = True
+        if opts.samples_format.lower() == 'png':
+            use_metadata = False
+            metadata = PngImagePlugin.PngInfo()
+            for key, value in image.info.items():
+                if isinstance(key, str) and isinstance(value, str):
+                    metadata.add_text(key, value)
+                    use_metadata = True
+            image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
+
+        elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
+            parameters = image.info.get('parameters', None)
+            exif_bytes = piexif.dump({
+                "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
+            })
+            if opts.samples_format.lower() in ("jpg", "jpeg"):
+                image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality)
+            else:
+                image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality)
+
+        else:
+            raise HTTPException(status_code=500, detail="Invalid image format")
 
-        image.save(
-            output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
-        )
         bytes_data = output_bytes.getvalue()
+
     return base64.b64encode(bytes_data)
 
 def api_middleware(app: FastAPI):
@@ -244,7 +255,7 @@ class Api:
         reqDict['image'] = decode_base64_to_image(reqDict['image'])
 
         with self.queue_lock:
-            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
+            result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
 
         return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
 
@@ -260,7 +271,7 @@ class Api:
         reqDict.pop('imageList')
 
         with self.queue_lock:
-            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
+            result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 

modules/errors.py

@@ -24,6 +24,18 @@ See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable
         """)
 
 
+already_displayed = {}
+
+
+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()

modules/extras.py

@@ -1,231 +1,16 @@
-from __future__ import annotations
-import math
 import os
 import re
-import sys
-import traceback
 import shutil
 
-import numpy as np
-from PIL import Image
 
 import torch
 import tqdm
 
-from typing import Callable, List, OrderedDict, Tuple
-from functools import partial
-from dataclasses import dataclass
-
-from modules import processing, shared, images, devices, sd_models, sd_samplers, sd_vae
-from modules.shared import opts
-import modules.gfpgan_model
-from modules.ui import plaintext_to_html
-import modules.codeformer_model
+from modules import shared, images, sd_models, sd_vae
+from modules.ui_common import plaintext_to_html
 import gradio as gr
 import safetensors.torch
 
-class LruCache(OrderedDict):
-    @dataclass(frozen=True)
-    class Key:
-        image_hash: int
-        info_hash: int
-        args_hash: int
-
-    @dataclass
-    class Value:
-        image: Image.Image
-        info: str
-
-    def __init__(self, max_size: int = 5, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._max_size = max_size
-
-    def get(self, key: LruCache.Key) -> LruCache.Value:
-        ret = super().get(key)
-        if ret is not None:
-            self.move_to_end(key)  # Move to end of eviction list
-        return ret
-
-    def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
-        self[key] = value
-        while len(self) > self._max_size:
-            self.popitem(last=False)
-
-
-cached_images: LruCache = LruCache(max_size=5)
-
-
-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):
-    devices.torch_gc()
-
-    shared.state.begin()
-    shared.state.job = 'extras'
-
-    imageArr = []
-    # Also keep track of original file names
-    imageNameArr = []
-    outputs = []
-
-    if extras_mode == 1:
-        #convert file to pillow image
-        for img in image_folder:
-            image = Image.open(img)
-            imageArr.append(image)
-            imageNameArr.append(os.path.splitext(img.orig_name)[0])
-    elif extras_mode == 2:
-        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
-
-        if input_dir == '':
-            return outputs, "Please select an input directory.", ''
-        image_list = shared.listfiles(input_dir)
-        for img in image_list:
-            try:
-                image = Image.open(img)
-            except Exception:
-                continue
-            imageArr.append(image)
-            imageNameArr.append(img)
-    else:
-        imageArr.append(image)
-        imageNameArr.append(None)
-
-    if extras_mode == 2 and output_dir != '':
-        outpath = output_dir
-    else:
-        outpath = opts.outdir_samples or opts.outdir_extras_samples
-
-    # Extra operation definitions
-
-    def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
-        shared.state.job = 'extras-gfpgan'
-        restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
-        res = Image.fromarray(restored_img)
-
-        if gfpgan_visibility < 1.0:
-            res = Image.blend(image, res, gfpgan_visibility)
-
-        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
-        return (res, info)
-
-    def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
-        shared.state.job = 'extras-codeformer'
-        restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
-        res = Image.fromarray(restored_img)
-
-        if codeformer_visibility < 1.0:
-            res = Image.blend(image, res, codeformer_visibility)
-
-        info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
-        return (res, info)
-
-    def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
-        shared.state.job = 'extras-upscale'
-        upscaler = shared.sd_upscalers[scaler_index]
-        res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-        if mode == 1 and crop:
-            cropped = Image.new("RGB", (resize_w, resize_h))
-            cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
-            res = cropped
-        return res
-
-    def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
-        # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
-        nonlocal upscaling_resize
-        if resize_mode == 1:
-            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
-            crop_info = " (crop)" if upscaling_crop else ""
-            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
-        return (image, info)
-
-    @dataclass
-    class UpscaleParams:
-        upscaler_idx: int
-        blend_alpha: float
-
-    def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
-        blended_result: Image.Image = None
-        image_hash: str = hash(np.array(image.getdata()).tobytes())
-        for upscaler in params:
-            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
-                            upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            cache_key = LruCache.Key(image_hash=image_hash,
-                                     info_hash=hash(info),
-                                     args_hash=hash(upscale_args))
-            cached_entry = cached_images.get(cache_key)
-            if cached_entry is None:
-                res = upscale(image, *upscale_args)
-                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
-                cached_images.put(cache_key, LruCache.Value(image=res, info=info))
-            else:
-                res, info = cached_entry.image, cached_entry.info
-
-            if blended_result is None:
-                blended_result = res
-            else:
-                blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
-        return (blended_result, info)
-
-    # Build a list of operations to run
-    facefix_ops: List[Callable] = []
-    facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
-    facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
-
-    upscale_ops: List[Callable] = []
-    upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
-
-    if upscaling_resize != 0:
-        step_params: List[UpscaleParams] = []
-        step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
-        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-            step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
-
-        upscale_ops.append(partial(run_upscalers_blend, step_params))
-
-    extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
-
-    for image, image_name in zip(imageArr, imageNameArr):
-        if image is None:
-            return outputs, "Please select an input image.", ''
-
-        shared.state.textinfo = f'Processing image {image_name}'
-        
-        existing_pnginfo = image.info or {}
-
-        image = image.convert("RGB")
-        info = ""
-        # Run each operation on each image
-        for op in extras_ops:
-            image, info = op(image, info)
-
-        if opts.use_original_name_batch and image_name is not None:
-            basename = os.path.splitext(os.path.basename(image_name))[0]
-        else:
-            basename = ''
-
-        if opts.enable_pnginfo: # append info before save
-            image.info = existing_pnginfo
-            image.info["extras"] = info
-
-        if save_output:
-            # Add upscaler name as a suffix.
-            suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
-            # Add second upscaler if applicable.
-            if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
-                suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
-
-            images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                            no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
-
-        if extras_mode != 2 or show_extras_results :
-            outputs.append(image)
-
-    devices.torch_gc()
-
-    return outputs, plaintext_to_html(info), ''
-
-def clear_cache():
-    cached_images.clear()
-
 
 def run_pnginfo(image):
     if image is None:

modules/interrogate.py

@@ -2,6 +2,7 @@ import os
 import sys
 import traceback
 from collections import namedtuple
+from pathlib import Path
 import re
 
 import torch
@@ -20,19 +21,20 @@ Category = namedtuple("Category", ["name", "topn", "items"])
 
 re_topn = re.compile(r"\.top(\d+)\.")
 
+def category_types():
+    return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]
+
 
 def download_default_clip_interrogate_categories(content_dir):
     print("Downloading CLIP categories...")
 
     tmpdir = content_dir + "_tmp"
+    category_types = ["artists", "flavors", "mediums", "movements"]
+
     try:
         os.makedirs(tmpdir)
-
-        torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/artists.txt", os.path.join(tmpdir, "artists.txt"))
-        torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/flavors.txt", os.path.join(tmpdir, "flavors.top3.txt"))
-        torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/mediums.txt", os.path.join(tmpdir, "mediums.txt"))
-        torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/movements.txt", os.path.join(tmpdir, "movements.txt"))
-
+        for category_type in category_types:
+            torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
         os.rename(tmpdir, content_dir)
 
     except Exception as e:
@@ -51,32 +53,38 @@ class InterrogateModels:
 
     def __init__(self, content_dir):
         self.loaded_categories = None
+        self.skip_categories = []
         self.content_dir = content_dir
         self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
 
     def categories(self):
-        if self.loaded_categories is not None:
-            return self.loaded_categories
-
-        self.loaded_categories = []
-
         if not os.path.exists(self.content_dir):
             download_default_clip_interrogate_categories(self.content_dir)
 
-        if os.path.exists(self.content_dir):
-            for filename in os.listdir(self.content_dir):
-                m = re_topn.search(filename)
-                topn = 1 if m is None else int(m.group(1))
+        if self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories:
+           return self.loaded_categories
 
-                with open(os.path.join(self.content_dir, filename), "r", encoding="utf8") as file:
+        self.loaded_categories = []
+
+        if os.path.exists(self.content_dir):
+            self.skip_categories = shared.opts.interrogate_clip_skip_categories
+            category_types = []
+            for filename in Path(self.content_dir).glob('*.txt'):
+                category_types.append(filename.stem)
+                if filename.stem in self.skip_categories:
+                    continue
+                m = re_topn.search(filename.stem)
+                topn = 1 if m is None else int(m.group(1))
+                with open(filename, "r", encoding="utf8") as file:
                     lines = [x.strip() for x in file.readlines()]
 
-                self.loaded_categories.append(Category(name=filename, topn=topn, items=lines))
+                self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines))
 
         return self.loaded_categories
 
     def load_blip_model(self):
-        import models.blip
+        with paths.Prioritize("BLIP"):
+            import models.blip
 
         files = modelloader.load_models(
             model_path=os.path.join(paths.models_path, "BLIP"),
@@ -139,6 +147,8 @@ class InterrogateModels:
     def rank(self, image_features, text_array, top_count=1):
         import clip
 
+        devices.torch_gc()
+
         if shared.opts.interrogate_clip_dict_limit != 0:
             text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
 

modules/paths.py

@@ -38,3 +38,17 @@ for d, must_exist, what, options in path_dirs:
         else:
             sys.path.append(d)
         paths[what] = d
+
+
+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

modules/postprocessing.py

@@ -0,0 +1,103 @@
+import os
+
+from PIL import Image
+
+from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
+from modules.shared import opts
+
+
+def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
+    devices.torch_gc()
+
+    shared.state.begin()
+    shared.state.job = 'extras'
+
+    image_data = []
+    image_names = []
+    outputs = []
+
+    if extras_mode == 1:
+        for img in image_folder:
+            image = Image.open(img)
+            image_data.append(image)
+            image_names.append(os.path.splitext(img.orig_name)[0])
+    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 = shared.listfiles(input_dir)
+        for filename in image_list:
+            try:
+                image = Image.open(filename)
+            except Exception:
+                continue
+            image_data.append(image)
+            image_names.append(filename)
+    else:
+        assert image, 'image not selected'
+
+        image_data.append(image)
+        image_names.append(None)
+
+    if extras_mode == 2 and output_dir != '':
+        outpath = output_dir
+    else:
+        outpath = opts.outdir_samples or opts.outdir_extras_samples
+
+    infotext = ''
+
+    for image, name in zip(image_data, image_names):
+        shared.state.textinfo = name
+
+        existing_pnginfo = image.info or {}
+
+        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 = ''
+
+        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])
+
+        if opts.enable_pnginfo:
+            pp.image.info = existing_pnginfo
+            pp.image.info["postprocessing"] = infotext
+
+        if save_output:
+            images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
+
+        if extras_mode != 2 or show_extras_results:
+            outputs.append(pp.image)
+
+    devices.torch_gc()
+
+    return outputs, ui_common.plaintext_to_html(infotext), ''
+
+
+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):
+    """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)

modules/scripts.py

@@ -7,7 +7,7 @@ from collections import namedtuple
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
-from modules import shared, paths, script_callbacks, extensions, script_loading
+from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
 
 AlwaysVisible = object()
 
@@ -150,8 +150,10 @@ def basedir():
     return current_basedir
 
 
-scripts_data = []
 ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
+
+scripts_data = []
+postprocessing_scripts_data = []
 ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
 
 
@@ -190,23 +192,31 @@ def list_files_with_name(filename):
 def load_scripts():
     global current_basedir
     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):
+        for key, script_class in module.__dict__.items():
+            if type(script_class) != type:
+                continue
+
+            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 sorted(scripts_list):
         try:
             if scriptfile.basedir != paths.script_path:
                 sys.path = [scriptfile.basedir] + sys.path
             current_basedir = scriptfile.basedir
 
-            module = script_loading.load_module(scriptfile.path)
-
-            for key, script_class in module.__dict__.items():
-                if type(script_class) == type and issubclass(script_class, Script):
-                    scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
+            script_module = script_loading.load_module(scriptfile.path)
+            register_scripts_from_module(script_module)
 
         except Exception:
             print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
@@ -413,6 +423,7 @@ class ScriptRunner:
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
+scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
 scripts_current: ScriptRunner = None
 
 
@@ -423,12 +434,13 @@ def reload_script_body_only():
 
 
 def reload_scripts():
-    global scripts_txt2img, scripts_img2img
+    global scripts_txt2img, scripts_img2img, scripts_postproc
 
     load_scripts()
 
     scripts_txt2img = ScriptRunner()
     scripts_img2img = ScriptRunner()
+    scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
 
 
 def IOComponent_init(self, *args, **kwargs):

modules/scripts_postprocessing.py

@@ -0,0 +1,147 @@
+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
+
+    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
+
+    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
+
+            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 = [x.lower().strip() for x in shared.opts.postprocessing_scipts_order.split(",")]
+
+        def script_score(name):
+            name = name.lower()
+            for i, possible_match in enumerate(scripts_order):
+                if possible_match in 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.Box() 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
+
+            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()

modules/sd_hijack_optimizations.py

@@ -9,7 +9,7 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
-from modules import shared
+from modules import shared, errors
 from modules.hypernetworks import hypernetwork
 
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -279,6 +279,21 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
     )
 
 
+def get_xformers_flash_attention_op(q, k, v):
+    if not shared.cmd_opts.xformers_flash_attention:
+        return None
+
+    try:
+        flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
+        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):
     h = self.heads
     q_in = self.to_q(x)
@@ -290,7 +305,8 @@ def xformers_attention_forward(self, x, context=None, mask=None):
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
+
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
 
     out = rearrange(out, 'b n h d -> b n (h d)', h=h)
     return self.to_out(out)
@@ -365,7 +381,7 @@ def xformers_attnblock_forward(self, x):
         q = q.contiguous()
         k = k.contiguous()
         v = v.contiguous()
-        out = xformers.ops.memory_efficient_attention(q, k, v)
+        out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
         out = rearrange(out, 'b (h w) c -> b c h w', h=h)
         out = self.proj_out(out)
         return x + out

modules/shared.py

@@ -57,6 +57,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
 parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
+parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
 parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
 parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
@@ -424,6 +425,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
     "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
+    "interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
     "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
     "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
@@ -431,6 +433,10 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
 }))
 
+options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+    "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, { "choices": ["cards", "thumbs"] }),
+}))
+
 options_templates.update(options_section(('ui', "User interface"), {
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
@@ -475,6 +481,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
 }))
 
+options_templates.update(options_section(('postprocessing', "Postprocessing"), {
+    'postprocessing_scipts_order': OptionInfo("upscale, gfpgan, codeformer", "Postprocessing operation order"),
+    'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+}))
+
 options_templates.update(options_section((None, "Hidden options"), {
     "disabled_extensions": OptionInfo([], "Disable those extensions"),
     "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),

modules/ui.py

@@ -5,7 +5,6 @@ import mimetypes
 import os
 import platform
 import random
-import subprocess as sp
 import sys
 import tempfile
 import time
@@ -20,7 +19,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
 from modules.paths import script_path
 
@@ -41,6 +40,7 @@ from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.textual_inversion import textual_inversion
 import modules.hypernetworks.ui
 from modules.generation_parameters_copypaste import image_from_url_text
+import modules.extras
 
 warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
 
@@ -86,7 +86,6 @@ css_hide_progressbar = """
 random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
 reuse_symbol = '\u267b\ufe0f'  # ♻️
 paste_symbol = '\u2199\ufe0f'  # ↙
-folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
@@ -95,78 +94,14 @@ extra_networks_symbol = '\U0001F3B4'  # 🎴
 
 
 def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
+    return ui_common.plaintext_to_html(text)
+
 
 def send_gradio_gallery_to_image(x):
     if len(x) == 0:
         return None
     return image_from_url_text(x[0])
 
-def save_files(js_data, images, do_make_zip, index):
-    import csv
-    filenames = []
-    fullfns = []
-
-    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
-    class MyObject:
-        def __init__(self, d=None):
-            if d is not None:
-                for key, value in d.items():
-                    setattr(self, key, value)
-
-    data = json.loads(js_data)
-
-    p = MyObject(data)
-    path = opts.outdir_save
-    save_to_dirs = opts.use_save_to_dirs_for_ui
-    extension: str = opts.samples_format
-    start_index = 0
-
-    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
-        images = [images[index]]
-        start_index = index
-
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
-    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
-        at_start = file.tell() == 0
-        writer = csv.writer(file)
-        if at_start:
-            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
-        for image_index, filedata in enumerate(images, start_index):
-            image = image_from_url_text(filedata)
-
-            is_grid = image_index < p.index_of_first_image
-            i = 0 if is_grid else (image_index - p.index_of_first_image)
-
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
-
-            filename = os.path.relpath(fullfn, path)
-            filenames.append(filename)
-            fullfns.append(fullfn)
-            if txt_fullfn:
-                filenames.append(os.path.basename(txt_fullfn))
-                fullfns.append(txt_fullfn)
-
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-
-    # Make Zip
-    if do_make_zip:
-        zip_filepath = os.path.join(path, "images.zip")
-
-        from zipfile import ZipFile
-        with ZipFile(zip_filepath, "w") as zip_file:
-            for i in range(len(fullfns)):
-                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]}")
-
-
 def visit(x, func, path=""):
     if hasattr(x, 'children'):
         for c in x.children:
@@ -444,19 +379,6 @@ def apply_setting(key, value):
     opts.save(shared.config_filename)
     return getattr(opts, key)
 
-
-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, gr.update()
-        return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
-    except Exception:
-        pass
-    # if the json parse or anything else fails, just return the old html_info
-    return html_info, gr.update()
-
-
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
     def refresh():
         refresh_method()
@@ -477,107 +399,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
 
 
 def create_output_panel(tabname, outdir):
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            elif "microsoft-standard-WSL2" in platform.uname().release:
-                sp.Popen(["wsl-open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
-    with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
-            with gr.Group(elem_id=f"{tabname}_gallery_container"):
-                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-
-            generation_info = None
-            with gr.Column():
-                with gr.Row(elem_id=f"image_buttons_{tabname}"):
-                    open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
-
-                    if tabname != "extras":
-                        save = gr.Button('Save', elem_id=f'save_{tabname}')
-                        save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
-
-                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
-
-                open_folder_button.click(
-                    fn=lambda: open_folder(opts.outdir_samples or outdir),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                if tabname != "extras":
-                    with gr.Row():
-                        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}')
-                        html_log = gr.HTML(elem_id=f'html_log_{tabname}')
-
-                        generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
-                        if tabname == 'txt2img' or tabname == 'img2img':
-                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
-                            generation_info_button.click(
-                                fn=update_generation_info,
-                                _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
-                                inputs=[generation_info, html_info, html_info],
-                                outputs=[html_info, html_info],
-                            )
-
-                        save.click(
-                            fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
-                            inputs=[
-                                generation_info,
-                                result_gallery,
-                                html_info,
-                                html_info,
-                            ],
-                            outputs=[
-                                download_files,
-                                html_log,
-                            ],
-                            show_progress=False,
-                        )
-
-                        save_zip.click(
-                            fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
-                            inputs=[
-                                generation_info,
-                                result_gallery,
-                                html_info,
-                                html_info,
-                            ],
-                            outputs=[
-                                download_files,
-                                html_log,
-                            ]
-                        )
-
-                else:
-                    html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
-                    html_info = gr.HTML(elem_id=f'html_info_{tabname}')
-                    html_log = gr.HTML(elem_id=f'html_log_{tabname}')
-
-                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
+    return ui_common.create_output_panel(tabname, outdir)
 
 
 def create_sampler_and_steps_selection(choices, tabname):
@@ -919,7 +741,7 @@ def create_ui():
                         seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
 
                     elif category == "checkboxes":
-                        with FormRow(elem_id="img2img_checkboxes"):
+                        with FormRow(elem_id="img2img_checkboxes", variant="compact"):
                             restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
                             tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
 
@@ -1106,86 +928,7 @@ def create_ui():
     modules.scripts.scripts_current = None
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='compact'):
-                with gr.Tabs(elem_id="mode_extras"):
-                    with gr.TabItem('Single Image', elem_id="extras_single_tab"):
-                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
-
-                    with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab"):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
-
-                    with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab"):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
-                        show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
-
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
-                with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by', elem_id="extras_scale_by_tab"):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
-                    with gr.TabItem('Scale to', elem_id="extras_scale_to_tab"):
-                        with gr.Group():
-                            with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
-
-                with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-
-                with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1, elem_id="extras_upscaler_2_visibility")
-
-                with gr.Group():
-                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan, elem_id="extras_gfpgan_visibility")
-
-                with gr.Group():
-                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_visibility")
-                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_weight")
-
-                with gr.Group():
-                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False, elem_id="extras_upscale_before_face_fix")
-
-            result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
-
-        submit.click(
-            fn=wrap_gradio_gpu_call(modules.extras.run_extras, extra_outputs=[None, '']),
-            _js="get_extras_tab_index",
-            inputs=[
-                dummy_component,
-                dummy_component,
-                extras_image,
-                image_batch,
-                extras_batch_input_dir,
-                extras_batch_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_before_face_fix,
-            ],
-            outputs=[
-                result_images,
-                html_info_x,
-                html_info,
-            ]
-        )
-        parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
-        extras_image.change(
-            fn=modules.extras.clear_cache,
-            inputs=[], outputs=[]
-        )
+        ui_postprocessing.create_ui()
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
         with gr.Row().style(equal_height=False):
@@ -1942,11 +1685,11 @@ def reload_javascript():
     if cmd_opts.theme is not None:
         inline += f"set_theme('{cmd_opts.theme}');"
 
-    head += f'<script type="text/javascript">{inline}</script>\n'
-
     for script in modules.scripts.list_scripts("javascript", ".js"):
         head += f'<script type="text/javascript" src="file={script.path}"></script>\n'
 
+    head += f'<script type="text/javascript">{inline}</script>\n'
+
     def template_response(*args, **kwargs):
         res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
         res.body = res.body.replace(b'</head>', f'{head}</head>'.encode("utf8"))

modules/ui_common.py

@@ -0,0 +1,202 @@
+import json
+import html
+import os
+import platform
+import sys
+
+import gradio as gr
+import subprocess as sp
+
+from modules import call_queue, shared
+from modules.generation_parameters_copypaste import image_from_url_text
+import modules.images
+
+folder_symbol = '\U0001f4c2'  # 📂
+
+
+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, gr.update()
+        return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
+    except Exception:
+        pass
+    # if the json parse or anything else fails, just return the old html_info
+    return html_info, gr.update()
+
+
+def plaintext_to_html(text):
+    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
+    return text
+
+
+def save_files(js_data, images, do_make_zip, index):
+    import csv
+    filenames = []
+    fullfns = []
+
+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+    class MyObject:
+        def __init__(self, d=None):
+            if d is not None:
+                for key, value in d.items():
+                    setattr(self, key, value)
+
+    data = json.loads(js_data)
+
+    p = MyObject(data)
+    path = shared.opts.outdir_save
+    save_to_dirs = shared.opts.use_save_to_dirs_for_ui
+    extension: str = shared.opts.samples_format
+    start_index = 0
+
+    if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+
+        images = [images[index]]
+        start_index = index
+
+    os.makedirs(shared.opts.outdir_save, exist_ok=True)
+
+    with open(os.path.join(shared.opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+        at_start = file.tell() == 0
+        writer = csv.writer(file)
+        if at_start:
+            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+
+        for image_index, filedata in enumerate(images, start_index):
+            image = image_from_url_text(filedata)
+
+            is_grid = image_index < p.index_of_first_image
+            i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+            fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+            filename = os.path.relpath(fullfn, path)
+            filenames.append(filename)
+            fullfns.append(fullfn)
+            if txt_fullfn:
+                filenames.append(os.path.basename(txt_fullfn))
+                fullfns.append(txt_fullfn)
+
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+
+    # Make Zip
+    if do_make_zip:
+        zip_filepath = os.path.join(path, "images.zip")
+
+        from zipfile import ZipFile
+        with ZipFile(zip_filepath, "w") as zip_file:
+            for i in range(len(fullfns)):
+                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]}")
+
+
+def create_output_panel(tabname, outdir):
+    from modules import shared
+    import modules.generation_parameters_copypaste as parameters_copypaste
+
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            elif "microsoft-standard-WSL2" in platform.uname().release:
+                sp.Popen(["wsl-open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+
+    with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
+        with gr.Group(elem_id=f"{tabname}_gallery_container"):
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+        generation_info = None
+        with gr.Column():
+            with gr.Row(elem_id=f"image_buttons_{tabname}"):
+                open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
+
+                if tabname != "extras":
+                    save = gr.Button('Save', elem_id=f'save_{tabname}')
+                    save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
+
+                buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+
+            open_folder_button.click(
+                fn=lambda: open_folder(shared.opts.outdir_samples or outdir),
+                inputs=[],
+                outputs=[],
+            )
+
+            if tabname != "extras":
+                with gr.Row():
+                    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}')
+                    html_log = gr.HTML(elem_id=f'html_log_{tabname}')
+
+                    generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
+                    if tabname == 'txt2img' or tabname == 'img2img':
+                        generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+                        generation_info_button.click(
+                            fn=update_generation_info,
+                            _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
+                            inputs=[generation_info, html_info, html_info],
+                            outputs=[html_info, html_info],
+                        )
+
+                    save.click(
+                        fn=call_queue.wrap_gradio_call(save_files),
+                        _js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
+                        inputs=[
+                            generation_info,
+                            result_gallery,
+                            html_info,
+                            html_info,
+                        ],
+                        outputs=[
+                            download_files,
+                            html_log,
+                        ],
+                        show_progress=False,
+                    )
+
+                    save_zip.click(
+                        fn=call_queue.wrap_gradio_call(save_files),
+                        _js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
+                        inputs=[
+                            generation_info,
+                            result_gallery,
+                            html_info,
+                            html_info,
+                        ],
+                        outputs=[
+                            download_files,
+                            html_log,
+                        ]
+                    )
+
+            else:
+                html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
+                html_info = gr.HTML(elem_id=f'html_info_{tabname}')
+                html_log = gr.HTML(elem_id=f'html_log_{tabname}')
+
+            parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+            return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log

modules/ui_components.py

@@ -47,3 +47,4 @@ class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
 
     def get_block_name(self):
         return "colorpicker"
+

modules/ui_extra_networks.py

@@ -3,6 +3,7 @@ import os.path
 from modules import shared
 import gradio as gr
 import json
+import html
 
 from modules.generation_parameters_copypaste import image_from_url_text
 
@@ -26,6 +27,7 @@ class ExtraNetworksPage:
         pass
 
     def create_html(self, tabname):
+        view = shared.opts.extra_networks_default_view
         items_html = ''
 
         for item in self.list_items():
@@ -36,7 +38,7 @@ class ExtraNetworksPage:
             items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
 
         res = f"""
-<div id='{tabname}_{self.name}_cards' class='extra-network-cards'>
+<div id='{tabname}_{self.name}_cards' class='extra-network-{view}'>
 {items_html}
 </div>
 """
@@ -53,12 +55,13 @@ class ExtraNetworksPage:
         preview = item.get("preview", None)
 
         args = {
-            "preview_html": "style='background-image: url(" + json.dumps(preview) + ")'" if preview else '',
+            "preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
             "prompt": item["prompt"],
             "tabname": json.dumps(tabname),
             "local_preview": json.dumps(item["local_preview"]),
             "name": item["name"],
-            "allow_negative_prompt": "true" if self.allow_negative_prompt else "false",
+            "card_clicked": '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"',
+            "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
         }
 
         return self.card_page.format(**args)

modules/ui_postprocessing.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from modules import scripts_postprocessing, scripts, shared, gfpgan_model, codeformer_model, ui_common, postprocessing, call_queue
+import modules.generation_parameters_copypaste as parameters_copypaste
+
+
+def create_ui():
+    tab_index = gr.State(value=0)
+
+    with gr.Row().style(equal_height=False, variant='compact'):
+        with gr.Column(variant='compact'):
+            with gr.Tabs(elem_id="mode_extras"):
+                with gr.TabItem('Single Image', elem_id="extras_single_tab") as tab_single:
+                    extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
+
+                with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
+                    image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
+
+                with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
+                    extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
+                    extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
+                    show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
+
+            submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+            script_inputs = scripts.scripts_postproc.setup_ui()
+
+        with gr.Column():
+            result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
+
+    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])
+
+    submit.click(
+        fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing, extra_outputs=[None, '']),
+        inputs=[
+            tab_index,
+            extras_image,
+            image_batch,
+            extras_batch_input_dir,
+            extras_batch_output_dir,
+            show_extras_results,
+            *script_inputs
+        ],
+        outputs=[
+            result_images,
+            html_info_x,
+            html_info,
+        ]
+    )
+
+    parameters_copypaste.add_paste_fields("extras", extras_image, None)
+
+    extras_image.change(
+        fn=scripts.scripts_postproc.image_changed,
+        inputs=[], outputs=[]
+    )

scripts/postprocessing_codeformer.py

@@ -0,0 +1,36 @@
+from PIL import Image
+import numpy as np
+
+from modules import scripts_postprocessing, codeformer_model
+import gradio as gr
+
+from modules.ui_components import FormRow
+
+
+class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing):
+    name = "CodeFormer"
+    order = 3000
+
+    def ui(self):
+        with FormRow():
+            codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, elem_id="extras_codeformer_visibility")
+            codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
+
+        return {
+            "codeformer_visibility": codeformer_visibility,
+            "codeformer_weight": codeformer_weight,
+        }
+
+    def process(self, pp: scripts_postprocessing.PostprocessedImage, codeformer_visibility, codeformer_weight):
+        if codeformer_visibility == 0:
+            return
+
+        restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)
+        res = Image.fromarray(restored_img)
+
+        if codeformer_visibility < 1.0:
+            res = Image.blend(pp.image, res, codeformer_visibility)
+
+        pp.image = res
+        pp.info["CodeFormer visibility"] = round(codeformer_visibility, 3)
+        pp.info["CodeFormer weight"] = round(codeformer_weight, 3)

scripts/postprocessing_gfpgan.py

@@ -0,0 +1,33 @@
+from PIL import Image
+import numpy as np
+
+from modules import scripts_postprocessing, gfpgan_model
+import gradio as gr
+
+from modules.ui_components import FormRow
+
+
+class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
+    name = "GFPGAN"
+    order = 2000
+
+    def ui(self):
+        with FormRow():
+            gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, elem_id="extras_gfpgan_visibility")
+
+        return {
+            "gfpgan_visibility": gfpgan_visibility,
+        }
+
+    def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility):
+        if gfpgan_visibility == 0:
+            return
+
+        restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
+        res = Image.fromarray(restored_img)
+
+        if gfpgan_visibility < 1.0:
+            res = Image.blend(pp.image, res, gfpgan_visibility)
+
+        pp.image = res
+        pp.info["GFPGAN visibility"] = round(gfpgan_visibility, 3)

scripts/postprocessing_upscale.py

@@ -0,0 +1,106 @@
+from PIL import Image
+import numpy as np
+
+from modules import scripts_postprocessing, shared
+import gradio as gr
+
+from modules.ui_components import FormRow
+
+
+upscale_cache = {}
+
+
+class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
+    name = "Upscale"
+    order = 1000
+
+    def ui(self):
+        selected_tab = gr.State(value=0)
+
+        with gr.Tabs(elem_id="extras_resize_mode"):
+            with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
+                upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+
+            with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
+                with FormRow():
+                    upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
+                    upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
+                    upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+
+        with FormRow():
+            extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+
+        with FormRow():
+            extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+            extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+
+        tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
+        tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
+
+        return {
+            "upscale_mode": selected_tab,
+            "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,
+        }
+
+    def upscale(self, image, info, upscaler, upscale_mode, upscale_by,  upscale_to_width, upscale_to_height, upscale_crop):
+        if upscale_mode == 1:
+            upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
+            info["Postprocess upscale to"] = f"{upscale_to_width}x{upscale_to_height}"
+        else:
+            info["Postprocess upscale by"] = upscale_by
+
+        cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by,  upscale_to_width, upscale_to_height, upscale_crop)
+        cached_image = upscale_cache.pop(cache_key, None)
+
+        if cached_image is not None:
+            image = cached_image
+        else:
+            image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path)
+
+        upscale_cache[cache_key] = image
+        if len(upscale_cache) > shared.opts.upscaling_max_images_in_cache:
+            upscale_cache.pop(next(iter(upscale_cache), None), None)
+
+        if upscale_mode == 1 and upscale_crop:
+            cropped = Image.new("RGB", (upscale_to_width, upscale_to_height))
+            cropped.paste(image, box=(upscale_to_width // 2 - image.width // 2, upscale_to_height // 2 - image.height // 2))
+            image = cropped
+            info["Postprocess crop to"] = f"{image.width}x{image.height}"
+
+        return image
+
+    def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
+        if upscaler_1_name == "None":
+            upscaler_1_name = None
+
+        upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_1_name]), None)
+        assert upscaler1 or (upscaler_1_name is None), f'could not find upscaler named {upscaler_1_name}'
+
+        if not upscaler1:
+            return
+
+        if upscaler_2_name == "None":
+            upscaler_2_name = None
+
+        upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != "None"]), None)
+        assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'
+
+        upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
+        pp.info[f"Postprocess upscaler"] = upscaler1.name
+
+        if upscaler2 and upscaler_2_visibility > 0:
+            second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
+            upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)
+
+            pp.info[f"Postprocess upscaler 2"] = upscaler2.name
+
+        pp.image = upscaled_image
+
+    def image_changed(self):
+        upscale_cache.clear()

scripts/xy_grid.py

@@ -184,6 +184,7 @@ axis_options = [
     AxisOption("Var. seed", int, apply_field("subseed")),
     AxisOption("Var. strength", float, apply_field("subseed_strength")),
     AxisOption("Steps", int, apply_field("steps")),
+    AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
     AxisOption("CFG Scale", float, apply_field("cfg_scale")),
     AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
     AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
@@ -307,7 +308,7 @@ class Script(scripts.Script):
                     y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
                     fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xy_grid_fill_y_tool_button", visible=False)
 
-        with gr.Row(variant="compact"):
+        with gr.Row(variant="compact", elem_id="axis_options"):
             draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
             include_lone_images = gr.Checkbox(label='Include Separate Images', value=False, elem_id=self.elem_id("include_lone_images"))
             no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
@@ -426,10 +427,21 @@ class Script(scripts.Script):
             total_steps = p.steps * len(xs) * len(ys)
 
         if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
-            total_steps *= 2
+            if x_opt.label == "Hires steps":
+                total_steps += sum(xs) * len(ys)
+            elif y_opt.label == "Hires steps":
+                total_steps += sum(ys) * len(xs)
+            elif p.hr_second_pass_steps:
+                total_steps += p.hr_second_pass_steps * len(xs) * len(ys)
+            else:
+                total_steps *= 2
 
-        print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
-        shared.total_tqdm.updateTotal(total_steps * p.n_iter)
+        total_steps *= p.n_iter
+
+        image_cell_count = p.n_iter * p.batch_size
+        cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else ""
+        print(f"X/Y plot will create {len(xs) * len(ys) * image_cell_count} images on a {len(xs)}x{len(ys)} grid{cell_console_text}. (Total steps to process: {total_steps})")
+        shared.total_tqdm.updateTotal(total_steps)
 
         grid_infotext = [None]
 

style.css

@@ -589,7 +589,7 @@ canvas[key="mask"] {
 
 /* Extensions */
 
-#tab_extensions table``{
+#tab_extensions table{
     border-collapse: collapse;
 }
 
@@ -718,6 +718,14 @@ footer {
     margin-left: -0.8em;
 }
 
+#img2img_copy_to_img2img, #img2img_copy_to_sketch, #img2img_copy_to_inpaint, #img2img_copy_to_inpaint_sketch{
+    margin-left: 0em;
+}
+
+#axis_options {
+    margin-left: 0em;
+}
+
 .inactive{
     opacity: 0.5;
 }
@@ -784,21 +792,78 @@ footer {
     display: inline-block;
     max-width: 16em;
     margin: 0.3em;
+    align-self: center;
 }
 
-.extra-network-cards .nocards{
+#txt2img_extra_view, #img2img_extra_view {
+    width: auto;
+}
+
+.extra-network-cards .nocards, .extra-network-thumbs .nocards{
     margin: 1.25em 0.5em 0.5em 0.5em;
 }
 
-.extra-network-cards .nocards h1{
+.extra-network-cards .nocards h1, .extra-network-thumbs .nocards h1{
     font-size: 1.5em;
     margin-bottom: 1em;
 }
 
-.extra-network-cards .nocards li{
+.extra-network-cards .nocards li, .extra-network-thumbs .nocards li{
     margin-left: 0.5em;
 }
 
+.extra-network-thumbs {
+    display: flex;
+    flex-flow: row wrap;
+    gap: 10px;
+}
+
+.extra-network-thumbs .card {
+    height: 6em;
+    width: 6em;
+    cursor: pointer;
+    background-image: url('./file=html/card-no-preview.png');
+    background-size: cover;
+    background-position: center center;
+    position: relative;
+}
+
+.extra-network-thumbs .card:hover .additional a {
+    display: block;
+}
+
+.extra-network-thumbs .actions .additional a {
+    background-image: url('./file=html/image-update.svg');
+    background-repeat: no-repeat;
+    background-size: cover;
+    background-position: center center;
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 24px;
+    height: 24px;
+    display: none;
+    font-size: 0;
+    text-align: -9999;
+}
+
+.extra-network-thumbs .actions .name {
+    position: absolute;
+    bottom: 0;
+    font-size: 10px;
+    padding: 3px;
+    width: 100%;
+    overflow: hidden;
+    white-space: nowrap;
+    text-overflow: ellipsis;
+    background: rgba(0,0,0,.5);
+}
+
+.extra-network-thumbs .card:hover .actions .name {
+    white-space: normal;
+    word-break: break-all;
+}
+
 .extra-network-cards .card{
     display: inline-block;
     margin: 0.5em;

webui.py

@@ -1,6 +1,5 @@
 import os
 import sys
-import threading
 import time
 import importlib
 import signal
@@ -8,6 +7,10 @@ import re
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
+from packaging import version
+
+import logging
+logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
 
 from modules import import_hook, errors, extra_networks
 from modules import extra_networks_hypernet, ui_extra_networks_hypernets, ui_extra_networks_textual_inversion
@@ -22,7 +25,6 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__:
 
 from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks
 import modules.codeformer_model as codeformer
-import modules.extras
 import modules.face_restoration
 import modules.gfpgan_model as gfpgan
 import modules.img2img
@@ -50,7 +52,32 @@ else:
     server_name = "0.0.0.0" if cmd_opts.listen else None
 
 
+def check_versions():
+    expected_torch_version = "1.13.1"
+
+    if version.parse(torch.__version__) < version.parse(expected_torch_version):
+        errors.print_error_explanation(f"""
+You are running torch {torch.__version__}.
+The program is tested to work with torch {expected_torch_version}.
+To reinstall the desired version, run with commandline flag --reinstall-torch.
+Beware that this will cause a lot of large files to be downloaded.
+        """.strip())
+
+    expected_xformers_version = "0.0.16rc425"
+    if shared.xformers_available:
+        import xformers
+
+        if version.parse(xformers.__version__) < version.parse(expected_xformers_version):
+            errors.print_error_explanation(f"""
+You are running xformers {xformers.__version__}.
+The program is tested to work with xformers {expected_xformers_version}.
+To reinstall the desired version, run with commandline flag --reinstall-xformers.
+            """.strip())
+
+
 def initialize():
+    check_versions()
+
     extensions.list_extensions()
     localization.list_localizations(cmd_opts.localizations_dir)