Merge branch 'master' into tensorboard

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -44,7 +44,9 @@ body:
     id: commit
     attributes:
       label: Commit where the problem happens
-      description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
+      description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
+    validations:
+      required: true
   - type: dropdown
     id: platforms
     attributes:

.github/ISSUE_TEMPLATE/config.yml

+blank_issues_enabled: false
+contact_links:
+  - name: WebUI Community Support
+    url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
+    about: Please ask and answer questions here.

.github/ISSUE_TEMPLATE/feature_request.yml

 name: Feature request
 description: Suggest an idea for this project
 title: "[Feature Request]: "
-labels: ["suggestion"]
+labels: ["enhancement"]
 
 body:
   - type: checkboxes

.github/PULL_REQUEST_TEMPLATE/pull_request_template.md → .github/pull_request_template.md

@@ -18,8 +18,8 @@ More technical discussion about your changes go here, plus anything that a maint
 
 List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box.
  - OS: [e.g. Windows, Linux]
- - Browser [e.g. chrome, safari]
- - Graphics card [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB]
+ - Browser: [e.g. chrome, safari]
+ - Graphics card: [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB]
 
 **Screenshots or videos of your changes**
 

.github/workflows/on_pull_request.yaml

@@ -19,22 +19,19 @@ jobs:
       - name: Checkout Code
         uses: actions/checkout@v3
       - name: Set up Python 3.10
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
         with:
           python-version: 3.10.6
-      - uses: actions/cache@v2
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
+          cache: pip
+          cache-dependency-path: |
+            **/requirements*txt
       - name: Install PyLint
         run: | 
           python -m pip install --upgrade pip
           pip install pylint
       # This lets PyLint check to see if it can resolve imports
       - name: Install dependencies
-        run : |
+        run: |
           export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
           python launch.py
       - name: Analysing the code with pylint

.github/workflows/run_tests.yaml

+name: Run basic features tests on CPU with empty SD model
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.10.6
+          cache: pip
+          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
+      - name: Upload main app stdout-stderr
+        uses: actions/upload-artifact@v3
+        if: always()
+        with:
+          name: stdout-stderr
+          path: |
+            test/stdout.txt
+            test/stderr.txt

.gitignore

 __pycache__
 *.ckpt
+*.safetensors
 *.pth
 /ESRGAN/*
 /SwinIR/*
@@ -27,4 +28,7 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
-.vscode
\ No newline at end of file
+.vscode
+/extensions
+/test/stdout.txt
+/test/stderr.txt

CODEOWNERS

 *       @AUTOMATIC1111
+
+# if you were managing a localization and were removed from this file, this is because
+# the intended way to do localizations now is via extensions. See:
+# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
+# Make a repo with your localization and since you are still listed as a collaborator
+# you can add it to the wiki page yourself. This change is because some people complained
+# the git commit log is cluttered with things unrelated to almost everyone and
+# because I believe this is the best overall for the project to handle localizations almost
+# entirely without my oversight.
+
+

README.md

 # Stable Diffusion web UI
 A browser interface based on Gradio library for Stable Diffusion.
 
-![](txt2img_Screenshot.png)
-
-Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) wiki page for extra scripts developed by users.
+![](screenshot.png)
 
 ## Features
 [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
@@ -11,6 +9,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - One click install and run script (but you still must install python and git)
 - Outpainting
 - Inpainting
+- Color Sketch
 - Prompt Matrix
 - Stable Diffusion Upscale
 - Attention, specify parts of text that the model should pay more attention to
@@ -23,6 +22,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
     - have as many embeddings as you want and use any names you like for them
     - use multiple embeddings with different numbers of vectors per token
     - works with half precision floating point numbers
+    - train embeddings on 8GB (also reports of 6GB working)
 - Extras tab with:
     - GFPGAN, neural network that fixes faces
     - CodeFormer, face restoration tool as an alternative to GFPGAN
@@ -37,14 +37,14 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Interrupt processing at any time
 - 4GB video card support (also reports of 2GB working)
 - Correct seeds for batches
-- Prompt length validation
-     - get length of prompt in tokens as you type
-     - get a warning after generation if some text was truncated
+- Live prompt token length validation
 - Generation parameters
      - parameters you used to generate images are saved with that image
      - in PNG chunks for PNG, in EXIF for JPEG
      - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
      - can be disabled in settings
+     - 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)
 - Mouseover hints for most UI elements
@@ -59,33 +59,44 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - CLIP interrogator, a button that tries to guess prompt from an image
 - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
 - Batch Processing, process a group of files using img2img
-- Img2img Alternative
+- Img2img Alternative, reverse Euler method of cross attention control
 - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
 - Reloading checkpoints on the fly
-- Checkpoint Merger, a tab that allows you to merge two checkpoints into one
+- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
 - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
 - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
      - separate prompts using uppercase `AND`
      - 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 (add --deepdanbooru to commandline args)
+- 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)
+- 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
+     - hypernetworks and embeddings options
+     - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
+- Clip skip
+- Use Hypernetworks
+- Use VAEs
+- Estimated completion time in progress bar
+- API
+- 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
 
 ## 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.
 
-Alternatively, use Google Colab:
+Alternatively, use online services (like Google Colab):
 
-- [Colab, maintained by Akaibu](https://colab.research.google.com/drive/1kw3egmSn-KgWsikYvOMjJkVDsPLjEMzl)
-- [Colab, original by me, outdated](https://colab.research.google.com/drive/1Iy-xW9t1-OQWhb0hNxueGij8phCyluOh).
+- [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"
 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. Place `model.ckpt` in the `models` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
-5. _*(Optional)*_ Place `GFPGANv1.4.pth` in the base directory, alongside `webui.py` (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
-6. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
+4. Place stable diffusion checkpoint (`model.ckpt`) in the `models/Stable-diffusion` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
+5. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
 
 ### Automatic Installation on Linux
 1. Install the dependencies:
@@ -113,6 +124,8 @@ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC
 The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
 
 ## Credits
+Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
+
 - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
 - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
 - GFPGAN - https://github.com/TencentARC/GFPGAN.git
@@ -121,15 +134,18 @@ The documentation was moved from this README over to the project's [wiki](https:
 - SwinIR - https://github.com/JingyunLiang/SwinIR
 - Swin2SR - https://github.com/mv-lab/swin2sr
 - LDSR - https://github.com/Hafiidz/latent-diffusion
+- MiDaS - https://github.com/isl-org/MiDaS
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
-- Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
-- InvokeAI, lstein - Cross Attention layer optimization - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
-- Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
+- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
+- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
+- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
+- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
 - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
 - xformers - https://github.com/facebookresearch/xformers
 - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
+- Security advice - RyotaK
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

configs/alt-diffusion-inference.yaml

+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: modules.xlmr.BertSeriesModelWithTransformation
+      params:
+        name: "XLMR-Large"
\ No newline at end of file

configs/v1-inference.yaml

+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

modules/ldsr_model_arch.py → extensions-builtin/LDSR/ldsr_model_arch.py

+import os
 import gc
 import time
 import warnings
@@ -8,27 +9,49 @@ import torchvision
 from PIL import Image
 from einops import rearrange, repeat
 from omegaconf import OmegaConf
+import safetensors.torch
 
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import instantiate_from_config, ismap
+from modules import shared, sd_hijack
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
+cached_ldsr_model: torch.nn.Module = None
+
 
 # Create LDSR Class
 class LDSR:
     def load_model_from_config(self, half_attention):
-        print(f"Loading model from {self.modelPath}")
-        pl_sd = torch.load(self.modelPath, map_location="cpu")
-        sd = pl_sd["state_dict"]
-        config = OmegaConf.load(self.yamlPath)
-        model = instantiate_from_config(config.model)
-        model.load_state_dict(sd, strict=False)
-        model.cuda()
-        if half_attention:
-            model = model.half()
-
-        model.eval()
+        global cached_ldsr_model
+
+        if shared.opts.ldsr_cached and cached_ldsr_model is not None:
+            print("Loading model from cache")
+            model: torch.nn.Module = cached_ldsr_model
+        else:
+            print(f"Loading model from {self.modelPath}")
+            _, extension = os.path.splitext(self.modelPath)
+            if extension.lower() == ".safetensors":
+                pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
+            else:
+                pl_sd = torch.load(self.modelPath, map_location="cpu")
+            sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
+            config = OmegaConf.load(self.yamlPath)
+            config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
+            model: torch.nn.Module = instantiate_from_config(config.model)
+            model.load_state_dict(sd, strict=False)
+            model = model.to(shared.device)
+            if half_attention:
+                model = model.half()
+            if shared.cmd_opts.opt_channelslast:
+                model = model.to(memory_format=torch.channels_last)
+
+            sd_hijack.model_hijack.hijack(model) # apply optimization
+            model.eval()
+
+            if shared.opts.ldsr_cached:
+                cached_ldsr_model = model
+
         return {"model": model}
 
     def __init__(self, model_path, yaml_path):
@@ -93,7 +116,8 @@ class LDSR:
         down_sample_method = 'Lanczos'
 
         gc.collect()
-        torch.cuda.empty_cache()
+        if torch.cuda.is_available:
+            torch.cuda.empty_cache()
 
         im_og = image
         width_og, height_og = im_og.size
@@ -101,8 +125,8 @@ class LDSR:
         down_sample_rate = target_scale / 4
         wd = width_og * down_sample_rate
         hd = height_og * down_sample_rate
-        width_downsampled_pre = int(wd)
-        height_downsampled_pre = int(hd)
+        width_downsampled_pre = int(np.ceil(wd))
+        height_downsampled_pre = int(np.ceil(hd))
 
         if down_sample_rate != 1:
             print(
@@ -110,7 +134,12 @@ class LDSR:
             im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
         else:
             print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        logs = self.run(model["model"], im_og, diffusion_steps, eta)
+        
+        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+        
+        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
 
         sample = logs["sample"]
         sample = sample.detach().cpu()
@@ -120,9 +149,14 @@ class LDSR:
         sample = np.transpose(sample, (0, 2, 3, 1))
         a = Image.fromarray(sample[0])
 
+        # remove padding
+        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
         del model
         gc.collect()
-        torch.cuda.empty_cache()
+        if torch.cuda.is_available:
+            torch.cuda.empty_cache()
+
         return a
 
 
@@ -137,7 +171,7 @@ def get_cond(selected_path):
     c = rearrange(c, '1 c h w -> 1 h w c')
     c = 2. * c - 1.
 
-    c = c.to(torch.device("cuda"))
+    c = c.to(shared.device)
     example["LR_image"] = c
     example["image"] = c_up
 

extensions-builtin/LDSR/preload.py

+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR'))

modules/ldsr_model.py → extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -5,8 +5,9 @@ import traceback
 from basicsr.utils.download_util import load_file_from_url
 
 from modules.upscaler import Upscaler, UpscalerData
-from modules.ldsr_model_arch import LDSR
-from modules import shared
+from ldsr_model_arch import LDSR
+from modules import shared, script_callbacks
+import sd_hijack_autoencoder, sd_hijack_ddpm_v1
 
 
 class UpscalerLDSR(Upscaler):
@@ -24,6 +25,7 @@ class UpscalerLDSR(Upscaler):
         yaml_path = os.path.join(self.model_path, "project.yaml")
         old_model_path = os.path.join(self.model_path, "model.pth")
         new_model_path = os.path.join(self.model_path, "model.ckpt")
+        safetensors_model_path = os.path.join(self.model_path, "model.safetensors")
         if os.path.exists(yaml_path):
             statinfo = os.stat(yaml_path)
             if statinfo.st_size >= 10485760:
@@ -32,8 +34,11 @@ class UpscalerLDSR(Upscaler):
         if os.path.exists(old_model_path):
             print("Renaming model from model.pth to model.ckpt")
             os.rename(old_model_path, new_model_path)
-        model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                   file_name="model.ckpt", progress=True)
+        if os.path.exists(safetensors_model_path):
+            model = safetensors_model_path
+        else:
+            model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                       file_name="model.ckpt", progress=True)
         yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
                                   file_name="project.yaml", progress=True)
 
@@ -52,3 +57,13 @@ class UpscalerLDSR(Upscaler):
             return img
         ddim_steps = shared.opts.ldsr_steps
         return ldsr.super_resolution(img, ddim_steps, self.scale)
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling")))
+    shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/ScuNET/preload.py

+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET'))

modules/scunet_model.py → extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -9,7 +9,7 @@ from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
 from modules import devices, modelloader
-from modules.scunet_model_arch import SCUNet as net
+from scunet_model_arch import SCUNet as net
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -49,14 +49,13 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         if model is None:
             return img
 
-        device = devices.device_scunet
+        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)
 
-        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
@@ -67,7 +66,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         return PIL.Image.fromarray(output, 'RGB')
 
     def load_model(self, path: str):
-        device = devices.device_scunet
+        device = devices.get_device_for('scunet')
         if "http" in path:
             filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
                                           progress=True)

extensions-builtin/SwinIR/preload.py

+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR'))

modules/swinir_model.py → extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -7,15 +7,14 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
-from modules import modelloader
-from modules.shared import cmd_opts, opts, device
-from modules.swinir_model_arch import SwinIR as net
-from modules.swinir_model_arch_v2 import Swin2SR as net2
+from modules import modelloader, devices, script_callbacks, shared
+from modules.shared import cmd_opts, opts
+from swinir_model_arch import SwinIR as net
+from swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
 
-precision_scope = (
-    torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
-)
+
+device_swinir = devices.get_device_for('swinir')
 
 
 class UpscalerSwinIR(Upscaler):
@@ -42,7 +41,7 @@ class UpscalerSwinIR(Upscaler):
         model = self.load_model(model_file)
         if model is None:
             return img
-        model = model.to(device)
+        model = model.to(device_swinir, dtype=devices.dtype)
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
@@ -94,25 +93,27 @@ class UpscalerSwinIR(Upscaler):
             model.load_state_dict(pretrained_model[params], strict=True)
         else:
             model.load_state_dict(pretrained_model, strict=True)
-        if not cmd_opts.no_half:
-            model = model.half()
         return model
 
 
 def upscale(
         img,
         model,
-        tile=opts.SWIN_tile,
-        tile_overlap=opts.SWIN_tile_overlap,
+        tile=None,
+        tile_overlap=None,
         window_size=8,
         scale=4,
 ):
+    tile = tile or opts.SWIN_tile
+    tile_overlap = tile_overlap or opts.SWIN_tile_overlap
+
+
     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(), precision_scope("cuda"):
+    img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
+    with torch.no_grad(), devices.autocast():
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
         w_pad = (w_old // window_size + 1) * window_size - w_old
@@ -139,8 +140,8 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     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(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
-    W = torch.zeros_like(E, dtype=torch.half, device=device)
+    E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
+    W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
 
     with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
         for h_idx in h_idx_list:
@@ -159,3 +160,13 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     output = E.div_(W)
 
     return output
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
+    shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

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

+// Stable Diffusion WebUI - Bracket checker
+// Version 1.0
+// By Hingashi no Florin/Bwin4L
+// 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 = evt.target;
+  tabName = evt.target.parentElement.parentElement.id.split("_")[0];
+  counterElt = document.querySelector('gradio-app').shadowRoot.querySelector('#' + tabName + '_token_counter');
+
+  promptName = evt.target.parentElement.parentElement.id.includes('neg') ? ' negative' : '';
+
+  errorStringParen  = '(' + tabName + promptName + ' prompt) - Different number of opening and closing parentheses detected.\n';
+  errorStringSquare = '[' + tabName + promptName + ' prompt] - Different number of opening and closing square brackets detected.\n';
+  errorStringCurly  = '{' + tabName + promptName + ' prompt} - 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, '');
+  }
+
+  if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
+    if(!counterElt.title.includes(errorStringCurly)) {
+      counterElt.title += errorStringCurly;
+    }
+  } else {
+    counterElt.title = counterElt.title.replace(errorStringCurly, '');
+  }
+
+  if(counterElt.title != '') {
+    counterElt.style = 'color: #FF5555;';
+  } else {
+    counterElt.style = '';
+  }
+}
+
+var shadowRootLoaded = setInterval(function() {
+  var shadowTextArea = document.querySelector('gradio-app').shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea');
+  if(shadowTextArea.length < 1) {
+      return false;
+  }
+
+  clearInterval(shadowRootLoaded);
+
+  document.querySelector('gradio-app').shadowRoot.querySelector('#txt2img_prompt').onkeyup = checkBrackets;
+  document.querySelector('gradio-app').shadowRoot.querySelector('#txt2img_neg_prompt').onkeyup = checkBrackets;
+  document.querySelector('gradio-app').shadowRoot.querySelector('#img2img_prompt').onkeyup = checkBrackets;
+  document.querySelector('gradio-app').shadowRoot.querySelector('#img2img_neg_prompt').onkeyup = checkBrackets;
+}, 1000);

extensions-builtin/roll-artist/scripts/roll-artist.py

+import random
+
+from modules import script_callbacks, shared
+import gradio as gr
+
+art_symbol = '\U0001f3a8'  # 🎨
+global_prompt = None
+related_ids = {"txt2img_prompt", "txt2img_clear_prompt", "img2img_prompt", "img2img_clear_prompt" }
+
+
+def roll_artist(prompt):
+    allowed_cats = set([x for x in shared.artist_db.categories() if len(shared.opts.random_artist_categories)==0 or x in shared.opts.random_artist_categories])
+    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+    return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+def add_roll_button(prompt):
+    roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+
+    roll.click(
+        fn=roll_artist,
+        _js="update_txt2img_tokens",
+        inputs=[
+            prompt,
+        ],
+        outputs=[
+            prompt,
+        ]
+    )
+
+
+def after_component(component, **kwargs):
+    global global_prompt
+
+    elem_id = kwargs.get('elem_id', None)
+    if elem_id not in related_ids:
+        return
+
+    if elem_id == "txt2img_prompt":
+        global_prompt = component
+    elif elem_id == "txt2img_clear_prompt":
+        add_roll_button(global_prompt)
+    elif elem_id == "img2img_prompt":
+        global_prompt = component
+    elif elem_id == "img2img_clear_prompt":
+        add_roll_button(global_prompt)
+
+
+script_callbacks.on_after_component(after_component)

html/footer.html

+<div>
+        <a href="/docs">API</a>
+         • 
+        <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
+         • 
+        <a href="https://gradio.app">Gradio</a>
+         • 
+        <a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
+</div>
+<br />
+<div class="versions">
+{versions}
+</div>

javascript/aspectRatioOverlay.js

@@ -3,12 +3,12 @@ let currentWidth = null;
 let currentHeight = null;
 let arFrameTimeout = setTimeout(function(){},0);
 
-function dimensionChange(e,dimname){
+function dimensionChange(e, is_width, is_height){
 
-	if(dimname == 'Width'){
+	if(is_width){
 		currentWidth = e.target.value*1.0
 	}
-	if(dimname == 'Height'){
+	if(is_height){
 		currentHeight = e.target.value*1.0
 	}
 
@@ -18,22 +18,13 @@ function dimensionChange(e,dimname){
 		return;
 	}
 
-	var img2imgMode = gradioApp().querySelector('#mode_img2img.tabs > div > button.rounded-t-lg.border-gray-200')
-	if(img2imgMode){
-		img2imgMode=img2imgMode.innerText
-	}else{
-		return;
-	}
-
-	var redrawImage = gradioApp().querySelector('div[data-testid=image] img');
-	var inpaintImage = gradioApp().querySelector('#img2maskimg div[data-testid=image] img')
-
 	var targetElement = null;
 
-	if(img2imgMode=='img2img' && redrawImage){
-		targetElement = redrawImage;
-	}else if(img2imgMode=='Inpaint' && inpaintImage){
-		targetElement = inpaintImage;
+    var tabIndex = get_tab_index('mode_img2img')
+	if(tabIndex == 0){
+		targetElement = gradioApp().querySelector('div[data-testid=image] img');
+	} else if(tabIndex == 1){
+		targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
 	}
 
 	if(targetElement){
@@ -98,22 +89,20 @@ onUiUpdate(function(){
 	var inImg2img   = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
 	if(inImg2img){
 		let inputs = gradioApp().querySelectorAll('input');
-		inputs.forEach(function(e){ 
-			let parentLabel = e.parentElement.querySelector('label')
-			if(parentLabel && parentLabel.innerText){
-				if(!e.classList.contains('scrollwatch')){
-					if(parentLabel.innerText == 'Width' || parentLabel.innerText == 'Height'){
-						e.addEventListener('input', function(e){dimensionChange(e,parentLabel.innerText)} )
-						e.classList.add('scrollwatch')
-					}
-					if(parentLabel.innerText == 'Width'){
-						currentWidth = e.value*1.0
-					}
-					if(parentLabel.innerText == 'Height'){
-						currentHeight = e.value*1.0
-					}
-				}
-			} 
+		inputs.forEach(function(e){
+		    var is_width = e.parentElement.id == "img2img_width"
+		    var is_height = e.parentElement.id == "img2img_height"
+
+			if((is_width || is_height) && !e.classList.contains('scrollwatch')){
+				e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
+				e.classList.add('scrollwatch')
+			}
+			if(is_width){
+				currentWidth = e.value*1.0
+			}
+			if(is_height){
+				currentHeight = e.value*1.0
+			}
 		})
 	}
 });

javascript/contextMenus.js

@@ -9,7 +9,7 @@ contextMenuInit = function(){
 
   function showContextMenu(event,element,menuEntries){
     let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
-    let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; 
+    let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
 
     let oldMenu = gradioApp().querySelector('#context-menu')
     if(oldMenu){
@@ -61,15 +61,15 @@ contextMenuInit = function(){
 
   }
 
-  function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
-    
-    currentItems = menuSpecs.get(targetEmementSelector)
-    
+  function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
+
+    currentItems = menuSpecs.get(targetElementSelector)
+
     if(!currentItems){
       currentItems = []
-      menuSpecs.set(targetEmementSelector,currentItems);
+      menuSpecs.set(targetElementSelector,currentItems);
     }
-    let newItem = {'id':targetEmementSelector+'_'+uid(), 
+    let newItem = {'id':targetElementSelector+'_'+uid(),
                    'name':entryName,
                    'func':entryFunction,
                    'isNew':true}
@@ -97,7 +97,7 @@ contextMenuInit = function(){
       if(source.id && source.id.indexOf('check_progress')>-1){
         return
       }
-      
+
       let oldMenu = gradioApp().querySelector('#context-menu')
       if(oldMenu){
         oldMenu.remove()
@@ -117,7 +117,7 @@ contextMenuInit = function(){
       })
     });
     eventListenerApplied=true
-  
+
   }
 
   return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
@@ -152,8 +152,8 @@ addContextMenuEventListener = initResponse[2];
     generateOnRepeat('#img2img_generate','#img2img_interrupt');
   })
 
-  let cancelGenerateForever = function(){ 
-    clearInterval(window.generateOnRepeatInterval) 
+  let cancelGenerateForever = function(){
+    clearInterval(window.generateOnRepeatInterval)
   }
 
   appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
@@ -162,7 +162,7 @@ addContextMenuEventListener = initResponse[2];
   appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
 
   appendContextMenuOption('#roll','Roll three',
-    function(){ 
+    function(){
       let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
       setTimeout(function(){rollbutton.click()},100)
       setTimeout(function(){rollbutton.click()},200)

javascript/dragdrop.js

@@ -9,11 +9,19 @@ function dropReplaceImage( imgWrap, files ) {
         return;
     }
 
+    const tmpFile = files[0];
+
     imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
     const callback = () => {
         const fileInput = imgWrap.querySelector('input[type="file"]');
         if ( fileInput ) {
-            fileInput.files = files;
+            if ( files.length === 0 ) {
+                files = new DataTransfer();
+                files.items.add(tmpFile);
+                fileInput.files = files.files;
+            } else {
+                fileInput.files = files;
+            }
             fileInput.dispatchEvent(new Event('change'));   
         }
     };
@@ -43,7 +51,7 @@ function dropReplaceImage( imgWrap, files ) {
 window.document.addEventListener('dragover', e => {
     const target = e.composedPath()[0];
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) {
+    if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
         return;
     }
     e.stopPropagation();

javascript/edit-attention.js

 addEventListener('keydown', (event) => {
 	let target = event.originalTarget || event.composedPath()[0];
-	if (!target.hasAttribute("placeholder")) return;
-	if (!target.placeholder.toLowerCase().includes("prompt")) return;
+	if (!target.matches("#toprow textarea.gr-text-input[placeholder]")) return;
 	if (! (event.metaKey || event.ctrlKey)) return;
 
 

javascript/extensions.js

+
+function extensions_apply(_, _){
+    disable = []
+    update = []
+    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
+        if(x.name.startsWith("enable_") && ! x.checked)
+            disable.push(x.name.substr(7))
+
+        if(x.name.startsWith("update_") && x.checked)
+            update.push(x.name.substr(7))
+    })
+
+    restart_reload()
+
+    return [JSON.stringify(disable), JSON.stringify(update)]
+}
+
+function extensions_check(){
+    gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
+        x.innerHTML = "Loading..."
+    })
+
+    return []
+}
+
+function install_extension_from_index(button, url){
+    button.disabled = "disabled"
+    button.value = "Installing..."
+
+    textarea = gradioApp().querySelector('#extension_to_install textarea')
+    textarea.value = url
+	textarea.dispatchEvent(new Event("input", { bubbles: true }))
+
+    gradioApp().querySelector('#install_extension_button').click()
+}

javascript/generationParams.js

+// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
+
+let txt2img_gallery, img2img_gallery, modal = undefined;
+onUiUpdate(function(){
+	if (!txt2img_gallery) {
+		txt2img_gallery = attachGalleryListeners("txt2img")
+	}
+	if (!img2img_gallery) {
+		img2img_gallery = attachGalleryListeners("img2img")
+	}
+	if (!modal) {
+		modal = gradioApp().getElementById('lightboxModal')
+		modalObserver.observe(modal,  { attributes : true, attributeFilter : ['style'] });
+	}
+});
+
+let modalObserver = new MutationObserver(function(mutations) {
+	mutations.forEach(function(mutationRecord) {
+		let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
+		if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
+			gradioApp().getElementById(selectedTab+"_generation_info_button").click()
+	});
+});
+
+function attachGalleryListeners(tab_name) {
+	gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
+	gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
+	gallery?.addEventListener('keydown', (e) => {
+		if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
+			gradioApp().getElementById(tab_name+"_generation_info_button").click()
+	});
+	return gallery;
+}

javascript/hints.js

@@ -4,8 +4,9 @@ titles = {
     "Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results",
     "Sampling method": "Which algorithm to use to produce the image",
 	"GFPGAN": "Restore low quality faces using GFPGAN neural network",
-	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
+	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
 	"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
+	"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", 
 
 	"Batch count": "How many batches of images to create",
 	"Batch size": "How many image to create in a single batch",
@@ -17,6 +18,7 @@ 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",
+    "\U0001F5D1": "Clear prompt",
     "\u{1f4cb}": "Apply selected styles to current prompt",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
@@ -62,8 +64,8 @@ titles = {
 
     "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
 
-    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [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]; leave empty for default.",
+    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [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]; 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.",
@@ -72,15 +74,13 @@ titles = {
     "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",
     "Apply style": "Insert selected styles into prompt fields",
-    "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.",
+    "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style uses that as a placeholder for your prompt when you use the style in the future.",
 
     "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
+    "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.",
 
     "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
 
-    "Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
-    "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
-
     "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
     "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
 
@@ -92,7 +92,19 @@ titles = {
     "Weighted sum": "Result = A * (1 - M) + B * M",
     "Add difference": "Result = A + (B - C) * M",
 
-    "Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+	"Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors",
+    "Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+
+    "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
+
+    "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resolution and lower quality.",
+    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resolution and extremely low quality.",
+
+    "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
+    "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.",
+    "Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.",
+    "Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.",
+    "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders."
 }
 
 

javascript/hires_fix.js

+
+function setInactive(elem, inactive){
+    console.log(elem)
+    if(inactive){
+        elem.classList.add('inactive')
+    } else{
+        elem.classList.remove('inactive')
+    }
+}
+
+function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
+    console.log(enable, width, height, hr_scale, hr_resize_x, hr_resize_y)
+
+    hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
+    hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
+    hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
+
+    gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
+
+    setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0)
+    setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0)
+    setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0)
+
+    return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]
+}

javascript/images_history.js

-var images_history_click_image = function(){
-    if (!this.classList.contains("transform")){        
-        var gallery = images_history_get_parent_by_class(this, "images_history_cantainor");
-        var buttons = gallery.querySelectorAll(".gallery-item");
-        var i = 0;
-        var hidden_list = [];
-        buttons.forEach(function(e){
-            if (e.style.display == "none"){
-                hidden_list.push(i);
-            }
-            i += 1;
-        })
-        if (hidden_list.length > 0){
-            setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
-        }        
-    }    
-    images_history_set_image_info(this); 
-}
-
-var images_history_click_tab = function(){
-    var tabs_box = gradioApp().getElementById("images_history_tab");
-    if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
-        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click();
-        tabs_box.classList.add(this.getAttribute("tabname"))
-    }                
-}
-
-function images_history_disabled_del(){
-    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
-        btn.setAttribute('disabled','disabled');
-    }); 
-}
-
-function images_history_get_parent_by_class(item, class_name){
-    var parent = item.parentElement;
-    while(!parent.classList.contains(class_name)){
-        parent = parent.parentElement;
-    }
-    return parent;  
-}
-
-function images_history_get_parent_by_tagname(item, tagname){
-    var parent = item.parentElement;
-    tagname = tagname.toUpperCase()
-    while(parent.tagName != tagname){
-        console.log(parent.tagName, tagname)
-        parent = parent.parentElement;
-    }  
-    return parent;
-}
-
-function images_history_hide_buttons(hidden_list, gallery){
-    var buttons = gallery.querySelectorAll(".gallery-item");
-    var num = 0;
-    buttons.forEach(function(e){
-        if (e.style.display == "none"){
-            num += 1;
-        }
-    });
-    if (num == hidden_list.length){
-        setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
-    } 
-    for( i in hidden_list){
-        buttons[hidden_list[i]].style.display = "none";
-    }    
-}
-
-function images_history_set_image_info(button){
-    var buttons = images_history_get_parent_by_tagname(button, "DIV").querySelectorAll(".gallery-item");
-    var index = -1;
-    var i = 0;
-    buttons.forEach(function(e){
-        if(e == button){
-            index = i;
-        }
-        if(e.style.display != "none"){
-            i += 1;
-        }        
-    });
-    var gallery = images_history_get_parent_by_class(button, "images_history_cantainor");
-    var set_btn = gallery.querySelector(".images_history_set_index");
-    var curr_idx = set_btn.getAttribute("img_index", index);  
-    if (curr_idx != index) {
-        set_btn.setAttribute("img_index", index);        
-        images_history_disabled_del();
-    }
-    set_btn.click();
-    
-}
-
-function images_history_get_current_img(tabname, image_path, files){
-    return [
-        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"), 
-        image_path, 
-        files
-    ];
-}
-
-function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){
-    image_index = parseInt(image_index);
-    var tab = gradioApp().getElementById(tabname + '_images_history');
-    var set_btn = tab.querySelector(".images_history_set_index");
-    var buttons = [];
-    tab.querySelectorAll(".gallery-item").forEach(function(e){
-        if (e.style.display != 'none'){
-            buttons.push(e);
-        }
-    });    
-    var img_num = buttons.length / 2;
-    if (img_num <= del_num){
-        setTimeout(function(tabname){
-            gradioApp().getElementById(tabname + '_images_history_renew_page').click();
-        }, 30, tabname); 
-    } else {
-        var next_img  
-        for (var i = 0; i < del_num; i++){
-            if (image_index + i < image_index + img_num){
-                buttons[image_index + i].style.display = 'none';
-                buttons[image_index + img_num + 1].style.display = 'none';
-                next_img = image_index + i + 1
-            }
-        }
-        var bnt;
-        if (next_img  >= img_num){
-            btn = buttons[image_index - del_num];
-        } else {            
-            btn = buttons[next_img];          
-        } 
-        setTimeout(function(btn){btn.click()}, 30, btn);
-    }
-    images_history_disabled_del();  
-    return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index];
-}
-
-function images_history_turnpage(img_path, page_index, image_index, tabname){
-    var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
-    buttons.forEach(function(elem) {
-        elem.style.display = 'block';
-    })
-    return [img_path, page_index, image_index, tabname];
-}
-
-function images_history_enable_del_buttons(){
-    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
-        btn.removeAttribute('disabled');
-    })
-}
-
-function images_history_init(){ 
-    var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page')
-    if (load_txt2img_button){        
-        for (var i in images_history_tab_list ){
-            tab = images_history_tab_list[i];
-            gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
-            gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index");
-            gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button");
-            gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");            
-                     
-        }
-        var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
-        tabs_box.setAttribute("id", "images_history_tab");        
-        var tab_btns = tabs_box.querySelectorAll("button");        
-        for (var i in images_history_tab_list){               
-            var tabname = images_history_tab_list[i]
-            tab_btns[i].setAttribute("tabname", tabname);
-
-            // this refreshes history upon tab switch
-            // until the history is known to work well, which is not the case now, we do not do this at startup
-            //tab_btns[i].addEventListener('click', images_history_click_tab);
-        }    
-        tabs_box.classList.add(images_history_tab_list[0]);
-
-        // same as above, at page load
-        //load_txt2img_button.click();
-    } else {
-        setTimeout(images_history_init, 500);
-    } 
-}
-
-var images_history_tab_list = ["txt2img", "img2img", "extras"];
-setTimeout(images_history_init, 500);
-document.addEventListener("DOMContentLoaded", function() {
-    var mutationObserver = new MutationObserver(function(m){
-        for (var i in images_history_tab_list ){
-            let tabname = images_history_tab_list[i]
-            var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
-            buttons.forEach(function(bnt){    
-                bnt.addEventListener('click', images_history_click_image, true);
-            });
-
-            // same as load_txt2img_button.click() above
-            /*
-            var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
-            if (cls_btn){
-                cls_btn.addEventListener('click', function(){
-                    gradioApp().getElementById(tabname + '_images_history_renew_page').click();
-                }, false);
-            }*/
-
-        }     
-    });
-    mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
-
-});
-
-

javascript/imageviewer.js

@@ -13,6 +13,15 @@ function showModal(event) {
     }
     lb.style.display = "block";
     lb.focus()
+
+    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
+    const tabImg2Img = gradioApp().getElementById("tab_img2img")
+    // show the save button in modal only on txt2img or img2img tabs
+    if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") {
+        gradioApp().getElementById("modal_save").style.display = "inline"
+    } else {
+        gradioApp().getElementById("modal_save").style.display = "none"
+    }
     event.stopPropagation()
 }
 
@@ -81,6 +90,25 @@ function modalImageSwitch(offset) {
     }
 }
 
+function saveImage(){
+    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
+    const tabImg2Img = gradioApp().getElementById("tab_img2img")
+    const saveTxt2Img = "save_txt2img"
+    const saveImg2Img = "save_img2img"
+    if (tabTxt2Img.style.display != "none") {
+        gradioApp().getElementById(saveTxt2Img).click()
+    } else if (tabImg2Img.style.display != "none") {
+        gradioApp().getElementById(saveImg2Img).click()
+    } else {
+        console.error("missing implementation for saving modal of this type")
+    }
+}
+
+function modalSaveImage(event) {
+    saveImage()
+    event.stopPropagation()
+}
+
 function modalNextImage(event) {
     modalImageSwitch(1)
     event.stopPropagation()
@@ -93,6 +121,9 @@ function modalPrevImage(event) {
 
 function modalKeyHandler(event) {
     switch (event.key) {
+        case "s":
+            saveImage()
+            break;
         case "ArrowLeft":
             modalPrevImage(event)
             break;
@@ -117,9 +148,10 @@ function showGalleryImage() {
                 if(e && e.parentElement.tagName == 'DIV'){
                     e.style.cursor='pointer'
                     e.style.userSelect='none'
-                    e.addEventListener('click', function (evt) {
-                        if(!opts.js_modal_lightbox) return;
+                    e.addEventListener('mousedown', 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);
                 }
@@ -198,6 +230,14 @@ document.addEventListener("DOMContentLoaded", function() {
     modalTileImage.title = "Preview tiling";
     modalControls.appendChild(modalTileImage)
 
+    const modalSave = document.createElement("span")
+    modalSave.className = "modalSave cursor"
+    modalSave.id = "modal_save"
+    modalSave.innerHTML = "🖫"
+    modalSave.addEventListener("click", modalSaveImage, true)
+    modalSave.title = "Save Image(s)"
+    modalControls.appendChild(modalSave)
+
     const modalClose = document.createElement('span')
     modalClose.className = 'modalClose cursor';
     modalClose.innerHTML = '×'

javascript/localization.js

@@ -108,6 +108,9 @@ function processNode(node){
 
 function dumpTranslations(){
     dumped = {}
+    if (localization.rtl) {
+        dumped.rtl = true
+    }
 
     Object.keys(original_lines).forEach(function(text){
         if(dumped[text] !== undefined)  return
@@ -129,6 +132,24 @@ onUiUpdate(function(m){
 
 document.addEventListener("DOMContentLoaded", function() {
     processNode(gradioApp())
+
+    if (localization.rtl) {  // if the language is from right to left,
+        (new MutationObserver((mutations, observer) => { // wait for the style to load
+            mutations.forEach(mutation => {
+                mutation.addedNodes.forEach(node => {
+                    if (node.tagName === 'STYLE') {
+                        observer.disconnect();
+
+                        for (const x of node.sheet.rules) {  // find all rtl media rules
+                            if (Array.from(x.media || []).includes('rtl')) {
+                                x.media.appendMedium('all');  // enable them
+                            }
+                        }
+                    }
+                })
+            });
+        })).observe(gradioApp(), { childList: true });
+    }
 })
 
 function download_localization() {

javascript/notification.js

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

javascript/progressbar.js

@@ -3,57 +3,75 @@ global_progressbars = {}
 galleries = {}
 galleryObservers = {}
 
+// this tracks launches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
+timeoutIds = {}
+
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
-    var progressbar = gradioApp().getElementById(id_progressbar)
+    // gradio 3.8's enlightened approach allows them to create two nested div elements inside each other with same id
+    // every time you use gr.HTML(elem_id='xxx'), so we handle this here
+    var progressbar = gradioApp().querySelector("#"+id_progressbar+" #"+id_progressbar)
+    var progressbarParent
+    if(progressbar){
+        progressbarParent = gradioApp().querySelector("#"+id_progressbar)
+    } else{
+        progressbar = gradioApp().getElementById(id_progressbar)
+        progressbarParent = null
+    }
+
     var skip = id_skip ? gradioApp().getElementById(id_skip) : null
     var interrupt = gradioApp().getElementById(id_interrupt)
-    
+
     if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
         if(progressbar.innerText){
-            let newtitle = 'Stable Diffusion - ' + progressbar.innerText
+            let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion';
             if(document.title != newtitle){
-                document.title =  newtitle;          
+                document.title =  newtitle;
             }
         }else{
             let newtitle = 'Stable Diffusion'
             if(document.title != newtitle){
-                document.title =  newtitle;          
+                document.title =  newtitle;
             }
         }
     }
-    
+
 	if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
 	    global_progressbars[id_progressbar] = progressbar
 
         var mutationObserver = new MutationObserver(function(m){
+            if(timeoutIds[id_part]) return;
+
             preview = gradioApp().getElementById(id_preview)
             gallery = gradioApp().getElementById(id_gallery)
 
             if(preview != null && gallery != null){
                 preview.style.width = gallery.clientWidth + "px"
                 preview.style.height = gallery.clientHeight + "px"
+                if(progressbarParent) progressbar.style.width = progressbarParent.clientWidth + "px"
 
 				//only watch gallery if there is a generation process going on
                 check_gallery(id_gallery);
 
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
-                if(!progressDiv){
+                if(progressDiv){
+                    timeoutIds[id_part] = window.setTimeout(function() {
+                        timeoutIds[id_part] = null
+                        requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt)
+                    }, 500)
+                } else{
                     if (skip) {
                         skip.style.display = "none"
                     }
                     interrupt.style.display = "none"
-			
+
                     //disconnect observer once generation finished, so user can close selected image if they want
                     if (galleryObservers[id_gallery]) {
                         galleryObservers[id_gallery].disconnect();
                         galleries[id_gallery] = null;
-                    }    
+                    }
                 }
-
-
             }
 
-            window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
         });
         mutationObserver.observe( progressbar, { childList:true, subtree:true })
 	}
@@ -74,14 +92,26 @@ function check_gallery(id_gallery){
             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 somenoe has a better solution please by all means
-                    setTimeout(function() { activeElement.focus() }, 1);
+                    // 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);
                 }
             }
         })

javascript/ui.js

-// various functions for interation with ui.py not large enough to warrant putting them in separate files
+// various functions for interaction with ui.py not large enough to warrant putting them in separate files
 
 function set_theme(theme){
     gradioURL = window.location.href
@@ -8,8 +8,8 @@ function set_theme(theme){
 }
 
 function selected_gallery_index(){
-    var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
-    var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
+    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 result = -1
     buttons.forEach(function(v, i){ if(v==button) { result = i } })
@@ -19,7 +19,7 @@ function selected_gallery_index(){
 
 function extract_image_from_gallery(gallery){
     if(gallery.length == 1){
-        return gallery[0]
+        return [gallery[0]]
     }
 
     index = selected_gallery_index()
@@ -28,7 +28,7 @@ function extract_image_from_gallery(gallery){
         return [null]
     }
 
-    return gallery[index];
+    return [gallery[index]];
 }
 
 function args_to_array(args){
@@ -45,16 +45,16 @@ function switch_to_txt2img(){
     return args_to_array(arguments);
 }
 
-function switch_to_img2img_img2img(){
+function switch_to_img2img(){
     gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
     gradioApp().getElementById('mode_img2img').querySelectorAll('button')[0].click();
 
     return args_to_array(arguments);
 }
 
-function switch_to_img2img_inpaint(){
+function switch_to_inpaint(){
     gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
-    gradioApp().getElementById('mode_img2img').querySelectorAll('button')[1].click();
+    gradioApp().getElementById('mode_img2img').querySelectorAll('button')[2].click();
 
     return args_to_array(arguments);
 }
@@ -65,26 +65,6 @@ function switch_to_extras(){
     return args_to_array(arguments);
 }
 
-function extract_image_from_gallery_txt2img(gallery){
-    switch_to_txt2img()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_img2img(gallery){
-    switch_to_img2img_img2img()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_inpaint(gallery){
-    switch_to_img2img_inpaint()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_extras(gallery){
-    switch_to_extras()
-    return extract_image_from_gallery(gallery);
-}
-
 function get_tab_index(tabId){
     var res = 0
 
@@ -120,7 +100,7 @@ function create_submit_args(args){
 
     // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
     // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
-    // I don't know why gradio is seding outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
+    // I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
     // If gradio at some point stops sending outputs, this may break something
     if(Array.isArray(res[res.length - 3])){
         res[res.length - 3] = null
@@ -151,6 +131,15 @@ function ask_for_style_name(_, prompt_text, negative_prompt_text) {
     return [name_, prompt_text, negative_prompt_text]
 }
 
+function confirm_clear_prompt(prompt, negative_prompt) {
+    if(confirm("Delete prompt?")) {
+        prompt = ""
+        negative_prompt = ""
+    }
+
+    return [prompt, negative_prompt]
+}
+
 
 
 opts = {}
@@ -199,6 +188,17 @@ onUiUpdate(function(){
 		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
 		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
 	}
+
+    show_all_pages = gradioApp().getElementById('settings_show_all_pages')
+    settings_tabs = gradioApp().querySelector('#settings div')
+    if(show_all_pages && settings_tabs){
+        settings_tabs.appendChild(show_all_pages)
+        show_all_pages.onclick = function(){
+            gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
+                elem.style.display = "block";
+            })
+        }
+    }
 })
 
 let txt2img_textarea, img2img_textarea = undefined;
@@ -228,4 +228,6 @@ function update_token_counter(button_id) {
 function restart_reload(){
     document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
     setTimeout(function(){location.reload()},2000)
+
+    return []
 }

modules/gfpgan_model.py

@@ -36,7 +36,9 @@ def gfpgann():
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
+    if hasattr(facexlib.detection.retinaface, 'device'):
+        facexlib.detection.retinaface.device = devices.device_gfpgan
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
     loaded_gfpgan_model = model
 
     return model