Merge branch 'dev' into test-fp8

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

@@ -64,11 +64,14 @@ class ExtraOptionsSection(scripts.Script):
                 p.override_settings[name] = value
 
 
-shared.options_templates.update(shared.options_section(('ui', "User interface"), {
-    "extra_options_txt2img": shared.OptionInfo([], "Options in main UI - txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
-    "extra_options_img2img": shared.OptionInfo([], "Options in main UI - img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
-    "extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
-    "extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
+shared.options_templates.update(shared.options_section(('settings_in_ui', "Settings in UI", "ui"), {
+    "settings_in_ui": shared.OptionHTML("""
+This page allows you to add some settings to the main interface of txt2img and img2img tabs.
+"""),
+    "extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
+    "extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
+    "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Number, {"precision": 0}).needs_reload_ui(),
+    "extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui()
 }))
 
 

extensions-builtin/hypertile/hypertile.py

@@ -6,7 +6,6 @@ Original author: @tfernd Github: https://github.com/tfernd/HyperTile
 
 from __future__ import annotations
 
-import functools
 from dataclasses import dataclass
 from typing import Callable
 
@@ -189,20 +188,27 @@ DEPTH_LAYERS_XL = {
 
 RNG_INSTANCE = random.Random()
 
-
-def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
+@cache
+def get_divisors(value: int, min_value: int, /, max_options: int = 1) -> list[int]:
     """
-    Returns a random divisor of value that
+    Returns divisors of value that
         x * min_value <= value
-    if max_options is 1, the behavior is deterministic
+    in big -> small order, amount of divisors is limited by max_options
     """
+    max_options = max(1, max_options) # at least 1 option should be returned
     min_value = min(min_value, value)
-
-    # All big divisors of value (inclusive)
     divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order
-
     ns = [value // i for i in divisors[:max_options]]  # has at least 1 element # big -> small order
+    return ns
+
 
+def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
+    """
+    Returns a random divisor of value that
+        x * min_value <= value
+    if max_options is 1, the behavior is deterministic
+    """
+    ns = get_divisors(value, min_value, max_options=max_options) # get cached divisors
     idx = RNG_INSTANCE.randint(0, len(ns) - 1)
 
     return ns[idx]
@@ -212,7 +218,7 @@ def set_hypertile_seed(seed: int) -> None:
     RNG_INSTANCE.seed(seed)
 
 
-@functools.cache
+@cache
 def largest_tile_size_available(width: int, height: int) -> int:
     """
     Calculates the largest tile size available for a given width and height

extensions-builtin/hypertile/scripts/hypertile_script.py

 import hypertile
 from modules import scripts, script_callbacks, shared
+from scripts.hypertile_xyz import add_axis_options
 
 
 class ScriptHypertile(scripts.Script):
@@ -17,7 +18,10 @@ class ScriptHypertile(scripts.Script):
         configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
 
     def before_hr(self, p, *args):
-        configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet)
+        # exclusive hypertile seed for the second pass
+        if not shared.opts.hypertile_enable_unet:
+            hypertile.set_hypertile_seed(p.all_seeds[0])
+        configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=shared.opts.hypertile_enable_unet_secondpass)
 
 
 def configure_hypertile(width, height, enable_unet=True):
@@ -57,12 +61,12 @@ def on_ui_settings():
         "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass"),
         "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
         "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-        "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-net swap size", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
+        "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
 
         "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE").info("minimal change in the generated picture"),
         "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
         "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-        "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
+        "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
     }
 
     for name, opt in options.items():
@@ -71,3 +75,4 @@ def on_ui_settings():
 
 
 script_callbacks.on_ui_settings(on_ui_settings)
+script_callbacks.on_before_ui(add_axis_options)

extensions-builtin/hypertile/scripts/hypertile_xyz.py

+from modules import scripts
+from modules.shared import opts
+
+xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
+
+def int_applier(value_name:str, min_range:int = -1, max_range:int = -1):
+    """
+    Returns a function that applies the given value to the given value_name in opts.data.
+    """
+    def validate(value_name:str, value:str):
+        value = int(value)
+        # validate value
+        if not min_range == -1:
+            assert value >= min_range, f"Value {value} for {value_name} must be greater than or equal to {min_range}"
+        if not max_range == -1:
+            assert value <= max_range, f"Value {value} for {value_name} must be less than or equal to {max_range}"
+    def apply_int(p, x, xs):
+        validate(value_name, x)
+        opts.data[value_name] = int(x)
+    return apply_int
+
+def bool_applier(value_name:str):
+    """
+    Returns a function that applies the given value to the given value_name in opts.data.
+    """
+    def validate(value_name:str, value:str):
+        assert value.lower() in ["true", "false"], f"Value {value} for {value_name} must be either true or false"
+    def apply_bool(p, x, xs):
+        validate(value_name, x)
+        value_boolean = x.lower() == "true"
+        opts.data[value_name] = value_boolean
+    return apply_bool
+
+def add_axis_options():
+    extra_axis_options = [
+        xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, bool_applier("hypertile_enable_unet"), choices=xyz_grid.boolean_choice(reverse=True)),
+        xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, bool_applier("hypertile_enable_unet_secondpass"), choices=xyz_grid.boolean_choice(reverse=True)),
+        xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, int_applier("hypertile_max_depth_unet", 0, 3), choices=lambda: [str(x) for x in range(4)]),
+        xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, int_applier("hypertile_max_tile_unet", 0, 512)),
+        xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, int_applier("hypertile_swap_size_unet", 0, 64)),
+        xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, bool_applier("hypertile_enable_vae"), choices=xyz_grid.boolean_choice(reverse=True)),
+        xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, int_applier("hypertile_max_depth_vae", 0, 3), choices=lambda: [str(x) for x in range(4)]),
+        xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, int_applier("hypertile_max_tile_vae", 0, 512)),
+        xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, int_applier("hypertile_swap_size_vae", 0, 64)),
+    ]
+    set_a = {opt.label for opt in xyz_grid.axis_options}
+    set_b = {opt.label for opt in extra_axis_options}
+    if set_a.intersection(set_b):
+        return
+
+    xyz_grid.axis_options.extend(extra_axis_options)

javascript/extraNetworks.js

@@ -130,6 +130,10 @@ function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePromp
     } else {
         promptContainer.insertBefore(prompt, promptContainer.firstChild);
     }
+
+    if (elem) {
+        elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt);
+    }
 }
 
 
@@ -388,3 +392,9 @@ function extraNetworksRefreshSingleCard(page, tabname, name) {
         }
     });
 }
+
+window.addEventListener("keydown", function(event) {
+    if (event.key == "Escape") {
+        closePopup();
+    }
+});

javascript/settings.js

@@ -44,3 +44,28 @@ onUiLoaded(function() {
 
     buttonShowAllPages.addEventListener("click", settingsShowAllTabs);
 });
+
+
+onOptionsChanged(function() {
+    if (gradioApp().querySelector('#settings .settings-category')) return;
+
+    var sectionMap = {};
+    gradioApp().querySelectorAll('#settings > div > button').forEach(function(x) {
+        sectionMap[x.textContent.trim()] = x;
+    });
+
+    opts._categories.forEach(function(x) {
+        var section = x[0];
+        var category = x[1];
+
+        var span = document.createElement('SPAN');
+        span.textContent = category;
+        span.className = 'settings-category';
+
+        var sectionElem = sectionMap[section];
+        if (!sectionElem) return;
+
+        sectionElem.parentElement.insertBefore(span, sectionElem);
+    });
+});
+

modules/cache.py

@@ -32,7 +32,7 @@ def dump_cache():
         with cache_lock:
             cache_filename_tmp = cache_filename + "-"
             with open(cache_filename_tmp, "w", encoding="utf8") as file:
-                json.dump(cache_data, file, indent=4)
+                json.dump(cache_data, file, indent=4, ensure_ascii=False)
 
             os.replace(cache_filename_tmp, cache_filename)
 

modules/devices.py

@@ -55,7 +55,7 @@ def get_optimal_device():
 
 
 def get_device_for(task):
-    if task in shared.cmd_opts.use_cpu:
+    if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu:
         return cpu
 
     return get_optimal_device()

modules/generation_parameters_copypaste.py

+from __future__ import annotations
 import base64
 import io
 import json
@@ -15,9 +16,6 @@ re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
 type_of_gr_update = type(gr.update())
 
-paste_fields = {}
-registered_param_bindings = []
-
 
 class ParamBinding:
     def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
@@ -30,6 +28,10 @@ class ParamBinding:
         self.paste_field_names = paste_field_names or []
 
 
+paste_fields: dict[str, dict] = {}
+registered_param_bindings: list[ParamBinding] = []
+
+
 def reset():
     paste_fields.clear()
     registered_param_bindings.clear()
@@ -113,7 +115,6 @@ def register_paste_params_button(binding: ParamBinding):
 
 
 def connect_paste_params_buttons():
-    binding: ParamBinding
     for binding in registered_param_bindings:
         destination_image_component = paste_fields[binding.tabname]["init_img"]
         fields = paste_fields[binding.tabname]["fields"]
@@ -313,6 +314,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "VAE Decoder" not in res:
         res["VAE Decoder"] = "Full"
 
+    skip = set(shared.opts.infotext_skip_pasting)
+    res = {k: v for k, v in res.items() if k not in skip}
+
     return res
 
 
@@ -443,3 +447,4 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
         outputs=[],
         show_progress=False,
     )
+

modules/gradio_extensons.py

@@ -47,10 +47,20 @@ def Block_get_config(self):
 
 
 def BlockContext_init(self, *args, **kwargs):
+    if scripts.scripts_current is not None:
+        scripts.scripts_current.before_component(self, **kwargs)
+
+    scripts.script_callbacks.before_component_callback(self, **kwargs)
+
     res = original_BlockContext_init(self, *args, **kwargs)
 
     add_classes_to_gradio_component(self)
 
+    scripts.script_callbacks.after_component_callback(self, **kwargs)
+
+    if scripts.scripts_current is not None:
+        scripts.scripts_current.after_component(self, **kwargs)
+
     return res
 
 

modules/mac_specific.py

 import logging
 
 import torch
+from torch import Tensor
 import platform
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
@@ -51,6 +52,17 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):
     return cumsum_func(input, *args, **kwargs)
 
 
+# MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046
+def interpolate_with_fp32_fallback(orig_func, *args, **kwargs) -> Tensor:
+    try:
+        return orig_func(*args, **kwargs)
+    except RuntimeError as e:
+        if "not implemented for" in str(e) and "Half" in str(e):
+            input_tensor = args[0]
+            return orig_func(input_tensor.to(torch.float32), *args[1:], **kwargs).to(input_tensor.dtype)
+        else:
+            print(f"An unexpected RuntimeError occurred: {str(e)}")
+
 if has_mps:
     if platform.mac_ver()[0].startswith("13.2."):
         # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
@@ -77,6 +89,9 @@ if has_mps:
         # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
         CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
 
+        # MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046
+        CondFunc('torch.nn.functional.interpolate', interpolate_with_fp32_fallback, None)
+
         # MPS workaround for https://github.com/pytorch/pytorch/issues/92311
         if platform.processor() == 'i386':
             for funcName in ['torch.argmax', 'torch.Tensor.argmax']:

modules/options.py

 import json
 import sys
+from dataclasses import dataclass
 
 import gradio as gr
 
@@ -8,13 +9,14 @@ from modules.shared_cmd_options import cmd_opts
 
 
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False, category_id=None):
         self.default = default
         self.label = label
         self.component = component
         self.component_args = component_args
         self.onchange = onchange
         self.section = section
+        self.category_id = category_id
         self.refresh = refresh
         self.do_not_save = False
 
@@ -63,7 +65,11 @@ class OptionHTML(OptionInfo):
 
 def options_section(section_identifier, options_dict):
     for v in options_dict.values():
-        v.section = section_identifier
+        if len(section_identifier) == 2:
+            v.section = section_identifier
+        elif len(section_identifier) == 3:
+            v.section = section_identifier[0:2]
+            v.category_id = section_identifier[2]
 
     return options_dict
 
@@ -158,7 +164,7 @@ class Options:
         assert not cmd_opts.freeze_settings, "saving settings is disabled"
 
         with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file, indent=4)
+            json.dump(self.data, file, indent=4, ensure_ascii=False)
 
     def same_type(self, x, y):
         if x is None or y is None:
@@ -206,6 +212,17 @@ class Options:
         d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
         d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
         d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
+
+        item_categories = {}
+        for item in self.data_labels.values():
+            category = categories.mapping.get(item.category_id)
+            category = "Uncategorized" if category is None else category.label
+            if category not in item_categories:
+                item_categories[category] = item.section[1]
+
+        # _categories is a list of pairs: [section, category]. Each section (a setting page) will get a special heading above it with the category as text.
+        d["_categories"] = [[v, k] for k, v in item_categories.items()] + [["Defaults", "Other"]]
+
         return json.dumps(d)
 
     def add_option(self, key, info):
@@ -214,15 +231,40 @@ class Options:
             self.data[key] = info.default
 
     def reorder(self):
-        """reorder settings so that all items related to section always go together"""
+        """Reorder settings so that:
+            - all items related to section always go together
+            - all sections belonging to a category go together
+            - sections inside a category are ordered alphabetically
+            - categories are ordered by creation order
+
+        Category is a superset of sections: for category "postprocessing" there could be multiple sections: "face restoration", "upscaling".
+
+        This function also changes items' category_id so that all items belonging to a section have the same category_id.
+        """
+
+        category_ids = {}
+        section_categories = {}
 
-        section_ids = {}
         settings_items = self.data_labels.items()
         for _, item in settings_items:
-            if item.section not in section_ids:
-                section_ids[item.section] = len(section_ids)
+            if item.section not in section_categories:
+                section_categories[item.section] = item.category_id
+
+        for _, item in settings_items:
+            item.category_id = section_categories.get(item.section)
+
+        for category_id in categories.mapping:
+            if category_id not in category_ids:
+                category_ids[category_id] = len(category_ids)
 
-        self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
+        def sort_key(x):
+            item: OptionInfo = x[1]
+            category_order = category_ids.get(item.category_id, len(category_ids))
+            section_order = item.section[1]
+
+            return category_order, section_order
+
+        self.data_labels = dict(sorted(settings_items, key=sort_key))
 
     def cast_value(self, key, value):
         """casts an arbitrary to the same type as this setting's value with key
@@ -245,3 +287,22 @@ class Options:
             value = expected_type(value)
 
         return value
+
+
+@dataclass
+class OptionsCategory:
+    id: str
+    label: str
+
+class OptionsCategories:
+    def __init__(self):
+        self.mapping = {}
+
+    def register_category(self, category_id, label):
+        if category_id in self.mapping:
+            return category_id
+
+        self.mapping[category_id] = OptionsCategory(category_id, label)
+
+
+categories = OptionsCategories()

modules/processing.py

@@ -679,8 +679,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Size": f"{p.width}x{p.height}",
         "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
         "Model": p.sd_model_name if opts.add_model_name_to_info else None,
-        "VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
-        "VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
+        "VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
+        "VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
         "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),

modules/scripts.py

@@ -560,17 +560,25 @@ class ScriptRunner:
             on_after.clear()
 
     def create_script_ui(self, script):
-        import modules.api.models as api_models
 
         script.args_from = len(self.inputs)
         script.args_to = len(self.inputs)
 
+        try:
+            self.create_script_ui_inner(script)
+        except Exception:
+            errors.report(f"Error creating UI for {script.name}: ", exc_info=True)
+
+    def create_script_ui_inner(self, script):
+        import modules.api.models as api_models
+
         controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
 
         if controls is None:
             return
 
         script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
+
         api_args = []
 
         for control in controls:

modules/sd_hijack.py

@@ -38,9 +38,6 @@ ldm.models.diffusion.ddpm.print = shared.ldm_print
 optimizers = []
 current_optimizer: sd_hijack_optimizations.SdOptimization = None
 
-ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
-sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
-
 def list_optimizers():
     new_optimizers = script_callbacks.list_optimizers_callback()
 
@@ -258,6 +255,9 @@ class StableDiffusionModelHijack:
 
         import modules.models.diffusion.ddpm_edit
 
+        ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
+        sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
+
         if isinstance(m, ldm.models.diffusion.ddpm.LatentDiffusion):
             sd_unet.original_forward = ldm_original_forward
         elif isinstance(m, modules.models.diffusion.ddpm_edit.LatentDiffusion):
@@ -303,6 +303,9 @@ class StableDiffusionModelHijack:
         self.layers = None
         self.clip = None
 
+        patches.undo(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward")
+        patches.undo(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward")
+
         sd_unet.original_forward = None
 
 

modules/sd_models.py

@@ -230,15 +230,19 @@ def select_checkpoint():
     return checkpoint_info
 
 
-checkpoint_dict_replacements = {
+checkpoint_dict_replacements_sd1 = {
     'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
     'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
     'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
 }
 
+checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
+    'conditioner.embedders.0.': 'cond_stage_model.',
+}
+
 
-def transform_checkpoint_dict_key(k):
-    for text, replacement in checkpoint_dict_replacements.items():
+def transform_checkpoint_dict_key(k, replacements):
+    for text, replacement in replacements.items():
         if k.startswith(text):
             k = replacement + k[len(text):]
 
@@ -249,9 +253,14 @@ def get_state_dict_from_checkpoint(pl_sd):
     pl_sd = pl_sd.pop("state_dict", pl_sd)
     pl_sd.pop("state_dict", None)
 
+    is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
+
     sd = {}
     for k, v in pl_sd.items():
-        new_key = transform_checkpoint_dict_key(k)
+        if is_sd2_turbo:
+            new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
+        else:
+            new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
 
         if new_key is not None:
             sd[new_key] = v

modules/shared_items.py

@@ -66,6 +66,22 @@ def reload_hypernetworks():
     shared.hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 
 
+def get_infotext_names():
+    from modules import generation_parameters_copypaste, shared
+    res = {}
+
+    for info in shared.opts.data_labels.values():
+        if info.infotext:
+            res[info.infotext] = 1
+
+    for tab_data in generation_parameters_copypaste.paste_fields.values():
+        for _, name in tab_data.get("fields") or []:
+            if isinstance(name, str):
+                res[name] = 1
+
+    return list(res)
+
+
 ui_reorder_categories_builtin_items = [
     "prompt",
     "image",

modules/shared_options.py

@@ -3,7 +3,7 @@ import gradio as gr
 from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes
 from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir  # noqa: F401
 from modules.shared_cmd_options import cmd_opts
-from modules.options import options_section, OptionInfo, OptionHTML
+from modules.options import options_section, OptionInfo, OptionHTML, categories
 
 options_templates = {}
 hide_dirs = shared.hide_dirs
@@ -21,7 +21,14 @@ restricted_opts = {
     "outdir_init_images"
 }
 
-options_templates.update(options_section(('saving-images', "Saving images/grids"), {
+categories.register_category("saving", "Saving images")
+categories.register_category("sd", "Stable Diffusion")
+categories.register_category("ui", "User Interface")
+categories.register_category("system", "System")
+categories.register_category("postprocessing", "Postprocessing")
+categories.register_category("training", "Training")
+
+options_templates.update(options_section(('saving-images', "Saving images/grids", "saving"), {
     "samples_save": OptionInfo(True, "Always save all generated images"),
     "samples_format": OptionInfo('png', 'File format for images'),
     "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
@@ -39,8 +46,6 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
     "grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
 
-    "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
-    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
     "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
     "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
@@ -67,7 +72,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "notification_volume": OptionInfo(100, "Notification sound volume", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}).info("in %"),
 }))
 
-options_templates.update(options_section(('saving-paths', "Paths for saving"), {
+options_templates.update(options_section(('saving-paths', "Paths for saving", "saving"), {
     "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
     "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
     "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
@@ -79,7 +84,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
     "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
 }))
 
-options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
+options_templates.update(options_section(('saving-to-dirs', "Saving to a directory", "saving"), {
     "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
     "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
     "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
@@ -87,21 +92,21 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))
 
-options_templates.update(options_section(('upscaling', "Upscaling"), {
+options_templates.update(options_section(('upscaling', "Upscaling", "postprocessing"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
     "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
 }))
 
-options_templates.update(options_section(('face-restoration', "Face restoration"), {
+options_templates.update(options_section(('face-restoration', "Face restoration", "postprocessing"), {
     "face_restoration": OptionInfo(False, "Restore faces", infotext='Face restoration').info("will use a third-party model on generation result to reconstruct faces"),
     "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in shared.face_restorers]}),
     "code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
     "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
 }))
 
-options_templates.update(options_section(('system', "System"), {
+options_templates.update(options_section(('system', "System", "system"), {
     "auto_launch_browser": OptionInfo("Local", "Automatically open webui in browser on startup", gr.Radio, lambda: {"choices": ["Disable", "Local", "Remote"]}),
     "enable_console_prompts": OptionInfo(shared.cmd_opts.enable_console_prompts, "Print prompts to console when generating with txt2img and img2img."),
     "show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
@@ -116,13 +121,13 @@ options_templates.update(options_section(('system', "System"), {
     "dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
 }))
 
-options_templates.update(options_section(('API', "API"), {
+options_templates.update(options_section(('API', "API", "system"), {
     "api_enable_requests": OptionInfo(True, "Allow http:// and https:// URLs for input images in API", restrict_api=True),
     "api_forbid_local_requests": OptionInfo(True, "Forbid URLs to local resources", restrict_api=True),
     "api_useragent": OptionInfo("", "User agent for requests", restrict_api=True),
 }))
 
-options_templates.update(options_section(('training', "Training"), {
+options_templates.update(options_section(('training', "Training", "training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
@@ -137,7 +142,7 @@ options_templates.update(options_section(('training', "Training"), {
     "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
 }))
 
-options_templates.update(options_section(('sd', "Stable Diffusion"), {
+options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles(shared.opts.sd_checkpoint_dropdown_use_short)}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
     "sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
     "sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
@@ -154,14 +159,14 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "hires_fix_refiner_pass": OptionInfo("second pass", "Hires fix: which pass to enable refiner for", gr.Radio, {"choices": ["first pass", "second pass", "both passes"]}, infotext="Hires refiner"),
 }))
 
-options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
+options_templates.update(options_section(('sdxl', "Stable Diffusion XL", "sd"), {
     "sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
     "sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
     "sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
     "sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
 }))
 
-options_templates.update(options_section(('vae', "VAE"), {
+options_templates.update(options_section(('vae', "VAE", "sd"), {
     "sd_vae_explanation": OptionHTML("""
 <abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
 image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
@@ -176,7 +181,7 @@ For img2img, VAE is used to process user's input image before the sampling, and
     "sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Decoder').info("method to decode latent to image"),
 }))
 
-options_templates.update(options_section(('img2img', "img2img"), {
+options_templates.update(options_section(('img2img', "img2img", "sd"), {
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Conditional mask weight'),
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.5, "step": 0.001}, infotext='Noise multiplier'),
     "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img and hires fix", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Extra noise').info("0 = disabled (default); should be lower than denoising strength"),
@@ -192,7 +197,7 @@ options_templates.update(options_section(('img2img', "img2img"), {
     "img2img_batch_show_results_limit": OptionInfo(32, "Show the first N batch img2img results in UI", gr.Slider, {"minimum": -1, "maximum": 1000, "step": 1}).info('0: disable, -1: show all images. Too many images can cause lag'),
 }))
 
-options_templates.update(options_section(('optimizations', "Optimizations"), {
+options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
     "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
     "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
     "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
@@ -205,7 +210,7 @@ options_templates.update(options_section(('optimizations', "Optimizations"), {
     "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."),
 }))
 
-options_templates.update(options_section(('compatibility', "Compatibility"), {
+options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
     "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
@@ -230,8 +235,9 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
     "deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
 }))
 
-options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+options_templates.update(options_section(('extra_networks', "Extra Networks", "sd"), {
     "extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
+    "extra_networks_dir_button_function": OptionInfo(False, "Add a '/' to the beginning of directory buttons").info("Buttons will display the contents of the selected directory without acting as a search filter."),
     "extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
     "extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
     "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
@@ -247,47 +253,64 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
     "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
 }))
 
-options_templates.update(options_section(('ui', "User interface"), {
-    "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
-    "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
-    "gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
-    "gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("an be any valid CSS value").needs_reload_ui(),
-    "return_grid": OptionInfo(True, "Show grid in results for web"),
-    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
-    "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
-    "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
-    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
-    "js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
-    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+options_templates.update(options_section(('ui_prompt_editing', "Prompt editing", "ui"), {
+    "keyedit_precision_attention": OptionInfo(0.1, "Precision for (attention:1.1) when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+    "keyedit_precision_extra": OptionInfo(0.05, "Precision for <extra networks:0.9> when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+    "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Word delimiters when editing the prompt with Ctrl+up/down"),
+    "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
+}))
+
+options_templates.update(options_section(('ui_gallery', "Gallery", "ui"), {
+    "return_grid": OptionInfo(True, "Show grid in gallery"),
+    "do_not_show_images": OptionInfo(False, "Do not show any images in gallery"),
+    "js_modal_lightbox": OptionInfo(True, "Full page image viewer: enable"),
+    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Full page image viewer: show images zoomed in by default"),
+    "js_modal_lightbox_gamepad": OptionInfo(False, "Full page image viewer: navigate with gamepad"),
+    "js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Full page image viewer: gamepad repeat period").info("in milliseconds"),
+    "gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("can be any valid CSS value, for example 768px or 20em").needs_reload_ui(),
+}))
+
+options_templates.update(options_section(('ui_alternatives', "UI alternatives", "ui"), {
+    "compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
     "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
     "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
-    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
-    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
-    "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Ctrl+up/down word delimiters"),
-    "keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
-    "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
-    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
-    "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
-    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
-    "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
     "sd_checkpoint_dropdown_use_short": OptionInfo(False, "Checkpoint dropdown: use filenames without paths").info("models in subdirectories like photo/sd15.ckpt will be listed as just sd15.ckpt"),
     "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
     "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
-    "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
     "txt2img_settings_accordion": OptionInfo(False, "Settings in txt2img hidden under Accordion").needs_reload_ui(),
     "img2img_settings_accordion": OptionInfo(False, "Settings in img2img hidden under Accordion").needs_reload_ui(),
-    "compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
+}))
+
+options_templates.update(options_section(('ui', "User interface", "ui"), {
+    "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
+    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
+    "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
+    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
+    "ui_reorder_list": OptionInfo([], "UI item order for txt2img/img2img tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
+    "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
+    "gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
+    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
+    "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
 }))
 
 
-options_templates.update(options_section(('infotext', "Infotext"), {
-    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
-    "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
-    "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
+options_templates.update(options_section(('infotext', "Infotext", "ui"), {
+    "infotext_explanation": OptionHTML("""
+Infotext is what this software calls the text that contains generation parameters and can be used to generate the same picture again.
+It is displayed in UI below the image. To use infotext, paste it into the prompt and click the ↙️ paste button.
+"""),
+    "enable_pnginfo": OptionInfo(True, "Write infotext to metadata of the generated image"),
+    "save_txt": OptionInfo(False, "Create a text file with infotext next to every generated image"),
+
+    "add_model_name_to_info": OptionInfo(True, "Add model name to infotext"),
+    "add_model_hash_to_info": OptionInfo(True, "Add model hash to infotext"),
+    "add_vae_name_to_info": OptionInfo(True, "Add VAE name to infotext"),
+    "add_vae_hash_to_info": OptionInfo(True, "Add VAE hash to infotext"),
+    "add_user_name_to_info": OptionInfo(False, "Add user name to infotext when authenticated"),
+    "add_version_to_infotext": OptionInfo(True, "Add program version to infotext"),
     "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
+    "infotext_skip_pasting": OptionInfo([], "Disregard fields from pasted infotext", ui_components.DropdownMulti, lambda: {"choices": shared_items.get_infotext_names()}),
     "infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
 <li>Ignore: keep prompt and styles dropdown as it is.</li>
 <li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
@@ -297,7 +320,7 @@ options_templates.update(options_section(('infotext', "Infotext"), {
 
 }))
 
-options_templates.update(options_section(('ui', "Live previews"), {
+options_templates.update(options_section(('ui', "Live previews", "ui"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
     "live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
@@ -310,7 +333,7 @@ options_templates.update(options_section(('ui', "Live previews"), {
     "live_preview_fast_interrupt": OptionInfo(False, "Return image with chosen live preview method on interrupt").info("makes interrupts faster"),
 }))
 
-options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
+options_templates.update(options_section(('sampler-params', "Sampler parameters", "sd"), {
     "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
     "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unpredictable results"),
     "eta_ancestral": OptionInfo(1.0, "Eta for k-diffusion samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"),
@@ -332,7 +355,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
 }))
 
-options_templates.update(options_section(('postprocessing', "Postprocessing"), {
+options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
     'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
     'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
     'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),

modules/styles.py

 import csv
+import fnmatch
 import os
 import os.path
 import re
@@ -10,6 +11,23 @@ class PromptStyle(typing.NamedTuple):
     name: str
     prompt: str
     negative_prompt: str
+    path: str = None
+
+
+def clean_text(text: str) -> str:
+    """
+    Iterating through a list of regular expressions and replacement strings, we
+    clean up the prompt and style text to make it easier to match against each
+    other.
+    """
+    re_list = [
+        ("multiple commas", re.compile("(,+\s+)+,?"), ", "),
+        ("multiple spaces", re.compile("\s{2,}"), " "),
+    ]
+    for _, regex, replace in re_list:
+        text = regex.sub(replace, text)
+
+    return text.strip(", ")
 
 
 def merge_prompts(style_prompt: str, prompt: str) -> str:
@@ -26,41 +44,64 @@ def apply_styles_to_prompt(prompt, styles):
     for style in styles:
         prompt = merge_prompts(style, prompt)
 
-    return prompt
+    return clean_text(prompt)
 
 
-re_spaces = re.compile("  +")
+def unwrap_style_text_from_prompt(style_text, prompt):
+    """
+    Checks the prompt to see if the style text is wrapped around it. If so,
+    returns True plus the prompt text without the style text. Otherwise, returns
+    False with the original prompt.
 
-
-def extract_style_text_from_prompt(style_text, prompt):
-    stripped_prompt = re.sub(re_spaces, " ", prompt.strip())
-    stripped_style_text = re.sub(re_spaces, " ", style_text.strip())
+    Note that the "cleaned" version of the style text is only used for matching
+    purposes here. It isn't returned; the original style text is not modified.
+    """
+    stripped_prompt = clean_text(prompt)
+    stripped_style_text = clean_text(style_text)
     if "{prompt}" in stripped_style_text:
-        left, right = stripped_style_text.split("{prompt}", 2)
+        # Work out whether the prompt is wrapped in the style text. If so, we
+        # return True and the "inner" prompt text that isn't part of the style.
+        try:
+            left, right = stripped_style_text.split("{prompt}", 2)
+        except ValueError as e:
+            # If the style text has multple "{prompt}"s, we can't split it into
+            # two parts. This is an error, but we can't do anything about it.
+            print(f"Unable to compare style text to prompt:\n{style_text}")
+            print(f"Error: {e}")
+            return False, prompt
         if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
-            prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
+            prompt = stripped_prompt[len(left) : len(stripped_prompt) - len(right)]
             return True, prompt
     else:
+        # Work out whether the given prompt ends with the style text. If so, we
+        # return True and the prompt text up to where the style text starts.
         if stripped_prompt.endswith(stripped_style_text):
-            prompt = stripped_prompt[:len(stripped_prompt)-len(stripped_style_text)]
-
-            if prompt.endswith(', '):
+            prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]
+            if prompt.endswith(", "):
                 prompt = prompt[:-2]
-
             return True, prompt
 
     return False, prompt
 
 
-def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
+def extract_original_prompts(style: PromptStyle, prompt, negative_prompt):
+    """
+    Takes a style and compares it to the prompt and negative prompt. If the style
+    matches, returns True plus the prompt and negative prompt with the style text
+    removed. Otherwise, returns False with the original prompt and negative prompt.
+    """
     if not style.prompt and not style.negative_prompt:
         return False, prompt, negative_prompt
 
-    match_positive, extracted_positive = extract_style_text_from_prompt(style.prompt, prompt)
+    match_positive, extracted_positive = unwrap_style_text_from_prompt(
+        style.prompt, prompt
+    )
     if not match_positive:
         return False, prompt, negative_prompt
 
-    match_negative, extracted_negative = extract_style_text_from_prompt(style.negative_prompt, negative_prompt)
+    match_negative, extracted_negative = unwrap_style_text_from_prompt(
+        style.negative_prompt, negative_prompt
+    )
     if not match_negative:
         return False, prompt, negative_prompt
 
@@ -69,25 +110,88 @@ def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
 
 class StyleDatabase:
     def __init__(self, path: str):
-        self.no_style = PromptStyle("None", "", "")
+        self.no_style = PromptStyle("None", "", "", None)
         self.styles = {}
         self.path = path
 
+        folder, file = os.path.split(self.path)
+        self.default_file = file.split("*")[0] + ".csv"
+        if self.default_file == ".csv":
+            self.default_file = "styles.csv"
+        self.default_path = os.path.join(folder, self.default_file)
+
+        self.prompt_fields = [field for field in PromptStyle._fields if field != "path"]
+
         self.reload()
 
     def reload(self):
+        """
+        Clears the style database and reloads the styles from the CSV file(s)
+        matching the path used to initialize the database.
+        """
         self.styles.clear()
 
-        if not os.path.exists(self.path):
+        path, filename = os.path.split(self.path)
+
+        if "*" in filename:
+            fileglob = filename.split("*")[0] + "*.csv"
+            filelist = []
+            for file in os.listdir(path):
+                if fnmatch.fnmatch(file, fileglob):
+                    filelist.append(file)
+                    # Add a visible divider to the style list
+                    half_len = round(len(file) / 2)
+                    divider = f"{'-' * (20 - half_len)} {file.upper()}"
+                    divider = f"{divider} {'-' * (40 - len(divider))}"
+                    self.styles[divider] = PromptStyle(
+                        f"{divider}", None, None, "do_not_save"
+                    )
+                    # Add styles from this CSV file
+                    self.load_from_csv(os.path.join(path, file))
+            if len(filelist) == 0:
+                print(f"No styles found in {path} matching {fileglob}")
+                return
+        elif not os.path.exists(self.path):
+            print(f"Style database not found: {self.path}")
             return
+        else:
+            self.load_from_csv(self.path)
 
-        with open(self.path, "r", encoding="utf-8-sig", newline='') as file:
+    def load_from_csv(self, path: str):
+        with open(path, "r", encoding="utf-8-sig", newline="") as file:
             reader = csv.DictReader(file, skipinitialspace=True)
             for row in reader:
+                # Ignore empty rows or rows starting with a comment
+                if not row or row["name"].startswith("#"):
+                    continue
                 # Support loading old CSV format with "name, text"-columns
                 prompt = row["prompt"] if "prompt" in row else row["text"]
                 negative_prompt = row.get("negative_prompt", "")
-                self.styles[row["name"]] = PromptStyle(row["name"], prompt, negative_prompt)
+                # Add style to database
+                self.styles[row["name"]] = PromptStyle(
+                    row["name"], prompt, negative_prompt, path
+                )
+
+    def get_style_paths(self) -> list():
+        """
+        Returns a list of all distinct paths, including the default path, of
+        files that styles are loaded from."""
+        # Update any styles without a path to the default path
+        for style in list(self.styles.values()):
+            if not style.path:
+                self.styles[style.name] = style._replace(path=self.default_path)
+
+        # Create a list of all distinct paths, including the default path
+        style_paths = set()
+        style_paths.add(self.default_path)
+        for _, style in self.styles.items():
+            if style.path:
+                style_paths.add(style.path)
+
+        # Remove any paths for styles that are just list dividers
+        style_paths.remove("do_not_save")
+
+        return list(style_paths)
 
     def get_style_prompts(self, styles):
         return [self.styles.get(x, self.no_style).prompt for x in styles]
@@ -96,20 +200,53 @@ class StyleDatabase:
         return [self.styles.get(x, self.no_style).negative_prompt for x in styles]
 
     def apply_styles_to_prompt(self, prompt, styles):
-        return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).prompt for x in styles])
+        return apply_styles_to_prompt(
+            prompt, [self.styles.get(x, self.no_style).prompt for x in styles]
+        )
 
     def apply_negative_styles_to_prompt(self, prompt, styles):
-        return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
-
-    def save_styles(self, path: str) -> None:
-        # Always keep a backup file around
-        if os.path.exists(path):
-            shutil.copy(path, f"{path}.bak")
-
-        with open(path, "w", encoding="utf-8-sig", newline='') as file:
-            writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
-            writer.writeheader()
-            writer.writerows(style._asdict() for k, style in self.styles.items())
+        return apply_styles_to_prompt(
+            prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles]
+        )
+
+    def save_styles(self, path: str = None) -> None:
+        # The path argument is deprecated, but kept for backwards compatibility
+        _ = path
+
+        # Update any styles without a path to the default path
+        for style in list(self.styles.values()):
+            if not style.path:
+                self.styles[style.name] = style._replace(path=self.default_path)
+
+        # Create a list of all distinct paths, including the default path
+        style_paths = set()
+        style_paths.add(self.default_path)
+        for _, style in self.styles.items():
+            if style.path:
+                style_paths.add(style.path)
+
+        # Remove any paths for styles that are just list dividers
+        style_paths.remove("do_not_save")
+
+        csv_names = [os.path.split(path)[1].lower() for path in style_paths]
+
+        for style_path in style_paths:
+            # Always keep a backup file around
+            if os.path.exists(style_path):
+                shutil.copy(style_path, f"{style_path}.bak")
+
+            # Write the styles to the CSV file
+            with open(style_path, "w", encoding="utf-8-sig", newline="") as file:
+                writer = csv.DictWriter(file, fieldnames=self.prompt_fields)
+                writer.writeheader()
+                for style in (s for s in self.styles.values() if s.path == style_path):
+                    # Skip style list dividers, e.g. "STYLES.CSV"
+                    if style.name.lower().strip("# ") in csv_names:
+                        continue
+                    # Write style fields, ignoring the path field
+                    writer.writerow(
+                        {k: v for k, v in style._asdict().items() if k != "path"}
+                    )
 
     def extract_styles_from_prompt(self, prompt, negative_prompt):
         extracted = []
@@ -120,7 +257,9 @@ class StyleDatabase:
             found_style = None
 
             for style in applicable_styles:
-                is_match, new_prompt, new_neg_prompt = extract_style_from_prompts(style, prompt, negative_prompt)
+                is_match, new_prompt, new_neg_prompt = extract_original_prompts(
+                    style, prompt, negative_prompt
+                )
                 if is_match:
                     found_style = style
                     prompt = new_prompt

modules/textual_inversion/autocrop.py

@@ -3,6 +3,8 @@ import requests
 import os
 import numpy as np
 from PIL import ImageDraw
+from modules import paths_internal
+from pkg_resources import parse_version
 
 GREEN = "#0F0"
 BLUE = "#00F"
@@ -25,7 +27,6 @@ def crop_image(im, settings):
         elif is_portrait(settings.crop_width, settings.crop_height):
             scale_by = settings.crop_height / im.height
 
-
     im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
     im_debug = im.copy()
 
@@ -69,6 +70,7 @@ def crop_image(im, settings):
 
     return results
 
+
 def focal_point(im, settings):
     corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
     entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
@@ -78,118 +80,120 @@ def focal_point(im, settings):
 
     weight_pref_total = 0
     if corner_points:
-      weight_pref_total += settings.corner_points_weight
+        weight_pref_total += settings.corner_points_weight
     if entropy_points:
-      weight_pref_total += settings.entropy_points_weight
+        weight_pref_total += settings.entropy_points_weight
     if face_points:
-      weight_pref_total += settings.face_points_weight
+        weight_pref_total += settings.face_points_weight
 
     corner_centroid = None
     if corner_points:
-      corner_centroid = centroid(corner_points)
-      corner_centroid.weight = settings.corner_points_weight / weight_pref_total
-      pois.append(corner_centroid)
+        corner_centroid = centroid(corner_points)
+        corner_centroid.weight = settings.corner_points_weight / weight_pref_total
+        pois.append(corner_centroid)
 
     entropy_centroid = None
     if entropy_points:
-      entropy_centroid = centroid(entropy_points)
-      entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
-      pois.append(entropy_centroid)
+        entropy_centroid = centroid(entropy_points)
+        entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
+        pois.append(entropy_centroid)
 
     face_centroid = None
     if face_points:
-      face_centroid = centroid(face_points)
-      face_centroid.weight = settings.face_points_weight / weight_pref_total
-      pois.append(face_centroid)
+        face_centroid = centroid(face_points)
+        face_centroid.weight = settings.face_points_weight / weight_pref_total
+        pois.append(face_centroid)
 
     average_point = poi_average(pois, settings)
 
     if settings.annotate_image:
-      d = ImageDraw.Draw(im)
-      max_size = min(im.width, im.height) * 0.07
-      if corner_centroid is not None:
-        color = BLUE
-        box = corner_centroid.bounding(max_size * corner_centroid.weight)
-        d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
-        d.ellipse(box, outline=color)
-        if len(corner_points) > 1:
-          for f in corner_points:
-            d.rectangle(f.bounding(4), outline=color)
-      if entropy_centroid is not None:
-        color = "#ff0"
-        box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
-        d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
-        d.ellipse(box, outline=color)
-        if len(entropy_points) > 1:
-          for f in entropy_points:
-            d.rectangle(f.bounding(4), outline=color)
-      if face_centroid is not None:
-        color = RED
-        box = face_centroid.bounding(max_size * face_centroid.weight)
-        d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
-        d.ellipse(box, outline=color)
-        if len(face_points) > 1:
-          for f in face_points:
-            d.rectangle(f.bounding(4), outline=color)
-
-      d.ellipse(average_point.bounding(max_size), outline=GREEN)
+        d = ImageDraw.Draw(im)
+        max_size = min(im.width, im.height) * 0.07
+        if corner_centroid is not None:
+            color = BLUE
+            box = corner_centroid.bounding(max_size * corner_centroid.weight)
+            d.text((box[0], box[1] - 15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
+            d.ellipse(box, outline=color)
+            if len(corner_points) > 1:
+                for f in corner_points:
+                    d.rectangle(f.bounding(4), outline=color)
+        if entropy_centroid is not None:
+            color = "#ff0"
+            box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
+            d.text((box[0], box[1] - 15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
+            d.ellipse(box, outline=color)
+            if len(entropy_points) > 1:
+                for f in entropy_points:
+                    d.rectangle(f.bounding(4), outline=color)
+        if face_centroid is not None:
+            color = RED
+            box = face_centroid.bounding(max_size * face_centroid.weight)
+            d.text((box[0], box[1] - 15), f"Face: {face_centroid.weight:.02f}", fill=color)
+            d.ellipse(box, outline=color)
+            if len(face_points) > 1:
+                for f in face_points:
+                    d.rectangle(f.bounding(4), outline=color)
+
+        d.ellipse(average_point.bounding(max_size), outline=GREEN)
 
     return average_point
 
 
 def image_face_points(im, settings):
     if settings.dnn_model_path is not None:
-      detector = cv2.FaceDetectorYN.create(
-          settings.dnn_model_path,
-          "",
-          (im.width, im.height),
-          0.9, # score threshold
-          0.3, # nms threshold
-          5000 # keep top k before nms
-      )
-      faces = detector.detect(np.array(im))
-      results = []
-      if faces[1] is not None:
-        for face in faces[1]:
-          x = face[0]
-          y = face[1]
-          w = face[2]
-          h = face[3]
-          results.append(
-            PointOfInterest(
-              int(x + (w * 0.5)), # face focus left/right is center
-              int(y + (h * 0.33)), # face focus up/down is close to the top of the head
-              size = w,
-              weight = 1/len(faces[1])
-            )
-          )
-      return results
+        detector = cv2.FaceDetectorYN.create(
+            settings.dnn_model_path,
+            "",
+            (im.width, im.height),
+            0.9,  # score threshold
+            0.3,  # nms threshold
+            5000  # keep top k before nms
+        )
+        faces = detector.detect(np.array(im))
+        results = []
+        if faces[1] is not None:
+            for face in faces[1]:
+                x = face[0]
+                y = face[1]
+                w = face[2]
+                h = face[3]
+                results.append(
+                    PointOfInterest(
+                        int(x + (w * 0.5)),  # face focus left/right is center
+                        int(y + (h * 0.33)),  # face focus up/down is close to the top of the head
+                        size=w,
+                        weight=1 / len(faces[1])
+                    )
+                )
+        return results
     else:
-      np_im = np.array(im)
-      gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-
-      tries = [
-        [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-        [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
-      ]
-      for t in tries:
-        classifier = cv2.CascadeClassifier(t[0])
-        minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
-        try:
-          faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-            minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-        except Exception:
-          continue
-
-        if faces:
-          rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-          return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
+        np_im = np.array(im)
+        gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+
+        tries = [
+            [f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01],
+            [f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05],
+            [f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05]
+        ]
+        for t in tries:
+            classifier = cv2.CascadeClassifier(t[0])
+            minsize = int(min(im.width, im.height) * t[1])  # at least N percent of the smallest side
+            try:
+                faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+                                                    minNeighbors=7, minSize=(minsize, minsize),
+                                                    flags=cv2.CASCADE_SCALE_IMAGE)
+            except Exception:
+                continue
+
+            if faces:
+                rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+                return [PointOfInterest((r[0] + r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0] - r[2]),
+                                        weight=1 / len(rects)) for r in rects]
     return []
 
 
@@ -198,7 +202,7 @@ def image_corner_points(im, settings):
 
     # naive attempt at preventing focal points from collecting at watermarks near the bottom
     gd = ImageDraw.Draw(grayscale)
-    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+    gd.rectangle([0, im.height * .9, im.width, im.height], fill="#999")
 
     np_im = np.array(grayscale)
 
@@ -206,7 +210,7 @@ def image_corner_points(im, settings):
         np_im,
         maxCorners=100,
         qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.06,
+        minDistance=min(grayscale.width, grayscale.height) * 0.06,
         useHarrisDetector=False,
     )
 
@@ -215,8 +219,8 @@ def image_corner_points(im, settings):
 
     focal_points = []
     for point in points:
-      x, y = point.ravel()
-      focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
+        x, y = point.ravel()
+        focal_points.append(PointOfInterest(x, y, size=4, weight=1 / len(points)))
 
     return focal_points
 
@@ -225,13 +229,13 @@ def image_entropy_points(im, settings):
     landscape = im.height < im.width
     portrait = im.height > im.width
     if landscape:
-      move_idx = [0, 2]
-      move_max = im.size[0]
+        move_idx = [0, 2]
+        move_max = im.size[0]
     elif portrait:
-      move_idx = [1, 3]
-      move_max = im.size[1]
+        move_idx = [1, 3]
+        move_max = im.size[1]
     else:
-      return []
+        return []
 
     e_max = 0
     crop_current = [0, 0, settings.crop_width, settings.crop_height]
@@ -241,14 +245,14 @@ def image_entropy_points(im, settings):
         e = image_entropy(crop)
 
         if (e > e_max):
-          e_max = e
-          crop_best = list(crop_current)
+            e_max = e
+            crop_best = list(crop_current)
 
         crop_current[move_idx[0]] += 4
         crop_current[move_idx[1]] += 4
 
-    x_mid = int(crop_best[0] + settings.crop_width/2)
-    y_mid = int(crop_best[1] + settings.crop_height/2)
+    x_mid = int(crop_best[0] + settings.crop_width / 2)
+    y_mid = int(crop_best[1] + settings.crop_height / 2)
 
     return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
 
@@ -294,22 +298,23 @@ def is_square(w, h):
     return w == h
 
 
-def download_and_cache_models(dirname):
-    download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
-    model_file_name = 'face_detection_yunet.onnx'
+model_dir_opencv = os.path.join(paths_internal.models_path, 'opencv')
+if parse_version(cv2.__version__) >= parse_version('4.8'):
+    model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet_2023mar.onnx')
+    model_url = 'https://github.com/opencv/opencv_zoo/blob/b6e370b10f641879a87890d44e42173077154a05/models/face_detection_yunet/face_detection_yunet_2023mar.onnx?raw=true'
+else:
+    model_file_path = os.path.join(model_dir_opencv, 'face_detection_yunet.onnx')
+    model_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
 
-    os.makedirs(dirname, exist_ok=True)
 
-    cache_file = os.path.join(dirname, model_file_name)
-    if not os.path.exists(cache_file):
-        print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
-        response = requests.get(download_url)
-        with open(cache_file, "wb") as f:
+def download_and_cache_models():
+    if not os.path.exists(model_file_path):
+        os.makedirs(model_dir_opencv, exist_ok=True)
+        print(f"downloading face detection model from '{model_url}' to '{model_file_path}'")
+        response = requests.get(model_url)
+        with open(model_file_path, "wb") as f:
             f.write(response.content)
-
-    if os.path.exists(cache_file):
-        return cache_file
-    return None
+    return model_file_path
 
 
 class PointOfInterest:

modules/textual_inversion/preprocess.py

@@ -3,7 +3,7 @@ from PIL import Image, ImageOps
 import math
 import tqdm
 
-from modules import paths, shared, images, deepbooru
+from modules import shared, images, deepbooru
 from modules.textual_inversion import autocrop
 
 
@@ -196,7 +196,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
             dnn_model_path = None
             try:
-                dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
+                dnn_model_path = autocrop.download_and_cache_models()
             except Exception as e:
                 print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
 

modules/ui_extensions.py

@@ -65,7 +65,7 @@ def save_config_state(name):
     filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")
     print(f"Saving backup of webui/extension state to {filename}.")
     with open(filename, "w", encoding="utf-8") as f:
-        json.dump(current_config_state, f, indent=4)
+        json.dump(current_config_state, f, indent=4, ensure_ascii=False)
     config_states.list_config_states()
     new_value = next(iter(config_states.all_config_states.keys()), "Current")
     new_choices = ["Current"] + list(config_states.all_config_states.keys())
@@ -335,6 +335,11 @@ def normalize_git_url(url):
     return url
 
 
+def get_extension_dirname_from_url(url):
+    *parts, last_part = url.split('/')
+    return normalize_git_url(last_part)
+
+
 def install_extension_from_url(dirname, url, branch_name=None):
     check_access()
 
@@ -346,10 +351,7 @@ def install_extension_from_url(dirname, url, branch_name=None):
     assert url, 'No URL specified'
 
     if dirname is None or dirname == "":
-        *parts, last_part = url.split('/')
-        last_part = normalize_git_url(last_part)
-
-        dirname = last_part
+        dirname = get_extension_dirname_from_url(url)
 
     target_dir = os.path.join(extensions.extensions_dir, dirname)
     assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
@@ -449,7 +451,8 @@ def get_date(info: dict, key):
 
 def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
     extlist = available_extensions["extensions"]
-    installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
+    installed_extensions = {extension.name for extension in extensions.extensions}
+    installed_extension_urls = {normalize_git_url(extension.remote) for extension in extensions.extensions if extension.remote is not None}
 
     tags = available_extensions.get("tags", {})
     tags_to_hide = set(hide_tags)
@@ -482,7 +485,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
         if url is None:
             continue
 
-        existing = installed_extension_urls.get(normalize_git_url(url), None)
+        existing = get_extension_dirname_from_url(url) in installed_extensions or normalize_git_url(url) in installed_extension_urls
         extension_tags = extension_tags + ["installed"] if existing else extension_tags
 
         if any(x for x in extension_tags if x in tags_to_hide):

modules/ui_extra_networks.py

@@ -151,8 +151,13 @@ class ExtraNetworksPage:
                         continue
 
                     subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
-                    while subdir.startswith("/"):
-                        subdir = subdir[1:]
+
+                    if shared.opts.extra_networks_dir_button_function:
+                        if not subdir.startswith("/"):
+                            subdir = "/" + subdir
+                    else:
+                        while subdir.startswith("/"):
+                            subdir = subdir[1:]
 
                     is_empty = len(os.listdir(x)) == 0
                     if not is_empty and not subdir.endswith("/"):
@@ -370,6 +375,9 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
 
     for page in ui.stored_extra_pages:
         with gr.Tab(page.title, elem_id=f"{tabname}_{page.id_page}", elem_classes=["extra-page"]) as tab:
+            with gr.Column(elem_id=f"{tabname}_{page.id_page}_prompts", elem_classes=["extra-page-prompts"]):
+                pass
+
             elem_id = f"{tabname}_{page.id_page}_cards_html"
             page_elem = gr.HTML('Loading...', elem_id=elem_id)
             ui.pages.append(page_elem)
@@ -400,7 +408,7 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
         allow_prompt = "true" if page.allow_prompt else "false"
         allow_negative_prompt = "true" if page.allow_negative_prompt else "false"
 
-        jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
+        jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}_prompts" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
 
         tab.select(fn=lambda: [gr.update(visible=True) for _ in tab_controls],  _js='function(){ ' + jscode + ' }', inputs=[], outputs=tab_controls, show_progress=False)
 

modules/ui_extra_networks_user_metadata.py

@@ -134,7 +134,7 @@ class UserMetadataEditor:
         basename, ext = os.path.splitext(filename)
 
         with open(basename + '.json', "w", encoding="utf8") as file:
-            json.dump(metadata, file, indent=4)
+            json.dump(metadata, file, indent=4, ensure_ascii=False)
 
     def save_user_metadata(self, name, desc, notes):
         user_metadata = self.get_user_metadata(name)

modules/ui_loadsave.py

@@ -141,7 +141,7 @@ class UiLoadsave:
 
     def write_to_file(self, current_ui_settings):
         with open(self.filename, "w", encoding="utf8") as file:
-            json.dump(current_ui_settings, file, indent=4)
+            json.dump(current_ui_settings, file, indent=4, ensure_ascii=False)
 
     def dump_defaults(self):
         """saves default values to a file unless tjhe file is present and there was an error loading default values at start"""

style.css

@@ -462,6 +462,15 @@ div.toprow-compact-tools{
     padding: 4px;
 }
 
+#settings > div.tab-nav .settings-category{
+    display: block;
+    margin: 1em 0 0.25em 0;
+    font-weight: bold;
+    text-decoration: underline;
+    cursor: default;
+    user-select: none;
+}
+
 #settings_result{
     height: 1.4em;
     margin: 0 1.2em;
@@ -637,6 +646,8 @@ table.popup-table .link{
     margin: auto;
     padding: 2em;
     z-index: 1001;
+    max-height: 90%;
+    max-width: 90%;
 }
 
 /* fullpage image viewer */
@@ -840,8 +851,16 @@ footer {
 
 /* extra networks UI */
 
-.extra-page .prompt{
-    margin: 0 0 0.5em 0;
+.extra-page > div.gap{
+    gap: 0;
+}
+
+.extra-page-prompts{
+    margin-bottom: 0;
+}
+
+.extra-page-prompts.extra-page-prompts-active{
+    margin-bottom: 1em;
 }
 
 .extra-network-cards{