")
+
+ if activation_text:
+ item["prompt"] += " + " + quote_js(" " + activation_text)
+
+ sd_version = item["user_metadata"].get("sd version")
+ if sd_version in network.SdVersion.__members__:
+ item["sd_version"] = sd_version
+ sd_version = network.SdVersion[sd_version]
+ else:
+ sd_version = lora_on_disk.sd_version
+
+ if shared.opts.lora_show_all or not enable_filter:
+ pass
+ elif sd_version == network.SdVersion.Unknown:
+ model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
+ if model_version.name in shared.opts.lora_hide_unknown_for_versions:
+ return None
+ elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL:
+ return None
+ elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2:
+ return None
+ elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1:
+ return None
+
+ return item
+
+ def list_items(self):
+ for index, name in enumerate(networks.available_networks):
+ item = self.create_item(name, index)
+
+ if item is not None:
+ yield item
+
+ def allowed_directories_for_previews(self):
+ return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat]
+
+ def create_user_metadata_editor(self, ui, tabname):
+ return LoraUserMetadataEditor(ui, tabname, self)
diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/__pycache__/preload.cpython-39.pyc b/stable-diffusion-webui/extensions-builtin/ScuNET/__pycache__/preload.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fde0ad9d5d4d77b0a83f4dc8d27fc528d2c56883
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diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/__pycache__/scunet_model_arch.cpython-39.pyc b/stable-diffusion-webui/extensions-builtin/ScuNET/__pycache__/scunet_model_arch.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7ba11fc1723cc9865e1fb7fdd2f16a7d6e73fba6
Binary files /dev/null and b/stable-diffusion-webui/extensions-builtin/ScuNET/__pycache__/scunet_model_arch.cpython-39.pyc differ
diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py b/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ce82b1d4349b24192b1915d022ed4fda9f31e5c
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py
@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+ parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET'))
diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/scripts/__pycache__/scunet_model.cpython-39.pyc b/stable-diffusion-webui/extensions-builtin/ScuNET/scripts/__pycache__/scunet_model.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a36de8afd3e7770a06dc1109607b58a65a3b2139
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diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/scripts/scunet_model.py b/stable-diffusion-webui/extensions-builtin/ScuNET/scripts/scunet_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..167d2f64b8e8ef1c506d89026e5d2ac8687d8098
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -0,0 +1,144 @@
+import sys
+
+import PIL.Image
+import numpy as np
+import torch
+from tqdm import tqdm
+
+import modules.upscaler
+from modules import devices, modelloader, script_callbacks, errors
+from scunet_model_arch import SCUNet
+
+from modules.modelloader import load_file_from_url
+from modules.shared import opts
+
+
+class UpscalerScuNET(modules.upscaler.Upscaler):
+ def __init__(self, dirname):
+ self.name = "ScuNET"
+ self.model_name = "ScuNET GAN"
+ self.model_name2 = "ScuNET PSNR"
+ self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
+ self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
+ self.user_path = dirname
+ super().__init__()
+ model_paths = self.find_models(ext_filter=[".pth"])
+ scalers = []
+ add_model2 = True
+ for file in model_paths:
+ if file.startswith("http"):
+ name = self.model_name
+ else:
+ name = modelloader.friendly_name(file)
+ if name == self.model_name2 or file == self.model_url2:
+ add_model2 = False
+ try:
+ scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+ scalers.append(scaler_data)
+ except Exception:
+ errors.report(f"Error loading ScuNET model: {file}", exc_info=True)
+ if add_model2:
+ scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
+ scalers.append(scaler_data2)
+ self.scalers = scalers
+
+ @staticmethod
+ @torch.no_grad()
+ def tiled_inference(img, model):
+ # test the image tile by tile
+ h, w = img.shape[2:]
+ tile = opts.SCUNET_tile
+ tile_overlap = opts.SCUNET_tile_overlap
+ if tile == 0:
+ return model(img)
+
+ device = devices.get_device_for('scunet')
+ assert tile % 8 == 0, "tile size should be a multiple of window_size"
+ sf = 1
+
+ stride = tile - tile_overlap
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+ E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device)
+
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
+ for h_idx in h_idx_list:
+
+ for w_idx in w_idx_list:
+
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+
+ out_patch = model(in_patch)
+ out_patch_mask = torch.ones_like(out_patch)
+
+ E[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch)
+ W[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch_mask)
+ pbar.update(1)
+ output = E.div_(W)
+
+ return output
+
+ def do_upscale(self, img: PIL.Image.Image, selected_file):
+
+ devices.torch_gc()
+
+ try:
+ model = self.load_model(selected_file)
+ except Exception as e:
+ print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
+ return img
+
+ device = devices.get_device_for('scunet')
+ tile = opts.SCUNET_tile
+ h, w = img.height, img.width
+ np_img = np.array(img)
+ np_img = np_img[:, :, ::-1] # RGB to BGR
+ np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
+ torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
+
+ if tile > h or tile > w:
+ _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
+ _img[:, :, :h, :w] = torch_img # pad image
+ torch_img = _img
+
+ torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+ torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
+ np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
+ del torch_img, torch_output
+ devices.torch_gc()
+
+ output = np_output.transpose((1, 2, 0)) # CHW to HWC
+ output = output[:, :, ::-1] # BGR to RGB
+ return PIL.Image.fromarray((output * 255).astype(np.uint8))
+
+ def load_model(self, path: str):
+ device = devices.get_device_for('scunet')
+ if path.startswith("http"):
+ # TODO: this doesn't use `path` at all?
+ filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
+ else:
+ filename = path
+ model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+ model.load_state_dict(torch.load(filename), strict=True)
+ model.eval()
+ for _, v in model.named_parameters():
+ v.requires_grad = False
+ model = model.to(device)
+
+ return model
+
+
+def on_ui_settings():
+ import gradio as gr
+ from modules import shared
+
+ shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
+ shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/scunet_model_arch.py b/stable-diffusion-webui/extensions-builtin/ScuNET/scunet_model_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..b51a880629baa492ffcbebe682bcf101f06699a6
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/ScuNET/scunet_model_arch.py
@@ -0,0 +1,268 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+from timm.models.layers import trunc_normal_, DropPath
+
+
+class WMSA(nn.Module):
+ """ Self-attention module in Swin Transformer
+ """
+
+ def __init__(self, input_dim, output_dim, head_dim, window_size, type):
+ super(WMSA, self).__init__()
+ self.input_dim = input_dim
+ self.output_dim = output_dim
+ self.head_dim = head_dim
+ self.scale = self.head_dim ** -0.5
+ self.n_heads = input_dim // head_dim
+ self.window_size = window_size
+ self.type = type
+ self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
+
+ self.relative_position_params = nn.Parameter(
+ torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
+
+ self.linear = nn.Linear(self.input_dim, self.output_dim)
+
+ trunc_normal_(self.relative_position_params, std=.02)
+ self.relative_position_params = torch.nn.Parameter(
+ self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
+ 2).transpose(
+ 0, 1))
+
+ def generate_mask(self, h, w, p, shift):
+ """ generating the mask of SW-MSA
+ Args:
+ shift: shift parameters in CyclicShift.
+ Returns:
+ attn_mask: should be (1 1 w p p),
+ """
+ # supporting square.
+ attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
+ if self.type == 'W':
+ return attn_mask
+
+ s = p - shift
+ attn_mask[-1, :, :s, :, s:, :] = True
+ attn_mask[-1, :, s:, :, :s, :] = True
+ attn_mask[:, -1, :, :s, :, s:] = True
+ attn_mask[:, -1, :, s:, :, :s] = True
+ attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
+ return attn_mask
+
+ def forward(self, x):
+ """ Forward pass of Window Multi-head Self-attention module.
+ Args:
+ x: input tensor with shape of [b h w c];
+ attn_mask: attention mask, fill -inf where the value is True;
+ Returns:
+ output: tensor shape [b h w c]
+ """
+ if self.type != 'W':
+ x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+
+ x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
+ h_windows = x.size(1)
+ w_windows = x.size(2)
+ # square validation
+ # assert h_windows == w_windows
+
+ x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
+ qkv = self.embedding_layer(x)
+ q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
+ sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
+ # Adding learnable relative embedding
+ sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
+ # Using Attn Mask to distinguish different subwindows.
+ if self.type != 'W':
+ attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
+ sim = sim.masked_fill_(attn_mask, float("-inf"))
+
+ probs = nn.functional.softmax(sim, dim=-1)
+ output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
+ output = rearrange(output, 'h b w p c -> b w p (h c)')
+ output = self.linear(output)
+ output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
+
+ if self.type != 'W':
+ output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
+
+ return output
+
+ def relative_embedding(self):
+ cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
+ relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
+ # negative is allowed
+ return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
+
+
+class Block(nn.Module):
+ def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+ """ SwinTransformer Block
+ """
+ super(Block, self).__init__()
+ self.input_dim = input_dim
+ self.output_dim = output_dim
+ assert type in ['W', 'SW']
+ self.type = type
+ if input_resolution <= window_size:
+ self.type = 'W'
+
+ self.ln1 = nn.LayerNorm(input_dim)
+ self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.ln2 = nn.LayerNorm(input_dim)
+ self.mlp = nn.Sequential(
+ nn.Linear(input_dim, 4 * input_dim),
+ nn.GELU(),
+ nn.Linear(4 * input_dim, output_dim),
+ )
+
+ def forward(self, x):
+ x = x + self.drop_path(self.msa(self.ln1(x)))
+ x = x + self.drop_path(self.mlp(self.ln2(x)))
+ return x
+
+
+class ConvTransBlock(nn.Module):
+ def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+ """ SwinTransformer and Conv Block
+ """
+ super(ConvTransBlock, self).__init__()
+ self.conv_dim = conv_dim
+ self.trans_dim = trans_dim
+ self.head_dim = head_dim
+ self.window_size = window_size
+ self.drop_path = drop_path
+ self.type = type
+ self.input_resolution = input_resolution
+
+ assert self.type in ['W', 'SW']
+ if self.input_resolution <= self.window_size:
+ self.type = 'W'
+
+ self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
+ self.type, self.input_resolution)
+ self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+ self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+
+ self.conv_block = nn.Sequential(
+ nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
+ nn.ReLU(True),
+ nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
+ )
+
+ def forward(self, x):
+ conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
+ conv_x = self.conv_block(conv_x) + conv_x
+ trans_x = Rearrange('b c h w -> b h w c')(trans_x)
+ trans_x = self.trans_block(trans_x)
+ trans_x = Rearrange('b h w c -> b c h w')(trans_x)
+ res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
+ x = x + res
+
+ return x
+
+
+class SCUNet(nn.Module):
+ # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
+ def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
+ super(SCUNet, self).__init__()
+ if config is None:
+ config = [2, 2, 2, 2, 2, 2, 2]
+ self.config = config
+ self.dim = dim
+ self.head_dim = 32
+ self.window_size = 8
+
+ # drop path rate for each layer
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
+
+ self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
+
+ begin = 0
+ self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution)
+ for i in range(config[0])] + \
+ [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
+
+ begin += config[0]
+ self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 2)
+ for i in range(config[1])] + \
+ [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
+
+ begin += config[1]
+ self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 4)
+ for i in range(config[2])] + \
+ [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
+
+ begin += config[2]
+ self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 8)
+ for i in range(config[3])]
+
+ begin += config[3]
+ self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
+ [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 4)
+ for i in range(config[4])]
+
+ begin += config[4]
+ self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
+ [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 2)
+ for i in range(config[5])]
+
+ begin += config[5]
+ self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
+ [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution)
+ for i in range(config[6])]
+
+ self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
+
+ self.m_head = nn.Sequential(*self.m_head)
+ self.m_down1 = nn.Sequential(*self.m_down1)
+ self.m_down2 = nn.Sequential(*self.m_down2)
+ self.m_down3 = nn.Sequential(*self.m_down3)
+ self.m_body = nn.Sequential(*self.m_body)
+ self.m_up3 = nn.Sequential(*self.m_up3)
+ self.m_up2 = nn.Sequential(*self.m_up2)
+ self.m_up1 = nn.Sequential(*self.m_up1)
+ self.m_tail = nn.Sequential(*self.m_tail)
+ # self.apply(self._init_weights)
+
+ def forward(self, x0):
+
+ h, w = x0.size()[-2:]
+ paddingBottom = int(np.ceil(h / 64) * 64 - h)
+ paddingRight = int(np.ceil(w / 64) * 64 - w)
+ x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
+
+ x1 = self.m_head(x0)
+ x2 = self.m_down1(x1)
+ x3 = self.m_down2(x2)
+ x4 = self.m_down3(x3)
+ x = self.m_body(x4)
+ x = self.m_up3(x + x4)
+ x = self.m_up2(x + x3)
+ x = self.m_up1(x + x2)
+ x = self.m_tail(x + x1)
+
+ x = x[..., :h, :w]
+
+ return x
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/__pycache__/preload.cpython-39.pyc b/stable-diffusion-webui/extensions-builtin/SwinIR/__pycache__/preload.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..61bcdb729dd3b5a67514fee64048a3f7bbd7ba1b
Binary files /dev/null and b/stable-diffusion-webui/extensions-builtin/SwinIR/__pycache__/preload.cpython-39.pyc differ
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/__pycache__/swinir_model_arch.cpython-39.pyc b/stable-diffusion-webui/extensions-builtin/SwinIR/__pycache__/swinir_model_arch.cpython-39.pyc
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diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py b/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py
new file mode 100644
index 0000000000000000000000000000000000000000..e912c6402bc80faa797cf2e95183101fb9a10286
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py
@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+ parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR'))
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/scripts/__pycache__/swinir_model.cpython-39.pyc b/stable-diffusion-webui/extensions-builtin/SwinIR/scripts/__pycache__/swinir_model.cpython-39.pyc
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diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/scripts/swinir_model.py b/stable-diffusion-webui/extensions-builtin/SwinIR/scripts/swinir_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae0d0e6a8ea04f3054c1e8e5baefd2f76b57f246
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -0,0 +1,192 @@
+import sys
+import platform
+
+import numpy as np
+import torch
+from PIL import Image
+from tqdm import tqdm
+
+from modules import modelloader, devices, script_callbacks, shared
+from modules.shared import opts, state
+from swinir_model_arch import SwinIR
+from swinir_model_arch_v2 import Swin2SR
+from modules.upscaler import Upscaler, UpscalerData
+
+SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
+
+device_swinir = devices.get_device_for('swinir')
+
+
+class UpscalerSwinIR(Upscaler):
+ def __init__(self, dirname):
+ self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs
+ self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings
+ self.name = "SwinIR"
+ self.model_url = SWINIR_MODEL_URL
+ self.model_name = "SwinIR 4x"
+ self.user_path = dirname
+ super().__init__()
+ scalers = []
+ model_files = self.find_models(ext_filter=[".pt", ".pth"])
+ for model in model_files:
+ if model.startswith("http"):
+ name = self.model_name
+ else:
+ name = modelloader.friendly_name(model)
+ model_data = UpscalerData(name, model, self)
+ scalers.append(model_data)
+ self.scalers = scalers
+
+ def do_upscale(self, img, model_file):
+ use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
+ and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
+ current_config = (model_file, opts.SWIN_tile)
+
+ if use_compile and self._cached_model_config == current_config:
+ model = self._cached_model
+ else:
+ self._cached_model = None
+ try:
+ model = self.load_model(model_file)
+ except Exception as e:
+ print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
+ return img
+ model = model.to(device_swinir, dtype=devices.dtype)
+ if use_compile:
+ model = torch.compile(model)
+ self._cached_model = model
+ self._cached_model_config = current_config
+ img = upscale(img, model)
+ devices.torch_gc()
+ return img
+
+ def load_model(self, path, scale=4):
+ if path.startswith("http"):
+ filename = modelloader.load_file_from_url(
+ url=path,
+ model_dir=self.model_download_path,
+ file_name=f"{self.model_name.replace(' ', '_')}.pth",
+ )
+ else:
+ filename = path
+ if filename.endswith(".v2.pth"):
+ model = Swin2SR(
+ upscale=scale,
+ in_chans=3,
+ img_size=64,
+ window_size=8,
+ img_range=1.0,
+ depths=[6, 6, 6, 6, 6, 6],
+ embed_dim=180,
+ num_heads=[6, 6, 6, 6, 6, 6],
+ mlp_ratio=2,
+ upsampler="nearest+conv",
+ resi_connection="1conv",
+ )
+ params = None
+ else:
+ model = SwinIR(
+ upscale=scale,
+ in_chans=3,
+ img_size=64,
+ window_size=8,
+ img_range=1.0,
+ depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+ embed_dim=240,
+ num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+ mlp_ratio=2,
+ upsampler="nearest+conv",
+ resi_connection="3conv",
+ )
+ params = "params_ema"
+
+ pretrained_model = torch.load(filename)
+ if params is not None:
+ model.load_state_dict(pretrained_model[params], strict=True)
+ else:
+ model.load_state_dict(pretrained_model, strict=True)
+ return model
+
+
+def upscale(
+ img,
+ model,
+ tile=None,
+ tile_overlap=None,
+ window_size=8,
+ scale=4,
+):
+ tile = tile or opts.SWIN_tile
+ tile_overlap = tile_overlap or opts.SWIN_tile_overlap
+
+
+ img = np.array(img)
+ img = img[:, :, ::-1]
+ img = np.moveaxis(img, 2, 0) / 255
+ img = torch.from_numpy(img).float()
+ img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
+ with torch.no_grad(), devices.autocast():
+ _, _, h_old, w_old = img.size()
+ h_pad = (h_old // window_size + 1) * window_size - h_old
+ w_pad = (w_old // window_size + 1) * window_size - w_old
+ img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
+ img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
+ output = inference(img, model, tile, tile_overlap, window_size, scale)
+ output = output[..., : h_old * scale, : w_old * scale]
+ output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+ if output.ndim == 3:
+ output = np.transpose(
+ output[[2, 1, 0], :, :], (1, 2, 0)
+ ) # CHW-RGB to HCW-BGR
+ output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
+ return Image.fromarray(output, "RGB")
+
+
+def inference(img, model, tile, tile_overlap, window_size, scale):
+ # test the image tile by tile
+ b, c, h, w = img.size()
+ tile = min(tile, h, w)
+ assert tile % window_size == 0, "tile size should be a multiple of window_size"
+ sf = scale
+
+ stride = tile - tile_overlap
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+ E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
+
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
+ for h_idx in h_idx_list:
+ if state.interrupted or state.skipped:
+ break
+
+ for w_idx in w_idx_list:
+ if state.interrupted or state.skipped:
+ break
+
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+ out_patch = model(in_patch)
+ out_patch_mask = torch.ones_like(out_patch)
+
+ E[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch)
+ W[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch_mask)
+ pbar.update(1)
+ output = E.div_(W)
+
+ return output
+
+
+def on_ui_settings():
+ import gradio as gr
+
+ shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
+ shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
+ if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows
+ shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py b/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..93b9327473a6e77c3a3dc6a7743e932c9083a996
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py
@@ -0,0 +1,867 @@
+# -----------------------------------------------------------------------------------
+# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
+# Originally Written by Ze Liu, Modified by Jingyun Liang.
+# -----------------------------------------------------------------------------------
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+
+class WindowAttention(nn.Module):
+ r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ """
+
+ def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim ** -0.5
+
+ # define a parameter table of relative position bias
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ trunc_normal_(self.relative_position_bias_table, std=.02)
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask=None):
+ """
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ q = q * self.scale
+ attn = (q @ k.transpose(-2, -1))
+
+ relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+ attn = self.softmax(attn)
+ else:
+ attn = self.softmax(attn)
+
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+ def flops(self, N):
+ # calculate flops for 1 window with token length of N
+ flops = 0
+ # qkv = self.qkv(x)
+ flops += N * self.dim * 3 * self.dim
+ # attn = (q @ k.transpose(-2, -1))
+ flops += self.num_heads * N * (self.dim // self.num_heads) * N
+ # x = (attn @ v)
+ flops += self.num_heads * N * N * (self.dim // self.num_heads)
+ # x = self.proj(x)
+ flops += N * self.dim * self.dim
+ return flops
+
+
+class SwinTransformerBlock(nn.Module):
+ r""" Swin Transformer Block.
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ if min(self.input_resolution) <= self.window_size:
+ # if window size is larger than input resolution, we don't partition windows
+ self.shift_size = 0
+ self.window_size = min(self.input_resolution)
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+ qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+ if self.shift_size > 0:
+ attn_mask = self.calculate_mask(self.input_resolution)
+ else:
+ attn_mask = None
+
+ self.register_buffer("attn_mask", attn_mask)
+
+ def calculate_mask(self, x_size):
+ # calculate attention mask for SW-MSA
+ H, W = x_size
+ img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
+ h_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ w_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+ return attn_mask
+
+ def forward(self, x, x_size):
+ H, W = x_size
+ B, L, C = x.shape
+ # assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = self.norm1(x)
+ x = x.view(B, H, W, C)
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ else:
+ shifted_x = x
+
+ # partition windows
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
+
+ # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+ if self.input_resolution == x_size:
+ attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
+ else:
+ attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+ x = x.view(B, H * W, C)
+
+ # FFN
+ x = shortcut + self.drop_path(x)
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+ def flops(self):
+ flops = 0
+ H, W = self.input_resolution
+ # norm1
+ flops += self.dim * H * W
+ # W-MSA/SW-MSA
+ nW = H * W / self.window_size / self.window_size
+ flops += nW * self.attn.flops(self.window_size * self.window_size)
+ # mlp
+ flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+ # norm2
+ flops += self.dim * H * W
+ return flops
+
+
+class PatchMerging(nn.Module):
+ r""" Patch Merging Layer.
+
+ Args:
+ input_resolution (tuple[int]): Resolution of input feature.
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.input_resolution = input_resolution
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(4 * dim)
+
+ def forward(self, x):
+ """
+ x: B, H*W, C
+ """
+ H, W = self.input_resolution
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+ assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+ x = x.view(B, H, W, C)
+
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
+
+ x = self.norm(x)
+ x = self.reduction(x)
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = H * W * self.dim
+ flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+ return flops
+
+
+class BasicLayer(nn.Module):
+ """ A basic Swin Transformer layer for one stage.
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList([
+ SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+ num_heads=num_heads, window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer)
+ for i in range(depth)])
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, x_size):
+ for blk in self.blocks:
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, x_size)
+ else:
+ x = blk(x, x_size)
+ if self.downsample is not None:
+ x = self.downsample(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+ def flops(self):
+ flops = 0
+ for blk in self.blocks:
+ flops += blk.flops()
+ if self.downsample is not None:
+ flops += self.downsample.flops()
+ return flops
+
+
+class RSTB(nn.Module):
+ """Residual Swin Transformer Block (RSTB).
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ img_size: Input image size.
+ patch_size: Patch size.
+ resi_connection: The convolutional block before residual connection.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+ img_size=224, patch_size=4, resi_connection='1conv'):
+ super(RSTB, self).__init__()
+
+ self.dim = dim
+ self.input_resolution = input_resolution
+
+ self.residual_group = BasicLayer(dim=dim,
+ input_resolution=input_resolution,
+ depth=depth,
+ num_heads=num_heads,
+ window_size=window_size,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path,
+ norm_layer=norm_layer,
+ downsample=downsample,
+ use_checkpoint=use_checkpoint)
+
+ if resi_connection == '1conv':
+ self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+ norm_layer=None)
+
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+ norm_layer=None)
+
+ def forward(self, x, x_size):
+ return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+ def flops(self):
+ flops = 0
+ flops += self.residual_group.flops()
+ H, W = self.input_resolution
+ flops += H * W * self.dim * self.dim * 9
+ flops += self.patch_embed.flops()
+ flops += self.patch_unembed.flops()
+
+ return flops
+
+
+class PatchEmbed(nn.Module):
+ r""" Image to Patch Embedding
+
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
+ if self.norm is not None:
+ x = self.norm(x)
+ return x
+
+ def flops(self):
+ flops = 0
+ H, W = self.img_size
+ if self.norm is not None:
+ flops += H * W * self.embed_dim
+ return flops
+
+
+class PatchUnEmbed(nn.Module):
+ r""" Image to Patch Unembedding
+
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ def forward(self, x, x_size):
+ B, HW, C = x.shape
+ x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
+ return x
+
+ def flops(self):
+ flops = 0
+ return flops
+
+
+class Upsample(nn.Sequential):
+ """Upsample module.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+ """
+
+ def __init__(self, scale, num_feat):
+ m = []
+ if (scale & (scale - 1)) == 0: # scale = 2^n
+ for _ in range(int(math.log(scale, 2))):
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(2))
+ elif scale == 3:
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(3))
+ else:
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+ super(Upsample, self).__init__(*m)
+
+
+class UpsampleOneStep(nn.Sequential):
+ """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+ Used in lightweight SR to save parameters.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+
+ """
+
+ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+ self.num_feat = num_feat
+ self.input_resolution = input_resolution
+ m = []
+ m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+ m.append(nn.PixelShuffle(scale))
+ super(UpsampleOneStep, self).__init__(*m)
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = H * W * self.num_feat * 3 * 9
+ return flops
+
+
+class SwinIR(nn.Module):
+ r""" SwinIR
+ A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
+
+ Args:
+ img_size (int | tuple(int)): Input image size. Default 64
+ patch_size (int | tuple(int)): Patch size. Default: 1
+ in_chans (int): Number of input image channels. Default: 3
+ embed_dim (int): Patch embedding dimension. Default: 96
+ depths (tuple(int)): Depth of each Swin Transformer layer.
+ num_heads (tuple(int)): Number of attention heads in different layers.
+ window_size (int): Window size. Default: 7
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+ drop_rate (float): Dropout rate. Default: 0
+ attn_drop_rate (float): Attention dropout rate. Default: 0
+ drop_path_rate (float): Stochastic depth rate. Default: 0.1
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+ upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+ img_range: Image range. 1. or 255.
+ upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+ resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+ """
+
+ def __init__(self, img_size=64, patch_size=1, in_chans=3,
+ embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
+ window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+ norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+ use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+ **kwargs):
+ super(SwinIR, self).__init__()
+ num_in_ch = in_chans
+ num_out_ch = in_chans
+ num_feat = 64
+ self.img_range = img_range
+ if in_chans == 3:
+ rgb_mean = (0.4488, 0.4371, 0.4040)
+ self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+ else:
+ self.mean = torch.zeros(1, 1, 1, 1)
+ self.upscale = upscale
+ self.upsampler = upsampler
+ self.window_size = window_size
+
+ #####################################################################################################
+ ################################### 1, shallow feature extraction ###################################
+ self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+ #####################################################################################################
+ ################################### 2, deep feature extraction ######################################
+ self.num_layers = len(depths)
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.num_features = embed_dim
+ self.mlp_ratio = mlp_ratio
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+ num_patches = self.patch_embed.num_patches
+ patches_resolution = self.patch_embed.patches_resolution
+ self.patches_resolution = patches_resolution
+
+ # merge non-overlapping patches into image
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+
+ # absolute position embedding
+ if self.ape:
+ self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+ trunc_normal_(self.absolute_pos_embed, std=.02)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ # stochastic depth
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
+
+ # build Residual Swin Transformer blocks (RSTB)
+ self.layers = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = RSTB(dim=embed_dim,
+ input_resolution=(patches_resolution[0],
+ patches_resolution[1]),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
+ norm_layer=norm_layer,
+ downsample=None,
+ use_checkpoint=use_checkpoint,
+ img_size=img_size,
+ patch_size=patch_size,
+ resi_connection=resi_connection
+
+ )
+ self.layers.append(layer)
+ self.norm = norm_layer(self.num_features)
+
+ # build the last conv layer in deep feature extraction
+ if resi_connection == '1conv':
+ self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+ #####################################################################################################
+ ################################ 3, high quality image reconstruction ################################
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR (to save parameters)
+ self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+ (patches_resolution[0], patches_resolution[1]))
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR (less artifacts)
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ if self.upscale == 4:
+ self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ @torch.jit.ignore
+ def no_weight_decay(self):
+ return {'absolute_pos_embed'}
+
+ @torch.jit.ignore
+ def no_weight_decay_keywords(self):
+ return {'relative_position_bias_table'}
+
+ def check_image_size(self, x):
+ _, _, h, w = x.size()
+ mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+ mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+ x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+ return x
+
+ def forward_features(self, x):
+ x_size = (x.shape[2], x.shape[3])
+ x = self.patch_embed(x)
+ if self.ape:
+ x = x + self.absolute_pos_embed
+ x = self.pos_drop(x)
+
+ for layer in self.layers:
+ x = layer(x, x_size)
+
+ x = self.norm(x) # B L C
+ x = self.patch_unembed(x, x_size)
+
+ return x
+
+ def forward(self, x):
+ H, W = x.shape[2:]
+ x = self.check_image_size(x)
+
+ self.mean = self.mean.type_as(x)
+ x = (x - self.mean) * self.img_range
+
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.conv_last(self.upsample(x))
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.upsample(x)
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ if self.upscale == 4:
+ x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ x = self.conv_last(self.lrelu(self.conv_hr(x)))
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ x_first = self.conv_first(x)
+ res = self.conv_after_body(self.forward_features(x_first)) + x_first
+ x = x + self.conv_last(res)
+
+ x = x / self.img_range + self.mean
+
+ return x[:, :, :H*self.upscale, :W*self.upscale]
+
+ def flops(self):
+ flops = 0
+ H, W = self.patches_resolution
+ flops += H * W * 3 * self.embed_dim * 9
+ flops += self.patch_embed.flops()
+ for layer in self.layers:
+ flops += layer.flops()
+ flops += H * W * 3 * self.embed_dim * self.embed_dim
+ flops += self.upsample.flops()
+ return flops
+
+
+if __name__ == '__main__':
+ upscale = 4
+ window_size = 8
+ height = (1024 // upscale // window_size + 1) * window_size
+ width = (720 // upscale // window_size + 1) * window_size
+ model = SwinIR(upscale=2, img_size=(height, width),
+ window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+ embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+ print(model)
+ print(height, width, model.flops() / 1e9)
+
+ x = torch.randn((1, 3, height, width))
+ x = model(x)
+ print(x.shape)
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..59219f69a9a7f8365628cb2f4f57f5cd0104147a
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch_v2.py
@@ -0,0 +1,1017 @@
+# -----------------------------------------------------------------------------------
+# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
+# Written by Conde and Choi et al.
+# -----------------------------------------------------------------------------------
+
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+class WindowAttention(nn.Module):
+ r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
+ """
+
+ def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
+ pretrained_window_size=(0, 0)):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.pretrained_window_size = pretrained_window_size
+ self.num_heads = num_heads
+
+ self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
+
+ # mlp to generate continuous relative position bias
+ self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+ nn.ReLU(inplace=True),
+ nn.Linear(512, num_heads, bias=False))
+
+ # get relative_coords_table
+ relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+ relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+ relative_coords_table = torch.stack(
+ torch.meshgrid([relative_coords_h,
+ relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
+ if pretrained_window_size[0] > 0:
+ relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+ relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+ else:
+ relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+ relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+ relative_coords_table *= 8 # normalize to -8, 8
+ relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+ torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+ self.register_buffer("relative_coords_table", relative_coords_table)
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=False)
+ if qkv_bias:
+ self.q_bias = nn.Parameter(torch.zeros(dim))
+ self.v_bias = nn.Parameter(torch.zeros(dim))
+ else:
+ self.q_bias = None
+ self.v_bias = None
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask=None):
+ """
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ qkv_bias = None
+ if self.q_bias is not None:
+ qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+ qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+ qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ # cosine attention
+ attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
+ logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
+ attn = attn * logit_scale
+
+ relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+ relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+ attn = self.softmax(attn)
+ else:
+ attn = self.softmax(attn)
+
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f'dim={self.dim}, window_size={self.window_size}, ' \
+ f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
+
+ def flops(self, N):
+ # calculate flops for 1 window with token length of N
+ flops = 0
+ # qkv = self.qkv(x)
+ flops += N * self.dim * 3 * self.dim
+ # attn = (q @ k.transpose(-2, -1))
+ flops += self.num_heads * N * (self.dim // self.num_heads) * N
+ # x = (attn @ v)
+ flops += self.num_heads * N * N * (self.dim // self.num_heads)
+ # x = self.proj(x)
+ flops += N * self.dim * self.dim
+ return flops
+
+class SwinTransformerBlock(nn.Module):
+ r""" Swin Transformer Block.
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resulotion.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ pretrained_window_size (int): Window size in pre-training.
+ """
+
+ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ if min(self.input_resolution) <= self.window_size:
+ # if window size is larger than input resolution, we don't partition windows
+ self.shift_size = 0
+ self.window_size = min(self.input_resolution)
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+ qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
+ pretrained_window_size=to_2tuple(pretrained_window_size))
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+ if self.shift_size > 0:
+ attn_mask = self.calculate_mask(self.input_resolution)
+ else:
+ attn_mask = None
+
+ self.register_buffer("attn_mask", attn_mask)
+
+ def calculate_mask(self, x_size):
+ # calculate attention mask for SW-MSA
+ H, W = x_size
+ img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
+ h_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ w_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+ return attn_mask
+
+ def forward(self, x, x_size):
+ H, W = x_size
+ B, L, C = x.shape
+ #assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = x.view(B, H, W, C)
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ else:
+ shifted_x = x
+
+ # partition windows
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
+
+ # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+ if self.input_resolution == x_size:
+ attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
+ else:
+ attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+ x = x.view(B, H * W, C)
+ x = shortcut + self.drop_path(self.norm1(x))
+
+ # FFN
+ x = x + self.drop_path(self.norm2(self.mlp(x)))
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+ def flops(self):
+ flops = 0
+ H, W = self.input_resolution
+ # norm1
+ flops += self.dim * H * W
+ # W-MSA/SW-MSA
+ nW = H * W / self.window_size / self.window_size
+ flops += nW * self.attn.flops(self.window_size * self.window_size)
+ # mlp
+ flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+ # norm2
+ flops += self.dim * H * W
+ return flops
+
+class PatchMerging(nn.Module):
+ r""" Patch Merging Layer.
+ Args:
+ input_resolution (tuple[int]): Resolution of input feature.
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.input_resolution = input_resolution
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(2 * dim)
+
+ def forward(self, x):
+ """
+ x: B, H*W, C
+ """
+ H, W = self.input_resolution
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+ assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+ x = x.view(B, H, W, C)
+
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
+
+ x = self.reduction(x)
+ x = self.norm(x)
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+ flops += H * W * self.dim // 2
+ return flops
+
+class BasicLayer(nn.Module):
+ """ A basic Swin Transformer layer for one stage.
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ pretrained_window_size (int): Local window size in pre-training.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+ pretrained_window_size=0):
+
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList([
+ SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+ num_heads=num_heads, window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer,
+ pretrained_window_size=pretrained_window_size)
+ for i in range(depth)])
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, x_size):
+ for blk in self.blocks:
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, x_size)
+ else:
+ x = blk(x, x_size)
+ if self.downsample is not None:
+ x = self.downsample(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+ def flops(self):
+ flops = 0
+ for blk in self.blocks:
+ flops += blk.flops()
+ if self.downsample is not None:
+ flops += self.downsample.flops()
+ return flops
+
+ def _init_respostnorm(self):
+ for blk in self.blocks:
+ nn.init.constant_(blk.norm1.bias, 0)
+ nn.init.constant_(blk.norm1.weight, 0)
+ nn.init.constant_(blk.norm2.bias, 0)
+ nn.init.constant_(blk.norm2.weight, 0)
+
+class PatchEmbed(nn.Module):
+ r""" Image to Patch Embedding
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ B, C, H, W = x.shape
+ # FIXME look at relaxing size constraints
+ # assert H == self.img_size[0] and W == self.img_size[1],
+ # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+ x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
+ if self.norm is not None:
+ x = self.norm(x)
+ return x
+
+ def flops(self):
+ Ho, Wo = self.patches_resolution
+ flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+ if self.norm is not None:
+ flops += Ho * Wo * self.embed_dim
+ return flops
+
+class RSTB(nn.Module):
+ """Residual Swin Transformer Block (RSTB).
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ img_size: Input image size.
+ patch_size: Patch size.
+ resi_connection: The convolutional block before residual connection.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+ img_size=224, patch_size=4, resi_connection='1conv'):
+ super(RSTB, self).__init__()
+
+ self.dim = dim
+ self.input_resolution = input_resolution
+
+ self.residual_group = BasicLayer(dim=dim,
+ input_resolution=input_resolution,
+ depth=depth,
+ num_heads=num_heads,
+ window_size=window_size,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path,
+ norm_layer=norm_layer,
+ downsample=downsample,
+ use_checkpoint=use_checkpoint)
+
+ if resi_connection == '1conv':
+ self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+ norm_layer=None)
+
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+ norm_layer=None)
+
+ def forward(self, x, x_size):
+ return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+ def flops(self):
+ flops = 0
+ flops += self.residual_group.flops()
+ H, W = self.input_resolution
+ flops += H * W * self.dim * self.dim * 9
+ flops += self.patch_embed.flops()
+ flops += self.patch_unembed.flops()
+
+ return flops
+
+class PatchUnEmbed(nn.Module):
+ r""" Image to Patch Unembedding
+
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ def forward(self, x, x_size):
+ B, HW, C = x.shape
+ x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
+ return x
+
+ def flops(self):
+ flops = 0
+ return flops
+
+
+class Upsample(nn.Sequential):
+ """Upsample module.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+ """
+
+ def __init__(self, scale, num_feat):
+ m = []
+ if (scale & (scale - 1)) == 0: # scale = 2^n
+ for _ in range(int(math.log(scale, 2))):
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(2))
+ elif scale == 3:
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(3))
+ else:
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+ super(Upsample, self).__init__(*m)
+
+class Upsample_hf(nn.Sequential):
+ """Upsample module.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+ """
+
+ def __init__(self, scale, num_feat):
+ m = []
+ if (scale & (scale - 1)) == 0: # scale = 2^n
+ for _ in range(int(math.log(scale, 2))):
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(2))
+ elif scale == 3:
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(3))
+ else:
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+ super(Upsample_hf, self).__init__(*m)
+
+
+class UpsampleOneStep(nn.Sequential):
+ """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+ Used in lightweight SR to save parameters.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+
+ """
+
+ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+ self.num_feat = num_feat
+ self.input_resolution = input_resolution
+ m = []
+ m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+ m.append(nn.PixelShuffle(scale))
+ super(UpsampleOneStep, self).__init__(*m)
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = H * W * self.num_feat * 3 * 9
+ return flops
+
+
+
+class Swin2SR(nn.Module):
+ r""" Swin2SR
+ A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
+
+ Args:
+ img_size (int | tuple(int)): Input image size. Default 64
+ patch_size (int | tuple(int)): Patch size. Default: 1
+ in_chans (int): Number of input image channels. Default: 3
+ embed_dim (int): Patch embedding dimension. Default: 96
+ depths (tuple(int)): Depth of each Swin Transformer layer.
+ num_heads (tuple(int)): Number of attention heads in different layers.
+ window_size (int): Window size. Default: 7
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+ drop_rate (float): Dropout rate. Default: 0
+ attn_drop_rate (float): Attention dropout rate. Default: 0
+ drop_path_rate (float): Stochastic depth rate. Default: 0.1
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+ upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+ img_range: Image range. 1. or 255.
+ upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+ resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+ """
+
+ def __init__(self, img_size=64, patch_size=1, in_chans=3,
+ embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
+ window_size=7, mlp_ratio=4., qkv_bias=True,
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+ norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+ use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+ **kwargs):
+ super(Swin2SR, self).__init__()
+ num_in_ch = in_chans
+ num_out_ch = in_chans
+ num_feat = 64
+ self.img_range = img_range
+ if in_chans == 3:
+ rgb_mean = (0.4488, 0.4371, 0.4040)
+ self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+ else:
+ self.mean = torch.zeros(1, 1, 1, 1)
+ self.upscale = upscale
+ self.upsampler = upsampler
+ self.window_size = window_size
+
+ #####################################################################################################
+ ################################### 1, shallow feature extraction ###################################
+ self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+ #####################################################################################################
+ ################################### 2, deep feature extraction ######################################
+ self.num_layers = len(depths)
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.num_features = embed_dim
+ self.mlp_ratio = mlp_ratio
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+ num_patches = self.patch_embed.num_patches
+ patches_resolution = self.patch_embed.patches_resolution
+ self.patches_resolution = patches_resolution
+
+ # merge non-overlapping patches into image
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+
+ # absolute position embedding
+ if self.ape:
+ self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+ trunc_normal_(self.absolute_pos_embed, std=.02)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ # stochastic depth
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
+
+ # build Residual Swin Transformer blocks (RSTB)
+ self.layers = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = RSTB(dim=embed_dim,
+ input_resolution=(patches_resolution[0],
+ patches_resolution[1]),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
+ norm_layer=norm_layer,
+ downsample=None,
+ use_checkpoint=use_checkpoint,
+ img_size=img_size,
+ patch_size=patch_size,
+ resi_connection=resi_connection
+
+ )
+ self.layers.append(layer)
+
+ if self.upsampler == 'pixelshuffle_hf':
+ self.layers_hf = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = RSTB(dim=embed_dim,
+ input_resolution=(patches_resolution[0],
+ patches_resolution[1]),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
+ norm_layer=norm_layer,
+ downsample=None,
+ use_checkpoint=use_checkpoint,
+ img_size=img_size,
+ patch_size=patch_size,
+ resi_connection=resi_connection
+
+ )
+ self.layers_hf.append(layer)
+
+ self.norm = norm_layer(self.num_features)
+
+ # build the last conv layer in deep feature extraction
+ if resi_connection == '1conv':
+ self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+ #####################################################################################################
+ ################################ 3, high quality image reconstruction ################################
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ elif self.upsampler == 'pixelshuffle_aux':
+ self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+ self.conv_before_upsample = nn.Sequential(
+ nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.conv_after_aux = nn.Sequential(
+ nn.Conv2d(3, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+ elif self.upsampler == 'pixelshuffle_hf':
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.upsample_hf = Upsample_hf(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+ self.conv_before_upsample_hf = nn.Sequential(
+ nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR (to save parameters)
+ self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+ (patches_resolution[0], patches_resolution[1]))
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR (less artifacts)
+ assert self.upscale == 4, 'only support x4 now.'
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ @torch.jit.ignore
+ def no_weight_decay(self):
+ return {'absolute_pos_embed'}
+
+ @torch.jit.ignore
+ def no_weight_decay_keywords(self):
+ return {'relative_position_bias_table'}
+
+ def check_image_size(self, x):
+ _, _, h, w = x.size()
+ mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+ mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+ x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+ return x
+
+ def forward_features(self, x):
+ x_size = (x.shape[2], x.shape[3])
+ x = self.patch_embed(x)
+ if self.ape:
+ x = x + self.absolute_pos_embed
+ x = self.pos_drop(x)
+
+ for layer in self.layers:
+ x = layer(x, x_size)
+
+ x = self.norm(x) # B L C
+ x = self.patch_unembed(x, x_size)
+
+ return x
+
+ def forward_features_hf(self, x):
+ x_size = (x.shape[2], x.shape[3])
+ x = self.patch_embed(x)
+ if self.ape:
+ x = x + self.absolute_pos_embed
+ x = self.pos_drop(x)
+
+ for layer in self.layers_hf:
+ x = layer(x, x_size)
+
+ x = self.norm(x) # B L C
+ x = self.patch_unembed(x, x_size)
+
+ return x
+
+ def forward(self, x):
+ H, W = x.shape[2:]
+ x = self.check_image_size(x)
+
+ self.mean = self.mean.type_as(x)
+ x = (x - self.mean) * self.img_range
+
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.conv_last(self.upsample(x))
+ elif self.upsampler == 'pixelshuffle_aux':
+ bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
+ bicubic = self.conv_bicubic(bicubic)
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ aux = self.conv_aux(x) # b, 3, LR_H, LR_W
+ x = self.conv_after_aux(aux)
+ x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
+ x = self.conv_last(x)
+ aux = aux / self.img_range + self.mean
+ elif self.upsampler == 'pixelshuffle_hf':
+ # for classical SR with HF
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x_before = self.conv_before_upsample(x)
+ x_out = self.conv_last(self.upsample(x_before))
+
+ x_hf = self.conv_first_hf(x_before)
+ x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
+ x_hf = self.conv_before_upsample_hf(x_hf)
+ x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
+ x = x_out + x_hf
+ x_hf = x_hf / self.img_range + self.mean
+
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.upsample(x)
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ x = self.conv_last(self.lrelu(self.conv_hr(x)))
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ x_first = self.conv_first(x)
+ res = self.conv_after_body(self.forward_features(x_first)) + x_first
+ x = x + self.conv_last(res)
+
+ x = x / self.img_range + self.mean
+ if self.upsampler == "pixelshuffle_aux":
+ return x[:, :, :H*self.upscale, :W*self.upscale], aux
+
+ elif self.upsampler == "pixelshuffle_hf":
+ x_out = x_out / self.img_range + self.mean
+ return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
+
+ else:
+ return x[:, :, :H*self.upscale, :W*self.upscale]
+
+ def flops(self):
+ flops = 0
+ H, W = self.patches_resolution
+ flops += H * W * 3 * self.embed_dim * 9
+ flops += self.patch_embed.flops()
+ for layer in self.layers:
+ flops += layer.flops()
+ flops += H * W * 3 * self.embed_dim * self.embed_dim
+ flops += self.upsample.flops()
+ return flops
+
+
+if __name__ == '__main__':
+ upscale = 4
+ window_size = 8
+ height = (1024 // upscale // window_size + 1) * window_size
+ width = (720 // upscale // window_size + 1) * window_size
+ model = Swin2SR(upscale=2, img_size=(height, width),
+ window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+ embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+ print(model)
+ print(height, width, model.flops() / 1e9)
+
+ x = torch.randn((1, 3, height, width))
+ x = model(x)
+ print(x.shape)
diff --git a/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
new file mode 100644
index 0000000000000000000000000000000000000000..45c7600ac5f81bc8c7b233162d73f6551c8b5e8d
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
@@ -0,0 +1,962 @@
+onUiLoaded(async() => {
+ const elementIDs = {
+ img2imgTabs: "#mode_img2img .tab-nav",
+ inpaint: "#img2maskimg",
+ inpaintSketch: "#inpaint_sketch",
+ rangeGroup: "#img2img_column_size",
+ sketch: "#img2img_sketch"
+ };
+ const tabNameToElementId = {
+ "Inpaint sketch": elementIDs.inpaintSketch,
+ "Inpaint": elementIDs.inpaint,
+ "Sketch": elementIDs.sketch
+ };
+
+
+ // Helper functions
+ // Get active tab
+
+ /**
+ * Waits for an element to be present in the DOM.
+ */
+ const waitForElement = (id) => new Promise(resolve => {
+ const checkForElement = () => {
+ const element = document.querySelector(id);
+ if (element) return resolve(element);
+ setTimeout(checkForElement, 100);
+ };
+ checkForElement();
+ });
+
+ function getActiveTab(elements, all = false) {
+ const tabs = elements.img2imgTabs.querySelectorAll("button");
+
+ if (all) return tabs;
+
+ for (let tab of tabs) {
+ if (tab.classList.contains("selected")) {
+ return tab;
+ }
+ }
+ }
+
+ // Get tab ID
+ function getTabId(elements) {
+ const activeTab = getActiveTab(elements);
+ return tabNameToElementId[activeTab.innerText];
+ }
+
+ // Wait until opts loaded
+ async function waitForOpts() {
+ for (; ;) {
+ if (window.opts && Object.keys(window.opts).length) {
+ return window.opts;
+ }
+ await new Promise(resolve => setTimeout(resolve, 100));
+ }
+ }
+
+ // Detect whether the element has a horizontal scroll bar
+ function hasHorizontalScrollbar(element) {
+ return element.scrollWidth > element.clientWidth;
+ }
+
+ // Function for defining the "Ctrl", "Shift" and "Alt" keys
+ function isModifierKey(event, key) {
+ switch (key) {
+ case "Ctrl":
+ return event.ctrlKey;
+ case "Shift":
+ return event.shiftKey;
+ case "Alt":
+ return event.altKey;
+ default:
+ return false;
+ }
+ }
+
+ // Check if hotkey is valid
+ function isValidHotkey(value) {
+ const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"];
+ return (
+ (typeof value === "string" &&
+ value.length === 1 &&
+ /[a-z]/i.test(value)) ||
+ specialKeys.includes(value)
+ );
+ }
+
+ // Normalize hotkey
+ function normalizeHotkey(hotkey) {
+ return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey;
+ }
+
+ // Format hotkey for display
+ function formatHotkeyForDisplay(hotkey) {
+ return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey;
+ }
+
+ // Create hotkey configuration with the provided options
+ function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) {
+ const result = {}; // Resulting hotkey configuration
+ const usedKeys = new Set(); // Set of used hotkeys
+
+ // Iterate through defaultHotkeysConfig keys
+ for (const key in defaultHotkeysConfig) {
+ const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value
+ const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value
+
+ // Apply appropriate value for undefined, boolean, or object userValue
+ if (
+ userValue === undefined ||
+ typeof userValue === "boolean" ||
+ typeof userValue === "object" ||
+ userValue === "disable"
+ ) {
+ result[key] =
+ userValue === undefined ? defaultValue : userValue;
+ } else if (isValidHotkey(userValue)) {
+ const normalizedUserValue = normalizeHotkey(userValue);
+
+ // Check for conflicting hotkeys
+ if (!usedKeys.has(normalizedUserValue)) {
+ usedKeys.add(normalizedUserValue);
+ result[key] = normalizedUserValue;
+ } else {
+ console.error(
+ `Hotkey: ${formatHotkeyForDisplay(
+ userValue
+ )} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay(
+ defaultValue
+ )}`
+ );
+ result[key] = defaultValue;
+ }
+ } else {
+ console.error(
+ `Hotkey: ${formatHotkeyForDisplay(
+ userValue
+ )} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay(
+ defaultValue
+ )}`
+ );
+ result[key] = defaultValue;
+ }
+ }
+
+ return result;
+ }
+
+ // Disables functions in the config object based on the provided list of function names
+ function disableFunctions(config, disabledFunctions) {
+ // Bind the hasOwnProperty method to the functionMap object to avoid errors
+ const hasOwnProperty =
+ Object.prototype.hasOwnProperty.bind(functionMap);
+
+ // Loop through the disabledFunctions array and disable the corresponding functions in the config object
+ disabledFunctions.forEach(funcName => {
+ if (hasOwnProperty(funcName)) {
+ const key = functionMap[funcName];
+ config[key] = "disable";
+ }
+ });
+
+ // Return the updated config object
+ return config;
+ }
+
+ /**
+ * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio.
+ * If the image display property is set to 'none', the mask breaks. To fix this, the function
+ * temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds
+ * to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on
+ * very long images.
+ */
+ function restoreImgRedMask(elements) {
+ const mainTabId = getTabId(elements);
+
+ if (!mainTabId) return;
+
+ const mainTab = gradioApp().querySelector(mainTabId);
+ const img = mainTab.querySelector("img");
+ const imageARPreview = gradioApp().querySelector("#imageARPreview");
+
+ if (!img || !imageARPreview) return;
+
+ imageARPreview.style.transform = "";
+ if (parseFloat(mainTab.style.width) > 865) {
+ const transformString = mainTab.style.transform;
+ const scaleMatch = transformString.match(
+ /scale\(([-+]?[0-9]*\.?[0-9]+)\)/
+ );
+ let zoom = 1; // default zoom
+
+ if (scaleMatch && scaleMatch[1]) {
+ zoom = Number(scaleMatch[1]);
+ }
+
+ imageARPreview.style.transformOrigin = "0 0";
+ imageARPreview.style.transform = `scale(${zoom})`;
+ }
+
+ if (img.style.display !== "none") return;
+
+ img.style.display = "block";
+
+ setTimeout(() => {
+ img.style.display = "none";
+ }, 400);
+ }
+
+ const hotkeysConfigOpts = await waitForOpts();
+
+ // Default config
+ const defaultHotkeysConfig = {
+ canvas_hotkey_zoom: "Alt",
+ canvas_hotkey_adjust: "Ctrl",
+ canvas_hotkey_reset: "KeyR",
+ canvas_hotkey_fullscreen: "KeyS",
+ canvas_hotkey_move: "KeyF",
+ canvas_hotkey_overlap: "KeyO",
+ canvas_disabled_functions: [],
+ canvas_show_tooltip: true,
+ canvas_auto_expand: true,
+ canvas_blur_prompt: false,
+ };
+
+ const functionMap = {
+ "Zoom": "canvas_hotkey_zoom",
+ "Adjust brush size": "canvas_hotkey_adjust",
+ "Moving canvas": "canvas_hotkey_move",
+ "Fullscreen": "canvas_hotkey_fullscreen",
+ "Reset Zoom": "canvas_hotkey_reset",
+ "Overlap": "canvas_hotkey_overlap"
+ };
+
+ // Loading the configuration from opts
+ const preHotkeysConfig = createHotkeyConfig(
+ defaultHotkeysConfig,
+ hotkeysConfigOpts
+ );
+
+ // Disable functions that are not needed by the user
+ const hotkeysConfig = disableFunctions(
+ preHotkeysConfig,
+ preHotkeysConfig.canvas_disabled_functions
+ );
+
+ let isMoving = false;
+ let mouseX, mouseY;
+ let activeElement;
+
+ const elements = Object.fromEntries(
+ Object.keys(elementIDs).map(id => [
+ id,
+ gradioApp().querySelector(elementIDs[id])
+ ])
+ );
+ const elemData = {};
+
+ // Apply functionality to the range inputs. Restore redmask and correct for long images.
+ const rangeInputs = elements.rangeGroup ?
+ Array.from(elements.rangeGroup.querySelectorAll("input")) :
+ [
+ gradioApp().querySelector("#img2img_width input[type='range']"),
+ gradioApp().querySelector("#img2img_height input[type='range']")
+ ];
+
+ for (const input of rangeInputs) {
+ input?.addEventListener("input", () => restoreImgRedMask(elements));
+ }
+
+ function applyZoomAndPan(elemId, isExtension = true) {
+ const targetElement = gradioApp().querySelector(elemId);
+
+ if (!targetElement) {
+ console.log("Element not found");
+ return;
+ }
+
+ targetElement.style.transformOrigin = "0 0";
+
+ elemData[elemId] = {
+ zoom: 1,
+ panX: 0,
+ panY: 0
+ };
+ let fullScreenMode = false;
+
+ // Create tooltip
+ function createTooltip() {
+ const toolTipElemnt =
+ targetElement.querySelector(".image-container");
+ const tooltip = document.createElement("div");
+ tooltip.className = "canvas-tooltip";
+
+ // Creating an item of information
+ const info = document.createElement("i");
+ info.className = "canvas-tooltip-info";
+ info.textContent = "";
+
+ // Create a container for the contents of the tooltip
+ const tooltipContent = document.createElement("div");
+ tooltipContent.className = "canvas-tooltip-content";
+
+ // Define an array with hotkey information and their actions
+ const hotkeysInfo = [
+ {
+ configKey: "canvas_hotkey_zoom",
+ action: "Zoom canvas",
+ keySuffix: " + wheel"
+ },
+ {
+ configKey: "canvas_hotkey_adjust",
+ action: "Adjust brush size",
+ keySuffix: " + wheel"
+ },
+ {configKey: "canvas_hotkey_reset", action: "Reset zoom"},
+ {
+ configKey: "canvas_hotkey_fullscreen",
+ action: "Fullscreen mode"
+ },
+ {configKey: "canvas_hotkey_move", action: "Move canvas"},
+ {configKey: "canvas_hotkey_overlap", action: "Overlap"}
+ ];
+
+ // Create hotkeys array with disabled property based on the config values
+ const hotkeys = hotkeysInfo.map(info => {
+ const configValue = hotkeysConfig[info.configKey];
+ const key = info.keySuffix ?
+ `${configValue}${info.keySuffix}` :
+ configValue.charAt(configValue.length - 1);
+ return {
+ key,
+ action: info.action,
+ disabled: configValue === "disable"
+ };
+ });
+
+ for (const hotkey of hotkeys) {
+ if (hotkey.disabled) {
+ continue;
+ }
+
+ const p = document.createElement("p");
+ p.innerHTML = `${hotkey.key} - ${hotkey.action}`;
+ tooltipContent.appendChild(p);
+ }
+
+ // Add information and content elements to the tooltip element
+ tooltip.appendChild(info);
+ tooltip.appendChild(tooltipContent);
+
+ // Add a hint element to the target element
+ toolTipElemnt.appendChild(tooltip);
+ }
+
+ //Show tool tip if setting enable
+ if (hotkeysConfig.canvas_show_tooltip) {
+ createTooltip();
+ }
+
+ // In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui.
+ function fixCanvas() {
+ const activeTab = getActiveTab(elements).textContent.trim();
+
+ if (activeTab !== "img2img") {
+ const img = targetElement.querySelector(`${elemId} img`);
+
+ if (img && img.style.display !== "none") {
+ img.style.display = "none";
+ img.style.visibility = "hidden";
+ }
+ }
+ }
+
+ // Reset the zoom level and pan position of the target element to their initial values
+ function resetZoom() {
+ elemData[elemId] = {
+ zoomLevel: 1,
+ panX: 0,
+ panY: 0
+ };
+
+ if (isExtension) {
+ targetElement.style.overflow = "hidden";
+ }
+
+ targetElement.isZoomed = false;
+
+ fixCanvas();
+ targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`;
+
+ const canvas = gradioApp().querySelector(
+ `${elemId} canvas[key="interface"]`
+ );
+
+ toggleOverlap("off");
+ fullScreenMode = false;
+
+ const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']");
+ if (closeBtn) {
+ closeBtn.addEventListener("click", resetZoom);
+ }
+
+ if (canvas && isExtension) {
+ const parentElement = targetElement.closest('[id^="component-"]');
+ if (
+ canvas &&
+ parseFloat(canvas.style.width) > parentElement.offsetWidth &&
+ parseFloat(targetElement.style.width) > parentElement.offsetWidth
+ ) {
+ fitToElement();
+ return;
+ }
+
+ }
+
+ if (
+ canvas &&
+ !isExtension &&
+ parseFloat(canvas.style.width) > 865 &&
+ parseFloat(targetElement.style.width) > 865
+ ) {
+ fitToElement();
+ return;
+ }
+
+ targetElement.style.width = "";
+ }
+
+ // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements
+ function toggleOverlap(forced = "") {
+ const zIndex1 = "0";
+ const zIndex2 = "998";
+
+ targetElement.style.zIndex =
+ targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1;
+
+ if (forced === "off") {
+ targetElement.style.zIndex = zIndex1;
+ } else if (forced === "on") {
+ targetElement.style.zIndex = zIndex2;
+ }
+ }
+
+ // Adjust the brush size based on the deltaY value from a mouse wheel event
+ function adjustBrushSize(
+ elemId,
+ deltaY,
+ withoutValue = false,
+ percentage = 5
+ ) {
+ const input =
+ gradioApp().querySelector(
+ `${elemId} input[aria-label='Brush radius']`
+ ) ||
+ gradioApp().querySelector(
+ `${elemId} button[aria-label="Use brush"]`
+ );
+
+ if (input) {
+ input.click();
+ if (!withoutValue) {
+ const maxValue =
+ parseFloat(input.getAttribute("max")) || 100;
+ const changeAmount = maxValue * (percentage / 100);
+ const newValue =
+ parseFloat(input.value) +
+ (deltaY > 0 ? -changeAmount : changeAmount);
+ input.value = Math.min(Math.max(newValue, 0), maxValue);
+ input.dispatchEvent(new Event("change"));
+ }
+ }
+ }
+
+ // Reset zoom when uploading a new image
+ const fileInput = gradioApp().querySelector(
+ `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv`
+ );
+ fileInput.addEventListener("click", resetZoom);
+
+ // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables
+ function updateZoom(newZoomLevel, mouseX, mouseY) {
+ newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15));
+
+ elemData[elemId].panX +=
+ mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel;
+ elemData[elemId].panY +=
+ mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel;
+
+ targetElement.style.transformOrigin = "0 0";
+ targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;
+
+ toggleOverlap("on");
+ if (isExtension) {
+ targetElement.style.overflow = "visible";
+ }
+
+ return newZoomLevel;
+ }
+
+ // Change the zoom level based on user interaction
+ function changeZoomLevel(operation, e) {
+ if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) {
+ e.preventDefault();
+
+ let zoomPosX, zoomPosY;
+ let delta = 0.2;
+ if (elemData[elemId].zoomLevel > 7) {
+ delta = 0.9;
+ } else if (elemData[elemId].zoomLevel > 2) {
+ delta = 0.6;
+ }
+
+ zoomPosX = e.clientX;
+ zoomPosY = e.clientY;
+
+ fullScreenMode = false;
+ elemData[elemId].zoomLevel = updateZoom(
+ elemData[elemId].zoomLevel +
+ (operation === "+" ? delta : -delta),
+ zoomPosX - targetElement.getBoundingClientRect().left,
+ zoomPosY - targetElement.getBoundingClientRect().top
+ );
+
+ targetElement.isZoomed = true;
+ }
+ }
+
+ /**
+ * This function fits the target element to the screen by calculating
+ * the required scale and offsets. It also updates the global variables
+ * zoomLevel, panX, and panY to reflect the new state.
+ */
+
+ function fitToElement() {
+ //Reset Zoom
+ targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
+
+ let parentElement;
+
+ if (isExtension) {
+ parentElement = targetElement.closest('[id^="component-"]');
+ } else {
+ parentElement = targetElement.parentElement;
+ }
+
+
+ // Get element and screen dimensions
+ const elementWidth = targetElement.offsetWidth;
+ const elementHeight = targetElement.offsetHeight;
+
+ const screenWidth = parentElement.clientWidth;
+ const screenHeight = parentElement.clientHeight;
+
+ // Get element's coordinates relative to the parent element
+ const elementRect = targetElement.getBoundingClientRect();
+ const parentRect = parentElement.getBoundingClientRect();
+ const elementX = elementRect.x - parentRect.x;
+
+ // Calculate scale and offsets
+ const scaleX = screenWidth / elementWidth;
+ const scaleY = screenHeight / elementHeight;
+ const scale = Math.min(scaleX, scaleY);
+
+ const transformOrigin =
+ window.getComputedStyle(targetElement).transformOrigin;
+ const [originX, originY] = transformOrigin.split(" ");
+ const originXValue = parseFloat(originX);
+ const originYValue = parseFloat(originY);
+
+ const offsetX =
+ (screenWidth - elementWidth * scale) / 2 -
+ originXValue * (1 - scale);
+ const offsetY =
+ (screenHeight - elementHeight * scale) / 2.5 -
+ originYValue * (1 - scale);
+
+ // Apply scale and offsets to the element
+ targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
+
+ // Update global variables
+ elemData[elemId].zoomLevel = scale;
+ elemData[elemId].panX = offsetX;
+ elemData[elemId].panY = offsetY;
+
+ fullScreenMode = false;
+ toggleOverlap("off");
+ }
+
+ /**
+ * This function fits the target element to the screen by calculating
+ * the required scale and offsets. It also updates the global variables
+ * zoomLevel, panX, and panY to reflect the new state.
+ */
+
+ // Fullscreen mode
+ function fitToScreen() {
+ const canvas = gradioApp().querySelector(
+ `${elemId} canvas[key="interface"]`
+ );
+
+ if (!canvas) return;
+
+ if (canvas.offsetWidth > 862 || isExtension) {
+ targetElement.style.width = (canvas.offsetWidth + 2) + "px";
+ }
+
+ if (isExtension) {
+ targetElement.style.overflow = "visible";
+ }
+
+ if (fullScreenMode) {
+ resetZoom();
+ fullScreenMode = false;
+ return;
+ }
+
+ //Reset Zoom
+ targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
+
+ // Get scrollbar width to right-align the image
+ const scrollbarWidth =
+ window.innerWidth - document.documentElement.clientWidth;
+
+ // Get element and screen dimensions
+ const elementWidth = targetElement.offsetWidth;
+ const elementHeight = targetElement.offsetHeight;
+ const screenWidth = window.innerWidth - scrollbarWidth;
+ const screenHeight = window.innerHeight;
+
+ // Get element's coordinates relative to the page
+ const elementRect = targetElement.getBoundingClientRect();
+ const elementY = elementRect.y;
+ const elementX = elementRect.x;
+
+ // Calculate scale and offsets
+ const scaleX = screenWidth / elementWidth;
+ const scaleY = screenHeight / elementHeight;
+ const scale = Math.min(scaleX, scaleY);
+
+ // Get the current transformOrigin
+ const computedStyle = window.getComputedStyle(targetElement);
+ const transformOrigin = computedStyle.transformOrigin;
+ const [originX, originY] = transformOrigin.split(" ");
+ const originXValue = parseFloat(originX);
+ const originYValue = parseFloat(originY);
+
+ // Calculate offsets with respect to the transformOrigin
+ const offsetX =
+ (screenWidth - elementWidth * scale) / 2 -
+ elementX -
+ originXValue * (1 - scale);
+ const offsetY =
+ (screenHeight - elementHeight * scale) / 2 -
+ elementY -
+ originYValue * (1 - scale);
+
+ // Apply scale and offsets to the element
+ targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
+
+ // Update global variables
+ elemData[elemId].zoomLevel = scale;
+ elemData[elemId].panX = offsetX;
+ elemData[elemId].panY = offsetY;
+
+ fullScreenMode = true;
+ toggleOverlap("on");
+ }
+
+ // Handle keydown events
+ function handleKeyDown(event) {
+ // Disable key locks to make pasting from the buffer work correctly
+ if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") {
+ return;
+ }
+
+ // before activating shortcut, ensure user is not actively typing in an input field
+ if (!hotkeysConfig.canvas_blur_prompt) {
+ if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') {
+ return;
+ }
+ }
+
+
+ const hotkeyActions = {
+ [hotkeysConfig.canvas_hotkey_reset]: resetZoom,
+ [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
+ [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
+ };
+
+ const action = hotkeyActions[event.code];
+ if (action) {
+ event.preventDefault();
+ action(event);
+ }
+
+ if (
+ isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) ||
+ isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust)
+ ) {
+ event.preventDefault();
+ }
+ }
+
+ // Get Mouse position
+ function getMousePosition(e) {
+ mouseX = e.offsetX;
+ mouseY = e.offsetY;
+ }
+
+ // Simulation of the function to put a long image into the screen.
+ // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element.
+ // We hide the image and show it to the user when it is ready.
+
+ targetElement.isExpanded = false;
+ function autoExpand() {
+ const canvas = document.querySelector(`${elemId} canvas[key="interface"]`);
+ if (canvas) {
+ if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) {
+ targetElement.style.visibility = "hidden";
+ setTimeout(() => {
+ fitToScreen();
+ resetZoom();
+ targetElement.style.visibility = "visible";
+ targetElement.isExpanded = true;
+ }, 10);
+ }
+ }
+ }
+
+ targetElement.addEventListener("mousemove", getMousePosition);
+
+ //observers
+ // Creating an observer with a callback function to handle DOM changes
+ const observer = new MutationObserver((mutationsList, observer) => {
+ for (let mutation of mutationsList) {
+ // If the style attribute of the canvas has changed, by observation it happens only when the picture changes
+ if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
+ mutation.target.tagName.toLowerCase() === 'canvas') {
+ targetElement.isExpanded = false;
+ setTimeout(resetZoom, 10);
+ }
+ }
+ });
+
+ // Apply auto expand if enabled
+ if (hotkeysConfig.canvas_auto_expand) {
+ targetElement.addEventListener("mousemove", autoExpand);
+ // Set up an observer to track attribute changes
+ observer.observe(targetElement, {attributes: true, childList: true, subtree: true});
+ }
+
+ // Handle events only inside the targetElement
+ let isKeyDownHandlerAttached = false;
+
+ function handleMouseMove() {
+ if (!isKeyDownHandlerAttached) {
+ document.addEventListener("keydown", handleKeyDown);
+ isKeyDownHandlerAttached = true;
+
+ activeElement = elemId;
+ }
+ }
+
+ function handleMouseLeave() {
+ if (isKeyDownHandlerAttached) {
+ document.removeEventListener("keydown", handleKeyDown);
+ isKeyDownHandlerAttached = false;
+
+ activeElement = null;
+ }
+ }
+
+ // Add mouse event handlers
+ targetElement.addEventListener("mousemove", handleMouseMove);
+ targetElement.addEventListener("mouseleave", handleMouseLeave);
+
+ // Reset zoom when click on another tab
+ elements.img2imgTabs.addEventListener("click", resetZoom);
+ elements.img2imgTabs.addEventListener("click", () => {
+ // targetElement.style.width = "";
+ if (parseInt(targetElement.style.width) > 865) {
+ setTimeout(fitToElement, 0);
+ }
+ });
+
+ targetElement.addEventListener("wheel", e => {
+ // change zoom level
+ const operation = e.deltaY > 0 ? "-" : "+";
+ changeZoomLevel(operation, e);
+
+ // Handle brush size adjustment with ctrl key pressed
+ if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) {
+ e.preventDefault();
+
+ // Increase or decrease brush size based on scroll direction
+ adjustBrushSize(elemId, e.deltaY);
+ }
+ });
+
+ // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
+ function handleMoveKeyDown(e) {
+
+ // Disable key locks to make pasting from the buffer work correctly
+ if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") {
+ return;
+ }
+
+ // before activating shortcut, ensure user is not actively typing in an input field
+ if (!hotkeysConfig.canvas_blur_prompt) {
+ if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') {
+ return;
+ }
+ }
+
+
+ if (e.code === hotkeysConfig.canvas_hotkey_move) {
+ if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) {
+ e.preventDefault();
+ document.activeElement.blur();
+ isMoving = true;
+ }
+ }
+ }
+
+ function handleMoveKeyUp(e) {
+ if (e.code === hotkeysConfig.canvas_hotkey_move) {
+ isMoving = false;
+ }
+ }
+
+ document.addEventListener("keydown", handleMoveKeyDown);
+ document.addEventListener("keyup", handleMoveKeyUp);
+
+ // Detect zoom level and update the pan speed.
+ function updatePanPosition(movementX, movementY) {
+ let panSpeed = 2;
+
+ if (elemData[elemId].zoomLevel > 8) {
+ panSpeed = 3.5;
+ }
+
+ elemData[elemId].panX += movementX * panSpeed;
+ elemData[elemId].panY += movementY * panSpeed;
+
+ // Delayed redraw of an element
+ requestAnimationFrame(() => {
+ targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`;
+ toggleOverlap("on");
+ });
+ }
+
+ function handleMoveByKey(e) {
+ if (isMoving && elemId === activeElement) {
+ updatePanPosition(e.movementX, e.movementY);
+ targetElement.style.pointerEvents = "none";
+
+ if (isExtension) {
+ targetElement.style.overflow = "visible";
+ }
+
+ } else {
+ targetElement.style.pointerEvents = "auto";
+ }
+ }
+
+ // Prevents sticking to the mouse
+ window.onblur = function() {
+ isMoving = false;
+ };
+
+ // Checks for extension
+ function checkForOutBox() {
+ const parentElement = targetElement.closest('[id^="component-"]');
+ if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) {
+ resetZoom();
+ targetElement.isExpanded = true;
+ }
+
+ if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) {
+ resetZoom();
+ }
+
+ if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) {
+ resetZoom();
+ }
+ }
+
+ if (isExtension) {
+ targetElement.addEventListener("mousemove", checkForOutBox);
+ }
+
+
+ window.addEventListener('resize', (e) => {
+ resetZoom();
+
+ if (isExtension) {
+ targetElement.isExpanded = false;
+ targetElement.isZoomed = false;
+ }
+ });
+
+ gradioApp().addEventListener("mousemove", handleMoveByKey);
+
+
+ }
+
+ applyZoomAndPan(elementIDs.sketch, false);
+ applyZoomAndPan(elementIDs.inpaint, false);
+ applyZoomAndPan(elementIDs.inpaintSketch, false);
+
+ // Make the function global so that other extensions can take advantage of this solution
+ const applyZoomAndPanIntegration = async(id, elementIDs) => {
+ const mainEl = document.querySelector(id);
+ if (id.toLocaleLowerCase() === "none") {
+ for (const elementID of elementIDs) {
+ const el = await waitForElement(elementID);
+ if (!el) break;
+ applyZoomAndPan(elementID);
+ }
+ return;
+ }
+
+ if (!mainEl) return;
+ mainEl.addEventListener("click", async() => {
+ for (const elementID of elementIDs) {
+ const el = await waitForElement(elementID);
+ if (!el) break;
+ applyZoomAndPan(elementID);
+ }
+ }, {once: true});
+ };
+
+ window.applyZoomAndPan = applyZoomAndPan; // Only 1 elements, argument elementID, for example applyZoomAndPan("#txt2img_controlnet_ControlNet_input_image")
+
+ window.applyZoomAndPanIntegration = applyZoomAndPanIntegration; // for any extension
+
+ /*
+ The function `applyZoomAndPanIntegration` takes two arguments:
+
+ 1. `id`: A string identifier for the element to which zoom and pan functionality will be applied on click.
+ If the `id` value is "none", the functionality will be applied to all elements specified in the second argument without a click event.
+
+ 2. `elementIDs`: An array of string identifiers for elements. Zoom and pan functionality will be applied to each of these elements on click of the element specified by the first argument.
+ If "none" is specified in the first argument, the functionality will be applied to each of these elements without a click event.
+
+ Example usage:
+ applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]);
+ In this example, zoom and pan functionality will be applied to the element with the identifier "txt2img_controlnet_ControlNet_input_image" upon clicking the element with the identifier "txt2img_controlnet".
+ */
+
+ // More examples
+ // Add integration with ControlNet txt2img One TAB
+ // applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]);
+
+ // Add integration with ControlNet txt2img Tabs
+ // applyZoomAndPanIntegration("#txt2img_controlnet",Array.from({ length: 10 }, (_, i) => `#txt2img_controlnet_ControlNet-${i}_input_image`));
+
+ // Add integration with Inpaint Anything
+ // applyZoomAndPanIntegration("None", ["#ia_sam_image", "#ia_sel_mask"]);
+});
diff --git a/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/__pycache__/hotkey_config.cpython-39.pyc b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/__pycache__/hotkey_config.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b5524837da155e8da7997db82c99e6d3bab15481
Binary files /dev/null and b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/__pycache__/hotkey_config.cpython-39.pyc differ
diff --git a/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d8d2d1c014be5dc1bac24b2c71079351fe1177e
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
@@ -0,0 +1,15 @@
+import gradio as gr
+from modules import shared
+
+shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
+ "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+ "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+ "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
+ "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
+ "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
+ "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
+ "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
+ "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"),
+ "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
+ "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
+}))
diff --git a/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/style.css b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/style.css
new file mode 100644
index 0000000000000000000000000000000000000000..5d8054e65196408c97791727088088650f102b21
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/style.css
@@ -0,0 +1,66 @@
+.canvas-tooltip-info {
+ position: absolute;
+ top: 10px;
+ left: 10px;
+ cursor: help;
+ background-color: rgba(0, 0, 0, 0.3);
+ width: 20px;
+ height: 20px;
+ border-radius: 50%;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ flex-direction: column;
+
+ z-index: 100;
+}
+
+.canvas-tooltip-info::after {
+ content: '';
+ display: block;
+ width: 2px;
+ height: 7px;
+ background-color: white;
+ margin-top: 2px;
+}
+
+.canvas-tooltip-info::before {
+ content: '';
+ display: block;
+ width: 2px;
+ height: 2px;
+ background-color: white;
+}
+
+.canvas-tooltip-content {
+ display: none;
+ background-color: #f9f9f9;
+ color: #333;
+ border: 1px solid #ddd;
+ padding: 15px;
+ position: absolute;
+ top: 40px;
+ left: 10px;
+ width: 250px;
+ font-size: 16px;
+ opacity: 0;
+ border-radius: 8px;
+ box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
+
+ z-index: 100;
+}
+
+.canvas-tooltip:hover .canvas-tooltip-content {
+ display: block;
+ animation: fadeIn 0.5s;
+ opacity: 1;
+}
+
+@keyframes fadeIn {
+ from {opacity: 0;}
+ to {opacity: 1;}
+}
+
+.styler {
+ overflow:inherit !important;
+}
\ No newline at end of file
diff --git a/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/__pycache__/extra_options_section.cpython-39.pyc b/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/__pycache__/extra_options_section.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..baa70444eccf06b78423a882df91f5dbc6ad6157
Binary files /dev/null and b/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/__pycache__/extra_options_section.cpython-39.pyc differ
diff --git a/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/extra_options_section.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff8c9fc2c24e11a7dbed213bc749836ba9d194de
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/extra_options_section.py
@@ -0,0 +1,74 @@
+import math
+
+import gradio as gr
+from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
+from modules.ui_components import FormColumn
+
+
+class ExtraOptionsSection(scripts.Script):
+ section = "extra_options"
+
+ def __init__(self):
+ self.comps = None
+ self.setting_names = None
+
+ def title(self):
+ return "Extra options"
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img):
+ self.comps = []
+ self.setting_names = []
+ self.infotext_fields = []
+ extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
+
+ mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
+
+ with gr.Blocks() as interface:
+ with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():
+
+ row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols)
+
+ for row in range(row_count):
+ with gr.Row():
+ for col in range(shared.opts.extra_options_cols):
+ index = row * shared.opts.extra_options_cols + col
+ if index >= len(extra_options):
+ break
+
+ setting_name = extra_options[index]
+
+ with FormColumn():
+ comp = ui_settings.create_setting_component(setting_name)
+
+ self.comps.append(comp)
+ self.setting_names.append(setting_name)
+
+ setting_infotext_name = mapping.get(setting_name)
+ if setting_infotext_name is not None:
+ self.infotext_fields.append((comp, setting_infotext_name))
+
+ def get_settings_values():
+ res = [ui_settings.get_value_for_setting(key) for key in self.setting_names]
+ return res[0] if len(res) == 1 else res
+
+ interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False)
+
+ return self.comps
+
+ def before_process(self, p, *args):
+ for name, value in zip(self.setting_names, args):
+ if name not in p.override_settings:
+ p.override_settings[name] = value
+
+
+shared.options_templates.update(shared.options_section(('ui', "User interface"), {
+ "extra_options_txt2img": shared.OptionInfo([], "Options in main UI - txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
+ "extra_options_img2img": shared.OptionInfo([], "Options in main UI - img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
+ "extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
+ "extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
+}))
+
+
diff --git a/stable-diffusion-webui/extensions-builtin/mobile/javascript/mobile.js b/stable-diffusion-webui/extensions-builtin/mobile/javascript/mobile.js
new file mode 100644
index 0000000000000000000000000000000000000000..652f07ac7eceb7ac780d6c19c1be85480471491a
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/mobile/javascript/mobile.js
@@ -0,0 +1,32 @@
+var isSetupForMobile = false;
+
+function isMobile() {
+ for (var tab of ["txt2img", "img2img"]) {
+ var imageTab = gradioApp().getElementById(tab + '_results');
+ if (imageTab && imageTab.offsetParent && imageTab.offsetLeft == 0) {
+ return true;
+ }
+ }
+
+ return false;
+}
+
+function reportWindowSize() {
+ var currentlyMobile = isMobile();
+ if (currentlyMobile == isSetupForMobile) return;
+ isSetupForMobile = currentlyMobile;
+
+ for (var tab of ["txt2img", "img2img"]) {
+ var button = gradioApp().getElementById(tab + '_generate_box');
+ var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column');
+ target.insertBefore(button, target.firstElementChild);
+
+ gradioApp().getElementById(tab + '_results').classList.toggle('mobile', currentlyMobile);
+ }
+}
+
+window.addEventListener("resize", reportWindowSize);
+
+onUiLoaded(function() {
+ reportWindowSize();
+});
diff --git a/stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
new file mode 100644
index 0000000000000000000000000000000000000000..114cf94ccbf69b473757f2fc46443a39723a9269
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
@@ -0,0 +1,42 @@
+// Stable Diffusion WebUI - Bracket checker
+// By Hingashi no Florin/Bwin4L & @akx
+// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
+// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
+
+function checkBrackets(textArea, counterElt) {
+ var counts = {};
+ (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
+ counts[bracket] = (counts[bracket] || 0) + 1;
+ });
+ var errors = [];
+
+ function checkPair(open, close, kind) {
+ if (counts[open] !== counts[close]) {
+ errors.push(
+ `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
+ );
+ }
+ }
+
+ checkPair('(', ')', 'round brackets');
+ checkPair('[', ']', 'square brackets');
+ checkPair('{', '}', 'curly brackets');
+ counterElt.title = errors.join('\n');
+ counterElt.classList.toggle('error', errors.length !== 0);
+}
+
+function setupBracketChecking(id_prompt, id_counter) {
+ var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
+ var counter = gradioApp().getElementById(id_counter);
+
+ if (textarea && counter) {
+ textarea.addEventListener("input", () => checkBrackets(textarea, counter));
+ }
+}
+
+onUiLoaded(function() {
+ setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
+ setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
+ setupBracketChecking('img2img_prompt', 'img2img_token_counter');
+ setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
+});
diff --git a/stable-diffusion-webui/extensions/put extensions here.txt b/stable-diffusion-webui/extensions/put extensions here.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.bunfig.toml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.bunfig.toml
new file mode 100644
index 0000000000000000000000000000000000000000..d6bb75b00762b1759f1e4344fe1209bf6da429e7
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.bunfig.toml
@@ -0,0 +1,3 @@
+[install.lockfile]
+
+save = false
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.changelogrc.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.changelogrc.js
new file mode 100644
index 0000000000000000000000000000000000000000..9a2f5f98d8cd6109c7e60df4b427daf6d0251e82
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.changelogrc.js
@@ -0,0 +1 @@
+module.exports = require('@lobehub/lint').changelog;
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.commitlintrc.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.commitlintrc.js
new file mode 100644
index 0000000000000000000000000000000000000000..9b8c6ace5a00412b589a886fd24eb16b6717bf42
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.commitlintrc.js
@@ -0,0 +1 @@
+module.exports = require('@lobehub/lint').commitlint;
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.editorconfig b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.editorconfig
new file mode 100644
index 0000000000000000000000000000000000000000..ca3ba3b38703f88338d348c3a20da8ee4e75a55c
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.editorconfig
@@ -0,0 +1,20 @@
+# http://editorconfig.org
+root = true
+
+[*]
+indent_style = space
+indent_size = 2
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+
+# Python
+[*.py]
+indent_size = 4
+
+[*.md]
+trim_trailing_whitespace = false
+
+[Makefile]
+indent_style = tab
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.eslintignore b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.eslintignore
new file mode 100644
index 0000000000000000000000000000000000000000..9b68058ef3098a2e105b72e91fd38b383c4bd297
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.eslintignore
@@ -0,0 +1,31 @@
+# Eslintignore for LobeHub
+################################################################
+
+# dependencies
+node_modules
+
+# ci
+.coverage
+
+# test
+jest*
+_test_
+__test__
+
+# umi
+.umi
+.umi-production
+.umi-test
+.dumi/tmp*
+
+# production
+dist
+es
+lib
+logs
+
+# misc
+# add other ignore file below
+javascript
+style.css
+vite-env.d.ts
\ No newline at end of file
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.eslintrc.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.eslintrc.js
new file mode 100644
index 0000000000000000000000000000000000000000..47a3d33f9366c4a855af05bc0505c3d9eacd53c2
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.eslintrc.js
@@ -0,0 +1,19 @@
+const config = require('@lobehub/lint').eslint;
+
+config.rules['indent'] = ['off', 2];
+
+module.exports = {
+ ...config,
+ overrides: [
+ {
+ files: ['*.ts', '*.tsx'],
+ rules: {
+ 'linebreak-style': 0,
+ 'no-undef': 0,
+ 'object-curly-spacing': 0,
+ 'unicorn/prefer-add-event-listener': 0,
+ 'unused-imports/no-unused-imports': 0,
+ },
+ },
+ ],
+};
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/1_bug_report.yml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/1_bug_report.yml
new file mode 100644
index 0000000000000000000000000000000000000000..58805282ccab9473513ff1594e5555432523bfc9
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/1_bug_report.yml
@@ -0,0 +1,53 @@
+name: '🐛 反馈缺陷 Bug Report'
+description: '反馈一个问题缺陷 | Report an bug'
+title: '[Bug] '
+labels: '🐛 Bug'
+body:
+ - type: dropdown
+ attributes:
+ label: '💻 系统环境 | Operating System'
+ options:
+ - Windows
+ - macOS
+ - Ubuntu
+ - Other Linux
+ - Other
+ validations:
+ required: true
+ - type: dropdown
+ attributes:
+ label: '🌐 浏览器 | Browser'
+ options:
+ - Chrome
+ - Edge
+ - Safari
+ - Firefox
+ - Other
+ validations:
+ required: true
+ - type: input
+ attributes:
+ label: '📦 SD Wbui 版本或提交 | Version or Commit'
+ - type: input
+ attributes:
+ label: '📦 Lobe Theme 版本或提交 | Version or Commit'
+ - type: textarea
+ attributes:
+ label: '🐛 问题描述 | Bug Description'
+ description: |
+ 清晰且简洁地描述你期待发生的事情。请按照以下的故障排除步骤操作，更新 webui 和 lobe 主题到最新版本。如果问题仍然存在，尝试检查是否有其他插件与之冲突，并提供冲突插件的名称。
+ A clear and concise description of your problem. Please follow the troubleshooting steps below, update the webui and lobe theme to the latest version. If the problem persists, try to check if there is a conflict with other plugins and provide the name of the conflicting plugin.
+ validations:
+ required: true
+ - type: textarea
+ attributes:
+ label: '🚦 期望结果 | Expected Behavior'
+ description: A clear and concise description of what you expected to happen.
+ - type: textarea
+ attributes:
+ label: '📷 复现步骤 | Recurrence Steps'
+ description: A clear and concise description of how to recurrence.
+ - type: textarea
+ attributes:
+ label: '📝 补充信息 | Additional Information'
+ description: If your problem needs further explanation, or if the issue you're seeing cannot be reproduced in a gist, please add more information here.
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/2_feature_request.yml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/2_feature_request.yml
new file mode 100644
index 0000000000000000000000000000000000000000..edcf7d0643cc31fde3d574ca1316ec33729f9f38
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/2_feature_request.yml
@@ -0,0 +1,21 @@
+name: '🌠 功能需求 Feature Request'
+description: '需求或建议 | Suggest an idea'
+title: '[Request] '
+labels: '🌠 Feature Request'
+body:
+ - type: textarea
+ attributes:
+ label: '🥰 需求描述 | Feature Description'
+ description: Please add a clear and concise description of the problem you are seeking to solve with this feature request.
+ validations:
+ required: true
+ - type: textarea
+ attributes:
+ label: '🧐 解决方案 | Proposed Solution'
+ description: Describe the solution you'd like in a clear and concise manner.
+ validations:
+ required: true
+ - type: textarea
+ attributes:
+ label: '📝 补充信息 | Additional Information'
+ description: Add any other context about the problem here.
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/3_question.yml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/3_question.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ca3ca0952da6fe012e82f5a290eb017cebf9c46a
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/3_question.yml
@@ -0,0 +1,17 @@
+name: '😇 疑问或帮助 Help Wanted'
+description: '疑问或需要帮助 | Need help'
+title: '[Question] '
+labels: '😇 Help Wanted'
+body:
+ - type: textarea
+ attributes:
+ label: '🧐 问题描述 | Proposed Solution'
+ description: |
+ 清晰且简洁地描述你遇到的问题。请按照以下的故障排除步骤操作，更新 webui 和 lobe 主题到最新版本。如果问题仍然存在，尝试检查是否有其他插件与之冲突，并提供冲突插件的名称。
+ A clear and concise description of what your problem. Please follow the troubleshooting steps below, update the webui and lobe theme to the latest version. If the problem persists, try to check if there is a conflict with other plugins and provide the name of the conflicting plugin.
+ validations:
+ required: true
+ - type: textarea
+ attributes:
+ label: '📝 补充信息 | Additional Information'
+ description: Add any other context about the problem here.
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/other.md b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/other.md
new file mode 100644
index 0000000000000000000000000000000000000000..215dd1f39d8a39df3519c3409da444bfeaaef19d
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/ISSUE_TEMPLATE/other.md
@@ -0,0 +1,7 @@
+---
+name: '📝 其他 Other'
+about: '其他问题 | Other issues'
+title: ''
+labels: ''
+assignees: ''
+---
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/PULL_REQUEST_TEMPLATE.md b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000000000000000000000000000000000000..7707b1aa939fcf75eb8a66899366ba1c0bd9f416
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,17 @@
+#### 💻 变更类型 | Change Type
+
+
+
+- [ ] ✨ feat
+- [ ] 🐛 fix
+- [ ] 💄 style
+- [ ] 🔨 chore
+- [ ] 📝 docs
+
+#### 🔀 变更说明 | Description of Change
+
+
+
+#### 📝 补充信息 | Additional Information
+
+
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issue-auto-comments.yml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issue-auto-comments.yml
new file mode 100644
index 0000000000000000000000000000000000000000..1264fdf40fcfca6b3ec37cd89eb721d7981a3b72
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issue-auto-comments.yml
@@ -0,0 +1,73 @@
+name: Issue Auto Comment
+on:
+ issues:
+ types:
+ - opened
+ - closed
+ - assigned
+ pull_request_target:
+ types:
+ - opened
+ - closed
+
+permissions:
+ contents: read
+
+jobs:
+ run:
+ permissions:
+ issues: write # for actions-cool/issues-helper to update issues
+ pull-requests: write # for actions-cool/issues-helper to update PRs
+ runs-on: ubuntu-latest
+ steps:
+ - name: Auto Comment on Issues Opened
+ uses: wow-actions/auto-comment@v1
+ with:
+ GITHUB_TOKEN: ${{ secrets.GH_TOKEN}}
+ issuesOpened: |
+ 👀 @{{ author }}
+
+ Thank you for raising an issue. We will investigate into the matter and get back to you as soon as possible.
+ Please make sure you have given us as much context as possible.\
+ 非常感谢您提交 issue。我们会尽快调查此事，并尽快回复您。 请确保您已经提供了尽可能多的背景信息。
+ - name: Auto Comment on Issues Closed
+ uses: wow-actions/auto-comment@v1
+ with:
+ GITHUB_TOKEN: ${{ secrets.GH_TOKEN}}
+ issuesClosed: |
+ ✅ @{{ author }}
+
+ This issue is closed, If you have any questions, you can comment and reply.\
+ 此问题已经关闭。如果您有任何问题，可以留言并回复。
+ - name: Auto Comment on Pull Request Opened
+ uses: wow-actions/auto-comment@v1
+ with:
+ GITHUB_TOKEN: ${{ secrets.GH_TOKEN}}
+ pullRequestOpened: |
+ 👍 @{{ author }}
+
+ Thank you for raising your pull request and contributing to our Community
+ Please make sure you have followed our contributing guidelines. We will review it as soon as possible.
+ If you encounter any problems, please feel free to connect with us.\
+ 非常感谢您提出拉取请求并为我们的社区做出贡献，请确保您已经遵循了我们的贡献指南，我们会尽快审查它。
+ 如果您遇到任何问题，请随时与我们联系。
+ - name: Auto Comment on Pull Request Merged
+ uses: actions-cool/pr-welcome@main
+ if: github.event.pull_request.merged == true
+ with:
+ token: ${{ secrets.GH_TOKEN }}
+ comment: |
+ ❤️ Great PR @${{ github.event.pull_request.user.login }} ❤️
+
+ The growth of project is inseparable from user feedback and contribution, thanks for your contribution!\
+ 项目的成长离不开用户反馈和贡献，感谢您的贡献!
+ emoji: 'hooray'
+ pr-emoji: '+1, heart'
+ - name: Remove inactive
+ if: github.event.issue.state == 'open' && github.actor == github.event.issue.user.login
+ uses: actions-cool/issues-helper@v3
+ with:
+ actions: 'remove-labels'
+ token: ${{ secrets.GH_TOKEN }}
+ issue-number: ${{ github.event.issue.number }}
+ labels: 'Inactive'
\ No newline at end of file
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issue-close-require.yml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issue-close-require.yml
new file mode 100644
index 0000000000000000000000000000000000000000..bf1416db86627e8ec055c9aec732a390c6ef23e2
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issue-close-require.yml
@@ -0,0 +1,78 @@
+name: Issue Close Require
+
+on:
+ workflow_dispatch:
+ schedule:
+ - cron: '0 0 * * *'
+
+permissions:
+ contents: read
+
+jobs:
+ issue-check-inactive:
+ permissions:
+ issues: write # for actions-cool/issues-helper to update issues
+ pull-requests: write # for actions-cool/issues-helper to update PRs
+ runs-on: ubuntu-latest
+ steps:
+ - name: check-inactive
+ uses: actions-cool/issues-helper@v3
+ with:
+ actions: 'check-inactive'
+ token: ${{ secrets.GH_TOKEN }}
+ inactive-label: 'Inactive'
+ inactive-day: 30
+
+ issue-close-require:
+ permissions:
+ issues: write # for actions-cool/issues-helper to update issues
+ pull-requests: write # for actions-cool/issues-helper to update PRs
+ runs-on: ubuntu-latest
+ steps:
+ - name: need reproduce
+ uses: actions-cool/issues-helper@v3
+ with:
+ actions: 'close-issues'
+ token: ${{ secrets.GH_TOKEN }}
+ labels: '✅ Fixed'
+ inactive-day: 3
+ body: |
+ 👋 @{{ github.event.issue.user.login }}
+
+ Since the issue was labeled with `✅ Fixed`, but no response in 3 days. This issue will be closed. If you have any questions, you can comment and reply.\
+ 由于该 issue 被标记为已修复，同时 3 天未收到回应。现关闭 issue，若有任何问题，可评论回复。
+ - name: need reproduce
+ uses: actions-cool/issues-helper@v3
+ with:
+ actions: 'close-issues'
+ token: ${{ secrets.GH_TOKEN }}
+ labels: '🤔 Need Reproduce'
+ inactive-day: 3
+ body: |
+ 👋 @{{ github.event.issue.user.login }}
+
+ Since the issue was labeled with `🤔 Need Reproduce`, but no response in 3 days. This issue will be closed. If you have any questions, you can comment and reply.\
+ 由于该 issue 被标记为需要更多信息，却 3 天未收到回应。现关闭 issue，若有任何问题，可评论回复。
+ - name: need reproduce
+ uses: actions-cool/issues-helper@v3
+ with:
+ actions: 'close-issues'
+ token: ${{ secrets.GH_TOKEN }}
+ labels: "🙅🏻♀️ WON'T DO"
+ inactive-day: 3
+ body: |
+ 👋 @{{ github.event.issue.user.login }}
+
+ Since the issue was labeled with `🙅🏻♀️ WON'T DO`, and no response in 3 days. This issue will be closed. If you have any questions, you can comment and reply.\
+ 由于该 issue 被标记为暂不处理，同时 3 天未收到回应。现关闭 issue，若有任何问题，可评论回复。
+ - name: need reproduce
+ uses: actions-cool/issues-helper@v3
+ with:
+ actions: 'close-issues'
+ token: ${{ secrets.GH_TOKEN }}
+ labels: "Inactive"
+ inactive-day: 30
+ body: |
+ Since the issue was labeled with `Inactive`, and no response in 30 days. This issue will be closed. If you have any questions, you can comment and reply.
+
+ 由于该 issue 被标记为 Inactive，同时 30 天未收到回应。现关闭 issue，若有任何问题，可评论回复。
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issues-translate.yml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issues-translate.yml
new file mode 100644
index 0000000000000000000000000000000000000000..0c201dd6bbb787212c075fd0fac8b51a4402d580
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/issues-translate.yml
@@ -0,0 +1,14 @@
+name: Issue Translate
+on:
+ issue_comment:
+ types: [created]
+ issues:
+ types: [opened]
+
+jobs:
+ build:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: usthe/issues-translate-action@v2.7
+ with:
+ BOT_GITHUB_TOKEN: ${{ secrets.GH_TOKEN }}
\ No newline at end of file
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/release.yml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/release.yml
new file mode 100644
index 0000000000000000000000000000000000000000..b24a8bb312f660e35a9c525cff7b5f3e6ccf4a15
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/release.yml
@@ -0,0 +1,32 @@
+name: Release CI
+on:
+ push:
+ branches:
+ - main
+
+jobs:
+ release:
+ name: Release
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Install bun
+ uses: oven-sh/setup-bun@v1
+
+ - name: Install deps
+ run: bun i
+
+ - name: CI
+ run: bun run ci
+
+ - name: Test
+ run: bun run test
+
+# - name: Build
+# run: bun run build
+
+ - name: Release
+ run: bun run release
+ env:
+ GITHUB_TOKEN: ${{ secrets.GH_TOKEN }}
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/test.yml b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..9e0238fc109a04518937673bd6d0cdf06fe7683f
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.github/workflows/test.yml
@@ -0,0 +1,27 @@
+name: Test CI
+on:
+ pull_request:
+ push:
+ branches:
+ - '!main'
+jobs:
+ test:
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Install bun
+ uses: oven-sh/setup-bun@v1
+
+ - name: Install deps
+ run: bun i
+
+ - name: CI
+ run: bun run ci
+
+ - name: Test and coverage
+ run: bun run test:coverage
+
+ - name: Upload coverage to Codecov
+ uses: codecov/codecov-action@v3
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.gitignore b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..67e9fe2e4a02946288ed4b2270ff0a1ae1ae65f7
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.gitignore
@@ -0,0 +1,47 @@
+# Gitignore for LobeHub
+################################################################
+
+# general
+.DS_Store
+.idea
+.vscode
+.history
+.temp
+.env.local
+venv
+temp
+tmp
+
+# dependencies
+node_modules
+*.log
+*.lock
+package-lock.json
+
+# ci
+.coverage
+.eslintcache
+.stylelintcache
+
+# production
+dist
+es
+logs
+test-output
+
+# umi
+.umi
+.umi-production
+.umi-test
+.dumi/tmp*
+
+# husky
+.husky/prepare-commit-msg
+
+# misc
+# add other ignore file below
+__pycache__
+/lobe_theme_config.json
+/javascript/**/*
+!/javascript/main.js
+bun.lockb
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.husky/pre-commit b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.husky/pre-commit
new file mode 100644
index 0000000000000000000000000000000000000000..1e98954165c0e409b523dd7df0ff3ac1e9c6ff96
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.husky/pre-commit
@@ -0,0 +1,7 @@
+#!/usr/bin/env sh
+. "$(dirname -- "$0")/_/husky.sh"
+
+git add .
+npx --no-install lint-staged
+npm run test
+git add .
\ No newline at end of file
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.i18nrc.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.i18nrc.js
new file mode 100644
index 0000000000000000000000000000000000000000..319c6e876a0a6c7a25996e8218e7ce115648b37c
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.i18nrc.js
@@ -0,0 +1,16 @@
+const { description } = require('./package.json');
+const { defineConfig } = require('@lobehub/i18n-cli');
+const options = require('./locales/options.json');
+
+const outputLocales = options.map((item) => item.value).filter((item) => item !== 'en_US');
+
+module.exports = defineConfig({
+ reference: description,
+ entry: 'locales/en_US.json',
+ entryLocale: 'en_US',
+ output: 'locales',
+ outputLocales: outputLocales,
+ splitToken: 2500,
+ temperature: 0,
+ modelName: 'gpt-3.5-turbo',
+});
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.npmrc b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.npmrc
new file mode 100644
index 0000000000000000000000000000000000000000..3b1f0e24d29e764989f5782b0d01b6d72f45c384
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.npmrc
@@ -0,0 +1,11 @@
+lockfile=false
+resolution-mode=highest
+public-hoist-pattern[]=*@umijs/lint*
+public-hoist-pattern[]=*changelog*
+public-hoist-pattern[]=*remark*
+public-hoist-pattern[]=*eslint*
+public-hoist-pattern[]=*stylelint*
+public-hoist-pattern[]=*prettier*
+public-hoist-pattern[]=*commitlint*
+public-hoist-pattern[]=*postcss*
+public-hoist-pattern[]=*semantic-release*
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.prettierignore b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.prettierignore
new file mode 100644
index 0000000000000000000000000000000000000000..667e7e4639ad32faad9665185567b29f40e925b7
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.prettierignore
@@ -0,0 +1,61 @@
+# Prettierignore for LobeHub
+################################################################
+
+# general
+.DS_Store
+.editorconfig
+.idea
+.vscode
+.history
+.temp
+.env.local
+.husky
+.npmrc
+venv
+temp
+tmp
+LICENSE
+
+# dependencies
+node_modules
+*.log
+*.lock
+package-lock.json
+
+# ci
+.coverage
+.eslintcache
+.stylelintcache
+test-output
+
+# production
+dist
+es
+lib
+logs
+
+# umi
+.umi
+.umi-production
+.umi-test
+.dumi/tmp*
+
+# ignore files
+.*ignore
+
+# docker
+docker
+Dockerfile*
+
+# image
+*.webp
+*.gif
+*.png
+*.jpg
+
+# misc
+# add other ignore file below
+*.py
+style.css
+javascript/
+.stylelintignore
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.prettierrc.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.prettierrc.js
new file mode 100644
index 0000000000000000000000000000000000000000..f0355a9c1a75f39af6853448d78f3379db8d8e49
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.prettierrc.js
@@ -0,0 +1 @@
+module.exports = require('@lobehub/lint').prettier;
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.releaserc.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.releaserc.js
new file mode 100644
index 0000000000000000000000000000000000000000..37930011d9d43110103a00080cc654aa999e2169
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.releaserc.js
@@ -0,0 +1 @@
+module.exports = require('@lobehub/lint').semanticRelease;
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.remarkrc.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.remarkrc.js
new file mode 100644
index 0000000000000000000000000000000000000000..b673c10ed1cc74bc6662171a438db6ab8af090b5
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.remarkrc.js
@@ -0,0 +1 @@
+module.exports = require('@lobehub/lint').remarklint;
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.stylelintrc.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.stylelintrc.js
new file mode 100644
index 0000000000000000000000000000000000000000..a8eb2a4088f91064f4b038a256953ce05b088f3a
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/.stylelintrc.js
@@ -0,0 +1,13 @@
+module.exports = {
+ ...require('@lobehub/lint').stylelint,
+ rules: {
+ 'selector-class-pattern': null,
+ 'selector-id-pattern': null,
+ 'no-descending-specificity': [
+ true,
+ {
+ severity: 'warning',
+ },
+ ],
+ },
+};
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/CHANGELOG.md b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..7d3c17b03a971bf7ab0b21b036021735cccf0911
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/CHANGELOG.md
@@ -0,0 +1,1915 @@
+
+
+# Changelog
+
+### [Version 3.0.10](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.9...v3.0.10)
+
+Released on **2023-10-23**
+
+#### 💄 Styles
+
+- **misc**: Fix Context Menu style.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Fix Context Menu style ([e5f09d5](https://github.com/lobehub/sd-webui-lobe-theme/commit/e5f09d5))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.9](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.8...v3.0.9)
+
+Released on **2023-10-19**
+
+#### 💄 Styles
+
+- **misc**: Update theme style.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Update theme style ([98b3d86](https://github.com/lobehub/sd-webui-lobe-theme/commit/98b3d86))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.8](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.7...v3.0.8)
+
+Released on **2023-09-17**
+
+#### 💄 Styles
+
+- **misc**: Fix some style problem.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Fix some style problem ([75c1e99](https://github.com/lobehub/sd-webui-lobe-theme/commit/75c1e99))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.7](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.6...v3.0.7)
+
+Released on **2023-09-12**
+
+#### 💄 Styles
+
+- **misc**: Fix prompt higlight.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Fix prompt higlight ([f190c1a](https://github.com/lobehub/sd-webui-lobe-theme/commit/f190c1a))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.6](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.5...v3.0.6)
+
+Released on **2023-09-12**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix build.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix build ([410c22f](https://github.com/lobehub/sd-webui-lobe-theme/commit/410c22f))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.5](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.4...v3.0.5)
+
+Released on **2023-09-12**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix ci.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix ci ([45c831d](https://github.com/lobehub/sd-webui-lobe-theme/commit/45c831d))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.4](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.3...v3.0.4)
+
+Released on **2023-09-08**
+
+
+
+
+Improvements and Fixes
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.3](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.2...v3.0.3)
+
+Released on **2023-09-04**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix build, Update .gitignore.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix build ([d83c494](https://github.com/lobehub/sd-webui-lobe-theme/commit/d83c494))
+- **misc**: Update .gitignore ([c39ac28](https://github.com/lobehub/sd-webui-lobe-theme/commit/c39ac28))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.2](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.1...v3.0.2)
+
+Released on **2023-09-04**
+
+#### 🐛 Bug Fixes
+
+- **split-view**: Fix split view style.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **split-view**: Fix split view style ([f258eb0](https://github.com/lobehub/sd-webui-lobe-theme/commit/f258eb0))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 3.0.1](https://github.com/lobehub/sd-webui-lobe-theme/compare/v3.0.0...v3.0.1)
+
+Released on **2023-09-04**
+
+#### 🐛 Bug Fixes
+
+- **format**: Fix format button and preview sticky position.
+- **hightlight**: Fix prompt highlight.
+- **split-view**: Fix split view.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **format**: Fix format button and preview sticky position ([c320ec2](https://github.com/lobehub/sd-webui-lobe-theme/commit/c320ec2))
+- **hightlight**: Fix prompt highlight ([a2698af](https://github.com/lobehub/sd-webui-lobe-theme/commit/a2698af))
+- **split-view**: Fix split view ([f1d5ab2](https://github.com/lobehub/sd-webui-lobe-theme/commit/f1d5ab2))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 3.0.0](https://github.com/lobehub/sd-webui-lobe-theme/compare/v2.8.8...v3.0.0)
+
+Released on **2023-08-31**
+
+#### ✨ Features
+
+- **breaking-changes**: Compatible with sd webui v1.6 \[force major].
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **breaking-changes**: Compatible with sd webui v1.6 \[force major] ([76c3b08](https://github.com/lobehub/sd-webui-lobe-theme/commit/76c3b08))
+
+
+
+#### 💥 BREAKING CHANGES
+
+- **breaking-changes**: `Lobe Theme v3` is compatible with `SD WebUI v1.6` and is not backwards compatible. Versions below `< 1.6` should be moved to the branch [legacy-sd-webui-1.5](https://github.com/lobehub/sd-webui-lobe-theme/tree/legacy-sd-webui-1.5)
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.8.8](https://github.com/lobehub/sd-webui-lobe-theme/compare/v2.8.7...v2.8.8)
+
+Released on **2023-08-24**
+
+
+
+
+Improvements and Fixes
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.8.7](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.8.6...v2.8.7)
+
+Released on **2023-08-15**
+
+#### ♻ Code Refactoring
+
+- **misc**: Replace CDN.
+
+
+
+
+Improvements and Fixes
+
+#### Code refactoring
+
+- **misc**: Replace CDN ([bb1627f](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/bb1627f))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.8.6](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.8.5...v2.8.6)
+
+Released on **2023-08-03**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix types and extranetworks button style.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix types and extranetworks button style ([fa4d296](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/fa4d296))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.8.5](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.8.4...v2.8.5)
+
+Released on **2023-07-20**
+
+#### 💄 Styles
+
+- **sidebar**: Add `minWidth` to DraggablePanelContainer.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **sidebar**: Add `minWidth` to DraggablePanelContainer ([8c6f9da](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/8c6f9da))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.8.4](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.8.3...v2.8.4)
+
+Released on **2023-07-20**
+
+#### ♻ Code Refactoring
+
+- **misc**: Refactor store.
+
+
+
+
+Improvements and Fixes
+
+#### Code refactoring
+
+- **misc**: Refactor store ([c220794](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/c220794))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.8.3](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.8.2...v2.8.3)
+
+Released on **2023-07-17**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Locale.py.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Locale.py ([cc48800](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/cc48800))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.8.2](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.8.1...v2.8.2)
+
+Released on **2023-07-16**
+
+#### 🐛 Bug Fixes
+
+- **misc**: I18n load.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: I18n load ([9decee2](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/9decee2))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.8.1](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.8.0...v2.8.1)
+
+Released on **2023-07-08**
+
+#### 🐛 Bug Fixes
+
+- **setting**: Fix setting.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **setting**: Fix setting ([0305fc0](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/0305fc0))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.8.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.7.2...v2.8.0)
+
+Released on **2023-07-08**
+
+#### ✨ Features
+
+- **local-setting**: Add local setting support.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **local-setting**: Add local setting support ([cb7f77e](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/cb7f77e))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.7.2](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.7.1...v2.7.2)
+
+Released on **2023-07-06**
+
+#### 💄 Styles
+
+- **giscus**: Update giscus style.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **giscus**: Update giscus style ([de6f8eb](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/de6f8eb))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.7.1](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.7.0...v2.7.1)
+
+Released on **2023-07-06**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix quicksetting sidebar default width.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix quicksetting sidebar default width ([43bc9bf](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/43bc9bf))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.7.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.6.5...v2.7.0)
+
+Released on **2023-07-05**
+
+#### ✨ Features
+
+- **misc**: Support `PWA` progressive web app.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Support `PWA` progressive web app ([05728b8](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/05728b8))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.6.5](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.6.4...v2.6.5)
+
+Released on **2023-07-05**
+
+#### 💄 Styles
+
+- **highlight**: Fix highlight select color and support wildcards.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **highlight**: Fix highlight select color and support wildcards ([34547c4](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/34547c4))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.6.4](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.6.3...v2.6.4)
+
+Released on **2023-07-05**
+
+#### 🐛 Bug Fixes
+
+- **giscus**: Fix giscus lang.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **giscus**: Fix giscus lang ([a198ffb](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/a198ffb))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.6.3](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.6.2...v2.6.3)
+
+Released on **2023-07-05**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix setting render \[force patch].
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix setting render \[force patch] ([f147b11](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/f147b11))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.6.2](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.6.1...v2.6.2)
+
+Released on **2023-07-01**
+
+#### 🐛 Bug Fixes
+
+- **autocomplete**: Fix some style bug on autocomplete.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **autocomplete**: Fix some style bug on autocomplete ([6e290e2](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/6e290e2))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.6.1](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.6.0...v2.6.1)
+
+Released on **2023-06-30**
+
+#### 💄 Styles
+
+- **theme**: Add radial gradient background in reduce animation mode, and fix Negative Prompt Weight support.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **theme**: Add radial gradient background in reduce animation mode, and fix Negative Prompt Weight support ([4fdf4a8](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/4fdf4a8))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.6.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.5.0...v2.6.0)
+
+Released on **2023-06-30**
+
+#### ✨ Features
+
+- **prompt**: Add prompt syntax highlighting.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **prompt**: Add prompt syntax highlighting ([b51301d](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/b51301d))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.5.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.4.1...v2.5.0)
+
+Released on **2023-06-30**
+
+#### ✨ Features
+
+- **theme**: Better gray scales neutral color in theme settings.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **theme**: Better gray scales neutral color in theme settings ([cfb0e5a](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/cfb0e5a))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.4.1](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.4.0...v2.4.1)
+
+Released on **2023-06-29**
+
+#### 🐛 Bug Fixes
+
+- **i18n**: Fix i18n load fail.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **i18n**: Fix i18n load fail ([2434caa](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/2434caa))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.4.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.3.0...v2.4.0)
+
+Released on **2023-06-29**
+
+#### ✨ Features
+
+- **i18n**: Support i18n and welcome PR contributions.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **i18n**: Support i18n and welcome PR contributions ([a61f3b4](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/a61f3b4))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.3.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.2.2...v2.3.0)
+
+Released on **2023-06-29**
+
+#### ✨ Features
+
+- **theme-setting**: Add legacy kitchen color in setting panel.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **theme-setting**: Add legacy kitchen color in setting panel ([3e71048](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/3e71048))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.2.2](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.2.1...v2.2.2)
+
+Released on **2023-06-28**
+
+#### 🐛 Bug Fixes
+
+- **nav**: Fix nav click func.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **nav**: Fix nav click func ([8dbb992](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/8dbb992))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.2.1](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.2.0...v2.2.1)
+
+Released on **2023-06-28**
+
+#### 🐛 Bug Fixes
+
+- **split-preview**: Update unsplit preivew style and set the default value to false.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **split-preview**: Update unsplit preivew style and set the default value to false ([7b05f05](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/7b05f05))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.2.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.1.0...v2.2.0)
+
+Released on **2023-06-28**
+
+#### ✨ Features
+
+- **setting**: Add split preview setting.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **setting**: Add split preview setting ([d17bab4](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/d17bab4))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.1.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.0.4...v2.1.0)
+
+Released on **2023-06-28**
+
+#### ✨ Features
+
+- **misc**: Allow prompt textarea resize.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Allow prompt textarea resize ([8fa7dcf](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/8fa7dcf))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.0.4](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.0.3...v2.0.4)
+
+Released on **2023-06-28**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix build actions.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix build actions ([ea62ee2](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/ea62ee2))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.0.3](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.0.2...v2.0.3)
+
+Released on **2023-06-28**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix gradio button min width.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix gradio button min width ([0256795](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/0256795))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### [Version 2.0.2](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v2.0.1...v2.0.2)
+
+Released on **2023-06-28**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix extra network cards style.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix extra network cards style ([34dc891](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/34dc891))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## [Version 2.0.0](https://github.com/canisminor1990/sd-webui-lobe-theme/compare/v1.8.3...v2.0.0)
+
+Released on **2023-06-28**
+
+#### ♻ Code Refactoring
+
+- **wip**: Refactor with @lobehub/ui.
+- **misc**: Refactor layout and styles, refactor project.
+
+#### ✨ Features
+
+- **wip**: Theme modify.
+- **misc**: Add custom logo, add primary color setting, add split t2i i2t layout.
+
+#### 🐛 Bug Fixes
+
+- **build**: Fix rollup-plugin-terser config.
+- **misc**: Fix extra button, fix gallery style, fix setting typo.
+
+#### 💄 Styles
+
+- **misc**: Update, update, update grenerate button style, update layout style, update svg icon.
+
+
+
+
+Improvements and Fixes
+
+#### Code refactoring
+
+- **wip**: Refactor with @lobehub/ui ([e116458](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/e116458))
+- **misc**: Refactor layout and styles ([438cd1f](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/438cd1f))
+- **misc**: Refactor project ([b9046f1](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/b9046f1))
+
+#### What's improved
+
+- **wip**: Theme modify ([7cdb48d](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/7cdb48d))
+- **misc**: Add custom logo ([34ea7aa](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/34ea7aa))
+- **misc**: Add primary color setting ([406aba7](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/406aba7))
+- **misc**: Add split t2i i2t layout ([149d1d8](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/149d1d8))
+
+#### What's fixed
+
+- **build**: Fix rollup-plugin-terser config ([b46c7d4](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/b46c7d4))
+- **misc**: Fix extra button ([079083b](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/079083b))
+- **misc**: Fix gallery style ([dd17dad](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/dd17dad))
+- **misc**: Fix setting typo ([9419345](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/9419345))
+
+#### Styles
+
+- **misc**: Update ([6e6dad8](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/6e6dad8))
+- **misc**: Update ([ba811e6](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/ba811e6))
+- **misc**: Update grenerate button style ([48321e5](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/48321e5))
+- **misc**: Update layout style ([ac76d7a](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/ac76d7a))
+- **misc**: Update svg icon ([d7e13b6](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/d7e13b6))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.8.3
+
+Released on **2023-05-30**
+
+#### 💄 Styles
+
+- **misc**: Add remarkrc.js.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Add remarkrc.js ([32c7be2](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/32c7be2))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.8.2
+
+Released on **2023-05-30**
+
+#### 💄 Styles
+
+- **misc**: Update ESLint configuration and add Prettier configuration.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Update ESLint configuration and add Prettier configuration ([fefad8c](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/fefad8c))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.8.1
+
+Released on **2023-05-19**
+
+#### 🐛 Bug Fixes
+
+- **style**: Header render.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **style**: Header render ([35125b4](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/35125b4))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.8.0
+
+Released on **2023-05-19**
+
+#### ✨ Features
+
+- **header**: Better navbar.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **header**: Better navbar ([37e2230](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/37e2230))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.7.1
+
+Released on **2023-05-19**
+
+#### 🐛 Bug Fixes
+
+- **extranetworks**: Card mode display.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **extranetworks**: Card mode display ([1b3376e](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/1b3376e))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.7.0
+
+Released on **2023-04-26**
+
+#### ✨ Features
+
+- **misc**: Add giscus feedback.
+
+#### 🐛 Bug Fixes
+
+- **misc**: Ci, firefox scroll fixed \[47], setting reset button.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Add giscus feedback ([42c7590](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/42c7590))
+
+#### What's fixed
+
+- **misc**: Ci ([ed2401d](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/ed2401d))
+- **misc**: Firefox scroll fixed \[47] ([87e774d](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/87e774d))
+- **misc**: Setting reset button ([2a0f468](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/2a0f468))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.6.3
+
+Released on **2023-04-23**
+
+#### 🐛 Bug Fixes
+
+- **misc**: GradioApp append.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: GradioApp append ([4751e78](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/4751e78))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.6.2
+
+Released on **2023-04-22**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Setting crash, try to fix header bug.
+
+#### 💄 Styles
+
+- **misc**: Format of the code.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Setting crash, closes [#46](https://github.com/canisminor1990/sd-webui-lobe-theme/issues/46) ([4f1a030](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/4f1a030))
+- **misc**: Try to fix header bug, closes [#46](https://github.com/canisminor1990/sd-webui-lobe-theme/issues/46) ([afdbe53](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/afdbe53))
+
+#### Styles
+
+- **misc**: Format of the code ([f6b11ae](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/f6b11ae))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.6.1
+
+Released on **2023-04-22**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Fix typo.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Fix typo ([9faf33e](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/9faf33e))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.6.0
+
+Released on **2023-04-22**
+
+#### ✨ Features
+
+- **misc**: Add setting panel.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Add setting panel, closes [#44](https://github.com/canisminor1990/sd-webui-lobe-theme/issues/44) ([f2beaaa](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/f2beaaa))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.5.2
+
+Released on **2023-04-22**
+
+#### 💄 Styles
+
+- **misc**: Update tags style.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Update tags style ([5f6185c](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/5f6185c))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.5.1
+
+Released on **2023-04-22**
+
+#### 💄 Styles
+
+- **misc**: Fix sidebar style.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Fix sidebar style ([8738409](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/8738409))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.5.0
+
+Released on **2023-04-22**
+
+#### ✨ Features
+
+- **misc**: Add extra network sidebar.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Add extra network sidebar ([b6dc1f4](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/b6dc1f4))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.4.7
+
+Released on **2023-04-22**
+
+#### 💄 Styles
+
+- **misc**: Better tool button.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Better tool button ([096acaf](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/096acaf))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.4.6
+
+Released on **2023-04-22**
+
+#### 💄 Styles
+
+- **misc**: Fix tabs padding.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Fix tabs padding ([598ba55](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/598ba55))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.4.5
+
+Released on **2023-04-22**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Bug Croped outpaint extention fiexed, Bug Croped outpaint extention fixed.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Bug Croped outpaint extention fiexed, closes [#41](https://github.com/canisminor1990/sd-webui-lobe-theme/issues/41) ([ee2765d](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/ee2765d))
+- **misc**: Bug Croped outpaint extention fixed, closes [#41](https://github.com/canisminor1990/sd-webui-lobe-theme/issues/41) ([157bd50](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/157bd50))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.4.4
+
+Released on **2023-04-21**
+
+#### 💄 Styles
+
+- **misc**: Better block style.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Better block style ([c428e6e](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/c428e6e))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.4.3
+
+Released on **2023-04-21**
+
+#### 💄 Styles
+
+- **misc**: Fix code format.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Fix code format ([560a768](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/560a768))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.4.2
+
+Released on **2023-04-21**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Init theme error.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Init theme error ([edaa043](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/edaa043))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.4.1
+
+Released on **2023-04-21**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Dragpanel auto hide in mobile view, fixed.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Dragpanel auto hide in mobile view, fixed, closes [#35](https://github.com/canisminor1990/sd-webui-lobe-theme/issues/35) ([a7e5d47](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/a7e5d47))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.4.0
+
+Released on **2023-04-21**
+
+#### ✨ Features
+
+- **misc**: Add suggestions to prompt editor.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Add suggestions to prompt editor ([bebebe4](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/bebebe4))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.3.2
+
+Released on **2023-04-21**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Sidebar height.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Sidebar height ([74a4d2c](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/74a4d2c))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.3.1
+
+Released on **2023-04-21**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Send func, tags lost.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Send func, tags lost ([0be6f28](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/0be6f28))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.3.0
+
+Released on **2023-04-20**
+
+#### ✨ Features
+
+- **misc**: Add prompt editor.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Add prompt editor ([1428cc1](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/1428cc1))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.2.2
+
+Released on **2023-04-20**
+
+#### 💄 Styles
+
+- **misc**: Update sidebar.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Update sidebar ([25665df](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/25665df))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.2.1
+
+Released on **2023-04-20**
+
+#### 💄 Styles
+
+- **misc**: Update layout style.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Update layout style ([09f69da](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/09f69da))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.2.0
+
+Released on **2023-04-20**
+
+#### ✨ Features
+
+- **misc**: Add react layout.
+
+#### 🐛 Bug Fixes
+
+- **misc**: Build.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Add react layout ([b1b6263](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/b1b6263))
+
+#### What's fixed
+
+- **misc**: Build ([c84f2cb](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/c84f2cb))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.1.2
+
+Released on **2023-04-18**
+
+#### 💄 Styles
+
+- **misc**: Update types.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Update types ([1830004](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/1830004))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.1.1
+
+Released on **2023-04-14**
+
+#### 🐛 Bug Fixes
+
+- **misc**: Scrollbar.
+
+
+
+
+Improvements and Fixes
+
+#### What's fixed
+
+- **misc**: Scrollbar ([2dce630](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/2dce630))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.1.0
+
+Released on **2023-04-14**
+
+#### ✨ Features
+
+- **misc**: Support light/dark mode.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **misc**: Support light/dark mode ([135f4fc](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/135f4fc))
+
+
+
+
+
+[](#readme-top)
+
+
+
+### Version 1.0.1
+
+Released on **2023-04-10**
+
+#### 💄 Styles
+
+- **misc**: Support mobile view.
+
+
+
+
+Improvements and Fixes
+
+#### Styles
+
+- **misc**: Support mobile view ([38cb555](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/38cb555))
+
+
+
+
+
+[](#readme-top)
+
+
+
+## Version 1.0.0
+
+Released on **2023-04-08**
+
+#### ✨ Features
+
+- **break-change**: Full support gradio-3.23.0, full support gradio-3.23.0.
+- **misc**: Add antd token support, update style.
+
+#### 🐛 Bug Fixes
+
+- **misc**: Ci, ci, ci, style, style, style, style.
+
+#### 💄 Styles
+
+- **misc**: Add stylelint, update button/scroll/draopdown style and fix some bug.
+
+
+
+
+Improvements and Fixes
+
+#### What's improved
+
+- **break-change**: Full support gradio-3.23.0 ([0fb84da](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/0fb84da))
+- **break-change**: Full support gradio-3.23.0 ([e47578c](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/e47578c))
+- **misc**: Add antd token support ([6c71223](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/6c71223))
+- **misc**: Update style ([9509e3d](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/9509e3d))
+
+#### What's fixed
+
+- **misc**: Ci ([c850bf1](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/c850bf1))
+- **misc**: Ci ([87b56f2](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/87b56f2))
+- **misc**: Ci ([bb22f1b](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/bb22f1b))
+- **misc**: Style ([1e4f99a](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/1e4f99a))
+- **misc**: Style ([f237f16](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/f237f16))
+- **misc**: Style ([56df1ae](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/56df1ae))
+- **misc**: Style ([30e2b7b](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/30e2b7b))
+
+#### Styles
+
+- **misc**: Add stylelint ([d7b0b0a](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/d7b0b0a))
+- **misc**: Update button/scroll/draopdown style and fix some bug ([71a8728](https://github.com/canisminor1990/sd-webui-lobe-theme/commit/71a8728))
+
+
+
+
+
+[](#readme-top)
+
+
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/LICENSE b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..211d32e752cb61bd056436e8f7a806f12a626bb7
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/LICENSE
@@ -0,0 +1,663 @@
+ GNU AFFERO GENERAL PUBLIC LICENSE
+ Version 3, 19 November 2007
+
+ Copyright (c) 2023 AUTOMATIC1111
+
+ Copyright (C) 2007 Free Software Foundation, Inc.
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
+ The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+ The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works. By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+ When we speak of free software, we are referring to freedom, not
+price. Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+ Developers that use our General Public Licenses protect your rights
+with two steps: (1) assert copyright on the software, and (2) offer
+you this License which gives you legal permission to copy, distribute
+and/or modify the software.
+
+ A secondary benefit of defending all users' freedom is that
+improvements made in alternate versions of the program, if they
+receive widespread use, become available for other developers to
+incorporate. Many developers of free software are heartened and
+encouraged by the resulting cooperation. However, in the case of
+software used on network servers, this result may fail to come about.
+The GNU General Public License permits making a modified version and
+letting the public access it on a server without ever releasing its
+source code to the public.
+
+ The GNU Affero General Public License is designed specifically to
+ensure that, in such cases, the modified source code becomes available
+to the community. It requires the operator of a network server to
+provide the source code of the modified version running there to the
+users of that server. Therefore, public use of a modified version, on
+a publicly accessible server, gives the public access to the source
+code of the modified version.
+
+ An older license, called the Affero General Public License and
+published by Affero, was designed to accomplish similar goals. This is
+a different license, not a version of the Affero GPL, but Affero has
+released a new version of the Affero GPL which permits relicensing under
+this license.
+
+ The precise terms and conditions for copying, distribution and
+modification follow.
+
+ TERMS AND CONDITIONS
+
+ 0. Definitions.
+
+ "This License" refers to version 3 of the GNU Affero General Public License.
+
+ "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+ "The Program" refers to any copyrightable work licensed under this
+License. Each licensee is addressed as "you". "Licensees" and
+"recipients" may be individuals or organizations.
+
+ To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy. The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+ A "covered work" means either the unmodified Program or a work based
+on the Program.
+
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diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README-ru_RU.md b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README-ru_RU.md
new file mode 100644
index 0000000000000000000000000000000000000000..1656651eba126718b489f3926c3c776603f5ec75
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README-ru_RU.md
@@ -0,0 +1,404 @@
+
+
+
+
+
+
+
Lobe Theme
+
+Современная тема для стабильной диффузии webui
+
+Ранее известная как Kitchen theme
+
+[English ](./README.md)· [简体中文](./README-zh_CN.md) · Russian · [Changelog](./CHANGELOG.md) · [Report Bug][github-issues-link] · [Request Feature][github-issues-link]
+
+
+
+[![][github-release-shield]][github-release-link]
+[![][discord-shield]][discord-link]
+[![][github-releasedate-shield]][github-releasedate-link]
+[![][github-action-test-shield]][github-action-test-link]
+[![][github-action-release-shield]][github-action-release-link]
+[![][github-contributors-shield]][github-contributors-link]
+[![][github-forks-shield]][github-forks-link]
+[![][github-stars-shield]][github-stars-link]
+[![][github-issues-shield]][github-issues-link]
+[![][github-license-shield]][github-license-link]
+
+
+Table of contents
+
+#### TOC
+
+- [✨ Дополнения](#-дополнения)
+
+- [📦 Установка](#-установка)
+
+- [🤯 Использование](#-использование)
+
+- [🖥 Поддерживаемые браузеры](#-поддерживаемые-браузеры)
+
+- [⌨️ Локальная разработка](#️-локальная-разработка)
+
+- [🤝 Содействие](#-содействие)
+
+- [🔗 Ссылки](#-ссылки)
+
+ - [Больше продуктов](#больше-продуктов)
+ - [Благодарности](#благодарности)
+
+####
+
+
+
+## ✨ Дополнения
+
+- [x] 🌗 Поддержка светлой и темной темы, с возможностью быстрого переключения на панели навигации
+- [x] 🌈 Поддержка кастомных пользовательских и нейтральных цветов с возможностью настройки логотипа
+- [x] 🪄 Поддержка форматирования подсказки одним щелчком мыши с помощью простого редактора тегов
+- [x] 🎛️ Высоконастраиваемая боковая панель, с боковой панелью быстрых настроек слева и боковой панелью моделей справа
+- [x] 🖼️ Регулируемое соотношение холста, благодаря чему генерируемые изображения всегда отображаются сверху
+- [x] 📱 Mobile-friendly, с частичной оптимизацией под мобильные экраны
+- [x] 🌍 Поддержка i18n(Поддержка разных языков) и приветствуются [PR](https://github.com/lobehub/sd-webui-lobe-theme/tree/main/locales)
+- [x] 📝 Подсветка синтаксиса в поле ввода промта
+- [x] 📦 Поддержка [PWA](https://support.google.com/chrome/answer/9658361)
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 📦 Установка
+
+#### Метод 1
+
+Найдите `Lobe Theme` или `Kitchen Theme` в поисковике плагинов SD WebUI и установите их.
+
+> **Note**\
+> **Версия 2.0.0** была переименована в **Lobe Theme**.
+
+#### Метод 2
+
+В качестве расширения (рекомендуется) клонировать репозиторий в папку расширений:
+
+```shell
+git clone "https://github.com/lobehub/sd-webui-lobe-theme" extensions/lobe-theme
+```
+
+> **Important**\
+> Минимальные требования `gradio-3.41.2` & `sd-webui 1.6`
+
+#### Обновления
+
+Добавьте проект в избранное, и вы будете получать все уведомления о релизах непосредственно с GitHub \~ ⭐️
+
+Star History
+
+
+ 
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🤯 Использование
+
+![][feat-thememode]
+
+#### Светлая и темная темы
+
+> **Note**\
+> В правом верхнем углу панели навигации можно быстро переключаться между светлой и темной темами.
+
+Текущая тема поддерживает как светлые, так и темные темы. Если вы хотите принудительно включить темный режим, используйте аргумент `--theme=dark` для запуска WebUI. Например, под Windows ваш `webui-user.bat` должен содержать:
+
+```shell
+set COMMANDLINE_ARGS= --theme=dark
+```
+
+Кроме того, переключение можно осуществлять непосредственно через URL:
+
+```shell
+http://localhost:7860/?__theme=light
+http://localhost:7860/?__theme=dark
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-theme-modify]
+
+#### Кастомизация темы
+
+> **Note**\
+> Щелкните на значке `⚙` в правом верхнем углу, чтобы открыть панель настроек. В настоящее время доступны следующие настройки:
+
+- Тема
+ - Основной цвет: в настоящее время предлагается 13 цветовых комбинаций тем.
+ - Нейтральный цвет: в настоящее время предлагается 6 различных комбинаций оттенков серого.
+ - Тип логотипа: `Lobe`, `Kitchen`, `Custom`
+ - Пользовательский логотип: `Поддерживаются ссылки`, `base64`, и `emoji`. При вводе одного смайлика он будет автоматически заменен на 3D Fluent Emoji.
+ - Пользовательский заголовок: Настраиваемое название сайта/темы.
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-highlight]
+
+#### Подсветка синтаксиса подсказок
+
+Автоматически подсвечивать промт в соответствии с правилами синтаксиса Stable Diffusion.
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-sidebar]
+
+#### Настройка боковой панели
+
+> **Note**\
+> нажмите значок `⚙` в правом верхнем углу, чтобы открыть панель настроек. Текущие доступные настройки следующие:
+
+- **Текстовое поле промта**
+ - Режим отображения: `Фиксированная высота и скрол в поле ввода` | `Увеличение от вводимого текста`
+- **Боковая панель**
+ - По умолчанию развернуть: `true`
+ - Режим отображения: `Фиксированный` | `Плавающий`
+ - Ширина по умолчанию: `280`
+- **Панель с дом. сетями**
+ - Включено: `true`
+ - По умолчанию развернуть: `true`
+ - Режим отображения: `Фиксированный` | `Плавающий`
+ - Ширина по умолчанию: `340`
+ - Размер превью по умолчанию: `86`
+
+
+Recommended System Settings
+
+#### Доп. Сети
+
+- Превью
+- Ширина: 86
+- высота: 128
+
+
+
+#### Быстрые настройки (изменяются в настройках SD)
+
+```txt
+sd_model_checkpoint, sd_vae, CLIP_stop_at_last_layers, img2img_background_color, img2img_color_correction, samples_save, samples_format, grid_save, return_grid, n_rows, live_previews_enable, show_progress_every_n_steps, live_preview_refresh_period
+```
+
+
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-mobile-friendly]
+
+#### Адаптированно под мобильные устройства
+
+Выполнена частичная адаптация удобства работы с мобильными устройствами, включая откидную панель навигации, адаптацию боковой панели и т.д. Однако из-за высокой сложности и фиксированных значений стабильного диффузного интерфейса трудно обеспечить такое же удобство работы, как и в настольной версии. Приветствуется обратная связь для поиска новых идей.
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-pwa]
+
+#### Progress Web App
+
+Вы можете использовать Progressive Web Apps [PWA](https://support.google.com/chrome/answer/9658361) для быстрого стабильного запуска на компьютере или мобильном устройстве.
+
+- На компьютере откройте браузер Chrome.
+- Перейдите на стабильный сайт диффузии, который вы хотите установить
+- В правом верхнем углу адресной строки нажмите кнопку `Установить`
+- Follow the onscreen instructions to install the PWA
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+#### Форматирование подсказок
+
+Нажмите кнопку 🪄 под строкой Prompt, чтобы отформатировать слова подсказки одним щелчком мыши.
+
+> **Note**\
+> Преобразует полноразмерные знаки препинания в полуразмерные, уберет лишние пробелы, добавит недостающие запятые и перенесёт в конец Extra-Networks (Дополнительные) модели.
+
+Перед форматированием:
+
+```text
+photorealistic photo of a handsome male (wizard :1.2）， short beard, white wizard shirt, (with golden trim:0.8),
+```
+
+После форматирования:
+
+```text
+photorealistic photo of a handsome male, (wizard:1.2), short beard, white wizard shirt, (with golden trim:0.8), ,
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🖥 Поддерживаемые браузеры
+
+| [
](http://godban.github.io/browsers-support-badges/)
Chrome | [
](http://godban.github.io/browsers-support-badges/)
Edge | [
](http://godban.github.io/browsers-support-badges/)
Safari |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| last 2 versions | last 2 versions | last 2 versions |
+
+> **Warning**\`
+> В настоящее время известна проблема совместимости со стилями в браузере Firefox.
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## ⌨️ Локальная разработка
+
+Вы можете использовать Github Codespaces для разработки в режиме онлайн:
+
+[![][codespaces-shield]][codespaces-link]
+
+В качестве альтернативы можно клонировать его для локальной разработки. Для включения режима горячей загрузки необходимо предварительно запустить Stable-Diffusion на порту `7860`.
+
+[![][bun-shield]][bun-link]
+
+```bash
+$ git clone https://github.com/lobehub/sd-webui-lobe-theme.git
+$ cd sd-webui-lobe-theme
+$ bun install
+$ bun dev
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🤝 Содействие
+
+Любые виды вкладов приветствуются, если вас интересует внесение кода, не стесняйтесь просмотреть наши проблемы на GitHub [Issues][github-issues-link], чтобы показать нам, из чего вы сделаны.
+
+[![][pr-welcome-shield]][pr-welcome-link]
+
+[![][contributors-contrib]][contributors-link]
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🔗 Ссылки
+
+### Больше продуктов
+
+- **[🤖 Lobe Chat][lobe-chat] :** Это открытая, расширяемая (с вызовом функций) высокопроизводительная рамка для чат-ботов. Она поддерживает однокликовую бесплатную развертывание вашего частного веб-приложения ChatGPT/LLM.
+- **[🌏 Lobe i18n][lobe-i18n] :** Lobe i18n - это автоматизированный инструмент для процесса перевода i18n (интернационализация), работающий на основе ChatGPT. Он поддерживает такие функции, как автоматическое разделение больших файлов, инкрементные обновления и настраиваемые параметры для модели OpenAI, API прокси и температуры.
+- **[💌 Lobe Commit][lobe-commit] :** Lobe Commit - это инструмент CLI, который использует Langchain/ChatGPT для создания сообщений о коммитах на основе Gitmoji.
+
+### Благодарности
+
+- stable-diffusion-webui：
+- gradio-theme-gallery:
+- cozy-nest:
+- _before `1.0.0` version_
+ - sd-web-ui-quickcs:
+ - Dark-Themes-SD-WebUI-Automatic1111:
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+---
+
+📝 License
+
+[![][fossa-license-shield]][fossa-license-link]
+
+
+This project is [AGPL3](./LICENSE) licensed.
+
+
+
+[back-to-top]: https://img.shields.io/badge/-BACK_TO_TOP-151515?style=flat-square
+[bun-link]: https://bun.sh
+[bun-shield]: https://img.shields.io/badge/-speedup%20with%20bun-black?logo=bun&style=for-the-badge
+[codespaces-link]: https://codespaces.new/lobehub/sd-webui-lobe-theme
+[codespaces-shield]: https://github.com/codespaces/badge.svg
+[contributors-contrib]: https://contrib.rocks/image?repo=lobehub/sd-webui-lobe-theme
+[contributors-link]: https://github.com/lobehub/sd-webui-lobe-theme/graphs/contributors
+[cover]: https://gw.alipayobjects.com/zos/kitchen/8Ab%24hLJ5ur/cover.webp
+[discord-link]: https://discord.gg/AYFPHvv2jT
+[discord-shield]: https://img.shields.io/discord/1127171173982154893?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=flat-square
+[feat-highlight]: https://gw.alipayobjects.com/zos/kitchen/iD%24W4U2y3Y/feat_highlight.webp
+[feat-mobile-friendly]: https://gw.alipayobjects.com/zos/kitchen/WpWe6Hw8UT/feat_mobile_friendly.webp
+[feat-pwa]: https://gw.alipayobjects.com/zos/kitchen/az49akOKJT/feat_pwa.webp
+[feat-sidebar]: https://gw.alipayobjects.com/zos/kitchen/Olum2IjxCW/feat_sidebar.webp
+[feat-theme-modify]: https://gw.alipayobjects.com/zos/kitchen/CbhlynwJYg/feat_theme_modify.webp
+[feat-thememode]: https://gw.alipayobjects.com/zos/kitchen/nSFtJidWUR/feat_thememode.webp
+[fossa-license-link]: https://app.fossa.com/projects/git%2Bgithub.com%2Flobehub%2Fsd-webui-lobe-theme
+[fossa-license-shield]: https://app.fossa.com/api/projects/git%2Bgithub.com%2Flobehub%2Fsd-webui-lobe-theme.svg?type=large
+[github-action-release-link]: https://github.com/actions/workflows/lobehub/sd-webui-lobe-theme/release.yml
+[github-action-release-shield]: https://img.shields.io/github/actions/workflow/status/lobehub/sd-webui-lobe-theme/release.yml?label=release&labelColor=black&logo=githubactions&logoColor=white&style=flat-square
+[github-action-test-link]: https://github.com/actions/workflows/lobehub/sd-webui-lobe-theme/test.yml
+[github-action-test-shield]: https://img.shields.io/github/actions/workflow/status/lobehub/sd-webui-lobe-theme/test.yml?label=test&labelColor=black&logo=githubactions&logoColor=white&style=flat-square
+[github-contributors-link]: https://github.com/lobehub/sd-webui-lobe-theme/graphs/contributors
+[github-contributors-shield]: https://img.shields.io/github/contributors/lobehub/sd-webui-lobe-theme?color=c4f042&labelColor=black&style=flat-square
+[github-forks-link]: https://github.com/lobehub/sd-webui-lobe-theme/network/members
+[github-forks-shield]: https://img.shields.io/github/forks/lobehub/sd-webui-lobe-theme?color=8ae8ff&labelColor=black&style=flat-square
+[github-issues-link]: https://github.com/lobehub/sd-webui-lobe-theme/issues
+[github-issues-shield]: https://img.shields.io/github/issues/lobehub/sd-webui-lobe-theme?color=ff80eb&labelColor=black&style=flat-square
+[github-license-link]: https://github.com/lobehub/sd-webui-lobe-theme/blob/main/LICENSE
+[github-license-shield]: https://img.shields.io/github/license/lobehub/sd-webui-lobe-theme?color=white&labelColor=black&style=flat-square
+[github-release-link]: https://github.com/lobehub/sd-webui-lobe-theme/releases
+[github-release-shield]: https://img.shields.io/github/v/release/lobehub/sd-webui-lobe-theme?color=369eff&labelColor=black&logo=github&style=flat-square
+[github-releasedate-link]: https://github.com/lobehub/sd-webui-lobe-theme/releases
+[github-releasedate-shield]: https://img.shields.io/github/release-date/lobehub/sd-webui-lobe-theme?labelColor=black&style=flat-square
+[github-stars-link]: https://github.com/lobehub/sd-webui-lobe-theme/network/stargazers
+[github-stars-shield]: https://img.shields.io/github/stars/lobehub/sd-webui-lobe-theme?color=ffcb47&labelColor=black&style=flat-square
+[lobe-chat]: https://github.com/lobehub/lobe-chat
+[lobe-commit]: https://github.com/lobehub/lobe-commit/tree/master/packages/lobe-commit
+[lobe-i18n]: https://github.com/lobehub/lobe-commit/tree/master/packages/lobe-i18n
+[pr-welcome-link]: https://github.com/lobehub/lobe-chat/pulls
+[pr-welcome-shield]: https://img.shields.io/badge/🤯_pr_welcome-%E2%86%92-ffcb47?labelColor=black&style=for-the-badge
+[profile-link]: https://github.com/canisminor1990
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README-zh_CN.md b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README-zh_CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..26daaa5214bd902fa2d480a1e5d05a4658f41fb0
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README-zh_CN.md
@@ -0,0 +1,429 @@
+
+
+
+
+
+
+
Lobe Theme
+
+高定 Stable Diffusion 现代主题, 诸多体验优化
+
+历史版本为 Kitchen Theme
+
+[English](./README.md) · 简体中文 · [变更日志](./CHANGELOG.md) · [反馈问题][github-issues-link] · [功能建议][github-issues-link]
+
+
+
+[![][github-release-shield]][github-release-link]
+[![][discord-shield]][discord-link]
+[![][github-releasedate-shield]][github-releasedate-link]
+[![][github-action-test-shield]][github-action-test-link]
+[![][github-action-release-shield]][github-action-release-link]
+[![][github-contributors-shield]][github-contributors-link]
+[![][github-forks-shield]][github-forks-link]
+[![][github-stars-shield]][github-stars-link]
+[![][github-issues-shield]][github-issues-link]
+[![][github-license-shield]][github-license-link]
+
+
+文档目录
+
+#### TOC
+
+- [✨ 主题特性](#-主题特性)
+
+- [📦 插件安装](#-插件安装)
+
+- [🤯 使用说明](#-使用说明)
+
+- [🖥 兼容环境](#-兼容环境)
+
+- [📦 生态系统](#-生态系统)
+
+- [⌨️ 本地开发](#️-本地开发)
+
+- [🤝 参与贡献](#-参与贡献)
+
+- [🔗 链接](#-链接)
+
+ - [更多工具](#更多工具)
+ - [参考](#参考)
+
+####
+
+
+
+
+
+> **Warning**\
+> `Lobe Theme v3` 适配 `SD WebUI v1.6`, 并不向下兼容，`< 1.6` 以下版本移动到分支 [legacy-sd-webui-1.5](https://github.com/lobehub/sd-webui-lobe-theme/tree/legacy-sd-webui-1.5)
+
+## ✨ 主题特性
+
+- [x] 🌗 支持亮暗色主题，可以导航栏快速切换
+- [x] 🌈 支持主题颜色和中性色自定义，支持 Logo 自定义
+- [x] 🪄 支持 Prompt 一键格式化，并提供简易的标签编辑器
+- [x] 🎛️ 高定制侧边栏，左侧为快捷设置侧边栏，右侧为模型侧边栏
+- [x] 🖼️ 可调节画板比例，使生成图像始终置顶
+- [x] 📱 移动端友好，针对手机屏幕完成部分优化
+- [x] 🌍 支持 i18n 并欢迎提交 [PR](https://github.com/lobehub/sd-webui-lobe-theme/tree/main/locales) 贡献
+- [x] 📝 语法高亮的 Prompt 输入框
+- [x] 📦 支持 [PWA](https://support.google.com/chrome/answer/9658361?hl=zh-Hans) 渐进式 Web 应用
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 📦 插件安装
+
+#### 方法一
+
+通过 stable diffusion webui 插件市场搜索 `Lobe Theme` 或 `Kitchen Theme` 进行安装
+
+> **Note**\
+> **Version 2.0.0** 后更名为 **Lobe Theme**
+
+#### 方法二
+
+作为扩展（推荐） 将存储库克隆到您的扩展文件夹中：
+
+```shell
+git clone "https://github.com/lobehub/sd-webui-lobe-theme" extensions/lobe-theme
+```
+
+> **Important**\
+> 最低要求 `gradio-3.41.2` & `sd-webui 1.6`
+
+#### 保持更新
+
+收藏项目，你将从 GitHub 上无延迟地接收所有发布通知 \~ ⭐️
+
+Star History
+
+
+
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🤯 使用说明
+
+![][feat-thememode]
+
+#### 亮暗色主题
+
+> **Note**\
+> 可在右上角导航栏快速切换
+
+当前主题支持亮暗色双主题，如果你想强制使用暗色模式，使用参数 `--theme=dark` 启动您的 WebUI。例如，在 Windows 上，您的 `webui-user.bat` 应包括：
+
+```shell
+set COMMANDLINE_ARGS= --theme=dark
+```
+
+或通过 URL Props 直接切换
+
+```shell
+http://localhost:7860/?__theme=light
+http://localhost:7860/?__theme=dark
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-theme-modify]
+
+#### 主体定制
+
+> **Note**\
+> 右上角 `⚙` 图标打开设置面板，当前可用设置如下
+
+- **Theme**
+ - Primary color 主题色：目前提供 `13` 种主题颜色搭配
+ - Neutral color 中性色：目前提供 `6` 种不同色彩倾向灰阶搭配
+ - Logo type 徽标种类：`Lobe` `Kitchen` `自定义`
+ - Logo 自定义：支持 `img url` `base64` `emoji`，当填入单个 emoji 时会自动替换为 3D Fluent Emoji
+ - Title 标题自定义：自定义站点名称
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-highlight]
+
+#### Prompt 语法高亮
+
+按 Stable Diffusion 语法规则，自动染色 prompt 显示
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-sidebar]
+
+#### 侧边栏定制
+
+> **Note**\
+> 右上角 `⚙` 图标打开设置面板，当前可用设置如下
+
+- **Prompt Textarea**
+ - Display mode: `scroll 固定高度滚动` | `resizable 按文本行数缩放`
+- **Sidebar**
+ - Default expand 默认展开: `true`
+ - Display mode: `fixed 固定` | `float 悬浮`
+ - Default width 默认宽度: `280`
+- **ExtraNetwork Sidebar**
+ - Enable 启用: `true`
+ - Default expand 默认展开: `true`
+ - Display mode: `fixed 固定` | `float 悬浮`
+ - Default Width 默认宽度: `340`
+ - Default card size 模型卡片默认尺寸: `86`
+
+
+推荐系统设置
+
+#### Extra-Networks 扩展模型视窗
+
+- 拇指视图
+- 卡牌宽度: 86
+- 卡牌高度: 128
+
+
+
+#### Quick-Setting 快捷设置
+
+```txt
+sd_model_checkpoint, sd_vae, CLIP_stop_at_last_layers, img2img_background_color, img2img_color_correction, samples_save, samples_format, grid_save, return_grid, n_rows, live_previews_enable, show_progress_every_n_steps, live_preview_refresh_period
+```
+
+
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-mobile-friendly]
+
+#### 移动端适配
+
+针对移动端完成部分便捷性适配设计，面包屑折叠导航栏，侧边栏适配等，但有余 stable diffusion 界面复杂度和定值难度较高无法保证和桌面端相同的使用体验，有更多想法欢迎反馈。
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-pwa]
+
+#### PWA 渐进式 Web 应用
+
+可以使用渐进式 Web 应用 [PWA](https://support.google.com/chrome/answer/9658361?hl=zh-Hans) 在计算机或移动设备上享受快速 Stable Diffusion 体验
+
+- 在计算机上打开 Chrome
+- 打开 Stable Diffusion 网页
+- 在地址栏的右上角，点击 `安装` 图标
+- 按照屏幕上的说明安装该 PWA
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+#### 提示词格式化
+
+点击 Prompt 下方 🪄 按钮即可将提示词一键格式化
+
+> **Note**\
+> 全角标点转半角，去除多余空格，补充缺失逗号，把 Extra-Networks 模型移动到末尾
+
+格式化前
+
+```text
+photorealistic photo of a handsome male (wizard :1.2）， short beard, white wizard shirt, (with golden trim:0.8),
+```
+
+格式化后
+
+```text
+photorealistic photo of a handsome male, (wizard:1.2), short beard, white wizard shirt, (with golden trim:0.8), ,
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🖥 兼容环境
+
+| [](http://godban.github.io/browsers-support-badges/)
Chrome | [](http://godban.github.io/browsers-support-badges/)
Edge | [](http://godban.github.io/browsers-support-badges/)
Safari |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| last 2 versions | last 2 versions | last 2 versions |
+
+> **Warning**\
+> 目前已知道在 Firefox 火狐浏览器上存在样式兼容问题
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 📦 生态系统
+
+| NPM | 代码库 | 描述 | 版本 |
+| ------------------------------- | ------------------------------------- | ----------------------------------------------------------------------------------------------------- | --------------------------------------- |
+| [@lobehub/ui][lobe-ui-link] | [lobehub/lobe-ui][lobe-ui-github] | Lobe UI 是一个专为构建 AIGC 网页应用程序而设计的开源 UI 组件库。 | [![][lobe-ui-shield]][lobe-ui-link] |
+| [@lobehub/lint][lobe-lint-link] | [lobehub/lobe-lint][lobe-lint-github] | LobeLint 为 LobeHub 提供 ESlint，Stylelint，Commitlint，Prettier，Remark 和 Semantic Release 的配置。 | [![][lobe-lint-shield]][lobe-lint-link] |
+| @lobehub/assets | [lobehub/assets][lobe-assets-github] | LobeHub 的 Logo 资源、favicon、网页字体。 | |
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## ⌨️ 本地开发
+
+你可以使用 Github Codespaces 进行在线开发：
+
+[![][codespaces-shield]][codespaces-link]
+
+或者克隆到本地开发, 热更新开发模式需要将 stable diffuison 提前启动到 `7860` 端口:
+
+[![][bun-shield]][bun-link]
+
+```bash
+$ git clone https://github.com/lobehub/sd-webui-lobe-theme.git
+$ cd sd-webui-lobe-theme
+$ bun install
+$ bun dev
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🤝 参与贡献
+
+我们非常欢迎各种形式的贡献。如果你对贡献代码感兴趣，可以查看我们的 GitHub [Issues][github-issues-link]，大展身手，向我们展示你的奇思妙想。
+
+[![][pr-welcome-shield]][pr-welcome-link]
+
+[![][contributors-contrib]][contributors-link]
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🔗 链接
+
+### 更多工具
+
+- [🤖 Lobe Chat][lobe-chat] : LobeChat 是一个开源的、可扩展的（Function Calling）高性能聊天机器人框架, 支持一键免费部署私人 ChatGPT/LLM 网页应用程序。。
+- [🌏 Lobe i18n][lobe-i18n] : Lobe i18n 是一个由 ChatGPT 驱动的 i18n（国际化）翻译过程的自动化工具。它支持自动分割大文件、增量更新，以及为 OpenAI 模型、API 代理和温度提供定制选项的功能。
+- [💌 Lobe Commit][lobe-commit] : Lobe Commit 是一个 CLI 工具，它利用 Langchain/ChatGPT 生成基于 Gitmoji 的提交消息。
+
+### 参考
+
+- stable-diffusion-webui：
+- gradio-theme-gallery:
+- cozy-nest:
+- _before `1.0.0` version_
+ - sd-web-ui-quickcs:
+ - Dark-Themes-SD-WebUI-Automatic1111:
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+---
+
+📝 License
+
+[![][fossa-license-shield]][fossa-license-link]
+
+
+This project is [AGPL3](./LICENSE) licensed.
+
+
+
+[back-to-top]: https://img.shields.io/badge/-BACK_TO_TOP-151515?style=flat-square
+[bun-link]: https://bun.sh
+[bun-shield]: https://img.shields.io/badge/-speedup%20with%20bun-black?logo=bun&style=for-the-badge
+[codespaces-link]: https://codespaces.new/lobehub/sd-webui-lobe-theme
+[codespaces-shield]: https://github.com/codespaces/badge.svg
+[contributors-contrib]: https://contrib.rocks/image?repo=lobehub/sd-webui-lobe-theme
+[contributors-link]: https://github.com/lobehub/sd-webui-lobe-theme/graphs/contributors
+[cover]: https://gw.alipayobjects.com/zos/kitchen/8Ab%24hLJ5ur/cover.webp
+[discord-link]: https://discord.gg/AYFPHvv2jT
+[discord-shield]: https://img.shields.io/discord/1127171173982154893?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=flat-square
+[feat-highlight]: https://gw.alipayobjects.com/zos/kitchen/iD%24W4U2y3Y/feat_highlight.webp
+[feat-mobile-friendly]: https://gw.alipayobjects.com/zos/kitchen/WpWe6Hw8UT/feat_mobile_friendly.webp
+[feat-pwa]: https://gw.alipayobjects.com/zos/kitchen/az49akOKJT/feat_pwa.webp
+[feat-sidebar]: https://gw.alipayobjects.com/zos/kitchen/Olum2IjxCW/feat_sidebar.webp
+[feat-theme-modify]: https://gw.alipayobjects.com/zos/kitchen/CbhlynwJYg/feat_theme_modify.webp
+[feat-thememode]: https://gw.alipayobjects.com/zos/kitchen/nSFtJidWUR/feat_thememode.webp
+[fossa-license-link]: https://app.fossa.com/projects/git%2Bgithub.com%2Flobehub%2Fsd-webui-lobe-theme
+[fossa-license-shield]: https://app.fossa.com/api/projects/git%2Bgithub.com%2Flobehub%2Fsd-webui-lobe-theme.svg?type=large
+[github-action-release-link]: https://github.com/actions/workflows/lobehub/sd-webui-lobe-theme/release.yml
+[github-action-release-shield]: https://img.shields.io/github/actions/workflow/status/lobehub/sd-webui-lobe-theme/release.yml?label=release&labelColor=black&logo=githubactions&logoColor=white&style=flat-square
+[github-action-test-link]: https://github.com/actions/workflows/lobehub/sd-webui-lobe-theme/test.yml
+[github-action-test-shield]: https://img.shields.io/github/actions/workflow/status/lobehub/sd-webui-lobe-theme/test.yml?label=test&labelColor=black&logo=githubactions&logoColor=white&style=flat-square
+[github-contributors-link]: https://github.com/lobehub/sd-webui-lobe-theme/graphs/contributors
+[github-contributors-shield]: https://img.shields.io/github/contributors/lobehub/sd-webui-lobe-theme?color=c4f042&labelColor=black&style=flat-square
+[github-forks-link]: https://github.com/lobehub/sd-webui-lobe-theme/network/members
+[github-forks-shield]: https://img.shields.io/github/forks/lobehub/sd-webui-lobe-theme?color=8ae8ff&labelColor=black&style=flat-square
+[github-issues-link]: https://github.com/lobehub/sd-webui-lobe-theme/issues
+[github-issues-shield]: https://img.shields.io/github/issues/lobehub/sd-webui-lobe-theme?color=ff80eb&labelColor=black&style=flat-square
+[github-license-link]: https://github.com/lobehub/sd-webui-lobe-theme/blob/main/LICENSE
+[github-license-shield]: https://img.shields.io/github/license/lobehub/sd-webui-lobe-theme?color=white&labelColor=black&style=flat-square
+[github-release-link]: https://github.com/lobehub/sd-webui-lobe-theme/releases
+[github-release-shield]: https://img.shields.io/github/v/release/lobehub/sd-webui-lobe-theme?color=369eff&labelColor=black&logo=github&style=flat-square
+[github-releasedate-link]: https://github.com/lobehub/sd-webui-lobe-theme/releases
+[github-releasedate-shield]: https://img.shields.io/github/release-date/lobehub/sd-webui-lobe-theme?labelColor=black&style=flat-square
+[github-stars-link]: https://github.com/lobehub/sd-webui-lobe-theme/network/stargazers
+[github-stars-shield]: https://img.shields.io/github/stars/lobehub/sd-webui-lobe-theme?color=ffcb47&labelColor=black&style=flat-square
+[lobe-assets-github]: https://github.com/lobehub/lobe-assets
+[lobe-chat]: https://github.com/lobehub/lobe-chat
+[lobe-commit]: https://github.com/lobehub/lobe-commit/tree/master/packages/lobe-commit
+[lobe-i18n]: https://github.com/lobehub/lobe-commit/tree/master/packages/lobe-i18n
+[lobe-lint-github]: https://github.com/lobehub/lobe-lint
+[lobe-lint-link]: https://www.npmjs.com/package/@lobehub/lint
+[lobe-lint-shield]: https://img.shields.io/npm/v/@lobehub/lint?color=369eff&labelColor=black&logo=npm&logoColor=white&style=flat-square
+[lobe-ui-github]: https://github.com/lobehub/lobe-ui
+[lobe-ui-link]: https://www.npmjs.com/package/@lobehub/ui
+[lobe-ui-shield]: https://img.shields.io/npm/v/@lobehub/ui?color=369eff&labelColor=black&logo=npm&logoColor=white&style=flat-square
+[pr-welcome-link]: https://github.com/lobehub/lobe-chat/pulls
+[pr-welcome-shield]: https://img.shields.io/badge/🤯_pr_welcome-%E2%86%92-ffcb47?labelColor=black&style=for-the-badge
+[profile-link]: https://github.com/canisminor1990
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README.md b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4074031a1f3ed41116adc513256278150d2aabd2
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/README.md
@@ -0,0 +1,427 @@
+
+
+
+
+
+
+
Lobe Theme
+
+The modern theme for stable diffusion webui
+
+Legacy verison a.k.a kitchen theme
+
+English · [Russian ](./README-ru_RU.md)· [简体中文](./README-zh_CN.md) · [Changelog](./CHANGELOG.md) · [Report Bug][github-issues-link] · [Request Feature][github-issues-link]
+
+
+
+[![][github-release-shield]][github-release-link]
+[![][discord-shield]][discord-link]
+[![][github-releasedate-shield]][github-releasedate-link]
+[![][github-action-test-shield]][github-action-test-link]
+[![][github-action-release-shield]][github-action-release-link]
+[![][github-contributors-shield]][github-contributors-link]
+[![][github-forks-shield]][github-forks-link]
+[![][github-stars-shield]][github-stars-link]
+[![][github-issues-shield]][github-issues-link]
+[![][github-license-shield]][github-license-link]
+
+
+Table of contents
+
+#### TOC
+
+- [✨ Features](#-features)
+
+- [📦 Installation](#-installation)
+
+- [🤯 Usage](#-usage)
+
+- [🖥 Environment Support](#-environment-support)
+
+- [📦 Ecosystem](#-ecosystem)
+
+- [⌨️ Local Development](#️-local-development)
+
+- [🤝 Contributing](#-contributing)
+
+- [🔗 Links](#-links)
+
+ - [More Products](#more-products)
+ - [Credits](#credits)
+
+####
+
+
+
+## ✨ Features
+
+- [x] 🌗 Supports light and dark themes, with the ability to quickly switch in the navigation bar
+- [x] 🌈 Supports custom theme colors and neutral colors, with the option to customize the logo
+- [x] 🪄 Supports one-click formatting of the prompt, with a simple tag editor provided
+- [x] 🎛️ Highly customizable sidebar, with a quick settings sidebar on the left and a model sidebar on the right
+- [x] 🖼️ Adjustable canvas ratio, ensuring that generated images are always displayed at the top
+- [x] 📱 Mobile-friendly, with partial optimization for mobile screens
+- [x] 🌍Support i18n and welcome [PR](https://github.com/lobehub/sd-webui-lobe-theme/tree/main/locales) contributions
+- [x] 📝 Syntax highlighting in the prompt input box
+- [x] 📦 Support [PWA](https://support.google.com/chrome/answer/9658361) progressive web app
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 📦 Installation
+
+#### Method 1
+
+Search for `Lobe Theme` or `Kitchen Theme` in the stable diffusion webui plugin market and install it.
+
+> **Note**\
+> **Version 2.0.0** was renamed to **Lobe Theme**
+
+#### Method 2
+
+As an extension (recommended), clone the repository to your extension folder:
+
+```shell
+git clone "https://github.com/lobehub/sd-webui-lobe-theme" extensions/lobe-theme
+```
+
+> **Important**\
+> minimum requirements `gradio-3.41.2` & `sd-webui 1.6`
+
+#### Upstream Sync
+
+Star Us，You will receive all releases notifications from GitHub without any delay \~ ⭐️
+
+Star History
+
+
+
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🤯 Usage
+
+![][feat-thememode]
+
+#### Light and Dark Themes
+
+> **Note**\
+> You can quickly switch between light and dark themes in the upper-right corner of the navigation bar.
+
+The current theme supports both light and dark themes. If you want to force dark mode, use the `--theme=dark` argument to launch your WebUI. For example, on Windows, your `webui-user.bat` should include:
+
+```shell
+set COMMANDLINE_ARGS= --theme=dark
+```
+
+Alternatively, you can switch directly through URL Props:
+
+```shell
+http://localhost:7860/?__theme=light
+http://localhost:7860/?__theme=dark
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-theme-modify]
+
+#### Theme Customization
+
+> **Note**\
+> Click the `⚙` icon in the upper-right corner to open the settings panel. The current available settings are as follows:
+
+- **Theme**
+ - Primary color: Currently offers `13` theme color combinations
+ - Neutral color: Currently offers `6` different grayscale color combinations
+ - Logo type: `Lobe`, `Kitchen`, `Custom`
+ - Custom logo: Supports `img url`, `base64`, and `emoji`. When a single emoji is entered, it will be automatically replaced with 3D Fluent Emoji.
+ - Custom title: Customize the site name.
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-highlight]
+
+#### Prompt Syntax Highlighting
+
+Automatically colorize prompt display according to the Stable Diffusion syntax rules
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-sidebar]
+
+#### Sidebar Customization
+
+> **Note**\
+> Click the `⚙` icon in the upper-right corner to open the settings panel. The current available settings are as follows:
+
+- **Prompt Textarea**
+ - Display mode: `scroll fixed height` | `resizable by text lines`
+- **Sidebar**
+ - Default expand: `true`
+ - Display mode: `fixed` | `float`
+ - Default width: `280`
+- **ExtraNetwork Sidebar**
+ - Enable: `true`
+ - Default expand: `true`
+ - Display mode: `fixed` | `float`
+ - Default width: `340`
+ - Default card size: `86`
+
+
+Recommended System Settings
+
+#### Extra-Networks
+
+- thumbs
+- width: 86
+- height: 128
+
+
+
+#### Quick-Setting
+
+```txt
+sd_model_checkpoint, sd_vae, CLIP_stop_at_last_layers, img2img_background_color, img2img_color_correction, samples_save, samples_format, grid_save, return_grid, n_rows, live_previews_enable, show_progress_every_n_steps, live_preview_refresh_period
+```
+
+
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-mobile-friendly]
+
+#### Mobile Adaptation
+
+Partial convenience adaptation design has been completed for mobile devices, including breadcrumb foldable navigation bar, sidebar adaptation, etc. However, due to the high complexity and fixed values of the stable diffusion interface, it is difficult to ensure the same user experience as the desktop version. Feedback is welcome for more ideas.
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+![][feat-pwa]
+
+#### Progress Web App
+
+You can use Progressive Web Apps [PWA](https://support.google.com/chrome/answer/9658361) for a fast stable diffusion experience on your computer or mobile device.
+
+- On your computer, open Chrome.
+- Go to stable diffusion website you want to install
+- At the top right of the address bar, click `Install`
+- Follow the onscreen instructions to install the PWA
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+#### Prompt formatting
+
+Click the 🪄 button below Prompt to format the prompt words with one click.
+
+> **Note**\
+> Convert full-width punctuation to half-width, remove extra spaces, add missing commas, and move the Extra-Networks model to the end.
+
+Before formatting:
+
+```text
+photorealistic photo of a handsome male (wizard :1.2）， short beard, white wizard shirt, (with golden trim:0.8),
+```
+
+After formatting:
+
+```text
+photorealistic photo of a handsome male, (wizard:1.2), short beard, white wizard shirt, (with golden trim:0.8), ,
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🖥 Environment Support
+
+| [](http://godban.github.io/browsers-support-badges/)
Chrome | [](http://godban.github.io/browsers-support-badges/)
Edge | [](http://godban.github.io/browsers-support-badges/)
Safari |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| last 2 versions | last 2 versions | last 2 versions |
+
+> **Warning**\
+> There is currently a known compatibility issue with styles on Firefox browser.
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 📦 Ecosystem
+
+| NPM | Repository | Description | Version |
+| ------------------------------- | ------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
+| [@lobehub/ui][lobe-ui-link] | [lobehub/lobe-ui][lobe-ui-github] | Lobe UI is an open-source UI component library dedicated to building AIGC web applications. | [![][lobe-ui-shield]][lobe-ui-link] |
+| [@lobehub/lint][lobe-lint-link] | [lobehub/lobe-lint][lobe-lint-github] | LobeLint provides configurations for ESlint, Stylelint, Commitlint, Prettier, Remark, and Semantic Release for LobeHub. | [![][lobe-lint-shield]][lobe-lint-link] |
+| @lobehub/assets | [lobehub/assets][lobe-assets-github] | Logo assets, favicons, webfonts for LobeHub. | |
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## ⌨️ Local Development
+
+You can use Github Codespaces for online development:
+
+[![][codespaces-shield]][codespaces-link]
+
+Alternatively, you can clone it for local development. To enable hot-reloading mode, you need to start stable diffuison on port `7860` in advance.
+
+[![][bun-shield]][bun-link]
+
+```bash
+$ git clone https://github.com/lobehub/sd-webui-lobe-theme.git
+$ cd sd-webui-lobe-theme
+$ bun install
+$ bun dev
+```
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🤝 Contributing
+
+Contributions of all types are more than welcome, if you are interested in contributing code, feel free to check out our GitHub [Issues][github-issues-link] to get stuck in to show us what you’re made of.
+
+[![][pr-welcome-shield]][pr-welcome-link]
+
+[![][contributors-contrib]][contributors-link]
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+## 🔗 Links
+
+### More Products
+
+- **[🤖 Lobe Chat][lobe-chat] :** An open-source, extensible (Function Calling), high-performance chatbot framework. It supports one-click free deployment of your private ChatGPT/LLM web application.
+- **[🌏 Lobe i18n][lobe-i18n] :** Lobe i18n is an automation tool for the i18n (internationalization) translation process, powered by ChatGPT. It supports features such as automatic splitting of large files, incremental updates, and customization options for the OpenAI model, API proxy, and temperature.
+- **[💌 Lobe Commit][lobe-commit] :** Lobe Commit is a CLI tool that leverages Langchain/ChatGPT to generate Gitmoji-based commit messages.
+
+### Credits
+
+- stable-diffusion-webui：
+- gradio-theme-gallery:
+- cozy-nest:
+- _before `1.0.0` version_
+ - sd-web-ui-quickcs:
+ - Dark-Themes-SD-WebUI-Automatic1111:
+
+
+
+[![][back-to-top]](#readme-top)
+
+
+
+---
+
+📝 License
+
+[![][fossa-license-shield]][fossa-license-link]
+
+
+This project is [AGPL3](./LICENSE) licensed.
+
+
+
+[back-to-top]: https://img.shields.io/badge/-BACK_TO_TOP-151515?style=flat-square
+[bun-link]: https://bun.sh
+[bun-shield]: https://img.shields.io/badge/-speedup%20with%20bun-black?logo=bun&style=for-the-badge
+[codespaces-link]: https://codespaces.new/lobehub/sd-webui-lobe-theme
+[codespaces-shield]: https://github.com/codespaces/badge.svg
+[contributors-contrib]: https://contrib.rocks/image?repo=lobehub/sd-webui-lobe-theme
+[contributors-link]: https://github.com/lobehub/sd-webui-lobe-theme/graphs/contributors
+[cover]: https://gw.alipayobjects.com/zos/kitchen/8Ab%24hLJ5ur/cover.webp
+[discord-link]: https://discord.gg/AYFPHvv2jT
+[discord-shield]: https://img.shields.io/discord/1127171173982154893?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=flat-square
+[feat-highlight]: https://gw.alipayobjects.com/zos/kitchen/iD%24W4U2y3Y/feat_highlight.webp
+[feat-mobile-friendly]: https://gw.alipayobjects.com/zos/kitchen/WpWe6Hw8UT/feat_mobile_friendly.webp
+[feat-pwa]: https://gw.alipayobjects.com/zos/kitchen/az49akOKJT/feat_pwa.webp
+[feat-sidebar]: https://gw.alipayobjects.com/zos/kitchen/Olum2IjxCW/feat_sidebar.webp
+[feat-theme-modify]: https://gw.alipayobjects.com/zos/kitchen/CbhlynwJYg/feat_theme_modify.webp
+[feat-thememode]: https://gw.alipayobjects.com/zos/kitchen/nSFtJidWUR/feat_thememode.webp
+[fossa-license-link]: https://app.fossa.com/projects/git%2Bgithub.com%2Flobehub%2Fsd-webui-lobe-theme
+[fossa-license-shield]: https://app.fossa.com/api/projects/git%2Bgithub.com%2Flobehub%2Fsd-webui-lobe-theme.svg?type=large
+[github-action-release-link]: https://github.com/actions/workflows/lobehub/sd-webui-lobe-theme/release.yml
+[github-action-release-shield]: https://img.shields.io/github/actions/workflow/status/lobehub/sd-webui-lobe-theme/release.yml?label=release&labelColor=black&logo=githubactions&logoColor=white&style=flat-square
+[github-action-test-link]: https://github.com/actions/workflows/lobehub/sd-webui-lobe-theme/test.yml
+[github-action-test-shield]: https://img.shields.io/github/actions/workflow/status/lobehub/sd-webui-lobe-theme/test.yml?label=test&labelColor=black&logo=githubactions&logoColor=white&style=flat-square
+[github-contributors-link]: https://github.com/lobehub/sd-webui-lobe-theme/graphs/contributors
+[github-contributors-shield]: https://img.shields.io/github/contributors/lobehub/sd-webui-lobe-theme?color=c4f042&labelColor=black&style=flat-square
+[github-forks-link]: https://github.com/lobehub/sd-webui-lobe-theme/network/members
+[github-forks-shield]: https://img.shields.io/github/forks/lobehub/sd-webui-lobe-theme?color=8ae8ff&labelColor=black&style=flat-square
+[github-issues-link]: https://github.com/lobehub/sd-webui-lobe-theme/issues
+[github-issues-shield]: https://img.shields.io/github/issues/lobehub/sd-webui-lobe-theme?color=ff80eb&labelColor=black&style=flat-square
+[github-license-link]: https://github.com/lobehub/sd-webui-lobe-theme/blob/main/LICENSE
+[github-license-shield]: https://img.shields.io/github/license/lobehub/sd-webui-lobe-theme?color=white&labelColor=black&style=flat-square
+[github-release-link]: https://github.com/lobehub/sd-webui-lobe-theme/releases
+[github-release-shield]: https://img.shields.io/github/v/release/lobehub/sd-webui-lobe-theme?color=369eff&labelColor=black&logo=github&style=flat-square
+[github-releasedate-link]: https://github.com/lobehub/sd-webui-lobe-theme/releases
+[github-releasedate-shield]: https://img.shields.io/github/release-date/lobehub/sd-webui-lobe-theme?labelColor=black&style=flat-square
+[github-stars-link]: https://github.com/lobehub/sd-webui-lobe-theme/network/stargazers
+[github-stars-shield]: https://img.shields.io/github/stars/lobehub/sd-webui-lobe-theme?color=ffcb47&labelColor=black&style=flat-square
+[lobe-assets-github]: https://github.com/lobehub/lobe-assets
+[lobe-chat]: https://github.com/lobehub/lobe-chat
+[lobe-commit]: https://github.com/lobehub/lobe-commit/tree/master/packages/lobe-commit
+[lobe-i18n]: https://github.com/lobehub/lobe-commit/tree/master/packages/lobe-i18n
+[lobe-lint-github]: https://github.com/lobehub/lobe-lint
+[lobe-lint-link]: https://www.npmjs.com/package/@lobehub/lint
+[lobe-lint-shield]: https://img.shields.io/npm/v/@lobehub/lint?color=369eff&labelColor=black&logo=npm&logoColor=white&style=flat-square
+[lobe-ui-github]: https://github.com/lobehub/lobe-ui
+[lobe-ui-link]: https://www.npmjs.com/package/@lobehub/ui
+[lobe-ui-shield]: https://img.shields.io/npm/v/@lobehub/ui?color=369eff&labelColor=black&logo=npm&logoColor=white&style=flat-square
+[pr-welcome-link]: https://github.com/lobehub/lobe-chat/pulls
+[pr-welcome-shield]: https://img.shields.io/badge/🤯_pr_welcome-%E2%86%92-ffcb47?labelColor=black&style=for-the-badge
+[profile-link]: https://github.com/canisminor1990
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/__pycache__/preload.cpython-39.pyc b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/__pycache__/preload.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..17d7a84a5f1cbf8c381e3eb4625b5937225eed36
Binary files /dev/null and b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/__pycache__/preload.cpython-39.pyc differ
diff --git a/stable-diffusion-webui/extensions/sd-webui-lobe-theme/javascript/main.js b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/javascript/main.js
new file mode 100644
index 0000000000000000000000000000000000000000..810159362b612b67c91d528774803b19c627e325
--- /dev/null
+++ b/stable-diffusion-webui/extensions/sd-webui-lobe-theme/javascript/main.js
@@ -0,0 +1,1857 @@
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+/**
+ * @license React
+ * react-dom.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
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t=r.getMeta();return Yg(Yg({},r.getOnlyChild(e(r.getControlled(),t,r.props.fieldContext))),{},{isFunction:!0})}var n=jv(e);return 1===n.length&&B.isValidElement(n[0])?{child:n[0],isFunction:!1}:{child:n,isFunction:!1}})),Lf(dy(r),"getValue",(function(e){var t=r.props.fieldContext.getFieldsValue,n=r.getNamePath();return Dy(e||t(!0),n)})),Lf(dy(r),"getControlled",(function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=r.props,n=t.trigger,o=t.validateTrigger,a=t.getValueFromEvent,i=t.normalize,s=t.valuePropName,l=t.getValueProps,c=t.fieldContext,u=void 0!==o?o:c.validateTrigger,d=r.getNamePath(),f=c.getInternalHooks,p=c.getFieldsValue,h=f(hO).dispatch,g=r.getValue(),m=l||function(e){return Lf({},s,e)},b=e[n],v=Yg(Yg({},e),m(g));return v[n]=function(){var e;r.touched=!0,r.dirty=!0,r.triggerMetaEvent();for(var t=arguments.length,n=new Array(t),o=0;o=0&&t<=n.length?(c.keys=[].concat(Kg(c.keys.slice(0,t)),[c.id],Kg(c.keys.slice(t))),a([].concat(Kg(n.slice(0,t)),[e],Kg(n.slice(t))))):(c.keys=[].concat(Kg(c.keys),[c.id]),a([].concat(Kg(n),[e]))),c.id+=1},remove:function(e){var t=l(),n=new Set(Array.isArray(e)?e:[e]);n.size<=0||(c.keys=c.keys.filter((function(e,t){return!n.has(t)})),a(t.filter((function(e,t){return!n.has(t)}))))},move:function(e,t){if(e!==t){var n=l();e<0||e>=n.length||t<0||t>=n.length||(c.keys=sD(c.keys,e,t),a(sD(n,e,t)))}}},f=o||[];return Array.isArray(f)||(f=[]),r(f.map((function(e,t){var n=c.keys[t];return void 0===n&&(c.keys[t]=c.id,n=c.keys[t],c.id+=1),{name:t,key:n,isListField:!0}})),d,t)}))))}Lf(dD,"contextType",mO),Lf(dD,"defaultProps",{trigger:"onChange",valuePropName:"value"});var hD="__@field_split__";function gD(e){return e.map((function(e){return"".concat(Ff(e),":").concat(e)})).join(hD)}var mD=function(){function e(){mp(this,e),Lf(this,"kvs",new Map)}return 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e===hO?(n.formHooked=!0,{dispatch:n.dispatch,initEntityValue:n.initEntityValue,registerField:n.registerField,useSubscribe:n.useSubscribe,setInitialValues:n.setInitialValues,destroyForm:n.destroyForm,setCallbacks:n.setCallbacks,setValidateMessages:n.setValidateMessages,getFields:n.getFields,setPreserve:n.setPreserve,getInitialValue:n.getInitialValue,registerWatch:n.registerWatch}):(rm(!1,"`getInternalHooks` is internal usage. 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Field can not overwrite it."));else{var a=t.get(o);if(a&&a.size>1)rm(!1,"Multiple Field with path '".concat(o.join("."),"' set 'initialValue'. Can not decide which one to pick."));else if(a){var i=n.getFieldValue(o);r.isListField()||e.skipExist&&void 0!==i||n.updateStore(jy(n.store,o,Kg(a)[0].value))}}}}))})),Lf(this,"resetFields",(function(e){n.warningUnhooked();var t=n.store;if(!e)return n.updateStore(_y(n.initialValues)),n.resetWithFieldInitialValue(),n.notifyObservers(t,null,{type:"reset"}),void n.notifyWatch();var r=e.map(nD);r.forEach((function(e){var t=n.getInitialValue(e);n.updateStore(jy(n.store,e,t))})),n.resetWithFieldInitialValue({namePathList:r}),n.notifyObservers(t,r,{type:"reset"}),n.notifyWatch(r)})),Lf(this,"setFields",(function(e){n.warningUnhooked();var t=n.store,r=[];e.forEach((function(e){var o=e.name,a=zf(e,bD),i=nD(o);r.push(i),"value"in a&&n.updateStore(jy(n.store,i,a.value)),n.notifyObservers(t,[i],{type:"setField",data:e})})),n.notifyWatch(r)})),Lf(this,"getFields",(function(){return n.getFieldEntities(!0).map((function(e){var t=e.getNamePath(),r=Yg(Yg({},e.getMeta()),{},{name:t,value:n.getFieldValue(t)});return Object.defineProperty(r,"originRCField",{value:!0}),r}))})),Lf(this,"initEntityValue",(function(e){var t=e.props.initialValue;if(void 0!==t){var r=e.getNamePath();void 0===Dy(n.store,r)&&n.updateStore(jy(n.store,r,t))}})),Lf(this,"isMergedPreserve",(function(e){var t=void 0!==e?e:n.preserve;return null==t||t})),Lf(this,"registerField",(function(e){n.fieldEntities.push(e);var t=e.getNamePath();if(n.notifyWatch([t]),void 0!==e.props.initialValue){var r=n.store;n.resetWithFieldInitialValue({entities:[e],skipExist:!0}),n.notifyObservers(r,[e.getNamePath()],{type:"valueUpdate",source:"internal"})}return function(r,o){var a=arguments.length>2&&void 0!==arguments[2]?arguments[2]:[];if(n.fieldEntities=n.fieldEntities.filter((function(t){return t!==e})),!n.isMergedPreserve(o)&&(!r||a.length>1)){var i=r?void 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+/**
+ * @license
+ * Copyright 2017 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */
+var b;m[g]=!0,m.elementProperties=new Map,m.elementStyles=[],m.shadowRootOptions={mode:"open"},null==d||d({ReactiveElement:m}),(null!==(s=l.reactiveElementVersions)&&void 0!==s?s:l.reactiveElementVersions=[]).push("1.6.2");const v=window,y=v.trustedTypes,w=y?y.createPolicy("lit-html",{createHTML:e=>e}):void 0,x="$lit$",S=`lit$${(Math.random()+"").slice(9)}$`,k="?"+S,C=`<${k}>`,E=document,O=()=>E.createComment(""),D=e=>null===e||"object"!=typeof e&&"function"!=typeof e,$=Array.isArray,j="[ \t\n\f\r]",P=/<(?:(!--|\/[^a-zA-Z])|(\/?[a-zA-Z][^>\s]*)|(\/?$))/g,N=/-->/g,_=/>/g,I=RegExp(`>|${j}(?:([^\\s"'>=/]+)(${j}*=${j}*(?:[^ \t\n\f\r"'\`<>=]|("|')|))|$)`,"g"),T=/'/g,R=/"/g,A=/^(?:script|style|textarea|title)$/i,F=(H=1,(e,...t)=>({_$litType$:H,strings:e,values:t})),M=Symbol.for("lit-noChange"),L=Symbol.for("lit-nothing"),z=new WeakMap,B=E.createTreeWalker(E,129,null,!1);var H;class W{constructor({strings:e,_$litType$:t},n){let r;this.parts=[];let o=0,a=0;const i=e.length-1,s=this.parts,[l,c]=((e,t)=>{const n=e.length-1,r=[];let o,a=2===t?"":"");if(!Array.isArray(e)||!e.hasOwnProperty("raw"))throw Error("invalid template strings array");return[void 0!==w?w.createHTML(s):s,r]})(e,t);if(this.el=W.createElement(l,n),B.currentNode=this.el.content,2===t){const e=this.el.content,t=e.firstChild;t.remove(),e.append(...t.childNodes)}for(;null!==(r=B.nextNode())&&s.length0){r.textContent=y?y.emptyScript:"";for(let n=0;n$(e)||"function"==typeof(null==e?void 0:e[Symbol.iterator]))(e)?this.T(e):this._(e)}k(e){return this._$AA.parentNode.insertBefore(e,this._$AB)}$(e){this._$AH!==e&&(this._$AR(),this._$AH=this.k(e))}_(e){this._$AH!==L&&D(this._$AH)?this._$AA.nextSibling.data=e:this.$(E.createTextNode(e)),this._$AH=e}g(e){var t;const{values:n,_$litType$:r}=e,o="number"==typeof r?this._$AC(e):(void 0===r.el&&(r.el=W.createElement(r.h,this.options)),r);if((null===(t=this._$AH)||void 0===t?void 0:t._$AD)===o)this._$AH.v(n);else{const e=new G(o,this),t=e.u(this.options);e.v(n),this.$(t),this._$AH=e}}_$AC(e){let t=z.get(e.strings);return void 0===t&&z.set(e.strings,t=new W(e)),t}T(e){$(this._$AH)||(this._$AH=[],this._$AR());const t=this._$AH;let n,r=0;for(const o of e)r===t.length?t.push(n=new V(this.k(O()),this.k(O()),this,this.options)):n=t[r],n._$AI(o),r++;r2||""!==n[0]||""!==n[1]?(this._$AH=Array(n.length-1).fill(new String),this.strings=n):this._$AH=L}get tagName(){return this.element.tagName}get _$AU(){return this._$AM._$AU}_$AI(e,t=this,n,r){const o=this.strings;let a=!1;if(void 0===o)e=U(this,e,t,0),a=!D(e)||e!==this._$AH&&e!==M,a&&(this._$AH=e);else{const r=e;let i,s;for(e=o[0],i=0;i{var r,o;const a=null!==(r=null==n?void 0:n.renderBefore)&&void 0!==r?r:t;let i=a._$litPart$;if(void 0===i){const e=null!==(o=null==n?void 0:n.renderBefore)&&void 0!==o?o:null;a._$litPart$=i=new V(t.insertBefore(O(),e),e,void 0,n??{})}return i._$AI(e),i})(t,this.renderRoot,this.renderOptions)}connectedCallback(){var e;super.connectedCallback(),null===(e=this._$Do)||void 0===e||e.setConnected(!0)}disconnectedCallback(){var e;super.disconnectedCallback(),null===(e=this._$Do)||void 0===e||e.setConnected(!1)}render(){return M}};ne.finalized=!0,ne._$litElement$=!0,null===(ee=globalThis.litElementHydrateSupport)||void 0===ee||ee.call(globalThis,{LitElement:ne});const re=globalThis.litElementPolyfillSupport;null==re||re({LitElement:ne}),(null!==(te=globalThis.litElementVersions)&&void 0!==te?te:globalThis.litElementVersions=[]).push("3.3.2");
+/**
+ * @license
+ * Copyright 2017 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */
+const oe=(e,t)=>"method"===t.kind&&t.descriptor&&!("value"in t.descriptor)?{...t,finisher(n){n.createProperty(t.key,e)}}:{kind:"field",key:Symbol(),placement:"own",descriptor:{},originalKey:t.key,initializer(){"function"==typeof t.initializer&&(this[t.key]=t.initializer.call(this))},finisher(n){n.createProperty(t.key,e)}};
+/**
+ * @license
+ * Copyright 2017 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */function ae(e){return(t,n)=>void 0!==n?((e,t,n)=>{t.constructor.createProperty(n,e)})(e,t,n):oe(e,t)}
+/**
+ * @license
+ * Copyright 2021 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */var ie;null===(ie=window.HTMLSlotElement)||void 0===ie||ie.prototype.assignedElements;
+/**
+ * @license
+ * Copyright 2020 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */
+const se=2;
+/**
+ * @license
+ * Copyright 2017 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */let le=class{constructor(e){}get _$AU(){return this._$AM._$AU}_$AT(e,t,n){this._$Ct=e,this._$AM=t,this._$Ci=n}_$AS(e,t){return this.update(e,t)}update(e,t){return this.render(...t)}};
+/**
+ * @license
+ * Copyright 2017 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */const ce=(e,t)=>{var n,r;const o=e._$AN;if(void 0===o)return!1;for(const a of o)null===(r=(n=a)._$AO)||void 0===r||r.call(n,t,!1),ce(a,t);return!0},ue=e=>{let t,n;do{if(void 0===(t=e._$AM))break;n=t._$AN,n.delete(e),e=t}while(0===(null==n?void 0:n.size))},de=e=>{for(let t;t=e._$AM;e=t){let n=t._$AN;if(void 0===n)t._$AN=n=new Set;else if(n.has(e))break;n.add(e),he(t)}};function fe(e){void 0!==this._$AN?(ue(this),this._$AM=e,de(this)):this._$AM=e}function pe(e,t=!1,n=0){const r=this._$AH,o=this._$AN;if(void 0!==o&&0!==o.size)if(t)if(Array.isArray(r))for(let a=n;a{var t,n,r,o;e.type==se&&(null!==(t=(r=e)._$AP)&&void 0!==t||(r._$AP=pe),null!==(n=(o=e)._$AQ)&&void 0!==n||(o._$AQ=fe))};class ge extends le{constructor(){super(...arguments),this._$AN=void 0}_$AT(e,t,n){super._$AT(e,t,n),de(this),this.isConnected=e._$AU}_$AO(e,t=!0){var n,r;e!==this.isConnected&&(this.isConnected=e,e?null===(n=this.reconnected)||void 0===n||n.call(this):null===(r=this.disconnected)||void 0===r||r.call(this)),t&&(ce(this,e),ue(this))}setValue(e){if((e=>void 0===e.strings)(this._$Ct))this._$Ct._$AI(e,this);else{const t=[...this._$Ct._$AH];t[this._$Ci]=e,this._$Ct._$AI(t,this,0)}}disconnected(){}reconnected(){}}
+/**
+ * @license
+ * Copyright 2020 Google LLC
+ * SPDX-License-Identifier: BSD-3-Clause
+ */class me{}const be=new WeakMap,ve=(e=>(...t)=>({_$litDirective$:e,values:t}))(class extends ge{render(e){return L}update(e,[t]){var n;const r=t!==this.G;return r&&void 0!==this.G&&this.ot(void 0),(r||this.rt!==this.lt)&&(this.G=t,this.ct=null===(n=e.options)||void 0===n?void 0:n.host,this.ot(this.lt=e.element)),L}ot(e){var t;if("function"==typeof this.G){const n=null!==(t=this.ct)&&void 0!==t?t:globalThis;let r=be.get(n);void 0===r&&(r=new WeakMap,be.set(n,r)),void 0!==r.get(this.G)&&this.G.call(this.ct,void 0),r.set(this.G,e),void 0!==e&&this.G.call(this.ct,e)}else this.G.value=e}get rt(){var e,t,n;return"function"==typeof this.G?null===(t=be.get(null!==(e=this.ct)&&void 0!==e?e:globalThis))||void 0===t?void 0:t.get(this.G):null===(n=this.G)||void 0===n?void 0:n.value}disconnected(){this.rt===this.lt&&this.ot(void 0)}reconnected(){this.ot(this.lt)}});var ye=Object.defineProperty,we=Object.getOwnPropertyDescriptor,xe=(e,t,n,r)=>{for(var o,a=r>1?void 0:r?we(t,n):t,i=e.length-1;i>=0;i--)(o=e[i])&&(a=(r?o(t,n,a):o(a))||a);return r&&a&&ye(t,n,a),a};e.GiscusWidget=class extends ne{constructor(){super(),this.GISCUS_SESSION_KEY="giscus-session",this.GISCUS_DEFAULT_HOST="https://giscus.app",this.ERROR_SUGGESTION="Please consider reporting this error at https://github.com/giscus/giscus/issues/new.",this.__session="",this._iframeRef=new me,this.messageEventHandler=this.handleMessageEvent.bind(this),this.hasLoaded=!1,this.host=this.GISCUS_DEFAULT_HOST,this.strict="0",this.reactionsEnabled="1",this.emitMetadata="0",this.inputPosition="bottom",this.theme="light",this.lang="en",this.loading="eager",this.setupSession(),window.addEventListener("message",this.messageEventHandler)}get iframeRef(){var e;return null==(e=this._iframeRef)?void 0:e.value}get _host(){try{return new URL(this.host),this.host}catch{return this.GISCUS_DEFAULT_HOST}}disconnectedCallback(){super.disconnectedCallback(),window.removeEventListener("message",this.messageEventHandler)}_formatError(e){return`[giscus] An error occurred. 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+
+ `}},e.GiscusWidget.styles=((e,...t)=>{const n=1===e.length?e[0]:t.reduce(((t,n,r)=>t+(e=>{if(!0===e._$cssResult$)return e.cssText;if("number"==typeof e)return e;throw Error("Value passed to 'css' function must be a 'css' function result: "+e+". Use 'unsafeCSS' to pass non-literal values, but take care to ensure page security.")})(n)+e[r+1]),e[0]);return new a(n,e,r)})`
+ :host,
+ iframe {
+ width: 100%;
+ border: none;
+ min-height: 150px;
+ color-scheme: light dark;
+ }
+
+ iframe.loading {
+ opacity: 0;
+ }
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