Merge branch 'dev' into master

.eslintrc.js

@@ -50,13 +50,14 @@ module.exports = {
     globals: {
         //script.js
         gradioApp: "readonly",
+        executeCallbacks: "readonly",
+        onAfterUiUpdate: "readonly",
+        onOptionsChanged: "readonly",
         onUiLoaded: "readonly",
         onUiUpdate: "readonly",
-        onOptionsChanged: "readonly",
         uiCurrentTab: "writable",
-        uiElementIsVisible: "readonly",
         uiElementInSight: "readonly",
-        executeCallbacks: "readonly",
+        uiElementIsVisible: "readonly",
         //ui.js
         opts: "writable",
         all_gallery_buttons: "readonly",
@@ -84,5 +85,7 @@ module.exports = {
         // imageviewer.js
         modalPrevImage: "readonly",
         modalNextImage: "readonly",
+        // token-counters.js
+        setupTokenCounters: "readonly",
     }
 };

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -43,8 +43,8 @@ body:
   - type: input
     id: commit
     attributes:
-      label: Commit where the problem happens
-      description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
+      label: Version or Commit where the problem happens
+      description: "Which webui version or commit are you running ? (Do not write *Latest Version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Version: v1.2.3** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)"
     validations:
       required: true
   - type: dropdown
@@ -80,6 +80,23 @@ body:
         - AMD GPUs (RX 5000 below)
         - CPU
         - Other GPUs
+  - type: dropdown
+    id: cross_attention_opt
+    attributes:
+      label: Cross attention optimization
+      description: What cross attention optimization are you using, Settings -> Optimizations -> Cross attention optimization
+      multiple: false
+      options:
+        - Automatic
+        - xformers
+        - sdp-no-mem
+        - sdp
+        - Doggettx
+        - V1 
+        - InvokeAI
+        - "None "
+    validations:
+      required: true
   - type: dropdown
     id: browsers
     attributes:

.github/workflows/on_pull_request.yaml

@@ -18,7 +18,7 @@ jobs:
           #     not to have GHA download an (at the time of writing) 4 GB cache
           #     of PyTorch and other dependencies.
       - name: Install Ruff
-        run: pip install ruff==0.0.265
+        run: pip install ruff==0.0.272
       - name: Run Ruff
         run: ruff .
   lint-js:

.github/workflows/run_tests.yaml

@@ -50,7 +50,7 @@ jobs:
           python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
       - name: Kill test server
         if: always()
-        run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10
+        run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
       - name: Show coverage
         run: |
           python -m coverage combine .coverage*

CHANGELOG.md

@@ -1,3 +1,60 @@
+## 1.4.0
+
+### Features:
+ * zoom controls for inpainting
+ * run basic torch calculation at startup in parallel to reduce the performance impact of first generation
+ * option to pad prompt/neg prompt to be same length
+ * remove taming_transformers dependency
+ * custom k-diffusion scheduler settings
+ * add an option to show selected settings in main txt2img/img2img UI
+ * sysinfo tab in settings
+ * infer styles from prompts when pasting params into the UI
+ * an option to control the behavior of the above
+
+### Minor:
+ * bump Gradio to 3.32.0
+ * bump xformers to 0.0.20
+ * Add option to disable token counters
+ * tooltip fixes & optimizations
+ * make it possible to configure filename for the zip download
+ * `[vae_filename]` pattern for filenames
+ * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE
+ * change UI reorder setting to multiselect
+ * read version info form CHANGELOG.md if git version info is not available
+ * link footer API to Wiki when API is not active
+ * persistent conds cache (opt-in optimization)
+ 
+### Extensions:
+ * After installing extensions, webui properly restarts the process rather than reloads the UI 
+ * Added VAE listing to web API. Via: /sdapi/v1/sd-vae
+ * custom unet support
+ * Add onAfterUiUpdate callback
+ * refactor EmbeddingDatabase.register_embedding() to allow unregistering
+ * add before_process callback for scripts
+ * add ability for alwayson scripts to specify section and let user reorder those sections
+ 
+### Bug Fixes:
+ * Fix dragging text to prompt
+ * fix incorrect quoting for infotext values with colon in them
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
+ * Fix s_min_uncond default type int
+ * Fix for #10643 (Inpainting mask sometimes not working)
+ * fix bad styling for thumbs view in extra networks #10639
+ * fix for empty list of optimizations #10605
+ * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility
+ * fix --ui-debug-mode exit
+ * patch GitPython to not use leaky persistent processes
+ * fix duplicate Cross attention optimization after UI reload
+ * torch.cuda.is_available() check for SdOptimizationXformers
+ * fix hires fix using wrong conds in second pass if using Loras.
+ * handle exception when parsing generation parameters from png info
+ * fix upcast attention dtype error
+ * forcing Torch Version to 1.13.1 for RX 5000 series GPUs
+ * split mask blur into X and Y components, patch Outpainting MK2 accordingly
+ * don't die when a LoRA is a broken symlink
+ * allow activation of Generate Forever during generation
+
+
 ## 1.3.2
 
 ### Bug Fixes:

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -1,12 +1,9 @@
 import os
-import sys
-import traceback
-
-from basicsr.utils.download_util import load_file_from_url
 
+from modules.modelloader import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
-from modules import shared, script_callbacks
+from modules import shared, script_callbacks, errors
 import sd_hijack_autoencoder  # noqa: F401
 import sd_hijack_ddpm_v1  # noqa: F401
 
@@ -45,22 +42,17 @@ class UpscalerLDSR(Upscaler):
         if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
             model = local_safetensors_path
         else:
-            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True)
+            model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")
 
-        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True)
+        yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")
 
-        try:
-            return LDSR(model, yaml)
-
-        except Exception:
-            print("Error importing LDSR:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-        return None
+        return LDSR(model, yaml)
 
     def do_upscale(self, img, path):
-        ldsr = self.load_model(path)
-        if ldsr is None:
-            print("NO LDSR!")
+        try:
+            ldsr = self.load_model(path)
+        except Exception:
+            errors.report(f"Failed loading LDSR model {path}", exc_info=True)
             return img
         ddim_steps = shared.opts.ldsr_steps
         return ldsr.super_resolution(img, ddim_steps, self.scale)

extensions-builtin/LDSR/sd_hijack_autoencoder.py

@@ -10,7 +10,7 @@ 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 vqvae_quantize import VectorQuantizer2 as VectorQuantizer
 from ldm.modules.diffusionmodules.model import Encoder, Decoder
 from ldm.util import instantiate_from_config
 
@@ -91,8 +91,9 @@ class VQModel(pl.LightningModule):
                     del sd[k]
         missing, unexpected = self.load_state_dict(sd, strict=False)
         print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
+        if missing:
             print(f"Missing Keys: {missing}")
+        if unexpected:
             print(f"Unexpected Keys: {unexpected}")
 
     def on_train_batch_end(self, *args, **kwargs):

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

@@ -195,9 +195,9 @@ class DDPMV1(pl.LightningModule):
         missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
             sd, strict=False)
         print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
+        if missing:
             print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
+        if unexpected:
             print(f"Unexpected Keys: {unexpected}")
 
     def q_mean_variance(self, x_start, t):

extensions-builtin/LDSR/vqvae_quantize.py

@@ -0,0 +1,147 @@
+# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
+# where the license is as follows:
+#
+# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
+# OR OTHER DEALINGS IN THE SOFTWARE./
+
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+
+
+class VectorQuantizer2(nn.Module):
+    """
+    Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
+    avoids costly matrix multiplications and allows for post-hoc remapping of indices.
+    """
+
+    # NOTE: due to a bug the beta term was applied to the wrong term. for
+    # backwards compatibility we use the buggy version by default, but you can
+    # specify legacy=False to fix it.
+    def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
+                 sane_index_shape=False, legacy=True):
+        super().__init__()
+        self.n_e = n_e
+        self.e_dim = e_dim
+        self.beta = beta
+        self.legacy = legacy
+
+        self.embedding = nn.Embedding(self.n_e, self.e_dim)
+        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+
+        self.remap = remap
+        if self.remap is not None:
+            self.register_buffer("used", torch.tensor(np.load(self.remap)))
+            self.re_embed = self.used.shape[0]
+            self.unknown_index = unknown_index  # "random" or "extra" or integer
+            if self.unknown_index == "extra":
+                self.unknown_index = self.re_embed
+                self.re_embed = self.re_embed + 1
+            print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
+                  f"Using {self.unknown_index} for unknown indices.")
+        else:
+            self.re_embed = n_e
+
+        self.sane_index_shape = sane_index_shape
+
+    def remap_to_used(self, inds):
+        ishape = inds.shape
+        assert len(ishape) > 1
+        inds = inds.reshape(ishape[0], -1)
+        used = self.used.to(inds)
+        match = (inds[:, :, None] == used[None, None, ...]).long()
+        new = match.argmax(-1)
+        unknown = match.sum(2) < 1
+        if self.unknown_index == "random":
+            new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
+        else:
+            new[unknown] = self.unknown_index
+        return new.reshape(ishape)
+
+    def unmap_to_all(self, inds):
+        ishape = inds.shape
+        assert len(ishape) > 1
+        inds = inds.reshape(ishape[0], -1)
+        used = self.used.to(inds)
+        if self.re_embed > self.used.shape[0]:  # extra token
+            inds[inds >= self.used.shape[0]] = 0  # simply set to zero
+        back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
+        return back.reshape(ishape)
+
+    def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
+        assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
+        assert rescale_logits is False, "Only for interface compatible with Gumbel"
+        assert return_logits is False, "Only for interface compatible with Gumbel"
+        # reshape z -> (batch, height, width, channel) and flatten
+        z = rearrange(z, 'b c h w -> b h w c').contiguous()
+        z_flattened = z.view(-1, self.e_dim)
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
+            torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
+            torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
+
+        min_encoding_indices = torch.argmin(d, dim=1)
+        z_q = self.embedding(min_encoding_indices).view(z.shape)
+        perplexity = None
+        min_encodings = None
+
+        # compute loss for embedding
+        if not self.legacy:
+            loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
+                   torch.mean((z_q - z.detach()) ** 2)
+        else:
+            loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \
+                   torch.mean((z_q - z.detach()) ** 2)
+
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # reshape back to match original input shape
+        z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
+
+        if self.remap is not None:
+            min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1)  # add batch axis
+            min_encoding_indices = self.remap_to_used(min_encoding_indices)
+            min_encoding_indices = min_encoding_indices.reshape(-1, 1)  # flatten
+
+        if self.sane_index_shape:
+            min_encoding_indices = min_encoding_indices.reshape(
+                z_q.shape[0], z_q.shape[2], z_q.shape[3])
+
+        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
+
+    def get_codebook_entry(self, indices, shape):
+        # shape specifying (batch, height, width, channel)
+        if self.remap is not None:
+            indices = indices.reshape(shape[0], -1)  # add batch axis
+            indices = self.unmap_to_all(indices)
+            indices = indices.reshape(-1)  # flatten again
+
+        # get quantized latent vectors
+        z_q = self.embedding(indices)
+
+        if shape is not None:
+            z_q = z_q.view(shape)
+            # reshape back to match original input shape
+            z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+        return z_q

extensions-builtin/Lora/extra_networks_lora.py

@@ -9,14 +9,14 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):
         additional = shared.opts.sd_lora
 
-        if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
+        if additional != "None" and additional in lora.available_loras and not any(x for x in params_list if x.items[0] == additional):
             p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 
         names = []
         multipliers = []
         for params in params_list:
-            assert len(params.items) > 0
+            assert params.items
 
             names.append(params.items[0])
             multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)

extensions-builtin/Lora/lora.py

@@ -219,7 +219,7 @@ def load_lora(name, lora_on_disk):
         else:
             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:
+    if keys_failed_to_match:
         print(f"Failed to match keys when loading Lora {lora_on_disk.filename}: {keys_failed_to_match}")
 
     return lora
@@ -267,7 +267,7 @@ def load_loras(names, multipliers=None):
         lora.multiplier = multipliers[i] if multipliers else 1.0
         loaded_loras.append(lora)
 
-    if len(failed_to_load_loras) > 0:
+    if failed_to_load_loras:
         sd_hijack.model_hijack.comments.append("Failed to find Loras: " + ", ".join(failed_to_load_loras))
 
 
@@ -448,7 +448,11 @@ def list_available_loras():
             continue
 
         name = os.path.splitext(os.path.basename(filename))[0]
-        entry = LoraOnDisk(name, filename)
+        try:
+            entry = LoraOnDisk(name, filename)
+        except OSError:  # should catch FileNotFoundError and PermissionError etc.
+            errors.report(f"Failed to load LoRA {name} from {filename}", exc_info=True)
+            continue
 
         available_loras[name] = entry
 

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -13,7 +13,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         lora.list_available_loras()
 
     def list_items(self):
-        for name, lora_on_disk in lora.available_loras.items():
+        for index, (name, lora_on_disk) in enumerate(lora.available_loras.items()):
             path, ext = os.path.splitext(lora_on_disk.filename)
 
             alias = lora_on_disk.get_alias()
@@ -27,6 +27,8 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
                 "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,
+                "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
+
             }
 
     def allowed_directories_for_previews(self):

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -1,17 +1,15 @@
-import os.path
 import sys
-import traceback
 
 import PIL.Image
 import numpy as np
 import torch
 from tqdm import tqdm
 
-from basicsr.utils.download_util import load_file_from_url
-
 import modules.upscaler
-from modules import devices, modelloader, script_callbacks
-from scunet_model_arch import SCUNet as net
+from modules import devices, modelloader, script_callbacks, errors
+from scunet_model_arch import SCUNet
+
+from modules.modelloader import load_file_from_url
 from modules.shared import opts
 
 
@@ -28,7 +26,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         scalers = []
         add_model2 = True
         for file in model_paths:
-            if "http" in file:
+            if file.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(file)
@@ -38,8 +36,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
                 scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
                 scalers.append(scaler_data)
             except Exception:
-                print(f"Error loading ScuNET model: {file}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error loading ScuNET model: {file}", exc_info=True)
         if add_model2:
             scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
             scalers.append(scaler_data2)
@@ -90,9 +87,10 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
 
         torch.cuda.empty_cache()
 
-        model = self.load_model(selected_file)
-        if model is None:
-            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
+        try:
+            model = self.load_model(selected_file)
+        except Exception as e:
+            print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
             return img
 
         device = devices.get_device_for('scunet')
@@ -120,15 +118,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
 
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
-        if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True)
+        if path.startswith("http"):
+            # TODO: this doesn't use `path` at all?
+            filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
         else:
             filename = path
-        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
-            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
-            return None
-
-        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        model = SCUNet(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 _, v in model.named_parameters():

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -1,17 +1,17 @@
-import os
+import sys
 
 import numpy as np
 import torch
 from PIL import Image
-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 opts, state
-from swinir_model_arch import SwinIR as net
-from swinir_model_arch_v2 import Swin2SR as net2
+from swinir_model_arch import SwinIR
+from swinir_model_arch_v2 import Swin2SR
 from modules.upscaler import Upscaler, UpscalerData
 
+SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
 
 device_swinir = devices.get_device_for('swinir')
 
@@ -19,16 +19,14 @@ device_swinir = devices.get_device_for('swinir')
 class UpscalerSwinIR(Upscaler):
     def __init__(self, dirname):
         self.name = "SwinIR"
-        self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
-                         "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
-                         "-L_x4_GAN.pth "
+        self.model_url = SWINIR_MODEL_URL
         self.model_name = "SwinIR 4x"
         self.user_path = dirname
         super().__init__()
         scalers = []
         model_files = self.find_models(ext_filter=[".pt", ".pth"])
         for model in model_files:
-            if "http" in model:
+            if model.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(model)
@@ -37,8 +35,10 @@ class UpscalerSwinIR(Upscaler):
         self.scalers = scalers
 
     def do_upscale(self, img, model_file):
-        model = self.load_model(model_file)
-        if model is None:
+        try:
+            model = self.load_model(model_file)
+        except Exception as e:
+            print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
             return img
         model = model.to(device_swinir, dtype=devices.dtype)
         img = upscale(img, model)
@@ -49,30 +49,31 @@ class UpscalerSwinIR(Upscaler):
         return img
 
     def load_model(self, path, scale=4):
-        if "http" in path:
-            dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
-            filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True)
+        if path.startswith("http"):
+            filename = modelloader.load_file_from_url(
+                url=path,
+                model_dir=self.model_download_path,
+                file_name=f"{self.model_name.replace(' ', '_')}.pth",
+            )
         else:
             filename = path
-        if filename is None or not os.path.exists(filename):
-            return None
         if filename.endswith(".v2.pth"):
-            model = net2(
-            upscale=scale,
-            in_chans=3,
-            img_size=64,
-            window_size=8,
-            img_range=1.0,
-            depths=[6, 6, 6, 6, 6, 6],
-            embed_dim=180,
-            num_heads=[6, 6, 6, 6, 6, 6],
-            mlp_ratio=2,
-            upsampler="nearest+conv",
-            resi_connection="1conv",
+            model = Swin2SR(
+                upscale=scale,
+                in_chans=3,
+                img_size=64,
+                window_size=8,
+                img_range=1.0,
+                depths=[6, 6, 6, 6, 6, 6],
+                embed_dim=180,
+                num_heads=[6, 6, 6, 6, 6, 6],
+                mlp_ratio=2,
+                upsampler="nearest+conv",
+                resi_connection="1conv",
             )
             params = None
         else:
-            model = net(
+            model = SwinIR(
                 upscale=scale,
                 in_chans=3,
                 img_size=64,

extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js

@@ -0,0 +1,748 @@
+onUiLoaded(async() => {
+    const elementIDs = {
+        img2imgTabs: "#mode_img2img .tab-nav",
+        inpaint: "#img2maskimg",
+        inpaintSketch: "#inpaint_sketch",
+        rangeGroup: "#img2img_column_size",
+        sketch: "#img2img_sketch"
+    };
+    const tabNameToElementId = {
+        "Inpaint sketch": elementIDs.inpaintSketch,
+        "Inpaint": elementIDs.inpaint,
+        "Sketch": elementIDs.sketch
+    };
+
+    // Helper functions
+    // Get active tab
+    function getActiveTab(elements, all = false) {
+        const tabs = elements.img2imgTabs.querySelectorAll("button");
+
+        if (all) return tabs;
+
+        for (let tab of tabs) {
+            if (tab.classList.contains("selected")) {
+                return tab;
+            }
+        }
+    }
+
+    // Get tab ID
+    function getTabId(elements) {
+        const activeTab = getActiveTab(elements);
+        return tabNameToElementId[activeTab.innerText];
+    }
+
+    // Wait until opts loaded
+    async function waitForOpts() {
+        for (;;) {
+            if (window.opts && Object.keys(window.opts).length) {
+                return window.opts;
+            }
+            await new Promise(resolve => setTimeout(resolve, 100));
+        }
+    }
+
+    // Function for defining the "Ctrl", "Shift" and "Alt" keys
+    function isModifierKey(event, key) {
+        switch (key) {
+        case "Ctrl":
+            return event.ctrlKey;
+        case "Shift":
+            return event.shiftKey;
+        case "Alt":
+            return event.altKey;
+        default:
+            return false;
+        }
+    }
+
+    // Check if hotkey is valid
+    function isValidHotkey(value) {
+        const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"];
+        return (
+            (typeof value === "string" &&
+                value.length === 1 &&
+                /[a-z]/i.test(value)) ||
+            specialKeys.includes(value)
+        );
+    }
+
+    // Normalize hotkey
+    function normalizeHotkey(hotkey) {
+        return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey;
+    }
+
+    // Format hotkey for display
+    function formatHotkeyForDisplay(hotkey) {
+        return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey;
+    }
+
+    // Create hotkey configuration with the provided options
+    function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) {
+        const result = {}; // Resulting hotkey configuration
+        const usedKeys = new Set(); // Set of used hotkeys
+
+        // Iterate through defaultHotkeysConfig keys
+        for (const key in defaultHotkeysConfig) {
+            const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value
+            const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value
+
+            // Apply appropriate value for undefined, boolean, or object userValue
+            if (
+                userValue === undefined ||
+                typeof userValue === "boolean" ||
+                typeof userValue === "object" ||
+                userValue === "disable"
+            ) {
+                result[key] =
+                    userValue === undefined ? defaultValue : userValue;
+            } else if (isValidHotkey(userValue)) {
+                const normalizedUserValue = normalizeHotkey(userValue);
+
+                // Check for conflicting hotkeys
+                if (!usedKeys.has(normalizedUserValue)) {
+                    usedKeys.add(normalizedUserValue);
+                    result[key] = normalizedUserValue;
+                } else {
+                    console.error(
+                        `Hotkey: ${formatHotkeyForDisplay(
+                            userValue
+                        )} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay(
+                            defaultValue
+                        )}`
+                    );
+                    result[key] = defaultValue;
+                }
+            } else {
+                console.error(
+                    `Hotkey: ${formatHotkeyForDisplay(
+                        userValue
+                    )} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay(
+                        defaultValue
+                    )}`
+                );
+                result[key] = defaultValue;
+            }
+        }
+
+        return result;
+    }
+
+    // Disables functions in the config object based on the provided list of function names
+    function disableFunctions(config, disabledFunctions) {
+        // Bind the hasOwnProperty method to the functionMap object to avoid errors
+        const hasOwnProperty =
+            Object.prototype.hasOwnProperty.bind(functionMap);
+
+        // Loop through the disabledFunctions array and disable the corresponding functions in the config object
+        disabledFunctions.forEach(funcName => {
+            if (hasOwnProperty(funcName)) {
+                const key = functionMap[funcName];
+                config[key] = "disable";
+            }
+        });
+
+        // Return the updated config object
+        return config;
+    }
+
+    /**
+     * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio.
+     * If the image display property is set to 'none', the mask breaks. To fix this, the function
+     * temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds
+     * to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on
+     * very long images.
+     */
+    function restoreImgRedMask(elements) {
+        const mainTabId = getTabId(elements);
+
+        if (!mainTabId) return;
+
+        const mainTab = gradioApp().querySelector(mainTabId);
+        const img = mainTab.querySelector("img");
+        const imageARPreview = gradioApp().querySelector("#imageARPreview");
+
+        if (!img || !imageARPreview) return;
+
+        imageARPreview.style.transform = "";
+        if (parseFloat(mainTab.style.width) > 865) {
+            const transformString = mainTab.style.transform;
+            const scaleMatch = transformString.match(
+                /scale\(([-+]?[0-9]*\.?[0-9]+)\)/
+            );
+            let zoom = 1; // default zoom
+
+            if (scaleMatch && scaleMatch[1]) {
+                zoom = Number(scaleMatch[1]);
+            }
+
+            imageARPreview.style.transformOrigin = "0 0";
+            imageARPreview.style.transform = `scale(${zoom})`;
+        }
+
+        if (img.style.display !== "none") return;
+
+        img.style.display = "block";
+
+        setTimeout(() => {
+            img.style.display = "none";
+        }, 400);
+    }
+
+    const hotkeysConfigOpts = await waitForOpts();
+
+    // Default config
+    const defaultHotkeysConfig = {
+        canvas_hotkey_zoom: "Alt",
+        canvas_hotkey_adjust: "Ctrl",
+        canvas_hotkey_reset: "KeyR",
+        canvas_hotkey_fullscreen: "KeyS",
+        canvas_hotkey_move: "KeyF",
+        canvas_hotkey_overlap: "KeyO",
+        canvas_disabled_functions: [],
+        canvas_show_tooltip: true
+    };
+
+    const functionMap = {
+        "Zoom": "canvas_hotkey_zoom",
+        "Adjust brush size": "canvas_hotkey_adjust",
+        "Moving canvas": "canvas_hotkey_move",
+        "Fullscreen": "canvas_hotkey_fullscreen",
+        "Reset Zoom": "canvas_hotkey_reset",
+        "Overlap": "canvas_hotkey_overlap"
+    };
+
+    // Loading the configuration from opts
+    const preHotkeysConfig = createHotkeyConfig(
+        defaultHotkeysConfig,
+        hotkeysConfigOpts
+    );
+
+    // Disable functions that are not needed by the user
+    const hotkeysConfig = disableFunctions(
+        preHotkeysConfig,
+        preHotkeysConfig.canvas_disabled_functions
+    );
+
+    let isMoving = false;
+    let mouseX, mouseY;
+    let activeElement;
+
+    const elements = Object.fromEntries(
+        Object.keys(elementIDs).map(id => [
+            id,
+            gradioApp().querySelector(elementIDs[id])
+        ])
+    );
+    const elemData = {};
+
+    // Apply functionality to the range inputs. Restore redmask and correct for long images.
+    const rangeInputs = elements.rangeGroup ?
+        Array.from(elements.rangeGroup.querySelectorAll("input")) :
+        [
+            gradioApp().querySelector("#img2img_width input[type='range']"),
+            gradioApp().querySelector("#img2img_height input[type='range']")
+        ];
+
+    for (const input of rangeInputs) {
+        input?.addEventListener("input", () => restoreImgRedMask(elements));
+    }
+
+    function applyZoomAndPan(elemId) {
+        const targetElement = gradioApp().querySelector(elemId);
+
+        if (!targetElement) {
+            console.log("Element not found");
+            return;
+        }
+
+        targetElement.style.transformOrigin = "0 0";
+
+        elemData[elemId] = {
+            zoom: 1,
+            panX: 0,
+            panY: 0
+        };
+        let fullScreenMode = false;
+
+        // Create tooltip
+        function createTooltip() {
+            const toolTipElemnt =
+                targetElement.querySelector(".image-container");
+            const tooltip = document.createElement("div");
+            tooltip.className = "canvas-tooltip";
+
+            // Creating an item of information
+            const info = document.createElement("i");
+            info.className = "canvas-tooltip-info";
+            info.textContent = "";
+
+            // Create a container for the contents of the tooltip
+            const tooltipContent = document.createElement("div");
+            tooltipContent.className = "canvas-tooltip-content";
+
+            // Define an array with hotkey information and their actions
+            const hotkeysInfo = [
+                {
+                    configKey: "canvas_hotkey_zoom",
+                    action: "Zoom canvas",
+                    keySuffix: " + wheel"
+                },
+                {
+                    configKey: "canvas_hotkey_adjust",
+                    action: "Adjust brush size",
+                    keySuffix: " + wheel"
+                },
+                {configKey: "canvas_hotkey_reset", action: "Reset zoom"},
+                {
+                    configKey: "canvas_hotkey_fullscreen",
+                    action: "Fullscreen mode"
+                },
+                {configKey: "canvas_hotkey_move", action: "Move canvas"},
+                {configKey: "canvas_hotkey_overlap", action: "Overlap"}
+            ];
+
+            // Create hotkeys array with disabled property based on the config values
+            const hotkeys = hotkeysInfo.map(info => {
+                const configValue = hotkeysConfig[info.configKey];
+                const key = info.keySuffix ?
+                    `${configValue}${info.keySuffix}` :
+                    configValue.charAt(configValue.length - 1);
+                return {
+                    key,
+                    action: info.action,
+                    disabled: configValue === "disable"
+                };
+            });
+
+            for (const hotkey of hotkeys) {
+                if (hotkey.disabled) {
+                    continue;
+                }
+
+                const p = document.createElement("p");
+                p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
+                tooltipContent.appendChild(p);
+            }
+
+            // Add information and content elements to the tooltip element
+            tooltip.appendChild(info);
+            tooltip.appendChild(tooltipContent);
+
+            // Add a hint element to the target element
+            toolTipElemnt.appendChild(tooltip);
+        }
+
+        //Show tool tip if setting enable
+        if (hotkeysConfig.canvas_show_tooltip) {
+            createTooltip();
+        }
+
+        // In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui.
+        function fixCanvas() {
+            const activeTab = getActiveTab(elements).textContent.trim();
+
+            if (activeTab !== "img2img") {
+                const img = targetElement.querySelector(`${elemId} img`);
+
+                if (img && img.style.display !== "none") {
+                    img.style.display = "none";
+                    img.style.visibility = "hidden";
+                }
+            }
+        }
+
+        // Reset the zoom level and pan position of the target element to their initial values
+        function resetZoom() {
+            elemData[elemId] = {
+                zoomLevel: 1,
+                panX: 0,
+                panY: 0
+            };
+
+            fixCanvas();
+            targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`;
+
+            const canvas = gradioApp().querySelector(
+                `${elemId} canvas[key="interface"]`
+            );
+
+            toggleOverlap("off");
+            fullScreenMode = false;
+
+            if (
+                canvas &&
+                parseFloat(canvas.style.width) > 865 &&
+                parseFloat(targetElement.style.width) > 865
+            ) {
+                fitToElement();
+                return;
+            }
+
+            targetElement.style.width = "";
+            if (canvas) {
+                targetElement.style.height = canvas.style.height;
+            }
+        }
+
+        // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements
+        function toggleOverlap(forced = "") {
+            const zIndex1 = "0";
+            const zIndex2 = "998";
+
+            targetElement.style.zIndex =
+                targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1;
+
+            if (forced === "off") {
+                targetElement.style.zIndex = zIndex1;
+            } else if (forced === "on") {
+                targetElement.style.zIndex = zIndex2;
+            }
+        }
+
+        // Adjust the brush size based on the deltaY value from a mouse wheel event
+        function adjustBrushSize(
+            elemId,
+            deltaY,
+            withoutValue = false,
+            percentage = 5
+        ) {
+            const input =
+                gradioApp().querySelector(
+                    `${elemId} input[aria-label='Brush radius']`
+                ) ||
+                gradioApp().querySelector(
+                    `${elemId} button[aria-label="Use brush"]`
+                );
+
+            if (input) {
+                input.click();
+                if (!withoutValue) {
+                    const maxValue =
+                        parseFloat(input.getAttribute("max")) || 100;
+                    const changeAmount = maxValue * (percentage / 100);
+                    const newValue =
+                        parseFloat(input.value) +
+                        (deltaY > 0 ? -changeAmount : changeAmount);
+                    input.value = Math.min(Math.max(newValue, 0), maxValue);
+                    input.dispatchEvent(new Event("change"));
+                }
+            }
+        }
+
+        // Reset zoom when uploading a new image
+        const fileInput = gradioApp().querySelector(
+            `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv`
+        );
+        fileInput.addEventListener("click", resetZoom);
+
+        // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables
+        function updateZoom(newZoomLevel, mouseX, mouseY) {
+            newZoomLevel = Math.max(0.5, Math.min(newZoomLevel, 15));
+
+            elemData[elemId].panX +=
+                mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel;
+            elemData[elemId].panY +=
+                mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel;
+
+            targetElement.style.transformOrigin = "0 0";
+            targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;
+
+            toggleOverlap("on");
+            return newZoomLevel;
+        }
+
+        // Change the zoom level based on user interaction
+        function changeZoomLevel(operation, e) {
+            if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) {
+                e.preventDefault();
+
+                let zoomPosX, zoomPosY;
+                let delta = 0.2;
+                if (elemData[elemId].zoomLevel > 7) {
+                    delta = 0.9;
+                } else if (elemData[elemId].zoomLevel > 2) {
+                    delta = 0.6;
+                }
+
+                zoomPosX = e.clientX;
+                zoomPosY = e.clientY;
+
+                fullScreenMode = false;
+                elemData[elemId].zoomLevel = updateZoom(
+                    elemData[elemId].zoomLevel +
+                        (operation === "+" ? delta : -delta),
+                    zoomPosX - targetElement.getBoundingClientRect().left,
+                    zoomPosY - targetElement.getBoundingClientRect().top
+                );
+            }
+        }
+
+        /**
+         * This function fits the target element to the screen by calculating
+         * the required scale and offsets. It also updates the global variables
+         * zoomLevel, panX, and panY to reflect the new state.
+         */
+
+        function fitToElement() {
+            //Reset Zoom
+            targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
+
+            // Get element and screen dimensions
+            const elementWidth = targetElement.offsetWidth;
+            const elementHeight = targetElement.offsetHeight;
+            const parentElement = targetElement.parentElement;
+            const screenWidth = parentElement.clientWidth;
+            const screenHeight = parentElement.clientHeight;
+
+            // Get element's coordinates relative to the parent element
+            const elementRect = targetElement.getBoundingClientRect();
+            const parentRect = parentElement.getBoundingClientRect();
+            const elementX = elementRect.x - parentRect.x;
+
+            // Calculate scale and offsets
+            const scaleX = screenWidth / elementWidth;
+            const scaleY = screenHeight / elementHeight;
+            const scale = Math.min(scaleX, scaleY);
+
+            const transformOrigin =
+                window.getComputedStyle(targetElement).transformOrigin;
+            const [originX, originY] = transformOrigin.split(" ");
+            const originXValue = parseFloat(originX);
+            const originYValue = parseFloat(originY);
+
+            const offsetX =
+                (screenWidth - elementWidth * scale) / 2 -
+                originXValue * (1 - scale);
+            const offsetY =
+                (screenHeight - elementHeight * scale) / 2.5 -
+                originYValue * (1 - scale);
+
+            // Apply scale and offsets to the element
+            targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
+
+            // Update global variables
+            elemData[elemId].zoomLevel = scale;
+            elemData[elemId].panX = offsetX;
+            elemData[elemId].panY = offsetY;
+
+            fullScreenMode = false;
+            toggleOverlap("off");
+        }
+
+        /**
+         * This function fits the target element to the screen by calculating
+         * the required scale and offsets. It also updates the global variables
+         * zoomLevel, panX, and panY to reflect the new state.
+         */
+
+        // Fullscreen mode
+        function fitToScreen() {
+            const canvas = gradioApp().querySelector(
+                `${elemId} canvas[key="interface"]`
+            );
+
+            if (!canvas) return;
+
+            if (canvas.offsetWidth > 862) {
+                targetElement.style.width = canvas.offsetWidth + "px";
+            }
+
+            if (fullScreenMode) {
+                resetZoom();
+                fullScreenMode = false;
+                return;
+            }
+
+            //Reset Zoom
+            targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
+
+            // Get scrollbar width to right-align the image
+            const scrollbarWidth =
+                window.innerWidth - document.documentElement.clientWidth;
+
+            // Get element and screen dimensions
+            const elementWidth = targetElement.offsetWidth;
+            const elementHeight = targetElement.offsetHeight;
+            const screenWidth = window.innerWidth - scrollbarWidth;
+            const screenHeight = window.innerHeight;
+
+            // Get element's coordinates relative to the page
+            const elementRect = targetElement.getBoundingClientRect();
+            const elementY = elementRect.y;
+            const elementX = elementRect.x;
+
+            // Calculate scale and offsets
+            const scaleX = screenWidth / elementWidth;
+            const scaleY = screenHeight / elementHeight;
+            const scale = Math.min(scaleX, scaleY);
+
+            // Get the current transformOrigin
+            const computedStyle = window.getComputedStyle(targetElement);
+            const transformOrigin = computedStyle.transformOrigin;
+            const [originX, originY] = transformOrigin.split(" ");
+            const originXValue = parseFloat(originX);
+            const originYValue = parseFloat(originY);
+
+            // Calculate offsets with respect to the transformOrigin
+            const offsetX =
+                (screenWidth - elementWidth * scale) / 2 -
+                elementX -
+                originXValue * (1 - scale);
+            const offsetY =
+                (screenHeight - elementHeight * scale) / 2 -
+                elementY -
+                originYValue * (1 - scale);
+
+            // Apply scale and offsets to the element
+            targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
+
+            // Update global variables
+            elemData[elemId].zoomLevel = scale;
+            elemData[elemId].panX = offsetX;
+            elemData[elemId].panY = offsetY;
+
+            fullScreenMode = true;
+            toggleOverlap("on");
+        }
+
+        // Handle keydown events
+        function handleKeyDown(event) {
+            const hotkeyActions = {
+                [hotkeysConfig.canvas_hotkey_reset]: resetZoom,
+                [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
+                [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
+            };
+
+            const action = hotkeyActions[event.code];
+            if (action) {
+                event.preventDefault();
+                action(event);
+            }
+
+            if (
+                isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) ||
+                isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust)
+            ) {
+                event.preventDefault();
+            }
+        }
+
+        // Get Mouse position
+        function getMousePosition(e) {
+            mouseX = e.offsetX;
+            mouseY = e.offsetY;
+        }
+
+        targetElement.addEventListener("mousemove", getMousePosition);
+
+        // Handle events only inside the targetElement
+        let isKeyDownHandlerAttached = false;
+
+        function handleMouseMove() {
+            if (!isKeyDownHandlerAttached) {
+                document.addEventListener("keydown", handleKeyDown);
+                isKeyDownHandlerAttached = true;
+
+                activeElement = elemId;
+            }
+        }
+
+        function handleMouseLeave() {
+            if (isKeyDownHandlerAttached) {
+                document.removeEventListener("keydown", handleKeyDown);
+                isKeyDownHandlerAttached = false;
+
+                activeElement = null;
+            }
+        }
+
+        // Add mouse event handlers
+        targetElement.addEventListener("mousemove", handleMouseMove);
+        targetElement.addEventListener("mouseleave", handleMouseLeave);
+
+        // Reset zoom when click on another tab
+        elements.img2imgTabs.addEventListener("click", resetZoom);
+        elements.img2imgTabs.addEventListener("click", () => {
+            // targetElement.style.width = "";
+            if (parseInt(targetElement.style.width) > 865) {
+                setTimeout(fitToElement, 0);
+            }
+        });
+
+        targetElement.addEventListener("wheel", e => {
+            // change zoom level
+            const operation = e.deltaY > 0 ? "-" : "+";
+            changeZoomLevel(operation, e);
+
+            // Handle brush size adjustment with ctrl key pressed
+            if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) {
+                e.preventDefault();
+
+                // Increase or decrease brush size based on scroll direction
+                adjustBrushSize(elemId, e.deltaY);
+            }
+        });
+
+        // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
+        function handleMoveKeyDown(e) {
+            if (e.code === hotkeysConfig.canvas_hotkey_move) {
+                if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) {
+                    e.preventDefault();
+                    document.activeElement.blur();
+                    isMoving = true;
+                }
+            }
+        }
+
+        function handleMoveKeyUp(e) {
+            if (e.code === hotkeysConfig.canvas_hotkey_move) {
+                isMoving = false;
+            }
+        }
+
+        document.addEventListener("keydown", handleMoveKeyDown);
+        document.addEventListener("keyup", handleMoveKeyUp);
+
+        // Detect zoom level and update the pan speed.
+        function updatePanPosition(movementX, movementY) {
+            let panSpeed = 2;
+
+            if (elemData[elemId].zoomLevel > 8) {
+                panSpeed = 3.5;
+            }
+
+            elemData[elemId].panX += movementX * panSpeed;
+            elemData[elemId].panY += movementY * panSpeed;
+
+            // Delayed redraw of an element
+            requestAnimationFrame(() => {
+                targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`;
+                toggleOverlap("on");
+            });
+        }
+
+        function handleMoveByKey(e) {
+            if (isMoving && elemId === activeElement) {
+                updatePanPosition(e.movementX, e.movementY);
+                targetElement.style.pointerEvents = "none";
+            } else {
+                targetElement.style.pointerEvents = "auto";
+            }
+        }
+
+        // Prevents sticking to the mouse
+        window.onblur = function() {
+            isMoving = false;
+        };
+
+        gradioApp().addEventListener("mousemove", handleMoveByKey);
+    }
+
+    applyZoomAndPan(elementIDs.sketch);
+    applyZoomAndPan(elementIDs.inpaint);
+    applyZoomAndPan(elementIDs.inpaintSketch);
+
+    // Make the function global so that other extensions can take advantage of this solution
+    window.applyZoomAndPan = applyZoomAndPan;
+});

extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py

@@ -0,0 +1,13 @@
+import gradio as gr
+from modules import shared
+
+shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
+    "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+    "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+    "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
+    "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
+    "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
+    "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
+    "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
+    "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
+}))

extensions-builtin/canvas-zoom-and-pan/style.css

@@ -0,0 +1,63 @@
+.canvas-tooltip-info {
+  position: absolute;
+  top: 10px;
+  left: 10px;
+  cursor: help;
+  background-color: rgba(0, 0, 0, 0.3);
+  width: 20px;
+  height: 20px;
+  border-radius: 50%;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  flex-direction: column; 
+
+  z-index: 100;
+}
+
+.canvas-tooltip-info::after {
+  content: '';
+  display: block;
+  width: 2px;
+  height: 7px;
+  background-color: white;
+  margin-top: 2px; 
+}
+
+.canvas-tooltip-info::before {
+  content: '';
+  display: block;
+  width: 2px;
+  height: 2px;
+  background-color: white;
+}
+
+.canvas-tooltip-content {
+  display: none;
+  background-color: #f9f9f9; 
+  color: #333; 
+  border: 1px solid #ddd;
+  padding: 15px; 
+  position: absolute;
+  top: 40px;
+  left: 10px;
+  width: 250px;
+  font-size: 16px;
+  opacity: 0;
+  border-radius: 8px;
+  box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); 
+
+  z-index: 100;
+}
+
+.canvas-tooltip:hover .canvas-tooltip-content {
+  display: block;
+  animation: fadeIn 0.5s; 
+  opacity: 1;
+}
+
+@keyframes fadeIn {
+  from {opacity: 0;}
+  to {opacity: 1;}
+}
+

extensions-builtin/extra-options-section/scripts/extra_options_section.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from modules import scripts, shared, ui_components, ui_settings
+from modules.ui_components import FormColumn
+
+
+class ExtraOptionsSection(scripts.Script):
+    section = "extra_options"
+
+    def __init__(self):
+        self.comps = None
+        self.setting_names = None
+
+    def title(self):
+        return "Extra options"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        self.comps = []
+        self.setting_names = []
+
+        with gr.Blocks() as interface:
+            with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and shared.opts.extra_options else gr.Group(), gr.Row():
+                for setting_name in shared.opts.extra_options:
+                    with FormColumn():
+                        comp = ui_settings.create_setting_component(setting_name)
+
+                    self.comps.append(comp)
+                    self.setting_names.append(setting_name)
+
+        def get_settings_values():
+            return [ui_settings.get_value_for_setting(key) for key in self.setting_names]
+
+        interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False)
+
+        return self.comps
+
+    def before_process(self, p, *args):
+        for name, value in zip(self.setting_names, args):
+            if name not in p.override_settings:
+                p.override_settings[name] = value
+
+
+shared.options_templates.update(shared.options_section(('ui', "User interface"), {
+    "extra_options": shared.OptionInfo([], "Options in main UI", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img/img2img interfaces").needs_restart(),
+    "extra_options_accordion": shared.OptionInfo(False, "Place options in main UI into an accordion")
+}))

html/extra-networks-card.html

@@ -1,4 +1,4 @@
-<div class='card' style={style} onclick={card_clicked}>
+<div class='card' style={style} onclick={card_clicked} {sort_keys}>
 	{background_image}
 	{metadata_button}
 	<div class='actions'>

html/footer.html

@@ -1,10 +1,12 @@
 <div>
-        <a href="/docs">API</a>
+        <a href="{api_docs}">API</a>
          • 
         <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
          • 
         <a href="https://gradio.app">Gradio</a>
          • 
+        <a href="#" onclick="showProfile('./internal/profile-startup'); return false;">Startup profile</a>
+         • 
         <a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
 </div>
 <br />

javascript/aspectRatioOverlay.js

@@ -81,7 +81,7 @@ function dimensionChange(e, is_width, is_height) {
 }
 
 
-onUiUpdate(function() {
+onAfterUiUpdate(function() {
     var arPreviewRect = gradioApp().querySelector('#imageARPreview');
     if (arPreviewRect) {
         arPreviewRect.style.display = 'none';

javascript/contextMenus.js

@@ -148,12 +148,18 @@ var addContextMenuEventListener = initResponse[2];
         500);
     };
 
-    appendContextMenuOption('#txt2img_generate', 'Generate forever', function() {
+    let generateOnRepeat_txt2img = function() {
         generateOnRepeat('#txt2img_generate', '#txt2img_interrupt');
-    });
-    appendContextMenuOption('#img2img_generate', 'Generate forever', function() {
+    };
+
+    let generateOnRepeat_img2img = function() {
         generateOnRepeat('#img2img_generate', '#img2img_interrupt');
-    });
+    };
+
+    appendContextMenuOption('#txt2img_generate', 'Generate forever', generateOnRepeat_txt2img);
+    appendContextMenuOption('#txt2img_interrupt', 'Generate forever', generateOnRepeat_txt2img);
+    appendContextMenuOption('#img2img_generate', 'Generate forever', generateOnRepeat_img2img);
+    appendContextMenuOption('#img2img_interrupt', 'Generate forever', generateOnRepeat_img2img);
 
     let cancelGenerateForever = function() {
         clearInterval(window.generateOnRepeatInterval);
@@ -167,6 +173,4 @@ var addContextMenuEventListener = initResponse[2];
 })();
 //End example Context Menu Items
 
-onUiUpdate(function() {
-    addContextMenuEventListener();
-});
+onAfterUiUpdate(addContextMenuEventListener);

javascript/dragdrop.js

@@ -48,12 +48,27 @@ function dropReplaceImage(imgWrap, files) {
     }
 }
 
+function eventHasFiles(e) {
+    if (!e.dataTransfer || !e.dataTransfer.files) return false;
+    if (e.dataTransfer.files.length > 0) return true;
+    if (e.dataTransfer.items.length > 0 && e.dataTransfer.items[0].kind == "file") return true;
+
+    return false;
+}
+
+function dragDropTargetIsPrompt(target) {
+    if (target?.placeholder && target?.placeholder.indexOf("Prompt") >= 0) return true;
+    if (target?.parentNode?.parentNode?.className?.indexOf("prompt") > 0) return true;
+    return false;
+}
+
 window.document.addEventListener('dragover', e => {
     const target = e.composedPath()[0];
-    const imgWrap = target.closest('[data-testid="image"]');
-    if (!imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
-        return;
-    }
+    if (!eventHasFiles(e)) return;
+
+    var targetImage = target.closest('[data-testid="image"]');
+    if (!dragDropTargetIsPrompt(target) && !targetImage) return;
+
     e.stopPropagation();
     e.preventDefault();
     e.dataTransfer.dropEffect = 'copy';
@@ -61,17 +76,31 @@ window.document.addEventListener('dragover', e => {
 
 window.document.addEventListener('drop', e => {
     const target = e.composedPath()[0];
-    if (target.placeholder.indexOf("Prompt") == -1) {
+    if (!eventHasFiles(e)) return;
+
+    if (dragDropTargetIsPrompt(target)) {
+        e.stopPropagation();
+        e.preventDefault();
+
+        let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
+
+        const imgParent = gradioApp().getElementById(prompt_target);
+        const files = e.dataTransfer.files;
+        const fileInput = imgParent.querySelector('input[type="file"]');
+        if (fileInput) {
+            fileInput.files = files;
+            fileInput.dispatchEvent(new Event('change'));
+        }
+    }
+
+    var targetImage = target.closest('[data-testid="image"]');
+    if (targetImage) {
+        e.stopPropagation();
+        e.preventDefault();
+        const files = e.dataTransfer.files;
+        dropReplaceImage(targetImage, files);
         return;
     }
-    const imgWrap = target.closest('[data-testid="image"]');
-    if (!imgWrap) {
-        return;
-    }
-    e.stopPropagation();
-    e.preventDefault();
-    const files = e.dataTransfer.files;
-    dropReplaceImage(imgWrap, files);
 });
 
 window.addEventListener('paste', e => {

javascript/extensions.js

@@ -72,3 +72,21 @@ function config_state_confirm_restore(_, config_state_name, config_restore_type)
     }
     return [confirmed, config_state_name, config_restore_type];
 }
+
+function toggle_all_extensions(event) {
+    gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) {
+        checkbox_el.checked = event.target.checked;
+    });
+}
+
+function toggle_extension() {
+    let all_extensions_toggled = true;
+    for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) {
+        if (!checkbox_el.checked) {
+            all_extensions_toggled = false;
+            break;
+        }
+    }
+
+    gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled;
+}

javascript/extraNetworks.js

@@ -3,10 +3,17 @@ function setupExtraNetworksForTab(tabname) {
 
     var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div');
     var search = gradioApp().querySelector('#' + tabname + '_extra_search textarea');
+    var sort = gradioApp().getElementById(tabname + '_extra_sort');
+    var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder');
     var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
 
     search.classList.add('search');
+    sort.classList.add('sort');
+    sortOrder.classList.add('sortorder');
+    sort.dataset.sortkey = 'sortDefault';
     tabs.appendChild(search);
+    tabs.appendChild(sort);
+    tabs.appendChild(sortOrder);
     tabs.appendChild(refresh);
 
     var applyFilter = function() {
@@ -26,8 +33,51 @@ function setupExtraNetworksForTab(tabname) {
         });
     };
 
+    var applySort = function() {
+        var reverse = sortOrder.classList.contains("sortReverse");
+        var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim();
+        sortKey = sortKey ? "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1) : "";
+        var sortKeyStore = sortKey ? sortKey + (reverse ? "Reverse" : "") : "";
+        if (!sortKey || sortKeyStore == sort.dataset.sortkey) {
+            return;
+        }
+
+        sort.dataset.sortkey = sortKeyStore;
+
+        var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
+        cards.forEach(function(card) {
+            card.originalParentElement = card.parentElement;
+        });
+        var sortedCards = Array.from(cards);
+        sortedCards.sort(function(cardA, cardB) {
+            var a = cardA.dataset[sortKey];
+            var b = cardB.dataset[sortKey];
+            if (!isNaN(a) && !isNaN(b)) {
+                return parseInt(a) - parseInt(b);
+            }
+
+            return (a < b ? -1 : (a > b ? 1 : 0));
+        });
+        if (reverse) {
+            sortedCards.reverse();
+        }
+        cards.forEach(function(card) {
+            card.remove();
+        });
+        sortedCards.forEach(function(card) {
+            card.originalParentElement.appendChild(card);
+        });
+    };
+
     search.addEventListener("input", applyFilter);
     applyFilter();
+    ["change", "blur", "click"].forEach(function(evt) {
+        sort.querySelector("input").addEventListener(evt, applySort);
+    });
+    sortOrder.addEventListener("click", function() {
+        sortOrder.classList.toggle("sortReverse");
+        applySort();
+    });
 
     extraNetworksApplyFilter[tabname] = applyFilter;
 }

javascript/generationParams.js

@@ -1,7 +1,7 @@
 // attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
 
 let txt2img_gallery, img2img_gallery, modal = undefined;
-onUiUpdate(function() {
+onAfterUiUpdate(function() {
     if (!txt2img_gallery) {
         txt2img_gallery = attachGalleryListeners("txt2img");
     }

javascript/hints.js

@@ -15,7 +15,7 @@ var titles = {
     "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
     "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
     "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
-    "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
+    "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomized",
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
     "\u{1f4be}": "Save style",
@@ -112,21 +112,29 @@ var titles = {
     "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
     "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
     "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
-    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
+    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order listed.",
     "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
 };
 
-function updateTooltipForSpan(span) {
-    if (span.title) return; // already has a title
+function updateTooltip(element) {
+    if (element.title) return; // already has a title
 
-    let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
+    let text = element.textContent;
+    let tooltip = localization[titles[text]] || titles[text];
 
     if (!tooltip) {
-        tooltip = localization[titles[span.value]] || titles[span.value];
+        let value = element.value;
+        if (value) tooltip = localization[titles[value]] || titles[value];
     }
 
     if (!tooltip) {
-        for (const c of span.classList) {
+        // Gradio dropdown options have `data-value`.
+        let dataValue = element.dataset.value;
+        if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue];
+    }
+
+    if (!tooltip) {
+        for (const c of element.classList) {
             if (c in titles) {
                 tooltip = localization[titles[c]] || titles[c];
                 break;
@@ -135,34 +143,53 @@ function updateTooltipForSpan(span) {
     }
 
     if (tooltip) {
-        span.title = tooltip;
+        element.title = tooltip;
     }
 }
 
-function updateTooltipForSelect(select) {
-    if (select.onchange != null) return;
+// Nodes to check for adding tooltips.
+const tooltipCheckNodes = new Set();
+// Timer for debouncing tooltip check.
+let tooltipCheckTimer = null;
 
-    select.onchange = function() {
-        select.title = localization[titles[select.value]] || titles[select.value] || "";
-    };
+function processTooltipCheckNodes() {
+    for (const node of tooltipCheckNodes) {
+        updateTooltip(node);
+    }
+    tooltipCheckNodes.clear();
 }
 
-var observedTooltipElements = {SPAN: 1, BUTTON: 1, SELECT: 1, P: 1};
-
-onUiUpdate(function(m) {
-    m.forEach(function(record) {
-        record.addedNodes.forEach(function(node) {
-            if (observedTooltipElements[node.tagName]) {
-                updateTooltipForSpan(node);
+onUiUpdate(function(mutationRecords) {
+    for (const record of mutationRecords) {
+        if (record.type === "childList" && record.target.classList.contains("options")) {
+            // This smells like a Gradio dropdown menu having changed,
+            // so let's enqueue an update for the input element that shows the current value.
+            let wrap = record.target.parentNode;
+            let input = wrap?.querySelector("input");
+            if (input) {
+                input.title = ""; // So we'll even have a chance to update it.
+                tooltipCheckNodes.add(input);
             }
-            if (node.tagName == "SELECT") {
-                updateTooltipForSelect(node);
+        }
+        for (const node of record.addedNodes) {
+            if (node.nodeType === Node.ELEMENT_NODE && !node.classList.contains("hide")) {
+                if (!node.title) {
+                    if (
+                        node.tagName === "SPAN" ||
+                        node.tagName === "BUTTON" ||
+                        node.tagName === "P" ||
+                        node.tagName === "INPUT" ||
+                        (node.tagName === "LI" && node.classList.contains("item")) // Gradio dropdown item
+                    ) {
+                        tooltipCheckNodes.add(node);
+                    }
+                }
+                node.querySelectorAll('span, button, p').forEach(n => tooltipCheckNodes.add(n));
             }
-
-            if (node.querySelectorAll) {
-                node.querySelectorAll('span, button, select, p').forEach(updateTooltipForSpan);
-                node.querySelectorAll('select').forEach(updateTooltipForSelect);
-            }
-        });
-    });
+        }
+    }
+    if (tooltipCheckNodes.size) {
+        clearTimeout(tooltipCheckTimer);
+        tooltipCheckTimer = setTimeout(processTooltipCheckNodes, 1000);
+    }
 });

javascript/imageMaskFix.js

@@ -39,5 +39,5 @@ function imageMaskResize() {
     });
 }
 
-onUiUpdate(imageMaskResize);
+onAfterUiUpdate(imageMaskResize);
 window.addEventListener('resize', imageMaskResize);

javascript/imageParams.js

@@ -1,18 +0,0 @@
-window.onload = (function() {
-    window.addEventListener('drop', e => {
-        const target = e.composedPath()[0];
-        if (target.placeholder.indexOf("Prompt") == -1) return;
-
-        let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
-
-        e.stopPropagation();
-        e.preventDefault();
-        const imgParent = gradioApp().getElementById(prompt_target);
-        const files = e.dataTransfer.files;
-        const fileInput = imgParent.querySelector('input[type="file"]');
-        if (fileInput) {
-            fileInput.files = files;
-            fileInput.dispatchEvent(new Event('change'));
-        }
-    });
-});

javascript/imageviewer.js

@@ -170,7 +170,7 @@ function modalTileImageToggle(event) {
     event.stopPropagation();
 }
 
-onUiUpdate(function() {
+onAfterUiUpdate(function() {
     var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img');
     if (fullImg_preview != null) {
         fullImg_preview.forEach(setupImageForLightbox);

javascript/imageviewerGamepad.js

@@ -1,7 +1,9 @@
+let gamepads = [];
+
 window.addEventListener('gamepadconnected', (e) => {
     const index = e.gamepad.index;
     let isWaiting = false;
-    setInterval(async() => {
+    gamepads[index] = setInterval(async() => {
         if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
         const gamepad = navigator.getGamepads()[index];
         const xValue = gamepad.axes[0];
@@ -24,6 +26,10 @@ window.addEventListener('gamepadconnected', (e) => {
     }, 10);
 });
 
+window.addEventListener('gamepaddisconnected', (e) => {
+    clearInterval(gamepads[e.gamepad.index]);
+});
+
 /*
 Primarily for vr controller type pointer devices.
 I use the wheel event because there's currently no way to do it properly with web xr.

javascript/notification.js

@@ -4,7 +4,7 @@ let lastHeadImg = null;
 
 let notificationButton = null;
 
-onUiUpdate(function() {
+onAfterUiUpdate(function() {
     if (notificationButton == null) {
         notificationButton = gradioApp().getElementById('request_notifications');
 

javascript/profilerVisualization.js

@@ -0,0 +1,153 @@
+
+function createRow(table, cellName, items) {
+    var tr = document.createElement('tr');
+    var res = [];
+
+    items.forEach(function(x, i) {
+        if (x === undefined) {
+            res.push(null);
+            return;
+        }
+
+        var td = document.createElement(cellName);
+        td.textContent = x;
+        tr.appendChild(td);
+        res.push(td);
+
+        var colspan = 1;
+        for (var n = i + 1; n < items.length; n++) {
+            if (items[n] !== undefined) {
+                break;
+            }
+
+            colspan += 1;
+        }
+
+        if (colspan > 1) {
+            td.colSpan = colspan;
+        }
+    });
+
+    table.appendChild(tr);
+
+    return res;
+}
+
+function showProfile(path, cutoff = 0.05) {
+    requestGet(path, {}, function(data) {
+        var table = document.createElement('table');
+        table.className = 'popup-table';
+
+        data.records['total'] = data.total;
+        var keys = Object.keys(data.records).sort(function(a, b) {
+            return data.records[b] - data.records[a];
+        });
+        var items = keys.map(function(x) {
+            return {key: x, parts: x.split('/'), time: data.records[x]};
+        });
+        var maxLength = items.reduce(function(a, b) {
+            return Math.max(a, b.parts.length);
+        }, 0);
+
+        var cols = createRow(table, 'th', ['record', 'seconds']);
+        cols[0].colSpan = maxLength;
+
+        function arraysEqual(a, b) {
+            return !(a < b || b < a);
+        }
+
+        var addLevel = function(level, parent, hide) {
+            var matching = items.filter(function(x) {
+                return x.parts[level] && !x.parts[level + 1] && arraysEqual(x.parts.slice(0, level), parent);
+            });
+            var sorted = matching.sort(function(a, b) {
+                return b.time - a.time;
+            });
+            var othersTime = 0;
+            var othersList = [];
+            var othersRows = [];
+            var childrenRows = [];
+            sorted.forEach(function(x) {
+                var visible = x.time >= cutoff && !hide;
+
+                var cells = [];
+                for (var i = 0; i < maxLength; i++) {
+                    cells.push(x.parts[i]);
+                }
+                cells.push(x.time.toFixed(3));
+                var cols = createRow(table, 'td', cells);
+                for (i = 0; i < level; i++) {
+                    cols[i].className = 'muted';
+                }
+
+                var tr = cols[0].parentNode;
+                if (!visible) {
+                    tr.classList.add("hidden");
+                }
+
+                if (x.time >= cutoff) {
+                    childrenRows.push(tr);
+                } else {
+                    othersTime += x.time;
+                    othersList.push(x.parts[level]);
+                    othersRows.push(tr);
+                }
+
+                var children = addLevel(level + 1, parent.concat([x.parts[level]]), true);
+                if (children.length > 0) {
+                    var cell = cols[level];
+                    var onclick = function() {
+                        cell.classList.remove("link");
+                        cell.removeEventListener("click", onclick);
+                        children.forEach(function(x) {
+                            x.classList.remove("hidden");
+                        });
+                    };
+                    cell.classList.add("link");
+                    cell.addEventListener("click", onclick);
+                }
+            });
+
+            if (othersTime > 0) {
+                var cells = [];
+                for (var i = 0; i < maxLength; i++) {
+                    cells.push(parent[i]);
+                }
+                cells.push(othersTime.toFixed(3));
+                cells[level] = 'others';
+                var cols = createRow(table, 'td', cells);
+                for (i = 0; i < level; i++) {
+                    cols[i].className = 'muted';
+                }
+
+                var cell = cols[level];
+                var tr = cell.parentNode;
+                var onclick = function() {
+                    tr.classList.add("hidden");
+                    cell.classList.remove("link");
+                    cell.removeEventListener("click", onclick);
+                    othersRows.forEach(function(x) {
+                        x.classList.remove("hidden");
+                    });
+                };
+
+                cell.title = othersList.join(", ");
+                cell.classList.add("link");
+                cell.addEventListener("click", onclick);
+
+                if (hide) {
+                    tr.classList.add("hidden");
+                }
+
+                childrenRows.push(tr);
+            }
+
+            return childrenRows;
+        };
+
+        addLevel(0, []);
+
+        popup(table);
+    });
+}
+

javascript/token-counters.js

@@ -0,0 +1,83 @@
+let promptTokenCountDebounceTime = 800;
+let promptTokenCountTimeouts = {};
+var promptTokenCountUpdateFunctions = {};
+
+function update_txt2img_tokens(...args) {
+    // Called from Gradio
+    update_token_counter("txt2img_token_button");
+    if (args.length == 2) {
+        return args[0];
+    }
+    return args;
+}
+
+function update_img2img_tokens(...args) {
+    // Called from Gradio
+    update_token_counter("img2img_token_button");
+    if (args.length == 2) {
+        return args[0];
+    }
+    return args;
+}
+
+function update_token_counter(button_id) {
+    if (opts.disable_token_counters) {
+        return;
+    }
+    if (promptTokenCountTimeouts[button_id]) {
+        clearTimeout(promptTokenCountTimeouts[button_id]);
+    }
+    promptTokenCountTimeouts[button_id] = setTimeout(
+        () => gradioApp().getElementById(button_id)?.click(),
+        promptTokenCountDebounceTime,
+    );
+}
+
+
+function recalculatePromptTokens(name) {
+    promptTokenCountUpdateFunctions[name]?.();
+}
+
+function recalculate_prompts_txt2img() {
+    // Called from Gradio
+    recalculatePromptTokens('txt2img_prompt');
+    recalculatePromptTokens('txt2img_neg_prompt');
+    return Array.from(arguments);
+}
+
+function recalculate_prompts_img2img() {
+    // Called from Gradio
+    recalculatePromptTokens('img2img_prompt');
+    recalculatePromptTokens('img2img_neg_prompt');
+    return Array.from(arguments);
+}
+
+function setupTokenCounting(id, id_counter, id_button) {
+    var prompt = gradioApp().getElementById(id);
+    var counter = gradioApp().getElementById(id_counter);
+    var textarea = gradioApp().querySelector(`#${id} > label > textarea`);
+
+    if (opts.disable_token_counters) {
+        counter.style.display = "none";
+        return;
+    }
+
+    if (counter.parentElement == prompt.parentElement) {
+        return;
+    }
+
+    prompt.parentElement.insertBefore(counter, prompt);
+    prompt.parentElement.style.position = "relative";
+
+    promptTokenCountUpdateFunctions[id] = function() {
+        update_token_counter(id_button);
+    };
+    textarea.addEventListener("input", promptTokenCountUpdateFunctions[id]);
+}
+
+function setupTokenCounters() {
+    setupTokenCounting('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
+    setupTokenCounting('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
+    setupTokenCounting('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
+    setupTokenCounting('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button');
+}

javascript/ui.js

@@ -248,29 +248,8 @@ function confirm_clear_prompt(prompt, negative_prompt) {
 }
 
 
-var promptTokecountUpdateFuncs = {};
-
-function recalculatePromptTokens(name) {
-    if (promptTokecountUpdateFuncs[name]) {
-        promptTokecountUpdateFuncs[name]();
-    }
-}
-
-function recalculate_prompts_txt2img() {
-    recalculatePromptTokens('txt2img_prompt');
-    recalculatePromptTokens('txt2img_neg_prompt');
-    return Array.from(arguments);
-}
-
-function recalculate_prompts_img2img() {
-    recalculatePromptTokens('img2img_prompt');
-    recalculatePromptTokens('img2img_neg_prompt');
-    return Array.from(arguments);
-}
-
-
 var opts = {};
-onUiUpdate(function() {
+onAfterUiUpdate(function() {
     if (Object.keys(opts).length != 0) return;
 
     var json_elem = gradioApp().getElementById('settings_json');
@@ -302,28 +281,7 @@ onUiUpdate(function() {
 
     json_elem.parentElement.style.display = "none";
 
-    function registerTextarea(id, id_counter, id_button) {
-        var prompt = gradioApp().getElementById(id);
-        var counter = gradioApp().getElementById(id_counter);
-        var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
-
-        if (counter.parentElement == prompt.parentElement) {
-            return;
-        }
-
-        prompt.parentElement.insertBefore(counter, prompt);
-        prompt.parentElement.style.position = "relative";
-
-        promptTokecountUpdateFuncs[id] = function() {
-            update_token_counter(id_button);
-        };
-        textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
-    }
-
-    registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
-    registerTextarea('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
-    registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
-    registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button');
+    setupTokenCounters();
 
     var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
     var settings_tabs = gradioApp().querySelector('#settings div');
@@ -354,33 +312,6 @@ onOptionsChanged(function() {
 });
 
 let txt2img_textarea, img2img_textarea = undefined;
-let wait_time = 800;
-let token_timeouts = {};
-
-function update_txt2img_tokens(...args) {
-    update_token_counter("txt2img_token_button");
-    if (args.length == 2) {
-        return args[0];
-    }
-    return args;
-}
-
-function update_img2img_tokens(...args) {
-    update_token_counter(
-        "img2img_token_button"
-    );
-    if (args.length == 2) {
-        return args[0];
-    }
-    return args;
-}
-
-function update_token_counter(button_id) {
-    if (token_timeouts[button_id]) {
-        clearTimeout(token_timeouts[button_id]);
-    }
-    token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
-}
 
 function restart_reload() {
     document.body.innerHTML = '<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';

javascript/ui_settings_hints.js

@@ -42,7 +42,7 @@ onOptionsChanged(function() {
 function settingsHintsShowQuicksettings() {
     requestGet("./internal/quicksettings-hint", {}, function(data) {
         var table = document.createElement('table');
-        table.className = 'settings-value-table';
+        table.className = 'popup-table';
 
         data.forEach(function(obj) {
             var tr = document.createElement('tr');

modules/api/api.py

@@ -14,7 +14,7 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -22,7 +22,8 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights,checkpoint_alisases
+from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_alisases
+from modules.sd_vae import vae_dict
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
@@ -108,7 +109,6 @@ def api_middleware(app: FastAPI):
         from rich.console import Console
         console = Console()
     except Exception:
-        import traceback
         rich_available = False
 
     @app.middleware("http")
@@ -139,11 +139,12 @@ def api_middleware(app: FastAPI):
             "errors": str(e),
         }
         if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
-            print(f"API error: {request.method}: {request.url} {err}")
+            message = f"API error: {request.method}: {request.url} {err}"
             if rich_available:
+                print(message)
                 console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
             else:
-                traceback.print_exc()
+                errors.report(message, exc_info=True)
         return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
 
     @app.middleware("http")
@@ -188,7 +189,9 @@ class Api:
         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[models.SamplerItem])
         self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
+        self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
         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/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
         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[models.FaceRestorerItem])
         self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
@@ -206,6 +209,11 @@ class Api:
         self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
         self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
 
+        if shared.cmd_opts.add_stop_route:
+            self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
+            self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
+            self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
+
         self.default_script_arg_txt2img = []
         self.default_script_arg_img2img = []
 
@@ -278,7 +286,7 @@ class Api:
             script_args[0] = selectable_idx + 1
 
         # Now check for always on scripts
-        if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
+        if request.alwayson_scripts:
             for alwayson_script_name in request.alwayson_scripts.keys():
                 alwayson_script = self.get_script(alwayson_script_name, script_runner)
                 if alwayson_script is None:
@@ -542,9 +550,20 @@ class Api:
             for upscaler in shared.sd_upscalers
         ]
 
+    def get_latent_upscale_modes(self):
+        return [
+            {
+                "name": upscale_mode,
+            }
+            for upscale_mode in [*(shared.latent_upscale_modes or {})]
+        ]
+
     def get_sd_models(self):
         return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
 
+    def get_sd_vaes(self):
+        return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()]
+
     def get_hypernetworks(self):
         return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
 
@@ -704,4 +723,16 @@ class Api:
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
-        uvicorn.run(self.app, host=server_name, port=port)
+        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=0)
+
+    def kill_webui(self):
+        restart.stop_program()
+
+    def restart_webui(self):
+        if restart.is_restartable():
+            restart.restart_program()
+        return Response(status_code=501)
+
+    def stop_webui(request):
+        shared.state.server_command = "stop"
+        return Response("Stopping.")

modules/api/models.py

@@ -241,6 +241,9 @@ class UpscalerItem(BaseModel):
     model_url: Optional[str] = Field(title="URL")
     scale: Optional[float] = Field(title="Scale")
 
+class LatentUpscalerModeItem(BaseModel):
+    name: str = Field(title="Name")
+
 class SDModelItem(BaseModel):
     title: str = Field(title="Title")
     model_name: str = Field(title="Model Name")
@@ -249,6 +252,10 @@ class SDModelItem(BaseModel):
     filename: str = Field(title="Filename")
     config: Optional[str] = Field(title="Config file")
 
+class SDVaeItem(BaseModel):
+    model_name: str = Field(title="Model Name")
+    filename: str = Field(title="Filename")
+
 class HypernetworkItem(BaseModel):
     name: str = Field(title="Name")
     path: Optional[str] = Field(title="Path")

modules/call_queue.py

@@ -1,10 +1,9 @@
+from functools import wraps
 import html
-import sys
 import threading
-import traceback
 import time
 
-from modules import shared, progress
+from modules import shared, progress, errors
 
 queue_lock = threading.Lock()
 
@@ -20,10 +19,11 @@ def wrap_queued_call(func):
 
 
 def wrap_gradio_gpu_call(func, extra_outputs=None):
+    @wraps(func)
     def f(*args, **kwargs):
 
         # if the first argument is a string that says "task(...)", it is treated as a job id
-        if len(args) > 0 and type(args[0]) == str and args[0][0:5] == "task(" and args[0][-1] == ")":
+        if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"):
             id_task = args[0]
             progress.add_task_to_queue(id_task)
         else:
@@ -47,6 +47,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 
 def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    @wraps(func)
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
         if run_memmon:
@@ -56,16 +57,14 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
         try:
             res = list(func(*args, **kwargs))
         except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {args} {kwargs}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
+            # When printing out our debug argument list,
+            # do not print out more than a 100 KB of text
+            max_debug_str_len = 131072
+            message = "Error completing request"
+            arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len]
+            if len(arg_str) > max_debug_str_len:
+                arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
+            errors.report(f"{message}\n{arg_str}", exc_info=True)
 
             shared.state.job = ""
             shared.state.job_count = 0
@@ -108,4 +107,3 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
         return tuple(res)
 
     return f
-

modules/cmd_args.py

@@ -11,7 +11,7 @@ parser.add_argument("--skip-python-version-check", action='store_true', help="la
 parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
 parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
 parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
-parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
+parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
 parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
 parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
 parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
@@ -106,4 +106,4 @@ parser.add_argument("--skip-version-check", action='store_true', help="Do not ch
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
 parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
-parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
+parser.add_argument('--add-stop-route', action='store_true', help='enable server stop/restart/kill via api')

modules/codeformer_model.py

@@ -1,13 +1,11 @@
 import os
-import sys
-import traceback
 
 import cv2
 import torch
 
 import modules.face_restoration
 import modules.shared
-from modules import shared, devices, modelloader
+from modules import shared, devices, modelloader, errors
 from modules.paths import models_path
 
 # codeformer people made a choice to include modified basicsr library to their project which makes
@@ -22,9 +20,7 @@ codeformer = None
 
 
 def setup_model(dirname):
-    global model_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
+    os.makedirs(model_path, exist_ok=True)
 
     path = modules.paths.paths.get("CodeFormer", None)
     if path is None:
@@ -105,8 +101,8 @@ def setup_model(dirname):
                             restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
                         del output
                         torch.cuda.empty_cache()
-                    except Exception as error:
-                        print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
+                    except Exception:
+                        errors.report('Failed inference for CodeFormer', exc_info=True)
                         restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
 
                     restored_face = restored_face.astype('uint8')
@@ -135,7 +131,6 @@ def setup_model(dirname):
         shared.face_restorers.append(codeformer)
 
     except Exception:
-        print("Error setting up CodeFormer:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report("Error setting up CodeFormer", exc_info=True)
 
    # sys.path = stored_sys_path

modules/config_states.py

@@ -3,8 +3,6 @@ Supports saving and restoring webui and extensions from a known working set of c
 """
 
 import os
-import sys
-import traceback
 import json
 import time
 import tqdm
@@ -13,7 +11,7 @@ from datetime import datetime
 from collections import OrderedDict
 import git
 
-from modules import shared, extensions
+from modules import shared, extensions, errors
 from modules.paths_internal import script_path, config_states_dir
 
 
@@ -53,8 +51,7 @@ def get_webui_config():
         if os.path.exists(os.path.join(script_path, ".git")):
             webui_repo = git.Repo(script_path)
     except Exception:
-        print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report(f"Error reading webui git info from {script_path}", exc_info=True)
 
     webui_remote = None
     webui_commit_hash = None
@@ -134,8 +131,7 @@ def restore_webui_config(config):
         if os.path.exists(os.path.join(script_path, ".git")):
             webui_repo = git.Repo(script_path)
     except Exception:
-        print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report(f"Error reading webui git info from {script_path}", exc_info=True)
         return
 
     try:
@@ -143,8 +139,7 @@ def restore_webui_config(config):
         webui_repo.git.reset(webui_commit_hash, hard=True)
         print(f"* Restored webui to commit {webui_commit_hash}.")
     except Exception:
-        print(f"Error restoring webui to commit {webui_commit_hash}:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report(f"Error restoring webui to commit{webui_commit_hash}")
 
 
 def restore_extension_config(config):

modules/devices.py

@@ -1,5 +1,7 @@
 import sys
 import contextlib
+from functools import lru_cache
+
 import torch
 from modules import errors
 
@@ -154,3 +156,19 @@ def test_for_nans(x, where):
     message += " Use --disable-nan-check commandline argument to disable this check."
 
     raise NansException(message)
+
+
+@lru_cache
+def first_time_calculation():
+    """
+    just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
+    spends about 2.7 seconds doing that, at least wih NVidia.
+    """
+
+    x = torch.zeros((1, 1)).to(device, dtype)
+    linear = torch.nn.Linear(1, 1).to(device, dtype)
+    linear(x)
+
+    x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
+    conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
+    conv2d(x)

modules/errors.py

@@ -1,8 +1,42 @@
 import sys
+import textwrap
 import traceback
 
 
+exception_records = []
+
+
+def record_exception():
+    _, e, tb = sys.exc_info()
+    if e is None:
+        return
+
+    if exception_records and exception_records[-1] == e:
+        return
+
+    exception_records.append((e, tb))
+
+    if len(exception_records) > 5:
+        exception_records.pop(0)
+
+
+def report(message: str, *, exc_info: bool = False) -> None:
+    """
+    Print an error message to stderr, with optional traceback.
+    """
+
+    record_exception()
+
+    for line in message.splitlines():
+        print("***", line, file=sys.stderr)
+    if exc_info:
+        print(textwrap.indent(traceback.format_exc(), "    "), file=sys.stderr)
+        print("---", file=sys.stderr)
+
+
 def print_error_explanation(message):
+    record_exception()
+
     lines = message.strip().split("\n")
     max_len = max([len(x) for x in lines])
 
@@ -12,9 +46,15 @@ def print_error_explanation(message):
     print('=' * max_len, file=sys.stderr)
 
 
-def display(e: Exception, task):
+def display(e: Exception, task, *, full_traceback=False):
+    record_exception()
+
     print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
-    print(traceback.format_exc(), file=sys.stderr)
+    te = traceback.TracebackException.from_exception(e)
+    if full_traceback:
+        # include frames leading up to the try-catch block
+        te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack)
+    print(*te.format(), sep="", file=sys.stderr)
 
     message = str(e)
     if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
@@ -28,6 +68,8 @@ already_displayed = {}
 
 
 def display_once(e: Exception, task):
+    record_exception()
+
     if task in already_displayed:
         return
 

modules/esrgan_model.py

@@ -1,15 +1,13 @@
-import os
+import sys
 
 import numpy as np
 import torch
 from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgan_model_arch as arch
 from modules import modelloader, images, devices
-from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
-
+from modules.upscaler import Upscaler, UpscalerData
 
 
 def mod2normal(state_dict):
@@ -134,7 +132,7 @@ class UpscalerESRGAN(Upscaler):
             scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
             scalers.append(scaler_data)
         for file in model_paths:
-            if "http" in file:
+            if file.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(file)
@@ -143,26 +141,25 @@ class UpscalerESRGAN(Upscaler):
             self.scalers.append(scaler_data)
 
     def do_upscale(self, img, selected_model):
-        model = self.load_model(selected_model)
-        if model is None:
+        try:
+            model = self.load_model(selected_model)
+        except Exception as e:
+            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
             return img
         model.to(devices.device_esrgan)
         img = esrgan_upscale(model, img)
         return img
 
     def load_model(self, path: str):
-        if "http" in path:
-            filename = load_file_from_url(
+        if path.startswith("http"):
+            # TODO: this doesn't use `path` at all?
+            filename = modelloader.load_file_from_url(
                 url=self.model_url,
                 model_dir=self.model_download_path,
                 file_name=f"{self.model_name}.pth",
-                progress=True,
             )
         else:
             filename = path
-        if not os.path.exists(filename) or filename is None:
-            print(f"Unable to load {self.model_path} from {filename}")
-            return None
 
         state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
 

modules/extensions.py

@@ -1,17 +1,13 @@
 import os
-import sys
 import threading
-import traceback
 
-import git
-
-from modules import shared
+from modules import shared, errors
+from modules.gitpython_hack import Repo
 from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401
 
 extensions = []
 
-if not os.path.exists(extensions_dir):
-    os.makedirs(extensions_dir)
+os.makedirs(extensions_dir, exist_ok=True)
 
 
 def active():
@@ -54,10 +50,9 @@ class Extension:
         repo = None
         try:
             if os.path.exists(os.path.join(self.path, ".git")):
-                repo = git.Repo(self.path)
+                repo = Repo(self.path)
         except Exception:
-            print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report(f"Error reading github repository info from {self.path}", exc_info=True)
 
         if repo is None or repo.bare:
             self.remote = None
@@ -72,8 +67,8 @@ class Extension:
                 self.commit_hash = commit.hexsha
                 self.version = self.commit_hash[:8]
 
-            except Exception as ex:
-                print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
+            except Exception:
+                errors.report(f"Failed reading extension data from Git repository ({self.name})", exc_info=True)
                 self.remote = None
 
         self.have_info_from_repo = True
@@ -94,7 +89,7 @@ class Extension:
         return res
 
     def check_updates(self):
-        repo = git.Repo(self.path)
+        repo = Repo(self.path)
         for fetch in repo.remote().fetch(dry_run=True):
             if fetch.flags != fetch.HEAD_UPTODATE:
                 self.can_update = True
@@ -116,7 +111,7 @@ class Extension:
         self.status = "latest"
 
     def fetch_and_reset_hard(self, commit='origin'):
-        repo = git.Repo(self.path)
+        repo = Repo(self.path)
         # Fix: `error: Your local changes to the following files would be overwritten by merge`,
         # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
         repo.git.fetch(all=True)

modules/extra_networks.py

@@ -32,6 +32,9 @@ class ExtraNetworkParams:
             else:
                 self.positional.append(item)
 
+    def __eq__(self, other):
+        return self.items == other.items
+
 
 class ExtraNetwork:
     def __init__(self, name):

modules/extra_networks_hypernet.py

@@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):
         additional = shared.opts.sd_hypernetwork
 
-        if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+        if additional != "None" and additional in shared.hypernetworks and not any(x for x in params_list if x.items[0] == additional):
             hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
             p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
@@ -17,7 +17,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
         names = []
         multipliers = []
         for params in params_list:
-            assert len(params.items) > 0
+            assert params.items
 
             names.append(params.items[0])
             multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)

modules/generation_parameters_copypaste.py

@@ -55,7 +55,7 @@ def image_from_url_text(filedata):
     if filedata is None:
         return None
 
-    if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False):
+    if type(filedata) == list and filedata and type(filedata[0]) == dict and filedata[0].get("is_file", False):
         filedata = filedata[0]
 
     if type(filedata) == dict and filedata.get("is_file", False):
@@ -265,19 +265,30 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         else:
             prompt += ("" if prompt == "" else "\n") + line
 
+    if shared.opts.infotext_styles != "Ignore":
+        found_styles, prompt, negative_prompt = shared.prompt_styles.extract_styles_from_prompt(prompt, negative_prompt)
+
+        if shared.opts.infotext_styles == "Apply":
+            res["Styles array"] = found_styles
+        elif shared.opts.infotext_styles == "Apply if any" and found_styles:
+            res["Styles array"] = found_styles
+
     res["Prompt"] = prompt
     res["Negative prompt"] = negative_prompt
 
     for k, v in re_param.findall(lastline):
-        if v[0] == '"' and v[-1] == '"':
-            v = unquote(v)
+        try:
+            if v[0] == '"' and v[-1] == '"':
+                v = unquote(v)
 
-        m = re_imagesize.match(v)
-        if m is not None:
-            res[f"{k}-1"] = m.group(1)
-            res[f"{k}-2"] = m.group(2)
-        else:
-            res[k] = v
+            m = re_imagesize.match(v)
+            if m is not None:
+                res[f"{k}-1"] = m.group(1)
+                res[f"{k}-2"] = m.group(2)
+            else:
+                res[k] = v
+        except Exception:
+            print(f"Error parsing \"{k}: {v}\"")
 
     # Missing CLIP skip means it was set to 1 (the default)
     if "Clip skip" not in res:
@@ -306,6 +317,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "RNG" not in res:
         res["RNG"] = "GPU"
 
+    if "Schedule type" not in res:
+        res["Schedule type"] = "Automatic"
+
+    if "Schedule max sigma" not in res:
+        res["Schedule max sigma"] = 0
+
+    if "Schedule min sigma" not in res:
+        res["Schedule min sigma"] = 0
+
+    if "Schedule rho" not in res:
+        res["Schedule rho"] = 0
+
     return res
 
 
@@ -318,6 +341,10 @@ infotext_to_setting_name_mapping = [
     ('Conditional mask weight', 'inpainting_mask_weight'),
     ('Model hash', 'sd_model_checkpoint'),
     ('ENSD', 'eta_noise_seed_delta'),
+    ('Schedule type', 'k_sched_type'),
+    ('Schedule max sigma', 'sigma_max'),
+    ('Schedule min sigma', 'sigma_min'),
+    ('Schedule rho', 'rho'),
     ('Noise multiplier', 'initial_noise_multiplier'),
     ('Eta', 'eta_ancestral'),
     ('Eta DDIM', 'eta_ddim'),
@@ -330,6 +357,7 @@ infotext_to_setting_name_mapping = [
     ('Token merging ratio hr', 'token_merging_ratio_hr'),
     ('RNG', 'randn_source'),
     ('NGMS', 's_min_uncond'),
+    ('Pad conds', 'pad_cond_uncond'),
 ]
 
 
@@ -421,7 +449,7 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
 
             vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
 
-            return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
+            return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
 
         paste_fields = paste_fields + [(override_settings_component, paste_settings)]
 
@@ -438,5 +466,3 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
         outputs=[],
         show_progress=False,
     )
-
-

modules/gfpgan_model.py

@@ -1,12 +1,10 @@
 import os
-import sys
-import traceback
 
 import facexlib
 import gfpgan
 
 import modules.face_restoration
-from modules import paths, shared, devices, modelloader
+from modules import paths, shared, devices, modelloader, errors
 
 model_dir = "GFPGAN"
 user_path = None
@@ -27,7 +25,7 @@ def gfpgann():
         return None
 
     models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
-    if len(models) == 1 and "http" in models[0]:
+    if len(models) == 1 and models[0].startswith("http"):
         model_file = models[0]
     elif len(models) != 0:
         latest_file = max(models, key=os.path.getctime)
@@ -72,11 +70,8 @@ gfpgan_constructor = None
 
 
 def setup_model(dirname):
-    global model_path
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-
     try:
+        os.makedirs(model_path, exist_ok=True)
         from gfpgan import GFPGANer
         from facexlib import detection, parsing  # noqa: F401
         global user_path
@@ -112,5 +107,4 @@ def setup_model(dirname):
 
         shared.face_restorers.append(FaceRestorerGFPGAN())
     except Exception:
-        print("Error setting up GFPGAN:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report("Error setting up GFPGAN", exc_info=True)

modules/gitpython_hack.py

@@ -0,0 +1,42 @@
+from __future__ import annotations
+
+import io
+import subprocess
+
+import git
+
+
+class Git(git.Git):
+    """
+    Git subclassed to never use persistent processes.
+    """
+
+    def _get_persistent_cmd(self, attr_name, cmd_name, *args, **kwargs):
+        raise NotImplementedError(f"Refusing to use persistent process: {attr_name} ({cmd_name} {args} {kwargs})")
+
+    def get_object_header(self, ref: str | bytes) -> tuple[str, str, int]:
+        ret = subprocess.check_output(
+            [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch-check"],
+            input=self._prepare_ref(ref),
+            cwd=self._working_dir,
+            timeout=2,
+        )
+        return self._parse_object_header(ret)
+
+    def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
+        # Not really streaming, per se; this buffers the entire object in memory.
+        # Shouldn't be a problem for our use case, since we're only using this for
+        # object headers (commit objects).
+        ret = subprocess.check_output(
+            [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch"],
+            input=self._prepare_ref(ref),
+            cwd=self._working_dir,
+            timeout=30,
+        )
+        bio = io.BytesIO(ret)
+        hexsha, typename, size = self._parse_object_header(bio.readline())
+        return (hexsha, typename, size, self.CatFileContentStream(size, bio))
+
+
+class Repo(git.Repo):
+    GitCommandWrapperType = Git

modules/hypernetworks/hypernetwork.py

@@ -2,8 +2,6 @@ import datetime
 import glob
 import html
 import os
-import sys
-import traceback
 import inspect
 
 import modules.textual_inversion.dataset
@@ -11,7 +9,7 @@ import torch
 import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
-from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint
+from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
 from modules.textual_inversion import textual_inversion, logging
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
@@ -325,17 +323,14 @@ def load_hypernetwork(name):
     if path is None:
         return None
 
-    hypernetwork = Hypernetwork()
-
     try:
+        hypernetwork = Hypernetwork()
         hypernetwork.load(path)
+        return hypernetwork
     except Exception:
-        print(f"Error loading hypernetwork {path}", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report(f"Error loading hypernetwork {path}", exc_info=True)
         return None
 
-    return hypernetwork
-
 
 def load_hypernetworks(names, multipliers=None):
     already_loaded = {}
@@ -770,7 +765,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
     except Exception:
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report("Exception in training hypernetwork", exc_info=True)
     finally:
         pbar.leave = False
         pbar.close()

modules/images.py

@@ -1,6 +1,4 @@
 import datetime
-import sys
-import traceback
 
 import pytz
 import io
@@ -21,6 +19,8 @@ from modules import sd_samplers, shared, script_callbacks, errors
 from modules.paths_internal import roboto_ttf_file
 from modules.shared import opts
 
+import modules.sd_vae as sd_vae
+
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 
 
@@ -336,8 +336,20 @@ def sanitize_filename_part(text, replace_spaces=True):
 
 
 class FilenameGenerator:
+    def get_vae_filename(self): #get the name of the VAE file.
+        if sd_vae.loaded_vae_file is None:
+            return "NoneType"
+        file_name = os.path.basename(sd_vae.loaded_vae_file)
+        split_file_name = file_name.split('.')
+        if len(split_file_name) > 1 and split_file_name[0] == '':
+            return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
+        else:
+            return split_file_name[0]
+
     replacements = {
         'seed': lambda self: self.seed if self.seed is not None else '',
+        'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
+        'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1],
         'steps': lambda self:  self.p and self.p.steps,
         'cfg': lambda self: self.p and self.p.cfg_scale,
         'width': lambda self: self.image.width,
@@ -354,19 +366,23 @@ class FilenameGenerator:
         'prompt_no_styles': lambda self: self.prompt_no_style(),
         'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
         'prompt_words': lambda self: self.prompt_words(),
-        'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
-        'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+        'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 or self.zip else self.p.batch_index + 1,
+        'batch_size': lambda self: self.p.batch_size,
+        'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
         'hasprompt': lambda self, *args: self.hasprompt(*args),  # accepts formats:[hasprompt<prompt1|default><prompt2>..]
         'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
         'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
+        'user': lambda self: self.p.user,
+        'vae_filename': lambda self: self.get_vae_filename(),
     }
     default_time_format = '%Y%m%d%H%M%S'
 
-    def __init__(self, p, seed, prompt, image):
+    def __init__(self, p, seed, prompt, image, zip=False):
         self.p = p
         self.seed = seed
         self.prompt = prompt
         self.image = image
+        self.zip = zip
 
     def hasprompt(self, *args):
         lower = self.prompt.lower()
@@ -390,7 +406,7 @@ class FilenameGenerator:
 
         prompt_no_style = self.prompt
         for style in shared.prompt_styles.get_style_prompts(self.p.styles):
-            if len(style) > 0:
+            if style:
                 for part in style.split("{prompt}"):
                     prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
 
@@ -399,7 +415,7 @@ class FilenameGenerator:
         return sanitize_filename_part(prompt_no_style, replace_spaces=False)
 
     def prompt_words(self):
-        words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0]
+        words = [x for x in re_nonletters.split(self.prompt or "") if x]
         if len(words) == 0:
             words = ["empty"]
         return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
@@ -407,7 +423,7 @@ class FilenameGenerator:
     def datetime(self, *args):
         time_datetime = datetime.datetime.now()
 
-        time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
+        time_format = args[0] if (args 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:
@@ -446,8 +462,7 @@ class FilenameGenerator:
                     replacement = fun(self, *pattern_args)
                 except Exception:
                     replacement = None
-                    print(f"Error adding [{pattern}] to filename", file=sys.stderr)
-                    print(traceback.format_exc(), file=sys.stderr)
+                    errors.report(f"Error adding [{pattern}] to filename", exc_info=True)
 
                 if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
                     continue
@@ -664,9 +679,10 @@ def read_info_from_image(image):
             items['exif comment'] = exif_comment
             geninfo = exif_comment
 
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
-                      'loop', 'background', 'timestamp', 'duration']:
-            items.pop(field, None)
+    for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+                    'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
+                    'icc_profile', 'chromaticity']:
+        items.pop(field, None)
 
     if items.get("Software", None) == "NovelAI":
         try:
@@ -677,8 +693,7 @@ def read_info_from_image(image):
 Negative prompt: {json_info["uc"]}
 Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
         except Exception:
-            print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report("Error parsing NovelAI image generation parameters", exc_info=True)
 
     return geninfo, items
 

modules/img2img.py

@@ -1,7 +1,9 @@
 import os
+from pathlib import Path
 
 import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
+import gradio as gr
 
 from modules import sd_samplers
 from modules.generation_parameters_copypaste import create_override_settings_dict
@@ -13,7 +15,7 @@ from modules.ui import plaintext_to_html
 import modules.scripts
 
 
-def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
+def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0):
     processing.fix_seed(p)
 
     images = shared.listfiles(input_dir)
@@ -21,9 +23,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
     is_inpaint_batch = False
     if inpaint_mask_dir:
         inpaint_masks = shared.listfiles(inpaint_mask_dir)
-        is_inpaint_batch = len(inpaint_masks) > 0
-    if is_inpaint_batch:
-        print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
+        is_inpaint_batch = bool(inpaint_masks)
+
+        if is_inpaint_batch:
+            print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
@@ -49,14 +52,31 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
             continue
         # Use the EXIF orientation of photos taken by smartphones.
         img = ImageOps.exif_transpose(img)
+
+        if to_scale:
+            p.width = int(img.width * scale_by)
+            p.height = int(img.height * scale_by)
+
         p.init_images = [img] * p.batch_size
 
+        image_path = Path(image)
         if is_inpaint_batch:
             # 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 mask_image_path not in inpaint_masks:
+            if len(inpaint_masks) == 1:
                 mask_image_path = inpaint_masks[0]
+            else:
+                # try to find corresponding mask for an image using simple filename matching
+                mask_image_dir = Path(inpaint_mask_dir)
+                masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*"))
+
+                if len(masks_found) == 0:
+                    print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.")
+                    continue
+
+                # it should contain only 1 matching mask
+                # otherwise user has many masks with the same name but different extensions
+                mask_image_path = masks_found[0]
+
             mask_image = Image.open(mask_image_path)
             p.image_mask = mask_image
 
@@ -65,7 +85,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
             proc = process_images(p)
 
         for n, processed_image in enumerate(proc.images):
-            filename = os.path.basename(image)
+            filename = image_path.name
 
             if n > 0:
                 left, right = os.path.splitext(filename)
@@ -78,7 +98,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, request: gr.Request, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     is_batch = mode == 5
@@ -92,7 +112,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     elif mode == 2:  # inpaint
         image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
         alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
-        mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
+        mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
+        mask = ImageChops.lighter(alpha_mask, mask).convert('L')
         image = image.convert("RGB")
     elif mode == 3:  # inpaint sketch
         image = inpaint_color_sketch
@@ -114,7 +135,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     if image is not None:
         image = ImageOps.exif_transpose(image)
 
-    if selected_scale_tab == 1:
+    if selected_scale_tab == 1 and not is_batch:
         assert image, "Can't scale by because no image is selected"
 
         width = int(image.width * scale_by)
@@ -160,6 +181,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     p.scripts = modules.scripts.scripts_img2img
     p.script_args = args
 
+    p.user = request.username
+
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
@@ -169,7 +192,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     if is_batch:
         assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
 
-        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)
+        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by)
 
         processed = Processed(p, [], p.seed, "")
     else:

modules/interrogate.py

@@ -1,6 +1,5 @@
 import os
 import sys
-import traceback
 from collections import namedtuple
 from pathlib import Path
 import re
@@ -216,8 +215,7 @@ class InterrogateModels:
                             res += f", {match}"
 
         except Exception:
-            print("Error interrogating", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report("Error interrogating", exc_info=True)
             res += "<error>"
 
         self.unload()

modules/launch_utils.py

@@ -7,7 +7,7 @@ import platform
 import json
 from functools import lru_cache
 
-from modules import cmd_args
+from modules import cmd_args, errors
 from modules.paths_internal import script_path, extensions_dir
 
 args, _ = cmd_args.parser.parse_known_args()
@@ -68,7 +68,13 @@ def git_tag():
     try:
         return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
     except Exception:
-        return "<none>"
+        try:
+            from pathlib import Path
+            changelog_md = Path(__file__).parent.parent / "CHANGELOG.md"
+            with changelog_md.open(encoding="utf-8") as file:
+                return next((line.strip() for line in file if line.strip()), "<none>")
+        except Exception:
+            return "<none>"
 
 
 def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
@@ -141,10 +147,10 @@ def git_clone(url, dir, name, commithash=None):
             return
 
         run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
-        run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
+        run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
         return
 
-    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
+    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
 
     if commithash is not None:
         run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
@@ -188,7 +194,7 @@ def run_extension_installer(extension_dir):
 
         print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
-        print(e, file=sys.stderr)
+        errors.report(str(e))
 
 
 def list_extensions(settings_file):
@@ -198,8 +204,8 @@ def list_extensions(settings_file):
         if os.path.isfile(settings_file):
             with open(settings_file, "r", encoding="utf8") as file:
                 settings = json.load(file)
-    except Exception as e:
-        print(e, file=sys.stderr)
+    except Exception:
+        errors.report("Could not load settings", exc_info=True)
 
     disabled_extensions = set(settings.get('disabled_extensions', []))
     disable_all_extensions = settings.get('disable_all_extensions', 'none')
@@ -223,23 +229,28 @@ def prepare_environment():
     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')
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
     clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
 
     stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
-    taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
     k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
     codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
-    taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
     k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
+    try:
+        # the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
+        os.remove(os.path.join(script_path, "tmp", "restart"))
+        os.environ.setdefault('SD_WEBUI_RESTARTING ', '1')
+    except OSError:
+        pass
+
     if not args.skip_python_version_check:
         check_python_version()
 
@@ -286,7 +297,6 @@ def prepare_environment():
     os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
 
     git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
-    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
     git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
     git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
     git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)

modules/localization.py

@@ -1,8 +1,7 @@
 import json
 import os
-import sys
-import traceback
 
+from modules import errors
 
 localizations = {}
 
@@ -31,7 +30,6 @@ def localization_js(current_localization_name: str) -> str:
             with open(fn, "r", encoding="utf8") as file:
                 data = json.load(file)
         except Exception:
-            print(f"Error loading localization from {fn}:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report(f"Error loading localization from {fn}", exc_info=True)
 
     return f"window.localization = {json.dumps(data)}"

modules/lowvram.py

@@ -15,6 +15,8 @@ def send_everything_to_cpu():
 
 
 def setup_for_low_vram(sd_model, use_medvram):
+    sd_model.lowvram = True
+
     parents = {}
 
     def send_me_to_gpu(module, _):
@@ -96,3 +98,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
         for block in diff_model.output_blocks:
             block.register_forward_pre_hook(send_me_to_gpu)
+
+
+def is_enabled(sd_model):
+    return getattr(sd_model, 'lowvram', False)

modules/modelloader.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import os
 import shutil
 import importlib
@@ -8,6 +10,29 @@ from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, Upscale
 from modules.paths import script_path, models_path
 
 
+def load_file_from_url(
+    url: str,
+    *,
+    model_dir: str,
+    progress: bool = True,
+    file_name: str | None = None,
+) -> str:
+    """Download a file from `url` into `model_dir`, using the file present if possible.
+
+    Returns the path to the downloaded file.
+    """
+    os.makedirs(model_dir, exist_ok=True)
+    if not file_name:
+        parts = urlparse(url)
+        file_name = os.path.basename(parts.path)
+    cached_file = os.path.abspath(os.path.join(model_dir, file_name))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        from torch.hub import download_url_to_file
+        download_url_to_file(url, cached_file, progress=progress)
+    return cached_file
+
+
 def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
     """
     A one-and done loader to try finding the desired models in specified directories.
@@ -46,9 +71,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
         if model_url is not None and len(output) == 0:
             if download_name is not None:
-                from basicsr.utils.download_util import load_file_from_url
-                dl = load_file_from_url(model_url, places[0], True, download_name)
-                output.append(dl)
+                output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
             else:
                 output.append(model_url)
 
@@ -59,7 +82,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
 
 def friendly_name(file: str):
-    if "http" in file:
+    if file.startswith("http"):
         file = urlparse(file).path
 
     file = os.path.basename(file)
@@ -95,8 +118,7 @@ def cleanup_models():
 
 def move_files(src_path: str, dest_path: str, ext_filter: str = None):
     try:
-        if not os.path.exists(dest_path):
-            os.makedirs(dest_path)
+        os.makedirs(dest_path, exist_ok=True)
         if os.path.exists(src_path):
             for file in os.listdir(src_path):
                 fullpath = os.path.join(src_path, file)

modules/models/diffusion/ddpm_edit.py

@@ -230,9 +230,9 @@ class DDPM(pl.LightningModule):
         missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
             sd, strict=False)
         print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
+        if missing:
             print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
+        if unexpected:
             print(f"Unexpected Keys: {unexpected}")
 
     def q_mean_variance(self, x_start, t):

modules/paths.py

@@ -20,7 +20,6 @@ assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possibl
 
 path_dirs = [
     (sd_path, 'ldm', 'Stable Diffusion', []),
-    (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
     (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
     (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
     (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),

modules/processing.py

@@ -1,4 +1,5 @@
 import json
+import logging
 import math
 import os
 import sys
@@ -6,14 +7,14 @@ import hashlib
 
 import torch
 import numpy as np
-from PIL import Image, ImageFilter, ImageOps
+from PIL import Image, ImageOps
 import random
 import cv2
 from skimage import exposure
 from typing import Any, Dict, List
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -23,7 +24,6 @@ import modules.images as images
 import modules.styles
 import modules.sd_models as sd_models
 import modules.sd_vae as sd_vae
-import logging
 from ldm.data.util import AddMiDaS
 from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
@@ -106,6 +106,9 @@ class StableDiffusionProcessing:
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
+    cached_uc = [None, None]
+    cached_c = [None, None]
+
     def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
@@ -171,15 +174,18 @@ class StableDiffusionProcessing:
 
         self.prompts = None
         self.negative_prompts = None
+        self.extra_network_data = None
         self.seeds = None
         self.subseeds = None
 
         self.step_multiplier = 1
-        self.cached_uc = [None, None]
-        self.cached_c = [None, None]
+        self.cached_uc = StableDiffusionProcessing.cached_uc
+        self.cached_c = StableDiffusionProcessing.cached_c
         self.uc = None
         self.c = None
 
+        self.user = None
+
     @property
     def sd_model(self):
         return shared.sd_model
@@ -288,8 +294,9 @@ class StableDiffusionProcessing:
         self.sampler = None
         self.c = None
         self.uc = None
-        self.cached_c = [None, None]
-        self.cached_uc = [None, None]
+        if not opts.experimental_persistent_cond_cache:
+            StableDiffusionProcessing.cached_c = [None, None]
+            StableDiffusionProcessing.cached_uc = [None, None]
 
     def get_token_merging_ratio(self, for_hr=False):
         if for_hr:
@@ -311,7 +318,7 @@ class StableDiffusionProcessing:
         self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
         self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
 
-    def get_conds_with_caching(self, function, required_prompts, steps, cache):
+    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
         """
         Returns the result of calling function(shared.sd_model, required_prompts, steps)
         using a cache to store the result if the same arguments have been used before.
@@ -320,27 +327,29 @@ class StableDiffusionProcessing:
         representing the previously used arguments, or None if no arguments
         have been used before. The second element is where the previously
         computed result is stored.
+
+        caches is a list with items described above.
         """
-        if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info) == cache[0]:
-            return cache[1]
+        for cache in caches:
+            if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
+                return cache[1]
+
+        cache = caches[0]
 
         with devices.autocast():
             cache[1] = function(shared.sd_model, required_prompts, steps)
 
-        cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info)
+        cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data)
         return cache[1]
 
     def setup_conds(self):
         sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
         self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
-
-        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
-        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, self.cached_c)
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
 
     def parse_extra_network_prompts(self):
-        self.prompts, extra_network_data = extra_networks.parse_prompts(self.prompts)
-
-        return extra_network_data
+        self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
 
 
 class Processed:
@@ -578,6 +587,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "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,
+        "User": p.user if opts.add_user_name_to_info else None,
     }
 
     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])
@@ -588,6 +598,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
+    if p.scripts is not None:
+        p.scripts.before_process(p)
+
     stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
 
     try:
@@ -673,10 +686,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
                 sd_vae_approx.model()
 
+            sd_unet.apply_unet()
+
         if state.job_count == -1:
             state.job_count = p.n_iter
 
-        extra_network_data = None
         for n in range(p.n_iter):
             p.iteration = n
 
@@ -697,11 +711,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(p.prompts) == 0:
                 break
 
-            extra_network_data = p.parse_extra_network_prompts()
+            p.parse_extra_network_prompts()
 
             if not p.disable_extra_networks:
                 with devices.autocast():
-                    extra_networks.activate(p, extra_network_data)
+                    extra_networks.activate(p, p.extra_network_data)
 
             if p.scripts is not None:
                 p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
@@ -736,7 +750,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             del samples_ddim
 
-            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+            if lowvram.is_enabled(shared.sd_model):
                 lowvram.send_everything_to_cpu()
 
             devices.torch_gc()
@@ -823,8 +837,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if opts.grid_save:
                 images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
-    if not p.disable_extra_networks and extra_network_data:
-        extra_networks.deactivate(p, extra_network_data)
+    if not p.disable_extra_networks and p.extra_network_data:
+        extra_networks.deactivate(p, p.extra_network_data)
 
     devices.torch_gc()
 
@@ -859,6 +873,8 @@ def old_hires_fix_first_pass_dimensions(width, height):
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
+    cached_hr_uc = [None, None]
+    cached_hr_c = [None, None]
 
     def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
         super().__init__(**kwargs)
@@ -891,6 +907,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_negative_prompts = None
         self.hr_extra_network_data = None
 
+        self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
+        self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
         self.hr_c = None
         self.hr_uc = None
 
@@ -970,7 +988,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
         if self.enable_hr and latent_scale_mode is None:
-            assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
+            if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
+                raise Exception(f"could not find upscaler named {self.hr_upscaler}")
 
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
@@ -1053,6 +1072,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             with devices.autocast():
                 extra_networks.activate(self, self.hr_extra_network_data)
 
+        with devices.autocast():
+            self.calculate_hr_conds()
+
         sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
 
         samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
@@ -1064,8 +1086,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         return samples
 
     def close(self):
+        super().close()
         self.hr_c = None
         self.hr_uc = None
+        if not opts.experimental_persistent_cond_cache:
+            StableDiffusionProcessingTxt2Img.cached_hr_uc = [None, None]
+            StableDiffusionProcessingTxt2Img.cached_hr_c = [None, None]
 
     def setup_prompts(self):
         super().setup_prompts()
@@ -1092,12 +1118,31 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_hr_prompts]
         self.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_hr_negative_prompts]
 
+    def calculate_hr_conds(self):
+        if self.hr_c is not None:
+            return
+
+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
+        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+
     def setup_conds(self):
         super().setup_conds()
 
+        self.hr_uc = None
+        self.hr_c = None
+
         if self.enable_hr:
-            self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
-            self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, self.cached_c)
+            if shared.opts.hires_fix_use_firstpass_conds:
+                self.calculate_hr_conds()
+
+            elif lowvram.is_enabled(shared.sd_model):  # if in lowvram mode, we need to calculate conds right away, before the cond NN is unloaded
+                with devices.autocast():
+                    extra_networks.activate(self, self.hr_extra_network_data)
+
+                self.calculate_hr_conds()
+
+                with devices.autocast():
+                    extra_networks.activate(self, self.extra_network_data)
 
     def parse_extra_network_prompts(self):
         res = super().parse_extra_network_prompts()
@@ -1114,7 +1159,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
+    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
@@ -1125,7 +1170,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.image_mask = mask
         self.latent_mask = None
         self.mask_for_overlay = None
-        self.mask_blur = mask_blur
+        if mask_blur is not None:
+            mask_blur_x = mask_blur
+            mask_blur_y = mask_blur
+        self.mask_blur_x = mask_blur_x
+        self.mask_blur_y = mask_blur_y
         self.inpainting_fill = inpainting_fill
         self.inpaint_full_res = inpaint_full_res
         self.inpaint_full_res_padding = inpaint_full_res_padding
@@ -1147,8 +1196,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if self.inpainting_mask_invert:
                 image_mask = ImageOps.invert(image_mask)
 
-            if self.mask_blur > 0:
-                image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
+            if self.mask_blur_x > 0:
+                np_mask = np.array(image_mask)
+                kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
+                np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
+                image_mask = Image.fromarray(np_mask)
+
+            if self.mask_blur_y > 0:
+                np_mask = np.array(image_mask)
+                kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
+                np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
+                image_mask = Image.fromarray(np_mask)
 
             if self.inpaint_full_res:
                 self.mask_for_overlay = image_mask

modules/prompt_parser.py

@@ -336,11 +336,11 @@ def parse_prompt_attention(text):
             round_brackets.append(len(res))
         elif text == '[':
             square_brackets.append(len(res))
-        elif weight is not None and len(round_brackets) > 0:
+        elif weight is not None and round_brackets:
             multiply_range(round_brackets.pop(), float(weight))
-        elif text == ')' and len(round_brackets) > 0:
+        elif text == ')' and round_brackets:
             multiply_range(round_brackets.pop(), round_bracket_multiplier)
-        elif text == ']' and len(square_brackets) > 0:
+        elif text == ']' and square_brackets:
             multiply_range(square_brackets.pop(), square_bracket_multiplier)
         else:
             parts = re.split(re_break, text)

modules/realesrgan_model.py

@@ -1,15 +1,13 @@
 import os
-import sys
-import traceback
 
 import numpy as np
 from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
 from realesrgan import RealESRGANer
 
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import cmd_opts, opts
-from modules import modelloader
+from modules import modelloader, errors
+
 
 class UpscalerRealESRGAN(Upscaler):
     def __init__(self, path):
@@ -36,8 +34,7 @@ class UpscalerRealESRGAN(Upscaler):
                     self.scalers.append(scaler)
 
         except Exception:
-            print("Error importing Real-ESRGAN:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report("Error importing Real-ESRGAN", exc_info=True)
             self.enable = False
             self.scalers = []
 
@@ -45,9 +42,10 @@ class UpscalerRealESRGAN(Upscaler):
         if not self.enable:
             return img
 
-        info = self.load_model(path)
-        if not os.path.exists(info.local_data_path):
-            print(f"Unable to load RealESRGAN model: {info.name}")
+        try:
+            info = self.load_model(path)
+        except Exception:
+            errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
             return img
 
         upsampler = RealESRGANer(
@@ -65,21 +63,17 @@ class UpscalerRealESRGAN(Upscaler):
         return image
 
     def load_model(self, path):
-        try:
-            info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None)
-
-            if info is None:
-                print(f"Unable to find model info: {path}")
-                return None
-
-            if info.local_data_path.startswith("http"):
-                info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_download_path, progress=True)
-
-            return info
-        except Exception as e:
-            print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-        return None
+        for scaler in self.scalers:
+            if scaler.data_path == path:
+                if scaler.local_data_path.startswith("http"):
+                    scaler.local_data_path = modelloader.load_file_from_url(
+                        scaler.data_path,
+                        model_dir=self.model_download_path,
+                    )
+                if not os.path.exists(scaler.local_data_path):
+                    raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
+                return scaler
+        raise ValueError(f"Unable to find model info: {path}")
 
     def load_models(self, _):
         return get_realesrgan_models(self)
@@ -135,5 +129,4 @@ def get_realesrgan_models(scaler):
         ]
         return models
     except Exception:
-        print("Error making Real-ESRGAN models list:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report("Error making Real-ESRGAN models list", exc_info=True)

modules/restart.py

@@ -0,0 +1,23 @@
+import os
+from pathlib import Path
+
+from modules.paths_internal import script_path
+
+
+def is_restartable() -> bool:
+    """
+    Return True if the webui is restartable (i.e. there is something watching to restart it with)
+    """
+    return bool(os.environ.get('SD_WEBUI_RESTART'))
+
+
+def restart_program() -> None:
+    """creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again"""
+
+    (Path(script_path) / "tmp" / "restart").touch()
+
+    stop_program()
+
+
+def stop_program() -> None:
+    os._exit(0)

modules/safe.py

@@ -2,8 +2,6 @@
 
 import pickle
 import collections
-import sys
-import traceback
 
 import torch
 import numpy
@@ -11,7 +9,10 @@ import _codecs
 import zipfile
 import re
 
+
 # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
+from modules import errors
+
 TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
 
 def encode(*args):
@@ -136,17 +137,20 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
             check_pt(filename, extra_handler)
 
     except pickle.UnpicklingError:
-        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-        print("-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
-        print("You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+        errors.report(
+            f"Error verifying pickled file from {filename}\n"
+            "-----> !!!! The file is most likely corrupted !!!! <-----\n"
+            "You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n",
+            exc_info=True,
+        )
         return None
-
     except Exception:
-        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-        print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
-        print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
+        errors.report(
+            f"Error verifying pickled file from {filename}\n"
+            f"The file may be malicious, so the program is not going to read it.\n"
+            f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n",
+            exc_info=True,
+        )
         return None
 
     return unsafe_torch_load(filename, *args, **kwargs)
@@ -190,4 +194,3 @@ with safe.Extra(handler):
 unsafe_torch_load = torch.load
 torch.load = load
 global_extra_handler = None
-

modules/script_callbacks.py

@@ -1,16 +1,16 @@
-import sys
-import traceback
-from collections import namedtuple
 import inspect
+import os
+from collections import namedtuple
 from typing import Optional, Dict, Any
 
 from fastapi import FastAPI
 from gradio import Blocks
 
+from modules import errors, timer
+
 
 def report_exception(c, job):
-    print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
-    print(traceback.format_exc(), file=sys.stderr)
+    errors.report(f"Error executing callback {job} for {c.script}", exc_info=True)
 
 
 class ImageSaveParams:
@@ -111,6 +111,7 @@ callback_map = dict(
     callbacks_before_ui=[],
     callbacks_on_reload=[],
     callbacks_list_optimizers=[],
+    callbacks_list_unets=[],
 )
 
 
@@ -123,6 +124,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
     for c in callback_map['callbacks_app_started']:
         try:
             c.callback(demo, app)
+            timer.startup_timer.record(os.path.basename(c.script))
         except Exception:
             report_exception(c, 'app_started_callback')
 
@@ -271,16 +273,28 @@ def list_optimizers_callback():
     return res
 
 
+def list_unets_callback():
+    res = []
+
+    for c in callback_map['callbacks_list_unets']:
+        try:
+            c.callback(res)
+        except Exception:
+            report_exception(c, 'list_unets')
+
+    return res
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
-    filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+    filename = stack[0].filename if stack else 'unknown file'
 
     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'
+    filename = stack[0].filename if stack else 'unknown file'
     if filename == 'unknown file':
         return
     for callback_list in callback_map.values():
@@ -430,3 +444,10 @@ def on_list_optimizers(callback):
     to it."""
 
     add_callback(callback_map['callbacks_list_optimizers'], callback)
+
+
+def on_list_unets(callback):
+    """register a function to be called when UI is making a list of alternative options for unet.
+    The function will be called with one argument, a list, and shall add objects of type modules.sd_unet.SdUnetOption to it."""
+
+    add_callback(callback_map['callbacks_list_unets'], callback)

modules/script_loading.py

@@ -1,8 +1,8 @@
 import os
-import sys
-import traceback
 import importlib.util
 
+from modules import errors
+
 
 def load_module(path):
     module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
@@ -27,5 +27,4 @@ def preload_extensions(extensions_dir, parser):
                 module.preload(parser)
 
         except Exception:
-            print(f"Error running preload() for {preload_script}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report(f"Error running preload() for {preload_script}", exc_info=True)

modules/scripts.py

@@ -1,12 +1,12 @@
 import os
 import re
 import sys
-import traceback
+import inspect
 from collections import namedtuple
 
 import gradio as gr
 
-from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
+from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing, errors, timer
 
 AlwaysVisible = object()
 
@@ -20,6 +20,9 @@ class Script:
     name = None
     """script's internal name derived from title"""
 
+    section = None
+    """name of UI section that the script's controls will be placed into"""
+
     filename = None
     args_from = None
     args_to = None
@@ -82,6 +85,15 @@ class Script:
 
         pass
 
+    def before_process(self, p, *args):
+        """
+        This function is called very early before processing begins for AlwaysVisible scripts.
+        You can modify the processing object (p) here, inject hooks, etc.
+        args contains all values returned by components from ui()
+        """
+
+        pass
+
     def process(self, p, *args):
         """
         This function is called before processing begins for AlwaysVisible scripts.
@@ -238,7 +250,7 @@ def load_scripts():
 
     def register_scripts_from_module(module):
         for script_class in module.__dict__.values():
-            if type(script_class) != type:
+            if not inspect.isclass(script_class):
                 continue
 
             if issubclass(script_class, Script):
@@ -264,12 +276,12 @@ def load_scripts():
             register_scripts_from_module(script_module)
 
         except Exception:
-            print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report(f"Error loading script: {scriptfile.filename}", exc_info=True)
 
         finally:
             sys.path = syspath
             current_basedir = paths.script_path
+            timer.startup_timer.record(scriptfile.filename)
 
     global scripts_txt2img, scripts_img2img, scripts_postproc
 
@@ -280,11 +292,9 @@ def load_scripts():
 
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
     try:
-        res = func(*args, **kwargs)
-        return res
+        return func(*args, **kwargs)
     except Exception:
-        print(f"Error calling: {filename}/{funcname}", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report(f"Error calling: {filename}/{funcname}", exc_info=True)
 
     return default
 
@@ -297,6 +307,7 @@ class ScriptRunner:
         self.titles = []
         self.infotext_fields = []
         self.paste_field_names = []
+        self.inputs = [None]
 
     def initialize_scripts(self, is_img2img):
         from modules import scripts_auto_postprocessing
@@ -324,69 +335,73 @@ class ScriptRunner:
                 self.scripts.append(script)
                 self.selectable_scripts.append(script)
 
-    def setup_ui(self):
+    def create_script_ui(self, script):
         import modules.api.models as api_models
 
+        script.args_from = len(self.inputs)
+        script.args_to = len(self.inputs)
+
+        controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
+
+        if controls is None:
+            return
+
+        script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
+        api_args = []
+
+        for control in controls:
+            control.custom_script_source = os.path.basename(script.filename)
+
+            arg_info = api_models.ScriptArg(label=control.label or "")
+
+            for field in ("value", "minimum", "maximum", "step", "choices"):
+                v = getattr(control, field, None)
+                if v is not None:
+                    setattr(arg_info, field, v)
+
+            api_args.append(arg_info)
+
+        script.api_info = api_models.ScriptInfo(
+            name=script.name,
+            is_img2img=script.is_img2img,
+            is_alwayson=script.alwayson,
+            args=api_args,
+        )
+
+        if script.infotext_fields is not None:
+            self.infotext_fields += script.infotext_fields
+
+        if script.paste_field_names is not None:
+            self.paste_field_names += script.paste_field_names
+
+        self.inputs += controls
+        script.args_to = len(self.inputs)
+
+    def setup_ui_for_section(self, section, scriptlist=None):
+        if scriptlist is None:
+            scriptlist = self.alwayson_scripts
+
+        for script in scriptlist:
+            if script.alwayson and script.section != section:
+                continue
+
+            with gr.Group(visible=script.alwayson) as group:
+                self.create_script_ui(script)
+
+            script.group = group
+
+    def prepare_ui(self):
+        self.inputs = [None]
+
+    def setup_ui(self):
         self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
 
-        inputs = [None]
-        inputs_alwayson = [True]
-
-        def create_script_ui(script, inputs, inputs_alwayson):
-            script.args_from = len(inputs)
-            script.args_to = len(inputs)
-
-            controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
-
-            if controls is None:
-                return
-
-            script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
-            api_args = []
-
-            for control in controls:
-                control.custom_script_source = os.path.basename(script.filename)
-
-                arg_info = api_models.ScriptArg(label=control.label or "")
-
-                for field in ("value", "minimum", "maximum", "step", "choices"):
-                    v = getattr(control, field, None)
-                    if v is not None:
-                        setattr(arg_info, field, v)
-
-                api_args.append(arg_info)
-
-            script.api_info = api_models.ScriptInfo(
-                name=script.name,
-                is_img2img=script.is_img2img,
-                is_alwayson=script.alwayson,
-                args=api_args,
-            )
-
-            if script.infotext_fields is not None:
-                self.infotext_fields += script.infotext_fields
-
-            if script.paste_field_names is not None:
-                self.paste_field_names += script.paste_field_names
-
-            inputs += controls
-            inputs_alwayson += [script.alwayson for _ in controls]
-            script.args_to = len(inputs)
-
-        for script in self.alwayson_scripts:
-            with gr.Group() as group:
-                create_script_ui(script, inputs, inputs_alwayson)
-
-            script.group = group
+        self.setup_ui_for_section(None)
 
         dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
-        inputs[0] = dropdown
+        self.inputs[0] = dropdown
 
-        for script in self.selectable_scripts:
-            with gr.Group(visible=False) as group:
-                create_script_ui(script, inputs, inputs_alwayson)
-
-            script.group = group
+        self.setup_ui_for_section(None, self.selectable_scripts)
 
         def select_script(script_index):
             selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
@@ -411,6 +426,7 @@ class ScriptRunner:
         )
 
         self.script_load_ctr = 0
+
         def onload_script_visibility(params):
             title = params.get('Script', None)
             if title:
@@ -421,10 +437,10 @@ class ScriptRunner:
             else:
                 return gr.update(visible=False)
 
-        self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
-        self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
+        self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
+        self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])
 
-        return inputs
+        return self.inputs
 
     def run(self, p, *args):
         script_index = args[0]
@@ -444,14 +460,21 @@ class ScriptRunner:
 
         return processed
 
+    def before_process(self, p):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.before_process(p, *script_args)
+            except Exception:
+                errors.report(f"Error running before_process: {script.filename}", exc_info=True)
+
     def process(self, p):
         for script in self.alwayson_scripts:
             try:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.process(p, *script_args)
             except Exception:
-                print(f"Error running process: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error running process: {script.filename}", exc_info=True)
 
     def before_process_batch(self, p, **kwargs):
         for script in self.alwayson_scripts:
@@ -459,8 +482,7 @@ class ScriptRunner:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.before_process_batch(p, *script_args, **kwargs)
             except Exception:
-                print(f"Error running before_process_batch: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error running before_process_batch: {script.filename}", exc_info=True)
 
     def process_batch(self, p, **kwargs):
         for script in self.alwayson_scripts:
@@ -468,8 +490,7 @@ class ScriptRunner:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.process_batch(p, *script_args, **kwargs)
             except Exception:
-                print(f"Error running process_batch: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error running process_batch: {script.filename}", exc_info=True)
 
     def postprocess(self, p, processed):
         for script in self.alwayson_scripts:
@@ -477,8 +498,7 @@ class ScriptRunner:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.postprocess(p, processed, *script_args)
             except Exception:
-                print(f"Error running postprocess: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error running postprocess: {script.filename}", exc_info=True)
 
     def postprocess_batch(self, p, images, **kwargs):
         for script in self.alwayson_scripts:
@@ -486,8 +506,7 @@ class ScriptRunner:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.postprocess_batch(p, *script_args, images=images, **kwargs)
             except Exception:
-                print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
 
     def postprocess_image(self, p, pp: PostprocessImageArgs):
         for script in self.alwayson_scripts:
@@ -495,24 +514,21 @@ class ScriptRunner:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.postprocess_image(p, pp, *script_args)
             except Exception:
-                print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
 
     def before_component(self, component, **kwargs):
         for script in self.scripts:
             try:
                 script.before_component(component, **kwargs)
             except Exception:
-                print(f"Error running before_component: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error running before_component: {script.filename}", exc_info=True)
 
     def after_component(self, component, **kwargs):
         for script in self.scripts:
             try:
                 script.after_component(component, **kwargs)
             except Exception:
-                print(f"Error running after_component: {script.filename}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report(f"Error running after_component: {script.filename}", exc_info=True)
 
     def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):

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, script_callbacks, errors
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
 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
@@ -43,7 +43,7 @@ def list_optimizers():
     optimizers.extend(new_optimizers)
 
 
-def apply_optimizations():
+def apply_optimizations(option=None):
     global current_optimizer
 
     undo_optimizations()
@@ -60,7 +60,7 @@ def apply_optimizations():
         current_optimizer.undo()
         current_optimizer = None
 
-    selection = shared.opts.cross_attention_optimization
+    selection = option or shared.opts.cross_attention_optimization
     if selection == "Automatic" and len(optimizers) > 0:
         matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
     else:
@@ -74,12 +74,13 @@ def apply_optimizations():
         matching_optimizer = optimizers[0]
 
     if matching_optimizer is not None:
-        print(f"Applying optimization: {matching_optimizer.name}... ", end='')
+        print(f"Applying attention optimization: {matching_optimizer.name}... ", end='')
         matching_optimizer.apply()
         print("done.")
         current_optimizer = matching_optimizer
         return current_optimizer.name
     else:
+        print("Disabling attention optimization")
         return ''
 
 
@@ -157,9 +158,9 @@ class StableDiffusionModelHijack:
     def __init__(self):
         self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
 
-    def apply_optimizations(self):
+    def apply_optimizations(self, option=None):
         try:
-            self.optimization_method = apply_optimizations()
+            self.optimization_method = apply_optimizations(option)
         except Exception as e:
             errors.display(e, "applying cross attention optimization")
             undo_optimizations()
@@ -196,6 +197,11 @@ class StableDiffusionModelHijack:
 
         self.layers = flatten(m)
 
+        if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+            ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+
+        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
+
     def undo_hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             m.cond_stage_model = m.cond_stage_model.wrapped
@@ -217,6 +223,8 @@ class StableDiffusionModelHijack:
         self.layers = None
         self.clip = None
 
+        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
+
     def apply_circular(self, enable):
         if self.circular_enabled == enable:
             return

modules/sd_hijack_clip.py

@@ -167,7 +167,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                 chunk.multipliers += [weight] * emb_len
                 position += embedding_length_in_tokens
 
-        if len(chunk.tokens) > 0 or len(chunks) == 0:
+        if chunk.tokens or not chunks:
             next_chunk(is_last=True)
 
         return chunks, token_count

modules/sd_hijack_clip_old.py

@@ -74,7 +74,7 @@ def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, text
 
     self.hijack.comments += hijack_comments
 
-    if len(used_custom_terms) > 0:
+    if used_custom_terms:
         embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
         self.hijack.comments.append(f"Used embeddings: {embedding_names}")
 

modules/sd_hijack_optimizations.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 import math
-import sys
-import traceback
 import psutil
 
 import torch
@@ -48,7 +46,7 @@ class SdOptimizationXformers(SdOptimization):
     priority = 100
 
     def is_available(self):
-        return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
+        return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
@@ -140,8 +138,7 @@ if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
         import xformers.ops
         shared.xformers_available = True
     except Exception:
-        print("Cannot import xformers", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        errors.report("Cannot import xformers", exc_info=True)
 
 
 def get_available_vram():
@@ -605,7 +602,7 @@ def sdp_attnblock_forward(self, x):
     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()
+        q, k, v = q.float(), k.float(), v.float()
     q = q.contiguous()
     k = k.contiguous()
     v = v.contiguous()

modules/sd_models.py

@@ -14,7 +14,7 @@ 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 import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
 import tomesd
@@ -95,8 +95,7 @@ except Exception:
 
 
 def setup_model():
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
+    os.makedirs(model_path, exist_ok=True)
 
     enable_midas_autodownload()
 
@@ -164,6 +163,7 @@ def model_hash(filename):
 
 
 def select_checkpoint():
+    """Raises `FileNotFoundError` if no checkpoints are found."""
     model_checkpoint = shared.opts.sd_model_checkpoint
 
     checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
@@ -171,14 +171,14 @@ def select_checkpoint():
         return checkpoint_info
 
     if len(checkpoints_list) == 0:
-        print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
+        error_message = "No checkpoints found. When searching for checkpoints, looked at:"
         if shared.cmd_opts.ckpt is not None:
-            print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
-        print(f" - directory {model_path}", file=sys.stderr)
+            error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
+        error_message += f"\n - directory {model_path}"
         if shared.cmd_opts.ckpt_dir is not None:
-            print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
-        print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
-        exit(1)
+            error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
+        error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
+        raise FileNotFoundError(error_message)
 
     checkpoint_info = next(iter(checkpoints_list.values()))
     if model_checkpoint is not None:
@@ -247,7 +247,12 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     _, extension = os.path.splitext(checkpoint_file)
     if extension.lower() == ".safetensors":
         device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
-        pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+
+        if not shared.opts.disable_mmap_load_safetensors:
+            pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+        else:
+            pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
+            pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
 
@@ -421,7 +426,7 @@ class SdModelData:
                 try:
                     load_model()
                 except Exception as e:
-                    errors.display(e, "loading stable diffusion model")
+                    errors.display(e, "loading stable diffusion model", full_traceback=True)
                     print("", file=sys.stderr)
                     print("Stable diffusion model failed to load", file=sys.stderr)
                     self.sd_model = None
@@ -506,6 +511,11 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
     timer.record("scripts callbacks")
 
+    with devices.autocast(), torch.no_grad():
+        sd_model.cond_stage_model_empty_prompt = sd_model.cond_stage_model([""])
+
+    timer.record("calculate empty prompt")
+
     print(f"Model loaded in {timer.summary()}.")
 
     return sd_model
@@ -525,6 +535,8 @@ def reload_model_weights(sd_model=None, info=None):
         if sd_model.sd_model_checkpoint == checkpoint_info.filename:
             return
 
+        sd_unet.apply_unet("None")
+
         if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
             lowvram.send_everything_to_cpu()
         else:

modules/sd_samplers_kdiffusion.py

@@ -20,7 +20,7 @@ samplers_k_diffusion = [
     ('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'], {"second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True, 'discard_next_to_last_sigma': True}),
+    ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
@@ -29,7 +29,7 @@ samplers_k_diffusion = [
     ('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', "second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True, 'discard_next_to_last_sigma': True}),
+    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
 ]
 
 samplers_data_k_diffusion = [
@@ -44,6 +44,14 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
+k_diffusion_scheduler = {
+    'Automatic': None,
+    'karras': k_diffusion.sampling.get_sigmas_karras,
+    'exponential': k_diffusion.sampling.get_sigmas_exponential,
+    'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
 
 class CFGDenoiser(torch.nn.Module):
     """
@@ -61,6 +69,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
         self.image_cfg_scale = None
+        self.padded_cond_uncond = False
 
     def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
         denoised_uncond = x_out[-uncond.shape[0]:]
@@ -125,6 +134,18 @@ class CFGDenoiser(torch.nn.Module):
             x_in = x_in[:-batch_size]
             sigma_in = sigma_in[:-batch_size]
 
+        self.padded_cond_uncond = False
+        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+            empty = shared.sd_model.cond_stage_model_empty_prompt
+            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+            if num_repeats < 0:
+                tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+                self.padded_cond_uncond = True
+            elif num_repeats > 0:
+                uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+                self.padded_cond_uncond = True
+
         if tensor.shape[1] == uncond.shape[1] or skip_uncond:
             if is_edit_model:
                 cond_in = torch.cat([tensor, uncond, uncond])
@@ -255,6 +276,13 @@ class KDiffusionSampler:
 
         try:
             return func()
+        except RecursionError:
+            print(
+                'Encountered RecursionError during sampling, returning last latent. '
+                'rho >5 with a polyexponential scheduler may cause this error. '
+                'You should try to use a smaller rho value instead.'
+            )
+            return self.last_latent
         except sd_samplers_common.InterruptedException:
             return self.last_latent
 
@@ -294,6 +322,31 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
+        elif opts.k_sched_type != "Automatic":
+            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+            sigmas_kwargs = {
+                'sigma_min': sigma_min,
+                'sigma_max': sigma_max,
+            }
+
+            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+            p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+            if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+                sigmas_kwargs['sigma_min'] = opts.sigma_min
+                p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+            if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+                sigmas_kwargs['sigma_max'] = opts.sigma_max
+                p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+            default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+            if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
+                sigmas_kwargs['rho'] = opts.rho
+                p.extra_generation_params["Schedule rho"] = opts.rho
+
+            sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
 
@@ -355,6 +408,9 @@ class KDiffusionSampler:
 
         samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
         return samples
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
@@ -388,5 +444,8 @@ class KDiffusionSampler:
             's_min_uncond': self.s_min_uncond
         }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
+        if self.model_wrap_cfg.padded_cond_uncond:
+            p.extra_generation_params["Pad conds"] = True
+
         return samples
 

modules/sd_unet.py

@@ -0,0 +1,92 @@
+import torch.nn
+import ldm.modules.diffusionmodules.openaimodel
+
+from modules import script_callbacks, shared, devices
+
+unet_options = []
+current_unet_option = None
+current_unet = None
+
+
+def list_unets():
+    new_unets = script_callbacks.list_unets_callback()
+
+    unet_options.clear()
+    unet_options.extend(new_unets)
+
+
+def get_unet_option(option=None):
+    option = option or shared.opts.sd_unet
+
+    if option == "None":
+        return None
+
+    if option == "Automatic":
+        name = shared.sd_model.sd_checkpoint_info.model_name
+
+        options = [x for x in unet_options if x.model_name == name]
+
+        option = options[0].label if options else "None"
+
+    return next(iter([x for x in unet_options if x.label == option]), None)
+
+
+def apply_unet(option=None):
+    global current_unet_option
+    global current_unet
+
+    new_option = get_unet_option(option)
+    if new_option == current_unet_option:
+        return
+
+    if current_unet is not None:
+        print(f"Dectivating unet: {current_unet.option.label}")
+        current_unet.deactivate()
+
+    current_unet_option = new_option
+    if current_unet_option is None:
+        current_unet = None
+
+        if not (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
+            shared.sd_model.model.diffusion_model.to(devices.device)
+
+        return
+
+    shared.sd_model.model.diffusion_model.to(devices.cpu)
+    devices.torch_gc()
+
+    current_unet = current_unet_option.create_unet()
+    current_unet.option = current_unet_option
+    print(f"Activating unet: {current_unet.option.label}")
+    current_unet.activate()
+
+
+class SdUnetOption:
+    model_name = None
+    """name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
+
+    label = None
+    """name of the unet in UI"""
+
+    def create_unet(self):
+        """returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
+        raise NotImplementedError()
+
+
+class SdUnet(torch.nn.Module):
+    def forward(self, x, timesteps, context, *args, **kwargs):
+        raise NotImplementedError()
+
+    def activate(self):
+        pass
+
+    def deactivate(self):
+        pass
+
+
+def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
+    if current_unet is not None:
+        return current_unet.forward(x, timesteps, context, *args, **kwargs)
+
+    return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
+

modules/shared.py

@@ -44,19 +44,6 @@ restricted_opts = {
     "outdir_init_images"
 }
 
-ui_reorder_categories = [
-    "inpaint",
-    "sampler",
-    "checkboxes",
-    "hires_fix",
-    "dimensions",
-    "cfg",
-    "seed",
-    "batch",
-    "override_settings",
-    "scripts",
-]
-
 # https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
 gradio_hf_hub_themes = [
     "gradio/glass",
@@ -273,6 +260,10 @@ class OptionInfo:
         self.comment_after += f"<span class='info'>({info})</span>"
         return self
 
+    def html(self, html):
+        self.comment_after += html
+        return self
+
     def needs_restart(self):
         self.comment_after += " <span class='info'>(requires restart)</span>"
         return self
@@ -318,6 +309,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
     "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
     "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
+    "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
     "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
 
     "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
@@ -384,6 +376,7 @@ options_templates.update(options_section(('system', "System"), {
     "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."),
     "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
+    "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
 }))
 
 options_templates.update(options_section(('training', "Training"), {
@@ -407,6 +400,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "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).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"),
+    "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
     "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."),
@@ -416,17 +410,19 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "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, "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}).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"),
+    "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 network; 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.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different vidocard vendors"),
+    "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 videocard vendors"),
 }))
 
 options_templates.update(options_section(('optimizations', "Optimizations"), {
     "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
-    "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_min_uncond": OptionInfo(0.0, "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"),
     "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_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
     "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
+    "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+    "experimental_persistent_cond_cache": OptionInfo(False, "persistent cond cache").info("Experimental, keep cond caches across jobs, reduce overhead."),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -435,6 +431,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
     "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
     "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
     "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
+    "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -488,16 +485,25 @@ options_templates.update(options_section(('ui', "User interface"), {
     "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(),
     "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
     "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").needs_restart(),
+    "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(),
     "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
     "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
+    "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(),
 }))
 
 options_templates.update(options_section(('infotext', "Infotext"), {
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
+    "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
     "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
-    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+    "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
+    "infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
+<li>Ignore: keep prompt and styles dropdown as it is.</li>
+<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
+<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
+<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
+</ul>"""),
+
 }))
 
 options_templates.update(options_section(('ui', "Live previews"), {
@@ -519,6 +525,10 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "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}),
+    'k_sched_type':  OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
+    'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
+    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
+    'rho':  OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
     '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").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"]}),
@@ -634,6 +644,10 @@ class Options:
         if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
             self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
 
+        # 1.4.0 ui_reorder
+        if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
+            self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
+
         bad_settings = 0
         for k, v in self.data.items():
             info = self.data_labels.get(k, None)

modules/shared_items.py

@@ -29,3 +29,41 @@ def cross_attention_optimizations():
     return ["Automatic"] + [x.title() for x in modules.sd_hijack.optimizers] + ["None"]
 
 
+def sd_unet_items():
+    import modules.sd_unet
+
+    return ["Automatic"] + [x.label for x in modules.sd_unet.unet_options] + ["None"]
+
+
+def refresh_unet_list():
+    import modules.sd_unet
+
+    modules.sd_unet.list_unets()
+
+
+ui_reorder_categories_builtin_items = [
+    "inpaint",
+    "sampler",
+    "checkboxes",
+    "hires_fix",
+    "dimensions",
+    "cfg",
+    "seed",
+    "batch",
+    "override_settings",
+]
+
+
+def ui_reorder_categories():
+    from modules import scripts
+
+    yield from ui_reorder_categories_builtin_items
+
+    sections = {}
+    for script in scripts.scripts_txt2img.scripts + scripts.scripts_img2img.scripts:
+        if isinstance(script.section, str):
+            sections[script.section] = 1
+
+    yield from sections
+
+    yield "scripts"

modules/styles.py

@@ -1,6 +1,7 @@
 import csv
 import os
 import os.path
+import re
 import typing
 import shutil
 
@@ -28,6 +29,44 @@ def apply_styles_to_prompt(prompt, styles):
     return prompt
 
 
+re_spaces = re.compile("  +")
+
+
+def extract_style_text_from_prompt(style_text, prompt):
+    stripped_prompt = re.sub(re_spaces, " ", prompt.strip())
+    stripped_style_text = re.sub(re_spaces, " ", style_text.strip())
+    if "{prompt}" in stripped_style_text:
+        left, right = stripped_style_text.split("{prompt}", 2)
+        if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
+            prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
+            return True, prompt
+    else:
+        if stripped_prompt.endswith(stripped_style_text):
+            prompt = stripped_prompt[:len(stripped_prompt)-len(stripped_style_text)]
+
+            if prompt.endswith(', '):
+                prompt = prompt[:-2]
+
+            return True, prompt
+
+    return False, prompt
+
+
+def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
+    if not style.prompt and not style.negative_prompt:
+        return False, prompt, negative_prompt
+
+    match_positive, extracted_positive = extract_style_text_from_prompt(style.prompt, prompt)
+    if not match_positive:
+        return False, prompt, negative_prompt
+
+    match_negative, extracted_negative = extract_style_text_from_prompt(style.negative_prompt, negative_prompt)
+    if not match_negative:
+        return False, prompt, negative_prompt
+
+    return True, extracted_positive, extracted_negative
+
+
 class StyleDatabase:
     def __init__(self, path: str):
         self.no_style = PromptStyle("None", "", "")
@@ -67,10 +106,34 @@ class StyleDatabase:
         if os.path.exists(path):
             shutil.copy(path, f"{path}.bak")
 
-        fd = os.open(path, os.O_RDWR|os.O_CREAT)
+        fd = os.open(path, os.O_RDWR | os.O_CREAT)
         with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
             # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
             # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
             writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
             writer.writeheader()
-            writer.writerows(style._asdict() for k,     style in self.styles.items())
+            writer.writerows(style._asdict() for k, style in self.styles.items())
+
+    def extract_styles_from_prompt(self, prompt, negative_prompt):
+        extracted = []
+
+        applicable_styles = list(self.styles.values())
+
+        while True:
+            found_style = None
+
+            for style in applicable_styles:
+                is_match, new_prompt, new_neg_prompt = extract_style_from_prompts(style, prompt, negative_prompt)
+                if is_match:
+                    found_style = style
+                    prompt = new_prompt
+                    negative_prompt = new_neg_prompt
+                    break
+
+            if not found_style:
+                break
+
+            applicable_styles.remove(found_style)
+            extracted.append(found_style.name)
+
+        return list(reversed(extracted)), prompt, negative_prompt

modules/sysinfo.py

@@ -0,0 +1,162 @@
+import json
+import os
+import sys
+import traceback
+
+import platform
+import hashlib
+import pkg_resources
+import psutil
+import re
+
+import launch
+from modules import paths_internal, timer
+
+checksum_token = "DontStealMyGamePlz__WINNERS_DONT_USE_DRUGS__DONT_COPY_THAT_FLOPPY"
+environment_whitelist = {
+    "GIT",
+    "INDEX_URL",
+    "WEBUI_LAUNCH_LIVE_OUTPUT",
+    "GRADIO_ANALYTICS_ENABLED",
+    "PYTHONPATH",
+    "TORCH_INDEX_URL",
+    "TORCH_COMMAND",
+    "REQS_FILE",
+    "XFORMERS_PACKAGE",
+    "GFPGAN_PACKAGE",
+    "CLIP_PACKAGE",
+    "OPENCLIP_PACKAGE",
+    "STABLE_DIFFUSION_REPO",
+    "K_DIFFUSION_REPO",
+    "CODEFORMER_REPO",
+    "BLIP_REPO",
+    "STABLE_DIFFUSION_COMMIT_HASH",
+    "K_DIFFUSION_COMMIT_HASH",
+    "CODEFORMER_COMMIT_HASH",
+    "BLIP_COMMIT_HASH",
+    "COMMANDLINE_ARGS",
+    "IGNORE_CMD_ARGS_ERRORS",
+}
+
+
+def pretty_bytes(num, suffix="B"):
+    for unit in ["", "K", "M", "G", "T", "P", "E", "Z", "Y"]:
+        if abs(num) < 1024 or unit == 'Y':
+            return f"{num:.0f}{unit}{suffix}"
+        num /= 1024
+
+
+def get():
+    res = get_dict()
+
+    text = json.dumps(res, ensure_ascii=False, indent=4)
+
+    h = hashlib.sha256(text.encode("utf8"))
+    text = text.replace(checksum_token, h.hexdigest())
+
+    return text
+
+
+re_checksum = re.compile(r'"Checksum": "([0-9a-fA-F]{64})"')
+
+
+def check(x):
+    m = re.search(re_checksum, x)
+    if not m:
+        return False
+
+    replaced = re.sub(re_checksum, f'"Checksum": "{checksum_token}"', x)
+
+    h = hashlib.sha256(replaced.encode("utf8"))
+    return h.hexdigest() == m.group(1)
+
+
+def get_dict():
+    ram = psutil.virtual_memory()
+
+    res = {
+        "Platform": platform.platform(),
+        "Python": platform.python_version(),
+        "Version": launch.git_tag(),
+        "Commit": launch.commit_hash(),
+        "Script path": paths_internal.script_path,
+        "Data path": paths_internal.data_path,
+        "Extensions dir": paths_internal.extensions_dir,
+        "Checksum": checksum_token,
+        "Commandline": sys.argv,
+        "Torch env info": get_torch_sysinfo(),
+        "Exceptions": get_exceptions(),
+        "CPU": {
+            "model": platform.processor(),
+            "count logical": psutil.cpu_count(logical=True),
+            "count physical": psutil.cpu_count(logical=False),
+        },
+        "RAM": {
+            x: pretty_bytes(getattr(ram, x, 0)) for x in ["total", "used", "free", "active", "inactive", "buffers", "cached", "shared"] if getattr(ram, x, 0) != 0
+        },
+        "Extensions": get_extensions(enabled=True),
+        "Inactive extensions": get_extensions(enabled=False),
+        "Environment": get_environment(),
+        "Config": get_config(),
+        "Startup": timer.startup_record,
+        "Packages": sorted([f"{pkg.key}=={pkg.version}" for pkg in pkg_resources.working_set]),
+    }
+
+    return res
+
+
+def format_traceback(tb):
+    return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
+
+
+def get_exceptions():
+    try:
+        from modules import errors
+
+        return [{"exception": str(e), "traceback": format_traceback(tb)} for e, tb in reversed(errors.exception_records)]
+    except Exception as e:
+        return str(e)
+
+
+def get_environment():
+    return {k: os.environ[k] for k in sorted(os.environ) if k in environment_whitelist}
+
+
+re_newline = re.compile(r"\r*\n")
+
+
+def get_torch_sysinfo():
+    try:
+        import torch.utils.collect_env
+        info = torch.utils.collect_env.get_env_info()._asdict()
+
+        return {k: re.split(re_newline, str(v)) if "\n" in str(v) else v for k, v in info.items()}
+    except Exception as e:
+        return str(e)
+
+
+def get_extensions(*, enabled):
+
+    try:
+        from modules import extensions
+
+        def to_json(x: extensions.Extension):
+            return {
+                "name": x.name,
+                "path": x.path,
+                "version": x.version,
+                "branch": x.branch,
+                "remote": x.remote,
+            }
+
+        return [to_json(x) for x in extensions.extensions if not x.is_builtin and x.enabled == enabled]
+    except Exception as e:
+        return str(e)
+
+
+def get_config():
+    try:
+        from modules import shared
+        return shared.opts.data
+    except Exception as e:
+        return str(e)

modules/textual_inversion/autocrop.py

@@ -77,27 +77,27 @@ def focal_point(im, settings):
     pois = []
 
     weight_pref_total = 0
-    if len(corner_points) > 0:
+    if corner_points:
       weight_pref_total += settings.corner_points_weight
-    if len(entropy_points) > 0:
+    if entropy_points:
       weight_pref_total += settings.entropy_points_weight
-    if len(face_points) > 0:
+    if face_points:
       weight_pref_total += settings.face_points_weight
 
     corner_centroid = None
-    if len(corner_points) > 0:
+    if corner_points:
       corner_centroid = centroid(corner_points)
       corner_centroid.weight = settings.corner_points_weight / weight_pref_total
       pois.append(corner_centroid)
 
     entropy_centroid = None
-    if len(entropy_points) > 0:
+    if entropy_points:
       entropy_centroid = centroid(entropy_points)
       entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
       pois.append(entropy_centroid)
 
     face_centroid = None
-    if len(face_points) > 0:
+    if face_points:
       face_centroid = centroid(face_points)
       face_centroid.weight = settings.face_points_weight / weight_pref_total
       pois.append(face_centroid)
@@ -187,7 +187,7 @@ def image_face_points(im, settings):
         except Exception:
           continue
 
-        if len(faces) > 0:
+        if faces:
           rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
           return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
     return []
@@ -298,8 +298,7 @@ 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'
     model_file_name = 'face_detection_yunet.onnx'
 
-    if not os.path.exists(dirname):
-        os.makedirs(dirname)
+    os.makedirs(dirname, exist_ok=True)
 
     cache_file = os.path.join(dirname, model_file_name)
     if not os.path.exists(cache_file):

modules/textual_inversion/dataset.py

@@ -32,7 +32,7 @@ class DatasetEntry:
 
 class PersonalizedBase(Dataset):
     def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if shared.opts.dataset_filename_word_regex else None
 
         self.placeholder_token = placeholder_token
 

modules/textual_inversion/logging.py

@@ -2,11 +2,51 @@ import datetime
 import json
 import os
 
-saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "clip_grad_mode", "clip_grad_value", "gradient_step", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file", "gradient_step", "latent_sampling_method"}
-saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}
-saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}
+saved_params_shared = {
+    "batch_size",
+    "clip_grad_mode",
+    "clip_grad_value",
+    "create_image_every",
+    "data_root",
+    "gradient_step",
+    "initial_step",
+    "latent_sampling_method",
+    "learn_rate",
+    "log_directory",
+    "model_hash",
+    "model_name",
+    "num_of_dataset_images",
+    "steps",
+    "template_file",
+    "training_height",
+    "training_width",
+}
+saved_params_ti = {
+    "embedding_name",
+    "num_vectors_per_token",
+    "save_embedding_every",
+    "save_image_with_stored_embedding",
+}
+saved_params_hypernet = {
+    "activation_func",
+    "add_layer_norm",
+    "hypernetwork_name",
+    "layer_structure",
+    "save_hypernetwork_every",
+    "use_dropout",
+    "weight_init",
+}
 saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
-saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}
+saved_params_previews = {
+    "preview_cfg_scale",
+    "preview_height",
+    "preview_negative_prompt",
+    "preview_prompt",
+    "preview_sampler_index",
+    "preview_seed",
+    "preview_steps",
+    "preview_width",
+}
 
 
 def save_settings_to_file(log_directory, all_params):

modules/textual_inversion/preprocess.py

@@ -47,7 +47,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
         caption += shared.interrogator.generate_caption(image)
 
     if params.process_caption_deepbooru:
-        if len(caption) > 0:
+        if caption:
             caption += ", "
         caption += deepbooru.model.tag_multi(image)
 
@@ -67,7 +67,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
 
     caption = caption.strip()
 
-    if len(caption) > 0:
+    if caption:
         with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
             file.write(caption)
 

modules/textual_inversion/textual_inversion.py

@@ -1,6 +1,4 @@
 import os
-import sys
-import traceback
 from collections import namedtuple
 
 import torch
@@ -14,7 +12,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from torch.utils.tensorboard import SummaryWriter
 
-from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint
+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -120,16 +118,29 @@ class EmbeddingDatabase:
         self.embedding_dirs.clear()
 
     def register_embedding(self, embedding, model):
-        self.word_embeddings[embedding.name] = embedding
-
-        ids = model.cond_stage_model.tokenize([embedding.name])[0]
+        return self.register_embedding_by_name(embedding, model, embedding.name)
 
+    def register_embedding_by_name(self, embedding, model, name):
+        ids = model.cond_stage_model.tokenize([name])[0]
         first_id = ids[0]
         if first_id not in self.ids_lookup:
             self.ids_lookup[first_id] = []
-
-        self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
-
+        if name in self.word_embeddings:
+            # remove old one from the lookup list
+            lookup = [x for x in self.ids_lookup[first_id] if x[1].name!=name]
+        else:
+            lookup = self.ids_lookup[first_id]
+        if embedding is not None:
+            lookup += [(ids, embedding)]
+        self.ids_lookup[first_id] = sorted(lookup, key=lambda x: len(x[0]), reverse=True)
+        if embedding is None:
+            # unregister embedding with specified name
+            if name in self.word_embeddings:
+                del self.word_embeddings[name]
+            if len(self.ids_lookup[first_id])==0:
+                del self.ids_lookup[first_id]
+            return None
+        self.word_embeddings[name] = embedding
         return embedding
 
     def get_expected_shape(self):
@@ -207,8 +218,7 @@ class EmbeddingDatabase:
 
                     self.load_from_file(fullfn, fn)
                 except Exception:
-                    print(f"Error loading embedding {fn}:", file=sys.stderr)
-                    print(traceback.format_exc(), file=sys.stderr)
+                    errors.report(f"Error loading embedding {fn}", exc_info=True)
                     continue
 
     def load_textual_inversion_embeddings(self, force_reload=False):
@@ -241,7 +251,7 @@ class EmbeddingDatabase:
         if self.previously_displayed_embeddings != displayed_embeddings:
             self.previously_displayed_embeddings = displayed_embeddings
             print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
-            if len(self.skipped_embeddings) > 0:
+            if self.skipped_embeddings:
                 print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
 
     def find_embedding_at_position(self, tokens, offset):
@@ -632,8 +642,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
         filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
         save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
     except Exception:
-        print(traceback.format_exc(), file=sys.stderr)
-        pass
+        errors.report("Error training embedding", exc_info=True)
     finally:
         pbar.leave = False
         pbar.close()

modules/timer.py

@@ -1,11 +1,30 @@
 import time
 
 
+class TimerSubcategory:
+    def __init__(self, timer, category):
+        self.timer = timer
+        self.category = category
+        self.start = None
+        self.original_base_category = timer.base_category
+
+    def __enter__(self):
+        self.start = time.time()
+        self.timer.base_category = self.original_base_category + self.category + "/"
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        elapsed_for_subcategroy = time.time() - self.start
+        self.timer.base_category = self.original_base_category
+        self.timer.add_time_to_record(self.original_base_category + self.category, elapsed_for_subcategroy)
+        self.timer.record(self.category)
+
+
 class Timer:
     def __init__(self):
         self.start = time.time()
         self.records = {}
         self.total = 0
+        self.base_category = ''
 
     def elapsed(self):
         end = time.time()
@@ -13,18 +32,29 @@ class Timer:
         self.start = end
         return res
 
-    def record(self, category, extra_time=0):
-        e = self.elapsed()
+    def add_time_to_record(self, category, amount):
         if category not in self.records:
             self.records[category] = 0
 
-        self.records[category] += e + extra_time
+        self.records[category] += amount
+
+    def record(self, category, extra_time=0):
+        e = self.elapsed()
+
+        self.add_time_to_record(self.base_category + category, e + extra_time)
+
         self.total += e + extra_time
 
+    def subcategory(self, name):
+        self.elapsed()
+
+        subcat = TimerSubcategory(self, name)
+        return subcat
+
     def summary(self):
         res = f"{self.total:.1f}s"
 
-        additions = [x for x in self.records.items() if x[1] >= 0.1]
+        additions = [(category, time_taken) for category, time_taken in self.records.items() if time_taken >= 0.1 and '/' not in category]
         if not additions:
             return res
 
@@ -34,5 +64,13 @@ class Timer:
 
         return res
 
+    def dump(self):
+        return {'total': self.total, 'records': self.records}
+
     def reset(self):
         self.__init__()
+
+
+startup_timer = Timer()
+
+startup_record = None

modules/txt2img.py

@@ -4,10 +4,10 @@ from modules.generation_parameters_copypaste import create_override_settings_dic
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
 from modules.ui import plaintext_to_html
+import gradio as gr
 
 
-
-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, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args):
+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, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     p = processing.StableDiffusionProcessingTxt2Img(
@@ -48,6 +48,8 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
     p.scripts = modules.scripts.scripts_txt2img
     p.script_args = args
 
+    p.user = request.username
+
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
 

modules/ui.py

@@ -1,21 +1,23 @@
+import datetime
 import json
 import mimetypes
 import os
 import sys
-import traceback
 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  # 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, ui_common, ui_postprocessing, progress, ui_loadsave
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path, data_path
+from modules.paths import script_path
+from modules.ui_common import create_refresh_button
+from modules.ui_gradio_extensions import reload_javascript
+
 
 from modules.shared import opts, cmd_opts
 
@@ -35,6 +37,8 @@ import modules.hypernetworks.ui
 from modules.generation_parameters_copypaste import image_from_url_text
 import modules.extras
 
+create_setting_component = ui_settings.create_setting_component
+
 warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
@@ -76,6 +80,7 @@ extra_networks_symbol = '\U0001F3B4'  # 🎴
 switch_values_symbol = '\U000021C5' # ⇅
 restore_progress_symbol = '\U0001F300' # 🌀
 detect_image_size_symbol = '\U0001F4D0'  # 📐
+up_down_symbol = '\u2195\ufe0f' # ↕️
 
 
 def plaintext_to_html(text):
@@ -231,9 +236,8 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
                 res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
 
         except json.decoder.JSONDecodeError:
-            if gen_info_string != '':
-                print("Error parsing JSON generation info:", file=sys.stderr)
-                print(gen_info_string, file=sys.stderr)
+            if gen_info_string:
+                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
 
         return [res, gr_show(False)]
 
@@ -368,25 +372,6 @@ def apply_setting(key, value):
     return getattr(opts, key)
 
 
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-    def refresh():
-        refresh_method()
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
-        for k, v in args.items():
-            setattr(refresh_component, k, v)
-
-        return gr.update(**(args or {}))
-
-    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
-    return refresh_button
-
-
 def create_output_panel(tabname, outdir):
     return ui_common.create_output_panel(tabname, outdir)
 
@@ -405,27 +390,17 @@ 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(","))}
+    user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
 
-    for _, 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_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
         yield category
 
 
-def get_value_for_setting(key):
-    value = getattr(opts, key)
-
-    info = opts.data_labels[key]
-    args = info.component_args() if callable(info.component_args) else info.component_args or {}
-    args = {k: v for k, v in args.items() if k not in {'precision'}}
-
-    return gr.update(value=value, **args)
-
-
 def create_override_settings_dropdown(tabname, row):
     dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
 
     dropdown.change(
-        fn=lambda x: gr.Dropdown.update(visible=len(x) > 0),
+        fn=lambda x: gr.Dropdown.update(visible=bool(x)),
         inputs=[dropdown],
         outputs=[dropdown],
     )
@@ -456,6 +431,8 @@ def create_ui():
 
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='compact', elem_id="txt2img_settings"):
+                modules.scripts.scripts_txt2img.prepare_ui()
+
                 for category in ordered_ui_categories():
                     if category == "sampler":
                         steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
@@ -524,6 +501,9 @@ def create_ui():
                         with FormGroup(elem_id="txt2img_script_container"):
                             custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
+                    else:
+                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)
+
             hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
 
             for component in hr_resolution_preview_inputs:
@@ -616,7 +596,8 @@ def create_ui():
                 outputs=[
                     txt2img_prompt,
                     txt_prompt_img
-                ]
+                ],
+                show_progress=False,
             )
 
             enable_hr.change(
@@ -641,6 +622,7 @@ def create_ui():
                 (subseed_strength, "Variation seed strength"),
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
+                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
@@ -779,6 +761,8 @@ def create_ui():
                 with FormRow():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
+                modules.scripts.scripts_img2img.prepare_ui()
+
                 for category in ordered_ui_categories():
                     if category == "sampler":
                         steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
@@ -789,7 +773,7 @@ def create_ui():
                                 selected_scale_tab = gr.State(value=0)
 
                                 with gr.Tabs():
-                                    with gr.Tab(label="Resize to") as tab_scale_to:
+                                    with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
                                         with FormRow():
                                             with gr.Column(elem_id="img2img_column_size", scale=4):
                                                 width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
@@ -798,7 +782,7 @@ def create_ui():
                                                 res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
                                                 detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
 
-                                    with gr.Tab(label="Resize by") as tab_scale_by:
+                                    with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
                                         scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
 
                                         with FormRow():
@@ -888,6 +872,8 @@ def create_ui():
                                     inputs=[],
                                     outputs=[inpaint_controls, mask_alpha],
                                 )
+                    else:
+                        modules.scripts.scripts_img2img.setup_ui_for_section(category)
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
 
@@ -902,7 +888,8 @@ def create_ui():
                 outputs=[
                     img2img_prompt,
                     img2img_prompt_img
-                ]
+                ],
+                show_progress=False,
             )
 
             img2img_args = dict(
@@ -1051,6 +1038,7 @@ def create_ui():
                 (subseed_strength, "Variation seed strength"),
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
+                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                 (denoising_strength, "Denoising strength"),
                 (mask_blur, "Mask blur"),
                 *modules.scripts.scripts_img2img.infotext_fields
@@ -1460,195 +1448,10 @@ def create_ui():
             outputs=[],
         )
 
-    def create_setting_component(key, is_quicksettings=False):
-        def fun():
-            return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
-        info = opts.data_labels[key]
-        t = type(info.default)
-
-        args = info.component_args() if callable(info.component_args) else info.component_args
-
-        if info.component is not None:
-            comp = info.component
-        elif t == str:
-            comp = gr.Textbox
-        elif t == int:
-            comp = gr.Number
-        elif t == bool:
-            comp = gr.Checkbox
-        else:
-            raise Exception(f'bad options item type: {t} for key {key}')
-
-        elem_id = f"setting_{key}"
-
-        if info.refresh is not None:
-            if is_quicksettings:
-                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
-            else:
-                with FormRow():
-                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                    create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
-        else:
-            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
-        return res
-
     loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
 
-    components = []
-    component_dict = {}
-    shared.settings_components = component_dict
-
-    script_callbacks.ui_settings_callback()
-    opts.reorder()
-
-    def run_settings(*args):
-        changed = []
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp == dummy_component:
-                continue
-
-            if opts.set(key, value):
-                changed.append(key)
-
-        try:
-            opts.save(shared.config_filename)
-        except RuntimeError:
-            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
-
-    def run_settings_single(value, key):
-        if not opts.same_type(value, opts.data_labels[key].default):
-            return gr.update(visible=True), opts.dumpjson()
-
-        if not opts.set(key, value):
-            return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
-        opts.save(shared.config_filename)
-
-        return get_value_for_setting(key), opts.dumpjson()
-
-    with gr.Blocks(analytics_enabled=False) as settings_interface:
-        with gr.Row():
-            with gr.Column(scale=6):
-                settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
-            with gr.Column():
-                restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
-
-        result = gr.HTML(elem_id="settings_result")
-
-        quicksettings_names = opts.quicksettings_list
-        quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
-
-        quicksettings_list = []
-
-        previous_section = None
-        current_tab = None
-        current_row = None
-        with gr.Tabs(elem_id="settings"):
-            for i, (k, item) in enumerate(opts.data_labels.items()):
-                section_must_be_skipped = item.section[0] is None
-
-                if previous_section != item.section and not section_must_be_skipped:
-                    elem_id, text = item.section
-
-                    if current_tab is not None:
-                        current_row.__exit__()
-                        current_tab.__exit__()
-
-                    gr.Group()
-                    current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
-                    current_tab.__enter__()
-                    current_row = gr.Column(variant='compact')
-                    current_row.__enter__()
-
-                    previous_section = item.section
-
-                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
-                    quicksettings_list.append((i, k, item))
-                    components.append(dummy_component)
-                elif section_must_be_skipped:
-                    components.append(dummy_component)
-                else:
-                    component = create_setting_component(k)
-                    component_dict[k] = component
-                    components.append(component)
-
-            if current_tab is not None:
-                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")
-                reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
-                with gr.Row():
-                    unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
-                    reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
-
-            with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
-                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()
-
-        def reload_sd_weights():
-            modules.sd_models.reload_model_weights()
-
-        unload_sd_model.click(
-            fn=unload_sd_weights,
-            inputs=[],
-            outputs=[]
-        )
-
-        reload_sd_model.click(
-            fn=reload_sd_weights,
-            inputs=[],
-            outputs=[]
-        )
-
-        request_notifications.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='function(){}'
-        )
-
-        download_localization.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='download_localization'
-        )
-
-        def reload_scripts():
-            modules.scripts.reload_script_body_only()
-            reload_javascript()  # need to refresh the html page
-
-        reload_script_bodies.click(
-            fn=reload_scripts,
-            inputs=[],
-            outputs=[]
-        )
-
-        restart_gradio.click(
-            fn=shared.state.request_restart,
-            _js='restart_reload',
-            inputs=[],
-            outputs=[],
-        )
+    settings = ui_settings.UiSettings()
+    settings.create_ui(loadsave, dummy_component)
 
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
@@ -1660,7 +1463,7 @@ def create_ui():
     ]
 
     interfaces += script_callbacks.ui_tabs_callback()
-    interfaces += [(settings_interface, "Settings", "settings")]
+    interfaces += [(settings.interface, "Settings", "settings")]
 
     extensions_interface = ui_extensions.create_ui()
     interfaces += [(extensions_interface, "Extensions", "extensions")]
@@ -1670,10 +1473,7 @@ def create_ui():
         shared.tab_names.append(label)
 
     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])):
-                component = create_setting_component(k, is_quicksettings=True)
-                component_dict[k] = component
+        settings.add_quicksettings()
 
         parameters_copypaste.connect_paste_params_buttons()
 
@@ -1701,58 +1501,20 @@ def create_ui():
             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())
+        footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
         gr.HTML(footer, elem_id="footer")
 
-        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
-        settings_submit.click(
-            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
-            inputs=components,
-            outputs=[text_settings, result],
-        )
-
-        for _i, k, _item in quicksettings_list:
-            component = component_dict[k]
-            info = opts.data_labels[k]
-
-            change_handler = component.release if hasattr(component, 'release') else component.change
-            change_handler(
-                fn=lambda value, k=k: run_settings_single(value, key=k),
-                inputs=[component],
-                outputs=[component, text_settings],
-                show_progress=info.refresh is not None,
-            )
+        settings.add_functionality(demo)
 
         update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
-        text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+        settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
         demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
 
-        button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
-        button_set_checkpoint.click(
-            fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
-            _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
-            inputs=[component_dict['sd_model_checkpoint'], dummy_component],
-            outputs=[component_dict['sd_model_checkpoint'], text_settings],
-        )
-
-        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
-        def get_settings_values():
-            return [get_value_for_setting(key) for key in component_keys]
-
-        demo.load(
-            fn=get_settings_values,
-            inputs=[],
-            outputs=[component_dict[k] for k in component_keys],
-            queue=False,
-        )
-
         def modelmerger(*args):
             try:
                 results = modules.extras.run_modelmerger(*args)
             except Exception as e:
-                print("Error loading/saving model file:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
+                errors.report("Error loading/saving model file", exc_info=True)
                 modules.sd_models.list_models()  # to remove the potentially missing models from the list
                 return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
             return results
@@ -1780,7 +1542,7 @@ def create_ui():
                 primary_model_name,
                 secondary_model_name,
                 tertiary_model_name,
-                component_dict['sd_model_checkpoint'],
+                settings.component_dict['sd_model_checkpoint'],
                 modelmerger_result,
             ]
         )
@@ -1794,70 +1556,6 @@ def create_ui():
     return demo
 
 
-def webpath(fn):
-    if fn.startswith(script_path):
-        web_path = os.path.relpath(fn, script_path).replace('\\', '/')
-    else:
-        web_path = os.path.abspath(fn)
-
-    return f'file={web_path}?{os.path.getmtime(fn)}'
-
-
-def javascript_html():
-    # Ensure localization is in `window` before scripts
-    head = f'<script type="text/javascript">{localization.localization_js(shared.opts.localization)}</script>\n'
-
-    script_js = os.path.join(script_path, "script.js")
-    head += f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
-
-    for script in modules.scripts.list_scripts("javascript", ".js"):
-        head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
-
-    for script in modules.scripts.list_scripts("javascript", ".mjs"):
-        head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
-
-    if cmd_opts.theme:
-        head += f'<script type="text/javascript">set_theme(\"{cmd_opts.theme}\");</script>\n'
-
-    return head
-
-
-def css_html():
-    head = ""
-
-    def stylesheet(fn):
-        return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
-
-    for cssfile in modules.scripts.list_files_with_name("style.css"):
-        if not os.path.isfile(cssfile):
-            continue
-
-        head += stylesheet(cssfile)
-
-    if os.path.exists(os.path.join(data_path, "user.css")):
-        head += stylesheet(os.path.join(data_path, "user.css"))
-
-    return head
-
-
-def reload_javascript():
-    js = javascript_html()
-    css = css_html()
-
-    def template_response(*args, **kwargs):
-        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
-        res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
-    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-
-
 def versions_html():
     import torch
     import launch
@@ -1901,3 +1599,17 @@ def setup_ui_api(app):
     app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
 
     app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
+
+    app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
+
+    def download_sysinfo(attachment=False):
+        from fastapi.responses import PlainTextResponse
+
+        text = sysinfo.get()
+        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+
+        return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
+
+    app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
+    app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+

modules/ui_common.py

@@ -10,8 +10,11 @@ import subprocess as sp
 from modules import call_queue, shared
 from modules.generation_parameters_copypaste import image_from_url_text
 import modules.images
+from modules.ui_components import ToolButton
+
 
 folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
 
 
 def update_generation_info(generation_info, html_info, img_index):
@@ -50,9 +53,10 @@ def save_files(js_data, images, do_make_zip, index):
     save_to_dirs = shared.opts.use_save_to_dirs_for_ui
     extension: str = shared.opts.samples_format
     start_index = 0
+    only_one = False
 
     if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
+        only_one = True
         images = [images[index]]
         start_index = index
 
@@ -70,6 +74,7 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
+            p.batch_index = image_index-1
             fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
@@ -83,7 +88,10 @@ def save_files(js_data, images, do_make_zip, index):
 
     # Make Zip
     if do_make_zip:
-        zip_filepath = os.path.join(path, "images.zip")
+        zip_fileseed = p.all_seeds[index-1] if only_one else p.all_seeds[0]
+        namegen = modules.images.FilenameGenerator(p, zip_fileseed, p.all_prompts[0], image, True)
+        zip_filename = namegen.apply(shared.opts.grid_zip_filename_pattern or "[datetime]_[[model_name]]_[seed]-[seed_last]")
+        zip_filepath = os.path.join(path, f"{zip_filename}.zip")
 
         from zipfile import ZipFile
         with ZipFile(zip_filepath, "w") as zip_file:
@@ -211,3 +219,23 @@ Requested path was: {f}
                 ))
 
             return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    def refresh():
+        refresh_method()
+        args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+        for k, v in args.items():
+            setattr(refresh_component, k, v)
+
+        return gr.update(**(args or {}))
+
+    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
+    refresh_button.click(
+        fn=refresh,
+        inputs=[],
+        outputs=[refresh_component]
+    )
+    return refresh_button
+

modules/ui_extensions.py

@@ -1,10 +1,8 @@
 import json
 import os.path
-import sys
 import threading
 import time
 from datetime import datetime
-import traceback
 
 import git
 
@@ -13,7 +11,7 @@ import html
 import shutil
 import errno
 
-from modules import extensions, shared, paths, config_states
+from modules import extensions, shared, paths, config_states, errors, restart
 from modules.paths_internal import config_states_dir
 from modules.call_queue import wrap_gradio_gpu_call
 
@@ -46,13 +44,16 @@ def apply_and_restart(disable_list, update_list, disable_all):
         try:
             ext.fetch_and_reset_hard()
         except Exception:
-            print(f"Error getting updates for {ext.name}:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report(f"Error getting updates for {ext.name}", exc_info=True)
 
     shared.opts.disabled_extensions = disabled
     shared.opts.disable_all_extensions = disable_all
     shared.opts.save(shared.config_filename)
-    shared.state.request_restart()
+
+    if restart.is_restartable():
+        restart.restart_program()
+    else:
+        restart.stop_program()
 
 
 def save_config_state(name):
@@ -113,8 +114,7 @@ def check_updates(id_task, disable_list):
             if 'FETCH_HEAD' not in str(e):
                 raise
         except Exception:
-            print(f"Error checking updates for {ext.name}:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report(f"Error checking updates for {ext.name}", exc_info=True)
 
         shared.state.nextjob()
 
@@ -138,7 +138,10 @@ def extension_table():
     <table id="extensions">
         <thead>
             <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>
+                    <input class="gr-check-radio gr-checkbox all_extensions_toggle" type="checkbox" {'checked="checked"' if all(ext.enabled for ext in extensions.extensions) else ''} onchange="toggle_all_extensions(event)" />
+                    <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>Branch</th>
                 <th>Version</th>
@@ -170,7 +173,7 @@ def extension_table():
 
         code += f"""
             <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><label{style}><input class="gr-check-radio gr-checkbox extension_toggle" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''} onchange="toggle_extension(event)" />{html.escape(ext.name)}</label></td>
                 <td>{remote}</td>
                 <td>{ext.branch}</td>
                 <td>{version_link}</td>
@@ -325,6 +328,11 @@ def normalize_git_url(url):
 def install_extension_from_url(dirname, url, branch_name=None):
     check_access()
 
+    if isinstance(dirname, str):
+        dirname = dirname.strip()
+    if isinstance(url, str):
+        url = url.strip()
+
     assert url, 'No URL specified'
 
     if dirname is None or dirname == "":
@@ -337,7 +345,8 @@ def install_extension_from_url(dirname, url, branch_name=None):
     assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
 
     normalized_url = normalize_git_url(url)
-    assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
+    if any(x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url):
+        raise Exception(f'Extension with this URL is already installed: {url}')
 
     tmpdir = os.path.join(paths.data_path, "tmp", dirname)
 
@@ -453,7 +462,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
         existing = installed_extension_urls.get(normalize_git_url(url), None)
         extension_tags = extension_tags + ["installed"] if existing else extension_tags
 
-        if len([x for x in extension_tags if x in tags_to_hide]) > 0:
+        if any(x for x in extension_tags if x in tags_to_hide):
             hidden += 1
             continue
 
@@ -490,8 +499,14 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
 
 
 def preload_extensions_git_metadata():
+    t0 = time.time()
     for extension in extensions.extensions:
         extension.read_info_from_repo()
+    print(
+        f"preload_extensions_git_metadata for "
+        f"{len(extensions.extensions)} extensions took "
+        f"{time.time() - t0:.2f}s"
+    )
 
 
 def create_ui():
@@ -506,7 +521,8 @@ def create_ui():
             with gr.TabItem("Installed", id="installed"):
 
                 with gr.Row(elem_id="extensions_installed_top"):
-                    apply = gr.Button(value="Apply and restart UI", variant="primary")
+                    apply_label = ("Apply and restart UI" if restart.is_restartable() else "Apply and quit")
+                    apply = gr.Button(value=apply_label, variant="primary")
                     check = gr.Button(value="Check for updates")
                     extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
                     extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
@@ -555,9 +571,9 @@ def create_ui():
                 available_extensions_table = gr.HTML()
 
                 refresh_available_extensions_button.click(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update(), gr.update()]),
                     inputs=[available_extensions_index, hide_tags, sort_column],
-                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text],
+                    outputs=[available_extensions_index, available_extensions_table, hide_tags, search_extensions_text, install_result],
                 )
 
                 install_extension_button.click(

modules/ui_extra_networks.py

@@ -4,6 +4,7 @@ from pathlib import Path
 
 from modules import shared
 from modules.images import read_info_from_image, save_image_with_geninfo
+from modules.ui import up_down_symbol
 import gradio as gr
 import json
 import html
@@ -185,6 +186,8 @@ class ExtraNetworksPage:
         if search_only and shared.opts.extra_networks_hidden_models == "Never":
             return ""
 
+        sort_keys = " ".join([html.escape(f'data-sort-{k}={v}') for k, v in item.get("sort_keys", {}).items()]).strip()
+
         args = {
             "background_image": background_image,
             "style": f"'display: none; {height}{width}'",
@@ -198,10 +201,23 @@ class ExtraNetworksPage:
             "search_term": item.get("search_term", ""),
             "metadata_button": metadata_button,
             "search_only": " search_only" if search_only else "",
+            "sort_keys": sort_keys,
         }
 
         return self.card_page.format(**args)
 
+    def get_sort_keys(self, path):
+        """
+        List of default keys used for sorting in the UI.
+        """
+        pth = Path(path)
+        stat = pth.stat()
+        return {
+            "date_created": int(stat.st_ctime or 0),
+            "date_modified": int(stat.st_mtime or 0),
+            "name": pth.name.lower(),
+        }
+
     def find_preview(self, path):
         """
         Find a preview PNG for a given path (without extension) and call link_preview on it.
@@ -296,6 +312,8 @@ def create_ui(container, button, tabname):
                 page_elem.change(fn=lambda: None, _js='function(){applyExtraNetworkFilter(' + json.dumps(tabname) + '); return []}', inputs=[], outputs=[])
 
     gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
+    gr.Dropdown(choices=['Default Sort', 'Date Created', 'Date Modified', 'Name'], value='Default Sort', elem_id=tabname+"_extra_sort", multiselect=False, visible=False, show_label=False, interactive=True)
+    gr.Button(up_down_symbol, elem_id=tabname+"_extra_sortorder")
     button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
 
     ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)

modules/ui_extra_networks_checkpoints.py

@@ -14,7 +14,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
 
     def list_items(self):
         checkpoint: sd_models.CheckpointInfo
-        for name, checkpoint in sd_models.checkpoints_list.items():
+        for index, (name, checkpoint) in enumerate(sd_models.checkpoints_list.items()):
             path, ext = os.path.splitext(checkpoint.filename)
             yield {
                 "name": checkpoint.name_for_extra,
@@ -24,6 +24,8 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
                 "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
                 "onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
                 "local_preview": f"{path}.{shared.opts.samples_format}",
+                "sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},
+
             }
 
     def allowed_directories_for_previews(self):

modules/ui_extra_networks_hypernets.py

@@ -12,7 +12,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
         shared.reload_hypernetworks()
 
     def list_items(self):
-        for name, path in shared.hypernetworks.items():
+        for index, (name, path) in enumerate(shared.hypernetworks.items()):
             path, ext = os.path.splitext(path)
 
             yield {
@@ -23,6 +23,8 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
                 "search_term": self.search_terms_from_path(path),
                 "prompt": json.dumps(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
                 "local_preview": f"{path}.preview.{shared.opts.samples_format}",
+                "sort_keys": {'default': index, **self.get_sort_keys(path + ext)},
+
             }
 
     def allowed_directories_for_previews(self):

modules/ui_extra_networks_textual_inversion.py

@@ -13,7 +13,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
         sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
 
     def list_items(self):
-        for embedding in sd_hijack.model_hijack.embedding_db.word_embeddings.values():
+        for index, embedding in enumerate(sd_hijack.model_hijack.embedding_db.word_embeddings.values()):
             path, ext = os.path.splitext(embedding.filename)
             yield {
                 "name": embedding.name,
@@ -23,6 +23,8 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
                 "search_term": self.search_terms_from_path(embedding.filename),
                 "prompt": json.dumps(embedding.name),
                 "local_preview": f"{path}.preview.{shared.opts.samples_format}",
+                "sort_keys": {'default': index, **self.get_sort_keys(embedding.filename)},
+
             }
 
     def allowed_directories_for_previews(self):

modules/ui_gradio_extensions.py

@@ -0,0 +1,69 @@
+import os
+import gradio as gr
+
+from modules import localization, shared, scripts
+from modules.paths import script_path, data_path
+
+
+def webpath(fn):
+    if fn.startswith(script_path):
+        web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+    else:
+        web_path = os.path.abspath(fn)
+
+    return f'file={web_path}?{os.path.getmtime(fn)}'
+
+
+def javascript_html():
+    # Ensure localization is in `window` before scripts
+    head = f'<script type="text/javascript">{localization.localization_js(shared.opts.localization)}</script>\n'
+
+    script_js = os.path.join(script_path, "script.js")
+    head += f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
+
+    for script in scripts.list_scripts("javascript", ".js"):
+        head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
+
+    for script in scripts.list_scripts("javascript", ".mjs"):
+        head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
+
+    if shared.cmd_opts.theme:
+        head += f'<script type="text/javascript">set_theme(\"{shared.cmd_opts.theme}\");</script>\n'
+
+    return head
+
+
+def css_html():
+    head = ""
+
+    def stylesheet(fn):
+        return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
+
+    for cssfile in scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
+        head += stylesheet(cssfile)
+
+    if os.path.exists(os.path.join(data_path, "user.css")):
+        head += stylesheet(os.path.join(data_path, "user.css"))
+
+    return head
+
+
+def reload_javascript():
+    js = javascript_html()
+    css = css_html()
+
+    def template_response(*args, **kwargs):
+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+        res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
+        res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
+        res.init_headers()
+        return res
+
+    gr.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+    shared.GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse

modules/ui_settings.py

@@ -0,0 +1,289 @@
+import gradio as gr
+
+from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo
+from modules.call_queue import wrap_gradio_call
+from modules.shared import opts
+from modules.ui_components import FormRow
+from modules.ui_gradio_extensions import reload_javascript
+
+
+def get_value_for_setting(key):
+    value = getattr(opts, key)
+
+    info = opts.data_labels[key]
+    args = info.component_args() if callable(info.component_args) else info.component_args or {}
+    args = {k: v for k, v in args.items() if k not in {'precision'}}
+
+    return gr.update(value=value, **args)
+
+
+def create_setting_component(key, is_quicksettings=False):
+    def fun():
+        return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+    info = opts.data_labels[key]
+    t = type(info.default)
+
+    args = info.component_args() if callable(info.component_args) else info.component_args
+
+    if info.component is not None:
+        comp = info.component
+    elif t == str:
+        comp = gr.Textbox
+    elif t == int:
+        comp = gr.Number
+    elif t == bool:
+        comp = gr.Checkbox
+    else:
+        raise Exception(f'bad options item type: {t} for key {key}')
+
+    elem_id = f"setting_{key}"
+
+    if info.refresh is not None:
+        if is_quicksettings:
+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+            ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
+        else:
+            with FormRow():
+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
+    else:
+        res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+    return res
+
+
+class UiSettings:
+    submit = None
+    result = None
+    interface = None
+    components = None
+    component_dict = None
+    dummy_component = None
+    quicksettings_list = None
+    quicksettings_names = None
+    text_settings = None
+
+    def run_settings(self, *args):
+        changed = []
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, self.components):
+            assert comp == self.dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, self.components):
+            if comp == self.dummy_component:
+                continue
+
+            if opts.set(key, value):
+                changed.append(key)
+
+        try:
+            opts.save(shared.config_filename)
+        except RuntimeError:
+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed{": " if changed else ""}{", ".join(changed)}.'
+
+    def run_settings_single(self, value, key):
+        if not opts.same_type(value, opts.data_labels[key].default):
+            return gr.update(visible=True), opts.dumpjson()
+
+        if not opts.set(key, value):
+            return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+        opts.save(shared.config_filename)
+
+        return get_value_for_setting(key), opts.dumpjson()
+
+    def create_ui(self, loadsave, dummy_component):
+        self.components = []
+        self.component_dict = {}
+        self.dummy_component = dummy_component
+
+        shared.settings_components = self.component_dict
+
+        script_callbacks.ui_settings_callback()
+        opts.reorder()
+
+        with gr.Blocks(analytics_enabled=False) as settings_interface:
+            with gr.Row():
+                with gr.Column(scale=6):
+                    self.submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+                with gr.Column():
+                    restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
+
+            self.result = gr.HTML(elem_id="settings_result")
+
+            self.quicksettings_names = opts.quicksettings_list
+            self.quicksettings_names = {x: i for i, x in enumerate(self.quicksettings_names) if x != 'quicksettings'}
+
+            self.quicksettings_list = []
+
+            previous_section = None
+            current_tab = None
+            current_row = None
+            with gr.Tabs(elem_id="settings"):
+                for i, (k, item) in enumerate(opts.data_labels.items()):
+                    section_must_be_skipped = item.section[0] is None
+
+                    if previous_section != item.section and not section_must_be_skipped:
+                        elem_id, text = item.section
+
+                        if current_tab is not None:
+                            current_row.__exit__()
+                            current_tab.__exit__()
+
+                        gr.Group()
+                        current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
+                        current_tab.__enter__()
+                        current_row = gr.Column(variant='compact')
+                        current_row.__enter__()
+
+                        previous_section = item.section
+
+                    if k in self.quicksettings_names and not shared.cmd_opts.freeze_settings:
+                        self.quicksettings_list.append((i, k, item))
+                        self.components.append(dummy_component)
+                    elif section_must_be_skipped:
+                        self.components.append(dummy_component)
+                    else:
+                        component = create_setting_component(k)
+                        self.component_dict[k] = component
+                        self.components.append(component)
+
+                if current_tab is not None:
+                    current_row.__exit__()
+                    current_tab.__exit__()
+
+                with gr.TabItem("Defaults", id="defaults", elem_id="settings_tab_defaults"):
+                    loadsave.create_ui()
+
+                with gr.TabItem("Sysinfo", id="sysinfo", elem_id="settings_tab_sysinfo"):
+                    gr.HTML('<a href="./internal/sysinfo-download" class="sysinfo_big_link" download>Download system info</a><br /><a href="./internal/sysinfo">(or open as text in a new page)</a>', elem_id="sysinfo_download")
+
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            sysinfo_check_file = gr.File(label="Check system info for validity", type='binary')
+                        with gr.Column(scale=1):
+                            sysinfo_check_output = gr.HTML("", elem_id="sysinfo_validity")
+                        with gr.Column(scale=100):
+                            pass
+
+                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")
+                    reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+                    with gr.Row():
+                        unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+                        reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
+
+                with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
+                    gr.HTML(shared.html("licenses.html"), elem_id="licenses")
+
+                gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+
+                self.text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+
+            unload_sd_model.click(
+                fn=sd_models.unload_model_weights,
+                inputs=[],
+                outputs=[]
+            )
+
+            reload_sd_model.click(
+                fn=sd_models.reload_model_weights,
+                inputs=[],
+                outputs=[]
+            )
+
+            request_notifications.click(
+                fn=lambda: None,
+                inputs=[],
+                outputs=[],
+                _js='function(){}'
+            )
+
+            download_localization.click(
+                fn=lambda: None,
+                inputs=[],
+                outputs=[],
+                _js='download_localization'
+            )
+
+            def reload_scripts():
+                scripts.reload_script_body_only()
+                reload_javascript()  # need to refresh the html page
+
+            reload_script_bodies.click(
+                fn=reload_scripts,
+                inputs=[],
+                outputs=[]
+            )
+
+            restart_gradio.click(
+                fn=shared.state.request_restart,
+                _js='restart_reload',
+                inputs=[],
+                outputs=[],
+            )
+
+            def check_file(x):
+                if x is None:
+                    return ''
+
+                if sysinfo.check(x.decode('utf8', errors='ignore')):
+                    return 'Valid'
+
+                return 'Invalid'
+
+            sysinfo_check_file.change(
+                fn=check_file,
+                inputs=[sysinfo_check_file],
+                outputs=[sysinfo_check_output],
+            )
+
+        self.interface = settings_interface
+
+    def add_quicksettings(self):
+        with gr.Row(elem_id="quicksettings", variant="compact"):
+            for _i, k, _item in sorted(self.quicksettings_list, key=lambda x: self.quicksettings_names.get(x[1], x[0])):
+                component = create_setting_component(k, is_quicksettings=True)
+                self.component_dict[k] = component
+
+    def add_functionality(self, demo):
+        self.submit.click(
+            fn=wrap_gradio_call(lambda *args: self.run_settings(*args), extra_outputs=[gr.update()]),
+            inputs=self.components,
+            outputs=[self.text_settings, self.result],
+        )
+
+        for _i, k, _item in self.quicksettings_list:
+            component = self.component_dict[k]
+            info = opts.data_labels[k]
+
+            change_handler = component.release if hasattr(component, 'release') else component.change
+            change_handler(
+                fn=lambda value, k=k: self.run_settings_single(value, key=k),
+                inputs=[component],
+                outputs=[component, self.text_settings],
+                show_progress=info.refresh is not None,
+            )
+
+        button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
+        button_set_checkpoint.click(
+            fn=lambda value, _: self.run_settings_single(value, key='sd_model_checkpoint'),
+            _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+            inputs=[self.component_dict['sd_model_checkpoint'], self.dummy_component],
+            outputs=[self.component_dict['sd_model_checkpoint'], self.text_settings],
+        )
+
+        component_keys = [k for k in opts.data_labels.keys() if k in self.component_dict]
+
+        def get_settings_values():
+            return [get_value_for_setting(key) for key in component_keys]
+
+        demo.load(
+            fn=get_settings_values,
+            inputs=[],
+            outputs=[self.component_dict[k] for k in component_keys],
+            queue=False,
+        )

modules/ui_tempdir.py

@@ -31,7 +31,7 @@ def check_tmp_file(gradio, filename):
     return False
 
 
-def save_pil_to_file(self, pil_image, dir=None):
+def save_pil_to_file(self, pil_image, dir=None, format="png"):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
     if already_saved_as and os.path.isfile(already_saved_as):
         register_tmp_file(shared.demo, already_saved_as)

modules/upscaler.py

@@ -53,8 +53,8 @@ class Upscaler:
 
     def upscale(self, img: PIL.Image, scale, selected_model: str = None):
         self.scale = scale
-        dest_w = int(img.width * scale)
-        dest_h = int(img.height * scale)
+        dest_w = int((img.width * scale) // 8 * 8)
+        dest_h = int((img.height * scale) // 8 * 8)
 
         for _ in range(3):
             shape = (img.width, img.height)
@@ -77,7 +77,7 @@ class Upscaler:
         pass
 
     def find_models(self, ext_filter=None) -> list:
-        return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path)
+        return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path, ext_filter=ext_filter)
 
     def update_status(self, prompt):
         print(f"\nextras: {prompt}", file=shared.progress_print_out)

requirements.txt

@@ -1,32 +1,32 @@
-astunparse
-blendmodes
-accelerate
-basicsr
-gfpgan
-gradio==3.31.0
-numpy
-omegaconf
-opencv-contrib-python
-requests
-piexif
+GitPython
 Pillow
-pytorch_lightning==1.7.7
-realesrgan
-scikit-image>=0.19
-timm==0.4.12
-transformers==4.25.1
-torch
-einops
-jsonmerge
+accelerate
+
+basicsr
+blendmodes
 clean-fid
-resize-right
-torchdiffeq
+einops
+gfpgan
+gradio==3.32.0
+inflection
+jsonmerge
 kornia
 lark
-inflection
-GitPython
-torchsde
-safetensors
+numpy
+omegaconf
+
+piexif
 psutil
-rich
+pytorch_lightning
+realesrgan
+requests
+resize-right
+
+safetensors
+scikit-image>=0.19
+timm
 tomesd
+torch
+torchdiffeq
+torchsde
+transformers==4.25.1

requirements_versions.txt

@@ -1,29 +1,30 @@
-blendmodes==2022
-transformers==4.25.1
+GitPython==3.1.30
+Pillow==9.5.0
 accelerate==0.18.0
 basicsr==1.4.2
+blendmodes==2022
+clean-fid==0.1.35
+einops==0.4.1
+fastapi==0.94.0
 gfpgan==1.3.8
 gradio==3.32.0
-numpy==1.23.5
-Pillow==9.5.0
-realesrgan==0.3.0
-torch
-omegaconf==2.2.3
-pytorch_lightning==1.9.4
-scikit-image==0.20.0
-timm==0.6.7
-piexif==1.1.3
-einops==0.4.1
+httpcore<=0.15
+inflection==0.5.1
 jsonmerge==1.8.0
-clean-fid==0.1.35
-resize-right==0.0.2
-torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
-inflection==0.5.1
-GitPython==3.1.30
-torchsde==0.2.5
+numpy==1.23.5
+omegaconf==2.2.3
+piexif==1.1.3
+psutil~=5.9.5
+pytorch_lightning==1.9.4
+realesrgan==0.3.0
+resize-right==0.0.2
 safetensors==0.3.1
-httpcore<=0.15
-fastapi==0.94.0
+scikit-image==0.20.0
+timm==0.6.7
 tomesd==0.1.2
+torch
+torchdiffeq==0.2.3
+torchsde==0.2.5
+transformers==4.25.1