Merge branch 'dev' into master

.eslintignore

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

.eslintrc.js

+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-trailing-spaces": "error",
+        "no-whitespace-before-property": "error",
+        "object-curly-newline": ["error", {consistent: true, multiline: true}],
+        "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",
+        "no-multi-spaces": "error",
+        "object-curly-spacing": ["error", "never"],
+        "operator-linebreak": ["error", "after"],
+        "no-unused-vars": "off",
+        "no-redeclare": "off",
+    },
+    globals: {
+        // this file
+        module: "writable",
+        //script.js
+        gradioApp: "writable",
+        onUiLoaded: "writable",
+        onUiUpdate: "writable",
+        onOptionsChanged: "writable",
+        uiCurrentTab: "writable",
+        uiElementIsVisible: "writable",
+        executeCallbacks: "writable",
+        //ui.js
+        opts: "writable",
+        all_gallery_buttons: "writable",
+        selected_gallery_button: "writable",
+        selected_gallery_index: "writable",
+        args_to_array: "writable",
+        switch_to_txt2img: "writable",
+        switch_to_img2img_tab: "writable",
+        switch_to_img2img: "writable",
+        switch_to_sketch: "writable",
+        switch_to_inpaint: "writable",
+        switch_to_inpaint_sketch: "writable",
+        switch_to_extras: "writable",
+        get_tab_index: "writable",
+        create_submit_args: "writable",
+        restart_reload: "writable",
+        updateInput: "writable",
+        //extraNetworks.js
+        requestGet: "writable",
+        popup: "writable",
+        // from python
+        localization: "writable",
+        // progrssbar.js
+        randomId: "writable",
+        requestProgress: "writable",
+        // imageviewer.js
+        modalPrevImage: "writable",
+        modalNextImage: "writable",
+    }
+};

.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/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

@@ -17,8 +17,14 @@ jobs:
           cache: pip
           cache-dependency-path: |
             **/requirements*txt
+            launch.py
       - name: Run tests
         run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
+        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"
       - name: Upload main app stdout-stderr
         uses: actions/upload-artifact@v3
         if: always()

.gitignore

@@ -34,3 +34,5 @@ notification.mp3
 /test/stderr.txt
 /cache.json*
 /config_states/
+/node_modules
+/package-lock.json
\ No newline at end of file

README.md

@@ -99,6 +99,12 @@ 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/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).
@@ -158,5 +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)

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):

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/lora.py

-import glob
 import os
 import re
 import torch
@@ -177,7 +176,7 @@ def load_lora(name, filename):
         else:
             print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
             continue
-            assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
+            raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")
 
         with torch.no_grad():
             module.weight.copy_(weight)
@@ -189,7 +188,7 @@ def load_lora(name, filename):
         elif lora_key == "lora_down.weight":
             lora_module.down = module
         else:
-            assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
+            raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")
 
     if len(keys_failed_to_match) > 0:
         print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
@@ -207,7 +206,7 @@ def load_loras(names, multipliers=None):
     loaded_loras.clear()
 
     loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
-    if any([x is None for x in loras_on_disk]):
+    if any(x is None for x in loras_on_disk):
         list_available_loras()
 
         loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
@@ -314,7 +313,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
 
             print(f'failed to calculate lora weights for layer {lora_layer_name}')
 
-        setattr(self, "lora_current_names", wanted_names)
+        self.lora_current_names = wanted_names
 
 
 def lora_forward(module, input, original_forward):
@@ -348,8 +347,8 @@ def lora_forward(module, input, original_forward):
 
 
 def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
-    setattr(self, "lora_current_names", ())
-    setattr(self, "lora_weights_backup", None)
+    self.lora_current_names = ()
+    self.lora_weights_backup = None
 
 
 def lora_Linear_forward(self, input):
@@ -428,7 +427,7 @@ def infotext_pasted(infotext, params):
 
     added = []
 
-    for k, v in params.items():
+    for k in params:
         if not k.startswith("AddNet Model "):
             continue
 

extensions-builtin/Lora/scripts/lora_script.py

@@ -53,7 +53,7 @@ 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": ["None"] + [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"]}),
 }))
 

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -10,10 +10,9 @@ 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
-from modules import images
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -133,8 +132,19 @@ 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
@@ -45,7 +44,7 @@ class UpscalerSwinIR(Upscaler):
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
-        except:
+        except Exception:
             pass
         return img
 

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

@@ -5,7 +5,7 @@
 
 function checkBrackets(textArea, counterElt) {
     var counts = {};
-  (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
+    (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
         counts[bracket] = (counts[bracket] || 0) + 1;
     });
     var errors = [];
@@ -27,14 +27,14 @@ function checkBrackets(textArea, counterElt) {
 
 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);
 
     if (textarea && counter) {
         textarea.addEventListener("input", () => checkBrackets(textarea, counter));
     }
 }
 
-onUiLoaded(function () {
+onUiLoaded(function() {
     setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
     setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
     setupBracketChecking('img2img_prompt', 'img2img_token_counter');

html/extra-networks-card.html

@@ -6,7 +6,7 @@
 			<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{serach_only}'>{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>

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();
 
-		var viewportscale = Math.min(  targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
+        var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight);
 
-		var scaledx = targetElement.naturalWidth*viewportscale
-		var scaledy = targetElement.naturalHeight*viewportscale
+        var scaledx = targetElement.naturalWidth * viewportscale;
+        var scaledy = targetElement.naturalHeight * viewportscale;
 
-		var cleintRectTop    = (viewportOffset.top+window.scrollY)
-		var cleintRectLeft   = (viewportOffset.left+window.scrollX)
-		var cleintRectCentreY = cleintRectTop  + (targetElement.clientHeight/2)
-		var 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);
 
-		var arscale = Math.min(  scaledx/currentWidth, scaledy/currentHeight )
-		var arscaledx = currentWidth*arscale
-		var arscaledy = currentHeight*arscale
+        var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight);
+        var arscaledx = currentWidth * arscale;
+        var arscaledy = currentHeight * arscale;
 
-		var arRectTop    = cleintRectCentreY-(arscaledy/2)
-		var arRectLeft   = cleintRectCentreX-(arscaledx/2)
-		var arRectWidth  = arscaledx
-		var arRectHeight = arscaledy
+        var arRectTop = cleintRectCentreY - (arscaledy / 2);
+        var arRectLeft = cleintRectCentreX - (arscaledx / 2);
+        var arRectWidth = arscaledx;
+        var 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';
 
@@ -81,31 +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/contextMenus.js

 
-contextMenuInit = function(){
-  let eventListenerApplied=false;
+var contextMenuInit = function() {
+    let eventListenerApplied = false;
     let menuSpecs = new Map();
 
-  const uid = function(){
+    const uid = function() {
         return Date.now().toString(36) + Math.random().toString(36).substring(2);
-  }
+    };
 
-  function showContextMenu(event,element,menuEntries){
+    function showContextMenu(event, element, menuEntries) {
         let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
         let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
 
-    let oldMenu = gradioApp().querySelector('#context-menu')
-    if(oldMenu){
-      oldMenu.remove()
+        let oldMenu = gradioApp().querySelector('#context-menu');
+        if (oldMenu) {
+            oldMenu.remove();
         }
 
-    let baseStyle = window.getComputedStyle(uiCurrentTab)
+        let baseStyle = window.getComputedStyle(uiCurrentTab);
 
-    const contextMenu = document.createElement('nav')
-    contextMenu.id = "context-menu"
-    contextMenu.style.background = baseStyle.background
-    contextMenu.style.color = baseStyle.color
-    contextMenu.style.fontFamily = baseStyle.fontFamily
-    contextMenu.style.top = posy+'px'
-    contextMenu.style.left = posx+'px'
+        const contextMenu = document.createElement('nav');
+        contextMenu.id = "context-menu";
+        contextMenu.style.background = baseStyle.background;
+        contextMenu.style.color = baseStyle.color;
+        contextMenu.style.fontFamily = baseStyle.fontFamily;
+        contextMenu.style.top = posy + 'px';
+        contextMenu.style.left = posx + 'px';
 
 
 
-    const contextMenuList = document.createElement('ul')
+        const contextMenuList = document.createElement('ul');
         contextMenuList.className = 'context-menu-items';
         contextMenu.append(contextMenuList);
 
-    menuEntries.forEach(function(entry){
-      let contextMenuEntry = document.createElement('a')
-      contextMenuEntry.innerHTML = entry['name']
+        menuEntries.forEach(function(entry) {
+            let contextMenuEntry = document.createElement('a');
+            contextMenuEntry.innerHTML = entry['name'];
             contextMenuEntry.addEventListener("click", function() {
                 entry['func']();
-      })
+            });
             contextMenuList.append(contextMenuEntry);
 
-    })
+        });
 
-    gradioApp().appendChild(contextMenu)
+        gradioApp().appendChild(contextMenu);
 
         let menuWidth = contextMenu.offsetWidth + 4;
         let menuHeight = contextMenu.offsetHeight + 4;
@@ -50,117 +50,123 @@ contextMenuInit = function(){
         let windowWidth = window.innerWidth;
         let windowHeight = window.innerHeight;
 
-    if ( (windowWidth - posx) < menuWidth ) {
+        if ((windowWidth - posx) < menuWidth) {
             contextMenu.style.left = windowWidth - menuWidth + "px";
         }
 
-    if ( (windowHeight - posy) < menuHeight ) {
+        if ((windowHeight - posy) < menuHeight) {
             contextMenu.style.top = windowHeight - menuHeight + "px";
         }
 
     }
 
-  function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
+    function appendContextMenuOption(targetElementSelector, entryName, entryFunction) {
 
-    var currentItems = menuSpecs.get(targetElementSelector)
+        var currentItems = menuSpecs.get(targetElementSelector);
 
-    if(!currentItems){
-      currentItems = []
-      menuSpecs.set(targetElementSelector,currentItems);
+        if (!currentItems) {
+            currentItems = [];
+            menuSpecs.set(targetElementSelector, currentItems);
         }
-    let newItem = {'id':targetElementSelector+'_'+uid(),
-                   'name':entryName,
-                   'func':entryFunction,
-                   'isNew':true}
+        let newItem = {
+            id: targetElementSelector + '_' + uid(),
+            name: entryName,
+            func: entryFunction,
+            isNew: true
+        };
 
-    currentItems.push(newItem)
-    return newItem['id']
+        currentItems.push(newItem);
+        return newItem['id'];
     }
 
-  function removeContextMenuOption(uid){
+    function removeContextMenuOption(uid) {
         menuSpecs.forEach(function(v) {
-      let index = -1
-      v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
-      if(index>=0){
+            let index = -1;
+            v.forEach(function(e, ei) {
+                if (e['id'] == uid) {
+                    index = ei;
+                }
+            });
+            if (index >= 0) {
                 v.splice(index, 1);
             }
-    })
+        });
     }
 
-  function addContextMenuEventListener(){
-    if(eventListenerApplied){
+    function addContextMenuEventListener() {
+        if (eventListenerApplied) {
             return;
         }
         gradioApp().addEventListener("click", function(e) {
-      if(! e.isTrusted){
-        return
+            if (!e.isTrusted) {
+                return;
             }
 
-      let oldMenu = gradioApp().querySelector('#context-menu')
-      if(oldMenu){
-        oldMenu.remove()
+            let oldMenu = gradioApp().querySelector('#context-menu');
+            if (oldMenu) {
+                oldMenu.remove();
             }
         });
         gradioApp().addEventListener("contextmenu", function(e) {
-      let oldMenu = gradioApp().querySelector('#context-menu')
-      if(oldMenu){
-        oldMenu.remove()
+            let oldMenu = gradioApp().querySelector('#context-menu');
+            if (oldMenu) {
+                oldMenu.remove();
             }
-      menuSpecs.forEach(function(v,k) {
-        if(e.composedPath()[0].matches(k)){
-          showContextMenu(e,e.composedPath()[0],v)
-          e.preventDefault()
+            menuSpecs.forEach(function(v, k) {
+                if (e.composedPath()[0].matches(k)) {
+                    showContextMenu(e, e.composedPath()[0], v);
+                    e.preventDefault();
                 }
-      })
             });
-    eventListenerApplied=true
+        });
+        eventListenerApplied = true;
 
     }
 
-  return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
-}
+    return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener];
+};
 
-initResponse = contextMenuInit();
-appendContextMenuOption     = initResponse[0];
-removeContextMenuOption     = initResponse[1];
-addContextMenuEventListener = initResponse[2];
+var initResponse = contextMenuInit();
+var appendContextMenuOption = initResponse[0];
+var removeContextMenuOption = initResponse[1];
+var addContextMenuEventListener = initResponse[2];
 
-(function(){
+(function() {
     //Start example Context Menu Items
-  let generateOnRepeat = function(genbuttonid,interruptbuttonid){
+    let generateOnRepeat = function(genbuttonid, interruptbuttonid) {
         let genbutton = gradioApp().querySelector(genbuttonid);
         let interruptbutton = gradioApp().querySelector(interruptbuttonid);
-    if(!interruptbutton.offsetParent){
+        if (!interruptbutton.offsetParent) {
             genbutton.click();
         }
-    clearInterval(window.generateOnRepeatInterval)
-    window.generateOnRepeatInterval = setInterval(function(){
-      if(!interruptbutton.offsetParent){
+        clearInterval(window.generateOnRepeatInterval);
+        window.generateOnRepeatInterval = setInterval(function() {
+            if (!interruptbutton.offsetParent) {
                 genbutton.click();
             }
         },
-    500)
-  }
+        500);
+    };
 
-  appendContextMenuOption('#txt2img_generate','Generate forever',function(){
-    generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
-  })
-  appendContextMenuOption('#img2img_generate','Generate forever',function(){
-    generateOnRepeat('#img2img_generate','#img2img_interrupt');
-  })
+    appendContextMenuOption('#txt2img_generate', 'Generate forever', function() {
+        generateOnRepeat('#txt2img_generate', '#txt2img_interrupt');
+    });
+    appendContextMenuOption('#img2img_generate', 'Generate forever', function() {
+        generateOnRepeat('#img2img_generate', '#img2img_interrupt');
+    });
 
-  let cancelGenerateForever = function(){
-    clearInterval(window.generateOnRepeatInterval)
-  }
+    let cancelGenerateForever = function() {
+        clearInterval(window.generateOnRepeatInterval);
+    };
 
-  appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
-  appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
-  appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
-  appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
+    appendContextMenuOption('#txt2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
+    appendContextMenuOption('#txt2img_generate', 'Cancel generate forever', cancelGenerateForever);
+    appendContextMenuOption('#img2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
+    appendContextMenuOption('#img2img_generate', 'Cancel generate forever', cancelGenerateForever);
 
 })();
 //End example Context Menu Items
 
-onUiUpdate(function(){
-  addContextMenuEventListener()
+onUiUpdate(function() {
+    addContextMenuEventListener();
 });

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 +65,24 @@ 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 ) {
+    if (!visibleImageFields.length) {
         return;
     }
 
@@ -93,5 +93,6 @@ 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("[id*='_toprow'] [id*='_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
@@ -44,7 +44,7 @@ function keyupEditAttention(event){
         return true;
     }
 
-    function selectCurrentWord(){
+    function selectCurrentWord() {
         if (selectionStart !== selectionEnd) return false;
         const delimiters = opts.keyedit_delimiters + " \r\n\t";
 
@@ -69,20 +69,20 @@ function keyupEditAttention(event){
 
     event.preventDefault();
 
-    var closeCharacter = ')'
-    var 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);
@@ -97,7 +97,7 @@ function keyupEditAttention(event){
 
     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);
@@ -112,7 +112,7 @@ function keyupEditAttention(event){
     target.selectionStart = selectionStart;
     target.selectionEnd = selectionEnd;
 
-	updateInput(target)
+    updateInput(target);
 }
 
 addEventListener('keydown', (event) => {

javascript/extensions.js

 
-function extensions_apply(_disabled_list, _update_list, 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.substring(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.substring(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.substring(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...";
 
-    var 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) {
@@ -63,9 +66,9 @@ function config_state_confirm_restore(_, config_state_name, config_restore_type)
     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..."
-        })
+        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.selected')?.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) {
-	var 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",
@@ -66,8 +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], [denoising], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
-    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [denoising], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
+    "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.",
@@ -113,20 +114,18 @@ titles = {
     "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.",
     "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."
-}
-
+};
 
-onUiUpdate(function(){
-	gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
+function updateTooltipForSpan(span) {
     if (span.title) return; // already has a title
 
     let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
 
-    if(!tooltip){
+    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 = localization[titles[c]] || titles[c];
@@ -135,16 +134,35 @@ onUiUpdate(function(){
         }
     }
 
-		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.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 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) {
+    function setInactive(elem, inactive) {
+        elem.classList.toggle('inactive', !!inactive);
     }
 
-    var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
-    var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
-    var 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

@@ -4,17 +4,16 @@
  */
 function imageMaskResize() {
     const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
-    if ( ! canvases.length ) {
-    canvases_fixed = false; // TODO: this is unused..?
-    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;
     }
 
@@ -24,15 +23,15 @@ function imageMaskResize() {
     const nh = previewImage.naturalHeight;
     const portrait = nh > nw;
 
-    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%';
@@ -41,4 +40,4 @@ function imageMaskResize() {
 }
 
 onUiUpdate(imageMaskResize);
-window.addEventListener( 'resize', imageMaskResize);
+window.addEventListener('resize', imageMaskResize);

javascript/imageParams.js

-window.onload = (function(){
+window.onload = (function() {
     window.addEventListener('drop', e => {
         const target = e.composedPath()[0];
         if (target.placeholder.indexOf("Prompt") == -1) return;
@@ -10,7 +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 () => {
+    setInterval(async() => {
         if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
         const gamepad = navigator.getGamepads()[index];
         const xValue = gamepad.axes[0];
@@ -14,7 +14,7 @@ window.addEventListener('gamepadconnected', (e) => {
         }
         if (isWaiting) {
             await sleepUntil(() => {
-                const xValue = navigator.getGamepads()[index].axes[0]
+                const xValue = navigator.getGamepads()[index].axes[0];
                 if (xValue < 0.3 && xValue > -0.3) {
                     return true;
                 }

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',
@@ -17,111 +16,111 @@ ignore_ids_for_localization={
     setting_realesrgan_enabled_models: 'SPAN',
     extras_upscaler_1: 'SPAN',
     extras_upscaler_2: 'SPAN',
-}
+};
 
-re_num = /^[\.\d]+$/
-re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
+var re_num = /^[.\d]+$/;
+var re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u;
 
-original_lines = {}
-translated_lines = {}
+var original_lines = {};
+var 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;
 
-    var 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'){
-        var 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){
-    var 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(){
-    if(!hasLocalization()) {
+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 = {}
+    var dumped = {};
     if (localization.rtl) {
         dumped.rtl = true;
     }
 
     for (const text in original_lines) {
-        if(dumped[text] !== undefined) continue;
+        if (dumped[text] !== undefined) continue;
         dumped[text] = localization[text] || text;
     }
 
@@ -129,7 +128,7 @@ function dumpTranslations(){
 }
 
 function download_localization() {
-    var text = JSON.stringify(dumpTranslations(), null, 4)
+    var text = JSON.stringify(dumpTranslations(), null, 4);
 
     var element = document.createElement('a');
     element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
@@ -142,20 +141,20 @@ function download_localization() {
     document.body.removeChild(element);
 }
 
-document.addEventListener("DOMContentLoaded", function () {
+document.addEventListener("DOMContentLoaded", function() {
     if (!hasLocalization()) {
         return;
     }
 
-    onUiUpdate(function (m) {
-        m.forEach(function (mutation) {
-            mutation.addedNodes.forEach(function (node) {
-                processNode(node)
-            })
+    onUiUpdate(function(m) {
+        m.forEach(function(mutation) {
+            mutation.addedNodes.forEach(function(node) {
+                processNode(node);
+            });
+        });
     });
-    })
 
-    processNode(gradioApp())
+    processNode(gradioApp());
 
     if (localization.rtl) { // if the language is from right to left,
         (new MutationObserver((mutations, observer) => { // wait for the style to load
@@ -170,8 +169,8 @@ document.addEventListener("DOMContentLoaded", function () {
                             }
                         }
                     }
-                })
                 });
-        })).observe(gradioApp(), { childList: true });
+            });
+        })).observe(gradioApp(), {childList: true});
     }
-})
+});

javascript/notification.js

@@ -4,14 +4,14 @@ let lastHeadImg = 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){
+        if (notificationButton != null) {
             notificationButton.addEventListener('click', () => {
                 void Notification.requestPermission();
-            },true);
+            }, 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(){
+function rememberGallerySelection() {
 
 }
 
-function getGallerySelectedIndex(){
+function getGallerySelectedIndex() {
 
 }
 
-function request(url, data, handler, errorHandler){
+function request(url, data, handler, errorHandler) {
     var xhr = new XMLHttpRequest();
     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();
             }
         }
     };
@@ -31,147 +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, 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()
-    }
+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";
             }
 
-            let 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 > inactivityTimeout && !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 = args_to_array(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
 
-onUiLoaded(function(){
-    createLink = function(elem_id, text, href){
-        var a = document.createElement('A')
-        a.textContent = text
-        a.target = '_blank';
-
-        elem = gradioApp().querySelector('#'+elem_id)
-        elem.insertBefore(a, elem.querySelector('label'))
-
-        return a
+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);
+        }
+    });
+});
 
-    createLink("setting_samples_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
-    createLink("setting_directories_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
-
-    createLink("setting_quicksettings_list", "[info] ").addEventListener("click", function(event){
-        requestGet("./internal/quicksettings-hint", {}, function(data){
-            var table = document.createElement('table')
-            table.className = 'settings-value-table'
+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)
+        data.forEach(function(obj) {
+            var tr = document.createElement('tr');
+            var td = document.createElement('td');
+            td.textContent = obj.name;
+            tr.appendChild(td);
 
-                var td = document.createElement('td')
-                td.textContent = obj.label
-                tr.appendChild(td)
+            td = document.createElement('td');
+            td.textContent = obj.label;
+            tr.appendChild(td);
 
-                table.appendChild(tr)
-            })
+            table.appendChild(tr);
+        });
 
         popup(table);
-        })
     });
-})
-
-
+}

launch.py

@@ -3,25 +3,23 @@ import subprocess
 import os
 import sys
 import importlib.util
-import shlex
 import platform
 import json
+from functools import lru_cache
 
 from modules import cmd_args
 from modules.paths_internal import script_path, extensions_dir
 
-commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
-sys.argv += shlex.split(commandline_args)
-
 args, _ = cmd_args.parser.parse_known_args()
 
 python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
-stored_commit_hash = None
-stored_git_tag = None
 dir_repos = "repositories"
 
+# Whether to default to printing command output
+default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
+
 if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
     os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
 
@@ -57,65 +55,52 @@ Use --skip-python-version-check to suppress this warning.
 """)
 
 
+@lru_cache()
 def commit_hash():
-    global stored_commit_hash
-
-    if stored_commit_hash is not None:
-        return stored_commit_hash
-
     try:
-        stored_commit_hash = run(f"{git} rev-parse HEAD").strip()
+        return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
     except Exception:
-        stored_commit_hash = "<none>"
-
-    return stored_commit_hash
+        return "<none>"
 
 
+@lru_cache()
 def git_tag():
-    global stored_git_tag
-
-    if stored_git_tag is not None:
-        return stored_git_tag
-
     try:
-        stored_git_tag = run(f"{git} describe --tags").strip()
+        return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
     except Exception:
-        stored_git_tag = "<none>"
-
-    return stored_git_tag
+        return "<none>"
 
 
-def run(command, desc=None, errdesc=None, custom_env=None, live=False):
+def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
     if desc is not None:
         print(desc)
 
-    if live:
-        result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
-        if result.returncode != 0:
-            raise RuntimeError(f"""{errdesc or 'Error running command'}.
-Command: {command}
-Error code: {result.returncode}""")
+    run_kwargs = {
+        "args": command,
+        "shell": True,
+        "env": os.environ if custom_env is None else custom_env,
+        "encoding": 'utf8',
+        "errors": 'ignore',
+    }
 
-        return ""
+    if not live:
+        run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE
 
-    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
+    result = subprocess.run(**run_kwargs)
 
     if result.returncode != 0:
+        error_bits = [
+            f"{errdesc or 'Error running command'}.",
+            f"Command: {command}",
+            f"Error code: {result.returncode}",
+        ]
+        if result.stdout:
+            error_bits.append(f"stdout: {result.stdout}")
+        if result.stderr:
+            error_bits.append(f"stderr: {result.stderr}")
+        raise RuntimeError("\n".join(error_bits))
 
-        message = f"""{errdesc or 'Error running command'}.
-Command: {command}
-Error code: {result.returncode}
-stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else '<empty>'}
-stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else '<empty>'}
-"""
-        raise RuntimeError(message)
-
-    return result.stdout.decode(encoding="utf8", errors="ignore")
-
-
-def check_run(command):
-    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
-    return result.returncode == 0
+    return (result.stdout or "")
 
 
 def is_installed(package):
@@ -131,11 +116,7 @@ def repo_dir(name):
     return os.path.join(script_path, dir_repos, name)
 
 
-def run_python(code, desc=None, errdesc=None):
-    return run(f'"{python}" -c "{code}"', desc, errdesc)
-
-
-def run_pip(command, desc=None, live=False):
+def run_pip(command, desc=None, live=default_command_live):
     if args.skip_install:
         return
 
@@ -143,8 +124,9 @@ def run_pip(command, desc=None, live=False):
     return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
 
 
-def check_run_python(code):
-    return check_run(f'"{python}" -c "{code}"')
+def check_run_python(code: str) -> bool:
+    result = subprocess.run([python, "-c", code], capture_output=True, shell=False)
+    return result.returncode == 0
 
 
 def git_clone(url, dir, name, commithash=None):
@@ -237,13 +219,14 @@ def run_extensions_installers(settings_file):
 
 
 def prepare_environment():
-    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url https://download.pytorch.org/whl/cu118")
+    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
+    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
 
     xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
-    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
-    clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-    openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
+    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
+    clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
+    openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
 
     stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
     taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
@@ -270,8 +253,11 @@ def prepare_environment():
     if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
 
-    if not args.skip_torch_cuda_test:
-        run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
+    if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
+        raise RuntimeError(
+            'Torch is not able to use GPU; '
+            'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
+        )
 
     if not is_installed("gfpgan"):
         run_pip(f"install {gfpgan_package}", "gfpgan")
@@ -294,8 +280,8 @@ def prepare_environment():
         elif platform.system() == "Linux":
             run_pip(f"install {xformers_package}", "xformers")
 
-    if not is_installed("pyngrok") and args.ngrok:
-        run_pip("install pyngrok", "ngrok")
+    if not is_installed("ngrok") and args.ngrok:
+        run_pip("install ngrok", "ngrok")
 
     os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
 

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/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()
 
@@ -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'))
@@ -103,3 +105,4 @@ parser.add_argument("--skip-version-check", action='store_true', help="Do not ch
 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/config_states.py

@@ -14,7 +14,7 @@ from collections import OrderedDict
 import git
 
 from modules import shared, extensions
-from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path, config_states_dir
+from modules.paths_internal import script_path, config_states_dir
 
 
 all_config_states = OrderedDict()
@@ -35,7 +35,7 @@ def list_config_states():
                 j["filepath"] = path
                 config_states.append(j)
 
-    config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True))
+    config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
 
     for cs in config_states:
         timestamp = time.asctime(time.gmtime(cs["created_at"]))
@@ -83,6 +83,8 @@ def get_extension_config():
     ext_config = {}
 
     for ext in extensions.extensions:
+        ext.read_info_from_repo()
+
         entry = {
             "name": ext.name,
             "path": ext.path,

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

modules/esrgan_model.py

@@ -6,7 +6,7 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgan_model_arch as arch
-from modules import shared, modelloader, images, devices
+from modules import modelloader, images, devices
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
@@ -16,9 +16,7 @@ def mod2normal(state_dict):
     # this code is copied from https://github.com/victorca25/iNNfer
     if 'conv_first.weight' in state_dict:
         crt_net = {}
-        items = []
-        for k, v in state_dict.items():
-            items.append(k)
+        items = list(state_dict)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -52,9 +50,7 @@ def resrgan2normal(state_dict, nb=23):
     if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
         re8x = 0
         crt_net = {}
-        items = []
-        for k, v in state_dict.items():
-            items.append(k)
+        items = list(state_dict)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']

modules/esrgan_model_arch.py

@@ -2,7 +2,6 @@
 
 from collections import OrderedDict
 import math
-import functools
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -438,9 +437,11 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
     padding = padding if pad_type == 'zero' else 0
 
     if convtype=='PartialConv2D':
+        from torchvision.ops import PartialConv2d  # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
         c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
                dilation=dilation, bias=bias, groups=groups)
     elif convtype=='DeformConv2D':
+        from torchvision.ops import DeformConv2d  # not tested
         c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
                dilation=dilation, bias=bias, groups=groups)
     elif convtype=='Conv3D':

modules/extensions.py

 import os
 import sys
+import threading
 import traceback
 
-import time
-from datetime import datetime
 import git
 
 from modules import shared
-from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401
 
 extensions = []
 
@@ -25,6 +24,8 @@ def active():
 
 
 class Extension:
+    lock = threading.Lock()
+
     def __init__(self, name, path, enabled=True, is_builtin=False):
         self.name = name
         self.path = path
@@ -43,8 +44,13 @@ class Extension:
         if self.is_builtin or self.have_info_from_repo:
             return
 
-        self.have_info_from_repo = True
+        with self.lock:
+            if self.have_info_from_repo:
+                return
 
+            self.do_read_info_from_repo()
+
+    def do_read_info_from_repo(self):
         repo = None
         try:
             if os.path.exists(os.path.join(self.path, ".git")):
@@ -59,18 +65,18 @@ class Extension:
             try:
                 self.status = 'unknown'
                 self.remote = next(repo.remote().urls, None)
-                head = repo.head.commit
                 self.commit_date = repo.head.commit.committed_date
-                ts = time.asctime(time.gmtime(self.commit_date))
                 if repo.active_branch:
                     self.branch = repo.active_branch.name
-                self.commit_hash = head.hexsha
-                self.version = f'{self.commit_hash[:8]} ({ts})'
+                self.commit_hash = repo.head.commit.hexsha
+                self.version = repo.git.describe("--always", "--tags")  # compared to `self.commit_hash[:8]` this takes about 30% more time total but since we run it in parallel we don't care
 
             except Exception as ex:
                 print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
                 self.remote = None
 
+        self.have_info_from_repo = True
+
     def list_files(self, subdir, extension):
         from modules import scripts
 

modules/extra_networks.py

@@ -91,7 +91,7 @@ def deactivate(p, extra_network_data):
     """call deactivate for extra networks in extra_network_data in specified order, then call
     deactivate for all remaining registered networks"""
 
-    for extra_network_name, extra_network_args in extra_network_data.items():
+    for extra_network_name in extra_network_data:
         extra_network = extra_network_registry.get(extra_network_name, None)
         if extra_network is None:
             continue

modules/extra_networks_hypernet.py

-from modules import extra_networks, shared, extra_networks
+from modules import extra_networks, shared
 from modules.hypernetworks import hypernetwork
 
 

modules/extras.py

@@ -136,14 +136,14 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     result_is_instruct_pix2pix_model = False
 
     if theta_func2:
-        shared.state.textinfo = f"Loading B"
+        shared.state.textinfo = "Loading B"
         print(f"Loading {secondary_model_info.filename}...")
         theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
     else:
         theta_1 = None
 
     if theta_func1:
-        shared.state.textinfo = f"Loading C"
+        shared.state.textinfo = "Loading C"
         print(f"Loading {tertiary_model_info.filename}...")
         theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
 
@@ -242,9 +242,11 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     shared.state.textinfo = "Saving"
     print(f"Saving to {output_modelname}...")
 
-    metadata = {"format": "pt", "sd_merge_models": {}, "sd_merge_recipe": None}
+    metadata = None
 
     if save_metadata:
+        metadata = {"format": "pt"}
+
         merge_recipe = {
             "type": "webui", # indicate this model was merged with webui's built-in merger
             "primary_model_hash": primary_model_info.sha256,
@@ -262,15 +264,17 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
         }
         metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
 
+        sd_merge_models = {}
+
         def add_model_metadata(checkpoint_info):
             checkpoint_info.calculate_shorthash()
-            metadata["sd_merge_models"][checkpoint_info.sha256] = {
+            sd_merge_models[checkpoint_info.sha256] = {
                 "name": checkpoint_info.name,
                 "legacy_hash": checkpoint_info.hash,
                 "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
             }
 
-            metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
+            sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {}))
 
         add_model_metadata(primary_model_info)
         if secondary_model_info:
@@ -278,7 +282,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
         if tertiary_model_info:
             add_model_metadata(tertiary_model_info)
 
-        metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
+        metadata["sd_merge_models"] = json.dumps(sd_merge_models)
 
     _, extension = os.path.splitext(output_modelname)
     if extension.lower() == ".safetensors":

modules/generation_parameters_copypaste.py

 import base64
-import html
 import io
-import math
 import os
 import re
-from pathlib import Path
 
 import gradio as gr
 from modules.paths import data_path
 from modules import shared, ui_tempdir, script_callbacks
-import tempfile
 from PIL import Image
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
@@ -23,14 +19,14 @@ registered_param_bindings = []
 
 
 class ParamBinding:
-    def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]):
+    def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
         self.paste_button = paste_button
         self.tabname = tabname
         self.source_text_component = source_text_component
         self.source_image_component = source_image_component
         self.source_tabname = source_tabname
         self.override_settings_component = override_settings_component
-        self.paste_field_names = paste_field_names
+        self.paste_field_names = paste_field_names or []
 
 
 def reset():
@@ -251,7 +247,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         lines.append(lastline)
         lastline = ''
 
-    for i, line in enumerate(lines):
+    for line in lines:
         line = line.strip()
         if line.startswith("Negative prompt:"):
             done_with_prompt = True
@@ -312,6 +308,8 @@ infotext_to_setting_name_mapping = [
     ('UniPC skip type', 'uni_pc_skip_type'),
     ('UniPC order', 'uni_pc_order'),
     ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('Token merging ratio', 'token_merging_ratio'),
+    ('Token merging ratio hr', 'token_merging_ratio_hr'),
     ('RNG', 'randn_source'),
     ('NGMS', 's_min_uncond'),
 ]

modules/gfpgan_model.py

@@ -78,7 +78,7 @@ def setup_model(dirname):
 
     try:
         from gfpgan import GFPGANer
-        from facexlib import detection, parsing
+        from facexlib import detection, parsing  # noqa: F401
         global user_path
         global have_gfpgan
         global gfpgan_constructor

modules/hypernetworks/hypernetwork.py

-import csv
 import datetime
 import glob
 import html
@@ -18,7 +17,7 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
 
-from collections import defaultdict, deque
+from collections import deque
 from statistics import stdev, mean
 
 
@@ -178,34 +177,34 @@ class Hypernetwork:
 
     def weights(self):
         res = []
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 res += layer.parameters()
         return res
 
     def train(self, mode=True):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.train(mode=mode)
                 for param in layer.parameters():
                     param.requires_grad = mode
 
     def to(self, device):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.to(device)
 
         return self
 
     def set_multiplier(self, multiplier):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.multiplier = multiplier
 
         return self
 
     def eval(self):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.eval()
                 for param in layer.parameters():
@@ -404,7 +403,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     k = self.to_k(context_k)
     v = self.to_v(context_v)
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+    q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
 
     sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
 
@@ -620,7 +619,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
     try:
         sd_hijack_checkpoint.add()
 
-        for i in range((steps-initial_step) * gradient_step):
+        for _ in range((steps-initial_step) * gradient_step):
             if scheduler.finished:
                 break
             if shared.state.interrupted:

modules/hypernetworks/ui.py

 import html
-import os
-import re
 
 import gradio as gr
 import modules.hypernetworks.hypernetwork
 from modules import devices, sd_hijack, shared
 
 not_available = ["hardswish", "multiheadattention"]
-keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict if x not in not_available]
 
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
     filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
 
-    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
+    return gr.Dropdown.update(choices=sorted(shared.hypernetworks)), f"Created: {filename}", ""
 
 
 def train_hypernetwork(*args):

modules/images.py

@@ -13,17 +13,24 @@ import numpy as np
 import piexif
 import piexif.helper
 from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
-from fonts.ttf import Roboto
 import string
 import json
 import hashlib
 
 from modules import sd_samplers, shared, script_callbacks, errors
-from modules.shared import opts, cmd_opts
+from modules.paths_internal import roboto_ttf_file
+from modules.shared import opts
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 
 
+def get_font(fontsize: int):
+    try:
+        return ImageFont.truetype(opts.font or roboto_ttf_file, fontsize)
+    except Exception:
+        return ImageFont.truetype(roboto_ttf_file, fontsize)
+
+
 def image_grid(imgs, batch_size=1, rows=None):
     if rows is None:
         if opts.n_rows > 0:
@@ -142,14 +149,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
                 lines.append(word)
         return lines
 
-    def get_font(fontsize):
-        try:
-            return ImageFont.truetype(opts.font or Roboto, fontsize)
-        except Exception:
-            return ImageFont.truetype(Roboto, fontsize)
-
     def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
-        for i, line in enumerate(lines):
+        for line in lines:
             fnt = initial_fnt
             fontsize = initial_fontsize
             while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
@@ -409,13 +410,13 @@ class FilenameGenerator:
         time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
         try:
             time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
-        except pytz.exceptions.UnknownTimeZoneError as _:
+        except pytz.exceptions.UnknownTimeZoneError:
             time_zone = None
 
         time_zone_time = time_datetime.astimezone(time_zone)
         try:
             formatted_time = time_zone_time.strftime(time_format)
-        except (ValueError, TypeError) as _:
+        except (ValueError, TypeError):
             formatted_time = time_zone_time.strftime(self.default_time_format)
 
         return sanitize_filename_part(formatted_time, replace_spaces=False)
@@ -472,15 +473,52 @@ def get_next_sequence_number(path, basename):
     prefix_length = len(basename)
     for p in os.listdir(path):
         if p.startswith(basename):
-            l = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+            parts = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
             try:
-                result = max(int(l[0]), result)
+                result = max(int(parts[0]), result)
             except ValueError:
                 pass
 
     return result + 1
 
 
+def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None):
+    if extension is None:
+        extension = os.path.splitext(filename)[1]
+
+    image_format = Image.registered_extensions()[extension]
+
+    existing_pnginfo = existing_pnginfo or {}
+    if opts.enable_pnginfo:
+        existing_pnginfo['parameters'] = geninfo
+
+    if extension.lower() == '.png':
+        pnginfo_data = PngImagePlugin.PngInfo()
+        for k, v in (existing_pnginfo or {}).items():
+            pnginfo_data.add_text(k, str(v))
+
+        image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+
+    elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+        if image.mode == 'RGBA':
+            image = image.convert("RGB")
+        elif image.mode == 'I;16':
+            image = image.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
+
+        image.save(filename, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
+
+        if opts.enable_pnginfo and geninfo is not None:
+            exif_bytes = piexif.dump({
+                "Exif": {
+                    piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
+                },
+            })
+
+            piexif.insert(exif_bytes, filename)
+    else:
+        image.save(filename, format=image_format, quality=opts.jpeg_quality)
+
+
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     """Save an image.
 
@@ -565,38 +603,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     info = params.pnginfo.get(pnginfo_section_name, None)
 
     def _atomically_save_image(image_to_save, filename_without_extension, extension):
-        # save image with .tmp extension to avoid race condition when another process detects new image in the directory
+        """
+        save image with .tmp extension to avoid race condition when another process detects new image in the directory
+        """
         temp_file_path = f"{filename_without_extension}.tmp"
-        image_format = Image.registered_extensions()[extension]
 
-        if extension.lower() == '.png':
-            pnginfo_data = PngImagePlugin.PngInfo()
-            if opts.enable_pnginfo:
-                for k, v in params.pnginfo.items():
-                    pnginfo_data.add_text(k, str(v))
-
-            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
-
-        elif extension.lower() in (".jpg", ".jpeg", ".webp"):
-            if image_to_save.mode == 'RGBA':
-                image_to_save = image_to_save.convert("RGB")
-            elif image_to_save.mode == 'I;16':
-                image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
-
-            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
-
-            if opts.enable_pnginfo and info is not None:
-                exif_bytes = piexif.dump({
-                    "Exif": {
-                        piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
-                    },
-                })
-
-                piexif.insert(exif_bytes, temp_file_path)
-        else:
-            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
+        save_image_with_geninfo(image_to_save, info, temp_file_path, extension, params.pnginfo)
 
-        # atomically rename the file with correct extension
         os.replace(temp_file_path, filename_without_extension + extension)
 
     fullfn_without_extension, extension = os.path.splitext(params.filename)

modules/img2img.py

-import math
 import os
-import sys
-import traceback
 
 import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
 
-from modules import devices, sd_samplers
+from modules import sd_samplers
 from modules.generation_parameters_copypaste import create_override_settings_dict
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
-import modules.images as images
 import modules.scripts
 
 
@@ -59,7 +55,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
             # try to find corresponding mask for an image using simple filename matching
             mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
             # if not found use first one ("same mask for all images" use-case)
-            if not mask_image_path in inpaint_masks:
+            if mask_image_path not in inpaint_masks:
                 mask_image_path = inpaint_masks[0]
             mask_image = Image.open(mask_image_path)
             p.image_mask = mask_image

modules/interrogate.py

@@ -11,7 +11,6 @@ import torch.hub
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
-import modules.shared as shared
 from modules import devices, paths, shared, lowvram, modelloader, errors
 
 blip_image_eval_size = 384
@@ -160,7 +159,7 @@ class InterrogateModels:
             text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
 
         top_count = min(top_count, len(text_array))
-        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
+        text_tokens = clip.tokenize(list(text_array), truncate=True).to(devices.device_interrogate)
         text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
         text_features /= text_features.norm(dim=-1, keepdim=True)
 
@@ -208,8 +207,8 @@ class InterrogateModels:
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
 
-                for name, topn, items in self.categories():
-                    matches = self.rank(image_features, items, top_count=topn)
+                for cat in self.categories():
+                    matches = self.rank(image_features, cat.items, top_count=cat.topn)
                     for match, score in matches:
                         if shared.opts.interrogate_return_ranks:
                             res += f", ({match}:{score/100:.3f})"

modules/mac_specific.py

 import torch
 import platform
-from modules import paths
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
 

modules/modelloader.py

-import glob
 import os
 import shutil
 import importlib
@@ -40,7 +39,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                 if os.path.islink(full_path) and not os.path.exists(full_path):
                     print(f"Skipping broken symlink: {full_path}")
                     continue
-                if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
+                if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist):
                     continue
                 if full_path not in output:
                     output.append(full_path)
@@ -108,12 +107,12 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
                     print(f"Moving {file} from {src_path} to {dest_path}.")
                     try:
                         shutil.move(fullpath, dest_path)
-                    except:
+                    except Exception:
                         pass
             if len(os.listdir(src_path)) == 0:
                 print(f"Removing empty folder: {src_path}")
                 shutil.rmtree(src_path, True)
-    except:
+    except Exception:
         pass
 
 
@@ -127,7 +126,7 @@ def load_upscalers():
             full_model = f"modules.{model_name}_model"
             try:
                 importlib.import_module(full_model)
-            except:
+            except Exception:
                 pass
 
     datas = []

modules/models/diffusion/uni_pc/__init__.py

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

modules/models/diffusion/uni_pc/sampler.py

@@ -54,7 +54,8 @@ class UniPCSampler(object):
         if conditioning is not None:
             if isinstance(conditioning, dict):
                 ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
+                while isinstance(ctmp, list):
+                    ctmp = ctmp[0]
                 cbs = ctmp.shape[0]
                 if cbs != batch_size:
                     print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")

modules/ngrok.py

-from pyngrok import ngrok, conf, exception
+import ngrok
 
-def connect(token, port, region):
+# Connect to ngrok for ingress
+def connect(token, port, options):
     account = None
     if token is None:
         token = 'None'
@@ -10,28 +11,19 @@ def connect(token, port, region):
             token, username, password = token.split(':', 2)
             account = f"{username}:{password}"
 
-    config = conf.PyngrokConfig(
-        auth_token=token, region=region
-    )
+    # For all options see: https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py
+    if not options.get('authtoken_from_env'):
+        options['authtoken'] = token
+    if account:
+        options['basic_auth'] = account
+    if not options.get('session_metadata'):
+        options['session_metadata'] = 'stable-diffusion-webui'
 
-    # Guard for existing tunnels
-    existing = ngrok.get_tunnels(pyngrok_config=config)
-    if existing:
-        for established in existing:
-            # Extra configuration in the case that the user is also using ngrok for other tunnels
-            if established.config['addr'][-4:] == str(port):
-                public_url = existing[0].public_url
-                print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n'
-                    'You can use this link after the launch is complete.')
-                return
 
     try:
-        if account is None:
-            public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
-        else:
-            public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).public_url
-    except exception.PyngrokNgrokError:
-        print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
+        public_url = ngrok.connect(f"127.0.0.1:{port}", **options).url()
+    except Exception as e:
+        print(f'Invalid ngrok authtoken? ngrok connection aborted due to: {e}\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
     else:
         print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'

modules/paths.py

 import os
 import sys
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir  # noqa: F401
 
-import modules.safe
+import modules.safe  # noqa: F401
 
 
 # data_path = cmd_opts_pre.data

modules/sd_models_config.py

-import re
 import os
 
 import torch