diff --git a/README.MD b/README.MD index 0be687a..3fced77 100644 --- a/README.MD +++ b/README.MD @@ -4,7 +4,7 @@ Split some nodes of the dependencies that are prone to problems into [ComfyUI_LayerStyle_Advance](https://github.com/chflame163/ComfyUI_LayerStyle_Advance) repository. Including: LayerMask: BiRefNetUltra, LayerMask: BiRefNetUltraV2, LayerMask: LoadBiRefNetModel, LayerMask: LoadBiRefNetModelV2, LayerMask: EVFSAMUltra, LayerMask: Florence2Ultra, LayerMask: LoadFlorence2Model, LayerUtility: Florence2Image2Prompt, -LayerUtility: GetColorTone, LayerUtility: GetColorToneV2, LayerMask: HumanPartsUltra, +LayerUtility: GetColorTone, LayerUtility: GetColorToneV2, LayerMask: HumanPartsUltra, LayerMask: BenUltra, LayerMask: LoadBenModel, LayerUtility: ImageAutoCrop, LayerUtility: ImageAutoCropV2, LayerUtility: ImageAutoCropV3, LayerUtility: ImageRewardFilter, LayerUtility: LoadJoyCaption2Model, LayerUtility: JoyCaption2Split, LayerUtility: JoyCaption2, LayerUtility: JoyCaption2ExtraOptions, LayerUtility: LaMa, @@ -56,14 +56,14 @@ Some JSON workflow files in the ```workflow``` directory, That's examples of git clone https://github.com/chflame163/ComfyUI_LayerStyle.git ``` -* Or download the zip file and extracted, copy the resulting folder to ```ComfyUI\custom_nodes``` +* Or download the zip file and extracted, copy the resulting folder to ```ComfyUI\custom_ Nodes``` ### Install dependency packages * for ComfyUI official portable package, double-click the ```install_requirements.bat``` in the plugin directory, for Aki ComfyUI package double-click on the ```install_requirements_aki.bat``` in the plugin directory, and wait for the installation to complete. * Or install dependency packages, open the cmd window in the ComfyUI_LayerStyle plugin directory like - ```ComfyUI\custom_nodes\ComfyUI_LayerStyle``` and enter the following command, + ```ComfyUI\custom_ Nodes\ComfyUI_LayerStyle``` and enter the following command,   for ComfyUI official portable package, type: @@ -76,7 +76,7 @@ Some JSON workflow files in the ```workflow``` directory, That's examples of ``` ..\..\python\python.exe -s -m pip install -r requirements.txt -.\repair_dependency_aki.bat +.\repair_dependency.bat ``` * Restart ComfyUI. @@ -1702,35 +1702,6 @@ On the basis of RemBgUltra, the following changes have been made: -### 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. - - ### SegformerB2ClothesUltra ![image](image/segformer_ultra_example.jpg) diff --git a/README_CN.MD b/README_CN.MD index 7b5f893..05c1733 100644 --- a/README_CN.MD +++ b/README_CN.MD @@ -4,7 +4,7 @@ 分拆部分依赖易出问题的节点至[ComfyUI_LayerStyle_Advance](https://github.com/chflame163/ComfyUI_LayerStyle_Advance)仓库。包括下列节点： LayerMask: BiRefNetUltra, LayerMask: BiRefNetUltraV2, LayerMask: LoadBiRefNetModel, LayerMask: LoadBiRefNetModelV2, LayerMask: EVFSAMUltra, LayerMask: Florence2Ultra, LayerMask: LoadFlorence2Model, LayerUtility: Florence2Image2Prompt, -LayerUtility: GetColorTone, LayerUtility: GetColorToneV2, LayerMask: HumanPartsUltra, +LayerUtility: GetColorTone, LayerUtility: GetColorToneV2, LayerMask: HumanPartsUltra, LayerMask: BenUltra, LayerMask: LoadBenModel, LayerUtility: ImageAutoCrop, LayerUtility: ImageAutoCropV2, LayerUtility: ImageAutoCropV3, LayerUtility: ImageRewardFilter, LayerUtility: LoadJoyCaption2Model, LayerUtility: JoyCaption2Split, LayerUtility: JoyCaption2, LayerUtility: JoyCaption2ExtraOptions, LayerUtility: LaMa, @@ -19,7 +19,7 @@ LayerUtility: AddBlindWaterMark, LayerUtility: ShowBlindWaterMark, LayerMask: Yo 如果最近更新了插件，请安装[ComfyUI_LayerStyle_Advance](https://github.com/chflame163/ComfyUI_LayerStyle_Advance)保证之前的工作流不会缺失节点。 如果因为拆分仓库导致问题，请回滚插件版本到```3d4a3526a9d1a19671a133e9215077bda520ee5d``` -在插件目录打开终端，使用下列命令回滚版本： +在插件目录打开终端，使用下列命令回滚版本：**** ``` git reset --hard 3d4a3526a9d1a19671a133e9215077bda520ee5d ``` @@ -66,7 +66,7 @@ git clone https://github.com/chflame163/ComfyUI_LayerStyle.git ``` ..\..\python\python.exe -s -m pip install -r requirements.txt -.\repair_dependency_aki.bat +.\repair_dependency.bat ``` * 重新打开ComfyUI。 @@ -1504,34 +1504,6 @@ RemBgUltra的V2升级版，增加了VITMatte边缘处理方法。 * device: 设置是否使用cuda。 * max_megapixels: 设置vitmatte运算的最大尺寸。 -### 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。 - - ### SegformerB2ClothesUltra ![image](image/segformer_ultra_example.jpg) 为人物生成脸、头发、手臂、腿以及服饰的遮罩，主要用于分割服装。模型分割代码来自[StartHua](https://github.com/StartHua/Comfyui_segformer_b2_clothes)，感谢原作者。 diff --git a/py/ben_ultra.py b/py/ben_ultra.py deleted file mode 100644 index d0b42a5..0000000 --- a/py/ben_ultra.py +++ /dev/null @@ -1,1068 +0,0 @@ -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", - "LayerMask: LoadBenModel": "LayerMask: Load Ben Model", -} diff --git a/pyproject.toml b/pyproject.toml index 6221362..6452083 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,9 +1,9 @@ [project] name = "comfyui_layerstyle" description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress." -version = "2.0.3" +version = "2.0.4" license = "MIT" -dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "timm", "colour-science", "transformers", "blend_modes", "huggingface_hub", "diffusers", "loguru", "wandb"] +dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "timm", "colour-science", "transformers", "blend_modes", "huggingface_hub", "diffusers", "loguru"] [project.urls] Repository = "https://github.com/chflame163/ComfyUI_LayerStyle" diff --git a/requirements.txt b/requirements.txt index 3938128..9366558 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,4 +14,3 @@ blend_modes huggingface_hub>=0.23.4 diffusers loguru -wandb