Merge branch 'dev' into Branch_AddNewFilenameGen

.github/workflows/run_tests.yaml

@@ -18,7 +18,7 @@ jobs:
           cache-dependency-path: |
             **/requirements*txt
       - name: Run tests
-        run: python launch.py --tests --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
+        run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
       - name: Upload main app stdout-stderr
         uses: actions/upload-artifact@v3
         if: always()

.gitignore

@@ -32,4 +32,4 @@ notification.mp3
 /extensions
 /test/stdout.txt
 /test/stderr.txt
-/cache.json
+/cache.json*

README.md

@@ -13,9 +13,9 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Prompt Matrix
 - Stable Diffusion Upscale
 - Attention, specify parts of text that the model should pay more attention to
-    - a man in a ((tuxedo)) - will pay more attention to tuxedo
-    - a man in a (tuxedo:1.21) - alternative syntax
-    - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
+    - a man in a `((tuxedo))` - will pay more attention to tuxedo
+    - a man in a `(tuxedo:1.21)` - alternative syntax
+    - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
 - Loopback, run img2img processing multiple times
 - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
 - Textual Inversion
@@ -28,7 +28,7 @@ A browser interface based on Gradio library for Stable Diffusion.
     - CodeFormer, face restoration tool as an alternative to GFPGAN
     - RealESRGAN, neural network upscaler
     - ESRGAN, neural network upscaler with a lot of third party models
-    - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
+    - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
     - LDSR, Latent diffusion super resolution upscaling
 - Resizing aspect ratio options
 - Sampling method selection
@@ -46,7 +46,7 @@ A browser interface based on Gradio library for Stable Diffusion.
      - drag and drop an image/text-parameters to promptbox
 - Read Generation Parameters Button, loads parameters in promptbox to UI
 - Settings page
-- Running arbitrary python code from UI (must run with --allow-code to enable)
+- Running arbitrary python code from UI (must run with `--allow-code` to enable)
 - Mouseover hints for most UI elements
 - Possible to change defaults/mix/max/step values for UI elements via text config
 - Tiling support, a checkbox to create images that can be tiled like textures
@@ -69,7 +69,7 @@ A browser interface based on Gradio library for Stable Diffusion.
      - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts
-- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
+- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
 - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
 - Generate forever option
 - Training tab
@@ -78,11 +78,11 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Clip skip
 - Hypernetworks
 - Loras (same as Hypernetworks but more pretty)
-- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt. 
+- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt 
 - Can select to load a different VAE from settings screen
 - Estimated completion time in progress bar
 - API
-- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
 - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
 - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
 - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
@@ -91,7 +91,6 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
 - Now with a license!
 - Reorder elements in the UI from settings screen
-- 
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@@ -101,7 +100,7 @@ Alternatively, use online services (like Google Colab):
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
 
 ### Automatic Installation on Windows
-1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
+1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH".
 2. Install [git](https://git-scm.com/download/win).
 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
@@ -121,6 +120,7 @@ sudo pacman -S wget git python3
 bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
 ```
 3. Run `webui.sh`.
+4. Check `webui-user.sh` for options.
 ### Installation on Apple Silicon
 
 Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).

environment-wsl2.yaml

@@ -4,8 +4,8 @@ channels:
   - defaults
 dependencies:
   - python=3.10
-  - pip=22.2.2
-  - cudatoolkit=11.3
-  - pytorch=1.12.1
-  - torchvision=0.13.1
-  - numpy=1.23.1
+  - pip=23.0
+  - cudatoolkit=11.8
+  - pytorch=2.0
+  - torchvision=0.15
+  - numpy=1.23
\ No newline at end of file

extensions-builtin/Lora/extra_networks_lora.py

@@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):
         additional = shared.opts.sd_lora
 
-        if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
+        if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
             p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 

extensions-builtin/Lora/scripts/lora_script.py

@@ -9,7 +9,11 @@ from modules import script_callbacks, ui_extra_networks, extra_networks, shared
 
 def unload():
     torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
+    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
     torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
+    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
+    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
 
 
 def before_ui():
@@ -20,11 +24,27 @@ def before_ui():
 if not hasattr(torch.nn, 'Linear_forward_before_lora'):
     torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
 
+if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
+    torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
+
 if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
     torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
 
+if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
+    torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+
+if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
+    torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
+
+if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
+    torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
+
 torch.nn.Linear.forward = lora.lora_Linear_forward
+torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
 torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
+torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
 
 script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
@@ -32,7 +52,5 @@ script_callbacks.on_before_ui(before_ui)
 
 
 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
-    "lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),
-
+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
 }))

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -5,11 +5,15 @@ import traceback
 import PIL.Image
 import numpy as np
 import torch
+from tqdm import tqdm
+
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
 from modules import devices, modelloader
 from scunet_model_arch import SCUNet as net
+from modules.shared import opts
+from modules import images
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
             scalers.append(scaler_data2)
         self.scalers = scalers
 
-    def do_upscale(self, img: PIL.Image, selected_file):
+    @staticmethod
+    @torch.no_grad()
+    def tiled_inference(img, model):
+        # test the image tile by tile
+        h, w = img.shape[2:]
+        tile = opts.SCUNET_tile
+        tile_overlap = opts.SCUNET_tile_overlap
+        if tile == 0:
+            return model(img)
+
+        device = devices.get_device_for('scunet')
+        assert tile % 8 == 0, "tile size should be a multiple of window_size"
+        sf = 1
+
+        stride = tile - tile_overlap
+        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
+        W = torch.zeros_like(E, dtype=devices.dtype, device=device)
+
+        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
+            for h_idx in h_idx_list:
+
+                for w_idx in w_idx_list:
+
+                    in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+
+                    out_patch = model(in_patch)
+                    out_patch_mask = torch.ones_like(out_patch)
+
+                    E[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch)
+                    W[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch_mask)
+                    pbar.update(1)
+        output = E.div_(W)
+
+        return output
+
+    def do_upscale(self, img: PIL.Image.Image, selected_file):
+
         torch.cuda.empty_cache()
 
         model = self.load_model(selected_file)
         if model is None:
+            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
             return img
 
         device = devices.get_device_for('scunet')
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        img = np.moveaxis(img, 2, 0) / 255
-        img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
-
-        with torch.no_grad():
-            output = model(img)
-        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output = 255. * np.moveaxis(output, 0, 2)
-        output = output.astype(np.uint8)
-        output = output[:, :, ::-1]
+        tile = opts.SCUNET_tile
+        h, w = img.height, img.width
+        np_img = np.array(img)
+        np_img = np_img[:, :, ::-1]  # RGB to BGR
+        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW
+        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore
+
+        if tile > h or tile > w:
+            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
+            _img[:, :, :h, :w] = torch_img # pad image
+            torch_img = _img
+
+        torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
+        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
+        del torch_img, torch_output
         torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
+
+        output = np_output.transpose((1, 2, 0))  # CHW to HWC
+        output = output[:, :, ::-1]  # BGR to RGB
+        return PIL.Image.fromarray((output * 255).astype(np.uint8))
 
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
@@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         model = model.to(device)
 
         return model
-

extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js

 // Stable Diffusion WebUI - Bracket checker
-// Version 1.0
-// By Hingashi no Florin/Bwin4L
+// By Hingashi no Florin/Bwin4L & @akx
 // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
 // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
 
-function checkBrackets(evt, textArea, counterElt) {
-  errorStringParen  = '(...) - Different number of opening and closing parentheses detected.\n';
-  errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n';
-  errorStringCurly  = '{...} - Different number of opening and closing curly brackets detected.\n';
-
-  openBracketRegExp = /\(/g;
-  closeBracketRegExp = /\)/g;
-
-  openSquareBracketRegExp = /\[/g;
-  closeSquareBracketRegExp = /\]/g;
-
-  openCurlyBracketRegExp = /\{/g;
-  closeCurlyBracketRegExp = /\}/g;
-
-  totalOpenBracketMatches = 0;
-  totalCloseBracketMatches = 0;
-  totalOpenSquareBracketMatches = 0;
-  totalCloseSquareBracketMatches = 0;
-  totalOpenCurlyBracketMatches = 0;
-  totalCloseCurlyBracketMatches = 0;
-
-  openBracketMatches = textArea.value.match(openBracketRegExp);
-  if(openBracketMatches) {
-    totalOpenBracketMatches = openBracketMatches.length;
-  }
-
-  closeBracketMatches = textArea.value.match(closeBracketRegExp);
-  if(closeBracketMatches) {
-    totalCloseBracketMatches = closeBracketMatches.length;
-  }
-
-  openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
-  if(openSquareBracketMatches) {
-    totalOpenSquareBracketMatches = openSquareBracketMatches.length;
-  }
-
-  closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
-  if(closeSquareBracketMatches) {
-    totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
-  }
-
-  openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
-  if(openCurlyBracketMatches) {
-    totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
-  }
-
-  closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
-  if(closeCurlyBracketMatches) {
-    totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
-  }
-
-  if(totalOpenBracketMatches != totalCloseBracketMatches) {
-    if(!counterElt.title.includes(errorStringParen)) {
-      counterElt.title += errorStringParen;
-    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringParen, '');
-  }
-
-  if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
-    if(!counterElt.title.includes(errorStringSquare)) {
-      counterElt.title += errorStringSquare;
-    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringSquare, '');
-  }
+function checkBrackets(textArea, counterElt) {
+  var counts = {};
+  (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
+    counts[bracket] = (counts[bracket] || 0) + 1;
+  });
+  var errors = [];
 
-  if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
-    if(!counterElt.title.includes(errorStringCurly)) {
-      counterElt.title += errorStringCurly;
+  function checkPair(open, close, kind) {
+    if (counts[open] !== counts[close]) {
+      errors.push(
+        `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
+      );
     }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringCurly, '');
   }
 
-  if(counterElt.title != '') {
-    counterElt.classList.add('error');
-  } else {
-    counterElt.classList.remove('error');
-  }
+  checkPair('(', ')', 'round brackets');
+  checkPair('[', ']', 'square brackets');
+  checkPair('{', '}', 'curly brackets');
+  counterElt.title = errors.join('\n');
+  counterElt.classList.toggle('error', errors.length !== 0);
 }
 
-function setupBracketChecking(id_prompt, id_counter){
+function setupBracketChecking(id_prompt, id_counter) {
   var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
   var counter = gradioApp().getElementById(id_counter)
-    textarea.addEventListener("input", function(evt){
-        checkBrackets(evt, textarea, counter)
-    });
-}
-
-var shadowRootLoaded = setInterval(function() {
-    var shadowRoot = document.querySelector('gradio-app').shadowRoot;
-    if(! shadowRoot)  return false;
 
-    var shadowTextArea = shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea');
-    if(shadowTextArea.length < 1)  return false;
-
-    clearInterval(shadowRootLoaded);
+  if (textarea && counter) {
+    textarea.addEventListener("input", () => checkBrackets(textarea, counter));
+  }
+}
 
-    setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
-    setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
-    setupBracketChecking('img2img_prompt', 'imgimg_token_counter')
-    setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
-}, 1000);
+onUiLoaded(function () {
+  setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
+  setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
+  setupBracketChecking('img2img_prompt', 'img2img_token_counter');
+  setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
+});

html/extra-networks-card.html

-<div class='card' {preview_html} onclick={card_clicked}>
+<div class='card' style={style} onclick={card_clicked}>
 	{metadata_button}
 
 	<div class='actions'>

html/licenses.html

@@ -636,3 +636,29 @@ SOFTWARE.
    See the License for the specific language governing permissions and
    limitations under the License.
 </pre>
+
+<h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
+<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
+<pre>
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+</pre>
\ No newline at end of file

javascript/aspectRatioOverlay.js

@@ -12,7 +12,7 @@ function dimensionChange(e, is_width, is_height){
 		currentHeight = e.target.value*1.0
 	}
 
-	var inImg2img   = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
+	var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
 
 	if(!inImg2img){
 		return;
@@ -22,7 +22,7 @@ function dimensionChange(e, is_width, is_height){
 
     var tabIndex = get_tab_index('mode_img2img')
 	if(tabIndex == 0){ // img2img
-		targetElement = gradioApp().querySelector('div[data-testid=image] img');
+		targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
 	} else if(tabIndex == 1){ //Sketch
 		targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
 	} else if(tabIndex == 2){ // Inpaint
@@ -38,7 +38,7 @@ function dimensionChange(e, is_width, is_height){
 		if(!arPreviewRect){
 		    arPreviewRect = document.createElement('div')
 		    arPreviewRect.id = "imageARPreview";
-		    gradioApp().getRootNode().appendChild(arPreviewRect)
+		    gradioApp().appendChild(arPreviewRect)
 		}
 
 
@@ -91,7 +91,9 @@ onUiUpdate(function(){
 	if(arPreviewRect){
 		arPreviewRect.style.display = 'none';
 	}
-	var inImg2img   = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
+    var tabImg2img = gradioApp().querySelector("#tab_img2img");
+    if (tabImg2img) {
+        var inImg2img = tabImg2img.style.display == "block";
         if(inImg2img){
             let inputs = gradioApp().querySelectorAll('input');
             inputs.forEach(function(e){
@@ -110,4 +112,5 @@ onUiUpdate(function(){
                 }
             })
         }
+    }
 });

javascript/contextMenus.js

@@ -43,7 +43,7 @@ contextMenuInit = function(){
 
     })
 
-    gradioApp().getRootNode().appendChild(contextMenu)
+    gradioApp().appendChild(contextMenu)
 
     let menuWidth = contextMenu.offsetWidth + 4;
     let menuHeight = contextMenu.offsetHeight + 4;
@@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2];
   appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
   appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
 
-  appendContextMenuOption('#roll','Roll three',
-    function(){
-      let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
-      setTimeout(function(){rollbutton.click()},100)
-      setTimeout(function(){rollbutton.click()},200)
-      setTimeout(function(){rollbutton.click()},300)
-    }
-  )
 })();
 //End example Context Menu Items
 

javascript/edit-attention.js

 function keyupEditAttention(event){
 	let target = event.originalTarget || event.composedPath()[0];
-	if (!target.matches("[id*='_toprow'] textarea.gr-text-input[placeholder]")) return;
+	if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
 	if (! (event.metaKey || event.ctrlKey)) return;
 
 	let isPlus = event.key == "ArrowUp"
@@ -44,9 +44,27 @@ function keyupEditAttention(event){
 		return true;
     }
     
-	// If the user hasn't selected anything, let's select their current parenthesis block
-    if(! selectCurrentParenthesisBlock('<', '>')){
-        selectCurrentParenthesisBlock('(', ')')
+    function selectCurrentWord(){
+        if (selectionStart !== selectionEnd) return false;
+        const delimiters = opts.keyedit_delimiters + " \r\n\t";
+        
+        // seek backward until to find beggining
+        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+            selectionStart--;
+        }
+        
+        // seek forward to find end
+        while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+            selectionEnd++;
+        }
+
+        target.setSelectionRange(selectionStart, selectionEnd);
+        return true;
+    }
+
+	// If the user hasn't selected anything, let's select their current parenthesis block or word
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+        selectCurrentWord();
     }
 
 	event.preventDefault();
@@ -81,7 +99,13 @@ function keyupEditAttention(event){
 	weight = parseFloat(weight.toPrecision(12));
 	if(String(weight).length == 1) weight += ".0"
 
+    if (closeCharacter == ')' && weight == 1) {
+        text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
+        selectionStart--;
+        selectionEnd--;
+    } else {
         text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+    }
 
 	target.focus();
 	target.value = text;

javascript/extensions.js

 
-function extensions_apply(_, _){
+function extensions_apply(_, _, disable_all){
     var disable = []
     var update = []
 
@@ -13,10 +13,10 @@ function extensions_apply(_, _){
 
     restart_reload()
 
-    return [JSON.stringify(disable), JSON.stringify(update)]
+    return [JSON.stringify(disable), JSON.stringify(update), disable_all]
 }
 
-function extensions_check(){
+function extensions_check(_, _){
     var disable = []
 
     gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){

javascript/extraNetworks.js

@@ -139,3 +139,41 @@ function extraNetworksShowMetadata(text){
 
     popup(elem);
 }
+
+function requestGet(url, data, handler, errorHandler){
+    var xhr = new XMLHttpRequest();
+    var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
+    xhr.open("GET", url + "?" + args, true);
+
+    xhr.onreadystatechange = function () {
+        if (xhr.readyState === 4) {
+            if (xhr.status === 200) {
+                try {
+                    var js = JSON.parse(xhr.responseText);
+                    handler(js)
+                } catch (error) {
+                    console.error(error);
+                    errorHandler()
+                }
+            } else{
+                errorHandler()
+            }
+        }
+    };
+    var js = JSON.stringify(data);
+    xhr.send(js);
+}
+
+function extraNetworksRequestMetadata(event, extraPage, cardName){
+    showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
+
+    requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
+        if(data && data.metadata){
+            extraNetworksShowMetadata(data.metadata)
+        } else{
+            showError()
+        }
+    }, showError)
+
+    event.stopPropagation()
+}

javascript/generationParams.js

@@ -16,7 +16,7 @@ onUiUpdate(function(){
 
 let modalObserver = new MutationObserver(function(mutations) {
 	mutations.forEach(function(mutationRecord) {
-		let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
+		let selectedTab = gradioApp().querySelector('#tabs div button')?.innerText
 		if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
 			gradioApp().getElementById(selectedTab+"_generation_info_button").click()
 	});

javascript/hints.js

@@ -18,11 +18,10 @@ titles = {
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
     "\u{1f4be}": "Save style",
-    "\u{1f5d1}": "Clear prompt",
+    "\u{1f5d1}\ufe0f": "Clear prompt",
     "\u{1f4cb}": "Apply selected styles to current prompt",
     "\u{1f4d2}": "Paste available values into the field",
-    "\u{1f3b4}": "Show extra networks",
-
+    "\u{1f3b4}": "Show/hide extra networks",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
     "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
@@ -40,7 +39,6 @@ titles = {
     "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
 
     "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
-    "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
     
     "Skip": "Stop processing current image and continue processing.",
     "Interrupt": "Stop processing images and return any results accumulated so far.",
@@ -71,8 +69,10 @@ titles = {
     "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
     "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
 
-    "Loopback": "Process an image, use it as an input, repeat.",
-    "Loops": "How many times to repeat processing an image and using it as input for the next iteration",
+    "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
+    "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.",
+    "Final denoising strength": "The denoising strength for the final loop of each image in the batch.",
+    "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.",
 
     "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both",
     "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",

javascript/imageviewer.js

@@ -32,13 +32,7 @@ function negmod(n, m) {
 function updateOnBackgroundChange() {
     const modalImage = gradioApp().getElementById("modalImage")
     if (modalImage && modalImage.offsetParent) {
-        let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
-        let currentButton = null
-        allcurrentButtons.forEach(function(elem) {
-            if (elem.parentElement.offsetParent) {
-                currentButton = elem;
-            }
-        })
+        let currentButton = selected_gallery_button();
 
         if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
             modalImage.src = currentButton.children[0].src;
@@ -50,22 +44,10 @@ function updateOnBackgroundChange() {
 }
 
 function modalImageSwitch(offset) {
-    var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
-    var galleryButtons = []
-    allgalleryButtons.forEach(function(elem) {
-        if (elem.parentElement.offsetParent) {
-            galleryButtons.push(elem);
-        }
-    })
+    var galleryButtons = all_gallery_buttons();
 
     if (galleryButtons.length > 1) {
-        var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
-        var currentButton = null
-        allcurrentButtons.forEach(function(elem) {
-            if (elem.parentElement.offsetParent) {
-                currentButton = elem;
-            }
-        })
+        var currentButton = selected_gallery_button();
 
         var result = -1
         galleryButtons.forEach(function(v, i) {
@@ -136,20 +118,15 @@ function modalKeyHandler(event) {
     }
 }
 
-function showGalleryImage() {
-    setTimeout(function() {
-        fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
-
-        if (fullImg_preview != null) {
-            fullImg_preview.forEach(function function_name(e) {
+function setupImageForLightbox(e) {
 	if (e.dataset.modded)
 		return;
+
 	e.dataset.modded = true;
-                if(e && e.parentElement.tagName == 'DIV'){
 	e.style.cursor='pointer'
 	e.style.userSelect='none'
 
-                    var isFirefox = isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
+	var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
 
 	// For Firefox, listening on click first switched to next image then shows the lightbox.
 	// If you know how to fix this without switching to mousedown event, please.
@@ -158,15 +135,12 @@ function showGalleryImage() {
 
 	e.addEventListener(event, function (evt) {
 		if(!opts.js_modal_lightbox || evt.button != 0) return;
+
 		modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
 		evt.preventDefault()
 		showModal(evt)
 	}, true);
-                }
-            });
-        }
 
-    }, 100);
 }
 
 function modalZoomSet(modalImage, enable) {
@@ -199,21 +173,21 @@ function modalTileImageToggle(event) {
 }
 
 function galleryImageHandler(e) {
-    if (e && e.parentElement.tagName == 'BUTTON') {
+    //if (e && e.parentElement.tagName == 'BUTTON') {
         e.onclick = showGalleryImage;
-    }
+    //}
 }
 
 onUiUpdate(function() {
-    fullImg_preview = gradioApp().querySelectorAll('img.w-full')
+    fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
     if (fullImg_preview != null) {
-        fullImg_preview.forEach(galleryImageHandler);
+        fullImg_preview.forEach(setupImageForLightbox);
     }
     updateOnBackgroundChange();
 })
 
 document.addEventListener("DOMContentLoaded", function() {
-    const modalFragment = document.createDocumentFragment();
+    //const modalFragment = document.createDocumentFragment();
     const modal = document.createElement('div')
     modal.onclick = closeModal;
     modal.id = "lightboxModal";
@@ -277,9 +251,12 @@ document.addEventListener("DOMContentLoaded", function() {
 
     modal.appendChild(modalNext)
 
+    try {
+		gradioApp().appendChild(modal);
+	} catch (e) {
+		gradioApp().body.appendChild(modal);
+	}
 
-    gradioApp().getRootNode().appendChild(modal)
-
-    document.body.appendChild(modalFragment);
+    document.body.appendChild(modal);
 
 });

javascript/notification.js

@@ -15,7 +15,7 @@ onUiUpdate(function(){
         }
     }
 
-    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] img.h-full.w-full.overflow-hidden');
+    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] .thumbnail-item > img');
 
     if (galleryPreviews == null) return;
 

javascript/progressbar.js

 // code related to showing and updating progressbar shown as the image is being made
 
-
-galleries = {}
-storedGallerySelections = {}
-galleryObservers = {}
-
 function rememberGallerySelection(id_gallery){
-    storedGallerySelections[id_gallery] = getGallerySelectedIndex(id_gallery)
+
 }
 
 function getGallerySelectedIndex(id_gallery){
-    let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
-    let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
-
-     let currentlySelectedIndex = -1
-     galleryButtons.forEach(function(v, i){ if(v==galleryBtnSelected) { currentlySelectedIndex = i } })
-
-     return currentlySelectedIndex
-}
 
-// this is a workaround for https://github.com/gradio-app/gradio/issues/2984
-function check_gallery(id_gallery){
-    let gallery = gradioApp().getElementById(id_gallery)
-    // if gallery has no change, no need to setting up observer again.
-    if (gallery && galleries[id_gallery] !== gallery){
-        galleries[id_gallery] = gallery;
-        if(galleryObservers[id_gallery]){
-            galleryObservers[id_gallery].disconnect();
-        }
-
-        storedGallerySelections[id_gallery] = -1
-
-        galleryObservers[id_gallery] = new MutationObserver(function (){
-            let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
-            let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
-            let currentlySelectedIndex = getGallerySelectedIndex(id_gallery)
-            prevSelectedIndex = storedGallerySelections[id_gallery]
-            storedGallerySelections[id_gallery] = -1
-
-            if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
-                // automatically re-open previously selected index (if exists)
-                activeElement = gradioApp().activeElement;
-                let scrollX = window.scrollX;
-                let scrollY = window.scrollY;
-
-                galleryButtons[prevSelectedIndex].click();
-                showGalleryImage();
-
-                // When the gallery button is clicked, it gains focus and scrolls itself into view
-                // We need to scroll back to the previous position
-                setTimeout(function (){
-                    window.scrollTo(scrollX, scrollY);
-                }, 50);
-
-                if(activeElement){
-                    // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it
-                    // if someone has a better solution please by all means
-                    setTimeout(function (){
-                        activeElement.focus({
-                            preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it
-                        })
-                    }, 1);
-                }
-            }
-        })
-        galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-    }
 }
 
-onUiUpdate(function(){
-    check_gallery('txt2img_gallery')
-    check_gallery('img2img_gallery')
-})
-
 function request(url, data, handler, errorHandler){
     var xhr = new XMLHttpRequest();
     var url = url;
@@ -203,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                 return
             }
 
-            if(elapsedFromStart > 5 && !res.queued && !res.active){
+            if(elapsedFromStart > 40 && !res.queued && !res.active){
                 removeProgressBar()
                 return
             }

javascript/ui.js

@@ -7,9 +7,31 @@ function set_theme(theme){
     }
 }
 
+function all_gallery_buttons() {
+    var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnails > .thumbnail-item.thumbnail-small');
+    var visibleGalleryButtons = [];
+    allGalleryButtons.forEach(function(elem) {
+        if (elem.parentElement.offsetParent) {
+            visibleGalleryButtons.push(elem);
+        }
+    })
+    return visibleGalleryButtons;
+}
+
+function selected_gallery_button() {
+    var allCurrentButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnail-item.thumbnail-small.selected');
+    var visibleCurrentButton = null;
+    allCurrentButtons.forEach(function(elem) {
+        if (elem.parentElement.offsetParent) {
+            visibleCurrentButton = elem;
+        }
+    })
+    return visibleCurrentButton;
+}
+
 function selected_gallery_index(){
-    var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item')
-    var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2')
+    var buttons = all_gallery_buttons();
+    var button = selected_gallery_button();
 
     var result = -1
     buttons.forEach(function(v, i){ if(v==button) { result = i } })
@@ -18,14 +40,18 @@ function selected_gallery_index(){
 }
 
 function extract_image_from_gallery(gallery){
-    if(gallery.length == 1){
-        return [gallery[0]]
+    if (gallery.length == 0){
+        return [null];
+    }
+    if (gallery.length == 1){
+        return [gallery[0]];
     }
 
     index = selected_gallery_index()
 
     if (index < 0 || index >= gallery.length){
-        return [null]
+        // Use the first image in the gallery as the default
+        index = 0;
     }
 
     return [gallery[index]];
@@ -86,7 +112,7 @@ function get_tab_index(tabId){
     var res = 0
 
     gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){
-        if(button.className.indexOf('bg-white') != -1)
+        if(button.className.indexOf('selected') != -1)
             res = i
     })
 
@@ -255,7 +281,6 @@ onUiUpdate(function(){
         }
 
         prompt.parentElement.insertBefore(counter, prompt)
-        counter.classList.add("token-counter")
         prompt.parentElement.style.position = "relative"
 
 		promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }

launch.py

@@ -5,24 +5,25 @@ import sys
 import importlib.util
 import shlex
 import platform
-import argparse
 import json
 
-parser = argparse.ArgumentParser(add_help=False)
-parser.add_argument("--ui-settings-file", type=str, default='config.json')
-parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.realpath(__file__)))
-args, _ = parser.parse_known_args(sys.argv)
+from modules import cmd_args
+from modules.paths_internal import script_path, extensions_dir
 
-script_path = os.path.dirname(__file__)
-data_path = os.getcwd()
+commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+sys.argv += shlex.split(commandline_args)
+
+args, _ = cmd_args.parser.parse_known_args()
 
-dir_repos = "repositories"
-dir_extensions = "extensions"
 python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
 stored_commit_hash = None
 skip_install = False
+dir_repos = "repositories"
+
+if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
+    os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
 
 
 def check_python_version():
@@ -70,23 +71,6 @@ def commit_hash():
     return stored_commit_hash
 
 
-def extract_arg(args, name):
-    return [x for x in args if x != name], name in args
-
-
-def extract_opt(args, name):
-    opt = None
-    is_present = False
-    if name in args:
-        is_present = True
-        idx = args.index(name)
-        del args[idx]
-        if idx < len(args) and args[idx][0] != "-":
-            opt = args[idx]
-            del args[idx]
-    return args, is_present, opt
-
-
 def run(command, desc=None, errdesc=None, custom_env=None, live=False):
     if desc is not None:
         print(desc)
@@ -137,12 +121,12 @@ def run_python(code, desc=None, errdesc=None):
     return run(f'"{python}" -c "{code}"', desc, errdesc)
 
 
-def run_pip(args, desc=None):
+def run_pip(args, desc=None, live=False):
     if skip_install:
         return
 
     index_url_line = f' --index-url {index_url}' if index_url != '' else ''
-    return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+    return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
 
 
 def check_run_python(code):
@@ -222,26 +206,29 @@ def list_extensions(settings_file):
         print(e, file=sys.stderr)
 
     disabled_extensions = set(settings.get('disabled_extensions', []))
+    disable_all_extensions = settings.get('disable_all_extensions', 'none')
+
+    if disable_all_extensions != 'none':
+        return []
 
-    return [x for x in os.listdir(os.path.join(data_path, dir_extensions)) if x not in disabled_extensions]
+    return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
 
 
 def run_extensions_installers(settings_file):
-    if not os.path.isdir(dir_extensions):
+    if not os.path.isdir(extensions_dir):
         return
 
     for dirname_extension in list_extensions(settings_file):
-        run_extension_installer(os.path.join(dir_extensions, dirname_extension))
+        run_extension_installer(os.path.join(extensions_dir, dirname_extension))
 
 
 def prepare_environment():
     global skip_install
 
-    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")
+    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --index-url https://download.pytorch.org/whl/cu118")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
-    commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 
-    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     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")
@@ -252,27 +239,13 @@ def prepare_environment():
     codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
-    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
+    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
     k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-    sys.argv += shlex.split(commandline_args)
-
-    sys.argv, _ = extract_arg(sys.argv, '-f')
-    sys.argv, update_all_extensions = extract_arg(sys.argv, '--update-all-extensions')
-    sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
-    sys.argv, skip_python_version_check = extract_arg(sys.argv, '--skip-python-version-check')
-    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')
-    xformers = '--xformers' in sys.argv
-    ngrok = '--ngrok' in sys.argv
-
-    if not skip_python_version_check:
+    if not args.skip_python_version_check:
         check_python_version()
 
     commit = commit_hash()
@@ -280,10 +253,10 @@ def prepare_environment():
     print(f"Python {sys.version}")
     print(f"Commit hash: {commit}")
 
-    if reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
+    if args.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", live=True)
 
-    if not skip_torch_cuda_test:
+    if not args.skip_torch_cuda_test:
         run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
 
     if not is_installed("gfpgan"):
@@ -295,10 +268,10 @@ def prepare_environment():
     if not is_installed("open_clip"):
         run_pip(f"install {openclip_package}", "open_clip")
 
-    if (not is_installed("xformers") or reinstall_xformers) and xformers:
+    if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
         if platform.system() == "Windows":
             if platform.python_version().startswith("3.10"):
-                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
             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")
@@ -307,7 +280,7 @@ def prepare_environment():
         elif platform.system() == "Linux":
             run_pip(f"install {xformers_package}", "xformers")
 
-    if not is_installed("pyngrok") and ngrok:
+    if not is_installed("pyngrok") and args.ngrok:
         run_pip("install pyngrok", "ngrok")
 
     os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
@@ -323,22 +296,22 @@ def prepare_environment():
 
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
-    run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
+    run_pip(f"install -r \"{requirements_file}\"", "requirements")
 
     run_extensions_installers(settings_file=args.ui_settings_file)
 
-    if update_check:
+    if args.update_check:
         version_check(commit)
 
-    if update_all_extensions:
-        git_pull_recursive(os.path.join(data_path, dir_extensions))
+    if args.update_all_extensions:
+        git_pull_recursive(extensions_dir)
     
     if "--exit" in sys.argv:
         print("Exiting because of --exit argument")
         exit(0)
 
-    if run_tests:
-        exitcode = tests(test_dir)
+    if args.tests and not args.no_tests:
+        exitcode = tests(args.tests)
         exit(exitcode)
 
 
@@ -352,6 +325,8 @@ def tests(test_dir):
         sys.argv.append("--skip-torch-cuda-test")
     if "--disable-nan-check" not in sys.argv:
         sys.argv.append("--disable-nan-check")
+    if "--no-tests" not in sys.argv:
+        sys.argv.append("--no-tests")
 
     print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
 

modules/devices.py

@@ -92,14 +92,18 @@ def cond_cast_float(input):
 
 
 def randn(seed, shape):
+    from modules.shared import opts
+
     torch.manual_seed(seed)
-    if device.type == 'mps':
+    if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
 def randn_without_seed(shape):
-    if device.type == 'mps':
+    from modules.shared import opts
+
+    if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 

modules/extensions.py

@@ -5,16 +5,21 @@ import traceback
 import time
 import git
 
-from modules import paths, shared
+from modules import shared
+from modules.paths_internal import extensions_dir, extensions_builtin_dir
 
 extensions = []
-extensions_dir = os.path.join(paths.data_path, "extensions")
-extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
 
 if not os.path.exists(extensions_dir):
     os.makedirs(extensions_dir)
 
+
 def active():
+    if shared.opts.disable_all_extensions == "all":
+        return []
+    elif shared.opts.disable_all_extensions == "extra":
+        return [x for x in extensions if x.enabled and x.is_builtin]
+    else:
         return [x for x in extensions if x.enabled]
 
 
@@ -27,21 +32,29 @@ class Extension:
         self.can_update = False
         self.is_builtin = is_builtin
         self.version = ''
+        self.remote = None
+        self.have_info_from_repo = False
+
+    def read_info_from_repo(self):
+        if self.have_info_from_repo:
+            return
+
+        self.have_info_from_repo = True
 
         repo = None
         try:
-            if os.path.exists(os.path.join(path, ".git")):
-                repo = git.Repo(path)
+            if os.path.exists(os.path.join(self.path, ".git")):
+                repo = git.Repo(self.path)
         except Exception:
-            print(f"Error reading github repository info from {path}:", file=sys.stderr)
+            print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
         if repo is None or repo.bare:
             self.remote = None
         else:
             try:
-                self.remote = next(repo.remote().urls, None)
                 self.status = 'unknown'
+                self.remote = next(repo.remote().urls, None)
                 head = repo.head.commit
                 ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
                 self.version = f'{head.hexsha[:8]} ({ts})'
@@ -89,7 +102,12 @@ def list_extensions():
     if not os.path.isdir(extensions_dir):
         return
 
-    paths = []
+    if shared.opts.disable_all_extensions == "all":
+        print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+    elif shared.opts.disable_all_extensions == "extra":
+        print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
+
+    extension_paths = []
     for dirname in [extensions_dir, extensions_builtin_dir]:
         if not os.path.isdir(dirname):
             return
@@ -99,9 +117,8 @@ def list_extensions():
             if not os.path.isdir(path):
                 continue
 
-            paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+            extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
 
-    for dirname, path, is_builtin in paths:
+    for dirname, path, is_builtin in extension_paths:
         extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
         extensions.append(extension)
-

modules/extra_networks_hypernet.py

@@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):
         additional = shared.opts.sd_hypernetwork
 
-        if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+        if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
             p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 

modules/generation_parameters_copypaste.py

@@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
     restore_old_hires_fix_params(res)
 
+    # Missing RNG means the default was set, which is GPU RNG
+    if "RNG" not in res:
+        res["RNG"] = "GPU"
+
     return res
 
 
@@ -304,6 +308,7 @@ infotext_to_setting_name_mapping = [
     ('UniPC skip type', 'uni_pc_skip_type'),
     ('UniPC order', 'uni_pc_order'),
     ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('RNG', 'randn_source'),
 ]
 
 
@@ -401,9 +406,14 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
 
     button.click(
         fn=paste_func,
-        _js=f"recalculate_prompts_{tabname}",
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
+    button.click(
+        fn=None,
+        _js=f"recalculate_prompts_{tabname}",
+        inputs=[],
+        outputs=[],
+    )
 
 

modules/hypernetworks/hypernetwork.py

@@ -312,7 +312,7 @@ class Hypernetwork:
 
 def list_hypernetworks(path):
     res = {}
-    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
+    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":

modules/images.py

@@ -261,9 +261,12 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
 
         if scale > 1.0:
             upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
-            assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
-
+            if len(upscalers) == 0:
+                upscaler = shared.sd_upscalers[0]
+                print(f"could not find upscaler named {upscaler_name or '<empty string>'}, using {upscaler.name} as a fallback")
+            else:
                 upscaler = upscalers[0]
+
             im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
 
         if im.width != w or im.height != h:
@@ -350,6 +353,7 @@ class FilenameGenerator:
         'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
         'prompt_words': lambda self: self.prompt_words(),
         'hasprompt': lambda self, *args: self.hasprompt(*args), #accept formats:[hasprompt<prompt1|default><prompt2>..]
+        'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
     }
     default_time_format = '%Y%m%d%H%M%S'
 
@@ -662,6 +666,8 @@ Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}
 
 
 def image_data(data):
+    import gradio as gr
+
     try:
         image = Image.open(io.BytesIO(data))
         textinfo, _ = read_info_from_image(image)
@@ -677,7 +683,7 @@ def image_data(data):
     except Exception:
         pass
 
-    return '', None
+    return gr.update(), None
 
 
 def flatten(img, bgcolor):

modules/img2img.py

@@ -151,12 +151,13 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         override_settings=override_settings,
     )
 
-    p.scripts = modules.scripts.scripts_txt2img
+    p.scripts = modules.scripts.scripts_img2img
     p.script_args = args
 
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
+    if mask:
         p.extra_generation_params["Mask blur"] = mask_blur
 
     if is_batch:

modules/interrogate.py

@@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir):
     category_types = ["artists", "flavors", "mediums", "movements"]
 
     try:
-        os.makedirs(tmpdir)
+        os.makedirs(tmpdir, exist_ok=True)
         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)
@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir):
         errors.display(e, "downloading default CLIP interrogate categories")
     finally:
         if os.path.exists(tmpdir):
-            os.remove(tmpdir)
+            os.removedirs(tmpdir)
 
 
 class InterrogateModels:

modules/lowvram.py

@@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram):
     if hasattr(sd_model.cond_stage_model, 'model'):
         sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
 
-    # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
+    # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
-    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
+    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
     sd_model.to(devices.device)
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
 
     # register hooks for those the first three models
     sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
@@ -69,6 +69,8 @@ def setup_for_low_vram(sd_model, use_medvram):
     sd_model.first_stage_model.decode = first_stage_model_decode_wrap
     if sd_model.depth_model:
         sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
+    if sd_model.embedder:
+        sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     if hasattr(sd_model.cond_stage_model, 'model'):

modules/mac_specific.py

 import torch
+import platform
 from modules import paths
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
@@ -32,6 +33,10 @@ if has_mps:
     # MPS fix for randn in torchsde
     CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
 
+    if platform.mac_ver()[0].startswith("13.2."):
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
+        CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)
+
     if version.parse(torch.__version__) < version.parse("1.13"):
         # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
 
@@ -49,4 +54,6 @@ if has_mps:
         CondFunc('torch.cumsum', cumsum_fix_func, None)
         CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
         CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
-
+    if version.parse(torch.__version__) == version.parse("2.0"):
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
+        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6)

modules/modelloader.py

@@ -4,7 +4,6 @@ import shutil
 import importlib
 from urllib.parse import urlparse
 
-from basicsr.utils.download_util import load_file_from_url
 from modules import shared
 from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
 from modules.paths import script_path, models_path
@@ -59,6 +58,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
         if model_url is not None and len(output) == 0:
             if download_name is not None:
+                from basicsr.utils.download_util import load_file_from_url
                 dl = load_file_from_url(model_url, model_path, True, download_name)
                 output.append(dl)
             else:

modules/paths.py

-import argparse
 import os
 import sys
-import modules.safe
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
 
-script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+import modules.safe
 
-# Parse the --data-dir flag first so we can use it as a base for our other argument default values
-parser = argparse.ArgumentParser(add_help=False)
-parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
-cmd_opts_pre = parser.parse_known_args()[0]
-data_path = cmd_opts_pre.data_dir
-models_path = os.path.join(data_path, "models")
 
 # data_path = cmd_opts_pre.data
 sys.path.insert(0, script_path)

modules/paths_internal.py

+"""this module defines internal paths used by program and is safe to import before dependencies are installed in launch.py"""
+
+import argparse
+import os
+
+script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+
+sd_configs_path = os.path.join(script_path, "configs")
+sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
+sd_model_file = os.path.join(script_path, 'model.ckpt')
+default_sd_model_file = sd_model_file
+
+# Parse the --data-dir flag first so we can use it as a base for our other argument default values
+parser_pre = argparse.ArgumentParser(add_help=False)
+parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
+cmd_opts_pre = parser_pre.parse_known_args()[0]
+
+data_path = cmd_opts_pre.data_dir
+
+models_path = os.path.join(data_path, "models")
+extensions_dir = os.path.join(data_path, "extensions")
+extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")

modules/postprocessing.py

@@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
 
     if extras_mode == 1:
         for img in image_folder:
-            image = Image.open(img)
+            if isinstance(img, Image.Image):
+                image = img
+                fn = ''
+            else:
+                image = Image.open(os.path.abspath(img.name))
+                fn = os.path.splitext(img.orig_name)[0]
+
             image_data.append(image)
-            image_names.append(os.path.splitext(img.orig_name)[0])
+            image_names.append(fn)
     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'

modules/processing.py

@@ -3,6 +3,7 @@ import math
 import os
 import sys
 import warnings
+import hashlib
 
 import torch
 import numpy as np
@@ -78,11 +79,7 @@ def apply_overlay(image, paste_loc, index, overlays):
 
 
 def txt2img_image_conditioning(sd_model, x, width, height):
-    if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
-        # Dummy zero conditioning if we're not using inpainting model.
-        # Still takes up a bit of memory, but no encoder call.
-        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+    if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
@@ -94,6 +91,16 @@ def txt2img_image_conditioning(sd_model, x, width, height):
 
         return image_conditioning
 
+    elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
+
+        return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
+
+    else:
+        # Dummy zero conditioning if we're not using inpainting or unclip models.
+        # Still takes up a bit of memory, but no encoder call.
+        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
 
 class StableDiffusionProcessing:
     """
@@ -190,6 +197,14 @@ class StableDiffusionProcessing:
 
         return conditioning_image
 
+    def unclip_image_conditioning(self, source_image):
+        c_adm = self.sd_model.embedder(source_image)
+        if self.sd_model.noise_augmentor is not None:
+            noise_level = 0 # TODO: Allow other noise levels?
+            c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
+            c_adm = torch.cat((c_adm, noise_level_emb), 1)
+        return c_adm
+
     def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
         self.is_using_inpainting_conditioning = True
 
@@ -241,6 +256,9 @@ class StableDiffusionProcessing:
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
             return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
 
+        if self.sampler.conditioning_key == "crossattn-adm":
+            return self.unclip_image_conditioning(source_image)
+
         # Dummy zero conditioning if we're not using inpainting or depth model.
         return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
 
@@ -459,6 +477,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+        "Init image hash": getattr(p, 'init_img_hash', None),
+        "RNG": (opts.randn_source if opts.randn_source != "GPU" else None)
     }
 
     generation_params.update(p.extra_generation_params)
@@ -622,8 +642,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     processed = Processed(p, [], p.seed, "")
                     file.write(processed.infotext(p, 0))
 
-            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
-            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+            step_multiplier = 1
+            if not shared.opts.dont_fix_second_order_samplers_schedule:
+                try:
+                    step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
+                except:
+                    pass
+            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
+            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -689,6 +715,22 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text
                 output_images.append(image)
 
+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+                    image_mask = p.mask_for_overlay.convert('RGB')
+                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
+
+                    if opts.save_mask:
+                        images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+
+                    if opts.save_mask_composite:
+                        images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+
+                    if opts.return_mask:
+                        output_images.append(image_mask)
+                    
+                    if opts.return_mask_composite:
+                        output_images.append(image_mask_composite)
+
             del x_samples_ddim
 
             devices.torch_gc()
@@ -974,6 +1016,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.color_corrections = []
         imgs = []
         for img in self.init_images:
+
+            # Save init image
+            if opts.save_init_img:
+                self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
+                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+
             image = images.flatten(img, opts.img2img_background_color)
 
             if crop_region is None and self.resize_mode != 3:

modules/safe.py

 # this code is adapted from the script contributed by anon from /h/
 
-import io
 import pickle
 import collections
 import sys
@@ -12,11 +11,9 @@ import _codecs
 import zipfile
 import re
 
-
 # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
 TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
 
-
 def encode(*args):
     out = _codecs.encode(*args)
     return out
@@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler):
 
     def persistent_load(self, saved_id):
         assert saved_id[0] == 'storage'
-        return TypedStorage()
+        return TypedStorage(_internal=True)
 
     def find_class(self, module, name):
         if self.extra_handler is not None:

modules/scripts.py

@@ -239,7 +239,15 @@ def load_scripts():
             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):
+    def orderby(basedir):
+        # 1st webui, 2nd extensions-builtin, 3rd extensions
+        priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
+        for key in priority:
+            if basedir.startswith(key):
+                return priority[key]
+        return 9999
+
+    for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
         try:
             if scriptfile.basedir != paths.script_path:
                 sys.path = [scriptfile.basedir] + sys.path
@@ -513,6 +521,18 @@ def reload_scripts():
     scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
 
 
+def add_classes_to_gradio_component(comp):
+    """
+    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+    """
+
+    comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
+
+    if getattr(comp, 'multiselect', False):
+        comp.elem_classes.append('multiselect')
+
+
+
 def IOComponent_init(self, *args, **kwargs):
     if scripts_current is not None:
         scripts_current.before_component(self, **kwargs)
@@ -521,6 +541,8 @@ def IOComponent_init(self, *args, **kwargs):
 
     res = original_IOComponent_init(self, *args, **kwargs)
 
+    add_classes_to_gradio_component(self)
+
     script_callbacks.after_component_callback(self, **kwargs)
 
     if scripts_current is not None:
@@ -531,3 +553,15 @@ def IOComponent_init(self, *args, **kwargs):
 
 original_IOComponent_init = gr.components.IOComponent.__init__
 gr.components.IOComponent.__init__ = IOComponent_init
+
+
+def BlockContext_init(self, *args, **kwargs):
+    res = original_BlockContext_init(self, *args, **kwargs)
+
+    add_classes_to_gradio_component(self)
+
+    return res
+
+
+original_BlockContext_init = gr.blocks.BlockContext.__init__
+gr.blocks.BlockContext.__init__ = BlockContext_init

modules/scripts_postprocessing.py

@@ -109,7 +109,7 @@ class ScriptPostprocessingRunner:
         inputs = []
 
         for script in self.scripts_in_preferred_order():
-            with gr.Box() as group:
+            with gr.Row() as group:
                 self.create_script_ui(script, inputs)
 
             script.group = group

modules/sd_hijack_optimizations.py

@@ -337,7 +337,7 @@ def xformers_attention_forward(self, x, context=None, mask=None):
 
     dtype = q.dtype
     if shared.opts.upcast_attn:
-        q, k = q.float(), k.float()
+        q, k, v = q.float(), k.float(), v.float()
 
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
 
@@ -372,7 +372,7 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
 
     dtype = q.dtype
     if shared.opts.upcast_attn:
-        q, k = q.float(), k.float()
+        q, k, v = q.float(), k.float(), v.float()
 
     # the output of sdp = (batch, num_heads, seq_len, head_dim)
     hidden_states = torch.nn.functional.scaled_dot_product_attention(

modules/sd_hijack_unet.py

@@ -67,7 +67,7 @@ def hijack_ddpm_edit():
 unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
 CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
-if version.parse(torch.__version__) <= version.parse("1.13.1"):
+if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
     CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
     CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
     CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)

modules/sd_models.py

@@ -122,7 +122,7 @@ def list_models():
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
 
-    for filename in model_list:
+    for filename in sorted(model_list, key=str.lower):
         checkpoint_info = CheckpointInfo(filename)
         checkpoint_info.register()
 
@@ -178,7 +178,7 @@ def select_checkpoint():
     return checkpoint_info
 
 
-chckpoint_dict_replacements = {
+checkpoint_dict_replacements = {
     'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
     'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
     'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
@@ -186,7 +186,7 @@ chckpoint_dict_replacements = {
 
 
 def transform_checkpoint_dict_key(k):
-    for text, replacement in chckpoint_dict_replacements.items():
+    for text, replacement in checkpoint_dict_replacements.items():
         if k.startswith(text):
             k = replacement + k[len(text):]
 
@@ -383,6 +383,14 @@ def repair_config(sd_config):
     elif shared.cmd_opts.upcast_sampling:
         sd_config.model.params.unet_config.params.use_fp16 = True
 
+    if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
+        sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
+
+    # For UnCLIP-L, override the hardcoded karlo directory
+    if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
+        karlo_path = os.path.join(paths.models_path, 'karlo')
+        sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
+
 
 sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
 sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
@@ -494,7 +502,7 @@ def reload_model_weights(sd_model=None, info=None):
     if sd_model is None or checkpoint_config != sd_model.used_config:
         del sd_model
         checkpoints_loaded.clear()
-        load_model(checkpoint_info, already_loaded_state_dict=state_dict, time_taken_to_load_state_dict=timer.records["load weights from disk"])
+        load_model(checkpoint_info, already_loaded_state_dict=state_dict)
         return shared.sd_model
 
     try:
@@ -517,3 +525,23 @@ def reload_model_weights(sd_model=None, info=None):
     print(f"Weights loaded in {timer.summary()}.")
 
     return sd_model
+
+def unload_model_weights(sd_model=None, info=None):
+    from modules import lowvram, devices, sd_hijack
+    timer = Timer()
+
+    if shared.sd_model:
+
+        # shared.sd_model.cond_stage_model.to(devices.cpu)
+        # shared.sd_model.first_stage_model.to(devices.cpu)
+        shared.sd_model.to(devices.cpu)
+        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+        shared.sd_model = None
+        sd_model = None
+        gc.collect()
+        devices.torch_gc()
+        torch.cuda.empty_cache()
+
+    print(f"Unloaded weights {timer.summary()}.")
+
+    return sd_model
\ No newline at end of file

modules/sd_models_config.py

@@ -14,6 +14,8 @@ config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
 config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
 config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
 config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
+config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
+config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
 config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
 config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
 config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
@@ -65,9 +67,14 @@ def is_using_v_parameterization_for_sd2(state_dict):
 def guess_model_config_from_state_dict(sd, filename):
     sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
     diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+    sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
 
     if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
         return config_depth_model
+    elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
+        return config_unclip
+    elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
+        return config_unopenclip
 
     if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
         if diffusion_model_input.shape[1] == 9:

modules/sd_samplers_common.py

@@ -60,3 +60,13 @@ def store_latent(decoded):
 
 class InterruptedException(BaseException):
     pass
+
+
+if opts.randn_source == "CPU":
+    import torchsde._brownian.brownian_interval
+
+    def torchsde_randn(size, dtype, device, seed):
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+
+    torchsde._brownian.brownian_interval._randn = torchsde_randn

modules/sd_samplers_compvis.py

@@ -70,7 +70,12 @@ class VanillaStableDiffusionSampler:
 
         # Have to unwrap the inpainting conditioning here to perform pre-processing
         image_conditioning = None
+        uc_image_conditioning = None
         if isinstance(cond, dict):
+            if self.conditioning_key == "crossattn-adm":
+                image_conditioning = cond["c_adm"]
+                uc_image_conditioning = unconditional_conditioning["c_adm"]
+            else:
                 image_conditioning = cond["c_concat"][0]
             cond = cond["c_crossattn"][0]
             unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
@@ -98,6 +103,10 @@ class VanillaStableDiffusionSampler:
         # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
         # Note that they need to be lists because it just concatenates them later.
         if image_conditioning is not None:
+            if self.conditioning_key == "crossattn-adm":
+                cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
+                unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
+            else:
                 cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
                 unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
 
@@ -176,6 +185,10 @@ class VanillaStableDiffusionSampler:
 
         # Wrap the conditioning models with additional image conditioning for inpainting model
         if image_conditioning is not None:
+            if self.conditioning_key == "crossattn-adm":
+                conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
+                unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
+            else:
                 conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
                 unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
 
@@ -195,6 +208,10 @@ class VanillaStableDiffusionSampler:
         # Wrap the conditioning models with additional image conditioning for inpainting model
         # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
         if image_conditioning is not None:
+            if self.conditioning_key == "crossattn-adm":
+                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
+                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
+            else:
                 conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
                 unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
 

modules/sd_samplers_kdiffusion.py

@@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module):
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
+        if shared.sd_model.model.conditioning_key == "crossattn-adm":
+            image_uncond = torch.zeros_like(image_cond)
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} 
+        else:
+            image_uncond = image_cond
+            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} 
+
         if not is_edit_model:
             x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
             sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
         else:
             x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
             sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
 
         denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
         cfg_denoiser_callback(denoiser_params)
@@ -116,13 +123,13 @@ class CFGDenoiser(torch.nn.Module):
                 cond_in = torch.cat([tensor, uncond, uncond])
 
             if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
+                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
             else:
                 x_out = torch.zeros_like(x_in)
                 for batch_offset in range(0, x_out.shape[0], batch_size):
                     a = batch_offset
                     b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
         else:
             x_out = torch.zeros_like(x_in)
             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
@@ -135,9 +142,9 @@ class CFGDenoiser(torch.nn.Module):
                 else:
                     c_crossattn = torch.cat([tensor[a:b]], uncond)
 
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
 
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
 
         denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
         cfg_denoised_callback(denoised_params)
@@ -183,7 +190,7 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        if x.device.type == 'mps':
+        if opts.randn_source == "CPU" or x.device.type == 'mps':
             return torch.randn_like(x, device=devices.cpu).to(x.device)
         else:
             return torch.randn_like(x)

modules/textual_inversion/textual_inversion.py

@@ -152,7 +152,11 @@ class EmbeddingDatabase:
                 name = data.get('name', name)
             else:
                 data = extract_image_data_embed(embed_image)
+                if data:
                     name = data.get('name', name)
+                else:
+                    # if data is None, means this is not an embeding, just a preview image
+                    return
         elif ext in ['.BIN', '.PT']:
             data = torch.load(path, map_location="cpu")
         elif ext in ['.SAFETENSORS']:

modules/ui_common.py

@@ -125,12 +125,12 @@ Requested path was: {f}
 
     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)
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=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}')
+            with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
+                open_folder_button = gr.Button(folder_symbol, visible=not shared.cmd_opts.hide_ui_dir_config)
 
                 if tabname != "extras":
                     save = gr.Button('Save', elem_id=f'save_{tabname}')
@@ -145,11 +145,10 @@ Requested path was: {f}
             )
 
             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_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
                     html_log = gr.HTML(elem_id=f'html_log_{tabname}')
 
                     generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
@@ -160,6 +159,7 @@ Requested path was: {f}
                             _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
                             inputs=[generation_info, html_info, html_info],
                             outputs=[html_info, html_info],
+                            show_progress=False,
                         )
 
                     save.click(
@@ -195,7 +195,7 @@ Requested path was: {f}
 
             else:
                 html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
-                html_info = gr.HTML(elem_id=f'html_info_{tabname}')
+                html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
                 html_log = gr.HTML(elem_id=f'html_log_{tabname}')
 
             paste_field_names = []

modules/ui_components.py

 import gradio as gr
 
 
-class ToolButton(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, fits inside gradio forms"""
+class FormComponent:
+    def get_expected_parent(self):
+        return gr.components.Form
 
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool", **kwargs)
 
-    def get_block_name(self):
-        return "button"
+gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
 
 
-class ToolButtonTop(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
+class ToolButton(FormComponent, gr.Button):
+    """Small button with single emoji as text, fits inside gradio forms"""
 
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool-top", **kwargs)
+    def __init__(self, *args, **kwargs):
+        classes = kwargs.pop("elem_classes", [])
+        super().__init__(*args, elem_classes=["tool", *classes], **kwargs)
 
     def get_block_name(self):
         return "button"
 
 
-class FormRow(gr.Row, gr.components.FormComponent):
+class FormRow(FormComponent, gr.Row):
     """Same as gr.Row but fits inside gradio forms"""
 
     def get_block_name(self):
         return "row"
 
 
-class FormGroup(gr.Group, gr.components.FormComponent):
+class FormColumn(FormComponent, gr.Column):
+    """Same as gr.Column but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "column"
+
+
+class FormGroup(FormComponent, gr.Group):
     """Same as gr.Row but fits inside gradio forms"""
 
     def get_block_name(self):
         return "group"
 
 
-class FormHTML(gr.HTML, gr.components.FormComponent):
+class FormHTML(FormComponent, gr.HTML):
     """Same as gr.HTML but fits inside gradio forms"""
 
     def get_block_name(self):
         return "html"
 
 
-class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
+class FormColorPicker(FormComponent, gr.ColorPicker):
     """Same as gr.ColorPicker but fits inside gradio forms"""
 
     def get_block_name(self):
         return "colorpicker"
 
 
-class DropdownMulti(gr.Dropdown):
+class DropdownMulti(FormComponent, gr.Dropdown):
     """Same as gr.Dropdown but always multiselect"""
     def __init__(self, **kwargs):
         super().__init__(multiselect=True, **kwargs)
 
     def get_block_name(self):
         return "dropdown"
+
+
+class DropdownEditable(FormComponent, gr.Dropdown):
+    """Same as gr.Dropdown but allows editing value"""
+    def __init__(self, **kwargs):
+        super().__init__(allow_custom_value=True, **kwargs)
+
+    def get_block_name(self):
+        return "dropdown"
+

modules/ui_extra_networks.py

@@ -2,8 +2,10 @@ import glob
 import os.path
 import urllib.parse
 from pathlib import Path
+from PIL import PngImagePlugin
 
 from modules import shared
+from modules.images import read_info_from_image
 import gradio as gr
 import json
 import html
@@ -22,8 +24,7 @@ def register_page(page):
     allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
 
 
-def add_pages_to_demo(app):
-    def fetch_file(filename: str = ""):
+def fetch_file(filename: str = ""):
     from starlette.responses import FileResponse
 
     if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
@@ -36,7 +37,24 @@ def add_pages_to_demo(app):
     # would profit from returning 304
     return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
 
+
+def get_metadata(page: str = "", item: str = ""):
+    from starlette.responses import JSONResponse
+
+    page = next(iter([x for x in extra_pages if x.name == page]), None)
+    if page is None:
+        return JSONResponse({})
+
+    metadata = page.metadata.get(item)
+    if metadata is None:
+        return JSONResponse({})
+
+    return JSONResponse({"metadata": metadata})
+
+
+def add_pages_to_demo(app):
     app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
+    app.add_api_route("/sd_extra_networks/metadata", get_metadata, methods=["GET"])
 
 
 class ExtraNetworksPage:
@@ -45,6 +63,7 @@ class ExtraNetworksPage:
         self.name = title.lower()
         self.card_page = shared.html("extra-networks-card.html")
         self.allow_negative_prompt = False
+        self.metadata = {}
 
     def refresh(self):
         pass
@@ -66,6 +85,8 @@ class ExtraNetworksPage:
         view = shared.opts.extra_networks_default_view
         items_html = ''
 
+        self.metadata = {}
+
         subdirs = {}
         for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
             for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
@@ -86,12 +107,16 @@ class ExtraNetworksPage:
             subdirs = {"": 1, **subdirs}
 
         subdirs_html = "".join([f"""
-<button class='gr-button gr-button-lg gr-button-secondary{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
+<button class='lg secondary gradio-button custom-button{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
 {html.escape(subdir if subdir!="" else "all")}
 </button>
 """ for subdir in subdirs])
 
         for item in self.list_items():
+            metadata = item.get("metadata")
+            if metadata:
+                self.metadata[item["name"]] = metadata
+
             items_html += self.create_html_for_item(item, tabname)
 
         if items_html == '':
@@ -124,14 +149,16 @@ class ExtraNetworksPage:
         if onclick is None:
             onclick = '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
 
+        height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
+        width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
+        background_image = f"background-image: url(\"{html.escape(preview)}\");" if preview else ''
         metadata_button = ""
         metadata = item.get("metadata")
         if metadata:
-            metadata_onclick = '"' + html.escape(f"""extraNetworksShowMetadata({json.dumps(metadata)}); return false;""") + '"'
-            metadata_button = f"<div class='metadata-button' title='Show metadata' onclick={metadata_onclick}></div>"
+            metadata_button = f"<div class='metadata-button' title='Show metadata' onclick='extraNetworksRequestMetadata(event, {json.dumps(self.name)}, {json.dumps(item['name'])})'></div>"
 
         args = {
-            "preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
+            "style": f"'{height}{width}{background_image}'",
             "prompt": item.get("prompt", None),
             "tabname": json.dumps(tabname),
             "local_preview": json.dumps(item["local_preview"]),
@@ -215,6 +242,7 @@ def create_ui(container, button, tabname):
     with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
         for page in ui.stored_extra_pages:
             with gr.Tab(page.title):
+
                 page_elem = gr.HTML(page.create_html(ui.tabname))
                 ui.pages.append(page_elem)
 
@@ -226,10 +254,10 @@ def create_ui(container, button, tabname):
 
     def toggle_visibility(is_visible):
         is_visible = not is_visible
-        return is_visible, gr.update(visible=is_visible)
+        return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
 
     state_visible = gr.State(value=False)
-    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
+    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button])
 
     def refresh():
         res = []
@@ -264,6 +292,7 @@ def setup_ui(ui, gallery):
 
         img_info = images[index if index >= 0 else 0]
         image = image_from_url_text(img_info)
+        geninfo, items = read_info_from_image(image)
 
         is_allowed = False
         for extra_page in ui.stored_extra_pages:
@@ -273,6 +302,11 @@ def setup_ui(ui, gallery):
 
         assert is_allowed, f'writing to {filename} is not allowed'
 
+        if geninfo:
+            pnginfo_data = PngImagePlugin.PngInfo()
+            pnginfo_data.add_text('parameters', geninfo)
+            image.save(filename, pnginfo=pnginfo_data)
+        else:
             image.save(filename)
 
         return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]

modules/ui_postprocessing.py

@@ -13,7 +13,7 @@ def create_ui():
                     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")
+                    image_batch = gr.Files(label="Batch Process", interactive=True, 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")

requirements.txt

+astunparse
 blendmodes
 accelerate
 basicsr
 fonts
 font-roboto
 gfpgan
-gradio==3.16.2
+gradio==3.27
 invisible-watermark
 numpy
 omegaconf
@@ -30,3 +31,4 @@ GitPython
 torchsde
 safetensors
 psutil
+rich

requirements_versions.txt

 blendmodes==2022
 transformers==4.25.1
-accelerate==0.12.0
+accelerate==0.18.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.16.2
-numpy==1.23.3
+gradio==3.27
+numpy==1.23.5
 Pillow==9.4.0
 realesrgan==0.3.0
 torch
 omegaconf==2.2.3
-pytorch_lightning==1.7.6
+pytorch_lightning==1.9.4
 scikit-image==0.19.2
 fonts
 font-roboto
@@ -25,6 +25,6 @@ lark==1.1.2
 inflection==0.5.1
 GitPython==3.1.30
 torchsde==0.2.5
-safetensors==0.2.7
+safetensors==0.3.1
 httpcore<=0.15
 fastapi==0.94.0

script.js

 function gradioApp() {
     const elems = document.getElementsByTagName('gradio-app')
-    const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot
-    return !!gradioShadowRoot ? gradioShadowRoot : document;
+    const elem = elems.length == 0 ? document : elems[0]
+
+    if (elem !== document) elem.getElementById = function(id){ return document.getElementById(id) }
+    return elem.shadowRoot ? elem.shadowRoot : elem
 }
 
 function get_uiCurrentTab() {

scripts/custom_code.py

 import modules.scripts as scripts
 import gradio as gr
+import ast
+import copy
 
 from modules.processing import Processed
 from modules.shared import opts, cmd_opts, state
 
+
+def convertExpr2Expression(expr):
+    expr.lineno = 0
+    expr.col_offset = 0
+    result = ast.Expression(expr.value, lineno=0, col_offset = 0)
+
+    return result
+
+
+def exec_with_return(code, module):
+    """
+    like exec() but can return values
+    https://stackoverflow.com/a/52361938/5862977
+    """
+    code_ast = ast.parse(code)
+
+    init_ast = copy.deepcopy(code_ast)
+    init_ast.body = code_ast.body[:-1]
+
+    last_ast = copy.deepcopy(code_ast)
+    last_ast.body = code_ast.body[-1:]
+
+    exec(compile(init_ast, "<ast>", "exec"), module.__dict__)
+    if type(last_ast.body[0]) == ast.Expr:
+        return eval(compile(convertExpr2Expression(last_ast.body[0]), "<ast>", "eval"), module.__dict__)
+    else:
+        exec(compile(last_ast, "<ast>", "exec"), module.__dict__)
+
+
 class Script(scripts.Script):
 
     def title(self):
@@ -13,12 +44,23 @@ class Script(scripts.Script):
         return cmd_opts.allow_code
 
     def ui(self, is_img2img):
-        code = gr.Textbox(label="Python code", lines=1, elem_id=self.elem_id("code"))
+        example = """from modules.processing import process_images
+
+p.width = 768
+p.height = 768
+p.batch_size = 2
+p.steps = 10
+
+return process_images(p)
+"""
+
 
-        return [code]
+        code = gr.Code(value=example, language="python", label="Python code", elem_id=self.elem_id("code"))
+        indent_level = gr.Number(label='Indent level', value=2, precision=0, elem_id=self.elem_id("indent_level"))
 
+        return [code, indent_level]
 
-    def run(self, p, code):
+    def run(self, p, code, indent_level):
         assert cmd_opts.allow_code, '--allow-code option must be enabled'
 
         display_result_data = [[], -1, ""]
@@ -29,13 +71,20 @@ class Script(scripts.Script):
             display_result_data[2] = i
 
         from types import ModuleType
-        compiled = compile(code, '', 'exec')
         module = ModuleType("testmodule")
         module.__dict__.update(globals())
         module.p = p
         module.display = display
-        exec(compiled, module.__dict__)
 
-        return Processed(p, *display_result_data)
+        indent = " " * indent_level
+        indented = code.replace('\n', '\n' + indent)
+        body = f"""def __webuitemp__():
+{indent}{indented}
+__webuitemp__()"""
+
+        result = exec_with_return(body, module)
 
+        if isinstance(result, Processed):
+            return result
 
+        return Processed(p, *display_result_data)

scripts/img2imgalt.py

@@ -6,23 +6,21 @@ from tqdm import trange
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common
-from modules.processing import Processed
-from modules.shared import opts, cmd_opts, state
+from modules import processing, shared, sd_samplers, sd_samplers_common
 
 import torch
 import k_diffusion as K
 
-from PIL import Image
-from torch import autocast
-from einops import rearrange, repeat
-
-
 def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
 
     s_in = x.new_ones([x.shape[0]])
+    if shared.sd_model.parameterization == "v":
+        dnw = K.external.CompVisVDenoiser(shared.sd_model)
+        skip = 1
+    else:
         dnw = K.external.CompVisDenoiser(shared.sd_model)
+        skip = 0
     sigmas = dnw.get_sigmas(steps).flip(0)
 
     shared.state.sampling_steps = steps
@@ -37,7 +35,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
 
-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
         t = dnw.sigma_to_t(sigma_in)
 
         eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
@@ -69,7 +67,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
 
     s_in = x.new_ones([x.shape[0]])
+    if shared.sd_model.parameterization == "v":
+        dnw = K.external.CompVisVDenoiser(shared.sd_model)
+        skip = 1
+    else:
         dnw = K.external.CompVisDenoiser(shared.sd_model)
+        skip = 0
     sigmas = dnw.get_sigmas(steps).flip(0)
 
     shared.state.sampling_steps = steps
@@ -84,7 +87,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
 
-        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+        c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
 
         if i == 1:
             t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
@@ -213,4 +216,3 @@ class Script(scripts.Script):
         processed = processing.process_images(p)
 
         return processed
-

scripts/loopback.py

-import numpy as np
-from tqdm import trange
+import math
 
-import modules.scripts as scripts
 import gradio as gr
-
-from modules import processing, shared, sd_samplers, images
+import modules.scripts as scripts
+from modules import deepbooru, images, processing, shared
 from modules.processing import Processed
-from modules.sd_samplers import samplers
-from modules.shared import opts, cmd_opts, state
-from modules import deepbooru
+from modules.shared import opts, state
 
 
 class Script(scripts.Script):
@@ -20,39 +16,65 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):        
         loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
-        denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
+        final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
+        denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
         append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
 
-        return [loops, denoising_strength_change_factor, append_interrogation]
+        return [loops, final_denoising_strength, denoising_curve, append_interrogation]
 
-    def run(self, p, loops, denoising_strength_change_factor, append_interrogation):
+    def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
         processing.fix_seed(p)
         batch_count = p.n_iter
         p.extra_generation_params = {
-            "Denoising strength change factor": denoising_strength_change_factor,
+            "Final denoising strength": final_denoising_strength,
+            "Denoising curve": denoising_curve
         }
 
         p.batch_size = 1
         p.n_iter = 1
 
-        output_images, info = None, None
+        info = None
         initial_seed = None
         initial_info = None
+        initial_denoising_strength = p.denoising_strength
 
         grids = []
         all_images = []
         original_init_image = p.init_images
         original_prompt = p.prompt
+        original_inpainting_fill = p.inpainting_fill
         state.job_count = loops * batch_count
 
         initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
 
-        for n in range(batch_count):
+        def calculate_denoising_strength(loop):
+            strength = initial_denoising_strength
+
+            if loops == 1:
+                return strength
+
+            progress = loop / (loops - 1)
+            if denoising_curve == "Aggressive":
+                strength = math.sin((progress) * math.pi * 0.5)
+            elif denoising_curve == "Lazy":
+                strength = 1 - math.cos((progress) * math.pi * 0.5)
+            else:
+                strength = progress
+
+            change = (final_denoising_strength - initial_denoising_strength) * strength
+            return initial_denoising_strength + change
+
         history = []
 
+        for n in range(batch_count):
             # Reset to original init image at the start of each batch
             p.init_images = original_init_image
 
+            # Reset to original denoising strength
+            p.denoising_strength = initial_denoising_strength
+
+            last_image = None
+
             for i in range(loops):
                 p.n_iter = 1
                 p.batch_size = 1
@@ -72,25 +94,45 @@ class Script(scripts.Script):
 
                 processed = processing.process_images(p)
 
+                # Generation cancelled.
+                if state.interrupted:
+                    break
+
                 if initial_seed is None:
                     initial_seed = processed.seed
                     initial_info = processed.info
 
-                init_img = processed.images[0]
-
-                p.init_images = [init_img]
                 p.seed = processed.seed + 1
-                p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
-                history.append(processed.images[0])
+                p.denoising_strength = calculate_denoising_strength(i + 1)
+                
+                if state.skipped:
+                    break
+
+                last_image = processed.images[0]
+                p.init_images = [last_image]
+                p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
+
+                if batch_count == 1:
+                    history.append(last_image)
+                    all_images.append(last_image)
 
+            if batch_count > 1 and not state.skipped and not state.interrupted:
+                history.append(last_image)
+                all_images.append(last_image)
+
+            p.inpainting_fill = original_inpainting_fill
+                
+            if state.interrupted:
+                    break
+
+        if len(history) > 1:
             grid = images.image_grid(history, rows=1)
             if opts.grid_save:
                 images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
 
+            if opts.return_grid:
                 grids.append(grid)
-            all_images += history
                 
-        if opts.return_grid:
         all_images = grids + all_images
 
         processed = Processed(p, all_images, initial_seed, initial_info)

scripts/postprocessing_upscale.py

@@ -4,8 +4,8 @@ import numpy as np
 from modules import scripts_postprocessing, shared
 import gradio as gr
 
-from modules.ui_components import FormRow
-
+from modules.ui_components import FormRow, ToolButton
+from modules.ui import switch_values_symbol
 
 upscale_cache = {}
 
@@ -17,14 +17,19 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
     def ui(self):
         selected_tab = gr.State(value=0)
 
+        with gr.Column():
+            with FormRow():
                 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")
+                            with gr.Column(elem_id="upscaling_column_size", scale=4):
+                                upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
+                                upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
+                            with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn")
                                 upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
 
             with FormRow():
@@ -34,6 +39,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
                 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")
 
+        upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False)
         tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
         tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
 

webui-macos-env.sh

@@ -11,7 +11,7 @@ fi
 
 export install_dir="$HOME"
 export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
-export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1"
+export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118"
 export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
 export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
 export PYTORCH_ENABLE_MPS_FALLBACK=1

webui-user.sh

@@ -43,4 +43,7 @@
 # Uncomment to enable accelerated launch
 #export ACCELERATE="True"
 
+# Uncomment to disable TCMalloc
+#export NO_TCMALLOC="True"
+
 ###########################################

webui.py

@@ -4,6 +4,7 @@ import time
 import importlib
 import signal
 import re
+import warnings
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
@@ -17,6 +18,11 @@ from modules import paths, timer, import_hook, errors
 startup_timer = timer.Timer()
 
 import torch
+import pytorch_lightning # pytorch_lightning should be imported after torch, but it re-enables warnings on import so import once to disable them
+warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
+warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
+
+
 startup_timer.record("import torch")
 
 import gradio
@@ -64,11 +70,51 @@ else:
     server_name = "0.0.0.0" if cmd_opts.listen else None
 
 
+def fix_asyncio_event_loop_policy():
+    """
+        The default `asyncio` event loop policy only automatically creates
+        event loops in the main threads. Other threads must create event
+        loops explicitly or `asyncio.get_event_loop` (and therefore
+        `.IOLoop.current`) will fail. Installing this policy allows event
+        loops to be created automatically on any thread, matching the
+        behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
+    """
+
+    import asyncio
+
+    if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
+        # "Any thread" and "selector" should be orthogonal, but there's not a clean
+        # interface for composing policies so pick the right base.
+        _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy  # type: ignore
+    else:
+        _BasePolicy = asyncio.DefaultEventLoopPolicy
+
+    class AnyThreadEventLoopPolicy(_BasePolicy):  # type: ignore
+        """Event loop policy that allows loop creation on any thread.
+        Usage::
+
+            asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+        """
+
+        def get_event_loop(self) -> asyncio.AbstractEventLoop:
+            try:
+                return super().get_event_loop()
+            except (RuntimeError, AssertionError):
+                # This was an AssertionError in python 3.4.2 (which ships with debian jessie)
+                # and changed to a RuntimeError in 3.4.3.
+                # "There is no current event loop in thread %r"
+                loop = self.new_event_loop()
+                self.set_event_loop(loop)
+                return loop
+
+    asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+
+
 def check_versions():
     if shared.cmd_opts.skip_version_check:
         return
 
-    expected_torch_version = "1.13.1"
+    expected_torch_version = "2.0.0"
 
     if version.parse(torch.__version__) < version.parse(expected_torch_version):
         errors.print_error_explanation(f"""
@@ -81,7 +127,7 @@ there are reports of issues with training tab on the latest version.
 Use --skip-version-check commandline argument to disable this check.
         """.strip())
 
-    expected_xformers_version = "0.0.16rc425"
+    expected_xformers_version = "0.0.17"
     if shared.xformers_available:
         import xformers
 
@@ -96,6 +142,8 @@ Use --skip-version-check commandline argument to disable this check.
 
 
 def initialize():
+    fix_asyncio_event_loop_policy()
+
     check_versions()
 
     extensions.list_extensions()
@@ -123,9 +171,6 @@ def initialize():
     modules.scripts.load_scripts()
     startup_timer.record("load scripts")
 
-    modelloader.load_upscalers()
-    startup_timer.record("load upscalers")
-
     modules.sd_vae.refresh_vae_list()
     startup_timer.record("refresh VAE")
 
@@ -147,6 +192,7 @@ def initialize():
     shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
+    shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
     startup_timer.record("opts onchange")
 
     shared.reload_hypernetworks()
@@ -240,7 +286,7 @@ def webui():
         shared.demo = modules.ui.create_ui()
         startup_timer.record("create ui")
 
-        if cmd_opts.gradio_queue:
+        if not cmd_opts.no_gradio_queue:
             shared.demo.queue(64)
 
         gradio_auth_creds = []

webui.sh

@@ -23,7 +23,7 @@ fi
 # Install directory without trailing slash
 if [[ -z "${install_dir}" ]]
 then
-    install_dir="/home/$(whoami)"
+    install_dir="${HOME}"
 fi
 
 # Name of the subdirectory (defaults to stable-diffusion-webui)
@@ -172,15 +172,30 @@ else
     exit 1
 fi
 
+# Try using TCMalloc on Linux
+prepare_tcmalloc() {
+    if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
+        TCMALLOC="$(ldconfig -p | grep -Po "libtcmalloc.so.\d" | head -n 1)"
+        if [[ ! -z "${TCMALLOC}" ]]; then
+            echo "Using TCMalloc: ${TCMALLOC}"
+            export LD_PRELOAD="${TCMALLOC}"
+        else
+            printf "\e[1m\e[31mCannot locate TCMalloc (improves CPU memory usage)\e[0m\n"
+        fi
+    fi
+}
+
 if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
 then
     printf "\n%s\n" "${delimiter}"
     printf "Accelerating launch.py..."
     printf "\n%s\n" "${delimiter}"
+    prepare_tcmalloc
     exec accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
 else
     printf "\n%s\n" "${delimiter}"
     printf "Launching launch.py..."
     printf "\n%s\n" "${delimiter}"
+    prepare_tcmalloc
     exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
 fi