Merge branch 'dev' into startup-profile

.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

CHANGELOG.md

-## Upcoming 1.3.0
+## 1.3.0
 
 ### Features:
  * add UI to edit defaults
@@ -8,6 +8,10 @@
  * update extensions table: show branch, show date in separate column, and show version from tags if available
  * TAESD - another option for cheap live previews
  * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings
+ * calculate hashes for Lora
+ * add lora hashes to infotext
+ * when pasting infotext, use infotext's lora hashes to find local loras for `<lora:xxx:1>` entries whose hashes match loras the user has
+ * select cross attention optimization from UI
 
 ### Minor:
  * bump Gradio to 3.31.0
@@ -26,6 +30,8 @@
  * switch from pyngrok to ngrok-py
  * lazy-load images in extra networks UI
  * set "Navigate image viewer with gamepad" option to false by default, by request
+ * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...>
+ * allow hiding buttons in ui-config.json
 
 ### Extensions:
  * add /sdapi/v1/script-info api
@@ -35,6 +41,8 @@
  * add command and endpoint for graceful server stopping
  * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields
  * rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously)
+ * add /sdapi/v1/refresh-loras api checkpoint post request
+ * tests overhaul
 
 ### Bug Fixes:
  * fix an issue preventing the program from starting if the user specifies a bad Gradio theme
@@ -46,6 +54,8 @@
  * fix inability to merge checkpoint without adding metadata
  * fix extra networks' save preview image not adding infotext for jpeg/webm
  * remove blinking effect from text in hires fix and scale resolution preview
+ * make links to `http://<...>.git` extensions work in the extension tab
+ * fix bug with webui hanging at startup due to hanging git process
 
 
 ## 1.2.1

README.md

@@ -15,7 +15,7 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Attention, specify parts of text that the model should pay more attention to
     - a man in a `((tuxedo))` - will pay more attention to tuxedo
     - a man in a `(tuxedo:1.21)` - alternative syntax
-    - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
+    - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)
 - Loopback, run img2img processing multiple times
 - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
 - Textual Inversion

extensions-builtin/LDSR/scripts/ldsr_model.py

 import os
-import sys
-import traceback
 
 from basicsr.utils.download_util import load_file_from_url
 
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
-from modules import shared, script_callbacks
+from modules import shared, script_callbacks, errors
 import sd_hijack_autoencoder  # noqa: F401
 import sd_hijack_ddpm_v1  # noqa: F401
 
@@ -51,10 +49,8 @@ class UpscalerLDSR(Upscaler):
 
         try:
             return LDSR(model, yaml)
-
         except Exception:
-            print("Error importing LDSR:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
+            errors.report("Error importing LDSR", exc_info=True)
         return None
 
     def do_upscale(self, img, path):

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
 

extensions-builtin/LDSR/vqvae_quantize.py

+# 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/ScuNET/scripts/scunet_model.py

 import os.path
 import sys
-import traceback
 
 import PIL.Image
 import numpy as np
@@ -10,8 +9,9 @@ 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 modules import devices, modelloader, script_callbacks, errors
 from scunet_model_arch import SCUNet as net
+
 from modules.shared import opts
 
 
@@ -38,8 +38,7 @@ 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)

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

+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 len(shared.opts.extra_options) > 0 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")
+}))

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

@@ -167,6 +167,4 @@ 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) {
-        return;
+    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'));
+        }
     }
-    const imgWrap = target.closest('[data-testid="image"]');
-    if (!imgWrap) {
+
+    var targetImage = target.closest('[data-testid="image"]');
+    if (targetImage) {
+        e.stopPropagation();
+        e.preventDefault();
+        const files = e.dataTransfer.files;
+        dropReplaceImage(targetImage, files);
         return;
     }
-    e.stopPropagation();
-    e.preventDefault();
-    const files = e.dataTransfer.files;
-    dropReplaceImage(imgWrap, files);
 });
 
 window.addEventListener('paste', e => {

javascript/edit-attention.js

 function keyupEditAttention(event) {
     let target = event.originalTarget || event.composedPath()[0];
-    if (!target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
+    if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return;
     if (!(event.metaKey || event.ctrlKey)) return;
 
     let isPlus = event.key == "ArrowUp";

javascript/generationParams.js

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

javascript/hints.js

@@ -116,17 +116,25 @@ var titles = {
     "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);
-            }
-            if (node.tagName == "SELECT") {
-                updateTooltipForSelect(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.querySelectorAll) {
-                node.querySelectorAll('span, button, select, p').forEach(updateTooltipForSpan);
-                node.querySelectorAll('select').forEach(updateTooltipForSelect);
+        }
+        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 (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

-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

+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/token-counters.js

+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>';

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
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -23,6 +23,7 @@ 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
+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")
@@ -189,6 +190,7 @@ class Api:
         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/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])
@@ -541,6 +543,9 @@ class Api:
     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]
 
@@ -700,4 +705,4 @@ 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)

modules/api/models.py

@@ -249,6 +249,10 @@ 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

 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()
 
@@ -56,16 +54,14 @@ 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 +104,3 @@ 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")
@@ -62,7 +62,7 @@ parser.add_argument("--opt-split-attention-invokeai", action='store_true', help=
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
 parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
 parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="does not do anything")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
 parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")

modules/codeformer_model.py

 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
@@ -105,8 +103,8 @@ 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 +133,6 @@ 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

 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

 import sys
+import textwrap
 import traceback
 
 
+def report(message: str, *, exc_info: bool = False) -> None:
+    """
+    Print an error message to stderr, with optional traceback.
+    """
+    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):
     lines = message.strip().split("\n")
     max_len = max([len(x) for x in lines])
@@ -12,9 +24,13 @@ def print_error_explanation(message):
     print('=' * max_len, file=sys.stderr)
 
 
-def display(e: Exception, task):
+def display(e: Exception, task, *, full_traceback=False):
     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:

modules/extensions.py

 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 = []
@@ -54,10 +51,9 @@ 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
@@ -65,14 +61,15 @@ class Extension:
             try:
                 self.status = 'unknown'
                 self.remote = next(repo.remote().urls, None)
-                self.commit_date = repo.head.commit.committed_date
+                commit = repo.head.commit
+                self.commit_date = commit.committed_date
                 if repo.active_branch:
                     self.branch = repo.active_branch.name
-                self.commit_hash = repo.head.commit.hexsha
-                self.version = repo.git.describe("--always", "--tags")  # compared to `self.commit_hash[:8]` this takes about 30% more time total but since we run it in parallel we don't care
+                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
@@ -93,7 +90,7 @@ 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
@@ -115,7 +112,7 @@ 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

@@ -26,7 +26,7 @@ class ExtraNetworkParams:
         self.named = {}
 
         for item in self.items:
-            parts = item.split('=', 2)
+            parts = item.split('=', 2) if isinstance(item, str) else [item]
             if len(parts) == 2:
                 self.named[parts[0]] = parts[1]
             else:

modules/generation_parameters_copypaste.py

@@ -35,7 +35,7 @@ def reset():
 
 
 def quote(text):
-    if ',' not in str(text) and '\n' not in str(text):
+    if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
         return text
 
     return json.dumps(text, ensure_ascii=False)
@@ -306,6 +306,18 @@ 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 +330,10 @@ 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'),

modules/gfpgan_model.py

 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
@@ -112,5 +110,4 @@ 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

+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

 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,
+        '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()
@@ -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
@@ -488,14 +503,13 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
 
     image_format = Image.registered_extensions()[extension]
 
-    existing_pnginfo = existing_pnginfo or {}
-    if opts.enable_pnginfo:
-        existing_pnginfo['parameters'] = geninfo
-
     if extension.lower() == '.png':
-        pnginfo_data = PngImagePlugin.PngInfo()
-        for k, v in (existing_pnginfo or {}).items():
-            pnginfo_data.add_text(k, str(v))
+        if opts.enable_pnginfo:
+            pnginfo_data = PngImagePlugin.PngInfo()
+            for k, v in (existing_pnginfo or {}).items():
+                pnginfo_data.add_text(k, str(v))
+        else:
+            pnginfo_data = None
 
         image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
 
@@ -665,9 +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:
@@ -678,8 +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

@@ -92,7 +92,8 @@ 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

modules/interrogate.py

 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/localization.py

 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/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

 import json
+import logging
 import math
 import os
 import sys
@@ -13,7 +14,7 @@ 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
 
@@ -321,14 +321,13 @@ class StableDiffusionProcessing:
         have been used before. The second element is where the previously
         computed result is stored.
         """
-
-        if cache[0] is not None and (required_prompts, steps) == cache[0]:
+        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]
 
         with devices.autocast():
             cache[1] = function(shared.sd_model, required_prompts, steps)
 
-        cache[0] = (required_prompts, steps)
+        cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info)
         return cache[1]
 
     def setup_conds(self):
@@ -589,11 +588,15 @@ 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:
         # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
-        if sd_models.checkpoint_alisases.get(p.override_settings.get('sd_model_checkpoint')) is None:
+        override_checkpoint = p.override_settings.get('sd_model_checkpoint')
+        if override_checkpoint is not None and sd_models.checkpoint_alisases.get(override_checkpoint) is None:
             p.override_settings.pop('sd_model_checkpoint', None)
             sd_models.reload_model_weights()
 
@@ -674,6 +677,8 @@ 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
 

modules/realesrgan_model.py

 import os
-import sys
-import traceback
 
 import numpy as np
 from PIL import Image
@@ -9,7 +7,8 @@ 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 +35,7 @@ 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 = []
 
@@ -76,9 +74,8 @@ class UpscalerRealESRGAN(Upscaler):
                 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)
+        except Exception:
+            errors.report("Error making Real-ESRGAN models list", exc_info=True)
         return None
 
     def load_models(self, _):
@@ -135,5 +132,4 @@ 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/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

-import sys
-import traceback
-from collections import namedtuple
 import inspect
+from collections import namedtuple
 from typing import Optional, Dict, Any
 
 from fastapi import FastAPI
 from gradio import Blocks
 
-from modules import timer
+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:
@@ -113,6 +110,7 @@ callback_map = dict(
     callbacks_before_ui=[],
     callbacks_on_reload=[],
     callbacks_list_optimizers=[],
+    callbacks_list_unets=[],
 )
 
 
@@ -274,6 +272,18 @@ 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'
@@ -433,3 +443,10 @@ 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

 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/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
@@ -43,11 +43,16 @@ def list_optimizers():
     optimizers.extend(new_optimizers)
 
 
-def apply_optimizations():
+def apply_optimizations(option=None):
     global current_optimizer
 
     undo_optimizations()
 
+    if len(optimizers) == 0:
+        # a script can access the model very early, and optimizations would not be filled by then
+        current_optimizer = None
+        return ''
+
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
 
@@ -55,7 +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:
@@ -63,15 +68,19 @@ def apply_optimizations():
 
     if selection == "None":
         matching_optimizer = None
+    elif selection == "Automatic" and shared.cmd_opts.disable_opt_split_attention:
+        matching_optimizer = None
     elif matching_optimizer is None:
         matching_optimizer = optimizers[0]
 
     if matching_optimizer is not None:
-        print(f"Applying optimization: {matching_optimizer.name}")
+        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 ''
 
 
@@ -149,6 +158,13 @@ class StableDiffusionModelHijack:
     def __init__(self):
         self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
 
+    def apply_optimizations(self, option=None):
+        try:
+            self.optimization_method = apply_optimizations(option)
+        except Exception as e:
+            errors.display(e, "applying cross attention optimization")
+            undo_optimizations()
+
     def hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             model_embeddings = m.cond_stage_model.roberta.embeddings
@@ -168,11 +184,7 @@ class StableDiffusionModelHijack:
         if m.cond_stage_key == "edit":
             sd_hijack_unet.hijack_ddpm_edit()
 
-        try:
-            self.optimization_method = apply_optimizations()
-        except Exception as e:
-            errors.display(e, "applying cross attention optimization")
-            undo_optimizations()
+        self.apply_optimizations()
 
         self.clip = m.cond_stage_model
 
@@ -185,6 +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
@@ -206,6 +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_optimizations.py

+from __future__ import annotations
 import math
-import sys
-import traceback
 import psutil
 
 import torch
@@ -58,7 +57,7 @@ class SdOptimizationSdpNoMem(SdOptimization):
     name = "sdp-no-mem"
     label = "scaled dot product without memory efficient attention"
     cmd_opt = "opt_sdp_no_mem_attention"
-    priority = 90
+    priority = 80
 
     def is_available(self):
         return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
@@ -72,7 +71,7 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
     name = "sdp"
     label = "scaled dot product"
     cmd_opt = "opt_sdp_attention"
-    priority = 80
+    priority = 70
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
@@ -115,7 +114,7 @@ class SdOptimizationInvokeAI(SdOptimization):
 class SdOptimizationDoggettx(SdOptimization):
     name = "Doggettx"
     cmd_opt = "opt_split_attention"
-    priority = 20
+    priority = 90
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
@@ -139,8 +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():

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
@@ -164,6 +164,7 @@ 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 +172,14 @@ 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:
@@ -313,8 +314,6 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
 
         timer.record("apply half()")
 
-    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
-    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
     devices.dtype_unet = model.model.diffusion_model.dtype
     devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
 
@@ -423,7 +422,7 @@ 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
@@ -508,6 +507,11 @@ 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
@@ -527,6 +531,8 @@ 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

@@ -19,7 +19,8 @@ samplers_k_diffusion = [
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"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}),
+    ('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}),
     ('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'}),
@@ -27,7 +28,8 @@ samplers_k_diffusion = [
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "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}),
+    ('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}),
 ]
 
 samplers_data_k_diffusion = [
@@ -42,6 +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):
     """
@@ -123,6 +133,16 @@ class CFGDenoiser(torch.nn.Module):
             x_in = x_in[:-batch_size]
             sigma_in = sigma_in[:-batch_size]
 
+        # TODO add infotext entry
+        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)
+            elif num_repeats > 0:
+                uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+
         if tensor.shape[1] == uncond.shape[1] or skip_uncond:
             if is_edit_model:
                 cond_in = torch.cat([tensor, uncond, uncond])
@@ -228,7 +248,7 @@ class KDiffusionSampler:
         self.sampler_noises = None
         self.stop_at = None
         self.eta = None
-        self.config = None
+        self.config = None  # set by the function calling the constructor
         self.last_latent = None
         self.s_min_uncond = None
 
@@ -253,6 +273,13 @@ 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
 
@@ -292,6 +319,31 @@ 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())
 
@@ -337,13 +389,13 @@ class KDiffusionSampler:
         if 'sigmas' in parameters:
             extra_params_kwargs['sigmas'] = sigma_sched
 
-        if self.funcname == 'sample_dpmpp_sde':
+        if self.config.options.get('brownian_noise', False):
             noise_sampler = self.create_noise_sampler(x, sigmas, p)
             extra_params_kwargs['noise_sampler'] = noise_sampler
 
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
-        extra_args={
+        extra_args = {
             'cond': conditioning,
             'image_cond': image_conditioning,
             'uncond': unconditional_conditioning,
@@ -373,7 +425,7 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
-        if self.funcname == 'sample_dpmpp_sde':
+        if self.config.options.get('brownian_noise', False):
             noise_sampler = self.create_noise_sampler(x, sigmas, p)
             extra_params_kwargs['noise_sampler'] = noise_sampler
 

modules/sd_unet.py

+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

@@ -6,6 +6,7 @@ import threading
 import time
 
 import gradio as gr
+import torch
 import tqdm
 
 import modules.interrogate
@@ -43,19 +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",
@@ -76,6 +64,9 @@ cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_op
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
     (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
 
+devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
+devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
+
 device = devices.device
 weight_load_location = None if cmd_opts.lowram else "cpu"
 
@@ -314,6 +305,7 @@ 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"),
@@ -403,6 +395,7 @@ 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."),
@@ -414,15 +407,16 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "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"),
     "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"),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -484,9 +478,10 @@ 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"), {
@@ -515,6 +510,10 @@ 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"]}),
@@ -630,6 +629,10 @@ 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/textual_inversion/image_embedding.py

 import base64
 import json
+import warnings
+
 import numpy as np
 import zlib
-from PIL import Image, ImageDraw, ImageFont
+from PIL import Image, ImageDraw
 import torch
 
 
@@ -129,14 +131,17 @@ def extract_image_data_embed(image):
 
 
 def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
+    from modules.images import get_font
+    if textfont:
+        warnings.warn(
+            'passing in a textfont to caption_image_overlay is deprecated and does nothing',
+            DeprecationWarning,
+            stacklevel=2,
+        )
     from math import cos
 
     image = srcimage.copy()
     fontsize = 32
-    if textfont is None:
-        from modules.images import get_font
-        textfont = get_font(fontsize)
-
     factor = 1.5
     gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
     for y in range(image.size[1]):
@@ -147,12 +152,12 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
 
     draw = ImageDraw.Draw(image)
 
-    font = ImageFont.truetype(textfont, fontsize)
+    font = get_font(fontsize)
     padding = 10
 
     _, _, w, h = draw.textbbox((0, 0), title, font=font)
     fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
-    font = ImageFont.truetype(textfont, fontsize)
+    font = get_font(fontsize)
     _, _, w, h = draw.textbbox((0, 0), title, font=font)
     draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
 
@@ -163,7 +168,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
     fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
 
-    font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
+    font = get_font(min(fontsize_left, fontsize_mid, fontsize_right))
 
     draw.text((padding, image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
     draw.text((image.size[0]/2, image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))

modules/textual_inversion/textual_inversion.py

 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):
@@ -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/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

 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
 from modules.paths_internal import config_states_dir
 from modules.call_queue import wrap_gradio_gpu_call
 
@@ -46,8 +44,7 @@ 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
@@ -113,8 +110,7 @@ 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()
 
@@ -345,12 +341,12 @@ def install_extension_from_url(dirname, url, branch_name=None):
         shutil.rmtree(tmpdir, True)
         if not branch_name:
             # if no branch is specified, use the default branch
-            with git.Repo.clone_from(url, tmpdir) as repo:
+            with git.Repo.clone_from(url, tmpdir, filter=['blob:none']) as repo:
                 repo.remote().fetch()
                 for submodule in repo.submodules:
                     submodule.update()
         else:
-            with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:
+            with git.Repo.clone_from(url, tmpdir, filter=['blob:none'], branch=branch_name) as repo:
                 repo.remote().fetch()
                 for submodule in repo.submodules:
                     submodule.update()
@@ -490,8 +486,14 @@ 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():

modules/ui_gradio_extensions.py

+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_tempdir.py

@@ -3,7 +3,7 @@ import tempfile
 from collections import namedtuple
 from pathlib import Path
 
-import gradio as gr
+import gradio.components
 
 from PIL import PngImagePlugin
 
@@ -31,13 +31,16 @@ def check_tmp_file(gradio, filename):
     return False
 
 
-def save_pil_to_file(pil_image, dir=None):
+def save_pil_to_file(self, pil_image, dir=None):
     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)
+        filename = already_saved_as
 
-        file_obj = Savedfile(f'{already_saved_as}?{os.path.getmtime(already_saved_as)}')
-        return file_obj
+        if not shared.opts.save_images_add_number:
+            filename += f'?{os.path.getmtime(already_saved_as)}'
+
+        return filename
 
     if shared.opts.temp_dir != "":
         dir = shared.opts.temp_dir
@@ -51,11 +54,11 @@ def save_pil_to_file(pil_image, dir=None):
 
     file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
     pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
+    return file_obj.name
 
 
 # override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
+gradio.components.IOComponent.pil_to_temp_file = save_pil_to_file
 
 
 def on_tmpdir_changed():

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 = round((img.width * scale - 4) / 8) * 8
+        dest_h = round((img.height * scale - 4) / 8) * 8
 
         for _ in range(3):
             shape = (img.width, img.height)

requirements.txt

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

requirements_versions.txt

-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==3.31.0
+gradio==3.32.0
+httpcore<=0.15
+inflection==0.5.1
+jsonmerge==1.8.0
+kornia==0.6.7
+lark==1.1.2
 numpy==1.23.5
-Pillow==9.5.0
-realesrgan==0.3.0
-torch
 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
 scikit-image==0.20.0
 timm==0.6.7
-piexif==1.1.3
-einops==0.4.1
-jsonmerge==1.8.0
-clean-fid==0.1.35
-resize-right==0.0.2
+tomesd==0.1.2
+torch
 torchdiffeq==0.2.3
-kornia==0.6.7
-lark==1.1.2
-inflection==0.5.1
-GitPython==3.1.30
 torchsde==0.2.5
-safetensors==0.3.1
-httpcore<=0.15
-fastapi==0.94.0
-tomesd==0.1.2
+transformers==4.25.1

script.js

@@ -10,44 +10,94 @@ function gradioApp() {
     return elem.shadowRoot ? elem.shadowRoot : elem;
 }
 
+/**
+ * Get the currently selected top-level UI tab button (e.g. the button that says "Extras").
+ */
 function get_uiCurrentTab() {
-    return gradioApp().querySelector('#tabs button.selected');
+    return gradioApp().querySelector('#tabs > .tab-nav > button.selected');
 }
 
+/**
+ * Get the first currently visible top-level UI tab content (e.g. the div hosting the "txt2img" UI).
+ */
 function get_uiCurrentTabContent() {
-    return gradioApp().querySelector('.tabitem[id^=tab_]:not([style*="display: none"])');
+    return gradioApp().querySelector('#tabs > .tabitem[id^=tab_]:not([style*="display: none"])');
 }
 
 var uiUpdateCallbacks = [];
+var uiAfterUpdateCallbacks = [];
 var uiLoadedCallbacks = [];
 var uiTabChangeCallbacks = [];
 var optionsChangedCallbacks = [];
+var uiAfterUpdateTimeout = null;
 var uiCurrentTab = null;
 
+/**
+ * Register callback to be called at each UI update.
+ * The callback receives an array of MutationRecords as an argument.
+ */
 function onUiUpdate(callback) {
     uiUpdateCallbacks.push(callback);
 }
+
+/**
+ * Register callback to be called soon after UI updates.
+ * The callback receives no arguments.
+ *
+ * This is preferred over `onUiUpdate` if you don't need
+ * access to the MutationRecords, as your function will
+ * not be called quite as often.
+ */
+function onAfterUiUpdate(callback) {
+    uiAfterUpdateCallbacks.push(callback);
+}
+
+/**
+ * Register callback to be called when the UI is loaded.
+ * The callback receives no arguments.
+ */
 function onUiLoaded(callback) {
     uiLoadedCallbacks.push(callback);
 }
+
+/**
+ * Register callback to be called when the UI tab is changed.
+ * The callback receives no arguments.
+ */
 function onUiTabChange(callback) {
     uiTabChangeCallbacks.push(callback);
 }
+
+/**
+ * Register callback to be called when the options are changed.
+ * The callback receives no arguments.
+ * @param callback
+ */
 function onOptionsChanged(callback) {
     optionsChangedCallbacks.push(callback);
 }
 
-function runCallback(x, m) {
-    try {
-        x(m);
-    } catch (e) {
-        (console.error || console.log).call(console, e.message, e);
+function executeCallbacks(queue, arg) {
+    for (const callback of queue) {
+        try {
+            callback(arg);
+        } catch (e) {
+            console.error("error running callback", callback, ":", e);
+        }
     }
 }
-function executeCallbacks(queue, m) {
-    queue.forEach(function(x) {
-        runCallback(x, m);
-    });
+
+/**
+ * Schedule the execution of the callbacks registered with onAfterUiUpdate.
+ * The callbacks are executed after a short while, unless another call to this function
+ * is made before that time. IOW, the callbacks are executed only once, even
+ * when there are multiple mutations observed.
+ */
+function scheduleAfterUiUpdateCallbacks() {
+    clearTimeout(uiAfterUpdateTimeout);
+    uiAfterUpdateTimeout = setTimeout(function() {
+        executeCallbacks(uiAfterUpdateCallbacks);
+    }, 200);
 }
 
 var executedOnLoaded = false;
@@ -60,6 +110,7 @@ document.addEventListener("DOMContentLoaded", function() {
         }
 
         executeCallbacks(uiUpdateCallbacks, m);
+        scheduleAfterUiUpdateCallbacks();
         const newTab = get_uiCurrentTab();
         if (newTab && (newTab !== uiCurrentTab)) {
             uiCurrentTab = newTab;