add gc after using consistency dec

custom schedule
Fix linting
2023-11-07 13:01:10 +08:00 · 2023-11-07 12:35:56 +08:00 · 2023-11-07 11:00:24 +08:00 · 2023-11-07 10:52:29 +08:00
45 changed files with 188 additions and 1150 deletions
@@ -20,7 +20,7 @@ jobs:
          #     not to have GHA download an (at the time of writing) 4 GB cache
          #     of PyTorch and other dependencies.
      - name: Install Ruff
-        run: pip install ruff==0.1.6
+        run: pip install ruff==0.0.272
      - name: Run Ruff
        run: ruff .
  lint-js:
@@ -121,9 +121,7 @@ Alternatively, use online services (like Google Colab):
 # Debian-based:
 sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
 # Red Hat-based:
-sudo dnf install wget git python3 gperftools-libs libglvnd-glx 
+sudo dnf install wget git python3
 # openSUSE-based:
 sudo zypper install wget git python3 libtcmalloc4 libglvnd
 # Arch-based:
 sudo pacman -S wget git python3
 ```
@@ -149,7 +147,7 @@ For the purposes of getting Google and other search engines to crawl the wiki, h
 ## Credits
 Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
+- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
 - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
 - GFPGAN - https://github.com/TencentARC/GFPGAN.git
 - CodeFormer - https://github.com/sczhou/CodeFormer
@@ -176,6 +174,5 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
 - LyCORIS - KohakuBlueleaf
 - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
 - Hypertile - tfernd - https://github.com/tfernd/HyperTile
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)
@@ -19,50 +19,3 @@ def rebuild_cp_decomposition(up, down, mid):
    up = up.reshape(up.size(0), -1)
    down = down.reshape(down.size(0), -1)
    return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
 # copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
 def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
    '''
    return a tuple of two value of input dimension decomposed by the number closest to factor
    second value is higher or equal than first value.
    In LoRA with Kroneckor Product, first value is a value for weight scale.
    secon value is a value for weight.
    Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
    examples)
    factor
        -1               2                4               8               16               ...
    127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
    128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
    250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
    360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
    512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
    1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
    '''
    if factor > 0 and (dimension % factor) == 0:
        m = factor
        n = dimension // factor
        if m > n:
            n, m = m, n
        return m, n
    if factor < 0:
        factor = dimension
    m, n = 1, dimension
    length = m + n
    while m<n:
        new_m = m + 1
        while dimension%new_m != 0:
            new_m += 1
        new_n = dimension // new_m
        if new_m + new_n > length or new_m>factor:
            break
        else:
            m, n = new_m, new_n
    if m > n:
        n, m = m, n
    return m, n
@@ -1,97 +0,0 @@
 import torch
 import network
 from lyco_helpers import factorization
 from einops import rearrange
 class ModuleTypeOFT(network.ModuleType):
    def create_module(self, net: network.Network, weights: network.NetworkWeights):
        if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
            return NetworkModuleOFT(net, weights)
        return None
 # Supports both kohya-ss' implementation of COFT  https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
 # and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
 class NetworkModuleOFT(network.NetworkModule):
    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
        super().__init__(net, weights)
        self.lin_module = None
        self.org_module: list[torch.Module] = [self.sd_module]
        # kohya-ss
        if "oft_blocks" in weights.w.keys():
            self.is_kohya = True
            self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
            self.alpha = weights.w["alpha"] # alpha is constraint
            self.dim = self.oft_blocks.shape[0] # lora dim
        # LyCORIS
        elif "oft_diag" in weights.w.keys():
            self.is_kohya = False
            self.oft_blocks = weights.w["oft_diag"]
            # self.alpha is unused
            self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
        is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
        if is_linear:
            self.out_dim = self.sd_module.out_features
        elif is_conv:
            self.out_dim = self.sd_module.out_channels
        elif is_other_linear:
            self.out_dim = self.sd_module.embed_dim
        if self.is_kohya:
            self.constraint = self.alpha * self.out_dim
            self.num_blocks = self.dim
            self.block_size = self.out_dim // self.dim
        else:
            self.constraint = None
            self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
    def calc_updown_kb(self, orig_weight, multiplier):
        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
        oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
        R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
        R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
        # This errors out for MultiheadAttention, might need to be handled up-stream
        merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
        merged_weight = torch.einsum(
            'k n m, k n ... -> k m ...',
            R,
            merged_weight
        )
        merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
        updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
        output_shape = orig_weight.shape
        return self.finalize_updown(updown, orig_weight, output_shape)
    def calc_updown(self, orig_weight):
        # if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it
        multiplier = self.multiplier()
        return self.calc_updown_kb(orig_weight, multiplier)
    # override to remove the multiplier/scale factor; it's already multiplied in get_weight
    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
        if self.bias is not None:
            updown = updown.reshape(self.bias.shape)
            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
            updown = updown.reshape(output_shape)
        if len(output_shape) == 4:
            updown = updown.reshape(output_shape)
        if orig_weight.size().numel() == updown.size().numel():
            updown = updown.reshape(orig_weight.shape)
        if ex_bias is not None:
            ex_bias = ex_bias * self.multiplier()
        return updown, ex_bias
@@ -11,7 +11,6 @@ import network_ia3
 import network_lokr
 import network_full
 import network_norm
 import network_oft
 import torch
 from typing import Union
@@ -29,7 +28,6 @@ module_types = [
    network_full.ModuleTypeFull(),
    network_norm.ModuleTypeNorm(),
    network_glora.ModuleTypeGLora(),
    network_oft.ModuleTypeOFT(),
 ]
@@ -191,17 +189,6 @@ def load_network(name, network_on_disk):
                key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
                sd_module = shared.sd_model.network_layer_mapping.get(key, None)
        # kohya_ss OFT module
        elif sd_module is None and "oft_unet" in key_network_without_network_parts:
            key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
        # KohakuBlueLeaf OFT module
        if sd_module is None and "oft_diag" in key:
            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
        if sd_module is None:
            keys_failed_to_match[key_network] = key
            continue
@@ -17,8 +17,6 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
    def create_item(self, name, index=None, enable_filter=True):
        lora_on_disk = networks.available_networks.get(name)
        if lora_on_disk is None:
            return
        path, ext = os.path.splitext(lora_on_disk.filename)
@@ -68,10 +66,9 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
        return item
    def list_items(self):
-        # instantiate a list to protect against concurrent modification
+        for index, name in enumerate(networks.available_networks):
        names = list(networks.available_networks)
        for index, name in enumerate(names):
            item = self.create_item(name, index)
            if item is not None:
                yield item
@@ -1,345 +0,0 @@
 """
 Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
 Warn: The patch works well only if the input image has a width and height that are multiples of 128
 Original author: @tfernd Github: https://github.com/tfernd/HyperTile
 """
 from __future__ import annotations
 import functools
 from dataclasses import dataclass
 from typing import Callable
 from functools import wraps, cache
 import math
 import torch.nn as nn
 import random
 from einops import rearrange
@dataclass
 class HypertileParams:
    depth = 0
    layer_name = ""
    tile_size: int = 0
    swap_size: int = 0
    aspect_ratio: float = 1.0
    forward = None
    enabled = False
 # TODO add SD-XL layers
 DEPTH_LAYERS = {
    0: [
        # SD 1.5 U-Net (diffusers)
        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.1.1.transformer_blocks.0.attn1",
        "input_blocks.2.1.transformer_blocks.0.attn1",
        "output_blocks.9.1.transformer_blocks.0.attn1",
        "output_blocks.10.1.transformer_blocks.0.attn1",
        "output_blocks.11.1.transformer_blocks.0.attn1",
        # SD 1.5 VAE
        "decoder.mid_block.attentions.0",
        "decoder.mid.attn_1",
    ],
    1: [
        # SD 1.5 U-Net (diffusers)
        "down_blocks.1.attentions.0.transformer_blocks.0.attn1",
        "down_blocks.1.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.2.attentions.0.transformer_blocks.0.attn1",
        "up_blocks.2.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.2.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.4.1.transformer_blocks.0.attn1",
        "input_blocks.5.1.transformer_blocks.0.attn1",
        "output_blocks.6.1.transformer_blocks.0.attn1",
        "output_blocks.7.1.transformer_blocks.0.attn1",
        "output_blocks.8.1.transformer_blocks.0.attn1",
    ],
    2: [
        # SD 1.5 U-Net (diffusers)
        "down_blocks.2.attentions.0.transformer_blocks.0.attn1",
        "down_blocks.2.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.1.attentions.0.transformer_blocks.0.attn1",
        "up_blocks.1.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.1.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.7.1.transformer_blocks.0.attn1",
        "input_blocks.8.1.transformer_blocks.0.attn1",
        "output_blocks.3.1.transformer_blocks.0.attn1",
        "output_blocks.4.1.transformer_blocks.0.attn1",
        "output_blocks.5.1.transformer_blocks.0.attn1",
    ],
    3: [
        # SD 1.5 U-Net (diffusers)
        "mid_block.attentions.0.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "middle_block.1.transformer_blocks.0.attn1",
    ],
 }
 # XL layers, thanks for GitHub@gel-crabs for the help
 DEPTH_LAYERS_XL = {
    0: [
        # SD 1.5 U-Net (diffusers)
        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.4.1.transformer_blocks.0.attn1",
        "input_blocks.5.1.transformer_blocks.0.attn1",
        "output_blocks.3.1.transformer_blocks.0.attn1",
        "output_blocks.4.1.transformer_blocks.0.attn1",
        "output_blocks.5.1.transformer_blocks.0.attn1",
        # SD 1.5 VAE
        "decoder.mid_block.attentions.0",
        "decoder.mid.attn_1",
    ],
    1: [
        # SD 1.5 U-Net (diffusers)
        #"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
        #"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
        #"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
        #"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
        #"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.4.1.transformer_blocks.1.attn1",
        "input_blocks.5.1.transformer_blocks.1.attn1",
        "output_blocks.3.1.transformer_blocks.1.attn1",
        "output_blocks.4.1.transformer_blocks.1.attn1",
        "output_blocks.5.1.transformer_blocks.1.attn1",
        "input_blocks.7.1.transformer_blocks.0.attn1",
        "input_blocks.8.1.transformer_blocks.0.attn1",
        "output_blocks.0.1.transformer_blocks.0.attn1",
        "output_blocks.1.1.transformer_blocks.0.attn1",
        "output_blocks.2.1.transformer_blocks.0.attn1",
        "input_blocks.7.1.transformer_blocks.1.attn1",
        "input_blocks.8.1.transformer_blocks.1.attn1",
        "output_blocks.0.1.transformer_blocks.1.attn1",
        "output_blocks.1.1.transformer_blocks.1.attn1",
        "output_blocks.2.1.transformer_blocks.1.attn1",
        "input_blocks.7.1.transformer_blocks.2.attn1",
        "input_blocks.8.1.transformer_blocks.2.attn1",
        "output_blocks.0.1.transformer_blocks.2.attn1",
        "output_blocks.1.1.transformer_blocks.2.attn1",
        "output_blocks.2.1.transformer_blocks.2.attn1",
        "input_blocks.7.1.transformer_blocks.3.attn1",
        "input_blocks.8.1.transformer_blocks.3.attn1",
        "output_blocks.0.1.transformer_blocks.3.attn1",
        "output_blocks.1.1.transformer_blocks.3.attn1",
        "output_blocks.2.1.transformer_blocks.3.attn1",
        "input_blocks.7.1.transformer_blocks.4.attn1",
        "input_blocks.8.1.transformer_blocks.4.attn1",
        "output_blocks.0.1.transformer_blocks.4.attn1",
        "output_blocks.1.1.transformer_blocks.4.attn1",
        "output_blocks.2.1.transformer_blocks.4.attn1",
        "input_blocks.7.1.transformer_blocks.5.attn1",
        "input_blocks.8.1.transformer_blocks.5.attn1",
        "output_blocks.0.1.transformer_blocks.5.attn1",
        "output_blocks.1.1.transformer_blocks.5.attn1",
        "output_blocks.2.1.transformer_blocks.5.attn1",
        "input_blocks.7.1.transformer_blocks.6.attn1",
        "input_blocks.8.1.transformer_blocks.6.attn1",
        "output_blocks.0.1.transformer_blocks.6.attn1",
        "output_blocks.1.1.transformer_blocks.6.attn1",
        "output_blocks.2.1.transformer_blocks.6.attn1",
        "input_blocks.7.1.transformer_blocks.7.attn1",
        "input_blocks.8.1.transformer_blocks.7.attn1",
        "output_blocks.0.1.transformer_blocks.7.attn1",
        "output_blocks.1.1.transformer_blocks.7.attn1",
        "output_blocks.2.1.transformer_blocks.7.attn1",
        "input_blocks.7.1.transformer_blocks.8.attn1",
        "input_blocks.8.1.transformer_blocks.8.attn1",
        "output_blocks.0.1.transformer_blocks.8.attn1",
        "output_blocks.1.1.transformer_blocks.8.attn1",
        "output_blocks.2.1.transformer_blocks.8.attn1",
        "input_blocks.7.1.transformer_blocks.9.attn1",
        "input_blocks.8.1.transformer_blocks.9.attn1",
        "output_blocks.0.1.transformer_blocks.9.attn1",
        "output_blocks.1.1.transformer_blocks.9.attn1",
        "output_blocks.2.1.transformer_blocks.9.attn1",
    ],
    2: [
        # SD 1.5 U-Net (diffusers)
        "mid_block.attentions.0.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "middle_block.1.transformer_blocks.0.attn1",
        "middle_block.1.transformer_blocks.1.attn1",
        "middle_block.1.transformer_blocks.2.attn1",
        "middle_block.1.transformer_blocks.3.attn1",
        "middle_block.1.transformer_blocks.4.attn1",
        "middle_block.1.transformer_blocks.5.attn1",
        "middle_block.1.transformer_blocks.6.attn1",
        "middle_block.1.transformer_blocks.7.attn1",
        "middle_block.1.transformer_blocks.8.attn1",
        "middle_block.1.transformer_blocks.9.attn1",
    ],
    3 : [] # TODO - separate layers for SD-XL
 }
 RNG_INSTANCE = random.Random()
 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
    """
    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
    idx = RNG_INSTANCE.randint(0, len(ns) - 1)
    return ns[idx]
 def set_hypertile_seed(seed: int) -> None:
    RNG_INSTANCE.seed(seed)
@functools.cache
 def largest_tile_size_available(width: int, height: int) -> int:
    """
    Calculates the largest tile size available for a given width and height
    Tile size is always a power of 2
    """
    gcd = math.gcd(width, height)
    largest_tile_size_available = 1
    while gcd % (largest_tile_size_available * 2) == 0:
        largest_tile_size_available *= 2
    return largest_tile_size_available
 def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
    """
    Finds h and w such that h*w = hw and h/w = aspect_ratio
    We check all possible divisors of hw and return the closest to the aspect ratio
    """
    divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw
    pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw
    ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw
    closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio
    closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio
    return closest_pair
@cache
 def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
    """
    Finds h and w such that h*w = hw and h/w = aspect_ratio
    """
    h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
    # find h and w such that h*w = hw and h/w = aspect_ratio
    if h * w != hw:
        w_candidate = hw / h
        # check if w is an integer
        if not w_candidate.is_integer():
            h_candidate = hw / w
            # check if h is an integer
            if not h_candidate.is_integer():
                return iterative_closest_divisors(hw, aspect_ratio)
            else:
                h = int(h_candidate)
        else:
            w = int(w_candidate)
    return h, w
 def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable:
    @wraps(params.forward)
    def wrapper(*args, **kwargs):
        if not params.enabled:
            return params.forward(*args, **kwargs)
        latent_tile_size = max(128, params.tile_size) // 8
        x = args[0]
        # VAE
        if x.ndim == 4:
            b, c, h, w = x.shape
            nh = random_divisor(h, latent_tile_size, params.swap_size)
            nw = random_divisor(w, latent_tile_size, params.swap_size)
            if nh * nw > 1:
                x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw)  # split into nh * nw tiles
            out = params.forward(x, *args[1:], **kwargs)
            if nh * nw > 1:
                out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw)
        # U-Net
        else:
            hw: int = x.size(1)
            h, w = find_hw_candidates(hw, params.aspect_ratio)
            assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
            factor = 2 ** params.depth if scale_depth else 1
            nh = random_divisor(h, latent_tile_size * factor, params.swap_size)
            nw = random_divisor(w, latent_tile_size * factor, params.swap_size)
            if nh * nw > 1:
                x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
            out = params.forward(x, *args[1:], **kwargs)
            if nh * nw > 1:
                out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
                out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
        return out
    return wrapper
 def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False):
    hypertile_layers = getattr(model, "__webui_hypertile_layers", None)
    if hypertile_layers is None:
        if not enable:
            return
        hypertile_layers = {}
        layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS
        for depth in range(4):
            for layer_name, module in model.named_modules():
                if any(layer_name.endswith(try_name) for try_name in layers[depth]):
                    params = HypertileParams()
                    module.__webui_hypertile_params = params
                    params.forward = module.forward
                    params.depth = depth
                    params.layer_name = layer_name
                    module.forward = self_attn_forward(params)
                    hypertile_layers[layer_name] = 1
        model.__webui_hypertile_layers = hypertile_layers
    aspect_ratio = width / height
    tile_size = min(largest_tile_size_available(width, height), tile_size_max)
    for layer_name, module in model.named_modules():
        if layer_name in hypertile_layers:
            params = module.__webui_hypertile_params
            params.tile_size = tile_size
            params.swap_size = swap_size
            params.aspect_ratio = aspect_ratio
            params.enabled = enable and params.depth <= max_depth
@@ -1,73 +0,0 @@
 import hypertile
 from modules import scripts, script_callbacks, shared
 class ScriptHypertile(scripts.Script):
    name = "Hypertile"
    def title(self):
        return self.name
    def show(self, is_img2img):
        return scripts.AlwaysVisible
    def process(self, p, *args):
        hypertile.set_hypertile_seed(p.all_seeds[0])
        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)
 def configure_hypertile(width, height, enable_unet=True):
    hypertile.hypertile_hook_model(
        shared.sd_model.first_stage_model,
        width,
        height,
        swap_size=shared.opts.hypertile_swap_size_vae,
        max_depth=shared.opts.hypertile_max_depth_vae,
        tile_size_max=shared.opts.hypertile_max_tile_vae,
        enable=shared.opts.hypertile_enable_vae,
    )
    hypertile.hypertile_hook_model(
        shared.sd_model.model,
        width,
        height,
        swap_size=shared.opts.hypertile_swap_size_unet,
        max_depth=shared.opts.hypertile_max_depth_unet,
        tile_size_max=shared.opts.hypertile_max_tile_unet,
        enable=enable_unet,
        is_sdxl=shared.sd_model.is_sdxl
    )
 def on_ui_settings():
    import gradio as gr
    options = {
        "hypertile_explanation": shared.OptionHTML("""
    <a href='https://github.com/tfernd/HyperTile'>Hypertile</a> optimizes the self-attention layer within U-Net and VAE models,
    resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the
    benefit.
    """),
        "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net").info("noticeable change in details of the generated picture; if enabled, overrides the setting below"),
        "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_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}),
    }
    for name, opt in options.items():
        opt.section = ('hypertile', "Hypertile")
        shared.opts.add_option(name, opt)
 script_callbacks.on_ui_settings(on_ui_settings)
@@ -130,10 +130,6 @@ function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePromp
    } else {
        promptContainer.insertBefore(prompt, promptContainer.firstChild);
    }
    if (elem) {
        elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt);
    }
 }
@@ -26,11 +26,7 @@ onAfterUiUpdate(function() {
    lastHeadImg = headImg;
    // play notification sound if available
-    const notificationAudio = gradioApp().querySelector('#audio_notification audio');
+    gradioApp().querySelector('#audio_notification audio')?.play();
    if (notificationAudio) {
        notificationAudio.volume = opts.notification_volume / 100.0 || 1.0;
        notificationAudio.play();
    }
    if (document.hasFocus()) return;
@@ -44,28 +44,3 @@ 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);
    });
 });
@@ -93,8 +93,8 @@ class PydanticModelGenerator:
            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def
        }
        DynamicModel = create_model(self._model_name, **fields)
-        DynamicModel.model_config['populate_by_name'] = True
+        DynamicModel.__config__.allow_population_by_field_name = True
-        DynamicModel.model_config['frozen'] = True
+        DynamicModel.__config__.allow_mutation = True
        return DynamicModel
 StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
@@ -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, ensure_ascii=False)
+                json.dump(cache_data, file, indent=4)
            os.replace(cache_filename_tmp, cache_filename)
@@ -6,21 +6,6 @@ import traceback
 exception_records = []
 def format_traceback(tb):
    return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
 def format_exception(e, tb):
    return {"exception": str(e), "traceback": format_traceback(tb)}
 def get_exceptions():
    try:
        return list(reversed(exception_records))
    except Exception as e:
        return str(e)
 def record_exception():
    _, e, tb = sys.exc_info()
    if e is None:
@@ -29,7 +14,8 @@ def record_exception():
    if exception_records and exception_records[-1] == e:
        return
-    exception_records.append(format_exception(e, tb))
+    from modules import sysinfo
    exception_records.append(sysinfo.format_exception(e, tb))
    if len(exception_records) > 5:
        exception_records.pop(0)
@@ -1,14 +1,11 @@
 from __future__ import annotations
 import configparser
 import os
 import threading
 import re
 from modules import shared, errors, cache, scripts
 from modules.gitpython_hack import Repo
 from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401
 extensions = []
 os.makedirs(extensions_dir, exist_ok=True)
@@ -22,55 +19,11 @@ def active():
        return [x for x in extensions if x.enabled]
 class ExtensionMetadata:
    filename = "metadata.ini"
    config: configparser.ConfigParser
    canonical_name: str
    requires: list
    def __init__(self, path, canonical_name):
        self.config = configparser.ConfigParser()
        filepath = os.path.join(path, self.filename)
        if os.path.isfile(filepath):
            try:
                self.config.read(filepath)
            except Exception:
                errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True)
        self.canonical_name = self.config.get("Extension", "Name", fallback=canonical_name)
        self.canonical_name = canonical_name.lower().strip()
        self.requires = self.get_script_requirements("Requires", "Extension")
    def get_script_requirements(self, field, section, extra_section=None):
        """reads a list of requirements from the config; field is the name of the field in the ini file,
        like Requires or Before, and section is the name of the [section] in the ini file; additionally,
        reads more requirements from [extra_section] if specified."""
        x = self.config.get(section, field, fallback='')
        if extra_section:
            x = x + ', ' + self.config.get(extra_section, field, fallback='')
        return self.parse_list(x.lower())
    def parse_list(self, text):
        """converts a line from config ("ext1 ext2, ext3  ") into a python list (["ext1", "ext2", "ext3"])"""
        if not text:
            return []
        # both "," and " " are accepted as separator
        return [x for x in re.split(r"[,\s]+", text.strip()) if x]
 class Extension:
    lock = threading.Lock()
    cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version']
    metadata: ExtensionMetadata
-    def __init__(self, name, path, enabled=True, is_builtin=False, metadata=None):
+    def __init__(self, name, path, enabled=True, is_builtin=False):
        self.name = name
        self.path = path
        self.enabled = enabled
@@ -83,8 +36,6 @@ class Extension:
        self.branch = None
        self.remote = None
        self.have_info_from_repo = False
        self.metadata = metadata if metadata else ExtensionMetadata(self.path, name.lower())
        self.canonical_name = metadata.canonical_name
    def to_dict(self):
        return {x: getattr(self, x) for x in self.cached_fields}
@@ -105,7 +56,6 @@ class Extension:
                self.do_read_info_from_repo()
                return self.to_dict()
        try:
            d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
            self.from_dict(d)
@@ -186,6 +136,9 @@ class Extension:
 def list_extensions():
    extensions.clear()
    if not os.path.isdir(extensions_dir):
        return
    if shared.cmd_opts.disable_all_extensions:
        print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
    elif shared.opts.disable_all_extensions == "all":
@@ -195,43 +148,18 @@ def list_extensions():
    elif shared.opts.disable_all_extensions == "extra":
        print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
-    loaded_extensions = {}
+    extension_paths = []
-
+    for dirname in [extensions_dir, extensions_builtin_dir]:
    # scan through extensions directory and load metadata
    for dirname in [extensions_builtin_dir, extensions_dir]:
        if not os.path.isdir(dirname):
-            continue
+            return
        for extension_dirname in sorted(os.listdir(dirname)):
            path = os.path.join(dirname, extension_dirname)
            if not os.path.isdir(path):
                continue
-            canonical_name = extension_dirname
+            extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
            metadata = ExtensionMetadata(path, canonical_name)
-            # check for duplicated canonical names
+    for dirname, path, is_builtin in extension_paths:
-            already_loaded_extension = loaded_extensions.get(metadata.canonical_name)
+        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
            if already_loaded_extension is not None:
                errors.report(f'Duplicate canonical name "{canonical_name}" found in extensions "{extension_dirname}" and "{already_loaded_extension.name}". Former will be discarded.', exc_info=False)
                continue
            is_builtin = dirname == extensions_builtin_dir
            extension = Extension(name=extension_dirname, path=path, enabled=extension_dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin, metadata=metadata)
        extensions.append(extension)
            loaded_extensions[canonical_name] = extension
    # check for requirements
    for extension in extensions:
        for req in extension.metadata.requires:
            required_extension = loaded_extensions.get(req)
            if required_extension is None:
                errors.report(f'Extension "{extension.name}" requires "{req}" which is not installed.', exc_info=False)
                continue
            if not extension.enabled:
                errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False)
                continue
 extensions: list[Extension] = []
@@ -1,5 +1,3 @@
 from inspect import signature
 from functools import wraps
 import gradio as gr
 from modules import scripts, ui_tempdir, patches
@@ -66,77 +64,10 @@ def Blocks_get_config_file(self, *args, **kwargs):
    return config
-def gradio_component_compatibility_layer(component_function):
+original_IOComponent_init = patches.patch(__name__, obj=gr.components.IOComponent, field="__init__", replacement=IOComponent_init)
    @wraps(component_function)
    def patched_function(*args, **kwargs):
        original_signature = signature(component_function).parameters
        valid_kwargs = {k: v for k, v in kwargs.items() if k in original_signature}
        result = component_function(*args, **valid_kwargs)
        return result
    return patched_function
 sub_events = ['then', 'success']
 def gradio_component_events_compatibility_layer(component_function):
    @wraps(component_function)
    def patched_function(*args, **kwargs):
        kwargs['js'] = kwargs.get('js', kwargs.pop('_js', None))
        original_signature = signature(component_function).parameters
        valid_kwargs = {k: v for k, v in kwargs.items() if k in original_signature}
        result = component_function(*args, **valid_kwargs)
        for sub_event in sub_events:
            component_event_then_function = getattr(result, sub_event, None)
            if component_event_then_function:
                patched_component_event_then_function = gradio_component_sub_events_compatibility_layer(component_event_then_function)
                setattr(result, sub_event, patched_component_event_then_function)
        # original_component_event_then_function = patches.patch(f'{__name__}.', obj=result, field='then', replacement=patched_component_event_then_function)
        return result
    return patched_function
 def gradio_component_sub_events_compatibility_layer(component_function):
    @wraps(component_function)
    def patched_function(*args, **kwargs):
        kwargs['js'] = kwargs.get('js', kwargs.pop('_js', None))
        original_signature = signature(component_function).parameters
        valid_kwargs = {k: v for k, v in kwargs.items() if k in original_signature}
        result = component_function(*args, **valid_kwargs)
        return result
    return patched_function
 for component_name in set(gr.components.__all__ + gr.layouts.__all__):
    try:
        component = getattr(gr, component_name)
        component_init = getattr(component, '__init__')
        patched_component_init = gradio_component_compatibility_layer(component_init)
        original_IOComponent_init = patches.patch(f'{__name__}.{component_name}', obj=component, field="__init__", replacement=patched_component_init)
        component_events = set(getattr(component, 'EVENTS'))
        for component_event in component_events:
            component_event_function = getattr(component, component_event)
            patched_component_event_function = gradio_component_events_compatibility_layer(component_event_function)
            original_component_event_function = patches.patch(f'{__name__}.{component_name}.{component_event}', obj=component, field=component_event, replacement=patched_component_event_function)
    except Exception as e:
        print(e)
        pass
 gr.Box = gr.Group
 original_IOComponent_init = patches.patch(__name__, obj=gr.components.base.Component, field="__init__", replacement=IOComponent_init)
 original_Block_get_config = patches.patch(__name__, obj=gr.blocks.Block, field="get_config", replacement=Block_get_config)
 original_BlockContext_init = patches.patch(__name__, obj=gr.blocks.BlockContext, field="__init__", replacement=BlockContext_init)
 original_Blocks_get_config_file = patches.patch(__name__, obj=gr.blocks.Blocks, field="get_config_file", replacement=Blocks_get_config_file)
 ui_tempdir.install_ui_tempdir_override()
@@ -44,8 +44,6 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
    steps = p.steps
    override_settings = p.override_settings
    sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
    batch_results = None
    discard_further_results = False
    for i, image in enumerate(images):
        state.job = f"{i+1} out of {len(images)}"
        if state.skipped:
@@ -129,21 +127,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
        if proc is None:
            p.override_settings.pop('save_images_replace_action', None)
-            proc = process_images(p)
+            process_images(p)
        if not discard_further_results and proc:
            if batch_results:
                batch_results.images.extend(proc.images)
                batch_results.infotexts.extend(proc.infotexts)
            else:
                batch_results = proc
            if 0 <= shared.opts.img2img_batch_show_results_limit < len(batch_results.images):
                discard_further_results = True
                batch_results.images = batch_results.images[:int(shared.opts.img2img_batch_show_results_limit)]
                batch_results.infotexts = batch_results.infotexts[:int(shared.opts.img2img_batch_show_results_limit)]
    return batch_results
 def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
@@ -228,9 +212,9 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
    with closing(p):
        if is_batch:
            assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
            processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
-            if processed is None:
+            process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
            processed = Processed(p, [], p.seed, "")
        else:
            processed = modules.scripts.scripts_img2img.run(p, *args)
@@ -441,7 +441,7 @@ def dump_sysinfo():
    import datetime
    text = sysinfo.get()
-    filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
+    filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
    with open(filename, "w", encoding="utf8") as file:
        file.write(text)
@@ -1,41 +1,16 @@
 import os
 import logging
 try:
    from tqdm.auto import tqdm
    class TqdmLoggingHandler(logging.Handler):
        def __init__(self, level=logging.INFO):
            super().__init__(level)
        def emit(self, record):
            try:
                msg = self.format(record)
                tqdm.write(msg)
                self.flush()
            except Exception:
                self.handleError(record)
    TQDM_IMPORTED = True
 except ImportError:
    # tqdm does not exist before first launch
    # I will import once the UI finishes seting up the enviroment and reloads.
    TQDM_IMPORTED = False
 def setup_logging(loglevel):
    if loglevel is None:
        loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
    loghandlers = []
    if TQDM_IMPORTED:
        loghandlers.append(TqdmLoggingHandler())
    if loglevel:
        log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
        logging.basicConfig(
            level=log_level,
            format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S',
            handlers=loghandlers
        )
@@ -1,6 +1,5 @@
 import json
 import sys
 from dataclasses import dataclass
 import gradio as gr
@@ -9,14 +8,13 @@ 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, category_id=None):
+    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):
        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
@@ -65,11 +63,7 @@ class OptionHTML(OptionInfo):
 def options_section(section_identifier, options_dict):
    for v in options_dict.values():
        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
@@ -82,7 +76,7 @@ class Options:
    def __init__(self, data_labels: dict[str, OptionInfo], restricted_opts):
        self.data_labels = data_labels
-        self.data = {k: v.default for k, v in self.data_labels.items() if not v.do_not_save}
+        self.data = {k: v.default for k, v in self.data_labels.items()}
        self.restricted_opts = restricted_opts
    def __setattr__(self, key, value):
@@ -164,7 +158,7 @@ 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, ensure_ascii=False)
+            json.dump(self.data, file, indent=4)
    def same_type(self, x, y):
        if x is None or y is None:
@@ -212,59 +206,23 @@ 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):
        self.data_labels[key] = info
-        if key not in self.data and not info.do_not_save:
+        if key not in self.data:
            self.data[key] = info.default
    def reorder(self):
-        """Reorder settings so that:
+        """reorder settings so that all items related to section always go together"""
            - 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_categories:
+            if item.section not in section_ids:
-                section_categories[item.section] = item.category_id
+                section_ids[item.section] = len(section_ids)
-        for _, item in settings_items:
+        self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
            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)
        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
@@ -287,22 +245,3 @@ 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()
@@ -78,7 +78,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
        image_data.close()
    devices.torch_gc()
-    shared.state.end()
+
    return outputs, ui_common.plaintext_to_html(infotext), ''
@@ -296,7 +296,7 @@ class StableDiffusionProcessing:
        return conditioning
    def edit_image_conditioning(self, source_image):
-        conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
+        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
        return conditioning_image
@@ -799,6 +799,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
    infotexts = []
    output_images = []
    with torch.no_grad(), p.sd_model.ema_scope():
        with devices.autocast():
            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@@ -872,6 +873,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
            else:
                if opts.sd_vae_decode_method != 'Full':
                    p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
                x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
            x_samples_ddim = torch.stack(x_samples_ddim).float()
@@ -1145,7 +1147,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
        if not self.enable_hr:
            return samples
        devices.torch_gc()
        if self.latent_scale_mode is None:
            decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
@@ -1155,6 +1156,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
        with sd_models.SkipWritingToConfig():
            sd_models.reload_model_weights(info=self.hr_checkpoint_info)
        devices.torch_gc()
        return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
    def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@@ -1162,6 +1165,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
            return samples
        self.is_hr_pass = True
        target_width = self.hr_upscale_to_x
        target_height = self.hr_upscale_to_y
@@ -1250,6 +1254,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
        decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
        self.is_hr_pass = False
        return decoded_samples
    def close(self):
@@ -110,7 +110,7 @@ class ImageRNG:
        self.is_first = True
    def first(self):
-        noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], int(self.seed_resize_from_h) // 8, int(self.seed_resize_from_w // 8))
+        noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], self.seed_resize_from_h // 8, self.seed_resize_from_w // 8)
        xs = []
@@ -311,113 +311,20 @@ scripts_data = []
 postprocessing_scripts_data = []
 ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
 def topological_sort(dependencies):
    """Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
    Ignores errors relating to missing dependeencies or circular dependencies
    """
    visited = {}
    result = []
    def inner(name):
        visited[name] = True
        for dep in dependencies.get(name, []):
            if dep in dependencies and dep not in visited:
                inner(dep)
        result.append(name)
    for depname in dependencies:
        if depname not in visited:
            inner(depname)
    return result
@dataclass
 class ScriptWithDependencies:
    script_canonical_name: str
    file: ScriptFile
    requires: list
    load_before: list
    load_after: list
 def list_scripts(scriptdirname, extension, *, include_extensions=True):
-    scripts = {}
+    scripts_list = []
-    loaded_extensions = {ext.canonical_name: ext for ext in extensions.active()}
+    basedir = os.path.join(paths.script_path, scriptdirname)
-    loaded_extensions_scripts = {ext.canonical_name: [] for ext in extensions.active()}
+    if os.path.exists(basedir):
-
+        for filename in sorted(os.listdir(basedir)):
-    # build script dependency map
+            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
    root_script_basedir = os.path.join(paths.script_path, scriptdirname)
    if os.path.exists(root_script_basedir):
        for filename in sorted(os.listdir(root_script_basedir)):
            if not os.path.isfile(os.path.join(root_script_basedir, filename)):
                continue
            if os.path.splitext(filename)[1].lower() != extension:
                continue
            script_file = ScriptFile(paths.script_path, filename, os.path.join(root_script_basedir, filename))
            scripts[filename] = ScriptWithDependencies(filename, script_file, [], [], [])
    if include_extensions:
        for ext in extensions.active():
-            extension_scripts_list = ext.list_files(scriptdirname, extension)
+            scripts_list += ext.list_files(scriptdirname, extension)
            for extension_script in extension_scripts_list:
                if not os.path.isfile(extension_script.path):
                    continue
-                script_canonical_name = ("builtin/" if ext.is_builtin else "") + ext.canonical_name + "/" + extension_script.filename
+    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
                relative_path = scriptdirname + "/" + extension_script.filename
                script = ScriptWithDependencies(
                    script_canonical_name=script_canonical_name,
                    file=extension_script,
                    requires=ext.metadata.get_script_requirements("Requires", relative_path, scriptdirname),
                    load_before=ext.metadata.get_script_requirements("Before", relative_path, scriptdirname),
                    load_after=ext.metadata.get_script_requirements("After", relative_path, scriptdirname),
                )
                scripts[script_canonical_name] = script
                loaded_extensions_scripts[ext.canonical_name].append(script)
    for script_canonical_name, script in scripts.items():
        # load before requires inverse dependency
        # in this case, append the script name into the load_after list of the specified script
        for load_before in script.load_before:
            # if this requires an individual script to be loaded before
            other_script = scripts.get(load_before)
            if other_script:
                other_script.load_after.append(script_canonical_name)
            # if this requires an extension
            other_extension_scripts = loaded_extensions_scripts.get(load_before)
            if other_extension_scripts:
                for other_script in other_extension_scripts:
                    other_script.load_after.append(script_canonical_name)
        # if After mentions an extension, remove it and instead add all of its scripts
        for load_after in list(script.load_after):
            if load_after not in scripts and load_after in loaded_extensions_scripts:
                script.load_after.remove(load_after)
                for other_script in loaded_extensions_scripts.get(load_after, []):
                    script.load_after.append(other_script.script_canonical_name)
    dependencies = {}
    for script_canonical_name, script in scripts.items():
        for required_script in script.requires:
            if required_script not in scripts and required_script not in loaded_extensions:
                errors.report(f'Script "{script_canonical_name}" requires "{required_script}" to be loaded, but it is not.', exc_info=False)
        dependencies[script_canonical_name] = script.load_after
    ordered_scripts = topological_sort(dependencies)
    scripts_list = [scripts[script_canonical_name].file for script_canonical_name in ordered_scripts]
    return scripts_list
@@ -458,9 +365,15 @@ def load_scripts():
            elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
                postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
-    # here the scripts_list is already ordered
+    def orderby(basedir):
-    # processing_script is not considered though
+        # 1st webui, 2nd extensions-builtin, 3rd extensions
-    for scriptfile in scripts_list:
+        priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
        for key in priority:
            if basedir.startswith(key):
                return priority[key]
        return 9999
    for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
        try:
            if scriptfile.basedir != paths.script_path:
                sys.path = [scriptfile.basedir] + sys.path
@@ -3,7 +3,7 @@ from collections import namedtuple
 import numpy as np
 import torch
 from PIL import Image
-from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
+from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, sd_vae_consistency, shared, sd_models
 from modules.shared import opts, state
 import k_diffusion.sampling
@@ -31,7 +31,7 @@ def setup_img2img_steps(p, steps=None):
    return steps, t_enc
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3, "Consistency Decoder": 4}
 def samples_to_images_tensor(sample, approximation=None, model=None):
@@ -51,6 +51,13 @@ def samples_to_images_tensor(sample, approximation=None, model=None):
    elif approximation == 3:
        x_sample = sd_vae_taesd.decoder_model()(sample.to(devices.device, devices.dtype)).detach()
        x_sample = x_sample * 2 - 1
    elif approximation == 4:
        with devices.autocast(), torch.no_grad():
            x_sample = sd_vae_consistency.decoder_model()(
                sample.detach().to(devices.device, devices.dtype)/0.18215,
                schedule=[float(i.strip()) for i in shared.opts.sd_vae_consistency_schedule.split(',')],
            )
        sd_vae_consistency.unload()
    else:
        if model is None:
            model = shared.sd_model
@@ -60,7 +60,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
                while restart_times > 0:
                    restart_times -= 1
-                    step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))
+                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
    last_sigma = None
    for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
@@ -0,0 +1,34 @@
 """
 Consistency Decoder
 Improved decoding for stable diffusion vaes.
 https://github.com/openai/consistencydecoder
 """
 import os
 from modules import devices, paths_internal, shared
 from consistencydecoder import ConsistencyDecoder
 sd_vae_consistency_models = None
 model_path = os.path.join(paths_internal.models_path, 'consistencydecoder')
 def decoder_model():
    global sd_vae_consistency_models
    if getattr(shared.sd_model, 'is_sdxl', False):
        raise NotImplementedError("SDXL is not supported for consistency decoder")
    if sd_vae_consistency_models is not None:
        sd_vae_consistency_models.ckpt.to(devices.device)
        return sd_vae_consistency_models
    loaded_model = ConsistencyDecoder(devices.device, model_path)
    sd_vae_consistency_models = loaded_model
    return loaded_model
 def unload():
    global sd_vae_consistency_models
    if sd_vae_consistency_models is not None:
        devices.torch_gc()
        sd_vae_consistency_models.ckpt.to('cpu')
@@ -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, categories
+from modules.options import options_section, OptionInfo, OptionHTML
 options_templates = {}
 hide_dirs = shared.hide_dirs
@@ -21,14 +21,7 @@ restricted_opts = {
    "outdir_init_images"
 }
-categories.register_category("saving", "Saving images")
+options_templates.update(options_section(('saving-images', "Saving images/grids"), {
 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"),
@@ -71,10 +64,9 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
    "save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
    "notification_audio": OptionInfo(True, "Play notification sound after image generation").info("notification.mp3 should be present in the root directory").needs_reload_ui(),
    "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", "saving"), {
+options_templates.update(options_section(('saving-paths', "Paths for 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),
@@ -86,7 +78,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving", "s
    "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", "saving"), {
+options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
    "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"),
@@ -94,21 +86,21 @@ 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", "postprocessing"), {
+options_templates.update(options_section(('upscaling', "Upscaling"), {
    "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", "postprocessing"), {
+options_templates.update(options_section(('face-restoration', "Face restoration"), {
    "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", "system"), {
+options_templates.update(options_section(('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(),
@@ -123,13 +115,13 @@ options_templates.update(options_section(('system', "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", "system"), {
+options_templates.update(options_section(('API', "API"), {
    "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", "training"), {
+options_templates.update(options_section(('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."),
@@ -144,7 +136,7 @@ options_templates.update(options_section(('training', "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", "sd"), {
+options_templates.update(options_section(('sd', "Stable Diffusion"), {
    "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"),
@@ -161,14 +153,14 @@ options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
    "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", "sd"), {
+options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
    "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", "sd"), {
+options_templates.update(options_section(('vae', "VAE"), {
    "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
@@ -180,10 +172,11 @@ For img2img, VAE is used to process user's input image before the sampling, and
    "sd_vae_overrides_per_model_preferences": OptionInfo(True, "Selected VAE overrides per-model preferences").info("you can set per-model VAE either by editing user metadata for checkpoints, or by making the VAE have same name as checkpoint"),
    "auto_vae_precision": OptionInfo(True, "Automatically revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
    "sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Encoder').info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
-    "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"),
+    "sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD", "Consistency Decoder"]}, infotext='VAE Decoder').info("method to decode latent to image"),
    "sd_vae_consistency_schedule": OptionInfo("1.0, 0.5", "consistency schedule").info("sampling schedule for consistency decoder."),
 }))
-options_templates.update(options_section(('img2img', "img2img", "sd"), {
+options_templates.update(options_section(('img2img', "img2img"), {
    "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"),
@@ -196,10 +189,9 @@ options_templates.update(options_section(('img2img', "img2img", "sd"), {
    "img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_reload_ui(),
    "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
    "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
    "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", "sd"), {
+options_templates.update(options_section(('optimizations', "Optimizations"), {
    "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"),
@@ -210,7 +202,7 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd"
    "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
 }))
-options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {
+options_templates.update(options_section(('compatibility', "Compatibility"), {
    "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."),
@@ -235,7 +227,7 @@ 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", "sd"), {
+options_templates.update(options_section(('extra_networks', "Extra Networks"), {
    "extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
    "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}),
@@ -243,7 +235,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks", "s
    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
    "extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
    "extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
-    "extra_networks_card_order_field": OptionInfo("Path", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Path', 'Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
+    "extra_networks_card_order_field": OptionInfo("Name", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
    "extra_networks_card_order": OptionInfo("Ascending", "Default order for Extra Networks cards", gr.Dropdown, {"choices": ['Ascending', 'Descending']}).needs_reload_ui(),
    "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
@@ -252,7 +244,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks", "s
    "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", "ui"), {
+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"),
@@ -281,13 +273,11 @@ options_templates.update(options_section(('ui', "User interface", "ui"), {
    "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(('infotext', "Infotext", "ui"), {
+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"),
@@ -302,7 +292,7 @@ options_templates.update(options_section(('infotext', "Infotext", "ui"), {
 }))
-options_templates.update(options_section(('ui', "Live previews", "ui"), {
+options_templates.update(options_section(('ui', "Live previews"), {
    "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"]}),
@@ -315,7 +305,7 @@ options_templates.update(options_section(('ui', "Live previews", "ui"), {
    "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", "sd"), {
+options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
    "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"),
@@ -337,7 +327,7 @@ 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", "postprocessing"), {
+options_templates.update(options_section(('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}),
@@ -1,6 +1,7 @@
 import json
 import os
 import sys
 import traceback
 import platform
 import hashlib
@@ -83,7 +84,7 @@ def get_dict():
        "Checksum": checksum_token,
        "Commandline": get_argv(),
        "Torch env info": get_torch_sysinfo(),
-        "Exceptions": errors.get_exceptions(),
+        "Exceptions": get_exceptions(),
        "CPU": {
            "model": platform.processor(),
            "count logical": psutil.cpu_count(logical=True),
@@ -103,6 +104,21 @@ def get_dict():
    return res
 def format_traceback(tb):
    return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
 def format_exception(e, tb):
    return {"exception": str(e), "traceback": format_traceback(tb)}
 def get_exceptions():
    try:
        return list(reversed(errors.exception_records))
    except Exception as e:
        return str(e)
 def get_environment():
    return {k: os.environ[k] for k in sorted(os.environ) if k in environment_whitelist}
@@ -4,7 +4,6 @@ import os
 import sys
 from functools import reduce
 import warnings
 from contextlib import ExitStack
 import gradio as gr
 import gradio.utils
@@ -32,8 +31,8 @@ from modules.generation_parameters_copypaste import image_from_url_text
 create_setting_component = ui_settings.create_setting_component
-# warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
-# warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning)
+warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning)
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -271,11 +270,7 @@ def create_ui():
        extra_tabs.__enter__()
        with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
-            with ExitStack() as stack:
+            with gr.Column(variant='compact', elem_id="txt2img_settings"):
                if shared.opts.txt2img_settings_accordion:
                    stack.enter_context(gr.Accordion("Open for Settings", open=False))
                stack.enter_context(gr.Column(variant='compact', elem_id="txt2img_settings"))
                scripts.scripts_txt2img.prepare_ui()
                for category in ordered_ui_categories():
@@ -494,11 +489,7 @@ def create_ui():
        extra_tabs.__enter__()
        with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
-            with ExitStack() as stack:
+            with gr.Column(variant='compact', elem_id="img2img_settings"):
                if shared.opts.img2img_settings_accordion:
                    stack.enter_context(gr.Accordion("Open for Settings", open=False))
                stack.enter_context(gr.Column(variant='compact', elem_id="img2img_settings"))
                copy_image_buttons = []
                copy_image_destinations = {}
@@ -635,6 +626,12 @@ def create_ui():
                                    scale_by.release(**on_change_args)
                                    button_update_resize_to.click(**on_change_args)
                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
                                    for component in [init_img, sketch]:
                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
                            tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
                            tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
@@ -695,12 +692,6 @@ def create_ui():
                    if category not in {"accordions"}:
                        scripts.scripts_img2img.setup_ui_for_section(category)
            # the code below is meant to update the resolution label after the image in the image selection UI has changed.
            # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
            # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
            for component in [init_img, sketch]:
                component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
            def select_img2img_tab(tab):
                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
@@ -1308,7 +1299,7 @@ def setup_ui_api(app):
        from fastapi.responses import PlainTextResponse
        text = sysinfo.get()
-        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
+        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
        return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
@@ -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, ensure_ascii=False)
+        json.dump(current_config_state, f, indent=4)
    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())
@@ -279,7 +279,6 @@ class ExtraNetworksPage:
            "date_created": int(stat.st_ctime or 0),
            "date_modified": int(stat.st_mtime or 0),
            "name": pth.name.lower(),
            "path": str(pth.parent).lower(),
        }
    def find_preview(self, path):
@@ -370,9 +369,6 @@ 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)
@@ -386,7 +382,7 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
            related_tabs.append(tab)
    edit_search = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", elem_classes="search", placeholder="Search...", visible=False, interactive=True)
-    dropdown_sort = gr.Dropdown(choices=['Path', 'Name', 'Date Created', 'Date Modified', ], value=shared.opts.extra_networks_card_order_field, elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
+    dropdown_sort = gr.Dropdown(choices=['Name', 'Date Created', 'Date Modified', ], value=shared.opts.extra_networks_card_order_field, elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
    button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes=["sortorder"] + ([] if shared.opts.extra_networks_card_order == "Ascending" else ["sortReverse"]), visible=False, tooltip="Invert sort order")
    button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh", visible=False)
    checkbox_show_dirs = gr.Checkbox(True, label='Show dirs', elem_id=tabname+"_extra_show_dirs", elem_classes="show-dirs", visible=False)
@@ -403,7 +399,7 @@ 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}_prompts" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
+        jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}" + '", ' + 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)
@@ -17,9 +17,6 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
    def create_item(self, name, index=None, enable_filter=True):
        checkpoint: sd_models.CheckpointInfo = sd_models.checkpoint_aliases.get(name)
        if checkpoint is None:
            return
        path, ext = os.path.splitext(checkpoint.filename)
        return {
            "name": checkpoint.name_for_extra,
@@ -35,12 +32,9 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
        }
    def list_items(self):
        # instantiate a list to protect against concurrent modification
        names = list(sd_models.checkpoints_list)
        for index, name in enumerate(names):
-            item = self.create_item(name, index)
+            yield self.create_item(name, index)
            if item is not None:
                yield item
    def allowed_directories_for_previews(self):
        return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]
@@ -13,10 +13,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
        shared.reload_hypernetworks()
    def create_item(self, name, index=None, enable_filter=True):
-        full_path = shared.hypernetworks.get(name)
+        full_path = shared.hypernetworks[name]
        if full_path is None:
            return
        path, ext = os.path.splitext(full_path)
        sha256 = sha256_from_cache(full_path, f'hypernet/{name}')
        shorthash = sha256[0:10] if sha256 else None
@@ -34,12 +31,8 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
        }
    def list_items(self):
-        # instantiate a list to protect against concurrent modification
+        for index, name in enumerate(shared.hypernetworks):
-        names = list(shared.hypernetworks)
+            yield self.create_item(name, index)
        for index, name in enumerate(names):
            item = self.create_item(name, index)
            if item is not None:
                yield item
    def allowed_directories_for_previews(self):
        return [shared.cmd_opts.hypernetwork_dir]
@@ -14,8 +14,6 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
    def create_item(self, name, index=None, enable_filter=True):
        embedding = sd_hijack.model_hijack.embedding_db.word_embeddings.get(name)
        if embedding is None:
            return
        path, ext = os.path.splitext(embedding.filename)
        return {
@@ -31,12 +29,8 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
        }
    def list_items(self):
-        # instantiate a list to protect against concurrent modification
+        for index, name in enumerate(sd_hijack.model_hijack.embedding_db.word_embeddings):
-        names = list(sd_hijack.model_hijack.embedding_db.word_embeddings)
+            yield self.create_item(name, index)
        for index, name in enumerate(names):
            item = self.create_item(name, index)
            if item is not None:
                yield item
    def allowed_directories_for_previews(self):
        return list(sd_hijack.model_hijack.embedding_db.embedding_dirs)
@@ -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, ensure_ascii=False)
+            json.dump(metadata, file, indent=4)
    def save_user_metadata(self, name, desc, notes):
        user_metadata = self.get_user_metadata(name)
@@ -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, ensure_ascii=False)
+            json.dump(current_ui_settings, file, indent=4)
    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"""
@@ -68,10 +68,10 @@ class UiPromptStyles:
                self.copy = ui_components.ToolButton(value=styles_copy_symbol, elem_id=f"{tabname}_style_copy", tooltip="Copy main UI prompt to style.")
            with gr.Row():
-                self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3, elem_classes=["prompt"])
+                self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
            with gr.Row():
-                self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3, elem_classes=["prompt"])
+                self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3)
            with gr.Row():
                self.save = gr.Button('Save', variant='primary', elem_id=f'{tabname}_edit_style_save', visible=False)
@@ -61,7 +61,7 @@ def save_pil_to_file(self, pil_image, dir=None, format="png"):
 def install_ui_tempdir_override():
    """override save to file function so that it also writes PNG info"""
-    # gradio.components.IOComponent.pil_to_temp_file = save_pil_to_file
+    gradio.components.IOComponent.pil_to_temp_file = save_pil_to_file
 def on_tmpdir_changed():
@@ -16,7 +16,6 @@ exclude = [
 ignore = [
 	"E501", # Line too long
 	"E721", # Do not compare types, use `isinstance`
 	"E731", # Do not assign a `lambda` expression, use a `def`
 	"I001", # Import block is un-sorted or un-formatted
@@ -8,7 +8,7 @@ clean-fid
 einops
 fastapi>=0.90.1
 gfpgan
-gradio==4.7.1
+gradio==3.41.2
 inflection
 jsonmerge
 kornia
@@ -32,3 +32,5 @@ torch
 torchdiffeq
 torchsde
 transformers==4.30.2
 git+https://github.com/openai/consistencydecoder.git
@@ -5,9 +5,9 @@ basicsr==1.4.2
 blendmodes==2022
 clean-fid==0.1.35
 einops==0.4.1
-fastapi==0.104.1
+fastapi==0.94.0
 gfpgan==1.3.8
-gradio==4.7.1
+gradio==3.41.2
 httpcore==0.15
 inflection==0.5.1
 jsonmerge==1.8.0
@@ -30,3 +30,4 @@ torchdiffeq==0.2.3
 torchsde==0.2.6
 transformers==4.30.2
 httpx==0.24.1
 git+https://github.com/openai/consistencydecoder.git
@@ -133,18 +133,9 @@ document.addEventListener('keydown', function(e) {
    if (isEnter && isModifierKey) {
        if (interruptButton.style.display === 'block') {
            interruptButton.click();
-            const callback = (mutationList) => {
+            setTimeout(function() {
                for (const mutation of mutationList) {
                    if (mutation.type === 'attributes' && mutation.attributeName === 'style') {
                        if (interruptButton.style.display === 'none') {
                generateButton.click();
-                            observer.disconnect();
+            }, 500);
                        }
                    }
                }
            };
            const observer = new MutationObserver(callback);
            observer.observe(interruptButton, {attributes: true});
        } else {
            generateButton.click();
        }
@@ -462,15 +462,6 @@ 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;
@@ -849,16 +840,8 @@ footer {
 /* extra networks UI */
-.extra-page > div.gap{
+.extra-page .prompt{
-    gap: 0;
+    margin: 0 0 0.5em 0;
 }
 .extra-page-prompts{
    margin-bottom: 0;
 }
 .extra-page-prompts.extra-page-prompts-active{
    margin-bottom: 1em;
 }
 .extra-network-cards{
@@ -89,7 +89,7 @@ delimiter="################################################################"
 printf "\n%s\n" "${delimiter}"
 printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n"
-printf "\e[1m\e[34mTested on Debian 11 (Bullseye), Fedora 34+ and openSUSE Leap 15.4 or newer.\e[0m"
+printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m"
 printf "\n%s\n" "${delimiter}"
 # Do not run as root
@@ -223,7 +223,7 @@ fi
 # Try using TCMalloc on Linux
 prepare_tcmalloc() {
    if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
-        TCMALLOC="$(PATH=/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)"
+        TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)"
        if [[ ! -z "${TCMALLOC}" ]]; then
            echo "Using TCMalloc: ${TCMALLOC}"
            export LD_PRELOAD="${TCMALLOC}"
Author	SHA1	Message	Date
Kohaku-Blueleaf	a36a30fb93	add gc after using consistency dec	2023-11-07 13:01:10 +08:00
Kohaku-Blueleaf	2ea8726597	custom schedule	2023-11-07 12:35:56 +08:00
Kohaku-Blueleaf	5dbd0355b0	Fix linting	2023-11-07 11:00:24 +08:00
Kohaku-Blueleaf	64fd916334	Add consistency decoder	2023-11-07 10:52:29 +08:00