From 61660a4b557bcd4399262e18a3f02392baea0b65 Mon Sep 17 00:00:00 2001 From: chflame163 Date: Sat, 7 Dec 2024 13:34:37 +0800 Subject: [PATCH] move BenUltra and LoadBenModel to LayerStyleAdvance --- README.md | 29 ++ README_CN.MD | 28 ++ py/ben_ultra.py | 1069 ++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 4 +- requirements.txt | 6 +- 5 files changed, 1129 insertions(+), 7 deletions(-) create mode 100644 py/ben_ultra.py diff --git a/README.md b/README.md index 1fad02a..3c82419 100644 --- a/README.md +++ b/README.md @@ -593,6 +593,35 @@ Node Options: * zero_out_start: Set the ConditioningSetTimestepRange start value for Negative ConditioningZeroOut, which is the same as the ConditioningSetTimestepRange end value for Negative. +### BenUltra +It is the implementation of [PramaLLC/BEN](https://huggingface.co/PramaLLC/BEN) project in ComfyUI. Thank you to the original author. + +Download the ```BEN_Base.pth``` and ```config.json``` from [huggingface](https://huggingface.co/PramaLLC/BEN/tree/main) or [BaiduNetdisk](https://pan.baidu.com/s/17mdBxfBl_R97mtNHuiHsxQ?pwd=2jn3) and copy to ```ComfyUI/models/BEN``` folder. + +![image](image/ben_ultra_example.jpg) + +Node Options： +![image](image/ben_ultra_node.jpg) +* ben_model: Ben model input. +* image: Image input. +* detail_method: Edge processing methods. provides VITMatte, VITMatte(local), PyMatting, GuidedFilter. If the model has been downloaded after the first use of VITMatte, you can use VITMatte (local) afterwards. +* detail_erode: Mask the erosion range inward from the edge. the larger the value, the larger the range of inward repair. +* detail_dilate: The edge of the mask expands outward. the larger the value, the wider the range of outward repair. +* black_point: Edge black sampling threshold. +* white_point: Edge white sampling threshold. +* process_detail: Set to false here will skip edge processing to save runtime. +* max_megapixels: Set the maximum size for VitMate operations. + +### LoadBenModel +Load the BEN model. + + +Node Options: +![image](image/load_ben_model_node.jpg) + +* model: Select the model. Currently, only the Ben_Sase model is available for selection. + + ### SegmentAnythingUltra diff --git a/README_CN.MD b/README_CN.MD index ee9e892..a9d0b53 100644 --- a/README_CN.MD +++ b/README_CN.MD @@ -534,6 +534,34 @@ all_layers: 包含全部图层的批量图片。 * zero_out_start: 设置Negative ConditioningZeroOut的ConditioningSetTimestepRange start值, 此数值与Negative的ConditioningSetTimestepRange end值相同。 +### BenUltra +是[PramaLLC/BEN](https://huggingface.co/PramaLLC/BEN) 项目在ComfyUI中的实现。感谢原作者。 +从 [huggingface](https://huggingface.co/PramaLLC/BEN/tree/main) 或 [百度网盘](https://pan.baidu.com/s/17mdBxfBl_R97mtNHuiHsxQ?pwd=2jn3)下载```BEN_Base.pth``` 和 ```config.json``` 两个文件并复制到 ```ComfyUI/models/BEN```文件夹。 + +![image](image/ben_ultra_example.jpg) + +节点选项说明： +![image](image/ben_ultra_node.jpg) +* ben_model: Ben模型输入。 +* image: 图片输入。 +* detail_method: 边缘处理方法。提供了VITMatte, VITMatte(local), PyMatting, GuidedFilter。如果首次使用VITMatte后模型已经下载，之后可以使用VITMatte(local)。 +* detail_erode: 遮罩边缘向内侵蚀范围。数值越大，向内修复的范围越大。 +* detail_dilate: 遮罩边缘向外扩张范围。数值越大，向外修复的范围越大。 +* black_point: 边缘黑色采样阈值。 +* white_point: 边缘白色采样阈值。 +* process_detail: 此处设为False将跳过边缘处理以节省运行时间。 +* max_megapixels: 设置vitmatte运算的最大尺寸。 + +### LoadBenModel +加载BEN模型。 + + +节点选项说明: +![image](image/load_ben_model_node.jpg) + +* model: 选择模型。目前仅有Ben_Base 模型可选。 + + ### SegmentAnythingUltra 对[ComfyUI Segment Anything](https://github.com/storyicon/comfyui_segment_anything)的改进，使遮罩有更具细节的边缘，感谢原作者。 diff --git a/py/ben_ultra.py b/py/ben_ultra.py new file mode 100644 index 0000000..e5222af --- /dev/null +++ b/py/ben_ultra.py @@ -0,0 +1,1069 @@ +# layerstyle advance +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 einops import rearrange +from PIL import Image, ImageFilter, ImageOps +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ +from torchvision import transforms + + +class Mlp(nn.Module): + """ Multilayer perceptron.""" + + 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): + """ 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): + """ Forward function. + 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 + + +class SwinTransformerBlock(nn.Module): + """ Swin Transformer Block. + Args: + dim (int): Number of input channels. + 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, 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.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + 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) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # 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 + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # 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 + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + 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 + + +class PatchMerging(nn.Module): + """ Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + 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 + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + 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, + depth, + num_heads, + window_size=7, + 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.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock( + dim=dim, + 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(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W): + """ Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 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)) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + Args: + 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, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + 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): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """ Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + 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. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + 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. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + use_checkpoint=False): + super().__init__() + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.out_indices = out_indices + self.frozen_stages = frozen_stages + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]] + + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])) + 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 layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2 ** i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=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])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint) + self.layers.append(layer) + + num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in out_indices: + layer = norm_layer(num_features[i_layer]) + layer_name = f'norm{i_layer}' + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + + def forward(self, x): + + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic') + x = (x + absolute_pos_embed) # B Wh*Ww C + + outs = [x.contiguous()] + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + + + if i in self.out_indices: + norm_layer = getattr(self, f'norm{i}') + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs.append(out) + + return tuple(outs) + +def get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "gelu": + return F.gelu + + raise RuntimeError(F"activation should be gelu, not {activation}.") + + +def make_cbr(in_dim, out_dim): + return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1), nn.InstanceNorm2d(out_dim), nn.GELU()) + + +def make_cbg(in_dim, out_dim): + return nn.Sequential(nn.Conv2d(in_dim, out_dim, kernel_size=3, padding=1), nn.InstanceNorm2d(out_dim), nn.GELU()) + + +def rescale_to(x, scale_factor: float = 2, interpolation='nearest'): + return F.interpolate(x, scale_factor=scale_factor, mode=interpolation) + + +def resize_as(x, y, interpolation='bilinear'): + return F.interpolate(x, size=y.shape[-2:], mode=interpolation) + + +def image2patches(x): + """b c (hg h) (wg w) -> (hg wg b) c h w""" + x = rearrange(x, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2) + return x + + +def patches2image(x): + """(hg wg b) c h w -> b c (hg h) (wg w)""" + x = rearrange(x, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2) + return x +class PositionEmbeddingSine: + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + self.dim_t = torch.arange(0, self.num_pos_feats, dtype=torch.float32) + + def __call__(self, b, h, w): + device = self.dim_t.device + mask = torch.zeros([b, h, w], dtype=torch.bool, device=device) + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(dim=1, dtype=torch.float32) + x_embed = not_mask.cumsum(dim=2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = self.temperature ** (2 * (self.dim_t.to(device) // 2) / self.num_pos_feats) + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + + return torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + + +class MCLM(nn.Module): + def __init__(self, d_model, num_heads, pool_ratios=[1, 4, 8]): + super(MCLM, self).__init__() + self.attention = nn.ModuleList([ + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1) + ]) + + self.linear1 = nn.Linear(d_model, d_model * 2) + self.linear2 = nn.Linear(d_model * 2, d_model) + self.linear3 = nn.Linear(d_model, d_model * 2) + self.linear4 = nn.Linear(d_model * 2, d_model) + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(0.1) + self.dropout1 = nn.Dropout(0.1) + self.dropout2 = nn.Dropout(0.1) + self.activation = get_activation_fn('gelu') + self.pool_ratios = pool_ratios + self.p_poses = [] + self.g_pos = None + self.positional_encoding = PositionEmbeddingSine(num_pos_feats=d_model // 2, normalize=True) + + def forward(self, l, g): + """ + l: 4,c,h,w + g: 1,c,h,w + """ + b, c, h, w = l.size() + # 4,c,h,w -> 1,c,2h,2w + concated_locs = rearrange(l, '(hg wg b) c h w -> b c (hg h) (wg w)', hg=2, wg=2) + + pools = [] + for pool_ratio in self.pool_ratios: + # b,c,h,w + tgt_hw = (round(h / pool_ratio), round(w / pool_ratio)) + pool = F.adaptive_avg_pool2d(concated_locs, tgt_hw) + pools.append(rearrange(pool, 'b c h w -> (h w) b c')) + if self.g_pos is None: + pos_emb = self.positional_encoding(pool.shape[0], pool.shape[2], pool.shape[3]) + pos_emb = rearrange(pos_emb, 'b c h w -> (h w) b c') + self.p_poses.append(pos_emb) + pools = torch.cat(pools, 0) + if self.g_pos is None: + self.p_poses = torch.cat(self.p_poses, dim=0) + pos_emb = self.positional_encoding(g.shape[0], g.shape[2], g.shape[3]) + self.g_pos = rearrange(pos_emb, 'b c h w -> (h w) b c') + + device = pools.device + self.p_poses = self.p_poses.to(device) + self.g_pos = self.g_pos.to(device) + + + # attention between glb (q) & multisensory concated-locs (k,v) + g_hw_b_c = rearrange(g, 'b c h w -> (h w) b c') + + + g_hw_b_c = g_hw_b_c + self.dropout1(self.attention[0](g_hw_b_c + self.g_pos, pools + self.p_poses, pools)[0]) + g_hw_b_c = self.norm1(g_hw_b_c) + g_hw_b_c = g_hw_b_c + self.dropout2(self.linear2(self.dropout(self.activation(self.linear1(g_hw_b_c)).clone()))) + g_hw_b_c = self.norm2(g_hw_b_c) + + # attention between origin locs (q) & freashed glb (k,v) + l_hw_b_c = rearrange(l, "b c h w -> (h w) b c") + _g_hw_b_c = rearrange(g_hw_b_c, '(h w) b c -> h w b c', h=h, w=w) + _g_hw_b_c = rearrange(_g_hw_b_c, "(ng h) (nw w) b c -> (h w) (ng nw b) c", ng=2, nw=2) + outputs_re = [] + for i, (_l, _g) in enumerate(zip(l_hw_b_c.chunk(4, dim=1), _g_hw_b_c.chunk(4, dim=1))): + outputs_re.append(self.attention[i + 1](_l, _g, _g)[0]) # (h w) 1 c + outputs_re = torch.cat(outputs_re, 1) # (h w) 4 c + + l_hw_b_c = l_hw_b_c + self.dropout1(outputs_re) + l_hw_b_c = self.norm1(l_hw_b_c) + l_hw_b_c = l_hw_b_c + self.dropout2(self.linear4(self.dropout(self.activation(self.linear3(l_hw_b_c)).clone()))) + l_hw_b_c = self.norm2(l_hw_b_c) + + l = torch.cat((l_hw_b_c, g_hw_b_c), 1) # hw,b(5),c + return rearrange(l, "(h w) b c -> b c h w", h=h, w=w) ## (5,c,h*w) + + +class MCRM(nn.Module): + def __init__(self, d_model, num_heads, pool_ratios=[4, 8, 16], h=None): + super(MCRM, self).__init__() + self.attention = nn.ModuleList([ + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1), + nn.MultiheadAttention(d_model, num_heads, dropout=0.1) + ]) + self.linear3 = nn.Linear(d_model, d_model * 2) + self.linear4 = nn.Linear(d_model * 2, d_model) + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(0.1) + self.dropout1 = nn.Dropout(0.1) + self.dropout2 = nn.Dropout(0.1) + self.sigmoid = nn.Sigmoid() + self.activation = get_activation_fn('gelu') + self.sal_conv = nn.Conv2d(d_model, 1, 1) + self.pool_ratios = pool_ratios + + def forward(self, x): + device = x.device + b, c, h, w = x.size() + loc, glb = x.split([4, 1], dim=0) # 4,c,h,w; 1,c,h,w + + patched_glb = rearrange(glb, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2) + + token_attention_map = self.sigmoid(self.sal_conv(glb)) + token_attention_map = F.interpolate(token_attention_map, size=patches2image(loc).shape[-2:], mode='nearest') + loc = loc * rearrange(token_attention_map, 'b c (hg h) (wg w) -> (hg wg b) c h w', hg=2, wg=2) + + pools = [] + for pool_ratio in self.pool_ratios: + tgt_hw = (round(h / pool_ratio), round(w / pool_ratio)) + pool = F.adaptive_avg_pool2d(patched_glb, tgt_hw) + pools.append(rearrange(pool, 'nl c h w -> nl c (h w)')) # nl(4),c,hw + + pools = rearrange(torch.cat(pools, 2), "nl c nphw -> nl nphw 1 c") + loc_ = rearrange(loc, 'nl c h w -> nl (h w) 1 c') + + outputs = [] + for i, q in enumerate(loc_.unbind(dim=0)): # traverse all local patches + v = pools[i] + k = v + outputs.append(self.attention[i](q, k, v)[0]) + + outputs = torch.cat(outputs, 1) + src = loc.view(4, c, -1).permute(2, 0, 1) + self.dropout1(outputs) + src = self.norm1(src) + src = src + self.dropout2(self.linear4(self.dropout(self.activation(self.linear3(src)).clone()))) + src = self.norm2(src) + src = src.permute(1, 2, 0).reshape(4, c, h, w) # freshed loc + glb = glb + F.interpolate(patches2image(src), size=glb.shape[-2:], mode='nearest') # freshed glb + + return torch.cat((src, glb), 0), token_attention_map + + +class BEN_Base(nn.Module): + def __init__(self): + super().__init__() + + self.backbone = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12) + emb_dim = 128 + self.sideout5 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + self.sideout4 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + self.sideout3 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + self.sideout2 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + self.sideout1 = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + + self.output5 = make_cbr(1024, emb_dim) + self.output4 = make_cbr(512, emb_dim) + self.output3 = make_cbr(256, emb_dim) + self.output2 = make_cbr(128, emb_dim) + self.output1 = make_cbr(128, emb_dim) + + self.multifieldcrossatt = MCLM(emb_dim, 1, [1, 4, 8]) + self.conv1 = make_cbr(emb_dim, emb_dim) + self.conv2 = make_cbr(emb_dim, emb_dim) + self.conv3 = make_cbr(emb_dim, emb_dim) + self.conv4 = make_cbr(emb_dim, emb_dim) + self.dec_blk1 = MCRM(emb_dim, 1, [2, 4, 8]) + self.dec_blk2 = MCRM(emb_dim, 1, [2, 4, 8]) + self.dec_blk3 = MCRM(emb_dim, 1, [2, 4, 8]) + self.dec_blk4 = MCRM(emb_dim, 1, [2, 4, 8]) + + self.insmask_head = nn.Sequential( + nn.Conv2d(emb_dim, 384, kernel_size=3, padding=1), + nn.InstanceNorm2d(384), + nn.GELU(), + nn.Conv2d(384, 384, kernel_size=3, padding=1), + nn.InstanceNorm2d(384), + nn.GELU(), + nn.Conv2d(384, emb_dim, kernel_size=3, padding=1) + ) + + self.shallow = nn.Sequential(nn.Conv2d(3, emb_dim, kernel_size=3, padding=1)) + self.upsample1 = make_cbg(emb_dim, emb_dim) + self.upsample2 = make_cbg(emb_dim, emb_dim) + self.output = nn.Sequential(nn.Conv2d(emb_dim, 1, kernel_size=3, padding=1)) + + for m in self.modules(): + if isinstance(m, nn.GELU) or isinstance(m, nn.Dropout): + m.inplace = True + + def forward(self, x): + device = x.device + shallow = self.shallow(x) + glb = rescale_to(x, scale_factor=0.5, interpolation='bilinear') + loc = image2patches(x) + input = torch.cat((loc, glb), dim=0) + feature = self.backbone(input) + e5 = self.output5(feature[4]) # (5,128,16,16) + e4 = self.output4(feature[3]) # (5,128,32,32) + e3 = self.output3(feature[2]) # (5,128,64,64) + e2 = self.output2(feature[1]) # (5,128,128,128) + e1 = self.output1(feature[0]) # (5,128,128,128) + loc_e5, glb_e5 = e5.split([4, 1], dim=0) + e5 = self.multifieldcrossatt(loc_e5, glb_e5) # (4,128,16,16) + + e4, tokenattmap4 = self.dec_blk4(e4 + resize_as(e5, e4)) + e4 = self.conv4(e4) + e3, tokenattmap3 = self.dec_blk3(e3 + resize_as(e4, e3)) + e3 = self.conv3(e3) + e2, tokenattmap2 = self.dec_blk2(e2 + resize_as(e3, e2)) + e2 = self.conv2(e2) + e1, tokenattmap1 = self.dec_blk1(e1 + resize_as(e2, e1)) + e1 = self.conv1(e1) + loc_e1, glb_e1 = e1.split([4, 1], dim=0) + output1_cat = patches2image(loc_e1) # (1,128,256,256) + output1_cat = output1_cat + resize_as(glb_e1, output1_cat) + final_output = self.insmask_head(output1_cat) # (1,128,256,256) + final_output = final_output + resize_as(shallow, final_output) + final_output = self.upsample1(rescale_to(final_output)) + final_output = rescale_to(final_output + resize_as(shallow, final_output)) + final_output = self.upsample2(final_output) + final_output = self.output(final_output) + + return final_output.sigmoid() + + @torch.no_grad() + def inference(self,image): + image, h, w,original_image = rgb_loader_refiner(image) + + img_tensor = img_transform(image).unsqueeze(0).to(next(self.parameters()).device) + + res = self.forward(img_tensor) + + pred_array = postprocess_image(res, im_size=[w, h]) + + mask_image = Image.fromarray(pred_array, mode='L') + + blurred_mask = mask_image.filter(ImageFilter.GaussianBlur(radius=1)) + + original_image_rgba = original_image.convert("RGBA") + + foreground = original_image_rgba.copy() + + foreground.putalpha(blurred_mask) + + return blurred_mask, foreground + + def loadcheckpoints(self,model_path): + model_dict = torch.load(model_path, map_location="cpu", weights_only=True) + self.load_state_dict(model_dict['model_state_dict'], strict=True) + del model_path + + + + +def rgb_loader_refiner( original_image): + h, w = original_image.size + # # Apply EXIF orientation + image = ImageOps.exif_transpose(original_image) + # Convert to RGB if necessary + if image.mode != 'RGB': + image = image.convert('RGB') + + # Resize the image + image = image.resize((1024, 1024), resample=Image.LANCZOS) + + return image.convert('RGB'), h, w,original_image + +# Define the image transformation +img_transform = transforms.Compose([ + transforms.ToTensor(), + transforms.ConvertImageDtype(torch.float32), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +]) + +def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray: + result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear'), 0) + ma = torch.max(result) + mi = torch.min(result) + result = (result - mi) / (ma - mi) + im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8) + im_array = np.squeeze(im_array) + return im_array + + + +# ************ ComfyUI Nodes ************ + +import tqdm +import os +import torch +import folder_paths +from comfy.utils import ProgressBar +from .imagefunc import log,tensor2pil, image2mask, pil2tensor, get_files +from .imagefunc import mask_edge_detail, guided_filter_alpha, generate_VITMatte, generate_VITMatte_trimap, histogram_remap, RGB2RGBA + + + +def get_models(): + model_path = os.path.join(folder_paths.models_dir, 'BEN') + model_ext = [".pth"] + model_dict = get_files(model_path, model_ext) + return model_dict + +class LS_LoadBenModel: + + def __init__(self): + pass + + + @classmethod + def INPUT_TYPES(s): + model_list = list(get_models().keys()) + device_list = ["cuda", "cpu"] + return { + "required": { + "model": (model_list,), + "device": (device_list,), + }, + } + + RETURN_TYPES = ("BEN_MODEL",) + RETURN_NAMES = ("ben_model",) + FUNCTION = "load_ben_model" + CATEGORY = '😺dzNodes/LayerMask' + + def load_ben_model(self, model, device): + model_path = get_models()[model] + b_model = BEN_Base().to(device).eval() + b_model.loadcheckpoints(model_path) + + return ({"model":b_model, "device":device, "name":model},) + + +class LS_BenUltra: + + def __init__(self): + self.NODE_NAME = 'BenUltra' + + @classmethod + def INPUT_TYPES(cls): + + method_list = ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter', ] + return { + "required": { + "ben_model": ("BEN_MODEL",), + "image": ("IMAGE",), + "detail_method": (method_list,), + "detail_erode": ("INT", {"default": 4, "min": 1, "max": 255, "step": 1}), + "detail_dilate": ("INT", {"default": 2, "min": 1, "max": 255, "step": 1}), + "black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}), + "white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}), + "max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}), + "process_detail": ("BOOLEAN", {"default": False}), + }, + "optional": { + } + } + + RETURN_TYPES = ("IMAGE", "MASK", ) + RETURN_NAMES = ("image", "mask", ) + FUNCTION = "ben_ultra_v2" + CATEGORY = '😺dzNodes/LayerMask' + + def ben_ultra_v2(self, ben_model, image, detail_method, detail_erode, detail_dilate, + black_point, white_point, max_megapixels, process_detail): + ret_images = [] + ret_masks = [] + + b_model = ben_model['model'] + device= ben_model['device'] + + if detail_method == 'VITMatte(local)': + local_files_only = True + else: + local_files_only = False + + comfy_pbar = ProgressBar(len(image)) + tqdm_pbar = tqdm.tqdm(total=len(image), desc="Processing BEN", leave=False) + for i in image: + i = torch.unsqueeze(i, 0) + orig_image = tensor2pil(i).convert('RGB') + mask, _ = b_model.inference(orig_image) + _mask = image2mask(mask) + + detail_range = detail_erode + detail_dilate + if process_detail: + _mask += 0.01 + if detail_method == 'GuidedFilter': + _mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1) + _mask = tensor2pil(histogram_remap(_mask, black_point, white_point)) + elif detail_method == 'PyMatting': + _mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point)) + else: + _trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate) + _mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device, max_megapixels=max_megapixels) + _mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point)) + else: + _mask = tensor2pil(_mask) + + ret_image = RGB2RGBA(orig_image, _mask.convert('L')) + ret_images.append(pil2tensor(ret_image)) + ret_masks.append(image2mask(_mask)) + comfy_pbar.update(1) + tqdm_pbar.update(1) + + log(f"{self.NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish') + return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),) + +NODE_CLASS_MAPPINGS = { + "LayerMask: BenUltra": LS_BenUltra, + "LayerMask: LoadBenModel": LS_LoadBenModel, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LayerMask: BenUltra": "LayerMask: Ben Ultra(Advance)", + "LayerMask: LoadBenModel": "LayerMask: Load Ben Model(Advance)", +} diff --git a/pyproject.toml b/pyproject.toml index 89d9675..be98e18 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,9 +1,9 @@ [project] name = "comfyui_layerstyle_advance" description = "The nodes detached from ComfyUI Layer Style are mainly those with complex requirements for dependency packages." -version = "2.0.0" +version = "2.0.1" license = "MIT" -dependencies = ["numpy", "matplotlib", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "timm", "blend_modes", "transformers", "diffusers", "loguru", "colour-science", "huggingface_hub", "segment_anything", "addict", "omegaconf", "yapf", "wget", "iopath", "mediapipe", "typer_config", "fastapi", "rich", "google-generativeai", "ultralytics", "transparent-background", "accelerate", "onnxruntime", "bitsandbytes", "peft", "protobuf", "hydra-core", "blind-watermark", "qrcode", "pyzbar", "psd-tools"] +dependencies = ["numpy", "matplotlib", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "timm", "blend_modes", "transformers", "diffusers", "loguru", "colour-science", "huggingface_hub", "segment_anything", "addict", "omegaconf", "yapf", "wget", "iopath", "mediapipe", "typer_config", "fastapi", "rich", "google-generativeai", "ultralytics", "transparent-background", "accelerate", "onnxruntime", "bitsandbytes", "peft", "protobuf", "hydra-core", "blind-watermark", "qrcode", "pyzbar", "psd-tools", "wandb"] [project.urls] Repository = "https://github.com/chflame163/ComfyUI_LayerStyle_Advance" diff --git a/requirements.txt b/requirements.txt index 8bf977b..6399a0f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -33,8 +33,4 @@ hydra-core blind-watermark qrcode pyzbar -psd-tools - -#torchscale -# inference-cli>=0.13.0 -# inference-gpu[yolo-world]>=0.13.0 \ No newline at end of file +psd-tools \ No newline at end of file