clear GenerationParametersList before batch

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

.eslintrc.js

@@ -88,7 +88,6 @@ module.exports = {
         // imageviewer.js
         modalPrevImage: "readonly",
         modalNextImage: "readonly",
-        updateModalImageIfVisible: "readonly",
         // localStorage.js
         localSet: "readonly",
         localGet: "readonly",

.github/workflows/on_pull_request.yaml

@@ -22,7 +22,7 @@ jobs:
       - name: Install Ruff
         run: pip install ruff==0.3.3
       - name: Run Ruff
-        run: ruff check .
+        run: ruff .
   lint-js:
     name: eslint
     runs-on: ubuntu-latest

README.md

@@ -133,7 +133,7 @@ If your system is very new, you need to install python3.11 or python3.10:
 # Ubuntu 24.04
 sudo add-apt-repository ppa:deadsnakes/ppa
 sudo apt update
-sudo apt install python3.11 python3.11-venv
+sudo apt install python3.11
 
 # Manjaro/Arch
 sudo pacman -S yay

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

@@ -1,69 +1,36 @@
-// Stable Diffusion WebUI - Bracket Checker
-// By @Bwin4L, @akx, @w-e-w, @Haoming02
+// Stable Diffusion WebUI - Bracket checker
+// By Hingashi no Florin/Bwin4L & @akx
 // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
-// 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, counterElem) {
-    const pairs = [
-        ['(', ')', 'round brackets'],
-        ['[', ']', 'square brackets'],
-        ['{', '}', 'curly brackets']
-    ];
+// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
 
+function checkBrackets(textArea, counterElt) {
     const counts = {};
-    const errors = new Set();
-    let i = 0;
+    textArea.value.matchAll(/(?<!\\)(?:\\\\)*?([(){}[\]])/g).forEach(bracket => {
+        counts[bracket[1]] = (counts[bracket[1]] || 0) + 1;
+    });
+    const errors = [];
 
-    while (i < textArea.value.length) {
-        let char = textArea.value[i];
-        let escaped = false;
-        while (char === '\\' && i + 1 < textArea.value.length) {
-            escaped = !escaped;
-            i++;
-            char = textArea.value[i];
-        }
-
-        if (escaped) {
-            i++;
-            continue;
-        }
-
-        for (const [open, close, label] of pairs) {
-            if (char === open) {
-                counts[label] = (counts[label] || 0) + 1;
-            } else if (char === close) {
-                counts[label] = (counts[label] || 0) - 1;
-                if (counts[label] < 0) {
-                    errors.add(`Incorrect order of ${label}.`);
-                }
-            }
-        }
-
-        i++;
-    }
-
-    for (const [open, close, label] of pairs) {
-        if (counts[label] == undefined) {
-            continue;
-        }
-
-        if (counts[label] > 0) {
-            errors.add(`${open} ... ${close} - Detected ${counts[label]} more opening than closing ${label}.`);
-        } else if (counts[label] < 0) {
-            errors.add(`${open} ... ${close} - Detected ${-counts[label]} more closing than opening ${label}.`);
+    function checkPair(open, close, kind) {
+        if (counts[open] !== counts[close]) {
+            errors.push(
+                `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
+            );
         }
     }
 
-    counterElem.title = [...errors].join('\n');
-    counterElem.classList.toggle('error', errors.size !== 0);
+    checkPair('(', ')', 'round brackets');
+    checkPair('[', ']', 'square brackets');
+    checkPair('{', '}', 'curly brackets');
+    counterElt.title = errors.join('\n');
+    counterElt.classList.toggle('error', errors.length !== 0);
 }
 
 function setupBracketChecking(id_prompt, id_counter) {
-    const textarea = gradioApp().querySelector(`#${id_prompt} > label > textarea`);
-    const counter = gradioApp().getElementById(id_counter);
+    var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
+    var counter = gradioApp().getElementById(id_counter);
 
     if (textarea && counter) {
-        onEdit(`${id_prompt}_BracketChecking`, textarea, 400, () => checkBrackets(textarea, counter));
+        textarea.addEventListener("input", () => checkBrackets(textarea, counter));
     }
 }
 

html/footer.html

@@ -1,5 +1,5 @@
 <div>
-        <a href="{api_docs}" target="_blank">API</a>
+        <a href="{api_docs}">API</a>
          • 
         <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">GitHub</a>
          • 

javascript/imageviewer.js

@@ -54,7 +54,6 @@ function updateOnBackgroundChange() {
         updateModalImage();
     }
 }
-const updateModalImageIfVisible = updateOnBackgroundChange;
 
 function modalImageSwitch(offset) {
     var galleryButtons = all_gallery_buttons();
@@ -165,7 +164,6 @@ function modalLivePreviewToggle(event) {
     const modalToggleLivePreview = gradioApp().getElementById("modal_toggle_live_preview");
     opts.js_live_preview_in_modal_lightbox = !opts.js_live_preview_in_modal_lightbox;
     modalToggleLivePreview.innerHTML = opts.js_live_preview_in_modal_lightbox ? "🗇" : "🗆";
-    updateModalImageIfVisible();
     event.stopPropagation();
 }
 

javascript/progressbar.js

@@ -190,7 +190,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                         livePreview.className = 'livePreview';
                         gallery.insertBefore(livePreview, gallery.firstElementChild);
                     }
-                    updateModalImageIfVisible();
+
                     livePreview.appendChild(img);
                     if (livePreview.childElementCount > 2) {
                         livePreview.removeChild(livePreview.firstElementChild);

launch.py

@@ -6,11 +6,6 @@ git = launch_utils.git
 index_url = launch_utils.index_url
 dir_repos = launch_utils.dir_repos
 
-if args.uv:
-    from modules.uv_hook import patch
-    patch()
-
-
 commit_hash = launch_utils.commit_hash
 git_tag = launch_utils.git_tag
 

modules/cmd_args.py

@@ -126,4 +126,3 @@ parser.add_argument("--skip-load-model-at-start", action='store_true', help="if
 parser.add_argument("--unix-filenames-sanitization", action='store_true', help="allow any symbols except '/' in filenames. May conflict with your browser and file system")
 parser.add_argument("--filenames-max-length", type=int, default=128, help='maximal length of filenames of saved images. If you override it, it can conflict with your file system')
 parser.add_argument("--no-prompt-history", action='store_true', help="disable read prompt from last generation feature; settings this argument will not create '--data_path/params.txt' file")
-parser.add_argument("--uv", action='store_true', help="use the uv package manager")

modules/hashes.py

@@ -1,7 +1,7 @@
 import hashlib
 import os.path
 
-from modules import shared, errors
+from modules import shared
 import modules.cache
 
 dump_cache = modules.cache.dump_cache
@@ -32,7 +32,7 @@ def sha256_from_cache(filename, title, use_addnet_hash=False):
     cached_sha256 = hashes[title].get("sha256", None)
     cached_mtime = hashes[title].get("mtime", 0)
 
-    if ondisk_mtime != cached_mtime or cached_sha256 is None:
+    if ondisk_mtime > cached_mtime or cached_sha256 is None:
         return None
 
     return cached_sha256
@@ -82,31 +82,3 @@ def addnet_hash_safetensors(b):
 
     return hash_sha256.hexdigest()
 
-
-def partial_hash_from_cache(filename, *, ignore_cache: bool = False, digits: int = 8):
-    """old hash that only looks at a small part of the file and is prone to collisions
-    kept for compatibility, don't use this for new things
-    """
-    try:
-        filename = str(filename)
-        mtime = os.path.getmtime(filename)
-        hashes = cache('partial-hash')
-        cache_entry = hashes.get(filename, {})
-        cache_mtime = cache_entry.get("mtime", 0)
-        cache_hash = cache_entry.get("hash", None)
-        if mtime == cache_mtime and cache_hash and not ignore_cache:
-            return cache_hash[0:digits]
-
-        with open(filename, 'rb') as file:
-            m = hashlib.sha256()
-            file.seek(0x100000)
-            m.update(file.read(0x10000))
-            partial_hash = m.hexdigest()
-            hashes[filename] = {'mtime': mtime, 'hash': partial_hash}
-            return partial_hash[0:digits]
-
-    except FileNotFoundError:
-        pass
-    except Exception:
-        errors.report(f'Error calculating partial hash for {filename}', exc_info=True)
-    return 'NOFILE'

modules/images.py

@@ -409,7 +409,6 @@ class FilenameGenerator:
         'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
         'hasprompt': lambda self, *args: self.hasprompt(*args),  # accepts formats:[hasprompt<prompt1|default><prompt2>..]
         'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
-        'randn_source': lambda self: opts.data["randn_source"],
         'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
         'user': lambda self: self.p.user,
         'vae_filename': lambda self: self.get_vae_filename(),

modules/launch_utils.py

@@ -43,7 +43,9 @@ def check_python_version():
         supported_minors = [7, 8, 9, 10, 11]
 
     if not (major == 3 and minor in supported_minors):
-        errors.print_error_explanation(f"""
+        import modules.errors
+
+        modules.errors.print_error_explanation(f"""
 INCOMPATIBLE PYTHON VERSION
 
 This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
@@ -313,43 +315,9 @@ def requirements_met(requirements_file):
     return True
 
 
-def get_cuda_comp_cap():
-    """
-    Returns float of CUDA Compute Capability using nvidia-smi
-    Returns 0.0 on error
-    CUDA Compute Capability
-    ref https://developer.nvidia.com/cuda-gpus
-    ref https://en.wikipedia.org/wiki/CUDA
-    Blackwell consumer GPUs should return 12.0 data-center GPUs should return 10.0
-    """
-    try:
-        return max(map(float, subprocess.check_output(['nvidia-smi', '--query-gpu=compute_cap', '--format=noheader,csv'], text=True).splitlines()))
-    except Exception as _:
-        return 0.0
-
-
-def early_access_blackwell_wheels():
-    """For Blackwell GPUs, use Early Access PyTorch Wheels provided by Nvidia"""
-    print('deprecated early_access_blackwell_wheels')
-    if all([
-            os.environ.get('TORCH_INDEX_URL') is None,
-            sys.version_info.major == 3,
-            sys.version_info.minor in (10, 11, 12),
-            platform.system() == "Windows",
-            get_cuda_comp_cap() >= 10,  # Blackwell
-    ]):
-        base_repo = 'https://huggingface.co/w-e-w/torch-2.6.0-cu128.nv/resolve/main/'
-        ea_whl = {
-            10: f'{base_repo}torch-2.6.0+cu128.nv-cp310-cp310-win_amd64.whl#sha256=fef3de7ce8f4642e405576008f384304ad0e44f7b06cc1aa45e0ab4b6e70490d {base_repo}torchvision-0.20.0a0+cu128.nv-cp310-cp310-win_amd64.whl#sha256=50841254f59f1db750e7348b90a8f4cd6befec217ab53cbb03780490b225abef',
-            11: f'{base_repo}torch-2.6.0+cu128.nv-cp311-cp311-win_amd64.whl#sha256=6665c36e6a7e79e7a2cb42bec190d376be9ca2859732ed29dd5b7b5a612d0d26 {base_repo}torchvision-0.20.0a0+cu128.nv-cp311-cp311-win_amd64.whl#sha256=bbc0ee4938e35fe5a30de3613bfcd2d8ef4eae334cf8d49db860668f0bb47083',
-            12: f'{base_repo}torch-2.6.0+cu128.nv-cp312-cp312-win_amd64.whl#sha256=a3197f72379d34b08c4a4bcf49ea262544a484e8702b8c46cbcd66356c89def6 {base_repo}torchvision-0.20.0a0+cu128.nv-cp312-cp312-win_amd64.whl#sha256=235e7be71ac4e75b0f8e817bae4796d7bac8a67146d2037ab96394f2bdc63e6c'
-        }
-        return f'pip install {ea_whl.get(sys.version_info.minor)}'
-
-
 def prepare_environment():
-    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu128")
-    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.7.0 torchvision==0.22.0 --extra-index-url {torch_index_url}")
+    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
+    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.1.2 torchvision==0.16.2 --extra-index-url {torch_index_url}")
     if args.use_ipex:
         if platform.system() == "Windows":
             # The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main
@@ -373,12 +341,12 @@ def prepare_environment():
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     requirements_file_for_npu = os.environ.get('REQS_FILE_FOR_NPU', "requirements_npu.txt")
 
-    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.30')
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
     clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
 
     assets_repo = os.environ.get('ASSETS_REPO', "https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets.git")
-    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/w-e-w/stablediffusion.git")
+    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
     stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
     k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
@@ -422,24 +390,8 @@ def prepare_environment():
         )
     startup_timer.record("torch GPU test")
 
-    # Ensure build dependencies are installed before any package that might need them
-    def ensure_build_dependencies():
-        """Ensure essential build tools are available"""
-        if not is_installed("wheel"):
-            run_pip("install wheel", "wheel")
-        # Check setuptools version compatibility
-        try:
-            setuptools_version = run(f'"{python}" -c "import setuptools; print(setuptools.__version__)"', None, None).strip()
-            if setuptools_version >= "70":
-                run_pip("install setuptools==69.5.1", "setuptools")
-        except Exception:
-            # If setuptools check fails, install compatible version
-            run_pip("install setuptools==69.5.1", "setuptools")
-    # Install build dependencies early
-    ensure_build_dependencies()
-
     if not is_installed("clip"):
-        run_pip(f"install --no-build-isolation {clip_package}", "clip")
+        run_pip(f"install {clip_package}", "clip")
         startup_timer.record("install clip")
 
     if not is_installed("open_clip"):

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):
-                generation_params[key] = value[index]
-            elif callable(value):
+            if 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"):
-                    hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
-                self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
+                self.hijack.extra_generation_params["TI hashes"] = util.GenerationParametersList(hashes)
 
-        if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
-            self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
+        if opts.emphasis != 'Original' and any(x for x in texts if '(' in x or '[' in x):
+            self.hijack.extra_generation_params["Emphasis"] = EmphasisMode(opts.emphasis)
 
         if self.return_pooled:
             return torch.hstack(zs), zs[0].pooled

modules/sd_hijack_optimizations.py

@@ -54,7 +54,7 @@ class SdOptimizationXformers(SdOptimization):
     priority = 100
 
     def is_available(self):
-        return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (12, 0))
+        return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = xformers_attention_forward

modules/sd_models.py

@@ -13,7 +13,6 @@ from urllib import request
 import ldm.modules.midas as midas
 
 from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
-from modules.hashes import partial_hash_from_cache as model_hash  # noqa: F401 for backwards compatibility
 from modules.timer import Timer
 from modules.shared import opts
 import tomesd
@@ -88,7 +87,7 @@ class CheckpointInfo:
         self.name = name
         self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
-        self.hash = hashes.partial_hash_from_cache(filename)
+        self.hash = model_hash(filename)
 
         self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
         self.shorthash = self.sha256[0:10] if self.sha256 else None
@@ -201,6 +200,21 @@ def get_closet_checkpoint_match(search_string):
     return None
 
 
+def model_hash(filename):
+    """old hash that only looks at a small part of the file and is prone to collisions"""
+
+    try:
+        with open(filename, "rb") as file:
+            import hashlib
+            m = hashlib.sha256()
+
+            file.seek(0x100000)
+            m.update(file.read(0x10000))
+            return m.hexdigest()[0:8]
+    except FileNotFoundError:
+        return 'NOFILE'
+
+
 def select_checkpoint():
     """Raises `FileNotFoundError` if no checkpoints are found."""
     model_checkpoint = shared.opts.sd_model_checkpoint

modules/sd_schedulers.py

@@ -117,15 +117,12 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
 
 
 def beta_scheduler(n, sigma_min, sigma_max, inner_model, device):
-    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)
+    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """
     alpha = shared.opts.beta_dist_alpha
     beta = shared.opts.beta_dist_beta
-    curve = [stats.beta.ppf(x, alpha, beta) for x in np.linspace(1, 0, n)]
-
-    start = inner_model.sigma_to_t(torch.tensor(sigma_max))
-    end = inner_model.sigma_to_t(torch.tensor(sigma_min))
-    timesteps = [end + x * (start - end) for x in curve]
-    sigmas = [inner_model.t_to_sigma(ts) for ts in timesteps]
+    timesteps = 1 - np.linspace(0, 1, n)
+    timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps]
+    sigmas = [sigma_min + (x * (sigma_max-sigma_min)) for x in timesteps]
     sigmas += [0.0]
     return torch.FloatTensor(sigmas).to(device)
 

modules/shared_items.py

@@ -125,7 +125,7 @@ def ui_reorder_categories():
 
 def callbacks_order_settings():
     options = {
-        "callbacks_order_explanation": OptionHTML("""
+        "sd_vae_explanation": OptionHTML("""
     For categories below, callbacks added to dropdowns happen before others, in order listed.
     """),
 

modules/shared_options.py

@@ -33,12 +33,12 @@ 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', ui_components.DropdownEditable, {"choices": ("png", "jpg", "jpeg", "webp", "avif")}).info("manual input of <a href='https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html' target='_blank'>other formats</a> is possible, but compatibility is not guaranteed"),
+    "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"),
     "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
     "save_images_replace_action": OptionInfo("Replace", "Saving the image to an existing file", gr.Radio, {"choices": ["Replace", "Add number suffix"], **hide_dirs}),
     "grid_save": OptionInfo(True, "Always save all generated image grids"),
-    "grid_format": OptionInfo('png', 'File format for grids', ui_components.DropdownEditable, {"choices": ("png", "jpg", "jpeg", "webp", "avif")}).info("manual input of <a href='https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html' target='_blank'>other formats</a> is possible, but compatibility is not guaranteed"),
+    "grid_format": OptionInfo('png', 'File format for grids'),
     "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
     "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
     "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
@@ -128,7 +128,6 @@ options_templates.update(options_section(('system', "System", "system"), {
     "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
     "hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
     "dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
-    "concurrent_git_fetch_limit": OptionInfo(16, "Number of simultaneous extension update checks ", gr.Slider, {"step": 1, "minimum": 1, "maximum": 100}).info("reduce extension update check time"),
 }))
 
 options_templates.update(options_section(('profiler', "Profiler", "system"), {
@@ -407,8 +406,8 @@ 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'),
     'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models"),
     'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling; XYZ plot: Skip Early CFG"),
-    'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 5.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
-    'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 5.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
+    'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
+    'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
 }))
 
 options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {

modules/ui_extensions.py

@@ -1,6 +1,5 @@
 import json
 import os
-from concurrent.futures import ThreadPoolExecutor
 import threading
 import time
 from datetime import datetime, timezone
@@ -107,24 +106,18 @@ def check_updates(id_task, disable_list):
     exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
     shared.state.job_count = len(exts)
 
-    lock = threading.Lock()
+    for ext in exts:
+        shared.state.textinfo = ext.name
 
-    def _check_update(ext):
         try:
             ext.check_updates()
         except FileNotFoundError as e:
             if 'FETCH_HEAD' not in str(e):
                 raise
         except Exception:
-            with lock:
-                errors.report(f"Error checking updates for {ext.name}", exc_info=True)
-        with lock:
-            shared.state.textinfo = ext.name
-            shared.state.nextjob()
+            errors.report(f"Error checking updates for {ext.name}", exc_info=True)
 
-    with ThreadPoolExecutor(max_workers=max(1, int(shared.opts.concurrent_git_fetch_limit))) as executor:
-        for ext in exts:
-            executor.submit(_check_update, ext)
+        shared.state.nextjob()
 
     return extension_table(), ""
 

modules/util.py

@@ -1,4 +1,3 @@
-from __future__ import annotations
 import os
 import re
 
@@ -289,3 +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__()
+

modules/uv_hook.py

@@ -1,50 +0,0 @@
-import sys
-import copy
-import shlex
-import subprocess
-from functools import wraps
-
-BAD_FLAGS = ("--prefer-binary", '-I', '--ignore-installed')
-
-
-def patch():
-    if hasattr(subprocess, "__original_run"):
-        return
-
-    print("using uv")
-    try:
-        subprocess.run(['uv', '-V'])
-    except FileNotFoundError:
-        subprocess.run([sys.executable, '-m', 'pip', 'install', 'uv'])
-
-    subprocess.__original_run = subprocess.run
-
-    @wraps(subprocess.__original_run)
-    def patched_run(*args, **kwargs):
-        _kwargs = copy.copy(kwargs)
-        if args:
-            command, *_args = args
-        else:
-            command, _args = _kwargs.pop("args", ""), ()
-
-        if isinstance(command, str):
-            command = shlex.split(command)
-        else:
-            command = [arg.strip() for arg in command]
-
-        if not isinstance(command, list) or "pip" not in command:
-            return subprocess.__original_run(*args, **kwargs)
-
-        cmd = command[command.index("pip") + 1:]
-
-        cmd = [arg for arg in cmd if arg not in BAD_FLAGS]
-
-        modified_command = ["uv", "pip", *cmd]
-
-        cmd_str = shlex.join([*modified_command, *_args])
-        result = subprocess.__original_run(cmd_str, **_kwargs)
-        if result.returncode != 0:
-            return subprocess.__original_run(*args, **kwargs)
-        return result
-
-    subprocess.run = patched_run

script.js

@@ -182,7 +182,7 @@ document.addEventListener('keydown', function(e) {
         const lightboxModal = document.querySelector('#lightboxModal');
         if (!globalPopup || globalPopup.style.display === 'none') {
             if (document.activeElement === lightboxModal) return;
-            if (interruptButton?.style.display === 'block') {
+            if (interruptButton.style.display === 'block') {
                 interruptButton.click();
                 e.preventDefault();
             }

scripts/postprocessing_codeformer.py

@@ -29,10 +29,6 @@ class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing
         res = Image.fromarray(restored_img)
 
         if codeformer_visibility < 1.0:
-            if pp.image.size != res.size:
-                res = res.resize(pp.image.size)
-            if pp.image.mode != res.mode:
-                res = res.convert(pp.image.mode)
             res = Image.blend(pp.image, res, codeformer_visibility)
 
         pp.image = res

scripts/postprocessing_gfpgan.py

@@ -26,10 +26,6 @@ class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
         res = Image.fromarray(restored_img)
 
         if gfpgan_visibility < 1.0:
-            if pp.image.size != res.size:
-                res = res.resize(pp.image.size)
-            if pp.image.mode != res.mode:
-                res = res.convert(pp.image.mode)
             res = Image.blend(pp.image, res, gfpgan_visibility)
 
         pp.image = res

style.css

@@ -480,10 +480,8 @@ div.toprow-compact-tools{
 }
 
 #settings_result{
-    min-height: 1.4em;
+    height: 1.4em;
     margin: 0 1.2em;
-    max-height: calc(var(--text-md) * var(--line-sm) * 5);
-    overflow-y: auto;
 }
 
 table.popup-table{
@@ -602,7 +600,6 @@ table.popup-table .link{
     background: var(--background-fill-primary);
     width: 100%;
     height: 100%;
-    pointer-events: none;
 }
 
 .livePreview img{

webui.sh

@@ -127,7 +127,7 @@ then
 fi
 
 # Check prerequisites
-gpu_info=$(lspci 2>/dev/null | grep -E "VGA|Display|CMP")
+gpu_info=$(lspci 2>/dev/null | grep -E "VGA|Display")
 case "$gpu_info" in
     *"Navi 1"*)
         export HSA_OVERRIDE_GFX_VERSION=10.3.0