Merge b93e924e01 into b138133ffa

* Batch broadcasting in JoinImageWithAlpha node

README.md

@@ -31,7 +31,8 @@
 [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
 [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
 
-<img width="1590" height="795" alt="ComfyUI Screenshot" src="https://github.com/user-attachments/assets/4aab0bef-b413-4595-9766-a2c134676d27" />
+<img width="1590" height="795" alt="ComfyUI Screenshot" src="https://github.com/user-attachments/assets/36e065e0-bfae-4456-8c7f-8369d5ea48a2" />
+<br>
 </div>
 
 ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more...

comfy/cli_args.py

@@ -91,6 +91,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
+parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"

comfy/clip_vision.py

@@ -1,6 +1,7 @@
 from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
 import os
 import json
+import torch
 import logging
 
 import comfy.ops
@@ -9,6 +10,7 @@ import comfy.model_management
 import comfy.utils
 import comfy.clip_model
 import comfy.image_encoders.dino2
+import comfy.image_encoders.birefnet
 
 class Output:
     def __getitem__(self, key):
@@ -23,6 +25,7 @@ IMAGE_ENCODERS = {
     "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
     "siglip2_vision_model": comfy.clip_model.CLIPVisionModelProjection,
     "dinov2": comfy.image_encoders.dino2.Dinov2Model,
+    "birefnet": comfy.image_encoders.birefnet.BiRefNet
 }
 
 class ClipVisionModel():
@@ -34,6 +37,7 @@ class ClipVisionModel():
         self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
         self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
         self.model_type = config.get("model_type", "clip_vision_model")
+        self.resize_to_original = config.get("resize_to_original", False)
         self.config = config.copy()
         model_class = IMAGE_ENCODERS.get(self.model_type)
         if self.model_type == "siglip_vision_model":
@@ -57,11 +61,15 @@ class ClipVisionModel():
 
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
+        H, W = image.shape[1], image.shape[2]
         if self.model_type == "siglip2_vision_model":
             pixel_values = comfy.clip_model.siglip2_preprocess(image.to(self.load_device), size=self.image_size, patch_size=self.config.get("patch_size", 16), num_patches=self.config.get("num_patches", 256), mean=self.image_mean, std=self.image_std, crop=crop).float()
         else:
             pixel_values = comfy.clip_model.clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
         out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
+        if self.resize_to_original:
+            resized = torch.nn.functional.interpolate(out[0], size=(H, W), mode="bicubic", antialias=False)
+            out = (resized,) + out[1:]
 
         outputs = Output()
         outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
@@ -129,6 +137,9 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
         else:
             json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
 
+    elif "bb.layers.1.blocks.0.attn.relative_position_index" in sd:
+        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "birefnet.json")
+
     # Dinov2
     elif 'encoder.layer.39.layer_scale2.lambda1' in sd:
         json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json")

comfy/image_encoders/birefnet.json

@@ -0,0 +1,7 @@
+{
+    "model_type": "birefnet",
+    "image_std": [1.0, 1.0, 1.0],
+    "image_mean": [0.0, 0.0, 0.0],
+    "image_size": 1024,
+    "resize_to_original": true
+}

comfy/image_encoders/birefnet.py

@@ -0,0 +1,690 @@
+import torch
+import comfy.ops
+import numpy as np
+import torch.nn as nn
+from functools import partial
+import torch.nn.functional as F
+from torchvision.ops import deform_conv2d
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+CXT = [3072, 1536, 768, 384][1:][::-1][-3:]
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, device=None, dtype=None, operations=None):
+        super().__init__()
+
+        self.dim = dim
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.q = operations.Linear(dim, dim, bias=qkv_bias, device=device, dtype=dtype)
+        self.kv = operations.Linear(dim, dim * 2, bias=qkv_bias, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True)
+        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+        kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        k, v = kv[0], kv[1]
+
+        x = optimized_attention(
+            q, k, v, heads=self.num_heads, skip_output_reshape=True, skip_reshape=True
+        ).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+
+        return x
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = operations.Linear(in_features, hidden_features, device=device, dtype=dtype)
+        self.act = nn.GELU()
+        self.fc2 = operations.Linear(hidden_features, out_features, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.fc2(x)
+        return x
+
+
+def window_partition(x, window_size):
+    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):
+    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):
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, device=None, dtype=None, operations=None):
+
+        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
+
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads, device=device, dtype=dtype))
+
+        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], indexing='ij'))  # 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
+        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 = operations.Linear(dim, dim * 3, bias=qkv_bias, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=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]
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.long().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)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 norm_layer=nn.LayerNorm, device=None, dtype=None, operations=None):
+        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
+
+        self.norm1 = norm_layer(dim, device=device, dtype=dtype)
+        self.attn = WindowAttention(
+            dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, device=device, dtype=dtype, operations=operations)
+
+        self.norm2 = norm_layer(dim, device=device, dtype=dtype)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, device=device, dtype=dtype, operations=operations)
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        B, L, C = x.shape
+        H, W = self.H, self.W
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        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
+
+        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
+
+        x_windows = window_partition(shifted_x, self.window_size)
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
+
+        attn_windows = self.attn(x_windows, mask=attn_mask)
+
+        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
+
+        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)
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    def __init__(self, dim, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.dim = dim
+        self.reduction = operations.Linear(4 * dim, 2 * dim, bias=False, device=device, dtype=dtype)
+        self.norm = operations.LayerNorm(4 * dim, device=device, dtype=dtype)
+
+    def forward(self, x, H, W):
+        B, L, C = x.shape
+        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):
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size=7,
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 norm_layer=nn.LayerNorm,
+                 downsample=None,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+
+        # 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,
+                norm_layer=norm_layer,
+                device=device, dtype=dtype, operations=operations)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, device=device, dtype=dtype, operations=operations)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        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)
+        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
+            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):
+    def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None, device=None, dtype=None, operations=None):
+        super().__init__()
+        patch_size = (patch_size, patch_size)
+        self.patch_size = patch_size
+
+        self.in_channels = in_channels
+        self.embed_dim = embed_dim
+
+        self.proj = operations.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim, device=device, dtype=dtype)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        _, _, 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):
+    def __init__(self,
+                 pretrain_img_size=224,
+                 patch_size=4,
+                 in_channels=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,
+                 patch_norm=True,
+                 out_indices=(0, 1, 2, 3),
+                 frozen_stages=-1,
+                 device=None, dtype=None, operations=None):
+        super().__init__()
+
+        norm_layer = partial(operations.LayerNorm, device=device, dtype=dtype)
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
+            device=device, dtype=dtype, operations=operations,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        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,
+                norm_layer=norm_layer,
+                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                device=device, dtype=dtype, operations=operations)
+            self.layers.append(layer)
+
+        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        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)
+
+
+    def forward(self, x):
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+
+        outs = []
+        x = x.flatten(2).transpose(1, 2)
+        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)
+
+class DeformableConv2d(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 stride=1,
+                 padding=1,
+                 bias=False, device=None, dtype=None, operations=None):
+
+        super(DeformableConv2d, self).__init__()
+
+        kernel_size = kernel_size if type(kernel_size) is tuple else (kernel_size, kernel_size)
+        self.stride = stride if type(stride) is tuple else (stride, stride)
+        self.padding = padding
+
+        self.offset_conv = operations.Conv2d(in_channels,
+                                     2 * kernel_size[0] * kernel_size[1],
+                                     kernel_size=kernel_size,
+                                     stride=stride,
+                                     padding=self.padding,
+                                     bias=True, device=device, dtype=dtype)
+
+        self.modulator_conv = operations.Conv2d(in_channels,
+                                     1 * kernel_size[0] * kernel_size[1],
+                                     kernel_size=kernel_size,
+                                     stride=stride,
+                                     padding=self.padding,
+                                     bias=True, device=device, dtype=dtype)
+
+        self.regular_conv = operations.Conv2d(in_channels,
+                                      out_channels=out_channels,
+                                      kernel_size=kernel_size,
+                                      stride=stride,
+                                      padding=self.padding,
+                                      bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        offset = self.offset_conv(x)
+        modulator = 2. * torch.sigmoid(self.modulator_conv(x))
+        weight, bias, offload_info = comfy.ops.cast_bias_weight(self.regular_conv, x, offloadable=True)
+
+        x = deform_conv2d(
+            input=x,
+            offset=offset,
+            weight=weight,
+            bias=None,
+            padding=self.padding,
+            mask=modulator,
+            stride=self.stride,
+        )
+        comfy.ops.uncast_bias_weight(self.regular_conv, weight, bias, offload_info)
+        return x
+
+class BasicDecBlk(nn.Module):
+    def __init__(self, in_channels=64, out_channels=64, inter_channels=64, device=None, dtype=None, operations=None):
+        super(BasicDecBlk, self).__init__()
+        inter_channels = 64
+        self.conv_in = operations.Conv2d(in_channels, inter_channels, 3, 1, padding=1, device=device, dtype=dtype)
+        self.relu_in = nn.ReLU(inplace=True)
+        self.dec_att = ASPPDeformable(in_channels=inter_channels, device=device, dtype=dtype, operations=operations)
+        self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, padding=1, device=device, dtype=dtype)
+        self.bn_in = operations.BatchNorm2d(inter_channels, device=device, dtype=dtype)
+        self.bn_out = operations.BatchNorm2d(out_channels, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv_in(x)
+        x = self.bn_in(x)
+        x = self.relu_in(x)
+        x = self.dec_att(x)
+        x = self.conv_out(x)
+        x = self.bn_out(x)
+        return x
+
+
+class BasicLatBlk(nn.Module):
+    def __init__(self, in_channels=64, out_channels=64, device=None, dtype=None, operations=None):
+        super(BasicLatBlk, self).__init__()
+        self.conv = operations.Conv2d(in_channels, out_channels, 1, 1, 0, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x
+
+
+class _ASPPModuleDeformable(nn.Module):
+    def __init__(self, in_channels, planes, kernel_size, padding, device, dtype, operations):
+        super(_ASPPModuleDeformable, self).__init__()
+        self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
+                                            stride=1, padding=padding, bias=False, device=device, dtype=dtype, operations=operations)
+        self.bn = operations.BatchNorm2d(planes, device=device, dtype=dtype)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.atrous_conv(x)
+        x = self.bn(x)
+
+        return self.relu(x)
+
+
+class ASPPDeformable(nn.Module):
+    def __init__(self, in_channels, out_channels=None, parallel_block_sizes=[1, 3, 7], device=None, dtype=None, operations=None):
+        super(ASPPDeformable, self).__init__()
+        self.down_scale = 1
+        if out_channels is None:
+            out_channels = in_channels
+        self.in_channelster = 256 // self.down_scale
+
+        self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0, device=device, dtype=dtype, operations=operations)
+        self.aspp_deforms = nn.ModuleList([
+            _ASPPModuleDeformable(in_channels, self.in_channelster, conv_size, padding=int(conv_size//2), device=device, dtype=dtype, operations=operations)
+              for conv_size in parallel_block_sizes
+        ])
+
+        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
+                                             operations.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False, device=device, dtype=dtype),
+                                             operations.BatchNorm2d(self.in_channelster, device=device, dtype=dtype),
+                                             nn.ReLU(inplace=True))
+        self.conv1 = operations.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False, device=device, dtype=dtype)
+        self.bn1 = operations.BatchNorm2d(out_channels, device=device, dtype=dtype)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x1 = self.aspp1(x)
+        x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
+        x5 = self.global_avg_pool(x)
+        x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
+        x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
+
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        return x
+
+class BiRefNet(nn.Module):
+    def __init__(self, config=None, dtype=None, device=None, operations=None):
+        super(BiRefNet, self).__init__()
+        self.bb = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12, device=device, dtype=dtype, operations=operations)
+
+        channels = [1536, 768, 384, 192]
+        channels = [c * 2 for c in channels]
+        self.cxt = channels[1:][::-1][-3:]
+        self.squeeze_module = nn.Sequential(*[
+            BasicDecBlk(channels[0]+sum(self.cxt), channels[0], device=device, dtype=dtype, operations=operations)
+            for _ in range(1)
+        ])
+
+        self.decoder = Decoder(channels, device=device, dtype=dtype, operations=operations)
+
+    def forward_enc(self, x):
+        x1, x2, x3, x4 = self.bb(x)
+        B, C, H, W = x.shape
+        x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
+        x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
+        x4 = torch.cat(
+            (
+                *[
+                    F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                    F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                    F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
+                ][-len(CXT):],
+                x4
+            ),
+            dim=1
+        )
+        return (x1, x2, x3, x4)
+
+    def forward_ori(self, x):
+        (x1, x2, x3, x4) = self.forward_enc(x)
+        x4 = self.squeeze_module(x4)
+        features = [x, x1, x2, x3, x4]
+        scaled_preds = self.decoder(features)
+        return scaled_preds
+
+    def forward(self, pixel_values, intermediate_output=None):
+        scaled_preds = self.forward_ori(pixel_values)
+        return scaled_preds
+
+
+class Decoder(nn.Module):
+    def __init__(self, channels, device, dtype, operations):
+        super(Decoder, self).__init__()
+        # factory kwargs
+        fk = {"device":device, "dtype":dtype, "operations":operations}
+        DecoderBlock = partial(BasicDecBlk, **fk)
+        LateralBlock = partial(BasicLatBlk, **fk)
+        DBlock = partial(SimpleConvs, **fk)
+
+        self.split = True
+        N_dec_ipt = 64
+        ic = 64
+        ipt_cha_opt = 1
+        self.ipt_blk5 = DBlock(2**10*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
+        self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
+
+        self.decoder_block4 = DecoderBlock(channels[0]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[1])
+        self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt]), channels[2])
+        self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt]), channels[3])
+        self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt]), channels[3]//2)
+
+        fk = {"device":device, "dtype":dtype}
+
+        self.conv_out1 = nn.Sequential(operations.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt]), 1, 1, 1, 0, **fk))
+
+        self.lateral_block4 = LateralBlock(channels[1], channels[1])
+        self.lateral_block3 = LateralBlock(channels[2], channels[2])
+        self.lateral_block2 = LateralBlock(channels[3], channels[3])
+
+        self.conv_ms_spvn_4 = operations.Conv2d(channels[1], 1, 1, 1, 0, **fk)
+        self.conv_ms_spvn_3 = operations.Conv2d(channels[2], 1, 1, 1, 0, **fk)
+        self.conv_ms_spvn_2 = operations.Conv2d(channels[3], 1, 1, 1, 0, **fk)
+
+        _N = 16
+
+        self.gdt_convs_4 = nn.Sequential(operations.Conv2d(channels[0] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+        self.gdt_convs_3 = nn.Sequential(operations.Conv2d(channels[1] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+        self.gdt_convs_2 = nn.Sequential(operations.Conv2d(channels[2] // 2, _N, 3, 1, 1, **fk), operations.BatchNorm2d(_N, **fk), nn.ReLU(inplace=True))
+
+        [setattr(self, f"gdt_convs_pred_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)]
+        [setattr(self, f"gdt_convs_attn_{i}", nn.Sequential(operations.Conv2d(_N, 1, 1, 1, 0, **fk))) for i in range(2, 5)]
+
+    def get_patches_batch(self, x, p):
+        _size_h, _size_w = p.shape[2:]
+        patches_batch = []
+        for idx in range(x.shape[0]):
+            columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
+            patches_x = []
+            for column_x in columns_x:
+                patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
+            patch_sample = torch.cat(patches_x, dim=1)
+            patches_batch.append(patch_sample)
+        return torch.cat(patches_batch, dim=0)
+
+    def forward(self, features):
+        x, x1, x2, x3, x4 = features
+
+        patches_batch = self.get_patches_batch(x, x4) if self.split else x
+        x4 = torch.cat((x4, self.ipt_blk5(F.interpolate(patches_batch, size=x4.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p4 = self.decoder_block4(x4)
+        p4_gdt = self.gdt_convs_4(p4)
+        gdt_attn_4 = self.gdt_convs_attn_4(p4_gdt).sigmoid()
+        p4 = p4 * gdt_attn_4
+        _p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
+        _p3 = _p4 + self.lateral_block4(x3)
+
+        patches_batch = self.get_patches_batch(x, _p3) if self.split else x
+        _p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p3 = self.decoder_block3(_p3)
+
+        p3_gdt = self.gdt_convs_3(p3)
+        gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
+        p3 = p3 * gdt_attn_3
+        _p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
+        _p2 = _p3 + self.lateral_block3(x2)
+
+        patches_batch = self.get_patches_batch(x, _p2) if self.split else x
+        _p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p2 = self.decoder_block2(_p2)
+
+        p2_gdt = self.gdt_convs_2(p2)
+        gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
+        p2 = p2 * gdt_attn_2
+
+        _p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
+        _p1 = _p2 + self.lateral_block2(x1)
+
+        patches_batch = self.get_patches_batch(x, _p1) if self.split else x
+        _p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
+        _p1 = self.decoder_block1(_p1)
+        _p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
+
+        patches_batch = self.get_patches_batch(x, _p1) if self.split else x
+        _p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
+        p1_out = self.conv_out1(_p1)
+        fake = torch.empty_like(p1_out)
+        return p1_out, fake, fake, fake
+
+
+class SimpleConvs(nn.Module):
+    def __init__(
+        self, in_channels: int, out_channels: int, inter_channels=64, device=None, dtype=None, operations=None
+    ) -> None:
+        super().__init__()
+        self.conv1 = operations.Conv2d(in_channels, inter_channels, 3, 1, 1, device=device, dtype=dtype)
+        self.conv_out = operations.Conv2d(inter_channels, out_channels, 3, 1, 1, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.conv_out(self.conv1(x))

comfy/ops.py

@@ -559,6 +559,25 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
+    class BatchNorm2d(torch.nn.BatchNorm2d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
+
+        def forward_comfy_cast_weights(self, input):
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            running_mean = self.running_mean.to(device=input.device, dtype=weight.dtype) if self.running_mean is not None else None
+            running_var = self.running_var.to(device=input.device, dtype=weight.dtype) if self.running_var is not None else None
+            x = torch.nn.functional.batch_norm(input, running_mean, running_var, weight, bias, self.training, self.momentum, self.eps)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
+
+        def forward(self, *args, **kwargs):
+            run_every_op()
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
     class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
         def reset_parameters(self):
             return None
@@ -746,6 +765,9 @@ class manual_cast(disable_weight_init):
     class Conv3d(disable_weight_init.Conv3d):
         comfy_cast_weights = True
 
+    class BatchNorm2d(disable_weight_init.BatchNorm2d):
+        comfy_cast_weights = True
+
     class GroupNorm(disable_weight_init.GroupNorm):
         comfy_cast_weights = True
 

comfy/quant_ops.py

@@ -1,6 +1,8 @@
 import torch
 import logging
 
+from comfy.cli_args import args
+
 try:
     import comfy_kitchen as ck
     from comfy_kitchen.tensor import (
@@ -21,7 +23,15 @@ try:
             ck.registry.disable("cuda")
             logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
 
-    ck.registry.disable("triton")
+    if args.enable_triton_backend:
+        try:
+            import triton
+            logging.info("Found triton %s. Enabling comfy-kitchen triton backend.", triton.__version__)
+        except ImportError as e:
+            logging.error(f"Failed to import triton, Error: {e}, the comfy-kitchen triton backend will not be available.")
+            ck.registry.disable("triton")
+    else:
+        ck.registry.disable("triton")
     for k, v in ck.list_backends().items():
         logging.info(f"Found comfy_kitchen backend {k}: {v}")
 except ImportError as e:

comfy_extras/nodes_compositing.py

@@ -202,14 +202,11 @@ class JoinImageWithAlpha(io.ComfyNode):
 
     @classmethod
     def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
-        batch_size = min(len(image), len(alpha))
-        out_images = []
-
+        batch_size = max(len(image), len(alpha))
         alpha = 1.0 - resize_mask(alpha, image.shape[1:])
-        for i in range(batch_size):
-           out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
-
-        return io.NodeOutput(torch.stack(out_images))
+        alpha = comfy.utils.repeat_to_batch_size(alpha, batch_size)
+        image = comfy.utils.repeat_to_batch_size(image, batch_size)
+        return io.NodeOutput(torch.cat((image[..., :3], alpha.unsqueeze(-1)), dim=-1))
 
 
 class CompositingExtension(ComfyExtension):

comfy_extras/nodes_mask.py

@@ -40,10 +40,21 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
 
     inverse_mask = torch.ones_like(mask) - mask
 
-    source_portion = mask * source[..., :visible_height, :visible_width]
-    destination_portion = inverse_mask  * destination[..., top:bottom, left:right]
+    source_rgb = source[:, :3, :visible_height, :visible_width]
+    dest_slice = destination[..., top:bottom, left:right]
+
+    if destination.shape[1] == 4:
+        if torch.max(dest_slice) == 0:
+            destination[:, :3, top:bottom, left:right] = source_rgb
+            destination[:, 3:4, top:bottom, left:right] = mask
+        else:
+            destination[:, :3, top:bottom, left:right] = (mask * source_rgb) + (inverse_mask * dest_slice[:, :3])
+            destination[:, 3:4, top:bottom, left:right] = torch.max(mask, dest_slice[:, 3:4])
+    else:
+        source_portion = mask * source_rgb
+        destination_portion = inverse_mask * dest_slice
+        destination[..., top:bottom, left:right] = source_portion + destination_portion
 
-    destination[..., top:bottom, left:right] = source_portion + destination_portion
     return destination
 
 class LatentCompositeMasked(IO.ComfyNode):
@@ -83,18 +94,23 @@ class ImageCompositeMasked(IO.ComfyNode):
             search_aliases=["paste image", "overlay", "layer"],
             category="image",
             inputs=[
-                IO.Image.Input("destination"),
                 IO.Image.Input("source"),
                 IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Boolean.Input("resize_source", default=False),
+                IO.Image.Input("destination", optional=True),
                 IO.Mask.Input("mask", optional=True),
             ],
             outputs=[IO.Image.Output()],
         )
 
     @classmethod
-    def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
+    def execute(cls, source, x, y, resize_source, destination = None, mask = None) -> IO.NodeOutput:
+        if destination is None: # transparent rgba
+            B, H, W, C = source.shape
+            destination = torch.zeros((B, H, W, 4), dtype=source.dtype, device=source.device)
+            if C == 3:
+                source = torch.nn.functional.pad(source, (0, 1), value=1.0)
         destination, source = node_helpers.image_alpha_fix(destination, source)
         destination = destination.clone().movedim(-1, 1)
         output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
@@ -376,6 +392,28 @@ class GrowMask(IO.ComfyNode):
 
     expand_mask = execute  # TODO: remove
 
+class ClipVisionToMask(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="ClipVisionToMask",
+            inputs = [
+                IO.ClipVisionOutput.Input("clip_vision_output")
+            ],
+            outputs = [IO.Mask.Output("mask")]
+        )
+    @classmethod
+    def execute(cls, clip_vision_output):
+        if not isinstance(clip_vision_output, torch.Tensor):
+            mask = clip_vision_output["last_hidden_state"]
+        mask = mask.sigmoid()
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(0)
+        if mask.shape[1] != 1:
+            mask = mask.movedim(-1, 1)
+        return IO.NodeOutput(mask)
+
+    clip_vision_to_mask = execute
 
 class ThresholdMask(IO.ComfyNode):
     @classmethod
@@ -440,6 +478,7 @@ class MaskExtension(ComfyExtension):
             GrowMask,
             ThresholdMask,
             MaskPreview,
+            ClipVisionToMask
         ]
 
 

node_helpers.py

@@ -86,6 +86,6 @@ def image_alpha_fix(destination, source):
     if destination.shape[-1] < source.shape[-1]:
         source = source[...,:destination.shape[-1]]
     elif destination.shape[-1] > source.shape[-1]:
-        destination = torch.nn.functional.pad(destination, (0, 1))
-        destination[..., -1] = 1.0
+        source = torch.nn.functional.pad(source, (0, 1))
+        source[..., -1] = 1.0
     return destination, source

server.py

@@ -1,3 +1,4 @@
+import errno
 import os
 import sys
 import asyncio
@@ -1245,7 +1246,13 @@ class PromptServer():
             address = addr[0]
             port = addr[1]
             site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
-            await site.start()
+            try:
+                await site.start()
+            except OSError as e:
+                if e.errno == errno.EADDRINUSE:
+                    logging.error(f"Port {port} is already in use on address {address}. Please close the other application or use a different port with --port.")
+                    raise SystemExit(1)
+                raise
 
             if not hasattr(self, 'address'):
                 self.address = address #TODO: remove this