set min brush size to 1

remove the overly complex option of radius / area brush size change mode

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

@@ -226,6 +226,8 @@ onUiLoaded(async() => {
         canvas_show_tooltip: true,
         canvas_auto_expand: true,
         canvas_blur_prompt: false,
+        canvas_hotkey_undo: "KeyZ",
+        canvas_hotkey_clear: "KeyC",
     };
 
     const functionMap = {
@@ -236,7 +238,9 @@ onUiLoaded(async() => {
         "Moving canvas": "canvas_hotkey_move",
         "Fullscreen": "canvas_hotkey_fullscreen",
         "Reset Zoom": "canvas_hotkey_reset",
-        "Overlap": "canvas_hotkey_overlap"
+        "Overlap": "canvas_hotkey_overlap",
+        "Undo": "canvas_hotkey_undo",
+        "Clear": "canvas_hotkey_clear"
     };
 
     // Loading the configuration from opts
@@ -321,6 +325,8 @@ onUiLoaded(async() => {
                     action: "Adjust brush size",
                     keySuffix: " + wheel"
                 },
+                {configKey: "canvas_hotkey_undo", action: "Undo brush stroke"},
+                {configKey: "canvas_hotkey_clear", action: "Clear canvas"},
                 {configKey: "canvas_hotkey_reset", action: "Reset zoom"},
                 {
                     configKey: "canvas_hotkey_fullscreen",
@@ -464,22 +470,45 @@ onUiLoaded(async() => {
                 gradioApp().querySelector(
                     `${elemId} button[aria-label="Use brush"]`
                 );
-
             if (input) {
                 input.click();
                 if (!withoutValue) {
-                    const maxValue =
-                        parseFloat(input.getAttribute("max")) || 100;
-                    const changeAmount = maxValue * (percentage / 100);
-                    const newValue =
-                        parseFloat(input.value) +
-                        (deltaY > 0 ? -changeAmount : changeAmount);
-                    input.value = Math.min(Math.max(newValue, 0), maxValue);
+                    const maxValue = parseFloat(input.getAttribute("max")) || 100;
+                    const minValue = parseFloat(input.getAttribute("min")) || 1;
+                    // allow brush size up to 1/2 diagonal of the image, beyond gradio's arbitrary limit
+                    const canvasImg = gradioApp().querySelector(`${elemId} img`);
+                    if (canvasImg) {
+                        const maxDiameter = Math.sqrt(canvasImg.naturalWidth ** 2 + canvasImg.naturalHeight ** 2) / 2;
+                        if (maxDiameter > maxValue) {
+                            input.setAttribute("max", maxDiameter);
+                        }
+                        if (minValue > 1) {
+                            input.setAttribute("min", '1');
+                        }
+                    }
+                    const brush_factor = deltaY > 0 ? 1 - opts.canvas_hotkey_brush_factor : 1 + opts.canvas_hotkey_brush_factor;
+                    const currentRadius = parseFloat(input.value);
+                    let delta = Math.sqrt(currentRadius ** 2 * brush_factor) - currentRadius;
+                    // minimum brush size step of 1
+                    if (Math.abs(delta) < 1) {
+                        delta = deltaY > 0 ? -1 : 1;
+                    }
+                    const newValue = currentRadius + delta;
+                    input.value = Math.max(newValue, 1);
                     input.dispatchEvent(new Event("change"));
                 }
             }
         }
 
+        // Undo the last brush stroke by clicking the undo button
+        function undoBrushStroke() {
+            gradioApp().querySelector(`${elemId} button[aria-label='Undo']`).click();
+        }
+
+        function clearCanvas() {
+            gradioApp().querySelector(`${elemId} button[aria-label='Clear']`).click();
+        }
+
         // Reset zoom when uploading a new image
         const fileInput = gradioApp().querySelector(
             `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv`
@@ -699,7 +728,9 @@ onUiLoaded(async() => {
                 [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
                 [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen,
                 [hotkeysConfig.canvas_hotkey_shrink_brush]: () => adjustBrushSize(elemId, 10),
-                [hotkeysConfig.canvas_hotkey_grow_brush]: () => adjustBrushSize(elemId, -10)
+                [hotkeysConfig.canvas_hotkey_grow_brush]: () => adjustBrushSize(elemId, -10),
+                [hotkeysConfig.canvas_hotkey_undo]: undoBrushStroke,
+                [hotkeysConfig.canvas_hotkey_clear]: clearCanvas
             };
 
             const action = hotkeyActions[event.code];

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

@@ -2,16 +2,19 @@ import gradio as gr
 from modules import shared
 
 shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
-    "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
-    "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+    "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift", "Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+    "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift", "Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
     "canvas_hotkey_shrink_brush": shared.OptionInfo("Q", "Shrink the brush size"),
     "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_undo": shared.OptionInfo("Z", "Undo brush stroke"),
+    "canvas_hotkey_clear": shared.OptionInfo("C", "Clear canvas"),
     "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 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"),
-    "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size","Hotkey enlarge brush","Hotkey shrink brush","Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
+    "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom", "Adjust brush size", "Hotkey enlarge brush", "Hotkey shrink brush", "Undo", "Clear", "Moving canvas", "Fullscreen", "Reset Zoom", "Overlap"]}),
+    "canvas_hotkey_brush_factor": shared.OptionInfo(0.1, "Brush size change rate", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info('controls how much the brush size is changed when using hotkeys or scroll wheel'),
 }))

extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js

@@ -4,11 +4,11 @@
 // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
 
 function checkBrackets(textArea, counterElt) {
-    const counts = {};
-    textArea.value.matchAll(/(?<!\\)(?:\\\\)*?([(){}[\]])/g).forEach(bracket => {
-        counts[bracket[1]] = (counts[bracket[1]] || 0) + 1;
+    var counts = {};
+    (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
+        counts[bracket] = (counts[bracket] || 0) + 1;
     });
-    const errors = [];
+    var errors = [];
 
     function checkPair(open, close, kind) {
         if (counts[open] !== counts[close]) {

modules/processing.py

@@ -187,7 +187,6 @@ class StableDiffusionProcessing:
 
     cached_uc = [None, None]
     cached_c = [None, None]
-    hijack_generation_params_state_list = []
 
     comments: dict = None
     sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
@@ -481,10 +480,6 @@ class StableDiffusionProcessing:
 
         for cache in caches:
             if cache[0] is not None and cached_params == cache[0]:
-                if len(cache) == 3:
-                    generation_params_state, cached_params_2 = cache[2]
-                    if cached_params == cached_params_2:
-                        self.hijack_generation_params_state_list.extend(generation_params_state)
                 return cache[1]
 
         cache = caches[0]
@@ -492,25 +487,9 @@ class StableDiffusionProcessing:
         with devices.autocast():
             cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
 
-        generation_params_state = model_hijack.capture_generation_params_state()
-        self.hijack_generation_params_state_list.extend(generation_params_state)
-        if len(cache) == 2:
-            cache.append((generation_params_state, cached_params))
-        else:
-            cache[2] = (generation_params_state, cached_params)
-
         cache[0] = cached_params
         return cache[1]
 
-    def apply_hijack_generation_params(self):
-        self.extra_generation_params.update(model_hijack.extra_generation_params)
-        for func in self.hijack_generation_params_state_list:
-            try:
-                func(self.extra_generation_params)
-            except Exception:
-                errors.report('Failed to apply hijack generation params state', exc_info=True)
-        self.hijack_generation_params_state_list.clear()
-
     def setup_conds(self):
         prompts = prompt_parser.SdConditioning(self.prompts, width=self.width, height=self.height)
         negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)
@@ -523,8 +502,6 @@ class StableDiffusionProcessing:
         self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
         self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)
 
-        self.apply_hijack_generation_params()
-
     def get_conds(self):
         return self.c, self.uc
 
@@ -988,6 +965,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             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
@@ -1534,8 +1513,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data, total_steps)
         self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data, total_steps)
 
-        self.apply_hijack_generation_params()
-
     def setup_conds(self):
         if self.is_hr_pass:
             # if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model

modules/sd_hijack.py

@@ -6,7 +6,6 @@ from modules import devices, sd_hijack_optimizations, shared, script_callbacks,
 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, xlmr_m18
-from modules.util import GenerationParamsState
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
@@ -322,13 +321,6 @@ class StableDiffusionModelHijack:
         self.comments = []
         self.extra_generation_params = {}
 
-    def capture_generation_params_state(self):
-        state = []
-        for key in list(self.extra_generation_params):
-            if isinstance(self.extra_generation_params[key], GenerationParamsState):
-                state.append(self.extra_generation_params.pop(key))
-        return state
-
     def get_prompt_lengths(self, text):
         if self.clip is None:
             return "-", "-"

modules/sd_hijack_clip.py

@@ -3,9 +3,8 @@ from collections import namedtuple
 
 import torch
 
-from modules import prompt_parser, devices, sd_hijack, sd_emphasis, util
+from modules import prompt_parser, devices, sd_hijack, sd_emphasis
 from modules.shared import opts
-from modules.util import GenerationParamsState
 
 
 class PromptChunk:
@@ -28,31 +27,6 @@ chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenC
 are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
 
 
-class EmbeddingHashes(GenerationParamsState):
-    def __init__(self, hashes: list):
-        super().__init__()
-        self.hashes = hashes
-
-    def __call__(self, extra_generation_params):
-        unique_hashes = dict.fromkeys(self.hashes)
-        if existing_ti_hashes := extra_generation_params.get('TI hashes'):
-            unique_hashes.update(dict.fromkeys(existing_ti_hashes.split(', ')))
-        extra_generation_params['TI hashes'] = ', '.join(sorted(unique_hashes, key=util.natural_sort_key))
-
-
-class EmphasisMode(GenerationParamsState):
-    def __init__(self, texts):
-        super().__init__()
-        if opts.emphasis != 'Original' and any(x for x in texts if '(' in x or '[' in x):
-            self.emphasis = opts.emphasis
-        else:
-            self.emphasis = None
-
-    def __call__(self, extra_generation_params):
-        if self.emphasis:
-            extra_generation_params['Emphasis'] = self.emphasis
-
-
 class TextConditionalModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -264,9 +238,12 @@ class TextConditionalModel(torch.nn.Module):
                 hashes.append(f"{name}: {shorthash}")
 
             if hashes:
-                self.hijack.extra_generation_params["TI hashes"] = EmbeddingHashes(hashes)
+                if self.hijack.extra_generation_params.get("TI hashes"):
+                    hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
+                self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
 
-        self.hijack.extra_generation_params["Emphasis"] = EmphasisMode(texts)
+        if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
+            self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
 
         if self.return_pooled:
             return torch.hstack(zs), zs[0].pooled

modules/util.py

@@ -288,18 +288,3 @@ def compare_sha256(file_path: str, hash_prefix: str) -> bool:
         for chunk in iter(lambda: f.read(blksize), b""):
             hash_sha256.update(chunk)
     return hash_sha256.hexdigest().startswith(hash_prefix.strip().lower())
-
-
-class GenerationParamsState:
-    """A custom class used in StableDiffusionModelHijack for assigning extra_generation_params
-    generation_params assigned using this class will work properly with StableDiffusionProcessing.get_conds_with_caching()
-        if assigned directly the generation_params will not be populated if conda cache is used
-
-    Generation_params of this class will be captured (see StableDiffusionModelHijack.capture_generation_params_state) and stored with conda cache, and will be extracted in StableDiffusionProcessing.apply_hijack_generation_params()
-
-    To use this class, create a subclass with a __call__ method that takes extra_generation_params: dict as input
-
-    Example usage: sd_hijack_clip.EmbeddingHashes, sd_hijack_clip.EmphasisMode
-    """
-    def __call__(self, extra_generation_params: dict):
-        raise NotImplementedError