clear GenerationParametersList before batch

clears any generation parameters that are with the attribute to_be_clear_before_batch = True
prevent buildup of some parameters

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

@@ -226,8 +226,6 @@ onUiLoaded(async() => {
         canvas_show_tooltip: true,
         canvas_auto_expand: true,
         canvas_blur_prompt: false,
-        canvas_hotkey_undo: "KeyZ",
-        canvas_hotkey_clear: "KeyC",
     };
 
     const functionMap = {
@@ -238,9 +236,7 @@ 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
@@ -325,8 +321,6 @@ 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",
@@ -470,45 +464,22 @@ onUiLoaded(async() => {
                 gradioApp().querySelector(
                     `${elemId} button[aria-label="Use brush"]`
                 );
+
             if (input) {
                 input.click();
                 if (!withoutValue) {
-                    const maxValue = parseFloat(input.getAttribute("max")) || 100;
+                    const maxValue =
-                    const minValue = parseFloat(input.getAttribute("min")) || 1;
+                        parseFloat(input.getAttribute("max")) || 100;
-                    // allow brush size up to 1/2 diagonal of the image, beyond gradio's arbitrary limit
+                    const changeAmount = maxValue * (percentage / 100);
-                    const canvasImg = gradioApp().querySelector(`${elemId} img`);
+                    const newValue =
-                    if (canvasImg) {
+                        parseFloat(input.value) +
-                        const maxDiameter = Math.sqrt(canvasImg.naturalWidth ** 2 + canvasImg.naturalHeight ** 2) / 2;
+                        (deltaY > 0 ? -changeAmount : changeAmount);
-                        if (maxDiameter > maxValue) {
+                    input.value = Math.min(Math.max(newValue, 0), 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`
@@ -728,9 +699,7 @@ 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

@@ -7,14 +7,11 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas
     "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", "Undo", "Clear", "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","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) {
-    var counts = {};
+    const counts = {};
-    (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
+    textArea.value.matchAll(/(?<!\\)(?:\\\\)*?([(){}[\]])/g).forEach(bracket => {
-        counts[bracket] = (counts[bracket] || 0) + 1;
+        counts[bracket[1]] = (counts[bracket[1]] || 0) + 1;
     });
-    var errors = [];
+    const errors = [];
 
     function checkPair(open, close, kind) {
         if (counts[open] !== counts[close]) {

modules/processing.py

@@ -16,7 +16,7 @@ from skimage import exposure
 from typing import Any
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng, profiling
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng, profiling, util
 from modules.rng import slerp # noqa: F401
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
@@ -457,6 +457,20 @@ class StableDiffusionProcessing:
             opts.emphasis,
         )
 
+    def apply_generation_params_list(self, generation_params_states):
+        """add and apply generation_params_states to self.extra_generation_params"""
+        for key, value in generation_params_states.items():
+            if key in self.extra_generation_params and isinstance(current_value := self.extra_generation_params[key], util.GenerationParametersList):
+                self.extra_generation_params[key] = current_value + value
+            else:
+                self.extra_generation_params[key] = value
+
+    def clear_marked_generation_params(self):
+        """clears any generation parameters that are with the attribute to_be_clear_before_batch = True"""
+        for key, value in list(self.extra_generation_params.items()):
+            if getattr(value, 'to_be_clear_before_batch', False):
+                self.extra_generation_params.pop(key)
+
     def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
         """
         Returns the result of calling function(shared.sd_model, required_prompts, steps)
@@ -480,6 +494,10 @@ class StableDiffusionProcessing:
 
         for cache in caches:
             if cache[0] is not None and cached_params == cache[0]:
+                if len(cache) == 3:
+                    generation_params_states, cached_cached_params = cache[2]
+                    if cached_params == cached_cached_params:
+                        self.apply_generation_params_list(generation_params_states)
                 return cache[1]
 
         cache = caches[0]
@@ -487,6 +505,13 @@ class StableDiffusionProcessing:
         with devices.autocast():
             cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
 
+        generation_params_states = model_hijack.extract_generation_params_states()
+        self.apply_generation_params_list(generation_params_states)
+        if len(cache) == 2:
+            cache.append((generation_params_states, cached_params))
+        else:
+            cache[2] = (generation_params_states, cached_params)
+
         cache[0] = cached_params
         return cache[1]
 
@@ -502,6 +527,8 @@ 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.extra_generation_params.update(model_hijack.extra_generation_params)
+
     def get_conds(self):
         return self.c, self.uc
 
@@ -801,10 +828,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
 
     for key, value in generation_params.items():
         try:
-            if isinstance(value, list):
+            if callable(value):
-                generation_params[key] = value[index]
-            elif callable(value):
                 generation_params[key] = value(**locals())
+            elif isinstance(value, list):
+                generation_params[key] = value[index]
         except Exception:
             errors.report(f'Error creating infotext for key "{key}"', exc_info=True)
             generation_params[key] = None
@@ -938,6 +965,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted or state.stopping_generation:
                 break
 
+            p.clear_marked_generation_params()  # clean up some generation params are tagged to be cleared before batch
             sd_models.reload_model_weights()  # model can be changed for example by refiner
 
             p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
@@ -965,8 +993,6 @@ 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
@@ -1513,6 +1539,8 @@ 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.extra_generation_params.update(model_hijack.extra_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

@@ -2,7 +2,7 @@ import torch
 from torch.nn.functional import silu
 from types import MethodType
 
-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches, util
 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
@@ -321,6 +321,14 @@ class StableDiffusionModelHijack:
         self.comments = []
         self.extra_generation_params = {}
 
+    def extract_generation_params_states(self):
+        """Extracts GenerationParametersList so that they can be cached and restored later"""
+        states = {}
+        for key in list(self.extra_generation_params):
+            if isinstance(self.extra_generation_params[key], util.GenerationParametersList):
+                states[key] = self.extra_generation_params.pop(key)
+        return states
+
     def get_prompt_lengths(self, text):
         if self.clip is None:
             return "-", "-"

modules/sd_hijack_clip.py

@@ -3,7 +3,7 @@ from collections import namedtuple
 
 import torch
 
-from modules import prompt_parser, devices, sd_hijack, sd_emphasis
+from modules import prompt_parser, devices, sd_hijack, sd_emphasis, util
 from modules.shared import opts
 
 
@@ -27,6 +27,30 @@ chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenC
 are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
 
 
+class EmphasisMode(util.GenerationParametersList):
+    def __init__(self, emphasis_mode:str = None):
+        super().__init__()
+        self.emphasis_mode = emphasis_mode
+
+    def __call__(self, *args, **kwargs):
+        return self.emphasis_mode
+
+    def __add__(self, other):
+        if isinstance(other, EmphasisMode):
+            return self if self.emphasis_mode else other
+        elif isinstance(other, str):
+            return self.__str__() + other
+        return NotImplemented
+
+    def __radd__(self, other):
+        if isinstance(other, str):
+            return other + self.__str__()
+        return NotImplemented
+
+    def __str__(self):
+        return self.emphasis_mode if self.emphasis_mode else ''
+
+
 class TextConditionalModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -238,12 +262,10 @@ class TextConditionalModel(torch.nn.Module):
                 hashes.append(f"{name}: {shorthash}")
 
             if hashes:
-                if self.hijack.extra_generation_params.get("TI hashes"):
+                self.hijack.extra_generation_params["TI hashes"] = util.GenerationParametersList(hashes)
-                    hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
-                self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
 
-        if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
+        if opts.emphasis != 'Original' and any(x for x in texts if '(' in x or '[' in x):
-            self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
+            self.hijack.extra_generation_params["Emphasis"] = EmphasisMode(opts.emphasis)
 
         if self.return_pooled:
             return torch.hstack(zs), zs[0].pooled

modules/util.py

@@ -288,3 +288,49 @@ 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 GenerationParametersList(list):
+    """A special object used in sd_hijack.StableDiffusionModelHijack for setting extra_generation_params
+    due to StableDiffusionProcessing.get_conds_with_caching
+    extra_generation_params set in StableDiffusionModelHijack will be lost when cached is used
+
+    When an extra_generation_params is set in StableDiffusionModelHijack using this object,
+    the params will be extracted by StableDiffusionModelHijack.extract_generation_params_states
+    the extracted params will be cached in StableDiffusionProcessing.get_conds_with_caching
+    and applyed to StableDiffusionProcessing.extra_generation_params by StableDiffusionProcessing.apply_generation_params_states
+
+    Example see modules.sd_hijack_clip.TextConditionalModel.hijack.extra_generation_params 'TI hashes' 'Emphasis'
+
+    Depending on the use case the methods can be overwritten.
+    In general __call__ method should return str or None, as normally it's called in modules.processing.create_infotext.
+    When called by create_infotext it will access to the locals() of the caller,
+    if return str, the value will be written to infotext, if return None will be ignored.
+    """
+
+    def __init__(self, *args, to_be_clear_before_batch=True, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._to_be_clear_before_batch = to_be_clear_before_batch
+
+    def __call__(self, *args, **kwargs):
+        return ', '.join(sorted(set(self), key=natural_sort_key))
+
+    @property
+    def to_be_clear_before_batch(self):
+        return self._to_be_clear_before_batch
+
+    def __add__(self, other):
+        if isinstance(other, GenerationParametersList):
+            return self.__class__([*self, *other])
+        elif isinstance(other, str):
+            return self.__str__() + other
+        return NotImplemented
+
+    def __radd__(self, other):
+        if isinstance(other, str):
+            return other + self.__str__()
+        return NotImplemented
+
+    def __str__(self):
+        return self.__call__()
+