Merge branch 'dev' into find_vae

.github/workflows/on_pull_request.yaml

@@ -18,22 +18,29 @@ jobs:
     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
+          python-version: 3.11
-          cache: pip
+          # NB: there's no cache: pip here since we're not installing anything
-          cache-dependency-path: |
+          #     from the requirements.txt file(s) in the repository; it's faster
-            **/requirements*txt
+          #     not to have GHA download an (at the time of writing) 4 GB cache
-      - name: Install PyLint
+          #     of PyTorch and other dependencies.
-        run: | 
+      - name: Install Ruff
-          python -m pip install --upgrade pip
+        run: pip install ruff==0.0.265
-          pip install pylint
+      - name: Run Ruff
-      # This lets PyLint check to see if it can resolve imports
+        run: ruff .
-      - name: Install dependencies
+
-        run: |
+# The rest are currently disabled pending fixing of e.g. installing the torch dependency.
-          export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
+
-          python launch.py
+#      - name: Install PyLint
-      - name: Analysing the code with pylint
+#        run: |
-        run: |
+#          python -m pip install --upgrade pip
-          pylint $(git ls-files '*.py')
+#          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')

.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()

CHANGELOG.md

@@ -1,3 +1,59 @@
+## Upcoming 1.2.1
+
+### Features:
+ * add an option to always refer to lora by filenames
+
+### Bug Fixes:
+ * never refer to lora by an alias if multiple loras have same alias or the alias is called none
+ * fix upscalers disappearing after the user reloads UI
+ * allow bf16 in safe unpickler (resolves problems with loading some loras)
+ * allow web UI to be ran fully offline
+
+## 1.2.0
+
+### Features:
+ * do not wait for stable diffusion model to load at startup
+ * add filename patterns: [denoising]
+ * directory hiding for extra networks: dirs starting with . will hide their cards on extra network tabs unless specifically searched for
+ * Lora: for the `<...>` text in prompt, use name of Lora that is in the metdata of the file, if present, instead of filename (both can be used to activate lora)
+ * Lora: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
+ * Lora: Fix some Loras not working (ones that have 3x3 convolution layer)
+ * Lora: add an option to use old method of applying loras (producing same results as with kohya-ss)
+ * add version to infotext, footer and console output when starting
+ * add links to wiki for filename pattern settings
+ * add extended info for quicksettings setting and use multiselect input instead of a text field
+
+### Minor:
+ * gradio bumped to 3.29.0
+ * torch bumped to 2.0.1
+ * --subpath option for gradio for use with reverse proxy
+ * linux/OSX: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
+ * possible frontend optimization: do not apply localizations if there are none
+ * Add extra `None` option for VAE in XYZ plot
+ * print error to console when batch processing in img2img fails
+ * create HTML for extra network pages only on demand
+ * allow directories starting with . to still list their models for lora, checkpoints, etc
+ * put infotext options into their own category in settings tab
+ * do not show licenses page when user selects Show all pages in settings
+
+### Extensions:
+ * Tooltip localization support
+ * Add api method to get LoRA models with prompt
+
+### Bug Fixes:
+ * re-add /docs endpoint
+ * fix gamepad navigation
+ * make the lightbox fullscreen image function properly
+ * fix squished thumbnails in extras tab
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
+ * fix webui showing the same image if you configure the generation to always save results into same file
+ * fix bug with upscalers not working properly
+ * Fix MPS on PyTorch 2.0.1, Intel Macs
+ * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
+ * prevent Reload UI button/link from reloading the page when it's not yet ready
+ * fix prompts from file script failing to read contents from a drag/drop file
+
+
 ## 1.1.1
 ### Bug Fixes:
  * fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle

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:
@@ -131,11 +130,11 @@ class LDSR:
             im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
         else:
             print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        
+
         # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
         pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
         im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
-        
+
         logs = self.run(model["model"], im_padded, diffusion_steps, eta)
 
         sample = logs["sample"]
@@ -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

@@ -1,16 +1,21 @@
 # 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)
+ldm.models.autoencoder.VQModel = VQModel
-setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
+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
@@ -460,7 +460,7 @@ class LatentDiffusionV1(DDPMV1):
         self.instantiate_cond_stage(cond_stage_config)
         self.cond_stage_forward = cond_stage_forward
         self.clip_denoised = False
-        self.bbox_tokenizer = None  
+        self.bbox_tokenizer = None
 
         self.restarted_from_ckpt = False
         if ckpt_path is not None:
@@ -792,7 +792,7 @@ class LatentDiffusionV1(DDPMV1):
                 z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
 
                 # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  
+                if isinstance(self.first_stage_model, VQModelInterface):
                     output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
                                                                  force_not_quantize=predict_cids or force_not_quantize)
                                    for i in range(z.shape[-1])]
@@ -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):
@@ -900,7 +890,7 @@ class LatentDiffusionV1(DDPMV1):
 
         if hasattr(self, "split_input_params"):
             assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  
+            assert not return_ids
             ks = self.split_input_params["ks"]  # eg. (128, 128)
             stride = self.split_input_params["stride"]  # eg. (64, 64)
 
@@ -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 callback:
-            if img_callback: img_callback(img, i)
+                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 callback:
-            if img_callback: img_callback(img, i)
+                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)
+ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
-setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
+ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
-setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
+ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
-setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
+ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1

extensions-builtin/Lora/extra_networks_lora.py

@@ -1,6 +1,7 @@
 from modules import extra_networks, shared
 import lora
 
+
 class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def __init__(self):
         super().__init__('lora')

extensions-builtin/Lora/lora.py

@@ -1,10 +1,9 @@
-import glob
 import os
 import re
 import torch
 from typing import Union
 
-from modules import shared, devices, sd_models, errors
+from modules import shared, devices, sd_models, errors, scripts
 
 metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
 
@@ -93,6 +92,7 @@ class LoraOnDisk:
             self.metadata = m
 
         self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None)  # those are cover images and they are too big to display in UI as text
+        self.alias = self.metadata.get('ss_output_name', self.name)
 
 
 class LoraModule:
@@ -165,12 +165,14 @@ def load_lora(name, filename):
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
         elif type(sd_module) == torch.nn.MultiheadAttention:
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif type(sd_module) == torch.nn.Conv2d:
+        elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+        elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
+            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
         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)
@@ -182,7 +184,7 @@ 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}")
@@ -199,11 +201,11 @@ def load_loras(names, multipliers=None):
 
     loaded_loras.clear()
 
-    loras_on_disk = [available_loras.get(name, None) for name in names]
+    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_loras.get(name, None) for name in names]
+        loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
 
     for i, name in enumerate(names):
         lora = already_loaded.get(name, None)
@@ -232,6 +234,8 @@ def lora_calc_updown(lora, module, target):
 
         if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
             updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+        elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
+            updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
         else:
             updown = up @ down
 
@@ -240,6 +244,19 @@ def lora_calc_updown(lora, module, target):
         return updown
 
 
+def lora_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+    weights_backup = getattr(self, "lora_weights_backup", None)
+
+    if weights_backup is None:
+        return
+
+    if isinstance(self, torch.nn.MultiheadAttention):
+        self.in_proj_weight.copy_(weights_backup[0])
+        self.out_proj.weight.copy_(weights_backup[1])
+    else:
+        self.weight.copy_(weights_backup)
+
+
 def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
     """
     Applies the currently selected set of Loras to the weights of torch layer self.
@@ -264,12 +281,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
         self.lora_weights_backup = weights_backup
 
     if current_names != wanted_names:
-        if weights_backup is not None:
+        lora_restore_weights_from_backup(self)
-            if isinstance(self, torch.nn.MultiheadAttention):
-                self.in_proj_weight.copy_(weights_backup[0])
-                self.out_proj.weight.copy_(weights_backup[1])
-            else:
-                self.weight.copy_(weights_backup)
 
         for lora in loaded_loras:
             module = lora.modules.get(lora_layer_name, None)
@@ -297,15 +309,48 @@ 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):
+    """
+    Old way of applying Lora by executing operations during layer's forward.
+    Stacking many loras this way results in big performance degradation.
+    """
+
+    if len(loaded_loras) == 0:
+        return original_forward(module, input)
+
+    input = devices.cond_cast_unet(input)
+
+    lora_restore_weights_from_backup(module)
+    lora_reset_cached_weight(module)
+
+    res = original_forward(module, input)
+
+    lora_layer_name = getattr(module, 'lora_layer_name', None)
+    for lora in loaded_loras:
+        module = lora.modules.get(lora_layer_name, None)
+        if module is None:
+            continue
+
+        module.up.to(device=devices.device)
+        module.down.to(device=devices.device)
+
+        res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+
+    return res
 
 
 def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
-    setattr(self, "lora_current_names", ())
+    self.lora_current_names = ()
-    setattr(self, "lora_weights_backup", None)
+    self.lora_weights_backup = None
 
 
 def lora_Linear_forward(self, input):
+    if shared.opts.lora_functional:
+        return lora_forward(self, input, torch.nn.Linear_forward_before_lora)
+
     lora_apply_weights(self)
 
     return torch.nn.Linear_forward_before_lora(self, input)
@@ -318,6 +363,9 @@ def lora_Linear_load_state_dict(self, *args, **kwargs):
 
 
 def lora_Conv2d_forward(self, input):
+    if shared.opts.lora_functional:
+        return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora)
+
     lora_apply_weights(self)
 
     return torch.nn.Conv2d_forward_before_lora(self, input)
@@ -343,24 +391,65 @@ def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
 
 def list_available_loras():
     available_loras.clear()
+    available_lora_aliases.clear()
+    forbidden_lora_aliases.clear()
+    forbidden_lora_aliases.update({"none": 1})
 
     os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
 
-    candidates = \
+    candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
-        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.pt'), recursive=True) + \
-        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
-        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
-
     for filename in sorted(candidates, key=str.lower):
         if os.path.isdir(filename):
             continue
 
         name = os.path.splitext(os.path.basename(filename))[0]
+        entry = LoraOnDisk(name, filename)
 
-        available_loras[name] = LoraOnDisk(name, filename)
+        available_loras[name] = entry
 
+        if entry.alias in available_lora_aliases:
+            forbidden_lora_aliases[entry.alias.lower()] = 1
+
+        available_lora_aliases[name] = entry
+        available_lora_aliases[entry.alias] = entry
+
+
+re_lora_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
+
+
+def infotext_pasted(infotext, params):
+    if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
+        return  # if the other extension is active, it will handle those fields, no need to do anything
+
+    added = []
+
+    for k in params:
+        if not k.startswith("AddNet Model "):
+            continue
+
+        num = k[13:]
+
+        if params.get("AddNet Module " + num) != "LoRA":
+            continue
+
+        name = params.get("AddNet Model " + num)
+        if name is None:
+            continue
+
+        m = re_lora_name.match(name)
+        if m:
+            name = m.group(1)
+
+        multiplier = params.get("AddNet Weight A " + num, "1.0")
+
+        added.append(f"<lora:{name}:{multiplier}>")
+
+    if added:
+        params["Prompt"] += "\n" + "".join(added)
 
 available_loras = {}
+available_lora_aliases = {}
+forbidden_lora_aliases = {}
 loaded_loras = []
 
 list_available_loras()

extensions-builtin/Lora/scripts/lora_script.py

@@ -1,12 +1,12 @@
 import torch
 import gradio as gr
+from fastapi import FastAPI
 
 import lora
 import extra_networks_lora
 import ui_extra_networks_lora
 from modules import script_callbacks, ui_extra_networks, extra_networks, shared
 
-
 def unload():
     torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
     torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
@@ -49,8 +49,34 @@ torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention
 script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
 script_callbacks.on_before_ui(before_ui)
+script_callbacks.on_infotext_pasted(lora.infotext_pasted)
 
 
 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["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"]}),
 }))
+
+
+shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
+    "lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
+}))
+
+
+def create_lora_json(obj: lora.LoraOnDisk):
+    return {
+        "name": obj.name,
+        "alias": obj.alias,
+        "path": obj.filename,
+        "metadata": obj.metadata,
+    }
+
+
+def api_loras(_: gr.Blocks, app: FastAPI):
+    @app.get("/sdapi/v1/loras")
+    async def get_loras():
+        return [create_lora_json(obj) for obj in lora.available_loras.values()]
+
+
+script_callbacks.on_app_started(api_loras)
+

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -15,13 +15,19 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
     def list_items(self):
         for name, lora_on_disk in lora.available_loras.items():
             path, ext = os.path.splitext(lora_on_disk.filename)
+
+            if shared.opts.lora_preferred_name == "Filename" or lora_on_disk.alias.lower() in lora.forbidden_lora_aliases:
+                alias = name
+            else:
+                alias = lora_on_disk.alias
+
             yield {
                 "name": name,
                 "filename": path,
                 "preview": self.find_preview(path),
                 "description": self.find_description(path),
                 "search_term": self.search_terms_from_path(lora_on_disk.filename),
-                "prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
+                "prompt": json.dumps(f"<lora:{alias}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
                 "local_preview": f"{path}.{shared.opts.samples_format}",
                 "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
             }

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -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),
+        if self.type != 'W':
-                                                 dims=(1, 2))
+            output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
+
         return output
 
     def relative_embedding(self):
@@ -262,4 +265,4 @@ class SCUNet(nn.Module):
                 nn.init.constant_(m.bias, 0)
         elif isinstance(m, nn.LayerNorm):
             nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
+            nn.init.constant_(m.weight, 1.0)

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -1,4 +1,3 @@
-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
 
@@ -151,7 +150,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
             for w_idx in w_idx_list:
                 if state.interrupted or state.skipped:
                     break
-                
+
                 in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
                 out_patch = model(in_patch)
                 out_patch_mask = torch.ones_like(out_patch)

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,
@@ -805,7 +805,7 @@ class SwinIR(nn.Module):
     def forward(self, x):
         H, W = x.shape[2:]
         x = self.check_image_size(x)
-        
+
         self.mean = self.mean.type_as(x)
         x = (x - self.mean) * self.img_range
 
@@ -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
@@ -241,7 +241,7 @@ class SwinTransformerBlock(nn.Module):
             attn_mask = None
 
         self.register_buffer("attn_mask", attn_mask)
-        
+
     def calculate_mask(self, x_size):
         # calculate attention mask for SW-MSA
         H, W = x_size
@@ -263,7 +263,7 @@ class SwinTransformerBlock(nn.Module):
         attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
         attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
 
-        return attn_mask        
+        return attn_mask
 
     def forward(self, x, x_size):
         H, W = x_size
@@ -288,7 +288,7 @@ class SwinTransformerBlock(nn.Module):
             attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
         else:
             attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-            
+
         # merge windows
         attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
         shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
@@ -369,7 +369,7 @@ class PatchMerging(nn.Module):
         H, W = self.input_resolution
         flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
         flops += H * W * self.dim // 2
-        return flops    
+        return flops
 
 class BasicLayer(nn.Module):
     """ A basic Swin Transformer layer for one stage.
@@ -447,7 +447,7 @@ class BasicLayer(nn.Module):
             nn.init.constant_(blk.norm1.weight, 0)
             nn.init.constant_(blk.norm2.bias, 0)
             nn.init.constant_(blk.norm2.weight, 0)
-            
+
 class PatchEmbed(nn.Module):
     r""" Image to Patch Embedding
     Args:
@@ -492,7 +492,7 @@ class PatchEmbed(nn.Module):
         flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
         if self.norm is not None:
             flops += Ho * Wo * self.embed_dim
-        return flops           
+        return flops
 
 class RSTB(nn.Module):
     """Residual Swin Transformer Block (RSTB).
@@ -531,7 +531,7 @@ class RSTB(nn.Module):
                                          num_heads=num_heads,
                                          window_size=window_size,
                                          mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, 
+                                         qkv_bias=qkv_bias,
                                          drop=drop, attn_drop=attn_drop,
                                          drop_path=drop_path,
                                          norm_layer=norm_layer,
@@ -622,7 +622,7 @@ class Upsample(nn.Sequential):
         else:
             raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
         super(Upsample, self).__init__(*m)
-        
+
 class Upsample_hf(nn.Sequential):
     """Upsample module.
 
@@ -642,7 +642,7 @@ class Upsample_hf(nn.Sequential):
             m.append(nn.PixelShuffle(3))
         else:
             raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample_hf, self).__init__(*m)        
+        super(Upsample_hf, self).__init__(*m)
 
 
 class UpsampleOneStep(nn.Sequential):
@@ -667,8 +667,8 @@ class UpsampleOneStep(nn.Sequential):
         H, W = self.input_resolution
         flops = H * W * self.num_feat * 3 * 9
         return flops
-    
+
-    
+
 
 class Swin2SR(nn.Module):
     r""" Swin2SR
@@ -698,8 +698,8 @@ 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, 
+                 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,
                  use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
@@ -764,7 +764,7 @@ class Swin2SR(nn.Module):
                          num_heads=num_heads[i_layer],
                          window_size=window_size,
                          mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, 
+                         qkv_bias=qkv_bias,
                          drop=drop_rate, attn_drop=attn_drop_rate,
                          drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
                          norm_layer=norm_layer,
@@ -776,7 +776,7 @@ class Swin2SR(nn.Module):
 
                          )
             self.layers.append(layer)
-            
+
         if self.upsampler == 'pixelshuffle_hf':
             self.layers_hf = nn.ModuleList()
             for i_layer in range(self.num_layers):
@@ -787,7 +787,7 @@ class Swin2SR(nn.Module):
                              num_heads=num_heads[i_layer],
                              window_size=window_size,
                              mlp_ratio=self.mlp_ratio,
-                             qkv_bias=qkv_bias, 
+                             qkv_bias=qkv_bias,
                              drop=drop_rate, attn_drop=attn_drop_rate,
                              drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
                              norm_layer=norm_layer,
@@ -799,7 +799,7 @@ class Swin2SR(nn.Module):
 
                              )
                 self.layers_hf.append(layer)
-                        
+
         self.norm = norm_layer(self.num_features)
 
         # build the last conv layer in deep feature extraction
@@ -829,10 +829,10 @@ class Swin2SR(nn.Module):
             self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
             self.conv_after_aux = nn.Sequential(
                 nn.Conv2d(3, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))            
+                nn.LeakyReLU(inplace=True))
             self.upsample = Upsample(upscale, num_feat)
             self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            
+
         elif self.upsampler == 'pixelshuffle_hf':
             self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                                                       nn.LeakyReLU(inplace=True))
@@ -846,7 +846,7 @@ class Swin2SR(nn.Module):
                 nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                 nn.LeakyReLU(inplace=True))
             self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            
+
         elif self.upsampler == 'pixelshuffledirect':
             # for lightweight SR (to save parameters)
             self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
@@ -905,7 +905,7 @@ class Swin2SR(nn.Module):
         x = self.patch_unembed(x, x_size)
 
         return x
-    
+
     def forward_features_hf(self, x):
         x_size = (x.shape[2], x.shape[3])
         x = self.patch_embed(x)
@@ -919,7 +919,7 @@ class Swin2SR(nn.Module):
         x = self.norm(x)  # B L C
         x = self.patch_unembed(x, x_size)
 
-        return x    
+        return x
 
     def forward(self, x):
         H, W = x.shape[2:]
@@ -951,7 +951,7 @@ class Swin2SR(nn.Module):
             x = self.conv_after_body(self.forward_features(x)) + x
             x_before = self.conv_before_upsample(x)
             x_out = self.conv_last(self.upsample(x_before))
-            
+
             x_hf = self.conv_first_hf(x_before)
             x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
             x_hf = self.conv_before_upsample_hf(x_hf)
@@ -977,15 +977,15 @@ class Swin2SR(nn.Module):
             x_first = self.conv_first(x)
             res = self.conv_after_body(self.forward_features(x_first)) + x_first
             x = x + self.conv_last(res)
-        
+
         x = x / self.img_range + self.mean
         if self.upsampler == "pixelshuffle_aux":
             return x[:, :, :H*self.upscale, :W*self.upscale], aux
-        
+
         elif self.upsampler == "pixelshuffle_hf":
             x_out = x_out / self.img_range + self.mean
             return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
-        
+
         else:
             return x[:, :, :H*self.upscale, :W*self.upscale]
 
@@ -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()
@@ -1014,4 +1014,4 @@ if __name__ == '__main__':
 
     x = torch.randn((1, 3, height, width))
     x = model(x)
-    print(x.shape)
+    print(x.shape)

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'>{search_term}</span>
+			<span style="display:none" class='search_term{serach_only}'>{search_term}</span>
 		</div>
 		<span class='name'>{name}</span>
 		<span class='description'>{description}</span>

javascript/aspectRatioOverlay.js

@@ -45,29 +45,24 @@ function dimensionChange(e, is_width, is_height){
 
 		var viewportOffset = targetElement.getBoundingClientRect();
 
-		viewportscale = Math.min(  targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
+		var viewportscale = Math.min(  targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
 
-		scaledx = targetElement.naturalWidth*viewportscale
+		var scaledx = targetElement.naturalWidth*viewportscale
-		scaledy = targetElement.naturalHeight*viewportscale
+		var scaledy = targetElement.naturalHeight*viewportscale
 
-		cleintRectTop    = (viewportOffset.top+window.scrollY)
+		var cleintRectTop    = (viewportOffset.top+window.scrollY)
-		cleintRectLeft   = (viewportOffset.left+window.scrollX)
+		var cleintRectLeft   = (viewportOffset.left+window.scrollX)
-		cleintRectCentreY = cleintRectTop  + (targetElement.clientHeight/2)
+		var cleintRectCentreY = cleintRectTop  + (targetElement.clientHeight/2)
-		cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
+		var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
 
-		viewRectTop    = cleintRectCentreY-(scaledy/2)
+		var arscale = Math.min(  scaledx/currentWidth, scaledy/currentHeight )
-		viewRectLeft   = cleintRectCentreX-(scaledx/2)
+		var arscaledx = currentWidth*arscale
-		arRectWidth  = scaledx
+		var arscaledy = currentHeight*arscale
-		arRectHeight = scaledy
 
-		arscale = Math.min(  arRectWidth/currentWidth, arRectHeight/currentHeight )
+		var arRectTop    = cleintRectCentreY-(arscaledy/2)
-		arscaledx = currentWidth*arscale
+		var arRectLeft   = cleintRectCentreX-(arscaledx/2)
-		arscaledy = currentHeight*arscale
+		var arRectWidth  = arscaledx
-
+		var arRectHeight = arscaledy
-		arRectTop    = cleintRectCentreY-(arscaledy/2)
-		arRectLeft   = cleintRectCentreX-(arscaledx/2)
-		arRectWidth  = arscaledx
-		arRectHeight = arscaledy
 
 	    arPreviewRect.style.top  = arRectTop+'px';
 	    arPreviewRect.style.left = arRectLeft+'px';

javascript/contextMenus.js

@@ -4,7 +4,7 @@ contextMenuInit = function(){
   let menuSpecs = new Map();
 
   const uid = function(){
-    return Date.now().toString(36) + Math.random().toString(36).substr(2);
+    return Date.now().toString(36) + Math.random().toString(36).substring(2);
   }
 
   function showContextMenu(event,element,menuEntries){
@@ -16,8 +16,7 @@ contextMenuInit = function(){
       oldMenu.remove()
     }
 
-    let tabButton = uiCurrentTab
+    let baseStyle = window.getComputedStyle(uiCurrentTab)
-    let baseStyle = window.getComputedStyle(tabButton)
 
     const contextMenu = document.createElement('nav')
     contextMenu.id = "context-menu"
@@ -36,7 +35,7 @@ contextMenuInit = function(){
     menuEntries.forEach(function(entry){
       let contextMenuEntry = document.createElement('a')
       contextMenuEntry.innerHTML = entry['name']
-      contextMenuEntry.addEventListener("click", function(e) {
+      contextMenuEntry.addEventListener("click", function() {
         entry['func']();
       })
       contextMenuList.append(contextMenuEntry);
@@ -63,7 +62,7 @@ contextMenuInit = function(){
 
   function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
 
-    currentItems = menuSpecs.get(targetElementSelector)
+    var currentItems = menuSpecs.get(targetElementSelector)
 
     if(!currentItems){
       currentItems = []
@@ -79,7 +78,7 @@ contextMenuInit = function(){
   }
 
   function removeContextMenuOption(uid){
-    menuSpecs.forEach(function(v,k) {
+    menuSpecs.forEach(function(v) {
       let index = -1
       v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
       if(index>=0){
@@ -93,8 +92,7 @@ contextMenuInit = function(){
       return;
     }
     gradioApp().addEventListener("click", function(e) {
-      let source = e.composedPath()[0]
+      if(! e.isTrusted){
-      if(source.id && source.id.indexOf('check_progress')>-1){
         return
       }
 
@@ -112,7 +110,6 @@ contextMenuInit = function(){
         if(e.composedPath()[0].matches(k)){
           showContextMenu(e,e.composedPath()[0],v)
           e.preventDefault()
-          return
         }
       })
     });

javascript/edit-attention.js

@@ -69,8 +69,8 @@ function keyupEditAttention(event){
 
 	event.preventDefault();
 
-    closeCharacter = ')'
+    var closeCharacter = ')'
-    delta = opts.keyedit_precision_attention
+    var delta = opts.keyedit_precision_attention
 
     if (selectionStart > 0 && text[selectionStart - 1] == '<'){
         closeCharacter = '>'
@@ -91,8 +91,8 @@ function keyupEditAttention(event){
         selectionEnd += 1;
     }
 
-	end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+	var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
-	weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
+	var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
 	if (isNaN(weight)) return;
 
 	weight += isPlus ? delta : -delta;

javascript/extensions.js

@@ -1,14 +1,14 @@
 
-function extensions_apply(_, _, disable_all){
+function extensions_apply(_disabled_list, _update_list, disable_all){
     var disable = []
     var update = []
 
     gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
         if(x.name.startsWith("enable_") && ! x.checked)
-            disable.push(x.name.substr(7))
+            disable.push(x.name.substring(7))
 
         if(x.name.startsWith("update_") && x.checked)
-            update.push(x.name.substr(7))
+            update.push(x.name.substring(7))
     })
 
     restart_reload()
@@ -16,12 +16,12 @@ function extensions_apply(_, _, disable_all){
     return [JSON.stringify(disable), JSON.stringify(update), disable_all]
 }
 
-function extensions_check(_, _){
+function extensions_check(){
     var disable = []
 
     gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
         if(x.name.startsWith("enable_") && ! x.checked)
-            disable.push(x.name.substr(7))
+            disable.push(x.name.substring(7))
     })
 
     gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
@@ -41,7 +41,7 @@ function install_extension_from_index(button, url){
     button.disabled = "disabled"
     button.value = "Installing..."
 
-    textarea = gradioApp().querySelector('#extension_to_install textarea')
+    var textarea = gradioApp().querySelector('#extension_to_install textarea')
     textarea.value = url
     updateInput(textarea)
 

javascript/extraNetworks.js

@@ -1,4 +1,3 @@
-
 function setupExtraNetworksForTab(tabname){
     gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks')
 
@@ -10,16 +9,34 @@ function setupExtraNetworksForTab(tabname){
     tabs.appendChild(search)
     tabs.appendChild(refresh)
 
-    search.addEventListener("input", function(evt){
+    var applyFilter = function(){
-        searchTerm = search.value.toLowerCase()
+        var searchTerm = search.value.toLowerCase()
 
         gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
-            text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
+            var searchOnly = elem.querySelector('.search_only')
-            elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
+            var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
+
+            var visible = text.indexOf(searchTerm) != -1
+
+            if(searchOnly && searchTerm.length < 4){
+                visible = false
+            }
+
+            elem.style.display = visible ? "" : "none"
         })
-    });
+    }
+
+    search.addEventListener("input", applyFilter);
+    applyFilter();
+
+    extraNetworksApplyFilter[tabname] = applyFilter;
 }
 
+function applyExtraNetworkFilter(tabname){
+    setTimeout(extraNetworksApplyFilter[tabname], 1);
+}
+
+var extraNetworksApplyFilter = {}
 var activePromptTextarea = {};
 
 function setupExtraNetworks(){
@@ -55,7 +72,7 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text){
 
     var partToSearch = m[1]
     var replaced = false
-    var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, index){
+    var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
         m = found.match(re_extranet);
         if(m[1] == partToSearch){
             replaced = true;
@@ -96,9 +113,9 @@ function saveCardPreview(event, tabname, filename){
 }
 
 function extraNetworksSearchButton(tabs_id, event){
-    searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
+    var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
-    button = event.target
+    var button = event.target
-    text = button.classList.contains("search-all") ? "" : button.textContent.trim()
+    var text = button.classList.contains("search-all") ? "" : button.textContent.trim()
 
     searchTextarea.value = text
     updateInput(searchTextarea)
@@ -133,7 +150,7 @@ function popup(contents){
 }
 
 function extraNetworksShowMetadata(text){
-    elem = document.createElement('pre')
+    var elem = document.createElement('pre')
     elem.classList.add('popup-metadata');
     elem.textContent = text;
 
@@ -165,7 +182,7 @@ function requestGet(url, data, handler, errorHandler){
 }
 
 function extraNetworksRequestMetadata(event, extraPage, cardName){
-    showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
+    var showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
 
     requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
         if(data && data.metadata){

javascript/generationParams.js

@@ -23,7 +23,7 @@ let modalObserver = new MutationObserver(function(mutations) {
 });
 
 function attachGalleryListeners(tab_name) {
-	gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
+	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

javascript/hints.js

@@ -66,8 +66,8 @@ 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], [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 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], [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.",
     "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.",
@@ -118,16 +118,18 @@ titles = {
 
 onUiUpdate(function(){
 	gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
-		tooltip = titles[span.textContent];
+		if (span.title) return;  // already has a title
 
-		if(!tooltip){
+		let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
-		    tooltip = titles[span.value];
+
+    if(!tooltip){
+		    tooltip = localization[titles[span.value]] || titles[span.value];
 		}
 
 		if(!tooltip){
 			for (const c of span.classList) {
 				if (c in titles) {
-					tooltip = titles[c];
+					tooltip = localization[titles[c]] || titles[c];
 					break;
 				}
 			}
@@ -142,7 +144,7 @@ onUiUpdate(function(){
 	    if (select.onchange != null) return;
 
 	    select.onchange = function(){
-            select.title = titles[select.value] || "";
+            select.title = localization[titles[select.value]] || titles[select.value] || "";
 	    }
 	})
 })

javascript/hires_fix.js

@@ -1,16 +1,12 @@
 
-function setInactive(elem, inactive){
-    if(inactive){
-        elem.classList.add('inactive')
-    } else{
-        elem.classList.remove('inactive')
-    }
-}
-
 function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
-    hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
+    function setInactive(elem, inactive){
-    hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
+        elem.classList.toggle('inactive', !!inactive)
-    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" : ""
 

javascript/imageMaskFix.js

@@ -2,11 +2,10 @@
  * temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
  * @see https://github.com/gradio-app/gradio/issues/1721
  */
-window.addEventListener( 'resize', () => imageMaskResize());
 function imageMaskResize() {
     const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
     if ( ! canvases.length ) {
-    canvases_fixed = false;
+    canvases_fixed = false; // TODO: this is unused..?
     window.removeEventListener( 'resize', imageMaskResize );
     return;
     }
@@ -15,7 +14,7 @@ function imageMaskResize() {
     const previewImage = wrapper.previousElementSibling;
 
     if ( ! previewImage.complete ) {
-        previewImage.addEventListener( 'load', () => imageMaskResize());
+        previewImage.addEventListener( 'load', imageMaskResize);
         return;
     }
 
@@ -24,7 +23,6 @@ function imageMaskResize() {
     const nw = previewImage.naturalWidth;
     const nh = previewImage.naturalHeight;
     const portrait = nh > nw;
-    const factor = portrait;
 
     const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
     const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
@@ -40,6 +38,7 @@ function imageMaskResize() {
         c.style.maxHeight = '100%';
         c.style.objectFit = 'contain';
     });
- }
+}
-  
+
- onUiUpdate(() => imageMaskResize());
+onUiUpdate(imageMaskResize);
+window.addEventListener( 'resize', imageMaskResize);

javascript/imageParams.js

@@ -1,7 +1,6 @@
 window.onload = (function(){
     window.addEventListener('drop', e => {
         const target = e.composedPath()[0];
-        const idx = selected_gallery_index();
         if (target.placeholder.indexOf("Prompt") == -1) return;
 
         let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";

javascript/imageviewer.js

@@ -57,7 +57,7 @@ function modalImageSwitch(offset) {
         })
 
         if (result != -1) {
-            nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
+            var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
             nextButton.click()
             const modalImage = gradioApp().getElementById("modalImage");
             const modal = gradioApp().getElementById("lightboxModal");
@@ -144,15 +144,11 @@ function setupImageForLightbox(e) {
 }
 
 function modalZoomSet(modalImage, enable) {
-    if (enable) {
+    if(modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
-        modalImage.classList.add('modalImageFullscreen');
-    } else {
-        modalImage.classList.remove('modalImageFullscreen');
-    }
 }
 
 function modalZoomToggle(event) {
-    modalImage = gradioApp().getElementById("modalImage");
+    var modalImage = gradioApp().getElementById("modalImage");
     modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
     event.stopPropagation()
 }
@@ -179,7 +175,7 @@ function galleryImageHandler(e) {
 }
 
 onUiUpdate(function() {
-    fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
+    var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
     if (fullImg_preview != null) {
         fullImg_preview.forEach(setupImageForLightbox);
     }

javascript/imageviewerGamepad.js

@@ -1,36 +1,57 @@
-    let delay = 350//ms
+window.addEventListener('gamepadconnected', (e) => {
-    window.addEventListener('gamepadconnected', (e) => {
+    const index = e.gamepad.index;
-        console.log("Gamepad connected!")
+    let isWaiting = false;
-        const gamepad = e.gamepad;
+    setInterval(async () => {
-        setInterval(() => {
+        if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
-            const xValue = gamepad.axes[0].toFixed(2);
+        const gamepad = navigator.getGamepads()[index];
-            if (xValue < -0.3) {
+        const xValue = gamepad.axes[0];
-                modalPrevImage(e);
+        if (xValue <= -0.3) {
-            } else if (xValue > 0.3) {
-                modalNextImage(e);
-            }
-
-        }, delay);
-    });
-
-
-    /*
-    Primarily for vr controller type pointer devices.
-    I use the wheel event because there's currently no way to do it properly with web xr.
-     */
-
-    let isScrolling = false;
-    window.addEventListener('wheel', (e) => {
-        if (isScrolling) return;
-        isScrolling = true;
-
-        if (e.deltaX <= -0.6) {
             modalPrevImage(e);
-        } else if (e.deltaX >= 0.6) {
+            isWaiting = true;
+        } else if (xValue >= 0.3) {
             modalNextImage(e);
+            isWaiting = true;
         }
+        if (isWaiting) {
+            await sleepUntil(() => {
+                const xValue = navigator.getGamepads()[index].axes[0]
+                if (xValue < 0.3 && xValue > -0.3) {
+                    return true;
+                }
+            }, opts.js_modal_lightbox_gamepad_repeat);
+            isWaiting = false;
+        }
+    }, 10);
+});
 
-        setTimeout(() => {
+/*
-            isScrolling = false;
+Primarily for vr controller type pointer devices.
-        }, delay);
+I use the wheel event because there's currently no way to do it properly with web xr.
-    });
+ */
+let isScrolling = false;
+window.addEventListener('wheel', (e) => {
+    if (!opts.js_modal_lightbox_gamepad || isScrolling) return;
+    isScrolling = true;
+
+    if (e.deltaX <= -0.6) {
+        modalPrevImage(e);
+    } else if (e.deltaX >= 0.6) {
+        modalNextImage(e);
+    }
+
+    setTimeout(() => {
+        isScrolling = false;
+    }, opts.js_modal_lightbox_gamepad_repeat);
+});
+
+function sleepUntil(f, timeout) {
+    return new Promise((resolve) => {
+        const timeStart = new Date();
+        const wait = setInterval(function() {
+            if (f() || new Date() - timeStart > timeout) {
+                clearInterval(wait);
+                resolve();
+            }
+        }, 20);
+    });
+}

javascript/localization.js

@@ -25,6 +25,10 @@ re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
 original_lines = {}
 translated_lines = {}
 
+function hasLocalization() {
+    return window.localization && Object.keys(window.localization).length > 0;
+}
+
 function textNodesUnder(el){
     var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
     while(n=walk.nextNode()) a.push(n);
@@ -35,11 +39,11 @@ function canBeTranslated(node, text){
     if(! text) return false;
     if(! node.parentElement) return false;
 
-    parentType = node.parentElement.nodeName
+    var parentType = node.parentElement.nodeName
     if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
 
     if (parentType=='OPTION' || parentType=='SPAN'){
-        pnode = node
+        var pnode = node
         for(var level=0; level<4; level++){
             pnode = pnode.parentElement
             if(! pnode) break;
@@ -69,7 +73,7 @@ function getTranslation(text){
 }
 
 function processTextNode(node){
-    text = node.textContent.trim()
+    var text = node.textContent.trim()
 
     if(! canBeTranslated(node, text)) return
 
@@ -105,30 +109,52 @@ function processNode(node){
 }
 
 function dumpTranslations(){
-    dumped = {}
+    if(!hasLocalization()) {
+        // If we don't have any localization,
+        // we will not have traversed the app to find
+        // original_lines, so do that now.
+        processNode(gradioApp());
+    }
+    var dumped = {}
     if (localization.rtl) {
-        dumped.rtl = true
+        dumped.rtl = true;
     }
 
-    Object.keys(original_lines).forEach(function(text){
+    for (const text in original_lines) {
-        if(dumped[text] !== undefined)  return
+        if(dumped[text] !== undefined) continue;
+        dumped[text] = localization[text] || text;
+    }
 
-        dumped[text] = localization[text] || text
+    return dumped;
-    })
-
-    return dumped
 }
 
-onUiUpdate(function(m){
+function download_localization() {
-    m.forEach(function(mutation){
+    var text = JSON.stringify(dumpTranslations(), null, 4)
-        mutation.addedNodes.forEach(function(node){
-            processNode(node)
-        })
-    });
-})
 
+    var element = document.createElement('a');
+    element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
+    element.setAttribute('download', "localization.json");
+    element.style.display = 'none';
+    document.body.appendChild(element);
+
+    element.click();
+
+    document.body.removeChild(element);
+}
+
+document.addEventListener("DOMContentLoaded", function () {
+    if (!hasLocalization()) {
+        return;
+    }
+
+    onUiUpdate(function (m) {
+        m.forEach(function (mutation) {
+            mutation.addedNodes.forEach(function (node) {
+                processNode(node)
+            })
+        });
+    })
 
-document.addEventListener("DOMContentLoaded", function() {
     processNode(gradioApp())
 
     if (localization.rtl) {  // if the language is from right to left,
@@ -149,17 +175,3 @@ document.addEventListener("DOMContentLoaded", function() {
         })).observe(gradioApp(), { childList: true });
     }
 })
-
-function download_localization() {
-    text = JSON.stringify(dumpTranslations(), null, 4)
-
-    var element = document.createElement('a');
-    element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
-    element.setAttribute('download', "localization.json");
-    element.style.display = 'none';
-    document.body.appendChild(element);
-
-    element.click();
-
-    document.body.removeChild(element);
-}

javascript/notification.js

@@ -2,15 +2,15 @@
 
 let lastHeadImg = null;
 
-notificationButton = null
+let notificationButton = null;
 
 onUiUpdate(function(){
     if(notificationButton == null){
         notificationButton = gradioApp().getElementById('request_notifications')
 
         if(notificationButton != null){
-            notificationButton.addEventListener('click', function (evt) {
+            notificationButton.addEventListener('click', () => {
-                Notification.requestPermission();
+                void Notification.requestPermission();
             },true);
         }
     }

javascript/progressbar.js

@@ -1,16 +1,15 @@
 // code related to showing and updating progressbar shown as the image is being made
 
-function rememberGallerySelection(id_gallery){
+function rememberGallerySelection(){
 
 }
 
-function getGallerySelectedIndex(id_gallery){
+function getGallerySelectedIndex(){
 
 }
 
 function request(url, data, handler, errorHandler){
     var xhr = new XMLHttpRequest();
-    var url = url;
     xhr.open("POST", url, true);
     xhr.setRequestHeader("Content-Type", "application/json");
     xhr.onreadystatechange = function () {
@@ -107,7 +106,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                 divProgress.style.width = rect.width + "px";
             }
 
-            progressText = ""
+            let progressText = ""
 
             divInner.style.width = ((res.progress || 0) * 100.0) + '%'
             divInner.style.background = res.progress ? "" : "transparent"

javascript/ui.js

@@ -1,7 +1,7 @@
 // various functions for interaction with ui.py not large enough to warrant putting them in separate files
 
 function set_theme(theme){
-    gradioURL = window.location.href
+    var gradioURL = window.location.href
     if (!gradioURL.includes('?__theme=')) {
       window.location.replace(gradioURL + '?__theme=' + theme);
     }
@@ -47,7 +47,7 @@ function extract_image_from_gallery(gallery){
         return [gallery[0]];
     }
 
-    index = selected_gallery_index()
+    var index = selected_gallery_index()
 
     if (index < 0 || index >= gallery.length){
         // Use the first image in the gallery as the default
@@ -58,7 +58,7 @@ function extract_image_from_gallery(gallery){
 }
 
 function args_to_array(args){
-    res = []
+    var res = []
     for(var i=0;i<args.length;i++){
         res.push(args[i])
     }
@@ -138,7 +138,7 @@ function get_img2img_tab_index() {
 }
 
 function create_submit_args(args){
-    res = []
+    var res = []
     for(var i=0;i<args.length;i++){
         res.push(args[i])
     }
@@ -160,7 +160,7 @@ function showSubmitButtons(tabname, show){
 }
 
 function showRestoreProgressButton(tabname, show){
-    button = gradioApp().getElementById(tabname + "_restore_progress")
+    var button = gradioApp().getElementById(tabname + "_restore_progress")
     if(! button) return
 
     button.style.display = show ? "flex" : "none"
@@ -207,8 +207,9 @@ function submit_img2img(){
     return res
 }
 
-function restoreProgressTxt2img(x){
+function restoreProgressTxt2img(){
     showRestoreProgressButton("txt2img", false)
+    var id = localStorage.getItem("txt2img_task_id")
 
     id = localStorage.getItem("txt2img_task_id")
 
@@ -220,10 +221,11 @@ function restoreProgressTxt2img(x){
 
     return id
 }
-function restoreProgressImg2img(x){
-    showRestoreProgressButton("img2img", false)
 
-    id = localStorage.getItem("img2img_task_id")
+function restoreProgressImg2img(){
+    showRestoreProgressButton("img2img", false)
+    
+    var id = localStorage.getItem("img2img_task_id")
 
     if(id) {
         requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
@@ -252,7 +254,7 @@ function modelmerger(){
 
 
 function ask_for_style_name(_, prompt_text, negative_prompt_text) {
-    name_ = prompt('Style name:')
+    var name_ = prompt('Style name:')
     return [name_, prompt_text, negative_prompt_text]
 }
 
@@ -287,11 +289,11 @@ function recalculate_prompts_img2img(){
 }
 
 
-opts = {}
+var opts = {}
 onUiUpdate(function(){
 	if(Object.keys(opts).length != 0) return;
 
-	json_elem = gradioApp().getElementById('settings_json')
+	var json_elem = gradioApp().getElementById('settings_json')
 	if(json_elem == null) return;
 
     var textarea = json_elem.querySelector('textarea')
@@ -340,12 +342,15 @@ onUiUpdate(function(){
     registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button')
     registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button')
 
-    show_all_pages = gradioApp().getElementById('settings_show_all_pages')
+    var show_all_pages = gradioApp().getElementById('settings_show_all_pages')
-    settings_tabs = gradioApp().querySelector('#settings div')
+    var settings_tabs = gradioApp().querySelector('#settings div')
     if(show_all_pages && settings_tabs){
         settings_tabs.appendChild(show_all_pages)
         show_all_pages.onclick = function(){
             gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
+                if(elem.id == "settings_tab_licenses")
+                    return;
+
                 elem.style.display = "block";
             })
         }
@@ -353,9 +358,9 @@ onUiUpdate(function(){
 })
 
 onOptionsChanged(function(){
-    elem = gradioApp().getElementById('sd_checkpoint_hash')
+    var elem = gradioApp().getElementById('sd_checkpoint_hash')
-    sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
+    var sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
-    shorthash = sd_checkpoint_hash.substr(0,10)
+    var shorthash = sd_checkpoint_hash.substring(0,10)
 
 	if(elem && elem.textContent != shorthash){
 	    elem.textContent = shorthash
@@ -390,7 +395,16 @@ function update_token_counter(button_id) {
 
 function restart_reload(){
     document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
-    setTimeout(function(){location.reload()},2000)
+
+    var requestPing = function(){
+        requestGet("./internal/ping", {}, function(data){
+            location.reload();
+        }, function(){
+            setTimeout(requestPing, 500);
+        })
+    }
+
+    setTimeout(requestPing, 2000);
 
     return []
 }

javascript/ui_settings_hints.js

@@ -0,0 +1,62 @@
+// various hints and extra info for the settings tab
+
+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){
+            var comment = document.createElement('DIV')
+            comment.className = 'settings-comment'
+            comment.innerHTML = commentAfter
+            span.parentElement.insertBefore(comment, span.nextSibling)
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling)
+        }
+    })
+})
+
+function settingsHintsShowQuicksettings(){
+	requestGet("./internal/quicksettings-hint", {}, function(data){
+		var table = document.createElement('table')
+		table.className = 'settings-value-table'
+
+		data.forEach(function(obj){
+			var tr = document.createElement('tr')
+			var td = document.createElement('td')
+			td.textContent = obj.name
+			tr.appendChild(td)
+
+			var td = document.createElement('td')
+			td.textContent = obj.label
+			tr.appendChild(td)
+
+			table.appendChild(tr)
+		})
+
+		popup(table);
+	})
+}

launch.py

@@ -3,24 +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
 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'
 
@@ -56,51 +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(f"{git} rev-parse HEAD", encoding='utf8').strip()
     except Exception:
-        stored_commit_hash = "<none>"
+        return "<none>"
-
-    return stored_commit_hash
 
 
-def run(command, desc=None, errdesc=None, custom_env=None, live=False):
+@lru_cache()
+def git_tag():
+    try:
+        return subprocess.check_output(f"{git} describe --tags", encoding='utf8').strip()
+    except Exception:
+        return "<none>"
+
+
+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:
+    run_kwargs = {
-        result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
+        "args": command,
-        if result.returncode != 0:
+        "shell": True,
-            raise RuntimeError(f"""{errdesc or 'Error running command'}.
+        "env": os.environ if custom_env is None else custom_env,
-Command: {command}
+        "encoding": 'utf8',
-Error code: {result.returncode}""")
+        "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'}.
+    return (result.stdout or "")
-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
 
 
 def is_installed(package):
@@ -116,11 +116,7 @@ def repo_dir(name):
     return os.path.join(script_path, dir_repos, name)
 
 
-def run_python(code, desc=None, errdesc=None):
+def run_pip(command, desc=None, live=default_command_live):
-    return run(f'"{python}" -c "{code}"', desc, errdesc)
-
-
-def run_pip(command, desc=None, live=False):
     if args.skip_install:
         return
 
@@ -128,8 +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):
+def check_run_python(code: str) -> bool:
-    return check_run(f'"{python}" -c "{code}"')
+    result = subprocess.run([python, "-c", code], capture_output=True, shell=True)
+    return result.returncode == 0
 
 
 def git_clone(url, dir, name, commithash=None):
@@ -222,13 +219,14 @@ def run_extensions_installers(settings_file):
 
 
 def prepare_environment():
-    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --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")
+    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
-    clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
+    clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
-    openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
+    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")
@@ -246,15 +244,20 @@ def prepare_environment():
         check_python_version()
 
     commit = commit_hash()
+    tag = git_tag()
 
     print(f"Python {sys.version}")
+    print(f"Version: {tag}")
     print(f"Commit hash: {commit}")
 
     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:
+    if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
-        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'")
+        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")
@@ -302,7 +305,7 @@ def prepare_environment():
 
     if args.update_all_extensions:
         git_pull_recursive(extensions_dir)
-    
+
     if "--exit" in sys.argv:
         print("Exiting because of --exit argument")
         exit(0)

modules/api/api.py

@@ -15,7 +15,8 @@ from secrets import compare_digest
 
 import modules.shared as shared
 from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
-from modules.api.models import *
+from modules.api import models
+from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
 from modules.textual_inversion.preprocess import preprocess
@@ -25,21 +26,24 @@ from modules.sd_models import checkpoints_list, unload_model_weights, reload_mod
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
-from typing import List
+from typing import Dict, List, Any
 import piexif
 import piexif.helper
 
+
 def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
-    except:
+    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}")
+        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e
+
 
 def script_name_to_index(name, scripts):
     try:
         return [script.title().lower() for script in scripts].index(name.lower())
-    except:
+    except Exception as e:
-        raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
+        raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
+
 
 def validate_sampler_name(name):
     config = sd_samplers.all_samplers_map.get(name, None)
@@ -48,20 +52,23 @@ def validate_sampler_name(name):
 
     return name
 
+
 def setUpscalers(req: dict):
     reqDict = vars(req)
     reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
     reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
     return reqDict
 
+
 def decode_base64_to_image(encoding):
     if encoding.startswith("data:image/"):
         encoding = encoding.split(";")[1].split(",")[1]
     try:
         image = Image.open(BytesIO(base64.b64decode(encoding)))
         return image
-    except Exception as err:
+    except Exception as e:
-        raise HTTPException(status_code=500, detail="Invalid encoded image")
+        raise HTTPException(status_code=500, detail="Invalid encoded image") from e
+
 
 def encode_pil_to_base64(image):
     with io.BytesIO() as output_bytes:
@@ -92,6 +99,7 @@ def encode_pil_to_base64(image):
 
     return base64.b64encode(bytes_data)
 
+
 def api_middleware(app: FastAPI):
     rich_available = True
     try:
@@ -99,7 +107,7 @@ def api_middleware(app: FastAPI):
         import starlette # importing just so it can be placed on silent list
         from rich.console import Console
         console = Console()
-    except:
+    except Exception:
         import traceback
         rich_available = False
 
@@ -157,7 +165,7 @@ def api_middleware(app: FastAPI):
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
         if shared.cmd_opts.api_auth:
-            self.credentials = dict()
+            self.credentials = {}
             for auth in shared.cmd_opts.api_auth.split(","):
                 user, password = auth.split(":")
                 self.credentials[user] = password
@@ -166,36 +174,36 @@ class Api:
         self.app = app
         self.queue_lock = queue_lock
         api_middleware(self.app)
-        self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse)
-        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
+        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse)
-        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse)
-        self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse)
-        self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
+        self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse)
-        self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
+        self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse)
         self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
-        self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
+        self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
         self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
-        self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
+        self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
-        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
-        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
-        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
-        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
-        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
-        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
-        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
-        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
+        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
-        self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
+        self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
-        self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
+        self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
-        self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
+        self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
-        self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
+        self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
-        self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
+        self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
-        self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
+        self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
         self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
-        self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList)
+        self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
 
         self.default_script_arg_txt2img = []
         self.default_script_arg_img2img = []
@@ -219,17 +227,17 @@ class Api:
         script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
         script = script_runner.selectable_scripts[script_idx]
         return script, script_idx
-    
+
     def get_scripts_list(self):
         t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles]
         i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles]
 
-        return ScriptsList(txt2img = t2ilist, img2img = i2ilist)  
+        return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist)
 
     def get_script(self, script_name, script_runner):
         if script_name is None or script_name == "":
             return None, None
-        
+
         script_idx = script_name_to_index(script_name, script_runner.scripts)
         return script_runner.scripts[script_idx]
 
@@ -264,11 +272,11 @@ class Api:
         if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
             for alwayson_script_name in request.alwayson_scripts.keys():
                 alwayson_script = self.get_script(alwayson_script_name, script_runner)
-                if alwayson_script == None:
+                if alwayson_script is None:
                     raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
                 # Selectable script in always on script param check
-                if alwayson_script.alwayson == False:
+                if alwayson_script.alwayson is False:
-                    raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
+                    raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params")
                 # always on script with no arg should always run so you don't really need to add them to the requests
                 if "args" in request.alwayson_scripts[alwayson_script_name]:
                     # min between arg length in scriptrunner and arg length in the request
@@ -276,7 +284,7 @@ class Api:
                         script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
         return script_args
 
-    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
+    def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
         script_runner = scripts.scripts_txt2img
         if not script_runner.scripts:
             script_runner.initialize_scripts(False)
@@ -310,7 +318,7 @@ class Api:
             p.outpath_samples = opts.outdir_txt2img_samples
 
             shared.state.begin()
-            if selectable_scripts != None:
+            if selectable_scripts is not None:
                 p.script_args = script_args
                 processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
             else:
@@ -320,9 +328,9 @@ class Api:
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
-        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
+        return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
-    def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
+    def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
         init_images = img2imgreq.init_images
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
@@ -367,7 +375,7 @@ class Api:
             p.outpath_samples = opts.outdir_img2img_samples
 
             shared.state.begin()
-            if selectable_scripts != None:
+            if selectable_scripts is not None:
                 p.script_args = script_args
                 processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
             else:
@@ -381,9 +389,9 @@ class Api:
             img2imgreq.init_images = None
             img2imgreq.mask = None
 
-        return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
+        return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
 
-    def extras_single_image_api(self, req: ExtrasSingleImageRequest):
+    def extras_single_image_api(self, req: models.ExtrasSingleImageRequest):
         reqDict = setUpscalers(req)
 
         reqDict['image'] = decode_base64_to_image(reqDict['image'])
@@ -391,9 +399,9 @@ class Api:
         with self.queue_lock:
             result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
 
-        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
+        return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
 
-    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
+    def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest):
         reqDict = setUpscalers(req)
 
         image_list = reqDict.pop('imageList', [])
@@ -402,15 +410,15 @@ class Api:
         with self.queue_lock:
             result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
 
-        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
+        return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 
-    def pnginfoapi(self, req: PNGInfoRequest):
+    def pnginfoapi(self, req: models.PNGInfoRequest):
         if(not req.image.strip()):
-            return PNGInfoResponse(info="")
+            return models.PNGInfoResponse(info="")
 
         image = decode_base64_to_image(req.image.strip())
         if image is None:
-            return PNGInfoResponse(info="")
+            return models.PNGInfoResponse(info="")
 
         geninfo, items = images.read_info_from_image(image)
         if geninfo is None:
@@ -418,13 +426,13 @@ class Api:
 
         items = {**{'parameters': geninfo}, **items}
 
-        return PNGInfoResponse(info=geninfo, items=items)
+        return models.PNGInfoResponse(info=geninfo, items=items)
 
-    def progressapi(self, req: ProgressRequest = Depends()):
+    def progressapi(self, req: models.ProgressRequest = Depends()):
         # copy from check_progress_call of ui.py
 
         if shared.state.job_count == 0:
-            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
+            return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
 
         # avoid dividing zero
         progress = 0.01
@@ -446,9 +454,9 @@ class Api:
         if shared.state.current_image and not req.skip_current_image:
             current_image = encode_pil_to_base64(shared.state.current_image)
 
-        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
+        return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
 
-    def interrogateapi(self, interrogatereq: InterrogateRequest):
+    def interrogateapi(self, interrogatereq: models.InterrogateRequest):
         image_b64 = interrogatereq.image
         if image_b64 is None:
             raise HTTPException(status_code=404, detail="Image not found")
@@ -465,7 +473,7 @@ class Api:
             else:
                 raise HTTPException(status_code=404, detail="Model not found")
 
-        return InterrogateResponse(caption=processed)
+        return models.InterrogateResponse(caption=processed)
 
     def interruptapi(self):
         shared.state.interrupt()
@@ -570,36 +578,36 @@ class Api:
             filename = create_embedding(**args) # create empty embedding
             sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
             shared.state.end()
-            return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
+            return models.CreateResponse(info=f"create embedding filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info = "create embedding error: {error}".format(error = e))
+            return models.TrainResponse(info=f"create embedding error: {e}")
 
     def create_hypernetwork(self, args: dict):
         try:
             shared.state.begin()
             filename = create_hypernetwork(**args) # create empty embedding
             shared.state.end()
-            return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
+            return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
+            return models.TrainResponse(info=f"create hypernetwork error: {e}")
 
     def preprocess(self, args: dict):
         try:
             shared.state.begin()
             preprocess(**args) # quick operation unless blip/booru interrogation is enabled
             shared.state.end()
-            return PreprocessResponse(info = 'preprocess complete')
+            return models.PreprocessResponse(info = 'preprocess complete')
         except KeyError as e:
             shared.state.end()
-            return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
+            return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
         except AssertionError as e:
             shared.state.end()
-            return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
+            return models.PreprocessResponse(info=f"preprocess error: {e}")
         except FileNotFoundError as e:
             shared.state.end()
-            return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
+            return models.PreprocessResponse(info=f'preprocess error: {e}')
 
     def train_embedding(self, args: dict):
         try:
@@ -617,10 +625,10 @@ class Api:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
-            return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
+            return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
         except AssertionError as msg:
             shared.state.end()
-            return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
+            return models.TrainResponse(info=f"train embedding error: {msg}")
 
     def train_hypernetwork(self, args: dict):
         try:
@@ -641,14 +649,15 @@ class Api:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
-            return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error))
+            return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
-        except AssertionError as msg:
+        except AssertionError:
             shared.state.end()
-            return TrainResponse(info="train embedding error: {error}".format(error=error))
+            return models.TrainResponse(info=f"train embedding error: {error}")
 
     def get_memory(self):
         try:
-            import os, psutil
+            import os
+            import psutil
             process = psutil.Process(os.getpid())
             res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values
             ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe
@@ -675,10 +684,10 @@ class Api:
                     'events': warnings,
                 }
             else:
-                cuda = { 'error': 'unavailable' }
+                cuda = {'error': 'unavailable'}
         except Exception as err:
-            cuda = { 'error': f'{err}' }
+            cuda = {'error': f'{err}'}
-        return MemoryResponse(ram = ram, cuda = cuda)
+        return models.MemoryResponse(ram=ram, cuda=cuda)
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)

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)
+        if _options[key].default is not None:
-        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
+            _type = type(_options[key].default)
+        flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
 
 FlagsModel = create_model("Flags", **flags)
 
@@ -288,4 +289,4 @@ class MemoryResponse(BaseModel):
 
 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)")
+    img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")

modules/call_queue.py

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

modules/cmd_args.py

@@ -1,6 +1,6 @@
 import argparse
 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()
 
@@ -102,3 +102,4 @@ parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gra
 parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
+parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')

modules/codeformer/codeformer_arch.py

@@ -1,14 +1,12 @@
 # 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 modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
-from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
 
 def calc_mean_std(feat, eps=1e-5):
@@ -121,7 +119,7 @@ class TransformerSALayer(nn.Module):
                 tgt_mask: Optional[Tensor] = None,
                 tgt_key_padding_mask: Optional[Tensor] = None,
                 query_pos: Optional[Tensor] = None):
-        
+
         # self attention
         tgt2 = self.norm1(tgt)
         q = k = self.with_pos_embed(tgt2, query_pos)
@@ -161,10 +159,10 @@ class Fuse_sft_block(nn.Module):
 
 @ARCH_REGISTRY.register()
 class CodeFormer(VQAutoEncoder):
-    def __init__(self, dim_embd=512, n_head=8, n_layers=9, 
+    def __init__(self, dim_embd=512, n_head=8, n_layers=9,
                 codebook_size=1024, latent_size=256,
-                connect_list=['32', '64', '128', '256'],
+                connect_list=('32', '64', '128', '256'),
-                fix_modules=['quantize','generator']):
+                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:
@@ -181,14 +179,14 @@ class CodeFormer(VQAutoEncoder):
         self.feat_emb = nn.Linear(256, self.dim_embd)
 
         # transformer
-        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) 
+        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
                                     for _ in range(self.n_layers)])
 
         # logits_predict head
         self.idx_pred_layer = nn.Sequential(
             nn.LayerNorm(dim_embd),
             nn.Linear(dim_embd, codebook_size, bias=False))
-        
+
         self.channels = {
             '16': 512,
             '32': 256,
@@ -223,7 +221,7 @@ class CodeFormer(VQAutoEncoder):
         enc_feat_dict = {}
         out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
         for i, block in enumerate(self.encoder.blocks):
-            x = block(x) 
+            x = block(x)
             if i in out_list:
                 enc_feat_dict[str(x.shape[-1])] = x.clone()
 
@@ -268,11 +266,11 @@ class CodeFormer(VQAutoEncoder):
         fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
 
         for i, block in enumerate(self.generator.blocks):
-            x = block(x) 
+            x = block(x)
             if i in fuse_list: # fuse after i-th block
                 f_size = str(x.shape[-1])
                 if w>0:
                     x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
         out = x
         # logits doesn't need softmax before cross_entropy loss
-        return out, logits, lq_feat
+        return out, logits, lq_feat

modules/codeformer/vqgan_arch.py

@@ -5,17 +5,15 @@ 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
 
 def normalize(in_channels):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-    
+
 
 @torch.jit.script
 def swish(x):
@@ -212,15 +210,15 @@ class AttnBlock(nn.Module):
         # compute attention
         b, c, h, w = q.shape
         q = q.reshape(b, c, h*w)
-        q = q.permute(0, 2, 1)   
+        q = q.permute(0, 2, 1)
         k = k.reshape(b, c, h*w)
-        w_ = torch.bmm(q, k) 
+        w_ = torch.bmm(q, k)
         w_ = w_ * (int(c)**(-0.5))
         w_ = F.softmax(w_, dim=2)
 
         # attend to values
         v = v.reshape(b, c, h*w)
-        w_ = w_.permute(0, 2, 1) 
+        w_ = w_.permute(0, 2, 1)
         h_ = torch.bmm(v, w_)
         h_ = h_.reshape(b, c, h, w)
 
@@ -272,18 +270,18 @@ class Encoder(nn.Module):
     def forward(self, x):
         for block in self.blocks:
             x = block(x)
-            
+
         return x
 
 
 class Generator(nn.Module):
     def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
         super().__init__()
-        self.nf = nf 
+        self.nf = nf
-        self.ch_mult = ch_mult 
+        self.ch_mult = ch_mult
         self.num_resolutions = len(self.ch_mult)
         self.num_res_blocks = res_blocks
-        self.resolution = img_size 
+        self.resolution = img_size
         self.attn_resolutions = attn_resolutions
         self.in_channels = emb_dim
         self.out_channels = 3
@@ -317,29 +315,29 @@ class Generator(nn.Module):
         blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
 
         self.blocks = nn.ModuleList(blocks)
-   
+
 
     def forward(self, x):
         for block in self.blocks:
             x = block(x)
-            
+
         return x
 
-  
+
 @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()
-        self.in_channels = 3 
+        self.in_channels = 3
-        self.nf = nf 
+        self.nf = nf
-        self.n_blocks = res_blocks 
+        self.n_blocks = res_blocks
         self.codebook_size = codebook_size
         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,
@@ -365,11 +363,11 @@ class VQAutoEncoder(nn.Module):
                 self.kl_weight
             )
         self.generator = Generator(
-            self.nf, 
+            self.nf,
             self.embed_dim,
-            self.ch_mult, 
+            self.ch_mult,
-            self.n_blocks, 
+            self.n_blocks,
-            self.resolution, 
+            self.resolution,
             self.attn_resolutions
         )
 
@@ -434,4 +432,4 @@ class VQGANDiscriminator(nn.Module):
                 raise ValueError('Wrong params!')
 
     def forward(self, x):
-        return self.main(x)
+        return self.main(x)

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 img2tensor, tensor2img
-        from basicsr.utils import imwrite, 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"]))

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 = []
+        items = list(state_dict)
-        for k, v in state_dict.items():
-            items.append(k)
 
         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 = []
+        items = list(state_dict)
-        for k, v in state_dict.items():
-            items.append(k)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -156,13 +152,16 @@ class UpscalerESRGAN(Upscaler):
 
     def load_model(self, path: str):
         if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+            filename = load_file_from_url(
-                                          file_name="%s.pth" % self.model_name,
+                url=self.model_url,
-                                          progress=True)
+                model_dir=self.model_path,
+                file_name=f"{self.model_name}.pth",
+                progress=True,
+            )
         else:
             filename = path
         if not os.path.exists(filename) or filename is None:
-            print("Unable to load %s from %s" % (self.model_path, filename))
+            print(f"Unable to load {self.model_path} from {filename}")
             return None
 
         state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)

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
@@ -38,7 +37,7 @@ class RRDBNet(nn.Module):
         elif upsample_mode == 'pixelshuffle':
             upsample_block = pixelshuffle_block
         else:
-            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
+            raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
         if upscale == 3:
             upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
         else:
@@ -106,7 +105,7 @@ class ResidualDenseBlock_5C(nn.Module):
     Modified options that can be used:
         - "Partial Convolution based Padding" arXiv:1811.11718
         - "Spectral normalization" arXiv:1802.05957
-        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. 
+        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
             {Rakotonirina} and A. {Rasoanaivo}
     """
 
@@ -171,7 +170,7 @@ class GaussianNoise(nn.Module):
             scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
             sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
             x = x + sampled_noise
-        return x 
+        return x
 
 def conv1x1(in_planes, out_planes, stride=1):
     return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
@@ -261,10 +260,10 @@ class Upsample(nn.Module):
 
     def extra_repr(self):
         if self.scale_factor is not None:
-            info = 'scale_factor=' + str(self.scale_factor)
+            info = f'scale_factor={self.scale_factor}'
         else:
-            info = 'size=' + str(self.size)
+            info = f'size={self.size}'
-        info += ', mode=' + self.mode
+        info += f', mode={self.mode}'
         return info
 
 
@@ -350,7 +349,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
     elif act_type == 'sigmoid':  # [0, 1] range output
         layer = nn.Sigmoid()
     else:
-        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
+        raise NotImplementedError(f'activation layer [{act_type}] is not found')
     return layer
 
 
@@ -372,7 +371,7 @@ def norm(norm_type, nc):
     elif norm_type == 'none':
         def norm_layer(x): return Identity()
     else:
-        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
+        raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
     return layer
 
 
@@ -388,7 +387,7 @@ def pad(pad_type, padding):
     elif pad_type == 'zero':
         layer = nn.ZeroPad2d(padding)
     else:
-        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
+        raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
     return layer
 
 
@@ -432,15 +431,17 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
                pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
                spectral_norm=False):
     """ Conv layer with padding, normalization, activation """
-    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
+    assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
     padding = get_valid_padding(kernel_size, dilation)
     p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
     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

@@ -3,11 +3,10 @@ import sys
 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 = []
 

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

@@ -1,4 +1,4 @@
-from modules import extra_networks, shared, extra_networks
+from modules import extra_networks, shared
 from modules.hypernetworks import hypernetwork
 
 
@@ -10,7 +10,8 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
         additional = shared.opts.sd_hypernetwork
 
         if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
-            p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+            hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
+            p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 
         names = []

modules/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')
 
@@ -199,7 +199,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
                     result_is_inpainting_model = True
             else:
                 theta_0[key] = theta_func2(a, b, multiplier)
-            
+
             theta_0[key] = to_half(theta_0[key], save_as_half)
 
         shared.state.sampling_step += 1

modules/generation_parameters_copypaste.py

@@ -1,15 +1,11 @@
 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():
@@ -59,6 +55,7 @@ def image_from_url_text(filedata):
         is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
+        filename = filename.rsplit('?', 1)[0]
         return Image.open(filename)
 
     if type(filedata) == list:
@@ -129,6 +126,7 @@ def connect_paste_params_buttons():
                 _js=jsfunc,
                 inputs=[binding.source_image_component],
                 outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+                show_progress=False,
             )
 
         if binding.source_text_component is not None and fields is not None:
@@ -140,6 +138,7 @@ def connect_paste_params_buttons():
                 fn=lambda *x: x,
                 inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
                 outputs=[field for field, name in fields if name in paste_field_names],
+                show_progress=False,
             )
 
         binding.paste_button.click(
@@ -147,6 +146,7 @@ def connect_paste_params_buttons():
             _js=f"switch_to_{binding.tabname}",
             inputs=None,
             outputs=None,
+            show_progress=False,
         )
 
 
@@ -247,7 +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
@@ -265,8 +265,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
         m = re_imagesize.match(v)
         if m is not None:
-            res[k+"-1"] = m.group(1)
+            res[f"{k}-1"] = m.group(1)
-            res[k+"-2"] = m.group(2)
+            res[f"{k}-2"] = m.group(2)
         else:
             res[k] = v
 
@@ -308,6 +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'),
 ]
@@ -409,12 +411,14 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
         fn=paste_func,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
+        show_progress=False,
     )
     button.click(
         fn=None,
         _js=f"recalculate_prompts_{tabname}",
         inputs=[],
         outputs=[],
+        show_progress=False,
     )
 
 

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/hashes.py

@@ -13,7 +13,7 @@ cache_data = None
 
 
 def dump_cache():
-    with filelock.FileLock(cache_filename+".lock"):
+    with filelock.FileLock(f"{cache_filename}.lock"):
         with open(cache_filename, "w", encoding="utf8") as file:
             json.dump(cache_data, file, indent=4)
 
@@ -22,7 +22,7 @@ def cache(subsection):
     global cache_data
 
     if cache_data is None:
-        with filelock.FileLock(cache_filename+".lock"):
+        with filelock.FileLock(f"{cache_filename}.lock"):
             if not os.path.isfile(cache_filename):
                 cache_data = {}
             else:

modules/hypernetworks/hypernetwork.py

@@ -1,4 +1,3 @@
-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
 
@@ -541,7 +540,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
-    
+
     clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
     if clip_grad:
         clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
@@ -594,7 +593,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
             print(e)
 
     scaler = torch.cuda.amp.GradScaler()
-    
+
     batch_size = ds.batch_size
     gradient_step = ds.gradient_step
     # n steps = batch_size * gradient_step * n image processed
@@ -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:
@@ -637,7 +636,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
                 if clip_grad:
                     clip_grad_sched.step(hypernetwork.step)
-                
+
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     if use_weight:
@@ -658,14 +657,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
                     _loss_step += loss.item()
                 scaler.scale(loss).backward()
-                
+
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
                 loss_logging.append(_loss_step)
                 if clip_grad:
                     clip_grad(weights, clip_grad_sched.learn_rate)
-                
+
                 scaler.step(optimizer)
                 scaler.update()
                 hypernetwork.step += 1
@@ -675,7 +674,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 _loss_step = 0
 
                 steps_done = hypernetwork.step + 1
-                
+
                 epoch_num = hypernetwork.step // steps_per_epoch
                 epoch_step = hypernetwork.step % steps_per_epoch
 

modules/hypernetworks/ui.py

@@ -1,19 +1,17 @@
 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:
@@ -357,6 +358,7 @@ class FilenameGenerator:
         'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
         'hasprompt': lambda self, *args: self.hasprompt(*args),  # accepts formats:[hasprompt<prompt1|default><prompt2>..]
         'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
+        'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
     }
     default_time_format = '%Y%m%d%H%M%S'
 
@@ -365,7 +367,7 @@ class FilenameGenerator:
         self.seed = seed
         self.prompt = prompt
         self.image = image
-        
+
     def hasprompt(self, *args):
         lower = self.prompt.lower()
         if self.p is None or self.prompt is None:
@@ -408,13 +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)
@@ -466,14 +468,14 @@ def get_next_sequence_number(path, basename):
     """
     result = -1
     if basename != '':
-        basename = basename + "-"
+        basename = f"{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
 
@@ -535,7 +537,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         add_number = opts.save_images_add_number or file_decoration == ''
 
         if file_decoration != "" and add_number:
-            file_decoration = "-" + file_decoration
+            file_decoration = f"-{file_decoration}"
 
         file_decoration = namegen.apply(file_decoration) + suffix
 
@@ -565,7 +567,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     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
-        temp_file_path = filename_without_extension + ".tmp"
+        temp_file_path = f"{filename_without_extension}.tmp"
         image_format = Image.registered_extensions()[extension]
 
         if extension.lower() == '.png':
@@ -625,7 +627,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     if opts.save_txt and info is not None:
         txt_fullfn = f"{fullfn_without_extension}.txt"
         with open(txt_fullfn, "w", encoding="utf8") as file:
-            file.write(info + "\n")
+            file.write(f"{info}\n")
     else:
         txt_fullfn = None
 

modules/img2img.py

@@ -1,19 +1,15 @@
-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
 
 
@@ -48,7 +44,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
 
         try:
             img = Image.open(image)
-        except UnidentifiedImageError:
+        except UnidentifiedImageError as e:
+            print(e)
             continue
         # Use the EXIF orientation of photos taken by smartphones.
         img = ImageOps.exif_transpose(img)
@@ -58,7 +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
@@ -28,7 +27,7 @@ def category_types():
 def download_default_clip_interrogate_categories(content_dir):
     print("Downloading CLIP categories...")
 
-    tmpdir = content_dir + "_tmp"
+    tmpdir = f"{content_dir}_tmp"
     category_types = ["artists", "flavors", "mediums", "movements"]
 
     try:
@@ -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)
 
@@ -208,13 +207,13 @@ class InterrogateModels:
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
 
-                for name, topn, items in self.categories():
+                for cat in self.categories():
-                    matches = self.rank(image_features, items, top_count=topn)
+                    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})"
                         else:
-                            res += ", " + match
+                            res += f", {match}"
 
         except Exception:
             print("Error interrogating", file=sys.stderr)

modules/localization.py

@@ -23,7 +23,7 @@ def list_localizations(dirname):
         localizations[fn] = file.path
 
 
-def localization_js(current_localization_name):
+def localization_js(current_localization_name: str) -> str:
     fn = localizations.get(current_localization_name, None)
     data = {}
     if fn is not None:
@@ -34,4 +34,4 @@ def localization_js(current_localization_name):
             print(f"Error loading localization from {fn}:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
-    return f"var localization = {json.dumps(data)}\n"
+    return f"window.localization = {json.dumps(data)}"

modules/mac_specific.py

@@ -1,6 +1,5 @@
 import torch
 import platform
-from modules import paths
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
 
@@ -43,7 +42,7 @@ if has_mps:
         # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
         CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
                                                           lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
-        # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/80800
         CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
                                                                                         lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
         # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
@@ -54,6 +53,11 @@ if has_mps:
         CondFunc('torch.cumsum', cumsum_fix_func, None)
         CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
         CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
-    if version.parse(torch.__version__) == version.parse("2.0"):
+
         # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
-        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6)
+        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
+
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/92311
+        if platform.processor() == 'i386':
+            for funcName in ['torch.argmax', 'torch.Tensor.argmax']:
+                CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')

modules/masking.py

@@ -4,7 +4,7 @@ from PIL import Image, ImageFilter, ImageOps
 def get_crop_region(mask, pad=0):
     """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
     For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
-    
+
     h, w = mask.shape
 
     crop_left = 0

modules/modelloader.py

@@ -1,4 +1,3 @@
-import glob
 import os
 import shutil
 import importlib
@@ -22,9 +21,6 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
     """
     output = []
 
-    if ext_filter is None:
-        ext_filter = []
-
     try:
         places = []
 
@@ -39,22 +35,14 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
         places.append(model_path)
 
         for place in places:
-            if os.path.exists(place):
+            for full_path in shared.walk_files(place, allowed_extensions=ext_filter):
-                for file in glob.iglob(place + '**/**', recursive=True):
+                if os.path.islink(full_path) and not os.path.exists(full_path):
-                    full_path = file
+                    print(f"Skipping broken symlink: {full_path}")
-                    if os.path.isdir(full_path):
+                    continue
-                        continue
+                if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist):
-                    if os.path.islink(full_path) and not os.path.exists(full_path):
+                    continue
-                        print(f"Skipping broken symlink: {full_path}")
+                if full_path not in output:
-                        continue
+                    output.append(full_path)
-                    if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
-                        continue
-                    if len(ext_filter) != 0:
-                        model_name, extension = os.path.splitext(file)
-                        if extension not in ext_filter:
-                            continue
-                    if file not in output:
-                        output.append(full_path)
 
         if model_url is not None and len(output) == 0:
             if download_name is not None:
@@ -119,32 +107,15 @@ 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
 
 
-builtin_upscaler_classes = []
-forbidden_upscaler_classes = set()
-
-
-def list_builtin_upscalers():
-    load_upscalers()
-
-    builtin_upscaler_classes.clear()
-    builtin_upscaler_classes.extend(Upscaler.__subclasses__())
-
-
-def forbid_loaded_nonbuiltin_upscalers():
-    for cls in Upscaler.__subclasses__():
-        if cls not in builtin_upscaler_classes:
-            forbidden_upscaler_classes.add(cls)
-
-
 def load_upscalers():
     # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
     # so we'll try to import any _model.py files before looking in __subclasses__
@@ -155,15 +126,22 @@ def load_upscalers():
             full_model = f"modules.{model_name}_model"
             try:
                 importlib.import_module(full_model)
-            except:
+            except Exception:
                 pass
 
     datas = []
     commandline_options = vars(shared.cmd_opts)
-    for cls in Upscaler.__subclasses__():
-        if cls in forbidden_upscaler_classes:
-            continue
 
+    # some of upscaler classes will not go away after reloading their modules, and we'll end
+    # up with two copies of those classes. The newest copy will always be the last in the list,
+    # so we go from end to beginning and ignore duplicates
+    used_classes = {}
+    for cls in reversed(Upscaler.__subclasses__()):
+        classname = str(cls)
+        if classname not in used_classes:
+            used_classes[classname] = cls
+
+    for cls in reversed(used_classes.values()):
         name = cls.__name__
         cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
         scaler = cls(commandline_options.get(cmd_name, None))

modules/models/diffusion/ddpm_edit.py

@@ -52,7 +52,7 @@ class DDPM(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,
@@ -107,7 +107,7 @@ class DDPM(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, only_model=load_only_unet)
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
 
             # If initialing from EMA-only checkpoint, create EMA model after loading.
             if self.use_ema and not load_ema:
@@ -194,7 +194,9 @@ class DDPM(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):
+        ignore_keys = ignore_keys or []
+
         sd = torch.load(path, map_location="cpu")
         if "state_dict" in list(sd.keys()):
             sd = sd["state_dict"]
@@ -223,7 +225,7 @@ class DDPM(pl.LightningModule):
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
+                    print(f"Deleting key {k} from state_dict.")
                     del sd[k]
         missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
             sd, strict=False)
@@ -386,7 +388,7 @@ class DDPM(pl.LightningModule):
         _, loss_dict_no_ema = self.shared_step(batch)
         with self.ema_scope():
             _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+            loss_dict_ema = {f"{key}_ema": loss_dict_ema[key] for key in loss_dict_ema}
         self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
         self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
 
@@ -403,7 +405,7 @@ class DDPM(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)
@@ -411,7 +413,7 @@ class DDPM(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):
@@ -473,13 +475,13 @@ class LatentDiffusion(DDPM):
             conditioning_key = None
         ckpt_path = kwargs.pop("ckpt_path", None)
         ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, load_ema=load_ema, **kwargs)
+        super().__init__(*args, conditioning_key=conditioning_key, load_ema=load_ema, **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
@@ -891,16 +893,6 @@ class LatentDiffusion(DDPM):
                 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):
@@ -1140,7 +1132,7 @@ class LatentDiffusion(DDPM):
         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]
 
@@ -1171,8 +1163,10 @@ class LatentDiffusion(DDPM):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(x0_partial)
-            if callback: callback(i)
+            if callback:
-            if img_callback: img_callback(img, i)
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
         return img, intermediates
 
     @torch.no_grad()
@@ -1219,8 +1213,10 @@ class LatentDiffusion(DDPM):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(img)
-            if callback: callback(i)
+            if callback:
-            if img_callback: img_callback(img, i)
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
 
         if return_intermediates:
             return img, intermediates
@@ -1235,7 +1231,7 @@ class LatentDiffusion(DDPM):
         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,
@@ -1267,7 +1263,7 @@ class LatentDiffusion(DDPM):
 
         use_ddim = False
 
-        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,
@@ -1295,7 +1291,7 @@ class LatentDiffusion(DDPM):
 
         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:
@@ -1337,7 +1333,7 @@ class LatentDiffusion(DDPM):
 
             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.
@@ -1439,10 +1435,10 @@ class Layout2ImgDiffusion(LatentDiffusion):
     # 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]

modules/models/diffusion/uni_pc/__init__.py

@@ -1 +1 @@
-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/models/diffusion/uni_pc/uni_pc.py

@@ -1,7 +1,6 @@
 import torch
-import torch.nn.functional as F
 import math
-from tqdm.auto import trange
+import tqdm
 
 
 class NoiseScheduleVP:
@@ -94,7 +93,7 @@ class NoiseScheduleVP:
         """
 
         if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
+            raise ValueError(f"Unsupported noise schedule {schedule}. The schedule needs to be 'discrete' or 'linear' or 'cosine'")
 
         self.schedule = schedule
         if schedule == 'discrete':
@@ -179,13 +178,13 @@ def model_wrapper(
     model,
     noise_schedule,
     model_type="noise",
-    model_kwargs={},
+    model_kwargs=None,
     guidance_type="uncond",
     #condition=None,
     #unconditional_condition=None,
     guidance_scale=1.,
     classifier_fn=None,
-    classifier_kwargs={},
+    classifier_kwargs=None,
 ):
     """Create a wrapper function for the noise prediction model.
 
@@ -276,6 +275,9 @@ def model_wrapper(
         A noise prediction model that accepts the noised data and the continuous time as the inputs.
     """
 
+    model_kwargs = model_kwargs or {}
+    classifier_kwargs = classifier_kwargs or {}
+
     def get_model_input_time(t_continuous):
         """
         Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
@@ -342,7 +344,7 @@ def model_wrapper(
                 t_in = torch.cat([t_continuous] * 2)
                 if isinstance(condition, dict):
                     assert isinstance(unconditional_condition, dict)
-                    c_in = dict()
+                    c_in = {}
                     for k in condition:
                         if isinstance(condition[k], list):
                             c_in[k] = [torch.cat([
@@ -353,7 +355,7 @@ def model_wrapper(
                                 unconditional_condition[k],
                                 condition[k]])
                 elif isinstance(condition, list):
-                    c_in = list()
+                    c_in = []
                     assert isinstance(unconditional_condition, list)
                     for i in range(len(condition)):
                         c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
@@ -469,7 +471,7 @@ class UniPC:
             t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
             return t
         else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+            raise ValueError(f"Unsupported skip_type {skip_type}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'")
 
     def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
         """
@@ -757,40 +759,44 @@ class UniPC:
                 vec_t = timesteps[0].expand((x.shape[0]))
                 model_prev_list = [self.model_fn(x, vec_t)]
                 t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
+                with tqdm.tqdm(total=steps) as pbar:
-                for init_order in range(1, order):
+                    # Init the first `order` values by lower order multistep DPM-Solver.
-                    vec_t = timesteps[init_order].expand(x.shape[0])
+                    for init_order in range(1, order):
-                    x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
+                        vec_t = timesteps[init_order].expand(x.shape[0])
-                    if model_x is None:
+                        x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
-                        model_x = self.model_fn(x, vec_t)
-                    if self.after_update is not None:
-                        self.after_update(x, model_x)
-                    model_prev_list.append(model_x)
-                    t_prev_list.append(vec_t)
-                for step in trange(order, steps + 1):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    #print('this step order:', step_order)
-                    if step == steps:
-                        #print('do not run corrector at the last step')
-                        use_corrector = False
-                    else:
-                        use_corrector = True
-                    x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
-                    if self.after_update is not None:
-                        self.after_update(x, model_x)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
                         if model_x is None:
                             model_x = self.model_fn(x, vec_t)
-                        model_prev_list[-1] = model_x
+                        if self.after_update is not None:
+                            self.after_update(x, model_x)
+                        model_prev_list.append(model_x)
+                        t_prev_list.append(vec_t)
+                        pbar.update()
+
+                    for step in range(order, steps + 1):
+                        vec_t = timesteps[step].expand(x.shape[0])
+                        if lower_order_final:
+                            step_order = min(order, steps + 1 - step)
+                        else:
+                            step_order = order
+                        #print('this step order:', step_order)
+                        if step == steps:
+                            #print('do not run corrector at the last step')
+                            use_corrector = False
+                        else:
+                            use_corrector = True
+                        x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
+                        if self.after_update is not None:
+                            self.after_update(x, model_x)
+                        for i in range(order - 1):
+                            t_prev_list[i] = t_prev_list[i + 1]
+                            model_prev_list[i] = model_prev_list[i + 1]
+                        t_prev_list[-1] = vec_t
+                        # We do not need to evaluate the final model value.
+                        if step < steps:
+                            if model_x is None:
+                                model_x = self.model_fn(x, vec_t)
+                            model_prev_list[-1] = model_x
+                        pbar.update()
         else:
             raise NotImplementedError()
         if denoise_to_zero:

modules/ngrok.py

@@ -7,13 +7,13 @@ def connect(token, port, region):
     else:
         if ':' in token:
             # token = authtoken:username:password
-            account = token.split(':')[1] + ':' + token.split(':')[-1]
+            token, username, password = token.split(':', 2)
-            token = token.split(':')[0]
+            account = f"{username}:{password}"
 
     config = conf.PyngrokConfig(
         auth_token=token, region=region
     )
-    
+
     # Guard for existing tunnels
     existing = ngrok.get_tunnels(pyngrok_config=config)
     if existing:
@@ -24,7 +24,7 @@ def connect(token, port, region):
                 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

modules/paths.py

@@ -1,8 +1,8 @@
 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
@@ -16,7 +16,7 @@ for possible_sd_path in possible_sd_paths:
         sd_path = os.path.abspath(possible_sd_path)
         break
 
-assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
+assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}"
 
 path_dirs = [
     (sd_path, 'ldm', 'Stable Diffusion', []),

modules/paths_internal.py

@@ -2,8 +2,14 @@
 
 import argparse
 import os
+import sys
+import shlex
 
-script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+sys.argv += shlex.split(commandline_args)
+
+modules_path = os.path.dirname(os.path.realpath(__file__))
+script_path = os.path.dirname(modules_path)
 
 sd_configs_path = os.path.join(script_path, "configs")
 sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
@@ -12,7 +18,7 @@ default_sd_model_file = sd_model_file
 
 # Parse the --data-dir flag first so we can use it as a base for our other argument default values
 parser_pre = argparse.ArgumentParser(add_help=False)
-parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
+parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(modules_path), help="base path where all user data is stored", )
 cmd_opts_pre = parser_pre.parse_known_args()[0]
 
 data_path = cmd_opts_pre.data_dir
@@ -21,3 +27,5 @@ models_path = os.path.join(data_path, "models")
 extensions_dir = os.path.join(data_path, "extensions")
 extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
 config_states_dir = os.path.join(script_path, "config_states")
+
+roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')

modules/processing.py

@@ -2,7 +2,6 @@ import json
 import math
 import os
 import sys
-import warnings
 import hashlib
 
 import torch
@@ -11,10 +10,10 @@ from PIL import Image, ImageFilter, ImageOps
 import random
 import cv2
 from skimage import exposure
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -30,6 +29,13 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
+import tomesd
+
+# add a logger for the processing module
+logger = logging.getLogger(__name__)
+# manually set output level here since there is no option to do so yet through launch options
+# logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)s %(name)s %(message)s')
+
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -165,7 +171,7 @@ class StableDiffusionProcessing:
         self.all_subseeds = None
         self.iteration = 0
         self.is_hr_pass = False
-        
+
 
     @property
     def sd_model(self):
@@ -458,10 +464,21 @@ def fix_seed(p):
     p.subseed = get_fixed_seed(p.subseed)
 
 
+def program_version():
+    import launch
+
+    res = launch.git_tag()
+    if res == "<none>":
+        res = None
+
+    return res
+
+
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
     clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
+    enable_hr = getattr(p, 'enable_hr', False)
 
     generation_params = {
         "Steps": p.steps,
@@ -480,16 +497,19 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+        "Token merging ratio": None if opts.token_merging_ratio == 0 else opts.token_merging_ratio,
+        "Token merging ratio hr": None if not enable_hr or opts.token_merging_ratio_hr == 0 else opts.token_merging_ratio_hr,
         "Init image hash": getattr(p, 'init_img_hash', None),
         "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
+        "Version": program_version() if opts.add_version_to_infotext else None,
     }
 
     generation_params.update(p.extra_generation_params)
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -512,9 +532,18 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if k == 'sd_vae':
                 sd_vae.reload_vae_weights()
 
+        if opts.token_merging_ratio > 0:
+            sd_models.apply_token_merging(sd_model=p.sd_model, hr=False)
+            logger.debug(f"Token merging applied to first pass. Ratio: '{opts.token_merging_ratio}'")
+
         res = process_images_inner(p)
 
     finally:
+        # undo model optimizations made by tomesd
+        if opts.token_merging_ratio > 0:
+            tomesd.remove_patch(p.sd_model)
+            logger.debug('Token merging model optimizations removed')
+
         # restore opts to original state
         if p.override_settings_restore_afterwards:
             for k, v in stored_opts.items():
@@ -653,7 +682,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if not shared.opts.dont_fix_second_order_samplers_schedule:
                 try:
                     step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
-                except:
+                except Exception:
                     pass
             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
@@ -769,7 +798,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     devices.torch_gc()
 
-    res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
+    res = Processed(
+        p,
+        images_list=output_images,
+        seed=p.all_seeds[0],
+        info=infotext(),
+        comments="".join(f"\n\n{comment}" for comment in comments),
+        subseed=p.all_subseeds[0],
+        index_of_first_image=index_of_first_image,
+        infotexts=infotexts,
+    )
 
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
@@ -958,8 +996,22 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
+        # apply token merging optimizations from tomesd for high-res pass
+        if opts.token_merging_ratio_hr > 0:
+            # in case the user has used separate merge ratios
+            if opts.token_merging_ratio > 0:
+                tomesd.remove_patch(self.sd_model)
+                logger.debug('Adjusting token merging ratio for high-res pass')
+
+            sd_models.apply_token_merging(sd_model=self.sd_model, hr=True)
+            logger.debug(f"Applied token merging for high-res pass. Ratio: '{opts.token_merging_ratio_hr}'")
+
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
 
+        if opts.token_merging_ratio_hr > 0 or opts.token_merging_ratio > 0:
+            tomesd.remove_patch(self.sd_model)
+            logger.debug('Removed token merging optimizations from model')
+
         self.is_hr_pass = False
 
         return samples

modules/progress.py

@@ -95,8 +95,16 @@ def progressapi(req: ProgressRequest):
         image = shared.state.current_image
         if image is not None:
             buffered = io.BytesIO()
-            image.save(buffered, format="png")
+
-            live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii")
+            if opts.live_previews_image_format == "png":
+                # using optimize for large images takes an enormous amount of time
+                save_kwargs = {"optimize": max(*image.size) > 256}
+            else:
+                save_kwargs = {}
+
+            image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
+            base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
+            live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
             id_live_preview = shared.state.id_live_preview
         else:
             live_preview = None

modules/prompt_parser.py

@@ -54,18 +54,21 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     """
 
     def collect_steps(steps, tree):
-        l = [steps]
+        res = [steps]
+
         class CollectSteps(lark.Visitor):
             def scheduled(self, tree):
                 tree.children[-1] = float(tree.children[-1])
                 if tree.children[-1] < 1:
                     tree.children[-1] *= steps
                 tree.children[-1] = min(steps, int(tree.children[-1]))
-                l.append(tree.children[-1])
+                res.append(tree.children[-1])
+
             def alternate(self, tree):
-                l.extend(range(1, steps+1))
+                res.extend(range(1, steps+1))
+
         CollectSteps().visit(tree)
-        return sorted(set(l))
+        return sorted(set(res))
 
     def at_step(step, tree):
         class AtStep(lark.Transformer):
@@ -92,7 +95,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     def get_schedule(prompt):
         try:
             tree = schedule_parser.parse(prompt)
-        except lark.exceptions.LarkError as e:
+        except lark.exceptions.LarkError:
             if 0:
                 import traceback
                 traceback.print_exc()
@@ -140,7 +143,7 @@ def get_learned_conditioning(model, prompts, steps):
         conds = model.get_learned_conditioning(texts)
 
         cond_schedule = []
-        for i, (end_at_step, text) in enumerate(prompt_schedule):
+        for i, (end_at_step, _) in enumerate(prompt_schedule):
             cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
 
         cache[prompt] = cond_schedule
@@ -216,8 +219,8 @@ def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_s
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c):
         target_index = 0
-        for current, (end_at, cond) in enumerate(cond_schedule):
+        for current, entry in enumerate(cond_schedule):
-            if current_step <= end_at:
+            if current_step <= entry.end_at_step:
                 target_index = current
                 break
         res[i] = cond_schedule[target_index].cond
@@ -231,13 +234,13 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
     tensors = []
     conds_list = []
 
-    for batch_no, composable_prompts in enumerate(c.batch):
+    for composable_prompts in c.batch:
         conds_for_batch = []
 
-        for cond_index, composable_prompt in enumerate(composable_prompts):
+        for composable_prompt in composable_prompts:
             target_index = 0
-            for current, (end_at, cond) in enumerate(composable_prompt.schedules):
+            for current, entry in enumerate(composable_prompt.schedules):
-                if current_step <= end_at:
+                if current_step <= entry.end_at_step:
                     target_index = current
                     break
 

modules/realesrgan_model.py

@@ -17,9 +17,9 @@ class UpscalerRealESRGAN(Upscaler):
         self.user_path = path
         super().__init__()
         try:
-            from basicsr.archs.rrdbnet_arch import RRDBNet
+            from basicsr.archs.rrdbnet_arch import RRDBNet  # noqa: F401
-            from realesrgan import RealESRGANer
+            from realesrgan import RealESRGANer  # noqa: F401
-            from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+            from realesrgan.archs.srvgg_arch import SRVGGNetCompact  # noqa: F401
             self.enable = True
             self.scalers = []
             scalers = self.load_models(path)
@@ -28,9 +28,9 @@ class UpscalerRealESRGAN(Upscaler):
             for scaler in scalers:
                 if scaler.local_data_path.startswith("http"):
                     filename = modelloader.friendly_name(scaler.local_data_path)
-                    local = next(iter([local_model for local_model in local_model_paths if local_model.endswith(filename + '.pth')]), None)
+                    local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
-                    if local:
+                    if local_model_candidates:
-                        scaler.local_data_path = local
+                        scaler.local_data_path = local_model_candidates[0]
 
                 if scaler.name in opts.realesrgan_enabled_models:
                     self.scalers.append(scaler)
@@ -47,7 +47,7 @@ class UpscalerRealESRGAN(Upscaler):
 
         info = self.load_model(path)
         if not os.path.exists(info.local_data_path):
-            print("Unable to load RealESRGAN model: %s" % info.name)
+            print(f"Unable to load RealESRGAN model: {info.name}")
             return img
 
         upsampler = RealESRGANer(
@@ -134,6 +134,6 @@ def get_realesrgan_models(scaler):
             ),
         ]
         return models
-    except Exception as e:
+    except Exception:
         print("Error making Real-ESRGAN models list:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)

modules/safe.py

@@ -40,7 +40,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return getattr(collections, name)
         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:
             return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']:
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32', 'BFloat16Storage']:
             return getattr(torch, name)
         if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
             return getattr(torch.nn.modules.container, name)
@@ -95,16 +95,16 @@ def check_pt(filename, extra_handler):
 
     except zipfile.BadZipfile:
 
-        # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+        # if it's not a zip file, it's an old pytorch format, with five objects written to pickle
         with open(filename, "rb") as file:
             unpickler = RestrictedUnpickler(file)
             unpickler.extra_handler = extra_handler
-            for i in range(5):
+            for _ in range(5):
                 unpickler.load()
 
 
 def load(filename, *args, **kwargs):
-    return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs)
+    return load_with_extra(filename, *args, extra_handler=global_extra_handler, **kwargs)
 
 
 def load_with_extra(filename, extra_handler=None, *args, **kwargs):

modules/script_callbacks.py

@@ -32,27 +32,42 @@ class CFGDenoiserParams:
     def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
         self.x = x
         """Latent image representation in the process of being denoised"""
-        
+
         self.image_cond = image_cond
         """Conditioning image"""
-        
+
         self.sigma = sigma
         """Current sigma noise step value"""
-        
+
         self.sampling_step = sampling_step
         """Current Sampling step number"""
-        
+
         self.total_sampling_steps = total_sampling_steps
         """Total number of sampling steps planned"""
-        
+
         self.text_cond = text_cond
         """ Encoder hidden states of text conditioning from prompt"""
-        
+
         self.text_uncond = text_uncond
         """ Encoder hidden states of text conditioning from negative prompt"""
 
 
 class CFGDenoisedParams:
+    def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
+        self.x = x
+        """Latent image representation in the process of being denoised"""
+
+        self.sampling_step = sampling_step
+        """Current Sampling step number"""
+
+        self.total_sampling_steps = total_sampling_steps
+        """Total number of sampling steps planned"""
+
+        self.inner_model = inner_model
+        """Inner model reference used for denoising"""
+
+
+class AfterCFGCallbackParams:
     def __init__(self, x, sampling_step, total_sampling_steps):
         self.x = x
         """Latent image representation in the process of being denoised"""
@@ -87,6 +102,7 @@ callback_map = dict(
     callbacks_image_saved=[],
     callbacks_cfg_denoiser=[],
     callbacks_cfg_denoised=[],
+    callbacks_cfg_after_cfg=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
     callbacks_image_grid=[],
@@ -186,6 +202,14 @@ def cfg_denoised_callback(params: CFGDenoisedParams):
             report_exception(c, 'cfg_denoised_callback')
 
 
+def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
+    for c in callback_map['callbacks_cfg_after_cfg']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'cfg_after_cfg_callback')
+
+
 def before_component_callback(component, **kwargs):
     for c in callback_map['callbacks_before_component']:
         try:
@@ -240,7 +264,7 @@ def add_callback(callbacks, fun):
 
     callbacks.append(ScriptCallback(filename, fun))
 
-    
+
 def remove_current_script_callbacks():
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -332,6 +356,14 @@ def on_cfg_denoised(callback):
     add_callback(callback_map['callbacks_cfg_denoised'], callback)
 
 
+def on_cfg_after_cfg(callback):
+    """register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations are completed.
+    The callback is called with one argument:
+        - params: AfterCFGCallbackParams - parameters to be passed to the script for post-processing after cfg calculation.
+    """
+    add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
+
+
 def on_before_component(callback):
     """register a function to be called before a component is created.
     The callback is called with arguments:

modules/script_loading.py

@@ -2,7 +2,6 @@ import os
 import sys
 import traceback
 import importlib.util
-from types import ModuleType
 
 
 def load_module(path):

modules/scripts.py

@@ -163,7 +163,8 @@ class Script:
         """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
 
         need_tabname = self.show(True) == self.show(False)
-        tabname = ('img2img' if self.is_img2img else 'txt2txt') + "_" if need_tabname else ""
+        tabkind = 'img2img' if self.is_img2img else 'txt2txt'
+        tabname = f"{tabkind}_" if need_tabname else ""
         title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
 
         return f'script_{tabname}{title}_{item_id}'
@@ -230,7 +231,7 @@ def load_scripts():
     syspath = sys.path
 
     def register_scripts_from_module(module):
-        for key, script_class in module.__dict__.items():
+        for script_class in module.__dict__.values():
             if type(script_class) != type:
                 continue
 
@@ -294,9 +295,9 @@ class ScriptRunner:
 
         auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
 
-        for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
+        for script_data in auto_processing_scripts + scripts_data:
-            script = script_class()
+            script = script_data.script_class()
-            script.filename = path
+            script.filename = script_data.path
             script.is_txt2img = not is_img2img
             script.is_img2img = is_img2img
 
@@ -491,7 +492,7 @@ class ScriptRunner:
                 module = script_loading.load_module(script.filename)
                 cache[filename] = module
 
-            for key, script_class in module.__dict__.items():
+            for script_class in module.__dict__.values():
                 if type(script_class) == type and issubclass(script_class, Script):
                     self.scripts[si] = script_class()
                     self.scripts[si].filename = filename
@@ -526,7 +527,7 @@ def add_classes_to_gradio_component(comp):
     this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
     """
 
-    comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
+    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
 
     if getattr(comp, 'multiselect', False):
         comp.elem_classes.append('multiselect')

modules/scripts_auto_postprocessing.py

@@ -17,7 +17,7 @@ class ScriptPostprocessingForMainUI(scripts.Script):
         return self.postprocessing_controls.values()
 
     def postprocess_image(self, p, script_pp, *args):
-        args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)}
+        args_dict = dict(zip(self.postprocessing_controls, args))
 
         pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
         pp.info = {}

modules/scripts_postprocessing.py

@@ -66,9 +66,9 @@ class ScriptPostprocessingRunner:
     def initialize_scripts(self, scripts_data):
         self.scripts = []
 
-        for script_class, path, basedir, script_module in scripts_data:
+        for script_data in scripts_data:
-            script: ScriptPostprocessing = script_class()
+            script: ScriptPostprocessing = script_data.script_class()
-            script.filename = path
+            script.filename = script_data.path
 
             if script.name == "Simple Upscale":
                 continue
@@ -124,7 +124,7 @@ class ScriptPostprocessingRunner:
             script_args = args[script.args_from:script.args_to]
 
             process_args = {}
-            for (name, component), value in zip(script.controls.items(), script_args):
+            for (name, _component), value in zip(script.controls.items(), script_args):
                 process_args[name] = value
 
             script.process(pp, **process_args)

modules/sd_disable_initialization.py

@@ -61,7 +61,7 @@ class DisableInitialization:
                 if res is None:
                     res = original(url, *args, local_files_only=False, **kwargs)
                 return res
-            except Exception as e:
+            except Exception:
                 return original(url, *args, local_files_only=False, **kwargs)
 
         def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):

modules/sd_hijack.py

@@ -3,7 +3,7 @@ from torch.nn.functional import silu
 from types import MethodType
 
 import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
+from modules import devices, sd_hijack_optimizations, shared
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -34,10 +34,10 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
-    
+
     optimization_method = None
 
-    can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
+    can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention) # not everyone has torch 2.x to use sdp
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
@@ -92,12 +92,12 @@ def fix_checkpoint():
 def weighted_loss(sd_model, pred, target, mean=True):
     #Calculate the weight normally, but ignore the mean
     loss = sd_model._old_get_loss(pred, target, mean=False)
-    
+
     #Check if we have weights available
     weight = getattr(sd_model, '_custom_loss_weight', None)
     if weight is not None:
         loss *= weight
-    
+
     #Return the loss, as mean if specified
     return loss.mean() if mean else loss
 
@@ -105,7 +105,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
     try:
         #Temporarily append weights to a place accessible during loss calc
         sd_model._custom_loss_weight = w
-        
+
         #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
         #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
         if not hasattr(sd_model, '_old_get_loss'):
@@ -118,9 +118,9 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
         try:
             #Delete temporary weights if appended
             del sd_model._custom_loss_weight
-        except AttributeError as e:
+        except AttributeError:
             pass
-            
+
         #If we have an old loss function, reset the loss function to the original one
         if hasattr(sd_model, '_old_get_loss'):
             sd_model.get_loss = sd_model._old_get_loss
@@ -133,7 +133,7 @@ def apply_weighted_forward(sd_model):
 def undo_weighted_forward(sd_model):
     try:
         del sd_model.weighted_forward
-    except AttributeError as e:
+    except AttributeError:
         pass
 
 
@@ -184,7 +184,7 @@ class StableDiffusionModelHijack:
 
     def undo_hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
-            m.cond_stage_model = m.cond_stage_model.wrapped 
+            m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped

modules/sd_hijack_clip.py

@@ -223,7 +223,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
             self.hijack.fixes = [x.fixes for x in batch_chunk]
 
             for fixes in self.hijack.fixes:
-                for position, embedding in fixes:
+                for _position, embedding in fixes:
                     used_embeddings[embedding.name] = embedding
 
             z = self.process_tokens(tokens, multipliers)

modules/sd_hijack_clip_old.py

@@ -75,7 +75,8 @@ def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, text
     self.hijack.comments += hijack_comments
 
     if len(used_custom_terms) > 0:
-        self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+        embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
+        self.hijack.comments.append(f"Used embeddings: {embedding_names}")
 
     self.hijack.fixes = hijack_fixes
     return self.process_tokens(remade_batch_tokens, batch_multipliers)

modules/sd_hijack_inpainting.py

@@ -1,16 +1,10 @@
-import os
 import torch
 
-from einops import repeat
-from omegaconf import ListConfig
-
 import ldm.models.diffusion.ddpm
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 
-from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.ddim import noise_like
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 from ldm.models.diffusion.sampling_util import norm_thresholding
 
 
@@ -29,7 +23,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
             if isinstance(c, dict):
                 assert isinstance(unconditional_conditioning, dict)
-                c_in = dict()
+                c_in = {}
                 for k in c:
                     if isinstance(c[k], list):
                         c_in[k] = [

modules/sd_hijack_ip2p.py

@@ -1,8 +1,5 @@
-import collections
 import os.path
-import sys
+
-import gc
-import time
 
 def should_hijack_ip2p(checkpoint_info):
     from modules import sd_models_config
@@ -10,4 +7,4 @@ def should_hijack_ip2p(checkpoint_info):
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
     cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
 
-    return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename
+    return "pix2pix" in ckpt_basename and "pix2pix" not in cfg_basename

modules/sd_hijack_optimizations.py

@@ -49,7 +49,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     v_in = self.to_v(context_v)
     del context, context_k, context_v, x
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -62,10 +62,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
             end = i + 2
             s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
             s1 *= self.scale
-    
+
             s2 = s1.softmax(dim=-1)
             del s1
-    
+
             r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
             del s2
         del q, k, v
@@ -95,43 +95,43 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     with devices.without_autocast(disable=not shared.opts.upcast_attn):
         k_in = k_in * self.scale
-    
+
         del context, x
-    
+
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+        q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
         del q_in, k_in, v_in
-    
+
         r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-    
+
         mem_free_total = get_available_vram()
-    
+
         gb = 1024 ** 3
         tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
         modifier = 3 if q.element_size() == 2 else 2.5
         mem_required = tensor_size * modifier
         steps = 1
-    
+
         if mem_required > mem_free_total:
             steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
             # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
             #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-    
+
         if steps > 64:
             max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
             raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
                                f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
-    
+
         slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
         for i in range(0, q.shape[1], slice_size):
             end = i + slice_size
             s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
-    
+
             s2 = s1.softmax(dim=-1, dtype=q.dtype)
             del s1
-    
+
             r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
             del s2
-    
+
         del q, k, v
 
     r1 = r1.to(dtype)
@@ -228,8 +228,8 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
 
     with devices.without_autocast(disable=not shared.opts.upcast_attn):
         k = k * self.scale
-    
+
-        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))
         r = einsum_op(q, k, v)
     r = r.to(dtype)
     return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
@@ -256,6 +256,9 @@ def sub_quad_attention_forward(self, x, context=None, mask=None):
     k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
     v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
 
+    if q.device.type == 'mps':
+        q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
+
     dtype = q.dtype
     if shared.opts.upcast_attn:
         q, k = q.float(), k.float()
@@ -293,7 +296,6 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
     if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
         # the big matmul fits into our memory limit; do everything in 1 chunk,
         # i.e. send it down the unchunked fast-path
-        query_chunk_size = q_tokens
         kv_chunk_size = k_tokens
 
     with devices.without_autocast(disable=q.dtype == v.dtype):
@@ -332,7 +334,7 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     k_in = self.to_k(context_k)
     v_in = self.to_v(context_v)
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
+    q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -367,7 +369,7 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
     q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
     k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
     v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
-    
+
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -449,7 +451,7 @@ def cross_attention_attnblock_forward(self, x):
         h3 += x
 
         return h3
-    
+
 def xformers_attnblock_forward(self, x):
     try:
         h_ = x
@@ -458,7 +460,7 @@ def xformers_attnblock_forward(self, x):
         k = self.k(h_)
         v = self.v(h_)
         b, c, h, w = q.shape
-        q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
         dtype = q.dtype
         if shared.opts.upcast_attn:
             q, k = q.float(), k.float()
@@ -480,7 +482,7 @@ def sdp_attnblock_forward(self, x):
     k = self.k(h_)
     v = self.v(h_)
     b, c, h, w = q.shape
-    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+    q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
     dtype = q.dtype
     if shared.opts.upcast_attn:
         q, k = q.float(), k.float()
@@ -504,7 +506,7 @@ def sub_quad_attnblock_forward(self, x):
     k = self.k(h_)
     v = self.v(h_)
     b, c, h, w = q.shape
-    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+    q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
     q = q.contiguous()
     k = k.contiguous()
     v = v.contiguous()

modules/sd_hijack_unet.py

@@ -18,7 +18,7 @@ class TorchHijackForUnet:
         if hasattr(torch, item):
             return getattr(torch, item)
 
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
 
     def cat(self, tensors, *args, **kwargs):
         if len(tensors) == 2:

modules/sd_hijack_xlmr.py

@@ -1,8 +1,6 @@
-import open_clip.tokenizer
 import torch
 
 from modules import sd_hijack_clip, devices
-from modules.shared import opts
 
 
 class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):

modules/sd_models.py

@@ -2,6 +2,8 @@ import collections
 import os.path
 import sys
 import gc
+import threading
+
 import torch
 import re
 import safetensors.torch
@@ -13,9 +15,9 @@ import ldm.modules.midas as midas
 from ldm.util import instantiate_from_config
 
 from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
-from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
+import tomesd
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -45,7 +47,7 @@ class CheckpointInfo:
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
         self.hash = model_hash(filename)
 
-        self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
+        self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
         self.shorthash = self.sha256[0:10] if self.sha256 else None
 
         self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
@@ -67,7 +69,7 @@ class CheckpointInfo:
             checkpoint_alisases[id] = self
 
     def calculate_shorthash(self):
-        self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
+        self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
         if self.sha256 is None:
             return
 
@@ -85,8 +87,7 @@ class CheckpointInfo:
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
+    from transformers import logging, CLIPModel  # noqa: F401
-    from transformers import logging, CLIPModel
 
     logging.set_verbosity_error()
 except Exception:
@@ -165,7 +166,7 @@ def model_hash(filename):
 
 def select_checkpoint():
     model_checkpoint = shared.opts.sd_model_checkpoint
-        
+
     checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
     if checkpoint_info is not None:
         return checkpoint_info
@@ -237,7 +238,7 @@ def read_metadata_from_safetensors(filename):
             if isinstance(v, str) and v[0:1] == '{':
                 try:
                     res[k] = json.loads(v)
-                except Exception as e:
+                except Exception:
                     pass
 
         return res
@@ -372,7 +373,7 @@ def enable_midas_autodownload():
         if not os.path.exists(path):
             if not os.path.exists(midas_path):
                 mkdir(midas_path)
-    
+
             print(f"Downloading midas model weights for {model_type} to {path}")
             request.urlretrieve(midas_urls[model_type], path)
             print(f"{model_type} downloaded")
@@ -404,13 +405,39 @@ def repair_config(sd_config):
 sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
 sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
 
-def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
+
+class SdModelData:
+    def __init__(self):
+        self.sd_model = None
+        self.lock = threading.Lock()
+
+    def get_sd_model(self):
+        if self.sd_model is None:
+            with self.lock:
+                try:
+                    load_model()
+                except Exception as e:
+                    errors.display(e, "loading stable diffusion model")
+                    print("", file=sys.stderr)
+                    print("Stable diffusion model failed to load", file=sys.stderr)
+                    self.sd_model = None
+
+        return self.sd_model
+
+    def set_sd_model(self, v):
+        self.sd_model = v
+
+
+model_data = SdModelData()
+
+
+def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
-    if shared.sd_model:
+    if model_data.sd_model:
-        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
-        shared.sd_model = None
+        model_data.sd_model = None
         gc.collect()
         devices.torch_gc()
 
@@ -439,7 +466,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_
     try:
         with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
             sd_model = instantiate_from_config(sd_config.model)
-    except Exception as e:
+    except Exception:
         pass
 
     if sd_model is None:
@@ -464,7 +491,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_
     timer.record("hijack")
 
     sd_model.eval()
-    shared.sd_model = sd_model
+    model_data.sd_model = sd_model
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
 
@@ -484,7 +511,7 @@ def reload_model_weights(sd_model=None, info=None):
     checkpoint_info = info or select_checkpoint()
 
     if not sd_model:
-        sd_model = shared.sd_model
+        sd_model = model_data.sd_model
 
     if sd_model is None:  # previous model load failed
         current_checkpoint_info = None
@@ -512,11 +539,11 @@ def reload_model_weights(sd_model=None, info=None):
         del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info, already_loaded_state_dict=state_dict)
-        return shared.sd_model
+        return model_data.sd_model
 
     try:
         load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-    except Exception as e:
+    except Exception:
         print("Failed to load checkpoint, restoring previous")
         load_model_weights(sd_model, current_checkpoint_info, None, timer)
         raise
@@ -535,17 +562,15 @@ def reload_model_weights(sd_model=None, info=None):
 
     return sd_model
 
+
 def unload_model_weights(sd_model=None, info=None):
-    from modules import lowvram, devices, sd_hijack
+    from modules import devices, sd_hijack
     timer = Timer()
 
-    if shared.sd_model:
+    if model_data.sd_model:
-
+        model_data.sd_model.to(devices.cpu)
-        # shared.sd_model.cond_stage_model.to(devices.cpu)
+        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
-        # shared.sd_model.first_stage_model.to(devices.cpu)
+        model_data.sd_model = None
-        shared.sd_model.to(devices.cpu)
-        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
-        shared.sd_model = None
         sd_model = None
         gc.collect()
         devices.torch_gc()
@@ -554,3 +579,25 @@ def unload_model_weights(sd_model=None, info=None):
     print(f"Unloaded weights {timer.summary()}.")
 
     return sd_model
+
+
+def apply_token_merging(sd_model, hr: bool):
+    """
+    Applies speed and memory optimizations from tomesd.
+
+    Args:
+        hr (bool): True if called in the context of a high-res pass
+    """
+
+    ratio = shared.opts.token_merging_ratio
+    if hr:
+        ratio = shared.opts.token_merging_ratio_hr
+
+    tomesd.apply_patch(
+        sd_model,
+        ratio=ratio,
+        use_rand=False,  # can cause issues with some samplers
+        merge_attn=True,
+        merge_crossattn=False,
+        merge_mlp=False
+    )

modules/sd_models_config.py

@@ -1,4 +1,3 @@
-import re
 import os
 
 import torch
@@ -111,7 +110,7 @@ def find_checkpoint_config_near_filename(info):
     if info is None:
         return None
 
-    config = os.path.splitext(info.filename)[0] + ".yaml"
+    config = f"{os.path.splitext(info.filename)[0]}.yaml"
     if os.path.exists(config):
         return config
 

modules/sd_samplers.py

@@ -1,7 +1,7 @@
 from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
 
 # imports for functions that previously were here and are used by other modules
-from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
+from modules.sd_samplers_common import samples_to_image_grid, sample_to_image  # noqa: F401
 
 all_samplers = [
     *sd_samplers_kdiffusion.samplers_data_k_diffusion,

modules/sd_samplers_compvis.py

@@ -55,7 +55,7 @@ class VanillaStableDiffusionSampler:
     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
         x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
 
-        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
+        res = self.orig_p_sample_ddim(x_dec, cond, ts, *args, unconditional_conditioning=unconditional_conditioning, **kwargs)
 
         x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
 
@@ -83,7 +83,7 @@ class VanillaStableDiffusionSampler:
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
-        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
+        assert all(len(conds) == 1 for conds in conds_list), 'composition via AND is not supported for DDIM/PLMS samplers'
         cond = tensor
 
         # for DDIM, shapes must match, we can't just process cond and uncond independently;

modules/sd_samplers_kdiffusion.py

@@ -1,7 +1,6 @@
 from collections import deque
 import torch
 import inspect
-import einops
 import k_diffusion.sampling
 from modules import prompt_parser, devices, sd_samplers_common
 
@@ -9,6 +8,7 @@ from modules.shared import opts, state
 import modules.shared as shared
 from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
 
 samplers_k_diffusion = [
     ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
@@ -87,17 +87,17 @@ class CFGDenoiser(torch.nn.Module):
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
-        assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
 
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
         if shared.sd_model.model.conditioning_key == "crossattn-adm":
             image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} 
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
         else:
             image_uncond = image_cond
-            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} 
+            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
 
         if not is_edit_model:
             x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
@@ -161,7 +161,7 @@ class CFGDenoiser(torch.nn.Module):
             fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
             x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
 
-        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
+        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
         cfg_denoised_callback(denoised_params)
 
         devices.test_for_nans(x_out, "unet")
@@ -181,6 +181,10 @@ class CFGDenoiser(torch.nn.Module):
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
 
+        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+        cfg_after_cfg_callback(after_cfg_callback_params)
+        denoised = after_cfg_callback_params.x
+
         self.step += 1
         return denoised
 
@@ -198,7 +202,7 @@ class TorchHijack:
         if hasattr(torch, item):
             return getattr(torch, item)
 
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
 
     def randn_like(self, x):
         if self.sampler_noises:
@@ -317,7 +321,7 @@ class KDiffusionSampler:
 
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
-        
+
         extra_params_kwargs = self.initialize(p)
         parameters = inspect.signature(self.func).parameters
 
@@ -340,9 +344,9 @@ class KDiffusionSampler:
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
         extra_args={
-            'cond': conditioning, 
+            'cond': conditioning,
-            'image_cond': image_conditioning, 
+            'image_cond': image_conditioning,
-            'uncond': unconditional_conditioning, 
+            'uncond': unconditional_conditioning,
             'cond_scale': p.cfg_scale,
             's_min_uncond': self.s_min_uncond
         }
@@ -375,9 +379,9 @@ class KDiffusionSampler:
 
         self.last_latent = x
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
-            'cond': conditioning, 
+            'cond': conditioning,
-            'image_cond': image_conditioning, 
+            'image_cond': image_conditioning,
-            'uncond': unconditional_conditioning, 
+            'uncond': unconditional_conditioning,
             'cond_scale': p.cfg_scale,
             's_min_uncond': self.s_min_uncond
         }, disable=False, callback=self.callback_state, **extra_params_kwargs))

modules/sd_vae.py

@@ -1,8 +1,5 @@
-import torch
-import safetensors.torch
 import os
 import collections
-from collections import namedtuple
 from modules import paths, shared, devices, script_callbacks, sd_models
 import glob
 from copy import deepcopy

modules/shared.py

@@ -1,12 +1,9 @@
-import argparse
 import datetime
 import json
 import os
 import sys
 import time
-import requests
 
-from PIL import Image
 import gradio as gr
 import tqdm
 
@@ -15,7 +12,8 @@ import modules.memmon
 import modules.styles
 import modules.devices as devices
 from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
-from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir
+from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir  # noqa: F401
+from ldm.models.diffusion.ddpm import LatentDiffusion
 
 demo = None
 
@@ -201,8 +199,9 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
+
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
         self.default = default
         self.label = label
         self.component = component
@@ -211,9 +210,33 @@ class OptionInfo:
         self.section = section
         self.refresh = refresh
 
+        self.comment_before = comment_before
+        """HTML text that will be added after label in UI"""
+
+        self.comment_after = comment_after
+        """HTML text that will be added before label in UI"""
+
+    def link(self, label, url):
+        self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
+        return self
+
+    def js(self, label, js_func):
+        self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
+        return self
+
+    def info(self, info):
+        self.comment_after += f"<span class='info'>({info})</span>"
+        return self
+
+    def needs_restart(self):
+        self.comment_after += " <span class='info'>(requires restart)</span>"
+        return self
+
+
+
 
 def options_section(section_identifier, options_dict):
-    for k, v in options_dict.items():
+    for v in options_dict.values():
         v.section = section_identifier
 
     return options_dict
@@ -242,7 +265,7 @@ options_templates = {}
 options_templates.update(options_section(('saving-images', "Saving images/grids"), {
     "samples_save": OptionInfo(True, "Always save all generated images"),
     "samples_format": OptionInfo('png', 'File format for images'),
-    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
+    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
     "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
 
     "grid_save": OptionInfo(True, "Always save all generated image grids"),
@@ -261,10 +284,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
-    "export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
+    "export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
     "img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
     "target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
-    "img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),
+    "img_max_size_mp": OptionInfo(200, "Maximum image size", gr.Number).info("in megapixels"),
 
     "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
     "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
@@ -292,28 +315,26 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
     "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
     "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
+    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
     "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
-    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
-    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
+    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-    "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
     "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
-    "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
     "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
 }))
 
 options_templates.update(options_section(('system', "System"), {
     "show_warnings": OptionInfo(False, "Show warnings in console."),
-    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
+    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
     "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
@@ -338,20 +359,22 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
+    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
     "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
+    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
     "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-    "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+    "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
-    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
+    "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
-    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP nrtwork; 1 ignores none, 2 ignores one layer"),
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
-    "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
+    "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different vidocard vendors"),
+    "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
+    "token_merging_ratio_hr": OptionInfo(0.0, "Togen merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -363,80 +386,87 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
-    "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
+    "interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
-    "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
+    "interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
-    "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+    "interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
-    "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+    "interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
-    "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+    "interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
+    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
     "interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
-    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
+    "deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
-    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
+    "deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
-    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
+    "deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
-    "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
+    "deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
 }))
 
 options_templates.update(options_section(('extra_networks', "Extra Networks"), {
     "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
     "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
+    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
-    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
+    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
-    "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
+    "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
+    "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_restart(),
+    "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).needs_restart(),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
     "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
-    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "js_modal_lightbox_gamepad": OptionInfo(True, "Navigate image viewer with gamepad"),
+    "js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
+    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_restart(),
-    "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
+    "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_restart(),
     "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
     "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
-    "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
+    "keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
-    "quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_restart(),
-    "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
+    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
     "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
-    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
+    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
-    "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
+}))
-    "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
+
+options_templates.update(options_section(('infotext', "Infotext"), {
+    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
+    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
+    "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
+    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
 }))
 
 options_templates.update(options_section(('ui', "Live previews"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
+    "live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
-    "show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+    "show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
-    "show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
+    "show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}).info("Full = slow but pretty; Approx NN = fast but low quality; Approx cheap = super fast but terrible otherwise"),
     "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
-    "live_preview_refresh_period": OptionInfo(1000, "Progressbar/preview update period, in milliseconds")
+    "live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
+    "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}).needs_restart(),
-    "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
-    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
     "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
+    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
-    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
+    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
     'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
     'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
-    'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
+    'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
     'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
 }))
 
@@ -453,6 +483,7 @@ options_templates.update(options_section((None, "Hidden options"), {
     "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
 }))
 
+
 options_templates.update()
 
 
@@ -542,6 +573,10 @@ class Options:
         with open(filename, "r", encoding="utf8") as file:
             self.data = json.load(file)
 
+        # 1.1.1 quicksettings list migration
+        if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
+            self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
+
         bad_settings = 0
         for k, v in self.data.items():
             info = self.data_labels.get(k, None)
@@ -560,7 +595,9 @@ class Options:
             func()
 
     def dumpjson(self):
-        d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
+        d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
+        d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
+        d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
         return json.dumps(d)
 
     def add_option(self, key, info):
@@ -571,11 +608,11 @@ class Options:
 
         section_ids = {}
         settings_items = self.data_labels.items()
-        for k, item in settings_items:
+        for _, item in settings_items:
             if item.section not in section_ids:
                 section_ids[item.section] = len(section_ids)
 
-        self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
+        self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
 
     def cast_value(self, key, value):
         """casts an arbitrary to the same type as this setting's value with key
@@ -600,13 +637,37 @@ class Options:
         return value
 
 
-
 opts = Options()
 if os.path.exists(config_filename):
     opts.load(config_filename)
 
+
+class Shared(sys.modules[__name__].__class__):
+    """
+    this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
+    at program startup.
+    """
+
+    sd_model_val = None
+
+    @property
+    def sd_model(self):
+        import modules.sd_models
+
+        return modules.sd_models.model_data.get_sd_model()
+
+    @sd_model.setter
+    def sd_model(self, value):
+        import modules.sd_models
+
+        modules.sd_models.model_data.set_sd_model(value)
+
+
+sd_model: LatentDiffusion = None  # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
+sys.modules[__name__].__class__ = Shared
+
 settings_components = None
-"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
+"""assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
 
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
@@ -620,8 +681,6 @@ latent_upscale_modes = {
 
 sd_upscalers = []
 
-sd_model = None
-
 clip_model = None
 
 progress_print_out = sys.stdout
@@ -634,14 +693,19 @@ def reload_gradio_theme(theme_name=None):
     if not theme_name:
         theme_name = opts.gradio_theme
 
+    default_theme_args = dict(
+        font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
+        font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
+    )
+
     if theme_name == "Default":
-        gradio_theme = gr.themes.Default()
+        gradio_theme = gr.themes.Default(**default_theme_args)
     else:
         try:
             gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
-        except requests.exceptions.ConnectionError:
+        except Exception as e:
-            print("Can't access HuggingFace Hub, falling back to default Gradio theme")
+            errors.display(e, "changing gradio theme")
-            gradio_theme = gr.themes.Default()
+            gradio_theme = gr.themes.Default(**default_theme_args)
 
 
 
@@ -701,3 +765,20 @@ def html(filename):
             return file.read()
 
     return ""
+
+
+def walk_files(path, allowed_extensions=None):
+    if not os.path.exists(path):
+        return
+
+    if allowed_extensions is not None:
+        allowed_extensions = set(allowed_extensions)
+
+    for root, _, files in os.walk(path, followlinks=True):
+        for filename in files:
+            if allowed_extensions is not None:
+                _, ext = os.path.splitext(filename)
+                if ext not in allowed_extensions:
+                    continue
+
+            yield os.path.join(root, filename)

modules/styles.py

@@ -1,18 +1,9 @@
-# We need this so Python doesn't complain about the unknown StableDiffusionProcessing-typehint at runtime
-from __future__ import annotations
-
 import csv
 import os
 import os.path
 import typing
-import collections.abc as abc
-import tempfile
 import shutil
 
-if typing.TYPE_CHECKING:
-    # Only import this when code is being type-checked, it doesn't have any effect at runtime
-    from .processing import StableDiffusionProcessing
-
 
 class PromptStyle(typing.NamedTuple):
     name: str
@@ -74,7 +65,7 @@ class StyleDatabase:
     def save_styles(self, path: str) -> None:
         # Always keep a backup file around
         if os.path.exists(path):
-            shutil.copy(path, path + ".bak")
+            shutil.copy(path, f"{path}.bak")
 
         fd = os.open(path, os.O_RDWR|os.O_CREAT)
         with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:

modules/sub_quadratic_attention.py

@@ -179,7 +179,7 @@ def efficient_dot_product_attention(
             chunk_idx,
             min(query_chunk_size, q_tokens)
         )
-    
+
     summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)
     summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
     compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
@@ -201,14 +201,15 @@ def efficient_dot_product_attention(
             key=key,
             value=value,
         )
-    
+
-    # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
+    res = torch.zeros_like(query)
-    # and pass slices to be mutated, instead of torch.cat()ing the returned slices
+    for i in range(math.ceil(q_tokens / query_chunk_size)):
-    res = torch.cat([
+        attn_scores = compute_query_chunk_attn(
-        compute_query_chunk_attn(
             query=get_query_chunk(i * query_chunk_size),
             key=key,
             value=value,
-        ) for i in range(math.ceil(q_tokens / query_chunk_size))
+        )
-    ], dim=1)
+
+        res[:, i * query_chunk_size:i * query_chunk_size + attn_scores.shape[1], :] = attn_scores
+
     return res

modules/textual_inversion/autocrop.py

@@ -1,10 +1,8 @@
 import cv2
 import requests
 import os
-from collections import defaultdict
-from math import log, sqrt
 import numpy as np
-from PIL import Image, ImageDraw
+from PIL import ImageDraw
 
 GREEN = "#0F0"
 BLUE = "#00F"
@@ -12,63 +10,64 @@ RED = "#F00"
 
 
 def crop_image(im, settings):
-  """ Intelligently crop an image to the subject matter """
+    """ Intelligently crop an image to the subject matter """
 
-  scale_by = 1
+    scale_by = 1
-  if is_landscape(im.width, im.height):
+    if is_landscape(im.width, im.height):
-    scale_by = settings.crop_height / im.height
+        scale_by = settings.crop_height / im.height
-  elif is_portrait(im.width, im.height):
+    elif is_portrait(im.width, im.height):
-    scale_by = settings.crop_width / im.width
+        scale_by = settings.crop_width / im.width
-  elif is_square(im.width, im.height):
+    elif is_square(im.width, im.height):
-    if is_square(settings.crop_width, settings.crop_height):
+        if is_square(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_width / im.width
+            scale_by = settings.crop_width / im.width
-    elif is_landscape(settings.crop_width, settings.crop_height):
+        elif is_landscape(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_width / im.width
+            scale_by = settings.crop_width / im.width
-    elif is_portrait(settings.crop_width, settings.crop_height):
+        elif is_portrait(settings.crop_width, settings.crop_height):
-      scale_by = settings.crop_height / im.height
+            scale_by = settings.crop_height / im.height
 
-  im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
-  im_debug = im.copy()
 
-  focus = focal_point(im_debug, settings)
+    im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+    im_debug = im.copy()
 
-  # take the focal point and turn it into crop coordinates that try to center over the focal
+    focus = focal_point(im_debug, settings)
-  # point but then get adjusted back into the frame
-  y_half = int(settings.crop_height / 2)
-  x_half = int(settings.crop_width / 2)
 
-  x1 = focus.x - x_half
+    # take the focal point and turn it into crop coordinates that try to center over the focal
-  if x1 < 0:
+    # point but then get adjusted back into the frame
-      x1 = 0
+    y_half = int(settings.crop_height / 2)
-  elif x1 + settings.crop_width > im.width:
+    x_half = int(settings.crop_width / 2)
-      x1 = im.width - settings.crop_width
 
-  y1 = focus.y - y_half
+    x1 = focus.x - x_half
-  if y1 < 0:
+    if x1 < 0:
-      y1 = 0
+        x1 = 0
-  elif y1 + settings.crop_height > im.height:
+    elif x1 + settings.crop_width > im.width:
-      y1 = im.height - settings.crop_height
+        x1 = im.width - settings.crop_width
 
-  x2 = x1 + settings.crop_width
+    y1 = focus.y - y_half
-  y2 = y1 + settings.crop_height
+    if y1 < 0:
+        y1 = 0
+    elif y1 + settings.crop_height > im.height:
+        y1 = im.height - settings.crop_height
 
-  crop = [x1, y1, x2, y2]
+    x2 = x1 + settings.crop_width
+    y2 = y1 + settings.crop_height
 
-  results = []
+    crop = [x1, y1, x2, y2]
 
-  results.append(im.crop(tuple(crop)))
+    results = []
 
-  if settings.annotate_image:
+    results.append(im.crop(tuple(crop)))
-    d = ImageDraw.Draw(im_debug)
-    rect = list(crop)
-    rect[2] -= 1
-    rect[3] -= 1
-    d.rectangle(rect, outline=GREEN)
-    results.append(im_debug)
-    if settings.destop_view_image:
-      im_debug.show()
 
-  return results
+    if settings.annotate_image:
+        d = ImageDraw.Draw(im_debug)
+        rect = list(crop)
+        rect[2] -= 1
+        rect[3] -= 1
+        d.rectangle(rect, outline=GREEN)
+        results.append(im_debug)
+        if settings.destop_view_image:
+            im_debug.show()
+
+    return results
 
 def focal_point(im, settings):
     corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
@@ -88,7 +87,7 @@ def focal_point(im, settings):
     corner_centroid = None
     if len(corner_points) > 0:
       corner_centroid = centroid(corner_points)
-      corner_centroid.weight = settings.corner_points_weight / weight_pref_total 
+      corner_centroid.weight = settings.corner_points_weight / weight_pref_total
       pois.append(corner_centroid)
 
     entropy_centroid = None
@@ -100,7 +99,7 @@ def focal_point(im, settings):
     face_centroid = None
     if len(face_points) > 0:
       face_centroid = centroid(face_points)
-      face_centroid.weight = settings.face_points_weight / weight_pref_total 
+      face_centroid.weight = settings.face_points_weight / weight_pref_total
       pois.append(face_centroid)
 
     average_point = poi_average(pois, settings)
@@ -111,7 +110,7 @@ def focal_point(im, settings):
       if corner_centroid is not None:
         color = BLUE
         box = corner_centroid.bounding(max_size * corner_centroid.weight)
-        d.text((box[0], box[1]-15), "Edge: %.02f" % corner_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(corner_points) > 1:
           for f in corner_points:
@@ -119,7 +118,7 @@ def focal_point(im, settings):
       if entropy_centroid is not None:
         color = "#ff0"
         box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
-        d.text((box[0], box[1]-15), "Entropy: %.02f" % entropy_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(entropy_points) > 1:
           for f in entropy_points:
@@ -127,14 +126,14 @@ def focal_point(im, settings):
       if face_centroid is not None:
         color = RED
         box = face_centroid.bounding(max_size * face_centroid.weight)
-        d.text((box[0], box[1]-15), "Face: %.02f" % face_centroid.weight, fill=color)
+        d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
         d.ellipse(box, outline=color)
         if len(face_points) > 1:
           for f in face_points:
             d.rectangle(f.bounding(4), outline=color)
 
       d.ellipse(average_point.bounding(max_size), outline=GREEN)
-      
+
     return average_point
 
 
@@ -185,7 +184,7 @@ def image_face_points(im, settings):
         try:
           faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
             minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-        except:
+        except Exception:
           continue
 
         if len(faces) > 0:
@@ -262,10 +261,11 @@ def image_entropy(im):
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
 
+
 def centroid(pois):
-  x = [poi.x for poi in pois]
+    x = [poi.x for poi in pois]
-  y = [poi.y for poi in pois]
+    y = [poi.y for poi in pois]
-  return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
+    return PointOfInterest(sum(x) / len(pois), sum(y) / len(pois))
 
 
 def poi_average(pois, settings):
@@ -283,59 +283,59 @@ def poi_average(pois, settings):
 
 
 def is_landscape(w, h):
-  return w > h
+    return w > h
 
 
 def is_portrait(w, h):
-  return h > w
+    return h > w
 
 
 def is_square(w, h):
-  return w == h
+    return w == h
 
 
 def download_and_cache_models(dirname):
-  download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
+    download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
-  model_file_name = 'face_detection_yunet.onnx'
+    model_file_name = 'face_detection_yunet.onnx'
 
-  if not os.path.exists(dirname):
+    if not os.path.exists(dirname):
-    os.makedirs(dirname)
+        os.makedirs(dirname)
 
-  cache_file = os.path.join(dirname, model_file_name)
+    cache_file = os.path.join(dirname, model_file_name)
-  if not os.path.exists(cache_file):
+    if not os.path.exists(cache_file):
-    print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
+        print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
-    response = requests.get(download_url)
+        response = requests.get(download_url)
-    with open(cache_file, "wb") as f:
+        with open(cache_file, "wb") as f:
-      f.write(response.content)
+            f.write(response.content)
 
-  if os.path.exists(cache_file):
+    if os.path.exists(cache_file):
-    return cache_file
+        return cache_file
-  return None
+    return None
 
 
 class PointOfInterest:
-  def __init__(self, x, y, weight=1.0, size=10):
+    def __init__(self, x, y, weight=1.0, size=10):
-    self.x = x
+        self.x = x
-    self.y = y
+        self.y = y
-    self.weight = weight
+        self.weight = weight
-    self.size = size
+        self.size = size
 
-  def bounding(self, size):
+    def bounding(self, size):
-    return [
+        return [
-      self.x - size//2,
+            self.x - size // 2,
-      self.y - size//2,
+            self.y - size // 2,
-      self.x + size//2,
+            self.x + size // 2,
-      self.y + size//2
+            self.y + size // 2
-    ]
+        ]
 
 
 class Settings:
-  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
+    def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
-    self.crop_width = crop_width
+        self.crop_width = crop_width
-    self.crop_height = crop_height
+        self.crop_height = crop_height
-    self.corner_points_weight = corner_points_weight
+        self.corner_points_weight = corner_points_weight
-    self.entropy_points_weight = entropy_points_weight
+        self.entropy_points_weight = entropy_points_weight
-    self.face_points_weight = face_points_weight
+        self.face_points_weight = face_points_weight
-    self.annotate_image = annotate_image
+        self.annotate_image = annotate_image
-    self.destop_view_image = False
+        self.destop_view_image = False
-    self.dnn_model_path = dnn_model_path
+        self.dnn_model_path = dnn_model_path