Merge branch 'master' of github.com:AUTOMATIC1111/stable-diffusion-webui

.eslintignore

+extensions
+extensions-disabled
+repositories
+venv
\ No newline at end of file

.eslintrc.js

+/* global module */
+module.exports = {
+    env: {
+        browser: true,
+        es2021: true,
+    },
+    extends: "eslint:recommended",
+    parserOptions: {
+        ecmaVersion: "latest",
+    },
+    rules: {
+        "arrow-spacing": "error",
+        "block-spacing": "error",
+        "brace-style": "error",
+        "comma-dangle": ["error", "only-multiline"],
+        "comma-spacing": "error",
+        "comma-style": ["error", "last"],
+        "curly": ["error", "multi-line", "consistent"],
+        "eol-last": "error",
+        "func-call-spacing": "error",
+        "function-call-argument-newline": ["error", "consistent"],
+        "function-paren-newline": ["error", "consistent"],
+        "indent": ["error", 4],
+        "key-spacing": "error",
+        "keyword-spacing": "error",
+        "linebreak-style": ["error", "unix"],
+        "no-extra-semi": "error",
+        "no-mixed-spaces-and-tabs": "error",
+        "no-multi-spaces": "error",
+        "no-redeclare": ["error", {builtinGlobals: false}],
+        "no-trailing-spaces": "error",
+        "no-unused-vars": "off",
+        "no-whitespace-before-property": "error",
+        "object-curly-newline": ["error", {consistent: true, multiline: true}],
+        "object-curly-spacing": ["error", "never"],
+        "operator-linebreak": ["error", "after"],
+        "quote-props": ["error", "consistent-as-needed"],
+        "semi": ["error", "always"],
+        "semi-spacing": "error",
+        "semi-style": ["error", "last"],
+        "space-before-blocks": "error",
+        "space-before-function-paren": ["error", "never"],
+        "space-in-parens": ["error", "never"],
+        "space-infix-ops": "error",
+        "space-unary-ops": "error",
+        "switch-colon-spacing": "error",
+        "template-curly-spacing": ["error", "never"],
+        "unicode-bom": "error",
+    },
+    globals: {
+        //script.js
+        gradioApp: "readonly",
+        onUiLoaded: "readonly",
+        onUiUpdate: "readonly",
+        onOptionsChanged: "readonly",
+        uiCurrentTab: "writable",
+        uiElementIsVisible: "readonly",
+        uiElementInSight: "readonly",
+        executeCallbacks: "readonly",
+        //ui.js
+        opts: "writable",
+        all_gallery_buttons: "readonly",
+        selected_gallery_button: "readonly",
+        selected_gallery_index: "readonly",
+        switch_to_txt2img: "readonly",
+        switch_to_img2img_tab: "readonly",
+        switch_to_img2img: "readonly",
+        switch_to_sketch: "readonly",
+        switch_to_inpaint: "readonly",
+        switch_to_inpaint_sketch: "readonly",
+        switch_to_extras: "readonly",
+        get_tab_index: "readonly",
+        create_submit_args: "readonly",
+        restart_reload: "readonly",
+        updateInput: "readonly",
+        //extraNetworks.js
+        requestGet: "readonly",
+        popup: "readonly",
+        // from python
+        localization: "readonly",
+        // progrssbar.js
+        randomId: "readonly",
+        requestProgress: "readonly",
+        // imageviewer.js
+        modalPrevImage: "readonly",
+        modalNextImage: "readonly",
+    }
+};

.git-blame-ignore-revs

+# Apply ESlint
+9c54b78d9dde5601e916f308d9a9d6953ec39430
\ No newline at end of file

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -47,6 +47,15 @@ body:
       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** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
     validations:
       required: true
+  - type: dropdown
+    id: py-version
+    attributes:
+      label: What Python version are you running on ?
+      multiple: false
+      options:
+        - Python 3.10.x
+        - Python 3.11.x (above, no supported yet)
+        - Python 3.9.x (below, no recommended)
   - type: dropdown
     id: platforms
     attributes:
@@ -59,6 +68,18 @@ body:
         - iOS
         - Android
         - Other/Cloud
+  - type: dropdown
+    id: device
+    attributes:
+        label: What device are you running WebUI on?
+        multiple: true
+        options:
+        - Nvidia GPUs (RTX 20 above)
+        - Nvidia GPUs (GTX 16 below)
+        - AMD GPUs (RX 6000 above)
+        - AMD GPUs (RX 5000 below)
+        - CPU
+        - Other GPUs
   - type: dropdown
     id: browsers
     attributes:

.github/pull_request_template.md

-# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request!
+## Description
 
-If you have a large change, pay special attention to this paragraph:
+* a simple description of what you're trying to accomplish
+* a summary of changes in code
+* which issues it fixes, if any
 
-> Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature.
+## Screenshots/videos:
 
-Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on.
 
-**Describe what this pull request is trying to achieve.**
+## Checklist:
 
-A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code.
-
-**Additional notes and description of your changes**
-
-More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of.
-
-**Environment this was tested in**
-
-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]
-
-**Screenshots or videos of your changes**
-
-If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made.
-
-This is **required** for anything that touches the user interface.
+- [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
+- [ ] I have performed a self-review of my own code
+- [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style)
+- [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests)
\ No newline at end of file

.github/workflows/on_pull_request.yaml

-# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
 name: Run Linting/Formatting on Pull Requests
 
 on:
   - push
   - pull_request
-  # See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
-  # if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
-  # pull_request:  
-  #  branches:
-  #    - master
-  #  branches-ignore:
-  #    - development
 
 jobs:
-  lint:
+  lint-python:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout Code
         uses: actions/checkout@v3
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v4
+      - uses: actions/setup-python@v4
         with:
-          python-version: 3.10.6
-          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: |
-          export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
-          python launch.py
-      - name: Analysing the code with pylint
-        run: |
-          pylint $(git ls-files '*.py')
+          python-version: 3.11
+          # NB: there's no cache: pip here since we're not installing anything
+          #     from the requirements.txt file(s) in the repository; it's faster
+          #     not to have GHA download an (at the time of writing) 4 GB cache
+          #     of PyTorch and other dependencies.
+      - name: Install Ruff
+        run: pip install ruff==0.0.265
+      - name: Run Ruff
+        run: ruff .
+  lint-js:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - name: Install Node.js
+        uses: actions/setup-node@v3
+        with:
+          node-version: 18
+      - run: npm i --ci
+      - run: npm run lint

.github/workflows/run_tests.yaml

@@ -17,13 +17,54 @@ jobs:
           cache: pip
           cache-dependency-path: |
             **/requirements*txt
+            launch.py
+      - name: Install test dependencies
+        run: pip install wait-for-it -r requirements-test.txt
+        env:
+          PIP_DISABLE_PIP_VERSION_CHECK: "1"
+          PIP_PROGRESS_BAR: "off"
+      - name: Setup environment
+        run: python launch.py --skip-torch-cuda-test --exit
+        env:
+          PIP_DISABLE_PIP_VERSION_CHECK: "1"
+          PIP_PROGRESS_BAR: "off"
+          TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
+          WEBUI_LAUNCH_LIVE_OUTPUT: "1"
+          PYTHONUNBUFFERED: "1"
+      - name: Start test server
+        run: >
+          python -m coverage run
+          --data-file=.coverage.server
+          launch.py
+          --skip-prepare-environment
+          --skip-torch-cuda-test
+          --test-server
+          --no-half
+          --disable-opt-split-attention
+          --use-cpu all
+          --add-stop-route
+          2>&1 | tee output.txt &
       - name: Run tests
-        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
+        run: |
+          wait-for-it --service 127.0.0.1:7860 -t 600
+          python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
+      - name: Kill test server
+        if: always()
+        run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10
+      - name: Show coverage
+        run: |
+          python -m coverage combine .coverage*
+          python -m coverage report -i
+          python -m coverage html -i
+      - name: Upload main app output
+        uses: actions/upload-artifact@v3
+        if: always()
+        with:
+          name: output
+          path: output.txt
+      - name: Upload coverage HTML
         uses: actions/upload-artifact@v3
         if: always()
         with:
-          name: stdout-stderr
-          path: |
-            test/stdout.txt
-            test/stderr.txt
+          name: htmlcov
+          path: htmlcov

.gitignore

@@ -32,4 +32,8 @@ notification.mp3
 /extensions
 /test/stdout.txt
 /test/stderr.txt
-/cache.json
+/cache.json*
+/config_states/
+/node_modules
+/package-lock.json
+/.coverage*

README.md

@@ -15,7 +15,7 @@ A browser interface based on Gradio library for Stable Diffusion.
 - 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)
+    - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) 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
@@ -99,8 +99,14 @@ Alternatively, use online services (like Google Colab):
 
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
 
+### Installation on Windows 10/11 with NVidia-GPUs using release package
+1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
+2. Run `update.bat`.
+3. Run `run.bat`.
+> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
+
 ### 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/release/python-3106/) (Newer version of Python does not support torch), 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.
@@ -115,11 +121,12 @@ sudo dnf install wget git python3
 # Arch-based:
 sudo pacman -S wget git python3
 ```
-2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:
+2. Navigate to the directory you would like the webui to be installed and execute the following command:
 ```bash
 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).
@@ -157,5 +164,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
 - Security advice - RyotaK
 - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
+- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

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/LDSR/ldsr_model_arch.py

@@ -88,7 +88,7 @@ class LDSR:
 
         x_t = None
         logs = None
-        for n in range(n_runs):
+        for _ in range(n_runs):
             if custom_shape is not None:
                 x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
                 x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
@@ -110,7 +110,6 @@ class LDSR:
         diffusion_steps = int(steps)
         eta = 1.0
 
-        down_sample_method = 'Lanczos'
 
         gc.collect()
         if torch.cuda.is_available:
@@ -158,7 +157,7 @@ class LDSR:
 
 
 def get_cond(selected_path):
-    example = dict()
+    example = {}
     up_f = 4
     c = selected_path.convert('RGB')
     c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
@@ -196,7 +195,7 @@ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_s
 @torch.no_grad()
 def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
                               corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
-    log = dict()
+    log = {}
 
     z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
                                         return_first_stage_outputs=True,
@@ -244,7 +243,7 @@ def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize
         x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
         log["sample_noquant"] = x_sample_noquant
         log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
-    except:
+    except Exception:
         pass
 
     log["sample"] = x_sample

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -7,7 +7,8 @@ from basicsr.utils.download_util import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
 from modules import shared, script_callbacks
-import sd_hijack_autoencoder, sd_hijack_ddpm_v1
+import sd_hijack_autoencoder  # noqa: F401
+import sd_hijack_ddpm_v1  # noqa: F401
 
 
 class UpscalerLDSR(Upscaler):
@@ -25,22 +26,28 @@ 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")
+
+        local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
+        local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
+        local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
+        local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
+
         if os.path.exists(yaml_path):
             statinfo = os.stat(yaml_path)
             if statinfo.st_size >= 10485760:
                 print("Removing invalid LDSR YAML file.")
                 os.remove(yaml_path)
+
         if os.path.exists(old_model_path):
             print("Renaming model from model.pth to model.ckpt")
             os.rename(old_model_path, new_model_path)
-        if os.path.exists(safetensors_model_path):
-            model = safetensors_model_path
+
+        if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
+            model = local_safetensors_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)
+            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True)
+
+        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True)
 
         try:
             return LDSR(model, yaml)

extensions-builtin/LDSR/sd_hijack_autoencoder.py

 # The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
 # The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
 # As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
-
+import numpy as np
 import torch
 import pytorch_lightning as pl
 import torch.nn.functional as F
 from contextlib import contextmanager
+
+from torch.optim.lr_scheduler import LambdaLR
+
+from ldm.modules.ema import LitEma
 from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
 from ldm.modules.diffusionmodules.model import Encoder, Decoder
 from ldm.util import instantiate_from_config
 
 import ldm.models.autoencoder
+from packaging import version
 
 class VQModel(pl.LightningModule):
     def __init__(self,
@@ -19,7 +24,7 @@ class VQModel(pl.LightningModule):
                  n_embed,
                  embed_dim,
                  ckpt_path=None,
-                 ignore_keys=[],
+                 ignore_keys=None,
                  image_key="image",
                  colorize_nlabels=None,
                  monitor=None,
@@ -57,7 +62,7 @@ class VQModel(pl.LightningModule):
             print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
 
         if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
         self.scheduler_config = scheduler_config
         self.lr_g_factor = lr_g_factor
 
@@ -76,11 +81,11 @@ class VQModel(pl.LightningModule):
                 if context is not None:
                     print(f"{context}: Restored training weights")
 
-    def init_from_ckpt(self, path, ignore_keys=list()):
+    def init_from_ckpt(self, path, ignore_keys=None):
         sd = torch.load(path, map_location="cpu")["state_dict"]
         keys = list(sd.keys())
         for k in keys:
-            for ik in ignore_keys:
+            for ik in ignore_keys or []:
                 if k.startswith(ik):
                     print("Deleting key {} from state_dict.".format(k))
                     del sd[k]
@@ -165,7 +170,7 @@ class VQModel(pl.LightningModule):
     def validation_step(self, batch, batch_idx):
         log_dict = self._validation_step(batch, batch_idx)
         with self.ema_scope():
-            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+            self._validation_step(batch, batch_idx, suffix="_ema")
         return log_dict
 
     def _validation_step(self, batch, batch_idx, suffix=""):
@@ -232,7 +237,7 @@ class VQModel(pl.LightningModule):
         return self.decoder.conv_out.weight
 
     def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
-        log = dict()
+        log = {}
         x = self.get_input(batch, self.image_key)
         x = x.to(self.device)
         if only_inputs:
@@ -249,7 +254,8 @@ class VQModel(pl.LightningModule):
         if plot_ema:
             with self.ema_scope():
                 xrec_ema, _ = self(x)
-                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+                if x.shape[1] > 3:
+                    xrec_ema = self.to_rgb(xrec_ema)
                 log["reconstructions_ema"] = xrec_ema
         return log
 
@@ -264,7 +270,7 @@ class VQModel(pl.LightningModule):
 
 class VQModelInterface(VQModel):
     def __init__(self, embed_dim, *args, **kwargs):
-        super().__init__(embed_dim=embed_dim, *args, **kwargs)
+        super().__init__(*args, embed_dim=embed_dim, **kwargs)
         self.embed_dim = embed_dim
 
     def encode(self, x):
@@ -282,5 +288,5 @@ class VQModelInterface(VQModel):
         dec = self.decoder(quant)
         return dec
 
-setattr(ldm.models.autoencoder, "VQModel", VQModel)
-setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
+ldm.models.autoencoder.VQModel = VQModel
+ldm.models.autoencoder.VQModelInterface = VQModelInterface

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

@@ -48,7 +48,7 @@ class DDPMV1(pl.LightningModule):
                  beta_schedule="linear",
                  loss_type="l2",
                  ckpt_path=None,
-                 ignore_keys=[],
+                 ignore_keys=None,
                  load_only_unet=False,
                  monitor="val/loss",
                  use_ema=True,
@@ -100,7 +100,7 @@ class DDPMV1(pl.LightningModule):
         if monitor is not None:
             self.monitor = monitor
         if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
 
         self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
                                linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
@@ -182,13 +182,13 @@ class DDPMV1(pl.LightningModule):
                 if context is not None:
                     print(f"{context}: Restored training weights")
 
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+    def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
         sd = torch.load(path, map_location="cpu")
         if "state_dict" in list(sd.keys()):
             sd = sd["state_dict"]
         keys = list(sd.keys())
         for k in keys:
-            for ik in ignore_keys:
+            for ik in ignore_keys or []:
                 if k.startswith(ik):
                     print("Deleting key {} from state_dict.".format(k))
                     del sd[k]
@@ -375,7 +375,7 @@ class DDPMV1(pl.LightningModule):
 
     @torch.no_grad()
     def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
+        log = {}
         x = self.get_input(batch, self.first_stage_key)
         N = min(x.shape[0], N)
         n_row = min(x.shape[0], n_row)
@@ -383,7 +383,7 @@ class DDPMV1(pl.LightningModule):
         log["inputs"] = x
 
         # get diffusion row
-        diffusion_row = list()
+        diffusion_row = []
         x_start = x[:n_row]
 
         for t in range(self.num_timesteps):
@@ -444,13 +444,13 @@ class LatentDiffusionV1(DDPMV1):
             conditioning_key = None
         ckpt_path = kwargs.pop("ckpt_path", None)
         ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+        super().__init__(*args, conditioning_key=conditioning_key, **kwargs)
         self.concat_mode = concat_mode
         self.cond_stage_trainable = cond_stage_trainable
         self.cond_stage_key = cond_stage_key
         try:
             self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
+        except Exception:
             self.num_downs = 0
         if not scale_by_std:
             self.scale_factor = scale_factor
@@ -877,16 +877,6 @@ class LatentDiffusionV1(DDPMV1):
                 c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
         return self.p_losses(x, c, t, *args, **kwargs)
 
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
     def apply_model(self, x_noisy, t, cond, return_ids=False):
 
         if isinstance(cond, dict):
@@ -1126,7 +1116,7 @@ class LatentDiffusionV1(DDPMV1):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+                [x[:batch_size] for x in cond[key]] for key in cond}
             else:
                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
 
@@ -1157,8 +1147,10 @@ class LatentDiffusionV1(DDPMV1):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
         return img, intermediates
 
     @torch.no_grad()
@@ -1205,8 +1197,10 @@ class LatentDiffusionV1(DDPMV1):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
 
         if return_intermediates:
             return img, intermediates
@@ -1221,7 +1215,7 @@ class LatentDiffusionV1(DDPMV1):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+                [x[:batch_size] for x in cond[key]] for key in cond}
             else:
                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
         return self.p_sample_loop(cond,
@@ -1253,7 +1247,7 @@ class LatentDiffusionV1(DDPMV1):
 
         use_ddim = ddim_steps is not None
 
-        log = dict()
+        log = {}
         z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
                                            return_first_stage_outputs=True,
                                            force_c_encode=True,
@@ -1280,7 +1274,7 @@ class LatentDiffusionV1(DDPMV1):
 
         if plot_diffusion_rows:
             # get diffusion row
-            diffusion_row = list()
+            diffusion_row = []
             z_start = z[:n_row]
             for t in range(self.num_timesteps):
                 if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
@@ -1322,7 +1316,7 @@ class LatentDiffusionV1(DDPMV1):
 
             if inpaint:
                 # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                h, w = z.shape[2], z.shape[3]
                 mask = torch.ones(N, h, w).to(self.device)
                 # zeros will be filled in
                 mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
@@ -1424,10 +1418,10 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
     # TODO: move all layout-specific hacks to this class
     def __init__(self, cond_stage_key, *args, **kwargs):
         assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
-        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
+        super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
 
     def log_images(self, batch, N=8, *args, **kwargs):
-        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
+        logs = super().log_images(*args, batch=batch, N=N, **kwargs)
 
         key = 'train' if self.training else 'validation'
         dset = self.trainer.datamodule.datasets[key]
@@ -1443,7 +1437,7 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
         logs['bbox_image'] = cond_img
         return logs
 
-setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
-setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
-setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
-setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
+ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
+ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
+ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
+ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1

extensions-builtin/Lora/extra_networks_lora.py

 from modules import extra_networks, shared
 import lora
 
+
 class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def __init__(self):
         super().__init__('lora')
@@ -8,7 +9,7 @@ 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]))
 
@@ -22,5 +23,23 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
 
         lora.load_loras(names, multipliers)
 
+        if shared.opts.lora_add_hashes_to_infotext:
+            lora_hashes = []
+            for item in lora.loaded_loras:
+                shorthash = item.lora_on_disk.shorthash
+                if not shorthash:
+                    continue
+
+                alias = item.mentioned_name
+                if not alias:
+                    continue
+
+                alias = alias.replace(":", "").replace(",", "")
+
+                lora_hashes.append(f"{alias}: {shorthash}")
+
+            if lora_hashes:
+                p.extra_generation_params["Lora hashes"] = ", ".join(lora_hashes)
+
     def deactivate(self, p):
         pass

extensions-builtin/Lora/scripts/lora_script.py

+import re
+
 import torch
 import gradio as gr
+from fastapi import FastAPI
 
 import lora
 import extra_networks_lora
 import ui_extra_networks_lora
 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
@@ -49,8 +51,66 @@ torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention
 script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
 script_callbacks.on_before_ui(before_ui)
+script_callbacks.on_infotext_pasted(lora.infotext_pasted)
 
 
 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),
+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras),
+    "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
+    "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
+}))
+
+
+shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
+    "lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
 }))
+
+
+def create_lora_json(obj: lora.LoraOnDisk):
+    return {
+        "name": obj.name,
+        "alias": obj.alias,
+        "path": obj.filename,
+        "metadata": obj.metadata,
+    }
+
+
+def api_loras(_: gr.Blocks, app: FastAPI):
+    @app.get("/sdapi/v1/loras")
+    async def get_loras():
+        return [create_lora_json(obj) for obj in lora.available_loras.values()]
+
+    @app.post("/sdapi/v1/refresh-loras")
+    async def refresh_loras():
+        return lora.list_available_loras()
+
+
+script_callbacks.on_app_started(api_loras)
+
+re_lora = re.compile("<lora:([^:]+):")
+
+
+def infotext_pasted(infotext, d):
+    hashes = d.get("Lora hashes")
+    if not hashes:
+        return
+
+    hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
+    hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
+
+    def lora_replacement(m):
+        alias = m.group(1)
+        shorthash = hashes.get(alias)
+        if shorthash is None:
+            return m.group(0)
+
+        lora_on_disk = lora.available_lora_hash_lookup.get(shorthash)
+        if lora_on_disk is None:
+            return m.group(0)
+
+        return f'<lora:{lora_on_disk.get_alias()}:'
+
+    d["Prompt"] = re.sub(re_lora, lora_replacement, d["Prompt"])
+
+
+script_callbacks.on_infotext_pasted(infotext_pasted)

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -15,13 +15,16 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
     def list_items(self):
         for name, lora_on_disk in lora.available_loras.items():
             path, ext = os.path.splitext(lora_on_disk.filename)
+
+            alias = lora_on_disk.get_alias()
+
             yield {
                 "name": name,
                 "filename": path,
                 "preview": self.find_preview(path),
                 "description": self.find_description(path),
                 "search_term": self.search_terms_from_path(lora_on_disk.filename),
-                "prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
+                "prompt": json.dumps(f"<lora:{alias}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
                 "local_preview": f"{path}.{shared.opts.samples_format}",
                 "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
             }

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -5,11 +5,14 @@ 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 modules import devices, modelloader, script_callbacks
 from scunet_model_arch import SCUNet as net
+from modules.shared import opts
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -42,34 +45,83 @@ 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')
         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)
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True)
         else:
             filename = path
         if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
@@ -79,9 +131,19 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
         model.load_state_dict(torch.load(filename), strict=True)
         model.eval()
-        for k, v in model.named_parameters():
+        for _, v in model.named_parameters():
             v.requires_grad = False
         model = model.to(device)
 
         return model
 
+
+def on_ui_settings():
+    import gradio as gr
+    from modules import shared
+
+    shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
+    shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/ScuNET/scunet_model_arch.py

@@ -61,7 +61,9 @@ class WMSA(nn.Module):
         Returns:
             output: tensor shape [b h w c]
         """
-        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+        if self.type != 'W':
+            x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+
         x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
         h_windows = x.size(1)
         w_windows = x.size(2)
@@ -85,8 +87,9 @@ class WMSA(nn.Module):
         output = self.linear(output)
         output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
 
-        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
-                                                 dims=(1, 2))
+        if self.type != 'W':
+            output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
+
         return output
 
     def relative_embedding(self):

extensions-builtin/SwinIR/scripts/swinir_model.py

-import contextlib
 import os
 
 import numpy as np
@@ -8,7 +7,7 @@ from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
 from modules import modelloader, devices, script_callbacks, shared
-from modules.shared import cmd_opts, opts, state
+from modules.shared import opts, state
 from swinir_model_arch import SwinIR as net
 from swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
@@ -45,14 +44,14 @@ class UpscalerSwinIR(Upscaler):
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
-        except:
+        except Exception:
             pass
         return img
 
     def load_model(self, path, scale=4):
         if "http" in path:
             dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
-            filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
+            filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True)
         else:
             filename = path
         if filename is None or not os.path.exists(filename):

extensions-builtin/SwinIR/swinir_model_arch.py

@@ -644,7 +644,7 @@ class SwinIR(nn.Module):
     """
 
     def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
                  window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                  norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
@@ -844,7 +844,7 @@ class SwinIR(nn.Module):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()

extensions-builtin/SwinIR/swinir_model_arch_v2.py

@@ -74,7 +74,7 @@ class WindowAttention(nn.Module):
     """
 
     def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
-                 pretrained_window_size=[0, 0]):
+                 pretrained_window_size=(0, 0)):
 
         super().__init__()
         self.dim = dim
@@ -698,7 +698,7 @@ class Swin2SR(nn.Module):
     """
 
     def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
                  window_size=7, mlp_ratio=4., qkv_bias=True,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                  norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
@@ -994,7 +994,7 @@ class Swin2SR(nn.Module):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()

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)
+    var counter = gradioApp().getElementById(id_counter);
 
-    textarea.addEventListener("input", function(evt){
-        checkBrackets(evt, textarea, counter)
-    });
+    if (textarea && counter) {
+        textarea.addEventListener("input", () => checkBrackets(textarea, counter));
+    }
 }
 
-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')
-})
+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');
+});
\ No newline at end of file

html/extra-networks-card.html

 <div class='card' style={style} onclick={card_clicked}>
+	{background_image}
 	{metadata_button}
-
 	<div class='actions'>
 		<div class='additional'>
 			<ul>
 				<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
 			</ul>
-			<span style="display:none" class='search_term'>{search_term}</span>
+			<span style="display:none" class='search_term{search_only}'>{search_term}</span>
 		</div>
 		<span class='name'>{name}</span>
 		<span class='description'>{description}</span>
 	</div>
 </div>
-

html/licenses.html

@@ -662,3 +662,29 @@ 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>
+
+<h2><a href="https://github.com/madebyollin/taesd/blob/main/LICENSE">TAESD</a></h2>
+<small>Tiny AutoEncoder for Stable Diffusion option for live previews</small>
+<pre>
+MIT License
+
+Copyright (c) 2023 Ollin Boer Bohan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
\ No newline at end of file

javascript/aspectRatioOverlay.js

 
 let currentWidth = null;
 let currentHeight = null;
-let arFrameTimeout = setTimeout(function(){},0);
+let arFrameTimeout = setTimeout(function() {}, 0);
 
-function dimensionChange(e, is_width, is_height){
+function dimensionChange(e, is_width, is_height) {
 
-	if(is_width){
-		currentWidth = e.target.value*1.0
+    if (is_width) {
+        currentWidth = e.target.value * 1.0;
     }
-	if(is_height){
-		currentHeight = e.target.value*1.0
+    if (is_height) {
+        currentHeight = e.target.value * 1.0;
     }
 
     var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
 
-	if(!inImg2img){
+    if (!inImg2img) {
         return;
     }
 
     var targetElement = null;
 
-    var tabIndex = get_tab_index('mode_img2img')
-	if(tabIndex == 0){ // img2img
+    var tabIndex = get_tab_index('mode_img2img');
+    if (tabIndex == 0) { // img2img
         targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
-	} else if(tabIndex == 1){ //Sketch
+    } else if (tabIndex == 1) { //Sketch
         targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
-	} else if(tabIndex == 2){ // Inpaint
+    } else if (tabIndex == 2) { // Inpaint
         targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
-	} else if(tabIndex == 3){ // Inpaint sketch
+    } else if (tabIndex == 3) { // Inpaint sketch
         targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
     }
 
 
-	if(targetElement){
+    if (targetElement) {
 
         var arPreviewRect = gradioApp().querySelector('#imageARPreview');
-		if(!arPreviewRect){
-		    arPreviewRect = document.createElement('div')
+        if (!arPreviewRect) {
+            arPreviewRect = document.createElement('div');
             arPreviewRect.id = "imageARPreview";
-		    gradioApp().appendChild(arPreviewRect)
+            gradioApp().appendChild(arPreviewRect);
         }
 
 
 
         var viewportOffset = targetElement.getBoundingClientRect();
 
-		viewportscale = Math.min(  targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
+        var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight);
 
-		scaledx = targetElement.naturalWidth*viewportscale
-		scaledy = targetElement.naturalHeight*viewportscale
+        var scaledx = targetElement.naturalWidth * viewportscale;
+        var scaledy = targetElement.naturalHeight * viewportscale;
 
-		cleintRectTop    = (viewportOffset.top+window.scrollY)
-		cleintRectLeft   = (viewportOffset.left+window.scrollX)
-		cleintRectCentreY = cleintRectTop  + (targetElement.clientHeight/2)
-		cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
+        var cleintRectTop = (viewportOffset.top + window.scrollY);
+        var cleintRectLeft = (viewportOffset.left + window.scrollX);
+        var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2);
+        var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth / 2);
 
-		viewRectTop    = cleintRectCentreY-(scaledy/2)
-		viewRectLeft   = cleintRectCentreX-(scaledx/2)
-		arRectWidth  = scaledx
-		arRectHeight = scaledy
+        var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight);
+        var arscaledx = currentWidth * arscale;
+        var arscaledy = currentHeight * arscale;
 
-		arscale = Math.min(  arRectWidth/currentWidth, arRectHeight/currentHeight )
-		arscaledx = currentWidth*arscale
-		arscaledy = currentHeight*arscale
+        var arRectTop = cleintRectCentreY - (arscaledy / 2);
+        var arRectLeft = cleintRectCentreX - (arscaledx / 2);
+        var arRectWidth = arscaledx;
+        var arRectHeight = arscaledy;
 
-		arRectTop    = cleintRectCentreY-(arscaledy/2)
-		arRectLeft   = cleintRectCentreX-(arscaledx/2)
-		arRectWidth  = arscaledx
-		arRectHeight = arscaledy
-
-	    arPreviewRect.style.top  = arRectTop+'px';
-	    arPreviewRect.style.left = arRectLeft+'px';
-	    arPreviewRect.style.width = arRectWidth+'px';
-	    arPreviewRect.style.height = arRectHeight+'px';
+        arPreviewRect.style.top = arRectTop + 'px';
+        arPreviewRect.style.left = arRectLeft + 'px';
+        arPreviewRect.style.width = arRectWidth + 'px';
+        arPreviewRect.style.height = arRectHeight + 'px';
 
         clearTimeout(arFrameTimeout);
-	    arFrameTimeout = setTimeout(function(){
+        arFrameTimeout = setTimeout(function() {
             arPreviewRect.style.display = 'none';
-	    },2000);
+        }, 2000);
 
         arPreviewRect.style.display = 'block';
 
@@ -86,31 +81,33 @@ function dimensionChange(e, is_width, is_height){
 }
 
 
-onUiUpdate(function(){
+onUiUpdate(function() {
     var arPreviewRect = gradioApp().querySelector('#imageARPreview');
-	if(arPreviewRect){
+    if (arPreviewRect) {
         arPreviewRect.style.display = 'none';
     }
     var tabImg2img = gradioApp().querySelector("#tab_img2img");
     if (tabImg2img) {
         var inImg2img = tabImg2img.style.display == "block";
-        if(inImg2img){
+        if (inImg2img) {
             let inputs = gradioApp().querySelectorAll('input');
-            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')
+            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_width) {
+                    currentWidth = e.value * 1.0;
                 }
-                if(is_height){
-                    currentHeight = e.value*1.0
+                if (is_height) {
+                    currentHeight = e.value * 1.0;
                 }
-            })
+            });
         }
     }
 });

javascript/dragdrop.js

 // allows drag-dropping files into gradio image elements, and also pasting images from clipboard
 
-function isValidImageList( files ) {
+function isValidImageList(files) {
     return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type);
 }
 
-function dropReplaceImage( imgWrap, files ) {
-    if ( ! isValidImageList( files ) ) {
+function dropReplaceImage(imgWrap, files) {
+    if (!isValidImageList(files)) {
         return;
     }
 
@@ -14,8 +14,8 @@ function dropReplaceImage( imgWrap, files ) {
     imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
     const callback = () => {
         const fileInput = imgWrap.querySelector('input[type="file"]');
-        if ( fileInput ) {
-            if ( files.length === 0 ) {
+        if (fileInput) {
+            if (files.length === 0) {
                 files = new DataTransfer();
                 files.items.add(tmpFile);
                 fileInput.files = files.files;
@@ -26,32 +26,32 @@ function dropReplaceImage( imgWrap, files ) {
         }
     };
 
-    if ( imgWrap.closest('#pnginfo_image') ) {
+    if (imgWrap.closest('#pnginfo_image')) {
         // special treatment for PNG Info tab, wait for fetch request to finish
         const oldFetch = window.fetch;
-        window.fetch = async (input, options) => {
+        window.fetch = async(input, options) => {
             const response = await oldFetch(input, options);
-            if ( 'api/predict/' === input ) {
+            if ('api/predict/' === input) {
                 const content = await response.text();
                 window.fetch = oldFetch;
-                window.requestAnimationFrame( () => callback() );
+                window.requestAnimationFrame(() => callback());
                 return new Response(content, {
                     status: response.status,
                     statusText: response.statusText,
                     headers: response.headers
-                })
+                });
             }
             return response;
         };
     } else {
-        window.requestAnimationFrame( () => callback() );
+        window.requestAnimationFrame(() => callback());
     }
 }
 
 window.document.addEventListener('dragover', e => {
     const target = e.composedPath()[0];
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
+    if (!imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
         return;
     }
     e.stopPropagation();
@@ -65,24 +65,27 @@ window.document.addEventListener('drop', e => {
         return;
     }
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap ) {
+    if (!imgWrap) {
         return;
     }
     e.stopPropagation();
     e.preventDefault();
     const files = e.dataTransfer.files;
-    dropReplaceImage( imgWrap, files );
+    dropReplaceImage(imgWrap, files);
 });
 
 window.addEventListener('paste', e => {
     const files = e.clipboardData.files;
-    if ( ! isValidImageList( files ) ) {
+    if (!isValidImageList(files)) {
         return;
     }
 
     const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
-        .filter(el => uiElementIsVisible(el));
-    if ( ! visibleImageFields.length ) {
+        .filter(el => uiElementIsVisible(el))
+        .sort((a, b) => uiElementInSight(b) - uiElementInSight(a));
+
+
+    if (!visibleImageFields.length) {
         return;
     }
 
@@ -93,5 +96,6 @@ window.addEventListener('paste', e => {
         firstFreeImageField ?
             firstFreeImageField :
             visibleImageFields[visibleImageFields.length - 1]
-    , files );
+        , files
+    );
 });

javascript/edit-attention.js

-function keyupEditAttention(event){
+function keyupEditAttention(event) {
     let target = event.originalTarget || event.composedPath()[0];
-	if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
-	if (! (event.metaKey || event.ctrlKey)) return;
+    if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return;
+    if (!(event.metaKey || event.ctrlKey)) return;
 
-	let isPlus = event.key == "ArrowUp"
-	let isMinus = event.key == "ArrowDown"
+    let isPlus = event.key == "ArrowUp";
+    let isMinus = event.key == "ArrowDown";
     if (!isPlus && !isMinus) return;
 
     let selectionStart = target.selectionStart;
     let selectionEnd = target.selectionEnd;
     let text = target.value;
 
-    function selectCurrentParenthesisBlock(OPEN, CLOSE){
+    function selectCurrentParenthesisBlock(OPEN, CLOSE) {
         if (selectionStart !== selectionEnd) return false;
 
         // Find opening parenthesis around current cursor
@@ -44,27 +44,45 @@ 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();
 
-    closeCharacter = ')'
-    delta = opts.keyedit_precision_attention
+    var closeCharacter = ')';
+    var delta = opts.keyedit_precision_attention;
 
-    if (selectionStart > 0 && text[selectionStart - 1] == '<'){
-        closeCharacter = '>'
-        delta = opts.keyedit_precision_extra
+    if (selectionStart > 0 && text[selectionStart - 1] == '<') {
+        closeCharacter = '>';
+        delta = opts.keyedit_precision_extra;
     } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
 
         // do not include spaces at the end
-        while(selectionEnd > selectionStart && text[selectionEnd-1] == ' '){
+        while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
             selectionEnd -= 1;
         }
-        if(selectionStart == selectionEnd){
-            return
+        if (selectionStart == selectionEnd) {
+            return;
         }
 
         text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
@@ -73,22 +91,28 @@ function keyupEditAttention(event){
         selectionEnd += 1;
     }
 
-	end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
-	weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
+    var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
     if (isNaN(weight)) return;
 
     weight += isPlus ? delta : -delta;
     weight = parseFloat(weight.toPrecision(12));
-	if(String(weight).length == 1) weight += ".0"
+    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;
     target.selectionStart = selectionStart;
     target.selectionEnd = selectionEnd;
 
-	updateInput(target)
+    updateInput(target);
 }
 
 addEventListener('keydown', (event) => {

javascript/extensions.js

 
-function extensions_apply(_, _, disable_all){
-    var disable = []
-    var update = []
+function extensions_apply(_disabled_list, _update_list, disable_all) {
+    var disable = [];
+    var update = [];
 
-    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
-        if(x.name.startsWith("enable_") && ! x.checked)
-            disable.push(x.name.substr(7))
+    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
+        if (x.name.startsWith("enable_") && !x.checked) {
+            disable.push(x.name.substring(7));
+        }
 
-        if(x.name.startsWith("update_") && x.checked)
-            update.push(x.name.substr(7))
-    })
+        if (x.name.startsWith("update_") && x.checked) {
+            update.push(x.name.substring(7));
+        }
+    });
 
-    restart_reload()
+    restart_reload();
 
-    return [JSON.stringify(disable), JSON.stringify(update), disable_all]
+    return [JSON.stringify(disable), JSON.stringify(update), disable_all];
 }
 
-function extensions_check(_, _){
-    var disable = []
+function extensions_check() {
+    var disable = [];
 
-    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
-        if(x.name.startsWith("enable_") && ! x.checked)
-            disable.push(x.name.substr(7))
-    })
+    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
+        if (x.name.startsWith("enable_") && !x.checked) {
+            disable.push(x.name.substring(7));
+        }
+    });
 
-    gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
-        x.innerHTML = "Loading..."
-    })
+    gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
+        x.innerHTML = "Loading...";
+    });
 
 
-    var id = randomId()
-    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){
+    var id = randomId();
+    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() {
 
-    })
+    });
 
-    return [id, JSON.stringify(disable)]
+    return [id, JSON.stringify(disable)];
 }
 
-function install_extension_from_index(button, url){
-    button.disabled = "disabled"
-    button.value = "Installing..."
+function install_extension_from_index(button, url) {
+    button.disabled = "disabled";
+    button.value = "Installing...";
 
-    textarea = gradioApp().querySelector('#extension_to_install textarea')
-    textarea.value = url
-    updateInput(textarea)
+    var textarea = gradioApp().querySelector('#extension_to_install textarea');
+    textarea.value = url;
+    updateInput(textarea);
 
-    gradioApp().querySelector('#install_extension_button').click()
+    gradioApp().querySelector('#install_extension_button').click();
+}
+
+function config_state_confirm_restore(_, config_state_name, config_restore_type) {
+    if (config_state_name == "Current") {
+        return [false, config_state_name, config_restore_type];
+    }
+    let restored = "";
+    if (config_restore_type == "extensions") {
+        restored = "all saved extension versions";
+    } else if (config_restore_type == "webui") {
+        restored = "the webui version";
+    } else {
+        restored = "the webui version and all saved extension versions";
+    }
+    let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
+    if (confirmed) {
+        restart_reload();
+        gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
+            x.innerHTML = "Loading...";
+        });
+    }
+    return [confirmed, config_state_name, config_restore_type];
 }

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(){
+onUiUpdate(function() {
     if (!txt2img_gallery) {
-		txt2img_gallery = attachGalleryListeners("txt2img")
+        txt2img_gallery = attachGalleryListeners("txt2img");
     }
     if (!img2img_gallery) {
-		img2img_gallery = attachGalleryListeners("img2img")
+        img2img_gallery = attachGalleryListeners("img2img");
     }
     if (!modal) {
-		modal = gradioApp().getElementById('lightboxModal')
-		modalObserver.observe(modal,  { attributes : true, attributeFilter : ['style'] });
+        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()
+        let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.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());
+    var 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()
+        if (e.keyCode == 37 || e.keyCode == 39) { // left or right arrow
+            gradioApp().getElementById(tab_name + "_generation_info_button").click();
+        }
     });
     return gallery;
 }

javascript/hints.js

 // mouseover tooltips for various UI elements
 
-titles = {
+var 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",
@@ -9,6 +9,7 @@ titles = {
     "UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
     "DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution",
 
+    "\u{1F4D0}": "Auto detect size from img2img",
     "Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
     "Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
     "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
@@ -22,6 +23,7 @@ titles = {
     "\u{1f4cb}": "Apply selected styles to current prompt",
     "\u{1f4d2}": "Paste available values into the field",
     "\u{1f3b4}": "Show/hide extra networks",
+    "\u{1f300}": "Restore progress",
 
     "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",
@@ -65,8 +67,8 @@ 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_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]; leave empty for default.",
-    "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]; leave empty for default.",
+    "Images filename pattern": "Use tags like [seed] and [date] to define how filenames for images are chosen. Leave empty for default.",
+    "Directory name pattern": "Use tags like [seed] and [date] to define how subdirectories for images and grids are chosen. 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": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
@@ -85,7 +87,6 @@ titles = {
     "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%).",
 
     "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.",
 
     "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
     "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
@@ -111,37 +112,57 @@ titles = {
     "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
     "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
     "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
-    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited."
-}
+    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
+    "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
+};
 
+function updateTooltipForSpan(span) {
+    if (span.title) return; // already has a title
 
-onUiUpdate(function(){
-	gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
-		tooltip = titles[span.textContent];
+    let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
 
-		if(!tooltip){
-		    tooltip = titles[span.value];
+    if (!tooltip) {
+        tooltip = localization[titles[span.value]] || titles[span.value];
     }
 
-		if(!tooltip){
+    if (!tooltip) {
         for (const c of span.classList) {
             if (c in titles) {
-					tooltip = titles[c];
+                tooltip = localization[titles[c]] || titles[c];
                 break;
             }
         }
     }
 
-		if(tooltip){
+    if (tooltip) {
         span.title = tooltip;
     }
-	})
+}
 
-	gradioApp().querySelectorAll('select').forEach(function(select){
+function updateTooltipForSelect(select) {
     if (select.onchange != null) return;
 
-	    select.onchange = function(){
-            select.title = titles[select.value] || "";
+    select.onchange = function() {
+        select.title = localization[titles[select.value]] || titles[select.value] || "";
+    };
+}
+
+var observedTooltipElements = {SPAN: 1, BUTTON: 1, SELECT: 1, P: 1};
+
+onUiUpdate(function(m) {
+    m.forEach(function(record) {
+        record.addedNodes.forEach(function(node) {
+            if (observedTooltipElements[node.tagName]) {
+                updateTooltipForSpan(node);
+            }
+            if (node.tagName == "SELECT") {
+                updateTooltipForSelect(node);
+            }
+
+            if (node.querySelectorAll) {
+                node.querySelectorAll('span, button, select, p').forEach(updateTooltipForSpan);
+                node.querySelectorAll('select').forEach(updateTooltipForSelect);
             }
-	})
-})
+        });
+    });
+});

javascript/hires_fix.js

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

javascript/imageMaskFix.js

@@ -2,20 +2,18 @@
  * temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
  * @see https://github.com/gradio-app/gradio/issues/1721
  */
-window.addEventListener( 'resize', () => imageMaskResize());
 function imageMaskResize() {
     const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
-    if ( ! canvases.length ) {
-    canvases_fixed = false;
-    window.removeEventListener( 'resize', imageMaskResize );
+    if (!canvases.length) {
+        window.removeEventListener('resize', imageMaskResize);
         return;
     }
 
     const wrapper = canvases[0].closest('.touch-none');
     const previewImage = wrapper.previousElementSibling;
 
-    if ( ! previewImage.complete ) {
-        previewImage.addEventListener( 'load', () => imageMaskResize());
+    if (!previewImage.complete) {
+        previewImage.addEventListener('load', imageMaskResize);
         return;
     }
 
@@ -24,22 +22,22 @@ function imageMaskResize() {
     const nw = previewImage.naturalWidth;
     const nh = previewImage.naturalHeight;
     const portrait = nh > nw;
-    const factor = portrait;
 
-    const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
-    const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
+    const wW = Math.min(w, portrait ? h / nh * nw : w / nw * nw);
+    const wH = Math.min(h, portrait ? h / nh * nh : w / nw * nh);
 
     wrapper.style.width = `${wW}px`;
     wrapper.style.height = `${wH}px`;
     wrapper.style.left = `0px`;
     wrapper.style.top = `0px`;
 
-    canvases.forEach( c => {
+    canvases.forEach(c => {
         c.style.width = c.style.height = '';
         c.style.maxWidth = '100%';
         c.style.maxHeight = '100%';
         c.style.objectFit = 'contain';
     });
- }
+}
 
- onUiUpdate(() => imageMaskResize());
+onUiUpdate(imageMaskResize);
+window.addEventListener('resize', imageMaskResize);

javascript/imageParams.js

-window.onload = (function(){
+window.onload = (function() {
     window.addEventListener('drop', e => {
         const target = e.composedPath()[0];
-        const idx = selected_gallery_index();
         if (target.placeholder.indexOf("Prompt") == -1) return;
 
         let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
@@ -11,7 +10,7 @@ window.onload = (function(){
         const imgParent = gradioApp().getElementById(prompt_target);
         const files = e.dataTransfer.files;
         const fileInput = imgParent.querySelector('input[type="file"]');
-        if ( fileInput ) {
+        if (fileInput) {
             fileInput.files = files;
             fileInput.dispatchEvent(new Event('change'));
         }

javascript/imageviewerGamepad.js

+window.addEventListener('gamepadconnected', (e) => {
+    const index = e.gamepad.index;
+    let isWaiting = false;
+    setInterval(async() => {
+        if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
+        const gamepad = navigator.getGamepads()[index];
+        const xValue = gamepad.axes[0];
+        if (xValue <= -0.3) {
+            modalPrevImage(e);
+            isWaiting = true;
+        } else if (xValue >= 0.3) {
+            modalNextImage(e);
+            isWaiting = true;
+        }
+        if (isWaiting) {
+            await sleepUntil(() => {
+                const xValue = navigator.getGamepads()[index].axes[0];
+                if (xValue < 0.3 && xValue > -0.3) {
+                    return true;
+                }
+            }, opts.js_modal_lightbox_gamepad_repeat);
+            isWaiting = false;
+        }
+    }, 10);
+});
+
+/*
+Primarily for vr controller type pointer devices.
+I use the wheel event because there's currently no way to do it properly with web xr.
+ */
+let isScrolling = false;
+window.addEventListener('wheel', (e) => {
+    if (!opts.js_modal_lightbox_gamepad || isScrolling) return;
+    isScrolling = true;
+
+    if (e.deltaX <= -0.6) {
+        modalPrevImage(e);
+    } else if (e.deltaX >= 0.6) {
+        modalNextImage(e);
+    }
+
+    setTimeout(() => {
+        isScrolling = false;
+    }, opts.js_modal_lightbox_gamepad_repeat);
+});
+
+function sleepUntil(f, timeout) {
+    return new Promise((resolve) => {
+        const timeStart = new Date();
+        const wait = setInterval(function() {
+            if (f() || new Date() - timeStart > timeout) {
+                clearInterval(wait);
+                resolve();
+            }
+        }, 20);
+    });
+}

javascript/localization.js

 
 // localization = {} -- the dict with translations is created by the backend
 
-ignore_ids_for_localization={
+var ignore_ids_for_localization = {
     setting_sd_hypernetwork: 'OPTION',
     setting_sd_model_checkpoint: 'OPTION',
-    setting_realesrgan_enabled_models: 'OPTION',
     modelmerger_primary_model_name: 'OPTION',
     modelmerger_secondary_model_name: 'OPTION',
     modelmerger_tertiary_model_name: 'OPTION',
@@ -17,119 +16,145 @@ ignore_ids_for_localization={
     setting_realesrgan_enabled_models: 'SPAN',
     extras_upscaler_1: 'SPAN',
     extras_upscaler_2: 'SPAN',
-}
+};
+
+var re_num = /^[.\d]+$/;
+var re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u;
 
-re_num = /^[\.\d]+$/
-re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
+var original_lines = {};
+var translated_lines = {};
 
-original_lines = {}
-translated_lines = {}
+function hasLocalization() {
+    return window.localization && Object.keys(window.localization).length > 0;
+}
 
-function textNodesUnder(el){
-    var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
-    while(n=walk.nextNode()) a.push(n);
+function textNodesUnder(el) {
+    var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false);
+    while ((n = walk.nextNode())) a.push(n);
     return a;
 }
 
-function canBeTranslated(node, text){
-    if(! text) return false;
-    if(! node.parentElement) return false;
+function canBeTranslated(node, text) {
+    if (!text) return false;
+    if (!node.parentElement) return false;
 
-    parentType = node.parentElement.nodeName
-    if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
+    var parentType = node.parentElement.nodeName;
+    if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false;
 
-    if (parentType=='OPTION' || parentType=='SPAN'){
-        pnode = node
-        for(var level=0; level<4; level++){
-            pnode = pnode.parentElement
-            if(! pnode) break;
+    if (parentType == 'OPTION' || parentType == 'SPAN') {
+        var pnode = node;
+        for (var level = 0; level < 4; level++) {
+            pnode = pnode.parentElement;
+            if (!pnode) break;
 
-            if(ignore_ids_for_localization[pnode.id] == parentType) return false;
+            if (ignore_ids_for_localization[pnode.id] == parentType) return false;
         }
     }
 
-    if(re_num.test(text)) return false;
-    if(re_emoji.test(text)) return false;
-    return true
+    if (re_num.test(text)) return false;
+    if (re_emoji.test(text)) return false;
+    return true;
 }
 
-function getTranslation(text){
-    if(! text) return undefined
+function getTranslation(text) {
+    if (!text) return undefined;
 
-    if(translated_lines[text] === undefined){
-        original_lines[text] = 1
+    if (translated_lines[text] === undefined) {
+        original_lines[text] = 1;
     }
 
-    tl = localization[text]
-    if(tl !== undefined){
-        translated_lines[tl] = 1
+    var tl = localization[text];
+    if (tl !== undefined) {
+        translated_lines[tl] = 1;
     }
 
-    return tl
+    return tl;
 }
 
-function processTextNode(node){
-    text = node.textContent.trim()
+function processTextNode(node) {
+    var text = node.textContent.trim();
 
-    if(! canBeTranslated(node, text)) return
+    if (!canBeTranslated(node, text)) return;
 
-    tl = getTranslation(text)
-    if(tl !== undefined){
-        node.textContent = tl
+    var tl = getTranslation(text);
+    if (tl !== undefined) {
+        node.textContent = tl;
     }
 }
 
-function processNode(node){
-    if(node.nodeType == 3){
-        processTextNode(node)
-        return
+function processNode(node) {
+    if (node.nodeType == 3) {
+        processTextNode(node);
+        return;
     }
 
-    if(node.title){
-        tl = getTranslation(node.title)
-        if(tl !== undefined){
-            node.title = tl
+    if (node.title) {
+        let tl = getTranslation(node.title);
+        if (tl !== undefined) {
+            node.title = tl;
         }
     }
 
-    if(node.placeholder){
-        tl = getTranslation(node.placeholder)
-        if(tl !== undefined){
-            node.placeholder = tl
+    if (node.placeholder) {
+        let tl = getTranslation(node.placeholder);
+        if (tl !== undefined) {
+            node.placeholder = tl;
         }
     }
 
-    textNodesUnder(node).forEach(function(node){
-        processTextNode(node)
-    })
+    textNodesUnder(node).forEach(function(node) {
+        processTextNode(node);
+    });
 }
 
-function dumpTranslations(){
-    dumped = {}
+function dumpTranslations() {
+    if (!hasLocalization()) {
+        // If we don't have any localization,
+        // we will not have traversed the app to find
+        // original_lines, so do that now.
+        processNode(gradioApp());
+    }
+    var dumped = {};
     if (localization.rtl) {
-        dumped.rtl = true
+        dumped.rtl = true;
     }
 
-    Object.keys(original_lines).forEach(function(text){
-        if(dumped[text] !== undefined)  return
-
-        dumped[text] = localization[text] || text
-    })
+    for (const text in original_lines) {
+        if (dumped[text] !== undefined) continue;
+        dumped[text] = localization[text] || text;
+    }
 
-    return dumped
+    return dumped;
 }
 
-onUiUpdate(function(m){
-    m.forEach(function(mutation){
-        mutation.addedNodes.forEach(function(node){
-            processNode(node)
-        })
-    });
-})
+function download_localization() {
+    var text = JSON.stringify(dumpTranslations(), null, 4);
+
+    var element = document.createElement('a');
+    element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
+    element.setAttribute('download', "localization.json");
+    element.style.display = 'none';
+    document.body.appendChild(element);
 
+    element.click();
+
+    document.body.removeChild(element);
+}
 
 document.addEventListener("DOMContentLoaded", function() {
-    processNode(gradioApp())
+    if (!hasLocalization()) {
+        return;
+    }
+
+    onUiUpdate(function(m) {
+        m.forEach(function(mutation) {
+            mutation.addedNodes.forEach(function(node) {
+                processNode(node);
+            });
+        });
+    });
+
+    processNode(gradioApp());
 
     if (localization.rtl) { // if the language is from right to left,
         (new MutationObserver((mutations, observer) => { // wait for the style to load
@@ -144,22 +169,8 @@ document.addEventListener("DOMContentLoaded", function() {
                             }
                         }
                     }
-                })
                 });
-        })).observe(gradioApp(), { childList: true });
+            });
+        })).observe(gradioApp(), {childList: true});
     }
-})
-
-function download_localization() {
-    text = JSON.stringify(dumpTranslations(), null, 4)
-
-    var element = document.createElement('a');
-    element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
-    element.setAttribute('download', "localization.json");
-    element.style.display = 'none';
-    document.body.appendChild(element);
-
-    element.click();
-
-    document.body.removeChild(element);
-}
+});

javascript/notification.js

@@ -2,16 +2,16 @@
 
 let lastHeadImg = null;
 
-notificationButton = null
+let notificationButton = null;
 
-onUiUpdate(function(){
-    if(notificationButton == null){
-        notificationButton = gradioApp().getElementById('request_notifications')
+onUiUpdate(function() {
+    if (notificationButton == null) {
+        notificationButton = gradioApp().getElementById('request_notifications');
 
-        if(notificationButton != null){
-            notificationButton.addEventListener('click', function (evt) {
-                Notification.requestPermission();
-            },true);
+        if (notificationButton != null) {
+            notificationButton.addEventListener('click', () => {
+                void Notification.requestPermission();
+            }, true);
         }
     }
 
@@ -42,7 +42,7 @@ onUiUpdate(function(){
         }
     );
 
-    notification.onclick = function(_){
+    notification.onclick = function(_) {
         parent.focus();
         this.close();
     };

javascript/progressbar.js

 // code related to showing and updating progressbar shown as the image is being made
 
-function rememberGallerySelection(id_gallery){
+function rememberGallerySelection() {
 
 }
 
-function getGallerySelectedIndex(id_gallery){
+function getGallerySelectedIndex() {
 
 }
 
-function request(url, data, handler, errorHandler){
+function request(url, data, handler, errorHandler) {
     var xhr = new XMLHttpRequest();
-    var url = url;
     xhr.open("POST", url, true);
     xhr.setRequestHeader("Content-Type", "application/json");
-    xhr.onreadystatechange = function () {
+    xhr.onreadystatechange = function() {
         if (xhr.readyState === 4) {
             if (xhr.status === 200) {
                 try {
                     var js = JSON.parse(xhr.responseText);
-                    handler(js)
+                    handler(js);
                 } catch (error) {
                     console.error(error);
-                    errorHandler()
+                    errorHandler();
                 }
-            } else{
-                errorHandler()
+            } else {
+                errorHandler();
             }
         }
     };
@@ -32,147 +31,147 @@ function request(url, data, handler, errorHandler){
     xhr.send(js);
 }
 
-function pad2(x){
-    return x<10 ? '0'+x : x
+function pad2(x) {
+    return x < 10 ? '0' + x : x;
 }
 
-function formatTime(secs){
-    if(secs > 3600){
-        return pad2(Math.floor(secs/60/60)) + ":" + pad2(Math.floor(secs/60)%60) + ":" + pad2(Math.floor(secs)%60)
-    } else if(secs > 60){
-        return pad2(Math.floor(secs/60)) + ":" + pad2(Math.floor(secs)%60)
-    } else{
-        return Math.floor(secs) + "s"
+function formatTime(secs) {
+    if (secs > 3600) {
+        return pad2(Math.floor(secs / 60 / 60)) + ":" + pad2(Math.floor(secs / 60) % 60) + ":" + pad2(Math.floor(secs) % 60);
+    } else if (secs > 60) {
+        return pad2(Math.floor(secs / 60)) + ":" + pad2(Math.floor(secs) % 60);
+    } else {
+        return Math.floor(secs) + "s";
     }
 }
 
-function setTitle(progress){
-    var title = 'Stable Diffusion'
+function setTitle(progress) {
+    var title = 'Stable Diffusion';
 
-    if(opts.show_progress_in_title && progress){
+    if (opts.show_progress_in_title && progress) {
         title = '[' + progress.trim() + '] ' + title;
     }
 
-    if(document.title != title){
+    if (document.title != title) {
         document.title = title;
     }
 }
 
 
-function randomId(){
-    return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7)+")"
+function randomId() {
+    return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + ")";
 }
 
 // starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and
 // preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd.
 // calls onProgress every time there is a progress update
-function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress){
-    var dateStart = new Date()
-    var wasEverActive = false
-    var parentProgressbar = progressbarContainer.parentNode
-    var parentGallery = gallery ? gallery.parentNode : null
-
-    var divProgress = document.createElement('div')
-    divProgress.className='progressDiv'
-    divProgress.style.display = opts.show_progressbar ? "block" : "none"
-    var divInner = document.createElement('div')
-    divInner.className='progress'
-
-    divProgress.appendChild(divInner)
-    parentProgressbar.insertBefore(divProgress, progressbarContainer)
-
-    if(parentGallery){
-        var livePreview = document.createElement('div')
-        livePreview.className='livePreview'
-        parentGallery.insertBefore(livePreview, gallery)
-    }
-
-    var removeProgressBar = function(){
-        setTitle("")
-        parentProgressbar.removeChild(divProgress)
-        if(parentGallery) parentGallery.removeChild(livePreview)
-        atEnd()
-    }
+function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout = 40) {
+    var dateStart = new Date();
+    var wasEverActive = false;
+    var parentProgressbar = progressbarContainer.parentNode;
+    var parentGallery = gallery ? gallery.parentNode : null;
+
+    var divProgress = document.createElement('div');
+    divProgress.className = 'progressDiv';
+    divProgress.style.display = opts.show_progressbar ? "block" : "none";
+    var divInner = document.createElement('div');
+    divInner.className = 'progress';
+
+    divProgress.appendChild(divInner);
+    parentProgressbar.insertBefore(divProgress, progressbarContainer);
+
+    if (parentGallery) {
+        var livePreview = document.createElement('div');
+        livePreview.className = 'livePreview';
+        parentGallery.insertBefore(livePreview, gallery);
+    }
+
+    var removeProgressBar = function() {
+        setTitle("");
+        parentProgressbar.removeChild(divProgress);
+        if (parentGallery) parentGallery.removeChild(livePreview);
+        atEnd();
+    };
 
-    var fun = function(id_task, id_live_preview){
-        request("./internal/progress", {"id_task": id_task, "id_live_preview": id_live_preview}, function(res){
-            if(res.completed){
-                removeProgressBar()
-                return
+    var fun = function(id_task, id_live_preview) {
+        request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
+            if (res.completed) {
+                removeProgressBar();
+                return;
             }
 
-            var rect = progressbarContainer.getBoundingClientRect()
+            var rect = progressbarContainer.getBoundingClientRect();
 
-            if(rect.width){
+            if (rect.width) {
                 divProgress.style.width = rect.width + "px";
             }
 
-            progressText = ""
+            let progressText = "";
 
-            divInner.style.width = ((res.progress || 0) * 100.0) + '%'
-            divInner.style.background = res.progress ? "" : "transparent"
+            divInner.style.width = ((res.progress || 0) * 100.0) + '%';
+            divInner.style.background = res.progress ? "" : "transparent";
 
-            if(res.progress > 0){
-                progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%'
+            if (res.progress > 0) {
+                progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%';
             }
 
-            if(res.eta){
-                progressText += " ETA: " + formatTime(res.eta)
+            if (res.eta) {
+                progressText += " ETA: " + formatTime(res.eta);
             }
 
 
-            setTitle(progressText)
+            setTitle(progressText);
 
-            if(res.textinfo && res.textinfo.indexOf("\n") == -1){
-                progressText = res.textinfo + " " + progressText
+            if (res.textinfo && res.textinfo.indexOf("\n") == -1) {
+                progressText = res.textinfo + " " + progressText;
             }
 
-            divInner.textContent = progressText
+            divInner.textContent = progressText;
 
-            var elapsedFromStart = (new Date() - dateStart) / 1000
+            var elapsedFromStart = (new Date() - dateStart) / 1000;
 
-            if(res.active) wasEverActive = true;
+            if (res.active) wasEverActive = true;
 
-            if(! res.active && wasEverActive){
-                removeProgressBar()
-                return
+            if (!res.active && wasEverActive) {
+                removeProgressBar();
+                return;
             }
 
-            if(elapsedFromStart > 5 && !res.queued && !res.active){
-                removeProgressBar()
-                return
+            if (elapsedFromStart > inactivityTimeout && !res.queued && !res.active) {
+                removeProgressBar();
+                return;
             }
 
 
-            if(res.live_preview && gallery){
-                var rect = gallery.getBoundingClientRect()
-                if(rect.width){
-                    livePreview.style.width = rect.width + "px"
-                    livePreview.style.height = rect.height + "px"
+            if (res.live_preview && gallery) {
+                rect = gallery.getBoundingClientRect();
+                if (rect.width) {
+                    livePreview.style.width = rect.width + "px";
+                    livePreview.style.height = rect.height + "px";
                 }
 
                 var img = new Image();
                 img.onload = function() {
-                    livePreview.appendChild(img)
-                    if(livePreview.childElementCount > 2){
-                        livePreview.removeChild(livePreview.firstElementChild)
-                    }
+                    livePreview.appendChild(img);
+                    if (livePreview.childElementCount > 2) {
+                        livePreview.removeChild(livePreview.firstElementChild);
                     }
+                };
                 img.src = res.live_preview;
             }
 
 
-            if(onProgress){
-                onProgress(res)
+            if (onProgress) {
+                onProgress(res);
             }
 
             setTimeout(() => {
                 fun(id_task, res.id_live_preview);
-            }, opts.live_preview_refresh_period || 500)
-        }, function(){
-            removeProgressBar()
-        })
-    }
+            }, opts.live_preview_refresh_period || 500);
+        }, function() {
+            removeProgressBar();
+        });
+    };
 
-    fun(id_task, 0)
+    fun(id_task, 0);
 }

javascript/textualInversion.js

 
 
 
-function start_training_textual_inversion(){
-    gradioApp().querySelector('#ti_error').innerHTML=''
+function start_training_textual_inversion() {
+    gradioApp().querySelector('#ti_error').innerHTML = '';
 
-    var id = randomId()
-    requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function(){}, function(progress){
-        gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo
-    })
+    var id = randomId();
+    requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function() {}, function(progress) {
+        gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo;
+    });
 
-    var res = args_to_array(arguments)
+    var res = Array.from(arguments);
 
-    res[0] = id
+    res[0] = id;
 
-    return res
+    return res;
 }

javascript/ui_settings_hints.js

+// various hints and extra info for the settings tab
+
+var settingsHintsSetup = false;
+
+onOptionsChanged(function() {
+    if (settingsHintsSetup) return;
+    settingsHintsSetup = true;
+
+    gradioApp().querySelectorAll('#settings [id^=setting_]').forEach(function(div) {
+        var name = div.id.substr(8);
+        var commentBefore = opts._comments_before[name];
+        var commentAfter = opts._comments_after[name];
+
+        if (!commentBefore && !commentAfter) return;
+
+        var span = null;
+        if (div.classList.contains('gradio-checkbox')) span = div.querySelector('label span');
+        else if (div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild;
+        else if (div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild;
+        else span = div.querySelector('label span').firstChild;
+
+        if (!span) return;
+
+        if (commentBefore) {
+            var comment = document.createElement('DIV');
+            comment.className = 'settings-comment';
+            comment.innerHTML = commentBefore;
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
+            span.parentElement.insertBefore(comment, span);
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
+        }
+        if (commentAfter) {
+            comment = document.createElement('DIV');
+            comment.className = 'settings-comment';
+            comment.innerHTML = commentAfter;
+            span.parentElement.insertBefore(comment, span.nextSibling);
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling);
+        }
+    });
+});
+
+function settingsHintsShowQuicksettings() {
+    requestGet("./internal/quicksettings-hint", {}, function(data) {
+        var table = document.createElement('table');
+        table.className = 'settings-value-table';
+
+        data.forEach(function(obj) {
+            var tr = document.createElement('tr');
+            var td = document.createElement('td');
+            td.textContent = obj.name;
+            tr.appendChild(td);
+
+            td = document.createElement('td');
+            td.textContent = obj.label;
+            tr.appendChild(td);
+
+            table.appendChild(tr);
+        });
+
+        popup(table);
+    });
+}

modules/api/models.py

@@ -223,8 +223,9 @@ for key in _options:
     if(_options[key].dest != 'help'):
         flag = _options[key]
         _type = str
-        if _options[key].default is not None: _type = type(_options[key].default)
-        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
+        if _options[key].default is not None:
+            _type = type(_options[key].default)
+        flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
 
 FlagsModel = create_model("Flags", **flags)
 
@@ -286,6 +287,23 @@ class MemoryResponse(BaseModel):
     ram: dict = Field(title="RAM", description="System memory stats")
     cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
 
+
 class ScriptsList(BaseModel):
-    txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)")
-    img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")
+    txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
+    img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
\ No newline at end of file
+
+
+class ScriptArg(BaseModel):
+    label: str = Field(default=None, title="Label", description="Name of the argument in UI")
+    value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
+    minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
+    maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
+    step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
+    choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
+
+
+class ScriptInfo(BaseModel):
+    name: str = Field(default=None, title="Name", description="Script name")
+    is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
+    is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
+    args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")

modules/call_queue.py

@@ -35,6 +35,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
             try:
                 res = func(*args, **kwargs)
+                progress.record_results(id_task, res)
             finally:
                 progress.finish_task(id_task)
 
@@ -59,7 +60,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             max_debug_str_len = 131072 # (1024*1024)/8
 
             print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            argStr = f"Arguments: {args} {kwargs}"
             print(argStr[:max_debug_str_len], file=sys.stderr)
             if len(argStr) > max_debug_str_len:
                 print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
@@ -72,7 +73,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             if extra_outputs_array is None:
                 extra_outputs_array = [None, '']
 
-            res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
+            error_message = f'{type(e).__name__}: {e}'
+            res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
 
         shared.state.skipped = False
         shared.state.interrupted = False

modules/cmd_args.py

 import argparse
+import json
 import os
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file  # noqa: F401
 
 parser = argparse.ArgumentParser()
 
@@ -11,8 +12,8 @@ parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.
 parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
 parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
 parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
-parser.add_argument("--tests", type=str, default=None, help="launch.py argument: run tests in the specified directory")
-parser.add_argument("--no-tests", action='store_true', help="launch.py argument: do not run tests even if --tests option is specified")
+parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
+parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
 parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
 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")
 parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
@@ -39,7 +40,8 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision",
 parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
-parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
+parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
 parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
@@ -51,16 +53,16 @@ parser.add_argument("--xformers", action='store_true', help="enable xformers for
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
 parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
-parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
+parser.add_argument("--opt-split-attention", action='store_true', help="prefer Doggettx's cross-attention layer optimization for automatic choice of optimization")
+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="prefer memory efficient sub-quadratic cross-attention layer optimization for automatic choice of optimization")
 parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
 parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
 parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
-parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="prefer InvokeAI's cross-attention layer optimization for automatic choice of optimization")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
+parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
+parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
 parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
@@ -75,6 +77,7 @@ parser.add_argument("--gradio-auth", type=str, help='set gradio authentication l
 parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
 parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
 parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
+parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
@@ -95,9 +98,12 @@ parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(
 parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
 parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
 parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
 parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
+parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
+parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')

modules/codeformer/codeformer_arch.py

 # this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
 
 import math
-import numpy as np
 import torch
 from torch import nn, Tensor
 import torch.nn.functional as F
-from typing import Optional, List
+from typing import Optional
 
-from modules.codeformer.vqgan_arch import *
-from basicsr.utils import get_root_logger
+from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
 from basicsr.utils.registry import ARCH_REGISTRY
 
 def calc_mean_std(feat, eps=1e-5):
@@ -163,8 +161,8 @@ class Fuse_sft_block(nn.Module):
 class CodeFormer(VQAutoEncoder):
     def __init__(self, dim_embd=512, n_head=8, n_layers=9,
                 codebook_size=1024, latent_size=256,
-                connect_list=['32', '64', '128', '256'],
-                fix_modules=['quantize','generator']):
+                connect_list=('32', '64', '128', '256'),
+                fix_modules=('quantize', 'generator')):
         super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
 
         if fix_modules is not None:

modules/codeformer/vqgan_arch.py

@@ -5,11 +5,9 @@ VQGAN code, adapted from the original created by the Unleashing Transformers aut
 https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
 
 '''
-import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import copy
 from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
 
@@ -328,7 +326,7 @@ class Generator(nn.Module):
 
 @ARCH_REGISTRY.register()
 class VQAutoEncoder(nn.Module):
-    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
+    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
                 beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
         super().__init__()
         logger = get_root_logger()
@@ -339,7 +337,7 @@ class VQAutoEncoder(nn.Module):
         self.embed_dim = emb_dim
         self.ch_mult = ch_mult
         self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
+        self.attn_resolutions = attn_resolutions or [16]
         self.quantizer_type = quantizer
         self.encoder = Encoder(
             self.in_channels,

modules/codeformer_model.py

@@ -33,11 +33,9 @@ def setup_model(dirname):
     try:
         from torchvision.transforms.functional import normalize
         from modules.codeformer.codeformer_arch import CodeFormer
-        from basicsr.utils.download_util import load_file_from_url
-        from basicsr.utils import imwrite, img2tensor, tensor2img
+        from basicsr.utils import img2tensor, tensor2img
         from facelib.utils.face_restoration_helper import FaceRestoreHelper
         from facelib.detection.retinaface import retinaface
-        from modules.shared import cmd_opts
 
         net_class = CodeFormer
 
@@ -96,7 +94,7 @@ def setup_model(dirname):
                 self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
                 self.face_helper.align_warp_face()
 
-                for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
+                for cropped_face in self.face_helper.cropped_faces:
                     cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
                     normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
                     cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)

modules/deepbooru.py

@@ -2,7 +2,6 @@ import os
 import re
 
 import torch
-from PIL import Image
 import numpy as np
 
 from modules import modelloader, paths, deepbooru_model, devices, images, shared
@@ -79,7 +78,7 @@ class DeepDanbooru:
 
         res = []
 
-        filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")])
+        filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")}
 
         for tag in [x for x in tags if x not in filtertags]:
             probability = probability_dict[tag]

modules/devices.py

@@ -65,7 +65,7 @@ def enable_tf32():
 
         # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
         # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
-        if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
+        if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
             torch.backends.cudnn.benchmark = True
 
         torch.backends.cuda.matmul.allow_tf32 = True
@@ -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/extra_networks_hypernet.py

-from modules import extra_networks, shared, extra_networks
+from modules import extra_networks, shared
 from modules.hypernetworks import hypernetwork
 
 
@@ -9,8 +9,9 @@ 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:
-            p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+        if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+            hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
+            p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 
         names = []

modules/models/diffusion/uni_pc/__init__.py

-from .sampler import UniPCSampler
+from .sampler import UniPCSampler  # noqa: F401