Merge branch 'dev' into conrevo/fix-soft-inpaint

.eslintrc.js

@@ -78,6 +78,8 @@ module.exports = {
         //extraNetworks.js
         requestGet: "readonly",
         popup: "readonly",
+        // profilerVisualization.js
+        createVisualizationTable: "readonly",
         // from python
         localization: "readonly",
         // progrssbar.js

CHANGELOG.md

@@ -14,7 +14,7 @@
 * Add support for DAT upscaler models ([#14690](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14690), [#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039))
 * Extra Networks Tree View ([#14588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14588), [#14900](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14900))
 * NPU Support ([#14801](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14801))
-* Propmpt comments support
+* Prompt comments support
 
 ### Minor:
 * Allow pasting in WIDTHxHEIGHT strings into the width/height fields ([#14296](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14296))
@@ -59,7 +59,7 @@
 * modules/api/api.py: add api endpoint to refresh embeddings list ([#14715](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14715))
 * set_named_arg ([#14773](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14773))
 * add before_token_counter callback and use it for prompt comments
-* ResizeHandleRow - allow overriden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
+* ResizeHandleRow - allow overridden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
 
 ### Performance
 * Massive performance improvement for extra networks directories with a huge number of files in them in an attempt to tackle #14507 ([#14528](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14528))
@@ -101,7 +101,7 @@
 * Gracefully handle mtime read exception from cache ([#14933](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14933))
 * Only trigger interrupt on `Esc` when interrupt button visible ([#14932](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14932))
 * Disable prompt token counters option actually disables token counting rather than just hiding results.
-* avoid doble upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
+* avoid double upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
 * Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995))
 * fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006))
 * Fix resize-handle for mobile ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010), [#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065))
@@ -171,7 +171,7 @@
 * infotext updates: add option to disregard certain infotext fields, add option to not include VAE in infotext, add explanation to infotext settings page, move some options to infotext settings page
 * add FP32 fallback support on sd_vae_approx ([#14046](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046))
 * support XYZ scripts / split hires path from unet ([#14126](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14126))
-* allow use of mutiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125))
+* allow use of multiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125))
 * make extra network card description plaintext by default, with an option (Treat card description as HTML) to re-enable HTML as it was (originally by [#13241](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13241))
 
 ### Extensions and API:
@@ -308,7 +308,7 @@
  * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542))
  * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers:
    * makes all of them work with img2img
-   * makes prompt composition posssible (AND)
+   * makes prompt composition possible (AND)
    * makes them available for SDXL
  * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808))
  * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599))
@@ -484,7 +484,7 @@
  * user metadata system for custom networks
  * extended Lora metadata editor: set activation text, default weight, view tags, training info
  * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension)
- * show github stars for extenstions
+ * show github stars for extensions
  * img2img batch mode can read extra stuff from png info
  * img2img batch works with subdirectories
  * hotkeys to move prompt elements: alt+left/right
@@ -703,7 +703,7 @@
  * do not wait for Stable Diffusion model to load at startup
  * add filename patterns: `[denoising]`
  * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
- * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metdata of the file, if present, instead of filename (both can be used to activate LoRA)
+ * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metadata of the file, if present, instead of filename (both can be used to activate LoRA)
  * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
  * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
  * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
@@ -733,7 +733,7 @@
  * fix gamepad navigation
  * make the lightbox fullscreen image function properly
  * fix squished thumbnails in extras tab
- * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everything after you refreshed)
  * fix webui showing the same image if you configure the generation to always save results into same file
  * fix bug with upscalers not working properly
  * fix MPS on PyTorch 2.0.1, Intel Macs
@@ -751,7 +751,7 @@
  * switch to PyTorch 2.0.0 (except for AMD GPUs)
  * visual improvements to custom code scripts
  * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
- * add support for saving init images in img2img, and record their hashes in infotext for reproducability
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducibility
  * automatically select current word when adjusting weight with ctrl+up/down
  * add dropdowns for X/Y/Z plot
  * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs

_typos.toml

+[default.extend-words]
+# Part of "RGBa" (Pillow's pre-multiplied alpha RGB mode)
+Ba = "Ba"
+# HSA is something AMD uses for their GPUs
+HSA = "HSA"

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

@@ -301,7 +301,7 @@ class DDPMV1(pl.LightningModule):
         elif self.parameterization == "x0":
             target = x_start
         else:
-            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+            raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
 
         loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
 
@@ -880,7 +880,7 @@ class LatentDiffusionV1(DDPMV1):
     def apply_model(self, x_noisy, t, cond, return_ids=False):
 
         if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict
+            # hybrid case, cond is expected to be a dict
             pass
         else:
             if not isinstance(cond, list):
@@ -916,7 +916,7 @@ class LatentDiffusionV1(DDPMV1):
                 cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
 
             elif self.cond_stage_key == 'coordinates_bbox':
-                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+                assert 'original_image_size' in self.split_input_params, 'BoundingBoxRescaling is missing original_image_size'
 
                 # assuming padding of unfold is always 0 and its dilation is always 1
                 n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
@@ -926,7 +926,7 @@ class LatentDiffusionV1(DDPMV1):
                 num_downs = self.first_stage_model.encoder.num_resolutions - 1
                 rescale_latent = 2 ** (num_downs)
 
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+                # get top left positions of patches as conforming for the bbbox tokenizer, therefore we
                 # need to rescale the tl patch coordinates to be in between (0,1)
                 tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
                                          rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)

extensions-builtin/Lora/lyco_helpers.py

@@ -30,7 +30,7 @@ def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
     In LoRA with Kroneckor Product, first value is a value for weight scale.
     secon value is a value for weight.
 
-    Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
+    Because of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
 
     examples)
     factor

extensions-builtin/Lora/networks.py

@@ -355,7 +355,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
     """
     Applies the currently selected set of networks to the weights of torch layer self.
     If weights already have this particular set of networks applied, does nothing.
-    If not, restores orginal weights from backup and alters weights according to networks.
+    If not, restores original weights from backup and alters weights according to networks.
     """
 
     network_layer_name = getattr(self, 'network_layer_name', None)

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

@@ -292,7 +292,7 @@ onUiLoaded(async() => {
 
         // Create tooltip
         function createTooltip() {
-            const toolTipElemnt =
+            const toolTipElement =
                 targetElement.querySelector(".image-container");
             const tooltip = document.createElement("div");
             tooltip.className = "canvas-tooltip";
@@ -355,7 +355,7 @@ onUiLoaded(async() => {
             tooltip.appendChild(tooltipContent);
 
             // Add a hint element to the target element
-            toolTipElemnt.appendChild(tooltip);
+            toolTipElement.appendChild(tooltip);
         }
 
         //Show tool tip if setting enable

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

@@ -8,8 +8,8 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas
     "canvas_hotkey_grow_brush": shared.OptionInfo("W", "Enlarge the brush size"),
     "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
     "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
-    "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
-    "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
+    "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas position"),
+    "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, needed for testing"),
     "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
     "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"),
     "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),

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

 import math
 
 import gradio as gr
-from modules import scripts, shared, ui_components, ui_settings, infotext_utils
+from modules import scripts, shared, ui_components, ui_settings, infotext_utils, errors
 from modules.ui_components import FormColumn
 
 
@@ -42,7 +42,11 @@ class ExtraOptionsSection(scripts.Script):
                             setting_name = extra_options[index]
 
                             with FormColumn():
-                                comp = ui_settings.create_setting_component(setting_name)
+                                try:
+                                    comp = ui_settings.create_setting_component(setting_name)
+                                except KeyError:
+                                    errors.report(f"Can't add extra options for {setting_name} in ui")
+                                    continue
 
                             self.comps.append(comp)
                             self.setting_names.append(setting_name)

extensions-builtin/soft-inpainting/scripts/soft_inpainting.py

@@ -108,7 +108,7 @@ def latent_blend(settings, a, b, t):
 
 def get_modified_nmask(settings, nmask, sigma):
     """
-    Converts a negative mask representing the transparency of the original latent vectors being overlayed
+    Converts a negative mask representing the transparency of the original latent vectors being overlaid
     to a mask that is scaled according to the denoising strength for this step.
 
     Where:

html/extra-networks-pane.html

@@ -44,11 +44,11 @@
             <i class="extra-network-control--refresh-icon"></i>
         </div>
     </div>
-    <div class="extra-network-pane-content">
-        <div id='{tabname}_{extra_networks_tabname}_tree' class='extra-network-tree {tree_view_div_extra_class}'>
+    <div class="extra-network-pane-content resize-handle-row" style="display: {extra_network_pane_content_default_display};">
+        <div id='{tabname}_{extra_networks_tabname}_tree' class='extra-network-tree {tree_view_div_extra_class}' style='flex-basis: {extra_networks_tree_view_default_width}px; display: {tree_view_div_default_display};'>
             {tree_html}
         </div>
-        <div id='{tabname}_{extra_networks_tabname}_cards' class='extra-network-cards'>
+        <div id='{tabname}_{extra_networks_tabname}_cards' class='extra-network-cards' style='flex-grow: 1;'>
             {items_html}
         </div>
     </div>

javascript/aspectRatioOverlay.js

@@ -50,17 +50,17 @@ function dimensionChange(e, is_width, is_height) {
         var scaledx = targetElement.naturalWidth * viewportscale;
         var scaledy = targetElement.naturalHeight * viewportscale;
 
-        var cleintRectTop = (viewportOffset.top + window.scrollY);
-        var cleintRectLeft = (viewportOffset.left + window.scrollX);
-        var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2);
-        var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth / 2);
+        var clientRectTop = (viewportOffset.top + window.scrollY);
+        var clientRectLeft = (viewportOffset.left + window.scrollX);
+        var clientRectCentreY = clientRectTop + (targetElement.clientHeight / 2);
+        var clientRectCentreX = clientRectLeft + (targetElement.clientWidth / 2);
 
         var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight);
         var arscaledx = currentWidth * arscale;
         var arscaledy = currentHeight * arscale;
 
-        var arRectTop = cleintRectCentreY - (arscaledy / 2);
-        var arRectLeft = cleintRectCentreX - (arscaledx / 2);
+        var arRectTop = clientRectCentreY - (arscaledy / 2);
+        var arRectLeft = clientRectCentreX - (arscaledx / 2);
         var arRectWidth = arscaledx;
         var arRectHeight = arscaledy;
 

javascript/extraNetworks.js

@@ -290,7 +290,7 @@ function extraNetworksTreeProcessDirectoryClick(event, btn, tabname, extra_netwo
      * Processes `onclick` events when user clicks on directories in tree.
      *
      * Here is how the tree reacts to clicks for various states:
-     * unselected unopened directory: Diretory is selected and expanded.
+     * unselected unopened directory: Directory is selected and expanded.
      * unselected opened directory: Directory is selected.
      * selected opened directory: Directory is collapsed and deselected.
      * chevron is clicked: Directory is expanded or collapsed. Selected state unchanged.
@@ -447,7 +447,26 @@ function extraNetworksControlTreeViewOnClick(event, tabname, extra_networks_tabn
      * @param tabname                   The name of the active tab in the sd webui. Ex: txt2img, img2img, etc.
      * @param extra_networks_tabname    The id of the active extraNetworks tab. Ex: lora, checkpoints, etc.
      */
-    gradioApp().getElementById(tabname + "_" + extra_networks_tabname + "_tree").classList.toggle("hidden");
+    const tree = gradioApp().getElementById(tabname + "_" + extra_networks_tabname + "_tree");
+    const parent = tree.parentElement;
+    let resizeHandle = parent.querySelector('.resize-handle');
+    tree.classList.toggle("hidden");
+
+    if (tree.classList.contains("hidden")) {
+        tree.style.display = 'none';
+        parent.style.display = 'flex';
+        if (resizeHandle) {
+            resizeHandle.style.display = 'none';
+        }
+    } else {
+        tree.style.display = 'block';
+        parent.style.display = 'grid';
+        if (!resizeHandle) {
+            setupResizeHandle(parent);
+            resizeHandle = parent.querySelector('.resize-handle');
+        }
+        resizeHandle.style.display = 'block';
+    }
     event.currentTarget.classList.toggle("extra-network-control--enabled");
 }
 
@@ -509,12 +528,76 @@ function popupId(id) {
     popup(storedPopupIds[id]);
 }
 
+function extraNetworksFlattenMetadata(obj) {
+    const result = {};
+
+    // Convert any stringified JSON objects to actual objects
+    for (const key of Object.keys(obj)) {
+        if (typeof obj[key] === 'string') {
+            try {
+                const parsed = JSON.parse(obj[key]);
+                if (parsed && typeof parsed === 'object') {
+                    obj[key] = parsed;
+                }
+            } catch (error) {
+                continue;
+            }
+        }
+    }
+
+    // Flatten the object
+    for (const key of Object.keys(obj)) {
+        if (typeof obj[key] === 'object' && obj[key] !== null) {
+            const nested = extraNetworksFlattenMetadata(obj[key]);
+            for (const nestedKey of Object.keys(nested)) {
+                result[`${key}/${nestedKey}`] = nested[nestedKey];
+            }
+        } else {
+            result[key] = obj[key];
+        }
+    }
+
+    // Special case for handling modelspec keys
+    for (const key of Object.keys(result)) {
+        if (key.startsWith("modelspec.")) {
+            result[key.replaceAll(".", "/")] = result[key];
+            delete result[key];
+        }
+    }
+
+    // Add empty keys to designate hierarchy
+    for (const key of Object.keys(result)) {
+        const parts = key.split("/");
+        for (let i = 1; i < parts.length; i++) {
+            const parent = parts.slice(0, i).join("/");
+            if (!result[parent]) {
+                result[parent] = "";
+            }
+        }
+    }
+
+    return result;
+}
+
 function extraNetworksShowMetadata(text) {
+    try {
+        let parsed = JSON.parse(text);
+        if (parsed && typeof parsed === 'object') {
+            parsed = extraNetworksFlattenMetadata(parsed);
+            const table = createVisualizationTable(parsed, 0);
+            popup(table);
+            return;
+        }
+    } catch (error) {
+        console.eror(error);
+    }
+
     var elem = document.createElement('pre');
     elem.classList.add('popup-metadata');
     elem.textContent = text;
 
     popup(elem);
+    return;
 }
 
 function requestGet(url, data, handler, errorHandler) {

javascript/profilerVisualization.js

@@ -33,120 +33,141 @@ function createRow(table, cellName, items) {
     return res;
 }
 
-function showProfile(path, cutoff = 0.05) {
-    requestGet(path, {}, function(data) {
-        var table = document.createElement('table');
-        table.className = 'popup-table';
-
-        data.records['total'] = data.total;
-        var keys = Object.keys(data.records).sort(function(a, b) {
-            return data.records[b] - data.records[a];
+function createVisualizationTable(data, cutoff = 0, sort = "") {
+    var table = document.createElement('table');
+    table.className = 'popup-table';
+
+    var keys = Object.keys(data);
+    if (sort === "number") {
+        keys = keys.sort(function(a, b) {
+            return data[b] - data[a];
         });
-        var items = keys.map(function(x) {
-            return {key: x, parts: x.split('/'), time: data.records[x]};
+    } else {
+        keys = keys.sort();
+    }
+    var items = keys.map(function(x) {
+        return {key: x, parts: x.split('/'), value: data[x]};
+    });
+    var maxLength = items.reduce(function(a, b) {
+        return Math.max(a, b.parts.length);
+    }, 0);
+
+    var cols = createRow(
+        table,
+        'th',
+        [
+            cutoff === 0 ? 'key' : 'record',
+            cutoff === 0 ? 'value' : 'seconds'
+        ]
+    );
+    cols[0].colSpan = maxLength;
+
+    function arraysEqual(a, b) {
+        return !(a < b || b < a);
+    }
+
+    var addLevel = function(level, parent, hide) {
+        var matching = items.filter(function(x) {
+            return x.parts[level] && !x.parts[level + 1] && arraysEqual(x.parts.slice(0, level), parent);
         });
-        var maxLength = items.reduce(function(a, b) {
-            return Math.max(a, b.parts.length);
-        }, 0);
-
-        var cols = createRow(table, 'th', ['record', 'seconds']);
-        cols[0].colSpan = maxLength;
-
-        function arraysEqual(a, b) {
-            return !(a < b || b < a);
+        if (sort === "number") {
+            matching = matching.sort(function(a, b) {
+                return b.value - a.value;
+            });
+        } else {
+            matching = matching.sort();
         }
+        var othersTime = 0;
+        var othersList = [];
+        var othersRows = [];
+        var childrenRows = [];
+        matching.forEach(function(x) {
+            var visible = (cutoff === 0 && !hide) || (x.value >= cutoff && !hide);
+
+            var cells = [];
+            for (var i = 0; i < maxLength; i++) {
+                cells.push(x.parts[i]);
+            }
+            cells.push(cutoff === 0 ? x.value : x.value.toFixed(3));
+            var cols = createRow(table, 'td', cells);
+            for (i = 0; i < level; i++) {
+                cols[i].className = 'muted';
+            }
 
-        var addLevel = function(level, parent, hide) {
-            var matching = items.filter(function(x) {
-                return x.parts[level] && !x.parts[level + 1] && arraysEqual(x.parts.slice(0, level), parent);
-            });
-            var sorted = matching.sort(function(a, b) {
-                return b.time - a.time;
-            });
-            var othersTime = 0;
-            var othersList = [];
-            var othersRows = [];
-            var childrenRows = [];
-            sorted.forEach(function(x) {
-                var visible = x.time >= cutoff && !hide;
-
-                var cells = [];
-                for (var i = 0; i < maxLength; i++) {
-                    cells.push(x.parts[i]);
-                }
-                cells.push(x.time.toFixed(3));
-                var cols = createRow(table, 'td', cells);
-                for (i = 0; i < level; i++) {
-                    cols[i].className = 'muted';
-                }
-
-                var tr = cols[0].parentNode;
-                if (!visible) {
-                    tr.classList.add("hidden");
-                }
-
-                if (x.time >= cutoff) {
-                    childrenRows.push(tr);
-                } else {
-                    othersTime += x.time;
-                    othersList.push(x.parts[level]);
-                    othersRows.push(tr);
-                }
-
-                var children = addLevel(level + 1, parent.concat([x.parts[level]]), true);
-                if (children.length > 0) {
-                    var cell = cols[level];
-                    var onclick = function() {
-                        cell.classList.remove("link");
-                        cell.removeEventListener("click", onclick);
-                        children.forEach(function(x) {
-                            x.classList.remove("hidden");
-                        });
-                    };
-                    cell.classList.add("link");
-                    cell.addEventListener("click", onclick);
-                }
-            });
+            var tr = cols[0].parentNode;
+            if (!visible) {
+                tr.classList.add("hidden");
+            }
 
-            if (othersTime > 0) {
-                var cells = [];
-                for (var i = 0; i < maxLength; i++) {
-                    cells.push(parent[i]);
-                }
-                cells.push(othersTime.toFixed(3));
-                cells[level] = 'others';
-                var cols = createRow(table, 'td', cells);
-                for (i = 0; i < level; i++) {
-                    cols[i].className = 'muted';
-                }
+            if (cutoff === 0 || x.value >= cutoff) {
+                childrenRows.push(tr);
+            } else {
+                othersTime += x.value;
+                othersList.push(x.parts[level]);
+                othersRows.push(tr);
+            }
 
+            var children = addLevel(level + 1, parent.concat([x.parts[level]]), true);
+            if (children.length > 0) {
                 var cell = cols[level];
-                var tr = cell.parentNode;
                 var onclick = function() {
-                    tr.classList.add("hidden");
                     cell.classList.remove("link");
                     cell.removeEventListener("click", onclick);
-                    othersRows.forEach(function(x) {
+                    children.forEach(function(x) {
                         x.classList.remove("hidden");
                     });
                 };
-
-                cell.title = othersList.join(", ");
                 cell.classList.add("link");
                 cell.addEventListener("click", onclick);
+            }
+        });
 
-                if (hide) {
-                    tr.classList.add("hidden");
-                }
+        if (othersTime > 0) {
+            var cells = [];
+            for (var i = 0; i < maxLength; i++) {
+                cells.push(parent[i]);
+            }
+            cells.push(othersTime.toFixed(3));
+            cells[level] = 'others';
+            var cols = createRow(table, 'td', cells);
+            for (i = 0; i < level; i++) {
+                cols[i].className = 'muted';
+            }
 
-                childrenRows.push(tr);
+            var cell = cols[level];
+            var tr = cell.parentNode;
+            var onclick = function() {
+                tr.classList.add("hidden");
+                cell.classList.remove("link");
+                cell.removeEventListener("click", onclick);
+                othersRows.forEach(function(x) {
+                    x.classList.remove("hidden");
+                });
+            };
+
+            cell.title = othersList.join(", ");
+            cell.classList.add("link");
+            cell.addEventListener("click", onclick);
+
+            if (hide) {
+                tr.classList.add("hidden");
             }
 
-            return childrenRows;
-        };
+            childrenRows.push(tr);
+        }
+
+        return childrenRows;
+    };
 
-        addLevel(0, []);
+    addLevel(0, []);
+
+    return table;
+}
 
+function showProfile(path, cutoff = 0.05) {
+    requestGet(path, {}, function(data) {
+        data.records['total'] = data.total;
+        const table = createVisualizationTable(data.records, cutoff, "number");
         popup(table);
     });
 }

javascript/resizeHandle.js

@@ -22,6 +22,9 @@
     }
 
     function displayResizeHandle(parent) {
+        if (!parent.needHideOnMoblie) {
+            return true;
+        }
         if (window.innerWidth < GRADIO_MIN_WIDTH * 2 + PAD * 4) {
             parent.style.display = 'flex';
             parent.resizeHandle.style.display = "none";
@@ -41,7 +44,7 @@
 
             const ratio = newParentWidth / oldParentWidth;
 
-            const newWidthL = Math.max(Math.floor(ratio * widthL), GRADIO_MIN_WIDTH);
+            const newWidthL = Math.max(Math.floor(ratio * widthL), parent.minLeftColWidth);
             setLeftColGridTemplate(parent, newWidthL);
 
             R.parentWidth = newParentWidth;
@@ -64,7 +67,19 @@
 
         parent.style.display = 'grid';
         parent.style.gap = '0';
-        const gridTemplateColumns = `${parent.children[0].style.flexGrow}fr ${PAD}px ${parent.children[1].style.flexGrow}fr`;
+        let leftColTemplate = "";
+        if (parent.children[0].style.flexGrow) {
+            leftColTemplate = `${parent.children[0].style.flexGrow}fr`;
+            parent.minLeftColWidth = GRADIO_MIN_WIDTH;
+            parent.minRightColWidth = GRADIO_MIN_WIDTH;
+            parent.needHideOnMoblie = true;
+        } else {
+            leftColTemplate = parent.children[0].style.flexBasis;
+            parent.minLeftColWidth = parent.children[0].style.flexBasis.slice(0, -2) / 2;
+            parent.minRightColWidth = 0;
+            parent.needHideOnMoblie = false;
+        }
+        const gridTemplateColumns = `${leftColTemplate} ${PAD}px ${parent.children[1].style.flexGrow}fr`;
         parent.style.gridTemplateColumns = gridTemplateColumns;
         parent.style.originalGridTemplateColumns = gridTemplateColumns;
 
@@ -132,7 +147,7 @@
                 } else {
                     delta = R.screenX - evt.changedTouches[0].screenX;
                 }
-                const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH);
+                const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - R.parent.minRightColWidth - PAD), R.parent.minLeftColWidth);
                 setLeftColGridTemplate(R.parent, leftColWidth);
             }
         });
@@ -171,10 +186,15 @@
     setupResizeHandle = setup;
 })();
 
-onUiLoaded(function() {
+
+function setupAllResizeHandles() {
     for (var elem of gradioApp().querySelectorAll('.resize-handle-row')) {
-        if (!elem.querySelector('.resize-handle')) {
+        if (!elem.querySelector('.resize-handle') && !elem.children[0].classList.contains("hidden")) {
             setupResizeHandle(elem);
         }
     }
-});
+}
+
+
+onUiLoaded(setupAllResizeHandles);
+

javascript/ui.js

@@ -411,7 +411,7 @@ function switchWidthHeight(tabname) {
 
 var onEditTimers = {};
 
-// calls func after afterMs milliseconds has passed since the input elem has beed enited by user
+// calls func after afterMs milliseconds has passed since the input elem has been edited by user
 function onEdit(editId, elem, afterMs, func) {
     var edited = function() {
         var existingTimer = onEditTimers[editId];

modules/api/api.py

@@ -23,7 +23,7 @@ from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
-from PIL import PngImagePlugin, Image
+from PIL import PngImagePlugin
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
@@ -85,7 +85,7 @@ def decode_base64_to_image(encoding):
         headers = {'user-agent': opts.api_useragent} if opts.api_useragent else {}
         response = requests.get(encoding, timeout=30, headers=headers)
         try:
-            image = Image.open(BytesIO(response.content))
+            image = images.read(BytesIO(response.content))
             return image
         except Exception as e:
             raise HTTPException(status_code=500, detail="Invalid image url") from e
@@ -93,7 +93,7 @@ def decode_base64_to_image(encoding):
     if encoding.startswith("data:image/"):
         encoding = encoding.split(";")[1].split(",")[1]
     try:
-        image = Image.open(BytesIO(base64.b64decode(encoding)))
+        image = images.read(BytesIO(base64.b64decode(encoding)))
         return image
     except Exception as e:
         raise HTTPException(status_code=500, detail="Invalid encoded image") from e
@@ -360,7 +360,7 @@ class Api:
         return script_args
 
     def apply_infotext(self, request, tabname, *, script_runner=None, mentioned_script_args=None):
-        """Processes `infotext` field from the `request`, and sets other fields of the `request` accoring to what's in infotext.
+        """Processes `infotext` field from the `request`, and sets other fields of the `request` according to what's in infotext.
 
         If request already has a field set, and that field is encountered in infotext too, the value from infotext is ignored.
 
@@ -409,8 +409,8 @@ class Api:
         if request.override_settings is None:
             request.override_settings = {}
 
-        overriden_settings = infotext_utils.get_override_settings(params)
-        for _, setting_name, value in overriden_settings:
+        overridden_settings = infotext_utils.get_override_settings(params)
+        for _, setting_name, value in overridden_settings:
             if setting_name not in request.override_settings:
                 request.override_settings[setting_name] = value
 

modules/call_queue.py

@@ -100,8 +100,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             sys_pct = sys_peak/max(sys_total, 1) * 100
 
             toltip_a = "Active: peak amount of video memory used during generation (excluding cached data)"
-            toltip_r = "Reserved: total amout of video memory allocated by the Torch library "
-            toltip_sys = "System: peak amout of video memory allocated by all running programs, out of total capacity"
+            toltip_r = "Reserved: total amount of video memory allocated by the Torch library "
+            toltip_sys = "System: peak amount of video memory allocated by all running programs, out of total capacity"
 
             text_a = f"<abbr title='{toltip_a}'>A</abbr>: <span class='measurement'>{active_peak/1024:.2f} GB</span>"
             text_r = f"<abbr title='{toltip_r}'>R</abbr>: <span class='measurement'>{reserved_peak/1024:.2f} GB</span>"

modules/cmd_args.py

@@ -121,4 +121,6 @@ parser.add_argument('--api-server-stop', action='store_true', help='enable serve
 parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
 parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
 parser.add_argument("--disable-extra-extensions", action='store_true', help="prevent all extensions except built-in from running regardless of any other settings", default=False)
-parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui", )
+parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui")
+parser.add_argument("--unix-filenames-sanitization", action='store_true', help="allow any symbols except '/' in filenames. May conflict with your browser and file system")
+parser.add_argument("--filenames-max-length", type=int, default=128, help='maximal length of filenames of saved images. If you override it, it can conflict with your file system')

modules/devices.py

@@ -259,7 +259,7 @@ def test_for_nans(x, where):
 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.
+    spends about 2.7 seconds doing that, at least with NVidia.
     """
 
     x = torch.zeros((1, 1)).to(device, dtype)

modules/extra_networks.py

@@ -60,7 +60,7 @@ class ExtraNetwork:
         Where name matches the name of this ExtraNetwork object, and arg1:arg2:arg3 are any natural number of text arguments
         separated by colon.
 
-        Even if the user does not mention this ExtraNetwork in his prompt, the call will stil be made, with empty params_list -
+        Even if the user does not mention this ExtraNetwork in his prompt, the call will still be made, with empty params_list -
         in this case, all effects of this extra networks should be disabled.
 
         Can be called multiple times before deactivate() - each new call should override the previous call completely.

modules/hypernetworks/hypernetwork.py

@@ -95,6 +95,7 @@ class HypernetworkModule(torch.nn.Module):
                         zeros_(b)
                     else:
                         raise KeyError(f"Key {weight_init} is not defined as initialization!")
+        devices.torch_npu_set_device()
         self.to(devices.device)
 
     def fix_old_state_dict(self, state_dict):

modules/images.py

@@ -12,7 +12,7 @@ import re
 import numpy as np
 import piexif
 import piexif.helper
-from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin
+from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin, ImageOps
 import string
 import json
 import hashlib
@@ -321,13 +321,16 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
     return res
 
 
-invalid_filename_chars = '#<>:"/\\|?*\n\r\t'
+if not shared.cmd_opts.unix_filenames_sanitization:
+    invalid_filename_chars = '#<>:"/\\|?*\n\r\t'
+else:
+    invalid_filename_chars = '/'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
 re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
 re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
-max_filename_part_length = 128
+max_filename_part_length = shared.cmd_opts.filenames_max_length
 NOTHING_AND_SKIP_PREVIOUS_TEXT = object()
 
 
@@ -770,7 +773,7 @@ def image_data(data):
     import gradio as gr
 
     try:
-        image = Image.open(io.BytesIO(data))
+        image = read(io.BytesIO(data))
         textinfo, _ = read_info_from_image(image)
         return textinfo, None
     except Exception:
@@ -797,3 +800,30 @@ def flatten(img, bgcolor):
 
     return img.convert('RGB')
 
+
+def read(fp, **kwargs):
+    image = Image.open(fp, **kwargs)
+    image = fix_image(image)
+
+    return image
+
+
+def fix_image(image: Image.Image):
+    if image is None:
+        return None
+
+    try:
+        image = ImageOps.exif_transpose(image)
+        image = fix_png_transparency(image)
+    except Exception:
+        pass
+
+    return image
+
+
+def fix_png_transparency(image: Image.Image):
+    if image.mode not in ("RGB", "P") or not isinstance(image.info.get("transparency"), bytes):
+        return image
+
+    image = image.convert("RGBA")
+    return image

modules/img2img.py

@@ -6,7 +6,7 @@ import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
 import gradio as gr
 
-from modules import images as imgutil
+from modules import images
 from modules.infotext_utils import create_override_settings_dict, parse_generation_parameters
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
@@ -21,7 +21,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
     output_dir = output_dir.strip()
     processing.fix_seed(p)
 
-    images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
+    batch_images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
 
     is_inpaint_batch = False
     if inpaint_mask_dir:
@@ -31,9 +31,9 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
         if is_inpaint_batch:
             print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
 
-    print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
+    print(f"Will process {len(batch_images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
-    state.job_count = len(images) * p.n_iter
+    state.job_count = len(batch_images) * p.n_iter
 
     # extract "default" params to use in case getting png info fails
     prompt = p.prompt
@@ -46,8 +46,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
     sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
     batch_results = None
     discard_further_results = False
-    for i, image in enumerate(images):
-        state.job = f"{i+1} out of {len(images)}"
+    for i, image in enumerate(batch_images):
+        state.job = f"{i+1} out of {len(batch_images)}"
         if state.skipped:
             state.skipped = False
 
@@ -55,7 +55,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
             break
 
         try:
-            img = Image.open(image)
+            img = images.read(image)
         except UnidentifiedImageError as e:
             print(e)
             continue
@@ -86,7 +86,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
                 # otherwise user has many masks with the same name but different extensions
                 mask_image_path = masks_found[0]
 
-            mask_image = Image.open(mask_image_path)
+            mask_image = images.read(mask_image_path)
             p.image_mask = mask_image
 
         if use_png_info:
@@ -94,8 +94,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
                 info_img = img
                 if png_info_dir:
                     info_img_path = os.path.join(png_info_dir, os.path.basename(image))
-                    info_img = Image.open(info_img_path)
-                geninfo, _ = imgutil.read_info_from_image(info_img)
+                    info_img = images.read(info_img_path)
+                geninfo, _ = images.read_info_from_image(info_img)
                 parsed_parameters = parse_generation_parameters(geninfo)
                 parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
             except Exception:
@@ -175,9 +175,8 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         image = None
         mask = None
 
-    # Use the EXIF orientation of photos taken by smartphones.
-    if image is not None:
-        image = ImageOps.exif_transpose(image)
+    image = images.fix_image(image)
+    mask = images.fix_image(mask)
 
     if selected_scale_tab == 1 and not is_batch:
         assert image, "Can't scale by because no image is selected"

modules/infotext_utils.py

@@ -8,7 +8,7 @@ import sys
 
 import gradio as gr
 from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions
+from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions, images, prompt_parser
 from PIL import Image
 
 sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__]  # alias for old name
@@ -83,7 +83,7 @@ def image_from_url_text(filedata):
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
         filename = filename.rsplit('?', 1)[0]
-        return Image.open(filename)
+        return images.read(filename)
 
     if type(filedata) == list:
         if len(filedata) == 0:
@@ -95,7 +95,7 @@ def image_from_url_text(filedata):
         filedata = filedata[len("data:image/png;base64,"):]
 
     filedata = base64.decodebytes(filedata.encode('utf-8'))
-    image = Image.open(io.BytesIO(filedata))
+    image = images.read(io.BytesIO(filedata))
     return image
 
 
@@ -356,9 +356,15 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Cache FP16 weight for LoRA" not in res and res["FP8 weight"] != "Disable":
         res["Cache FP16 weight for LoRA"] = False
 
-    if "Emphasis" not in res:
+    prompt_attention = prompt_parser.parse_prompt_attention(prompt)
+    prompt_attention += prompt_parser.parse_prompt_attention(negative_prompt)
+    prompt_uses_emphasis = len(prompt_attention) != len([p for p in prompt_attention if p[1] == 1.0 or p[0] == 'BREAK'])
+    if "Emphasis" not in res and prompt_uses_emphasis:
         res["Emphasis"] = "Original"
 
+    if "Refiner switch by sampling steps" not in res:
+        res["Refiner switch by sampling steps"] = False
+
     infotext_versions.backcompat(res)
 
     for key in skip_fields:

modules/infotext_versions.py

@@ -5,6 +5,7 @@ import re
 
 v160 = version.parse("1.6.0")
 v170_tsnr = version.parse("v1.7.0-225")
+v180 = version.parse("1.8.0")
 
 
 def parse_version(text):
@@ -40,3 +41,5 @@ def backcompat(d):
     if ver < v170_tsnr:
         d["Downcast alphas_cumprod"] = True
 
+    if ver < v180 and d.get('Refiner'):
+        d["Refiner switch by sampling steps"] = True

modules/initialize.py

@@ -139,7 +139,7 @@ def initialize_rest(*, reload_script_modules=False):
         """
         Accesses shared.sd_model property to load model.
         After it's available, if it has been loaded before this access by some extension,
-        its optimization may be None because the list of optimizaers has neet been filled
+        its optimization may be None because the list of optimizers has not been filled
         by that time, so we apply optimization again.
         """
         from modules import devices

modules/launch_utils.py

@@ -55,7 +55,7 @@ and delete current Python and "venv" folder in WebUI's directory.
 
 You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
 
-{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
+{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre" if is_windows else ""}
 
 Use --skip-python-version-check to suppress this warning.
 """)

modules/mac_specific.py

@@ -12,7 +12,7 @@ log = logging.getLogger(__name__)
 
 # before torch version 1.13, has_mps is only available in nightly pytorch and macOS 12.3+,
 # use check `getattr` and try it for compatibility.
-# in torch version 1.13, backends.mps.is_available() and backends.mps.is_built() are introduced in to check mps availabilty,
+# in torch version 1.13, backends.mps.is_available() and backends.mps.is_built() are introduced in to check mps availability,
 # since torch 2.0.1+ nightly build, getattr(torch, 'has_mps', False) was deprecated, see https://github.com/pytorch/pytorch/pull/103279
 def check_for_mps() -> bool:
     if version.parse(torch.__version__) <= version.parse("2.0.1"):

modules/modelloader.py

@@ -110,7 +110,7 @@ def load_upscalers():
             except Exception:
                 pass
 
-    datas = []
+    data = []
     commandline_options = vars(shared.cmd_opts)
 
     # some of upscaler classes will not go away after reloading their modules, and we'll end
@@ -129,10 +129,10 @@ def load_upscalers():
         scaler = cls(commandline_model_path)
         scaler.user_path = commandline_model_path
         scaler.model_download_path = commandline_model_path or scaler.model_path
-        datas += scaler.scalers
+        data += scaler.scalers
 
     shared.sd_upscalers = sorted(
-        datas,
+        data,
         # Special case for UpscalerNone keeps it at the beginning of the list.
         key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
     )

modules/models/diffusion/ddpm_edit.py

@@ -341,7 +341,7 @@ class DDPM(pl.LightningModule):
         elif self.parameterization == "x0":
             target = x_start
         else:
-            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+            raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
 
         loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
 
@@ -901,7 +901,7 @@ class LatentDiffusion(DDPM):
     def apply_model(self, x_noisy, t, cond, return_ids=False):
 
         if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict
+            # hybrid case, cond is expected to be a dict
             pass
         else:
             if not isinstance(cond, list):
@@ -937,7 +937,7 @@ class LatentDiffusion(DDPM):
                 cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
 
             elif self.cond_stage_key == 'coordinates_bbox':
-                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+                assert 'original_image_size' in self.split_input_params, 'BoundingBoxRescaling is missing original_image_size'
 
                 # assuming padding of unfold is always 0 and its dilation is always 1
                 n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
@@ -947,7 +947,7 @@ class LatentDiffusion(DDPM):
                 num_downs = self.first_stage_model.encoder.num_resolutions - 1
                 rescale_latent = 2 ** (num_downs)
 
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+                # get top left positions of patches as conforming for the bbbox tokenizer, therefore we
                 # need to rescale the tl patch coordinates to be in between (0,1)
                 tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
                                          rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)

modules/postprocessing.py

@@ -17,10 +17,10 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
         if extras_mode == 1:
             for img in image_folder:
                 if isinstance(img, Image.Image):
-                    image = img
+                    image = images.fix_image(img)
                     fn = ''
                 else:
-                    image = Image.open(os.path.abspath(img.name))
+                    image = images.read(os.path.abspath(img.name))
                     fn = os.path.splitext(img.orig_name)[0]
                 yield image, fn
         elif extras_mode == 2:
@@ -56,7 +56,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
 
         if isinstance(image_placeholder, str):
             try:
-                image_data = Image.open(image_placeholder)
+                image_data = images.read(image_placeholder)
             except Exception:
                 continue
         else:

modules/processing.py

@@ -896,6 +896,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
 
+            p.setup_conds()
+
+            p.extra_generation_params.update(model_hijack.extra_generation_params)
+
             # params.txt should be saved after scripts.process_batch, since the
             # infotext could be modified by that callback
             # Example: a wildcard processed by process_batch sets an extra model
@@ -905,13 +909,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     processed = Processed(p, [])
                     file.write(processed.infotext(p, 0))
 
-            p.setup_conds()
-
             for comment in model_hijack.comments:
                 p.comment(comment)
 
-            p.extra_generation_params.update(model_hijack.extra_generation_params)
-
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 

modules/rng.py

@@ -34,7 +34,7 @@ def randn_local(seed, shape):
 
 
 def randn_like(x):
-    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized generator.
 
     Use either randn() or manual_seed() to initialize the generator."""
 
@@ -48,7 +48,7 @@ def randn_like(x):
 
 
 def randn_without_seed(shape, generator=None):
-    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized generator.
 
     Use either randn() or manual_seed() to initialize the generator."""
 

modules/scripts.py

@@ -92,7 +92,7 @@ class Script:
     """If true, the script setup will only be run in Gradio UI, not in API"""
 
     controls = None
-    """A list of controls retured by the ui()."""
+    """A list of controls returned by the ui()."""
 
     def title(self):
         """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
@@ -109,7 +109,7 @@ class Script:
 
     def show(self, is_img2img):
         """
-        is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
+        is_img2img is True if this function is called for the img2img interface, and False otherwise
 
         This function should return:
          - False if the script should not be shown in UI at all

modules/sd_emphasis.py

@@ -35,7 +35,7 @@ class EmphasisIgnore(Emphasis):
 
 class EmphasisOriginal(Emphasis):
     name = "Original"
-    description = "the orginal emphasis implementation"
+    description = "the original emphasis implementation"
 
     def after_transformers(self):
         original_mean = self.z.mean()
@@ -48,7 +48,7 @@ class EmphasisOriginal(Emphasis):
 
 class EmphasisOriginalNoNorm(EmphasisOriginal):
     name = "No norm"
-    description = "same as orginal, but without normalization (seems to work better for SDXL)"
+    description = "same as original, but without normalization (seems to work better for SDXL)"
 
     def after_transformers(self):
         self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)

modules/sd_hijack_clip.py

@@ -23,7 +23,7 @@ class PromptChunk:
 
 PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
 """An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
-chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
+chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
 are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
 
 
@@ -66,7 +66,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
 
     def encode_with_transformers(self, tokens):
         """
-        converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;
+        converts a batch of token ids (in python lists) into a single tensor with numeric representation of those tokens;
         All python lists with tokens are assumed to have same length, usually 77.
         if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
         model - can be 768 and 1024.
@@ -136,7 +136,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                 if token == self.comma_token:
                     last_comma = len(chunk.tokens)
 
-                # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
+                # this is when we are at the end of allotted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
                 # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
                 elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
                     break_location = last_comma + 1
@@ -206,7 +206,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, for SD2 it's 1024, and for SDXL it's 1280.
         An example shape returned by this function can be: (2, 77, 768).
         For SDXL, instead of returning one tensor avobe, it returns a tuple with two: the other one with shape (B, 1280) with pooled values.
-        Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
+        Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one element
         is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
         """
 
@@ -230,7 +230,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
             for fixes in self.hijack.fixes:
                 for _position, embedding in fixes:
                     used_embeddings[embedding.name] = embedding
-
+            devices.torch_npu_set_device()
             z = self.process_tokens(tokens, multipliers)
             zs.append(z)
 

modules/sd_models.py

@@ -784,7 +784,7 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
     If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
     If not, returns the model that can be used to load weights from checkpoint_info's file.
     If no such model exists, returns None.
-    Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
+    Additionally deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
     """
 
     already_loaded = None

modules/sd_samplers_cfg_denoiser.py

@@ -152,7 +152,7 @@ class CFGDenoiser(torch.nn.Module):
         if state.interrupted or state.skipped:
             raise sd_samplers_common.InterruptedException
 
-        if sd_samplers_common.apply_refiner(self):
+        if sd_samplers_common.apply_refiner(self, sigma):
             cond = self.sampler.sampler_extra_args['cond']
             uncond = self.sampler.sampler_extra_args['uncond']
 

modules/sd_samplers_common.py

@@ -155,8 +155,19 @@ def replace_torchsde_browinan():
 replace_torchsde_browinan()
 
 
-def apply_refiner(cfg_denoiser):
-    completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
+def apply_refiner(cfg_denoiser, sigma=None):
+    if opts.refiner_switch_by_sample_steps or sigma is None:
+        completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
+        cfg_denoiser.p.extra_generation_params["Refiner switch by sampling steps"] = True
+
+    else:
+        # torch.max(sigma) only to handle rare case where we might have different sigmas in the same batch
+        try:
+            timestep = torch.argmin(torch.abs(cfg_denoiser.inner_model.sigmas - torch.max(sigma)))
+        except AttributeError:  # for samplers that don't use sigmas (DDIM) sigma is actually the timestep
+            timestep = torch.max(sigma).to(dtype=int)
+        completed_ratio = (999 - timestep) / 1000
+
     refiner_switch_at = cfg_denoiser.p.refiner_switch_at
     refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
 

modules/shared.py

@@ -43,7 +43,7 @@ restricted_opts = None
 sd_model: sd_models_types.WebuiSdModel = None
 
 settings_components = None
-"""assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
+"""assigned from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
 
 tab_names = []
 

modules/shared_options.py

@@ -213,7 +213,7 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd"
     "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
     "pad_cond_uncond_v0": OptionInfo(False, "Pad prompt/negative prompt (v0)", infotext='Pad conds v0').info("alternative implementation for the above; used prior to 1.6.0 for DDIM sampler; overrides the above if set; WARNING: truncates negative prompt if it's too long; changes seeds"),
     "persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("do not recalculate conds from prompts if prompts have not changed since previous calculation"),
-    "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
+    "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond commandline argument"),
     "fp8_storage": OptionInfo("Disable", "FP8 weight", gr.Radio, {"choices": ["Disable", "Enable for SDXL", "Enable"]}).info("Use FP8 to store Linear/Conv layers' weight. Require pytorch>=2.1.0."),
     "cache_fp16_weight": OptionInfo(False, "Cache FP16 weight for LoRA").info("Cache fp16 weight when enabling FP8, will increase the quality of LoRA. Use more system ram."),
 }))
@@ -227,7 +227,8 @@ options_templates.update(options_section(('compatibility', "Compatibility", "sd"
     "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
     "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
     "use_old_scheduling": OptionInfo(False, "Use old prompt editing timelines.", infotext="Old prompt editing timelines").info("For [red:green:N]; old: If N < 1, it's a fraction of steps (and hires fix uses range from 0 to 1), if N >= 1, it's an absolute number of steps; new: If N has a decimal point in it, it's a fraction of steps (and hires fix uses range from 1 to 2), othewrwise it's an absolute number of steps"),
-    "use_downcasted_alpha_bar": OptionInfo(False, "Downcast model alphas_cumprod to fp16 before sampling. For reproducing old seeds.", infotext="Downcast alphas_cumprod")
+    "use_downcasted_alpha_bar": OptionInfo(False, "Downcast model alphas_cumprod to fp16 before sampling. For reproducing old seeds.", infotext="Downcast alphas_cumprod"),
+    "refiner_switch_by_sample_steps": OptionInfo(False, "Switch to refiner by sampling steps instead of model timesteps. Old behavior for refiner.", infotext="Refiner switch by sampling steps")
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate"), {
@@ -258,6 +259,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks", "s
     "extra_networks_card_order_field": OptionInfo("Path", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Path', 'Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
     "extra_networks_card_order": OptionInfo("Ascending", "Default order for Extra Networks cards", gr.Dropdown, {"choices": ['Ascending', 'Descending']}).needs_reload_ui(),
     "extra_networks_tree_view_default_enabled": OptionInfo(False, "Enables the Extra Networks directory tree view by default").needs_reload_ui(),
+    "extra_networks_tree_view_default_width": OptionInfo(180, "Default width for the Extra Networks directory tree view", gr.Number).needs_reload_ui(),
     "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
     "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
     "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
@@ -368,7 +370,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'rho':  OptionInfo(0.0, "rho", gr.Number, infotext='Schedule rho').info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}, infotext='ENSD').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", infotext='Discard penultimate sigma').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
-    'sgm_noise_multiplier': OptionInfo(False, "SGM noise multiplier", infotext='SGM noise multplier').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818").info("Match initial noise to official SDXL implementation - only useful for reproducing images"),
+    'sgm_noise_multiplier': OptionInfo(False, "SGM noise multiplier", infotext='SGM noise multiplier').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818").info("Match initial noise to official SDXL implementation - only useful for reproducing images"),
     'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}, infotext='UniPC variant'),
     'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, infotext='UniPC skip type'),
     'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),

modules/shared_state.py

@@ -157,10 +157,12 @@ class State:
             self.current_image_sampling_step = self.sampling_step
 
         except Exception:
-            # when switching models during genration, VAE would be on CPU, so creating an image will fail.
+            # when switching models during generation, VAE would be on CPU, so creating an image will fail.
             # we silently ignore this error
             errors.record_exception()
 
     def assign_current_image(self, image):
+        if shared.opts.live_previews_image_format == 'jpeg' and image.mode == 'RGBA':
+            image = image.convert('RGB')
         self.current_image = image
         self.id_live_preview += 1

modules/styles.py

@@ -42,7 +42,7 @@ def extract_style_text_from_prompt(style_text, prompt):
     stripped_style_text = style_text.strip()
 
     if "{prompt}" in stripped_style_text:
-        left, right = stripped_style_text.split("{prompt}", 2)
+        left, _, right = stripped_style_text.partition("{prompt}")
         if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
             prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
             return True, prompt

modules/textual_inversion/autocrop.py

@@ -65,7 +65,7 @@ def crop_image(im, settings):
         rect[3] -= 1
         d.rectangle(rect, outline=GREEN)
         results.append(im_debug)
-        if settings.destop_view_image:
+        if settings.desktop_view_image:
             im_debug.show()
 
     return results
@@ -341,5 +341,5 @@ class Settings:
         self.entropy_points_weight = entropy_points_weight
         self.face_points_weight = face_points_weight
         self.annotate_image = annotate_image
-        self.destop_view_image = False
+        self.desktop_view_image = False
         self.dnn_model_path = dnn_model_path

modules/textual_inversion/dataset.py

@@ -2,7 +2,6 @@ import os
 import numpy as np
 import PIL
 import torch
-from PIL import Image
 from torch.utils.data import Dataset, DataLoader, Sampler
 from torchvision import transforms
 from collections import defaultdict
@@ -10,7 +9,7 @@ from random import shuffle, choices
 
 import random
 import tqdm
-from modules import devices, shared
+from modules import devices, shared, images
 import re
 
 from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
@@ -61,7 +60,7 @@ class PersonalizedBase(Dataset):
             if shared.state.interrupted:
                 raise Exception("interrupted")
             try:
-                image = Image.open(path)
+                image = images.read(path)
                 #Currently does not work for single color transparency
                 #We would need to read image.info['transparency'] for that
                 if use_weight and 'A' in image.getbands():

modules/textual_inversion/image_embedding.py

@@ -193,11 +193,11 @@ if __name__ == '__main__':
 
     embedded_image = insert_image_data_embed(cap_image, test_embed)
 
-    retrived_embed = extract_image_data_embed(embedded_image)
+    retrieved_embed = extract_image_data_embed(embedded_image)
 
-    assert str(retrived_embed) == str(test_embed)
+    assert str(retrieved_embed) == str(test_embed)
 
-    embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
+    embedded_image2 = insert_image_data_embed(cap_image, retrieved_embed)
 
     assert embedded_image == embedded_image2
 

modules/textual_inversion/textual_inversion.py

@@ -172,7 +172,7 @@ class EmbeddingDatabase:
                 if data:
                     name = data.get('name', name)
                 else:
-                    # if data is None, means this is not an embeding, just a preview image
+                    # if data is None, means this is not an embedding, just a preview image
                     return
         elif ext in ['.BIN', '.PT']:
             data = torch.load(path, map_location="cpu")

modules/ui.py

@@ -269,6 +269,9 @@ def create_ui():
 
     parameters_copypaste.reset()
 
+    settings = ui_settings.UiSettings()
+    settings.register_settings()
+
     scripts.scripts_current = scripts.scripts_txt2img
     scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
@@ -1116,7 +1119,6 @@ def create_ui():
     loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
     ui_settings_from_file = loadsave.ui_settings.copy()
 
-    settings = ui_settings.UiSettings()
     settings.create_ui(loadsave, dummy_component)
 
     interfaces = [

modules/ui_common.py

@@ -105,7 +105,7 @@ def save_files(js_data, images, do_make_zip, index):
     logfile_path = os.path.join(shared.opts.outdir_save, "log.csv")
 
     # NOTE: ensure csv integrity when fields are added by
-    # updating headers and padding with delimeters where needed
+    # updating headers and padding with delimiters where needed
     if os.path.exists(logfile_path):
         update_logfile(logfile_path, fields)
 

modules/ui_components.py

@@ -88,7 +88,7 @@ class DropdownEditable(FormComponent, gr.Dropdown):
 class InputAccordion(gr.Checkbox):
     """A gr.Accordion that can be used as an input - returns True if open, False if closed.
 
-    Actaully just a hidden checkbox, but creates an accordion that follows and is followed by the state of the checkbox.
+    Actually just a hidden checkbox, but creates an accordion that follows and is followed by the state of the checkbox.
     """
 
     global_index = 0

modules/ui_extensions.py

@@ -380,7 +380,7 @@ def install_extension_from_url(dirname, url, branch_name=None):
         except OSError as err:
             if err.errno == errno.EXDEV:
                 # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
-                # Since we can't use a rename, do the slower but more versitile shutil.move()
+                # Since we can't use a rename, do the slower but more versatile shutil.move()
                 shutil.move(tmpdir, target_dir)
             else:
                 # Something else, not enough free space, permissions, etc.  rethrow it so that it gets handled.

modules/ui_extra_networks.py

@@ -531,9 +531,13 @@ class ExtraNetworksPage:
         data_sortkey = f"{data_sortmode}-{data_sortdir}-{len(self.items)}"
         tree_view_btn_extra_class = ""
         tree_view_div_extra_class = "hidden"
+        tree_view_div_default_display = "none"
+        extra_network_pane_content_default_display = "flex"
         if shared.opts.extra_networks_tree_view_default_enabled:
             tree_view_btn_extra_class = "extra-network-control--enabled"
             tree_view_div_extra_class = ""
+            tree_view_div_default_display = "block"
+            extra_network_pane_content_default_display = "grid"
 
         return self.pane_tpl.format(
             **{
@@ -546,6 +550,9 @@ class ExtraNetworksPage:
                 "tree_view_div_extra_class": tree_view_div_extra_class,
                 "tree_html": self.create_tree_view_html(tabname),
                 "items_html": self.create_card_view_html(tabname, none_message="Loading..." if empty else None),
+                "extra_networks_tree_view_default_width": shared.opts.extra_networks_tree_view_default_width,
+                "tree_view_div_default_display": tree_view_div_default_display,
+                "extra_network_pane_content_default_display": extra_network_pane_content_default_display,
             }
         )
 
@@ -693,7 +700,7 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
             return ui.pages_contents
 
         button_refresh = gr.Button("Refresh", elem_id=f"{tabname}_{page.extra_networks_tabname}_extra_refresh_internal", visible=False)
-        button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages).then(fn=lambda: None, _js="function(){ " + f"applyExtraNetworkFilter('{tabname}_{page.extra_networks_tabname}');" + " }")
+        button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages).then(fn=lambda: None, _js="function(){ " + f"applyExtraNetworkFilter('{tabname}_{page.extra_networks_tabname}');" + " }").then(fn=lambda: None, _js='setupAllResizeHandles')
 
     def create_html():
         ui.pages_contents = [pg.create_html(ui.tabname) for pg in ui.stored_extra_pages]
@@ -703,7 +710,7 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
             create_html()
         return ui.pages_contents
 
-    interface.load(fn=pages_html, inputs=[], outputs=ui.pages)
+    interface.load(fn=pages_html, inputs=[], outputs=ui.pages).then(fn=lambda: None, _js='setupAllResizeHandles')
 
     return ui
 

modules/ui_prompt_styles.py

@@ -67,7 +67,7 @@ class UiPromptStyles:
             with gr.Row():
                 self.selection = gr.Dropdown(label="Styles", elem_id=f"{tabname}_styles_edit_select", choices=list(shared.prompt_styles.styles), value=[], allow_custom_value=True, info="Styles allow you to add custom text to prompt. Use the {prompt} token in style text, and it will be replaced with user's prompt when applying style. Otherwise, style's text will be added to the end of the prompt.")
                 ui_common.create_refresh_button([self.dropdown, self.selection], shared.prompt_styles.reload, lambda: {"choices": list(shared.prompt_styles.styles)}, f"refresh_{tabname}_styles")
-                self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply_dialog", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
+                self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply_dialog", tooltip="Apply all selected styles from the style selection dropdown in main UI to the prompt.")
                 self.copy = ui_components.ToolButton(value=styles_copy_symbol, elem_id=f"{tabname}_style_copy", tooltip="Copy main UI prompt to style.")
 
             with gr.Row():

modules/ui_settings.py

@@ -98,6 +98,9 @@ class UiSettings:
 
         return get_value_for_setting(key), opts.dumpjson()
 
+    def register_settings(self):
+        script_callbacks.ui_settings_callback()
+
     def create_ui(self, loadsave, dummy_component):
         self.components = []
         self.component_dict = {}
@@ -105,7 +108,6 @@ class UiSettings:
 
         shared.settings_components = self.component_dict
 
-        script_callbacks.ui_settings_callback()
         opts.reorder()
 
         with gr.Blocks(analytics_enabled=False) as settings_interface:

modules/upscaler_utils.py

@@ -69,10 +69,8 @@ def upscale_with_model(
         for y, h, row in grid.tiles:
             newrow = []
             for x, w, tile in row:
-                logger.debug("Tile (%d, %d) %s...", x, y, tile)
                 output = upscale_pil_patch(model, tile)
                 scale_factor = output.width // tile.width
-                logger.debug("=> %s (scale factor %s)", output, scale_factor)
                 newrow.append([x * scale_factor, w * scale_factor, output])
                 p.update(1)
             newtiles.append([y * scale_factor, h * scale_factor, newrow])

scripts/outpainting_mk_2.py

@@ -102,7 +102,7 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
     shaped_noise_fft = _fft2(noise_rgb)
     shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase  # perform the actual shaping
 
-    brightness_variation = 0.  # color_variation # todo: temporarily tieing brightness variation to color variation for now
+    brightness_variation = 0.  # color_variation # todo: temporarily tying brightness variation to color variation for now
     contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
 
     # scikit-image is used for histogram matching, very convenient!

scripts/xyz_grid.py

@@ -45,7 +45,7 @@ def apply_prompt(p, x, xs):
 def apply_order(p, x, xs):
     token_order = []
 
-    # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
+    # Initially grab the tokens from the prompt, so they can be replaced in order of earliest seen
     for token in x:
         token_order.append((p.prompt.find(token), token))
 

style.css

@@ -1615,9 +1615,10 @@ body.resizing .resize-handle {
     display: inline-flex;
     visibility: hidden;
     color: var(--button-secondary-text-color);
-
+    width: 0;
 }
 
 .extra-network-tree .tree-list-content:hover .button-row {
     visibility: visible;
+    width: auto;
 }

webui.sh

@@ -158,9 +158,9 @@ then
     if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
     then
         export TORCH_COMMAND="pip install torch==2.0.1+rocm5.4.2 torchvision==0.15.2+rocm5.4.2 --index-url https://download.pytorch.org/whl/rocm5.4.2"
-    elif echo "$gpu_info" | grep -q "Huawei" && [[ -z "${TORCH_COMMAND}" ]]
+    elif npu-smi info 2>/dev/null
     then
-        export TORCH_COMMAND="pip install torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu; pip install torch_npu"
+        export TORCH_COMMAND="pip install torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu; pip install torch_npu==2.1.0"
     
     fi
 fi