Merge branch 'dev' into taesd-a

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -59,6 +59,18 @@ body:
         - iOS
         - Android
         - Other/Cloud
+  - type: dropdown
+    id: device
+    attributes:
+        label: What device are you running WebUI on?
+        multiple: true
+        options:
+        - Nvidia GPUs (RTX 20 above)
+        - Nvidia GPUs (GTX 16 below)
+        - AMD GPUs (RX 6000 above)
+        - AMD GPUs (RX 5000 below)
+        - CPU
+        - Other GPUs
   - type: dropdown
     id: browsers
     attributes:

.github/workflows/on_pull_request.yaml

@@ -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 +1,16 @@
+## 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
+ * fix localizations not working
+ * fix error for loras: 'LatentDiffusion' object has no attribute 'lora_layer_mapping'
+
 ## 1.2.0
 
 ### Features:

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/lora.py

@@ -1,4 +1,3 @@
-import glob
 import os
 import re
 import torch
@@ -133,6 +132,10 @@ def load_lora(name, filename):
 
     sd = sd_models.read_state_dict(filename)
 
+    # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
+    if not hasattr(shared.sd_model, 'lora_layer_mapping'):
+        assign_lora_names_to_compvis_modules(shared.sd_model)
+
     keys_failed_to_match = {}
     is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
 
@@ -173,7 +176,7 @@ def load_lora(name, filename):
         else:
             print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
             continue
-            assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
+            raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")
 
         with torch.no_grad():
             module.weight.copy_(weight)
@@ -185,7 +188,7 @@ def load_lora(name, filename):
         elif lora_key == "lora_down.weight":
             lora_module.down = module
         else:
-            assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
+            raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")
 
     if len(keys_failed_to_match) > 0:
         print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
@@ -203,7 +206,7 @@ def load_loras(names, multipliers=None):
     loaded_loras.clear()
 
     loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
-    if any([x is None for x in loras_on_disk]):
+    if any(x is None for x in loras_on_disk):
         list_available_loras()
 
         loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
@@ -310,7 +313,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
 
             print(f'failed to calculate lora weights for layer {lora_layer_name}')
 
-        setattr(self, "lora_current_names", wanted_names)
+        self.lora_current_names = wanted_names
 
 
 def lora_forward(module, input, original_forward):
@@ -344,8 +347,8 @@ def lora_forward(module, input, original_forward):
 
 
 def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
-    setattr(self, "lora_current_names", ())
+    self.lora_current_names = ()
-    setattr(self, "lora_weights_backup", None)
+    self.lora_weights_backup = None
 
 
 def lora_Linear_forward(self, input):
@@ -393,6 +396,8 @@ 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)
 
@@ -406,6 +411,9 @@ def list_available_loras():
 
         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
 
@@ -419,7 +427,7 @@ def infotext_pasted(infotext, params):
 
     added = []
 
-    for k, v in params.items():
+    for k in params:
         if not k.startswith("AddNet Model "):
             continue
 
@@ -445,6 +453,7 @@ def infotext_pasted(infotext, params):
 
 available_loras = {}
 available_lora_aliases = {}
+forbidden_lora_aliases = {}
 loaded_loras = []
 
 list_available_loras()

extensions-builtin/Lora/scripts/lora_script.py

@@ -53,7 +53,8 @@ script_callbacks.on_infotext_pasted(lora.infotext_pasted)
 
 
 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras),
+    "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
 }))
 
 

extensions-builtin/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:{lora_on_disk.alias}:") + " + 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)

javascript/extraNetworks.js

@@ -68,18 +68,27 @@ var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
 
 function tryToRemoveExtraNetworkFromPrompt(textarea, text){
     var m = text.match(re_extranet)
-    if(! m) return false
-
-    var partToSearch = m[1]
     var replaced = false
-    var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
+    var newTextareaText
-        m = found.match(re_extranet);
+    if(m) {
-        if(m[1] == partToSearch){
+        var partToSearch = m[1]
-            replaced = true;
+        newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
-            return ""
+            m = found.match(re_extranet);
-        }
+            if(m[1] == partToSearch){
-        return found;
+                replaced = true;
-    })
+                return ""
+            }
+            return found;
+        })
+    } else {
+        newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found){
+            if(found == text) {
+                replaced = true;
+                return ""
+            }
+            return found;
+        })
+    }
 
     if(replaced){
         textarea.value = newTextareaText

javascript/localization.js

@@ -109,18 +109,23 @@ function processNode(node){
 }
 
 function dumpTranslations(){
+    if(!hasLocalization()) {
+        // If we don't have any localization,
+        // we will not have traversed the app to find
+        // original_lines, so do that now.
+        processNode(gradioApp());
+    }
     var dumped = {}
     if (localization.rtl) {
-        dumped.rtl = true
+        dumped.rtl = true;
     }
 
-    Object.keys(original_lines).forEach(function(text){
+    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
 }
 
 function download_localization() {
@@ -137,7 +142,11 @@ function download_localization() {
     document.body.removeChild(element);
 }
 
-if(hasLocalization()) {
+document.addEventListener("DOMContentLoaded", function () {
+    if (!hasLocalization()) {
+        return;
+    }
+
     onUiUpdate(function (m) {
         m.forEach(function (mutation) {
             mutation.addedNodes.forEach(function (node) {
@@ -146,26 +155,23 @@ if(hasLocalization()) {
         });
     })
 
+    processNode(gradioApp())
 
-    document.addEventListener("DOMContentLoaded", function () {
+    if (localization.rtl) {  // if the language is from right to left,
-        processNode(gradioApp())
+        (new MutationObserver((mutations, observer) => { // wait for the style to load
+            mutations.forEach(mutation => {
+                mutation.addedNodes.forEach(node => {
+                    if (node.tagName === 'STYLE') {
+                        observer.disconnect();
 
-        if (localization.rtl) {  // if the language is from right to left,
+                        for (const x of node.sheet.rules) {  // find all rtl media rules
-            (new MutationObserver((mutations, observer) => { // wait for the style to load
+                            if (Array.from(x.media || []).includes('rtl')) {
-                mutations.forEach(mutation => {
+                                x.media.appendMedium('all');  // enable them
-                    mutation.addedNodes.forEach(node => {
-                        if (node.tagName === 'STYLE') {
-                            observer.disconnect();
-
-                            for (const x of node.sheet.rules) {  // find all rtl media rules
-                                if (Array.from(x.media || []).includes('rtl')) {
-                                    x.media.appendMedium('all');  // enable them
-                                }
                             }
                         }
-                    })
+                    }
-                });
+                })
-            })).observe(gradioApp(), { childList: true });
+            });
-        }
+        })).observe(gradioApp(), { childList: true });
-    })
+    }
-}
+})

javascript/ui_settings_hints.js

@@ -1,41 +1,62 @@
 // various hints and extra info for the settings tab
 
-onUiLoaded(function(){
+settingsHintsSetup = false
-    createLink = function(elem_id, text, href){
-        var a = document.createElement('A')
-        a.textContent = text
-        a.target = '_blank';
 
-        elem = gradioApp().querySelector('#'+elem_id)
+onOptionsChanged(function(){
-        elem.insertBefore(a, elem.querySelector('label'))
+    if(settingsHintsSetup) return
+    settingsHintsSetup = true
 
-        return a
+    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]
 
-    createLink("setting_samples_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
+        if(! commentBefore && !commentAfter) return
-    createLink("setting_directories_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
 
-    createLink("setting_quicksettings_list", "[info] ").addEventListener("click", function(event){
+        var span = null
-        requestGet("./internal/quicksettings-hint", {}, function(data){
+        if(div.classList.contains('gradio-checkbox')) span = div.querySelector('label span')
-            var table = document.createElement('table')
+        else if(div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild
-            table.className = 'settings-value-table'
+        else if(div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild
+        else span = div.querySelector('label span').firstChild
 
-            data.forEach(function(obj){
+        if(!span) return
-                var tr = document.createElement('tr')
-                var td = document.createElement('td')
-                td.textContent = obj.name
-                tr.appendChild(td)
 
-                var td = document.createElement('td')
+        if(commentBefore){
-                td.textContent = obj.label
+            var comment = document.createElement('DIV')
-                tr.appendChild(td)
+            comment.className = 'settings-comment'
-
+            comment.innerHTML = commentBefore
-                table.appendChild(tr)
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span)
-            })
+            span.parentElement.insertBefore(comment, span)
-
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span)
-            popup(table);
+        }
-        })
+        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

@@ -3,25 +3,23 @@ import subprocess
 import os
 import sys
 import importlib.util
-import shlex
 import platform
 import json
+from functools import lru_cache
 
 from modules import cmd_args
 from modules.paths_internal import script_path, extensions_dir
 
-commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
-sys.argv += shlex.split(commandline_args)
-
 args, _ = cmd_args.parser.parse_known_args()
 
 python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
-stored_commit_hash = None
-stored_git_tag = None
 dir_repos = "repositories"
 
+# Whether to default to printing command output
+default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
+
 if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
     os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
 
@@ -57,65 +55,52 @@ Use --skip-python-version-check to suppress this warning.
 """)
 
 
+@lru_cache()
 def commit_hash():
-    global stored_commit_hash
-
-    if stored_commit_hash is not None:
-        return stored_commit_hash
-
     try:
-        stored_commit_hash = run(f"{git} rev-parse HEAD").strip()
+        return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
     except Exception:
-        stored_commit_hash = "<none>"
+        return "<none>"
-
-    return stored_commit_hash
 
 
+@lru_cache()
 def git_tag():
-    global stored_git_tag
-
-    if stored_git_tag is not None:
-        return stored_git_tag
-
     try:
-        stored_git_tag = run(f"{git} describe --tags").strip()
+        return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
     except Exception:
-        stored_git_tag = "<none>"
+        return "<none>"
-
-    return stored_git_tag
 
 
-def run(command, desc=None, errdesc=None, custom_env=None, live=False):
+def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
     if desc is not None:
         print(desc)
 
-    if live:
+    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):
@@ -131,11 +116,7 @@ def repo_dir(name):
     return os.path.join(script_path, dir_repos, name)
 
 
-def run_python(code, desc=None, errdesc=None):
+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
 
@@ -143,8 +124,9 @@ def run_pip(command, desc=None, live=False):
     return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
 
 
-def check_run_python(code):
+def check_run_python(code: str) -> bool:
-    return check_run(f'"{python}" -c "{code}"')
+    result = subprocess.run([python, "-c", code], capture_output=True, shell=False)
+    return result.returncode == 0
 
 
 def git_clone(url, dir, name, commithash=None):
@@ -237,13 +219,14 @@ def run_extensions_installers(settings_file):
 
 
 def prepare_environment():
-    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url https://download.pytorch.org/whl/cu118")
+    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
+    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
 
     xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
-    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
+    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")
@@ -270,8 +253,11 @@ def prepare_environment():
     if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
 
-    if not args.skip_torch_cuda_test:
+    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")
@@ -319,7 +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=f"create embedding filename: {filename}")
+            return models.CreateResponse(info=f"create embedding filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info=f"create embedding 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=f"create hypernetwork filename: {filename}")
+            return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info=f"create hypernetwork 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=f"preprocess error: invalid token: {e}")
+            return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
         except AssertionError as e:
             shared.state.end()
-            return PreprocessResponse(info=f"preprocess error: {e}")
+            return models.PreprocessResponse(info=f"preprocess error: {e}")
         except FileNotFoundError as e:
             shared.state.end()
-            return PreprocessResponse(info=f'preprocess 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=f"train embedding complete: 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=f"train embedding error: {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=f"train embedding complete: 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=f"train embedding 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/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()
 
@@ -102,4 +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')
+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"]))
@@ -83,6 +83,8 @@ def get_extension_config():
     ext_config = {}
 
     for ext in extensions.extensions:
+        ext.read_info_from_repo()
+
         entry = {
             "name": ext.name,
             "path": ext.path,

modules/deepbooru.py

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

modules/devices.py

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

modules/esrgan_model.py

@@ -6,7 +6,7 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgan_model_arch as arch
-from modules import shared, modelloader, images, devices
+from modules import modelloader, images, devices
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
@@ -16,9 +16,7 @@ def mod2normal(state_dict):
     # this code is copied from https://github.com/victorca25/iNNfer
     if 'conv_first.weight' in state_dict:
         crt_net = {}
-        items = []
+        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']

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

modules/extensions.py

@@ -1,13 +1,12 @@
 import os
 import sys
+import threading
 import traceback
 
-import time
-from datetime import datetime
 import git
 
 from modules import shared
-from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401
 
 extensions = []
 
@@ -25,6 +24,8 @@ def active():
 
 
 class Extension:
+    lock = threading.Lock()
+
     def __init__(self, name, path, enabled=True, is_builtin=False):
         self.name = name
         self.path = path
@@ -43,8 +44,13 @@ class Extension:
         if self.is_builtin or self.have_info_from_repo:
             return
 
-        self.have_info_from_repo = True
+        with self.lock:
+            if self.have_info_from_repo:
+                return
 
+            self.do_read_info_from_repo()
+
+    def do_read_info_from_repo(self):
         repo = None
         try:
             if os.path.exists(os.path.join(self.path, ".git")):
@@ -59,18 +65,18 @@ class Extension:
             try:
                 self.status = 'unknown'
                 self.remote = next(repo.remote().urls, None)
-                head = repo.head.commit
                 self.commit_date = repo.head.commit.committed_date
-                ts = time.asctime(time.gmtime(self.commit_date))
                 if repo.active_branch:
                     self.branch = repo.active_branch.name
-                self.commit_hash = head.hexsha
+                self.commit_hash = repo.head.commit.hexsha
-                self.version = f'{self.commit_hash[:8]} ({ts})'
+                self.version = repo.git.describe("--always", "--tags")  # compared to `self.commit_hash[:8]` this takes about 30% more time total but since we run it in parallel we don't care
 
             except Exception as ex:
                 print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
                 self.remote = None
 
+        self.have_info_from_repo = True
+
     def list_files(self, subdir, extension):
         from modules import scripts
 

modules/extra_networks.py

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

modules/extra_networks_hypernet.py

@@ -1,4 +1,4 @@
-from modules import extra_networks, shared, extra_networks
+from modules import extra_networks, shared
 from modules.hypernetworks import hypernetwork
 
 

modules/extras.py

@@ -136,14 +136,14 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     result_is_instruct_pix2pix_model = False
 
     if theta_func2:
-        shared.state.textinfo = f"Loading B"
+        shared.state.textinfo = "Loading B"
         print(f"Loading {secondary_model_info.filename}...")
         theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
     else:
         theta_1 = None
 
     if theta_func1:
-        shared.state.textinfo = f"Loading C"
+        shared.state.textinfo = "Loading C"
         print(f"Loading {tertiary_model_info.filename}...")
         theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
 
@@ -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():
@@ -251,7 +247,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         lines.append(lastline)
         lastline = ''
 
-    for i, line in enumerate(lines):
+    for line in lines:
         line = line.strip()
         if line.startswith("Negative prompt:"):
             done_with_prompt = True
@@ -312,6 +308,8 @@ infotext_to_setting_name_mapping = [
     ('UniPC skip type', 'uni_pc_skip_type'),
     ('UniPC order', 'uni_pc_order'),
     ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('Token merging ratio', 'token_merging_ratio'),
+    ('Token merging ratio hr', 'token_merging_ratio_hr'),
     ('RNG', 'randn_source'),
     ('NGMS', 's_min_uncond'),
 ]

modules/gfpgan_model.py

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

modules/hypernetworks/hypernetwork.py

@@ -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:
@@ -366,7 +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:
@@ -409,13 +410,13 @@ class FilenameGenerator:
         time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
         try:
             time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
-        except pytz.exceptions.UnknownTimeZoneError as _:
+        except pytz.exceptions.UnknownTimeZoneError:
             time_zone = None
 
         time_zone_time = time_datetime.astimezone(time_zone)
         try:
             formatted_time = time_zone_time.strftime(time_format)
-        except (ValueError, TypeError) as _:
+        except (ValueError, TypeError):
             formatted_time = time_zone_time.strftime(self.default_time_format)
 
         return sanitize_filename_part(formatted_time, replace_spaces=False)
@@ -472,9 +473,9 @@ def get_next_sequence_number(path, basename):
     prefix_length = len(basename)
     for p in os.listdir(path):
         if p.startswith(basename):
-            l = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+            parts = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
             try:
-                result = max(int(l[0]), result)
+                result = max(int(parts[0]), result)
             except ValueError:
                 pass
 

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
 
 
@@ -59,7 +55,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
             # try to find corresponding mask for an image using simple filename matching
             mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
             # if not found use first one ("same mask for all images" use-case)
-            if not mask_image_path in inpaint_masks:
+            if mask_image_path not in inpaint_masks:
                 mask_image_path = inpaint_masks[0]
             mask_image = Image.open(mask_image_path)
             p.image_mask = mask_image

modules/interrogate.py

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

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
@@ -61,4 +60,4 @@ if has_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')
+                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
@@ -40,7 +39,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                 if os.path.islink(full_path) and not os.path.exists(full_path):
                     print(f"Skipping broken symlink: {full_path}")
                     continue
-                if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
+                if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist):
                     continue
                 if full_path not in output:
                     output.append(full_path)
@@ -108,29 +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():
-    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__
@@ -141,15 +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"]
@@ -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:
@@ -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]]))
@@ -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

@@ -13,7 +13,7 @@ def connect(token, port, region):
     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

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, sd_samplers_common
 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):
@@ -472,6 +478,11 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     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)
+
+    uses_ensd = opts.eta_noise_seed_delta != 0
+    if uses_ensd:
+        uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
 
     generation_params = {
         "Steps": p.steps,
@@ -489,15 +500,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "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,
+        "ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
+        "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,
+        **p.extra_generation_params,
         "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 = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
@@ -523,9 +535,18 @@ 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():
@@ -660,12 +681,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     processed = Processed(p, [], p.seed, "")
                     file.write(processed.infotext(p, 0))
 
-            step_multiplier = 1
+            sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
-            if not shared.opts.dont_fix_second_order_samplers_schedule:
+            step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
-                try:
-                    step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
-                except:
-                    pass
             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
 
@@ -978,8 +995,22 @@ 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

@@ -95,9 +95,16 @@ def progressapi(req: ProgressRequest):
         image = shared.state.current_image
         if image is not None:
             buffered = io.BytesIO()
-            image.save(buffered, format="png")
+
+            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/png;base64,{base64_image}"
+            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)
@@ -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

@@ -231,7 +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
 
@@ -295,9 +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
 
@@ -492,7 +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

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
@@ -216,6 +216,9 @@ class StableDiffusionModelHijack:
         self.comments = []
 
     def get_prompt_lengths(self, text):
+        if self.clip is None:
+            return "-", "-"
+
         _, token_count = self.clip.process_texts([text])
 
         return token_count, self.clip.get_target_prompt_token_count(token_count)

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_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))
@@ -296,7 +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):
@@ -335,7 +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
@@ -370,7 +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
@@ -452,7 +451,7 @@ def cross_attention_attnblock_forward(self, x):
         h3 += x
 
         return h3
-    
+
 def xformers_attnblock_forward(self, x):
     try:
         h_ = x
@@ -461,7 +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()
@@ -483,7 +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()
@@ -507,7 +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_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

@@ -15,9 +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))
@@ -87,8 +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:
@@ -167,7 +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
@@ -239,7 +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
@@ -374,7 +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")
@@ -415,6 +414,9 @@ class SdModelData:
     def get_sd_model(self):
         if self.sd_model is None:
             with self.lock:
+                if self.sd_model is not None:
+                    return self.sd_model
+
                 try:
                     load_model()
                 except Exception as e:
@@ -467,7 +469,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     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:
@@ -538,13 +540,12 @@ def reload_model_weights(sd_model=None, info=None):
 
     if sd_model is None or checkpoint_config != sd_model.used_config:
         del sd_model
-        checkpoints_loaded.clear()
         load_model(checkpoint_info, already_loaded_state_dict=state_dict)
         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
@@ -565,7 +566,7 @@ def reload_model_weights(sd_model=None, info=None):
 
 
 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 model_data.sd_model:
@@ -580,3 +581,25 @@ 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

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,
@@ -14,12 +14,18 @@ samplers_for_img2img = []
 samplers_map = {}
 
 
-def create_sampler(name, model):
+def find_sampler_config(name):
     if name is not None:
         config = all_samplers_map.get(name, None)
     else:
         config = all_samplers[0]
 
+    return config
+
+
+def create_sampler(name, model):
+    config = find_sampler_config(name)
+
     assert config is not None, f'bad sampler name: {name}'
 
     sampler = config.constructor(model)

modules/sd_samplers_common.py

@@ -2,7 +2,7 @@ from collections import namedtuple
 import numpy as np
 import torch
 from PIL import Image
-from modules import devices, processing, images, sd_vae_approx, sd_vae_taesd
+from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
 
 from modules.shared import opts, state
 import modules.shared as shared
@@ -64,6 +64,25 @@ def store_latent(decoded):
             shared.state.assign_current_image(sample_to_image(decoded))
 
 
+def is_sampler_using_eta_noise_seed_delta(p):
+    """returns whether sampler from config will use eta noise seed delta for image creation"""
+
+    sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
+
+    eta = p.eta
+
+    if eta is None and p.sampler is not None:
+        eta = p.sampler.eta
+
+    if eta is None and sampler_config is not None:
+        eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
+
+    if eta == 0:
+        return False
+
+    return sampler_config.options.get("uses_ensd", False)
+
+
 class InterruptedException(BaseException):
     pass
 

modules/sd_samplers_compvis.py

@@ -11,7 +11,7 @@ import modules.models.diffusion.uni_pc
 
 
 samplers_data_compvis = [
-    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True}),
     sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
     sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),
 ]
@@ -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;
@@ -134,7 +134,11 @@ class VanillaStableDiffusionSampler:
         self.update_step(x)
 
     def initialize(self, p):
-        self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
+        if self.is_ddim:
+            self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
+        else:
+            self.eta = 0.0
+
         if self.eta != 0.0:
             p.extra_generation_params["Eta DDIM"] = self.eta
 

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
 
@@ -9,25 +8,26 @@ 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'], {}),
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
-    ('Heun', 'sample_heun', ['k_heun'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
-    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True}),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
-    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True}),
 ]
 
 samplers_data_k_diffusion = [
@@ -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
 
@@ -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
@@ -88,10 +85,10 @@ def refresh_vae_list():
 
 
 def find_vae_near_checkpoint(checkpoint_file):
-    checkpoint_path = os.path.splitext(checkpoint_file)[0]
+    checkpoint_path = os.path.basename(checkpoint_file).rsplit('.', 1)[0]
-    for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]:
+    for vae_file in vae_dict.values():
-        if os.path.isfile(vae_location):
+        if os.path.basename(vae_file).startswith(checkpoint_path):
-            return vae_location
+            return vae_file
 
     return None
 

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 +12,7 @@ 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
@@ -202,8 +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
@@ -212,9 +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
@@ -243,7 +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"),
@@ -262,10 +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"),
@@ -293,28 +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."),
@@ -339,20 +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"), {
@@ -364,30 +386,32 @@ 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"),
@@ -400,17 +424,15 @@ options_templates.update(options_section(('ui', "User interface"), {
     "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"),
-    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
+    "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"), {
@@ -423,27 +445,28 @@ options_templates.update(options_section(('infotext', "Infotext"), {
 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", "TAESD"]}),
+    "show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = 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_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
     '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"),
 }))
 
@@ -460,6 +483,7 @@ options_templates.update(options_section((None, "Hidden options"), {
     "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
 }))
 
+
 options_templates.update()
 
 
@@ -571,7 +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):
@@ -582,11 +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
@@ -667,14 +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 Exception as e:
             errors.display(e, "changing gradio theme")
-            gradio_theme = gr.themes.Default()
+            gradio_theme = gr.themes.Default(**default_theme_args)
 
 
 
@@ -743,7 +774,7 @@ def walk_files(path, allowed_extensions=None):
     if allowed_extensions is not None:
         allowed_extensions = set(allowed_extensions)
 
-    for root, dirs, files in os.walk(path):
+    for root, _, files in os.walk(path, followlinks=True):
         for filename in files:
             if allowed_extensions is not None:
                 _, ext = os.path.splitext(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

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)
@@ -134,7 +133,7 @@ def focal_point(im, settings):
             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

modules/textual_inversion/dataset.py

@@ -118,7 +118,7 @@ class PersonalizedBase(Dataset):
                 weight = torch.ones(latent_sample.shape)
             else:
                 weight = None
-            
+
             if latent_sampling_method == "random":
                 entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
             else:
@@ -243,4 +243,4 @@ class BatchLoaderRandom(BatchLoader):
         return self
 
 def collate_wrapper_random(batch):
-    return BatchLoaderRandom(batch)
+    return BatchLoaderRandom(batch)

modules/textual_inversion/image_embedding.py

@@ -2,10 +2,8 @@ import base64
 import json
 import numpy as np
 import zlib
-from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
+from PIL import Image, ImageDraw, ImageFont
-from fonts.ttf import Roboto
 import torch
-from modules.shared import opts
 
 
 class EmbeddingEncoder(json.JSONEncoder):
@@ -17,7 +15,7 @@ class EmbeddingEncoder(json.JSONEncoder):
 
 class EmbeddingDecoder(json.JSONDecoder):
     def __init__(self, *args, **kwargs):
-        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+        json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs)
 
     def object_hook(self, d):
         if 'TORCHTENSOR' in d:
@@ -136,11 +134,8 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     image = srcimage.copy()
     fontsize = 32
     if textfont is None:
-        try:
+        from modules.images import get_font
-            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
+        textfont = get_font(fontsize)
-            textfont = opts.font or Roboto
-        except Exception:
-            textfont = Roboto
 
     factor = 1.5
     gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))

modules/textual_inversion/learn_schedule.py

@@ -12,7 +12,7 @@ class LearnScheduleIterator:
         self.it = 0
         self.maxit = 0
         try:
-            for i, pair in enumerate(pairs):
+            for pair in pairs:
                 if not pair.strip():
                     continue
                 tmp = pair.split(':')
@@ -32,8 +32,8 @@ class LearnScheduleIterator:
                     self.maxit += 1
                     return
             assert self.rates
-        except (ValueError, AssertionError):
+        except (ValueError, AssertionError) as e:
-            raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.')
+            raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') from e
 
 
     def __iter__(self):

modules/textual_inversion/preprocess.py

@@ -1,13 +1,9 @@
 import os
 from PIL import Image, ImageOps
 import math
-import platform
-import sys
 import tqdm
-import time
 
 from modules import paths, shared, images, deepbooru
-from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
 
 
@@ -129,7 +125,7 @@ def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, thr
         default=None
     )
     return wh and center_crop(image, *wh)
-    
+
 
 def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
     width = process_width

modules/textual_inversion/textual_inversion.py

@@ -1,7 +1,6 @@
 import os
 import sys
 import traceback
-import inspect
 from collections import namedtuple
 
 import torch
@@ -30,7 +29,7 @@ textual_inversion_templates = {}
 def list_textual_inversion_templates():
     textual_inversion_templates.clear()
 
-    for root, dirs, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
+    for root, _, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
         for fn in fns:
             path = os.path.join(root, fn)
 
@@ -167,8 +166,7 @@ class EmbeddingDatabase:
         # textual inversion embeddings
         if 'string_to_param' in data:
             param_dict = data['string_to_param']
-            if hasattr(param_dict, '_parameters'):
+            param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11
-                param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
             assert len(param_dict) == 1, 'embedding file has multiple terms in it'
             emb = next(iter(param_dict.items()))[1]
         # diffuser concepts
@@ -199,7 +197,7 @@ class EmbeddingDatabase:
         if not os.path.isdir(embdir.path):
             return
 
-        for root, dirs, fns in os.walk(embdir.path, followlinks=True):
+        for root, _, fns in os.walk(embdir.path, followlinks=True):
             for fn in fns:
                 try:
                     fullfn = os.path.join(root, fn)
@@ -216,7 +214,7 @@ class EmbeddingDatabase:
     def load_textual_inversion_embeddings(self, force_reload=False):
         if not force_reload:
             need_reload = False
-            for path, embdir in self.embedding_dirs.items():
+            for embdir in self.embedding_dirs.values():
                 if embdir.has_changed():
                     need_reload = True
                     break
@@ -229,7 +227,7 @@ class EmbeddingDatabase:
         self.skipped_embeddings.clear()
         self.expected_shape = self.get_expected_shape()
 
-        for path, embdir in self.embedding_dirs.items():
+        for embdir in self.embedding_dirs.values():
             self.load_from_dir(embdir)
             embdir.update()
 
@@ -325,16 +323,16 @@ def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epo
     tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)
 
 def tensorboard_add_scaler(tensorboard_writer, tag, value, step):
-    tensorboard_writer.add_scalar(tag=tag, 
+    tensorboard_writer.add_scalar(tag=tag,
         scalar_value=value, global_step=step)
 
 def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):
     # Convert a pil image to a torch tensor
     img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
-    img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], 
+    img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0],
         len(pil_image.getbands()))
     img_tensor = img_tensor.permute((2, 0, 1))
-                
+
     tensorboard_writer.add_image(tag, img_tensor, global_step=step)
 
 def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name="embedding"):
@@ -404,7 +402,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     if initial_step >= steps:
         shared.state.textinfo = "Model has already been trained beyond specified max steps"
         return embedding, 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 \
@@ -414,7 +412,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     old_parallel_processing_allowed = shared.parallel_processing_allowed
-    
+
     if shared.opts.training_enable_tensorboard:
         tensorboard_writer = tensorboard_setup(log_directory)
 
@@ -441,7 +439,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
             optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')
             if embedding.checksum() == optimizer_saved_dict.get('hash', None):
                 optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
-    
+
         if optimizer_state_dict is not None:
             optimizer.load_state_dict(optimizer_state_dict)
             print("Loaded existing optimizer from checkpoint")
@@ -470,7 +468,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     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:
@@ -487,7 +485,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 
                 if clip_grad:
                     clip_grad_sched.step(embedding.step)
-            
+
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     if use_weight:
@@ -515,7 +513,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
-                
+
                 if clip_grad:
                     clip_grad(embedding.vec, clip_grad_sched.learn_rate)
 
@@ -603,7 +601,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 
                         try:
                             vectorSize = list(data['string_to_param'].values())[0].shape[0]
-                        except Exception as e:
+                        except Exception:
                             vectorSize = '?'
 
                         checkpoint = sd_models.select_checkpoint()

modules/txt2img.py

@@ -1,18 +1,15 @@
 import modules.scripts
-from modules import sd_samplers
+from modules import sd_samplers, processing
 from modules.generation_parameters_copypaste import create_override_settings_dict
-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
-    StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
-import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
 def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
-    p = StableDiffusionProcessingTxt2Img(
+    p = processing.StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
         outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
@@ -53,7 +50,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-        processed = process_images(p)
+        processed = processing.process_images(p)
 
     p.close()
 

modules/ui.py

@@ -1,29 +1,23 @@
-import html
 import json
-import math
 import mimetypes
 import os
-import platform
-import random
 import sys
-import tempfile
-import time
 import traceback
-from functools import partial, reduce
+from functools import reduce
 import warnings
 
 import gradio as gr
 import gradio.routes
 import gradio.utils
 import numpy as np
-from PIL import Image, PngImagePlugin
+from PIL import Image, PngImagePlugin  # noqa: F401
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing, progress
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave
-from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
 from modules.paths import script_path, data_path
 
-from modules.shared import opts, cmd_opts, restricted_opts
+from modules.shared import opts, cmd_opts
 
 import modules.codeformer_model
 import modules.generation_parameters_copypaste as parameters_copypaste
@@ -34,7 +28,6 @@ import modules.shared as shared
 import modules.styles
 import modules.textual_inversion.ui
 from modules import prompt_parser
-from modules.images import save_image
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.textual_inversion import textual_inversion
@@ -93,16 +86,6 @@ def send_gradio_gallery_to_image(x):
         return None
     return image_from_url_text(x[0])
 
-def visit(x, func, path=""):
-    if hasattr(x, 'children'):
-        if isinstance(x, gr.Tabs) and x.elem_id is not None:
-            # Tabs element can't have a label, have to use elem_id instead
-            func(f"{path}/Tabs@{x.elem_id}", x)
-        for c in x.children:
-            visit(c, func, path)
-    elif x.label is not None:
-        func(f"{path}/{x.label}", x)
-
 
 def add_style(name: str, prompt: str, negative_prompt: str):
     if name is None:
@@ -246,7 +229,7 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
                 all_seeds = gen_info.get('all_seeds', [-1])
                 res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
 
-        except json.decoder.JSONDecodeError as e:
+        except json.decoder.JSONDecodeError:
             if gen_info_string != '':
                 print("Error parsing JSON generation info:", file=sys.stderr)
                 print(gen_info_string, file=sys.stderr)
@@ -423,7 +406,7 @@ def create_sampler_and_steps_selection(choices, tabname):
 def ordered_ui_categories():
     user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))}
 
-    for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+    for _, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
         yield category
 
 
@@ -736,8 +719,8 @@ def create_ui():
                     with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
                         gr.HTML(
-                            f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
-                            f"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
                             f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
                             f"{hidden}</p>"
                         )
@@ -746,7 +729,6 @@ def create_ui():
                         img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
 
                     img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
-                    img2img_image_inputs = [init_img, sketch, init_img_with_mask, inpaint_color_sketch]
 
                     for i, tab in enumerate(img2img_tabs):
                         tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
@@ -1189,7 +1171,7 @@ def create_ui():
                         process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
                         process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
                         process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
-                    
+
                     with gr.Column(visible=False) as process_multicrop_col:
                         gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
                         with gr.Row():
@@ -1201,7 +1183,7 @@ def create_ui():
                         with gr.Row():
                             process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
                             process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
-   
+
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
@@ -1230,7 +1212,7 @@ def create_ui():
                     )
 
                 def get_textual_inversion_template_names():
-                    return sorted([x for x in textual_inversion.textual_inversion_templates])
+                    return sorted(textual_inversion.textual_inversion_templates)
 
                 with gr.Tab(label="Train", id="train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
@@ -1238,13 +1220,13 @@ def create_ui():
                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
 
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
-                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
 
                     with FormRow():
                         embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
                         hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
-                    
+
                     with FormRow():
                         clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
                         clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
@@ -1290,8 +1272,8 @@ def create_ui():
 
             with gr.Column(elem_id='ti_gallery_container'):
                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
-                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                gr.HTML(elem_id="ti_progress", value="")
                 ti_outcome = gr.HTML(elem_id="ti_error", value="")
 
         create_embedding.click(
@@ -1479,6 +1461,8 @@ def create_ui():
 
         return res
 
+    loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
+
     components = []
     component_dict = {}
     shared.settings_components = component_dict
@@ -1566,6 +1550,9 @@ def create_ui():
                 current_row.__exit__()
                 current_tab.__exit__()
 
+            with gr.TabItem("Defaults", id="defaults", elem_id="settings_tab_defaults"):
+                loadsave.create_ui()
+
             with gr.TabItem("Actions", id="actions", elem_id="settings_tab_actions"):
                 request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
                 download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
@@ -1578,7 +1565,7 @@ def create_ui():
                 gr.HTML(shared.html("licenses.html"), elem_id="licenses")
 
             gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
-            
+
 
         def unload_sd_weights():
             modules.sd_models.unload_model_weights()
@@ -1639,7 +1626,7 @@ def create_ui():
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (train_interface, "Train", "ti"),
+        (train_interface, "Train", "train"),
     ]
 
     interfaces += script_callbacks.ui_tabs_callback()
@@ -1654,7 +1641,7 @@ def create_ui():
 
     with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings", variant="compact"):
-            for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
+            for _i, k, _item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
@@ -1667,8 +1654,18 @@ def create_ui():
                 with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
                     interface.render()
 
+            for interface, _label, ifid in interfaces:
+                if ifid in ["extensions", "settings"]:
+                    continue
+
+                loadsave.add_block(interface, ifid)
+
+            loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+
+            loadsave.setup_ui()
+
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+            gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
 
         footer = shared.html("footer.html")
         footer = footer.format(versions=versions_html())
@@ -1681,7 +1678,7 @@ def create_ui():
             outputs=[text_settings, result],
         )
 
-        for i, k, item in quicksettings_list:
+        for _i, k, _item in quicksettings_list:
             component = component_dict[k]
             info = opts.data_labels[k]
 
@@ -1755,97 +1752,8 @@ def create_ui():
             ]
         )
 
-    ui_config_file = cmd_opts.ui_config_file
+    loadsave.dump_defaults()
-    ui_settings = {}
+    demo.ui_loadsave = loadsave
-    settings_count = len(ui_settings)
-    error_loading = False
-
-    try:
-        if os.path.exists(ui_config_file):
-            with open(ui_config_file, "r", encoding="utf8") as file:
-                ui_settings = json.load(file)
-    except Exception:
-        error_loading = True
-        print("Error loading settings:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-    def loadsave(path, x):
-        def apply_field(obj, field, condition=None, init_field=None):
-            key = f"{path}/{field}"
-
-            if getattr(obj, 'custom_script_source', None) is not None:
-              key = f"customscript/{obj.custom_script_source}/{key}"
-
-            if getattr(obj, 'do_not_save_to_config', False):
-                return
-
-            saved_value = ui_settings.get(key, None)
-            if saved_value is None:
-                ui_settings[key] = getattr(obj, field)
-            elif condition and not condition(saved_value):
-                pass
-
-                # this warning is generally not useful;
-                # print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
-            else:
-                setattr(obj, field, saved_value)
-                if init_field is not None:
-                    init_field(saved_value)
-
-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
-            apply_field(x, 'visible')
-
-        if type(x) == gr.Slider:
-            apply_field(x, 'value')
-            apply_field(x, 'minimum')
-            apply_field(x, 'maximum')
-            apply_field(x, 'step')
-
-        if type(x) == gr.Radio:
-            apply_field(x, 'value', lambda val: val in x.choices)
-
-        if type(x) == gr.Checkbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Textbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Number:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Dropdown:
-            def check_dropdown(val):
-                if getattr(x, 'multiselect', False):
-                    return all([value in x.choices for value in val])
-                else:
-                    return val in x.choices
-
-            apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
-
-        def check_tab_id(tab_id):
-            tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
-            if type(tab_id) == str:
-                tab_ids = [t.id for t in tab_items]
-                return tab_id in tab_ids
-            elif type(tab_id) == int:
-                return tab_id >= 0 and tab_id < len(tab_items)
-            else:
-                return False
-
-        if type(x) == gr.Tabs:
-            apply_field(x, 'selected', check_tab_id)
-
-    visit(txt2img_interface, loadsave, "txt2img")
-    visit(img2img_interface, loadsave, "img2img")
-    visit(extras_interface, loadsave, "extras")
-    visit(modelmerger_interface, loadsave, "modelmerger")
-    visit(train_interface, loadsave, "train")
-
-    loadsave(f"webui/Tabs@{tabs.elem_id}", tabs)
-
-    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
-        with open(ui_config_file, "w", encoding="utf8") as file:
-            json.dump(ui_settings, file, indent=4)
 
     # Required as a workaround for change() event not triggering when loading values from ui-config.json
     interp_description.value = update_interp_description(interp_method.value)
@@ -1863,12 +1771,11 @@ def webpath(fn):
 
 
 def javascript_html():
-    script_js = os.path.join(script_path, "script.js")
+    # Ensure localization is in `window` before scripts
-    head = f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
+    head = f'<script type="text/javascript">{localization.localization_js(shared.opts.localization)}</script>\n'
 
-    inline = f"{localization.localization_js(shared.opts.localization)};"
+    script_js = os.path.join(script_path, "script.js")
-    if cmd_opts.theme is not None:
+    head += f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
-        inline += f"set_theme('{cmd_opts.theme}');"
 
     for script in modules.scripts.list_scripts("javascript", ".js"):
         head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
@@ -1876,7 +1783,8 @@ def javascript_html():
     for script in modules.scripts.list_scripts("javascript", ".mjs"):
         head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
 
-    head += f'<script type="text/javascript">{inline}</script>\n'
+    if cmd_opts.theme:
+        head += f'<script type="text/javascript">set_theme(\"{cmd_opts.theme}\");</script>\n'
 
     return head
 
@@ -1933,15 +1841,15 @@ def versions_html():
 
     return f"""
 version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
- • 
+&#x2000;•&#x2000;
 python: <span title="{sys.version}">{python_version}</span>
- • 
+&#x2000;•&#x2000;
 torch: {getattr(torch, '__long_version__',torch.__version__)}
- • 
+&#x2000;•&#x2000;
 xformers: {xformers_version}
- • 
+&#x2000;•&#x2000;
 gradio: {gr.__version__}
- • 
+&#x2000;•&#x2000;
 checkpoint: <a id="sd_checkpoint_hash">N/A</a>
 """
 

modules/ui_extensions.py

@@ -1,6 +1,7 @@
 import json
 import os.path
 import sys
+import threading
 import time
 from datetime import datetime
 import traceback
@@ -140,7 +141,9 @@ def extension_table():
             <tr>
                 <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
                 <th>URL</th>
-                <th><abbr title="Extension version">Version</abbr></th>
+                <th>Branch</th>
+                <th>Version</th>
+                <th>Date</th>
                 <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
             </tr>
         </thead>
@@ -148,6 +151,7 @@ def extension_table():
     """
 
     for ext in extensions.extensions:
+        ext: extensions.Extension
         ext.read_info_from_repo()
 
         remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
@@ -169,7 +173,9 @@ def extension_table():
             <tr>
                 <td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
                 <td>{remote}</td>
+                <td>{ext.branch}</td>
                 <td>{version_link}</td>
+                <td>{time.asctime(time.gmtime(ext.commit_date))}</td>
                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
             </tr>
     """
@@ -467,7 +473,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
                 <td>{html.escape(description)}<p class="info"><span class="date_added">Added: {html.escape(added)}</span></p></td>
                 <td>{install_code}</td>
             </tr>
-        
+
         """
 
         for tag in [x for x in extension_tags if x not in tags]:
@@ -484,13 +490,20 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
     return code, list(tags)
 
 
+def preload_extensions_git_metadata():
+    for extension in extensions.extensions:
+        extension.read_info_from_repo()
+
+
 def create_ui():
     import modules.ui
 
     config_states.list_config_states()
 
+    threading.Thread(target=preload_extensions_git_metadata).start()
+
     with gr.Blocks(analytics_enabled=False) as ui:
-        with gr.Tabs(elem_id="tabs_extensions") as tabs:
+        with gr.Tabs(elem_id="tabs_extensions"):
             with gr.TabItem("Installed", id="installed"):
 
                 with gr.Row(elem_id="extensions_installed_top"):
@@ -508,7 +521,8 @@ def create_ui():
 </span>
                     """
                 info = gr.HTML(html)
-                extensions_table = gr.HTML(lambda: extension_table())
+                extensions_table = gr.HTML('Loading...')
+                ui.load(fn=extension_table, inputs=[], outputs=[extensions_table])
 
                 apply.click(
                     fn=apply_and_restart,
@@ -535,9 +549,9 @@ def create_ui():
                     hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
                     sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
 
-                with gr.Row(): 
+                with gr.Row():
                     search_extensions_text = gr.Text(label="Search").style(container=False)
-                   
+
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()
 
@@ -579,9 +593,9 @@ def create_ui():
                 install_result = gr.HTML(elem_id="extension_install_result")
 
                 install_button.click(
-                    fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
+                    fn=modules.ui.wrap_gradio_call(lambda *args: [gr.update(), *install_extension_from_url(*args)], extra_outputs=[gr.update(), gr.update()]),
                     inputs=[install_dirname, install_url, install_branch],
-                    outputs=[extensions_table, install_result],
+                    outputs=[install_url, extensions_table, install_result],
                 )
 
             with gr.TabItem("Backup/Restore"):
@@ -595,7 +609,8 @@ def create_ui():
                     config_save_button = gr.Button(value="Save Current Config")
 
                 config_states_info = gr.HTML("")
-                config_states_table = gr.HTML(lambda: update_config_states_table("Current"))
+                config_states_table = gr.HTML("Loading...")
+                ui.load(fn=update_config_states_table, inputs=[config_states_list], outputs=[config_states_table])
 
                 config_save_button.click(fn=save_config_state, inputs=[config_save_name], outputs=[config_states_list, config_states_info])
 
@@ -608,4 +623,5 @@ def create_ui():
                     outputs=[config_states_table],
                 )
 
+
     return ui

modules/ui_extra_networks.py

@@ -1,4 +1,3 @@
-import glob
 import os.path
 import urllib.parse
 from pathlib import Path
@@ -27,11 +26,11 @@ def register_page(page):
 def fetch_file(filename: str = ""):
     from starlette.responses import FileResponse
 
-    if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
+    if not any(Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs):
         raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
 
     ext = os.path.splitext(filename)[1].lower()
-    if ext not in (".png", ".jpg", ".webp"):
+    if ext not in (".png", ".jpg", ".jpeg", ".webp"):
         raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
 
     # would profit from returning 304
@@ -91,7 +90,7 @@ class ExtraNetworksPage:
 
         subdirs = {}
         for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
-            for root, dirs, files in os.walk(parentdir):
+            for root, dirs, _ in os.walk(parentdir, followlinks=True):
                 for dirname in dirs:
                     x = os.path.join(root, dirname)
 
@@ -195,7 +194,7 @@ class ExtraNetworksPage:
         Find a preview PNG for a given path (without extension) and call link_preview on it.
         """
 
-        preview_extensions = ["png", "jpg", "webp"]
+        preview_extensions = ["png", "jpg", "jpeg", "webp"]
         if shared.opts.samples_format not in preview_extensions:
             preview_extensions.append(shared.opts.samples_format)
 
@@ -263,13 +262,13 @@ def create_ui(container, button, tabname):
     ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
     ui.tabname = tabname
 
-    with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
+    with gr.Tabs(elem_id=tabname+"_extra_tabs"):
         for page in ui.stored_extra_pages:
             page_id = page.title.lower().replace(" ", "_")
 
             with gr.Tab(page.title, id=page_id):
                 elem_id = f"{tabname}_{page_id}_cards_html"
-                page_elem = gr.HTML('', elem_id=elem_id)
+                page_elem = gr.HTML('Loading...', elem_id=elem_id)
                 ui.pages.append(page_elem)
 
                 page_elem.change(fn=lambda: None, _js='function(){applyExtraNetworkFilter(' + json.dumps(tabname) + '); return []}', inputs=[], outputs=[])
@@ -283,13 +282,24 @@ def create_ui(container, button, tabname):
     def toggle_visibility(is_visible):
         is_visible = not is_visible
 
-        if is_visible and not ui.pages_contents:
+        return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
+
+    def fill_tabs(is_empty):
+        """Creates HTML for extra networks' tabs when the extra networks button is clicked for the first time."""
+
+        if not ui.pages_contents:
             refresh()
 
-        return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary")), *ui.pages_contents
+        if is_empty:
+            return True, *ui.pages_contents
+
+        return True, *[gr.update() for _ in ui.pages_contents]
 
     state_visible = gr.State(value=False)
-    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button, *ui.pages])
+    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button], show_progress=False)
+
+    state_empty = gr.State(value=True)
+    button.click(fn=fill_tabs, inputs=[state_empty], outputs=[state_empty, *ui.pages], show_progress=False)
 
     def refresh():
         for pg in ui.stored_extra_pages:
@@ -327,7 +337,7 @@ def setup_ui(ui, gallery):
 
         is_allowed = False
         for extra_page in ui.stored_extra_pages:
-            if any([path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()]):
+            if any(path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()):
                 is_allowed = True
                 break
 

modules/ui_loadsave.py

@@ -0,0 +1,208 @@
+import json
+import os
+
+import gradio as gr
+
+from modules import errors
+from modules.ui_components import ToolButton
+
+
+class UiLoadsave:
+    """allows saving and restorig default values for gradio components"""
+
+    def __init__(self, filename):
+        self.filename = filename
+        self.ui_settings = {}
+        self.component_mapping = {}
+        self.error_loading = False
+        self.finalized_ui = False
+
+        self.ui_defaults_view = None
+        self.ui_defaults_apply = None
+        self.ui_defaults_review = None
+
+        try:
+            if os.path.exists(self.filename):
+                self.ui_settings = self.read_from_file()
+        except Exception as e:
+            self.error_loading = True
+            errors.display(e, "loading settings")
+
+    def add_component(self, path, x):
+        """adds component to the registry of tracked components"""
+
+        assert not self.finalized_ui
+
+        def apply_field(obj, field, condition=None, init_field=None):
+            key = f"{path}/{field}"
+
+            if getattr(obj, 'custom_script_source', None) is not None:
+              key = f"customscript/{obj.custom_script_source}/{key}"
+
+            if getattr(obj, 'do_not_save_to_config', False):
+                return
+
+            saved_value = self.ui_settings.get(key, None)
+            if saved_value is None:
+                self.ui_settings[key] = getattr(obj, field)
+            elif condition and not condition(saved_value):
+                pass
+            else:
+                setattr(obj, field, saved_value)
+                if init_field is not None:
+                    init_field(saved_value)
+
+            if field == 'value' and key not in self.component_mapping:
+                self.component_mapping[key] = x
+
+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
+            apply_field(x, 'visible')
+
+        if type(x) == gr.Slider:
+            apply_field(x, 'value')
+            apply_field(x, 'minimum')
+            apply_field(x, 'maximum')
+            apply_field(x, 'step')
+
+        if type(x) == gr.Radio:
+            apply_field(x, 'value', lambda val: val in x.choices)
+
+        if type(x) == gr.Checkbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Textbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Number:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Dropdown:
+            def check_dropdown(val):
+                if getattr(x, 'multiselect', False):
+                    return all(value in x.choices for value in val)
+                else:
+                    return val in x.choices
+
+            apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
+
+        def check_tab_id(tab_id):
+            tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
+            if type(tab_id) == str:
+                tab_ids = [t.id for t in tab_items]
+                return tab_id in tab_ids
+            elif type(tab_id) == int:
+                return 0 <= tab_id < len(tab_items)
+            else:
+                return False
+
+        if type(x) == gr.Tabs:
+            apply_field(x, 'selected', check_tab_id)
+
+    def add_block(self, x, path=""):
+        """adds all components inside a gradio block x to the registry of tracked components"""
+
+        if hasattr(x, 'children'):
+            if isinstance(x, gr.Tabs) and x.elem_id is not None:
+                # Tabs element can't have a label, have to use elem_id instead
+                self.add_component(f"{path}/Tabs@{x.elem_id}", x)
+            for c in x.children:
+                self.add_block(c, path)
+        elif x.label is not None:
+            self.add_component(f"{path}/{x.label}", x)
+
+    def read_from_file(self):
+        with open(self.filename, "r", encoding="utf8") as file:
+            return json.load(file)
+
+    def write_to_file(self, current_ui_settings):
+        with open(self.filename, "w", encoding="utf8") as file:
+            json.dump(current_ui_settings, file, indent=4)
+
+    def dump_defaults(self):
+        """saves default values to a file unless tjhe file is present and there was an error loading default values at start"""
+
+        if self.error_loading and os.path.exists(self.filename):
+            return
+
+        self.write_to_file(self.ui_settings)
+
+    def iter_changes(self, current_ui_settings, values):
+        """
+        given a dictionary with defaults from a file and current values from gradio elements, returns
+        an iterator over tuples of values that are not the same between the file and the current;
+        tuple contents are: path, old value, new value
+        """
+
+        for (path, component), new_value in zip(self.component_mapping.items(), values):
+            old_value = current_ui_settings.get(path)
+
+            choices = getattr(component, 'choices', None)
+            if isinstance(new_value, int) and choices:
+                if new_value >= len(choices):
+                    continue
+
+                new_value = choices[new_value]
+
+            if new_value == old_value:
+                continue
+
+            if old_value is None and new_value == '' or new_value == []:
+                continue
+
+            yield path, old_value, new_value
+
+    def ui_view(self, *values):
+        text = ["<table><thead><tr><th>Path</th><th>Old value</th><th>New value</th></thead><tbody>"]
+
+        for path, old_value, new_value in self.iter_changes(self.read_from_file(), values):
+            if old_value is None:
+                old_value = "<span class='ui-defaults-none'>None</span>"
+
+            text.append(f"<tr><td>{path}</td><td>{old_value}</td><td>{new_value}</td></tr>")
+
+        if len(text) == 1:
+            text.append("<tr><td colspan=3>No changes</td></tr>")
+
+        text.append("</tbody>")
+        return "".join(text)
+
+    def ui_apply(self, *values):
+        num_changed = 0
+
+        current_ui_settings = self.read_from_file()
+
+        for path, _, new_value in self.iter_changes(current_ui_settings.copy(), values):
+            num_changed += 1
+            current_ui_settings[path] = new_value
+
+        if num_changed == 0:
+            return "No changes."
+
+        self.write_to_file(current_ui_settings)
+
+        return f"Wrote {num_changed} changes."
+
+    def create_ui(self):
+        """creates ui elements for editing defaults UI, without adding any logic to them"""
+
+        gr.HTML(
+            f"This page allows you to change default values in UI elements on other tabs.<br />"
+            f"Make your changes, press 'View changes' to review the changed default values,<br />"
+            f"then press 'Apply' to write them to {self.filename}.<br />"
+            f"New defaults will apply after you restart the UI.<br />"
+        )
+
+        with gr.Row():
+            self.ui_defaults_view = gr.Button(value='View changes', elem_id="ui_defaults_view", variant="secondary")
+            self.ui_defaults_apply = gr.Button(value='Apply', elem_id="ui_defaults_apply", variant="primary")
+
+        self.ui_defaults_review = gr.HTML("")
+
+    def setup_ui(self):
+        """adds logic to elements created with create_ui; all add_block class must be made before this"""
+
+        assert not self.finalized_ui
+        self.finalized_ui = True
+
+        self.ui_defaults_view.click(fn=self.ui_view, inputs=list(self.component_mapping.values()), outputs=[self.ui_defaults_review])
+        self.ui_defaults_apply.click(fn=self.ui_apply, inputs=list(self.component_mapping.values()), outputs=[self.ui_defaults_review])

modules/ui_postprocessing.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from modules import scripts_postprocessing, scripts, shared, gfpgan_model, codeformer_model, ui_common, postprocessing, call_queue
+from modules import scripts, shared, ui_common, postprocessing, call_queue
 import modules.generation_parameters_copypaste as parameters_copypaste
 
 

modules/ui_tempdir.py

@@ -23,7 +23,7 @@ def register_tmp_file(gradio, filename):
 
 def check_tmp_file(gradio, filename):
     if hasattr(gradio, 'temp_file_sets'):
-        return any([filename in fileset for fileset in gradio.temp_file_sets])
+        return any(filename in fileset for fileset in gradio.temp_file_sets)
 
     if hasattr(gradio, 'temp_dirs'):
         return any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in gradio.temp_dirs)
@@ -72,7 +72,7 @@ def cleanup_tmpdr():
     if temp_dir == "" or not os.path.isdir(temp_dir):
         return
 
-    for root, dirs, files in os.walk(temp_dir, topdown=False):
+    for root, _, files in os.walk(temp_dir, topdown=False):
         for name in files:
             _, extension = os.path.splitext(name)
             if extension != ".png":

modules/upscaler.py

@@ -2,8 +2,6 @@ import os
 from abc import abstractmethod
 
 import PIL
-import numpy as np
-import torch
 from PIL import Image
 
 import modules.shared
@@ -43,9 +41,9 @@ class Upscaler:
             os.makedirs(self.model_path, exist_ok=True)
 
         try:
-            import cv2
+            import cv2  # noqa: F401
             self.can_tile = True
-        except:
+        except Exception:
             pass
 
     @abstractmethod
@@ -57,7 +55,7 @@ class Upscaler:
         dest_w = int(img.width * scale)
         dest_h = int(img.height * scale)
 
-        for i in range(3):
+        for _ in range(3):
             shape = (img.width, img.height)
 
             img = self.do_upscale(img, selected_model)

modules/xlmr.py

@@ -1,4 +1,4 @@
-from transformers import BertPreTrainedModel,BertModel,BertConfig
+from transformers import BertPreTrainedModel, BertConfig
 import torch.nn as nn
 import torch
 from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
@@ -28,7 +28,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
     config_class = BertSeriesConfig
 
     def __init__(self, config=None, **kargs):
-        # modify initialization for autoloading 
+        # modify initialization for autoloading
         if config is None:
             config = XLMRobertaConfig()
             config.attention_probs_dropout_prob= 0.1
@@ -74,7 +74,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
         text["attention_mask"] = torch.tensor(
             text['attention_mask']).to(device)
         features = self(**text)
-        return features['projection_state'] 
+        return features['projection_state']
 
     def forward(
         self,
@@ -134,4 +134,4 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
 
 class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
     base_model_prefix = 'roberta'
-    config_class= RobertaSeriesConfig
+    config_class= RobertaSeriesConfig

pyproject.toml

@@ -0,0 +1,32 @@
+[tool.ruff]
+
+target-version = "py39"
+
+extend-select = [
+  "B",
+  "C",
+  "I",
+  "W",
+]
+
+exclude = [
+	"extensions",
+	"extensions-disabled",
+]
+
+ignore = [
+	"E501", # Line too long
+	"E731", # Do not assign a `lambda` expression, use a `def`
+	
+	"I001", # Import block is un-sorted or un-formatted
+	"C901", # Function is too complex
+	"C408", # Rewrite as a literal
+	"W605", # invalid escape sequence, messes with some docstrings
+]
+
+[tool.ruff.per-file-ignores]
+"webui.py" = ["E402"]  # Module level import not at top of file
+
+[tool.ruff.flake8-bugbear]
+# Allow default arguments like, e.g., `data: List[str] = fastapi.Query(None)`.
+extend-immutable-calls = ["fastapi.Depends", "fastapi.security.HTTPBasic"]

requirements.txt

@@ -2,8 +2,6 @@ astunparse
 blendmodes
 accelerate
 basicsr
-fonts
-font-roboto
 gfpgan
 gradio==3.29.0
 numpy
@@ -31,3 +29,4 @@ torchsde
 safetensors
 psutil
 rich
+tomesd

requirements_versions.txt

@@ -5,19 +5,17 @@ basicsr==1.4.2
 gfpgan==1.3.8
 gradio==3.29.0
 numpy==1.23.5
-Pillow==9.4.0
+Pillow==9.5.0
 realesrgan==0.3.0
 torch
 omegaconf==2.2.3
 pytorch_lightning==1.9.4
-scikit-image==0.19.2
+scikit-image==0.20.0
-fonts
-font-roboto
 timm==0.6.7
 piexif==1.1.3
 einops==0.4.1
 jsonmerge==1.8.0
-clean-fid==0.1.29
+clean-fid==0.1.35
 resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
@@ -28,3 +26,4 @@ torchsde==0.2.5
 safetensors==0.3.1
 httpcore<=0.15
 fastapi==0.94.0
+tomesd==0.1.2

scripts/custom_code.py

@@ -4,7 +4,7 @@ import ast
 import copy
 
 from modules.processing import Processed
-from modules.shared import opts, cmd_opts, state
+from modules.shared import cmd_opts
 
 
 def convertExpr2Expression(expr):