Merge branch 'master' into cursor/cloud-fe-endpoints-6010

app/frontend_management.py

@@ -27,7 +27,7 @@ def frontend_install_warning_message():
     return f"""
 {get_missing_requirements_message()}
 
-This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
+The ComfyUI frontend is shipped in a pip package so it needs to be updated separately from the ComfyUI code.
 """.strip()
 
 def parse_version(version: str) -> tuple[int, int, int]:

app/node_replace_manager.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import logging
+
 from aiohttp import web
 
 from typing import TYPE_CHECKING, TypedDict
@@ -31,8 +33,22 @@ class NodeReplaceManager:
         self._replacements: dict[str, list[NodeReplace]] = {}
 
     def register(self, node_replace: NodeReplace):
-        """Register a node replacement mapping."""
-        self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)
+        """Register a node replacement mapping.
+
+        Idempotent: if a replacement with the same (old_node_id, new_node_id)
+        is already registered, the duplicate is ignored. This prevents stale
+        entries from accumulating when custom nodes are reloaded in the same
+        process (e.g. via ComfyUI-Manager).
+        """
+        existing = self._replacements.setdefault(node_replace.old_node_id, [])
+        for entry in existing:
+            if entry.new_node_id == node_replace.new_node_id:
+                logging.debug(
+                    "Node replacement %s -> %s already registered, ignoring duplicate.",
+                    node_replace.old_node_id, node_replace.new_node_id,
+                )
+                return
+        existing.append(node_replace)
 
     def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
         """Get replacements for an old node ID."""

comfy/background_removal/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/background_removal/birefnet.py

@@ -0,0 +1,689 @@
+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)
+        return p1_out
+
+
+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/bg_removal_model.py

@@ -0,0 +1,78 @@
+from .utils import load_torch_file
+import os
+import json
+import torch
+import logging
+
+import comfy.ops
+import comfy.model_patcher
+import comfy.model_management
+import comfy.clip_model
+import comfy.background_removal.birefnet
+
+BG_REMOVAL_MODELS = {
+    "birefnet": comfy.background_removal.birefnet.BiRefNet
+}
+
+class BackgroundRemovalModel():
+    def __init__(self, json_config):
+        with open(json_config) as f:
+            config = json.load(f)
+
+        self.image_size = config.get("image_size", 1024)
+        self.image_mean = config.get("image_mean", [0.0, 0.0, 0.0])
+        self.image_std = config.get("image_std", [1.0, 1.0, 1.0])
+        self.model_type = config.get("model_type", "birefnet")
+        self.config = config.copy()
+        model_class = BG_REMOVAL_MODELS.get(self.model_type)
+
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+        self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
+        self.model.eval()
+
+        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+
+    def load_sd(self, sd):
+        return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
+
+    def get_sd(self):
+        return self.model.state_dict()
+
+    def encode_image(self, image):
+        comfy.model_management.load_model_gpu(self.patcher)
+        H, W = image.shape[1], image.shape[2]
+        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=False)
+        out = self.model(pixel_values=pixel_values)
+        out = torch.nn.functional.interpolate(out, size=(H, W), mode="bicubic", antialias=False)
+
+        mask = out.sigmoid().to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(0)
+        if mask.shape[1] != 1:
+            mask = mask.movedim(-1, 1)
+
+        return mask
+
+
+def load_background_removal_model(sd):
+    if "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__)), "background_removal"), "birefnet.json")
+    else:
+        return None
+
+    bg_model = BackgroundRemovalModel(json_config)
+    m, u = bg_model.load_sd(sd)
+    if len(m) > 0:
+        logging.warning("missing background removal: {}".format(m))
+    u = set(u)
+    keys = list(sd.keys())
+    for k in keys:
+        if k not in u:
+            sd.pop(k)
+    return bg_model
+
+def load(ckpt_path):
+    sd = load_torch_file(ckpt_path)
+    return load_background_removal_model(sd)

comfy/hooks.py

@@ -93,7 +93,7 @@ class Hook:
         self.hook_scope = hook_scope
         '''Scope of where this hook should apply in terms of the conds used in sampling run.'''
         self.custom_should_register = default_should_register
-        '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
+        '''Can be overridden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
 
     @property
     def strength(self):

comfy/k_diffusion/sampling.py

@@ -1859,6 +1859,23 @@ def sample_ar_video(model, x, sigmas, extra_args=None, callback=None, disable=No
     output = torch.zeros_like(x)
     s_in = x.new_ones([x.shape[0]])
     current_start_frame = 0
+
+    # I2V: seed KV cache with the initial image latent before the denoising loop
+    initial_latent = transformer_options.get("ar_config", {}).get("initial_latent", None)
+    if initial_latent is not None:
+        initial_latent = inner_model.process_latent_in(initial_latent).to(device=device, dtype=model_dtype)
+        n_init = initial_latent.shape[2]
+        output[:, :, :n_init] = initial_latent
+
+        ar_state = {"start_frame": 0, "kv_caches": kv_caches, "crossattn_caches": crossattn_caches}
+        transformer_options["ar_state"] = ar_state
+        zero_sigma = sigmas.new_zeros([1])
+        _ = model(initial_latent, zero_sigma * s_in, **extra_args)
+
+        current_start_frame = n_init
+        remaining = lat_t - n_init
+        num_blocks = -(-remaining // num_frame_per_block)
+
     num_sigma_steps = len(sigmas) - 1
     total_real_steps = num_blocks * num_sigma_steps
     step_count = 0

comfy/ldm/modules/diffusionmodules/util.py

@@ -140,7 +140,7 @@ def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
     alphas = alphacums[ddim_timesteps]
     alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
 
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
+    # according to the formula provided in https://arxiv.org/abs/2010.02502
     sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
     if verbose:
         logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')

comfy/ldm/sam3/detector.py

@@ -561,7 +561,8 @@ class SAM3Model(nn.Module):
         return high_res_masks
 
     def forward_video(self, images, initial_masks, pbar=None, text_prompts=None,
-                       new_det_thresh=0.5, max_objects=0, detect_interval=1):
+                       new_det_thresh=0.5, max_objects=0, detect_interval=1,
+                       target_device=None, target_dtype=None):
         """Track video with optional per-frame text-prompted detection."""
         bb = self.detector.backbone["vision_backbone"]
 
@@ -589,8 +590,10 @@ class SAM3Model(nn.Module):
             return self.tracker.track_video_with_detection(
                 backbone_fn, images, initial_masks, detect_fn,
                 new_det_thresh=new_det_thresh, max_objects=max_objects,
-                detect_interval=detect_interval, backbone_obj=bb, pbar=pbar)
+                detect_interval=detect_interval, backbone_obj=bb, pbar=pbar,
+                target_device=target_device, target_dtype=target_dtype)
         # SAM3 (non-multiplex) — no detection support, requires initial masks
         if initial_masks is None:
             raise ValueError("SAM3 (non-multiplex) requires initial_mask for video tracking")
-        return self.tracker.track_video(backbone_fn, images, initial_masks, pbar=pbar, backbone_obj=bb)
+        return self.tracker.track_video(backbone_fn, images, initial_masks, pbar=pbar, backbone_obj=bb,
+                                         target_device=target_device, target_dtype=target_dtype)

comfy/ldm/sam3/tracker.py

@@ -200,8 +200,13 @@ def pack_masks(masks):
 
 def unpack_masks(packed):
     """Unpack bit-packed [*, H, W//8] uint8 to bool [*, H, W*8]."""
-    shifts = torch.arange(8, device=packed.device)
-    return ((packed.unsqueeze(-1) >> shifts) & 1).view(*packed.shape[:-1], -1).bool()
+    bits = torch.tensor([1, 2, 4, 8, 16, 32, 64, 128], dtype=torch.uint8, device=packed.device)
+    return (packed.unsqueeze(-1) & bits).bool().view(*packed.shape[:-1], -1)
+
+
+def _prep_frame(images, idx, device, dt, size):
+    """Slice CPU full-res frames, transfer to GPU in target dtype, and resize to (size, size)."""
+    return comfy.utils.common_upscale(images[idx].to(device=device, dtype=dt), size, size, "bicubic", crop="disabled")
 
 
 def _compute_backbone(backbone_fn, frame, frame_idx=None):
@@ -1078,16 +1083,19 @@ class SAM3Tracker(nn.Module):
         # SAM3: drop last FPN level
         return vision_feats[:-1], vision_pos[:-1], feat_sizes[:-1]
 
-    def _track_single_object(self, backbone_fn, images, initial_mask, pbar=None):
+    def _track_single_object(self, backbone_fn, images, initial_mask, pbar=None,
+                             target_device=None, target_dtype=None):
         """Track one object, computing backbone per frame to save VRAM."""
         N = images.shape[0]
-        device, dt = images.device, images.dtype
+        device = target_device if target_device is not None else images.device
+        dt = target_dtype if target_dtype is not None else images.dtype
+        size = self.image_size
         output_dict = {"cond_frame_outputs": {}, "non_cond_frame_outputs": {}}
         all_masks = []
 
         for frame_idx in tqdm(range(N), desc="tracking"):
             vision_feats, vision_pos, feat_sizes = self._compute_backbone_frame(
-                backbone_fn, images[frame_idx:frame_idx + 1], frame_idx=frame_idx)
+                backbone_fn, _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), frame_idx=frame_idx)
             mask_input = None
             if frame_idx == 0:
                 mask_input = F.interpolate(initial_mask.to(device=device, dtype=dt),
@@ -1114,12 +1122,13 @@ class SAM3Tracker(nn.Module):
 
         return torch.cat(all_masks, dim=0)  # [N, 1, H, W]
 
-    def track_video(self, backbone_fn, images, initial_masks, pbar=None, **kwargs):
+    def track_video(self, backbone_fn, images, initial_masks, pbar=None,
+                    target_device=None, target_dtype=None, **kwargs):
         """Track one or more objects across video frames.
 
         Args:
             backbone_fn: callable that returns (sam2_features, sam2_positions, trunk_out) for a frame
-            images: [N, 3, 1008, 1008] video frames
+            images: [N, 3, H, W] CPU full-res video frames (resized per-frame to self.image_size)
             initial_masks: [N_obj, 1, H, W] binary masks for first frame (one per object)
             pbar: optional progress bar
 
@@ -1130,7 +1139,8 @@ class SAM3Tracker(nn.Module):
         per_object = []
         for obj_idx in range(N_obj):
             obj_masks = self._track_single_object(
-                backbone_fn, images, initial_masks[obj_idx:obj_idx + 1], pbar=pbar)
+                backbone_fn, images, initial_masks[obj_idx:obj_idx + 1], pbar=pbar,
+                target_device=target_device, target_dtype=target_dtype)
             per_object.append(obj_masks)
 
         return torch.cat(per_object, dim=1)  # [N, N_obj, H, W]
@@ -1632,11 +1642,18 @@ class SAM31Tracker(nn.Module):
             return det_scores[new_dets].tolist() if det_scores is not None else [0.0] * new_dets.sum().item()
         return []
 
+    INTERNAL_MAX_OBJECTS = 64  # Hard ceiling on accumulated tracks; max_objects=0 or any value above this is clamped here.
+
     def track_video_with_detection(self, backbone_fn, images, initial_masks, detect_fn=None,
                                    new_det_thresh=0.5, max_objects=0, detect_interval=1,
-                                   backbone_obj=None, pbar=None):
+                                   backbone_obj=None, pbar=None, target_device=None, target_dtype=None):
         """Track with optional per-frame detection. Returns [N, max_N_obj, H, W] mask logits."""
-        N, device, dt = images.shape[0], images.device, images.dtype
+        if max_objects <= 0 or max_objects > self.INTERNAL_MAX_OBJECTS:
+            max_objects = self.INTERNAL_MAX_OBJECTS
+        N = images.shape[0]
+        device = target_device if target_device is not None else images.device
+        dt = target_dtype if target_dtype is not None else images.dtype
+        size = self.image_size
         output_dict = {"cond_frame_outputs": {}, "non_cond_frame_outputs": {}}
         all_masks = []
         idev = comfy.model_management.intermediate_device()
@@ -1656,7 +1673,7 @@ class SAM31Tracker(nn.Module):
                 prefetch = True
             except RuntimeError:
                 pass
-        cur_bb = self._compute_backbone_frame(backbone_fn, images[0:1], frame_idx=0)
+        cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(0, 1), device, dt, size), frame_idx=0)
 
         for frame_idx in tqdm(range(N), desc="tracking"):
             vision_feats, vision_pos, feat_sizes, high_res_prop, trunk_out = cur_bb
@@ -1666,7 +1683,7 @@ class SAM31Tracker(nn.Module):
                 backbone_stream.wait_stream(torch.cuda.current_stream(device))
                 with torch.cuda.stream(backbone_stream):
                     next_bb = self._compute_backbone_frame(
-                        backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
+                        backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
 
             # Per-frame detection with NMS (skip if no detect_fn, or interval/max not met)
             det_masks = torch.empty(0, device=device)
@@ -1687,7 +1704,7 @@ class SAM31Tracker(nn.Module):
                 current_out = self._condition_with_masks(
                     initial_masks.to(device=device, dtype=dt), frame_idx, vision_feats, vision_pos,
                     feat_sizes, high_res_prop, output_dict, N, mux_state, backbone_obj,
-                    images[frame_idx:frame_idx + 1], trunk_out)
+                    _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), trunk_out)
                 last_occluded = torch.full((mux_state.total_valid_entries,), -1, device=device, dtype=torch.long)
                 obj_scores = [1.0] * mux_state.total_valid_entries
                 if keep_alive is not None:
@@ -1702,7 +1719,7 @@ class SAM31Tracker(nn.Module):
                     current_out = self._condition_with_masks(
                         det_masks, frame_idx, vision_feats, vision_pos, feat_sizes, high_res_prop,
                         output_dict, N, mux_state, backbone_obj,
-                        images[frame_idx:frame_idx + 1], trunk_out, threshold=0.0)
+                        _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), trunk_out, threshold=0.0)
                     last_occluded = torch.full((mux_state.total_valid_entries,), -1, device=device, dtype=torch.long)
                     obj_scores = det_scores[:mux_state.total_valid_entries].tolist()
                     if keep_alive is not None:
@@ -1718,7 +1735,7 @@ class SAM31Tracker(nn.Module):
                             torch.cuda.current_stream(device).wait_stream(backbone_stream)
                             cur_bb = next_bb
                         else:
-                            cur_bb = self._compute_backbone_frame(backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
+                            cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
                     continue
             else:
                 N_obj = mux_state.total_valid_entries
@@ -1768,7 +1785,7 @@ class SAM31Tracker(nn.Module):
                     torch.cuda.current_stream(device).wait_stream(backbone_stream)
                     cur_bb = next_bb
                 else:
-                    cur_bb = self._compute_backbone_frame(backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
+                    cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
 
         if not all_masks or all(m is None for m in all_masks):
             return {"packed_masks": None, "n_frames": N, "scores": []}

comfy/ops.py

@@ -562,6 +562,25 @@ 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
@@ -749,6 +768,9 @@ 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_api/latest/_io.py

@@ -17,6 +17,7 @@ if TYPE_CHECKING:
     from spandrel import ImageModelDescriptor
     from comfy.clip_vision import ClipVisionModel
     from comfy.clip_vision import Output as ClipVisionOutput_
+    from comfy.bg_removal_model import BackgroundRemovalModel
     from comfy.controlnet import ControlNet
     from comfy.hooks import HookGroup, HookKeyframeGroup
     from comfy.model_patcher import ModelPatcher
@@ -614,6 +615,11 @@ class Model(ComfyTypeIO):
     if TYPE_CHECKING:
         Type = ModelPatcher
 
+@comfytype(io_type="BACKGROUND_REMOVAL")
+class BackgroundRemoval(ComfyTypeIO):
+    if TYPE_CHECKING:
+        Type = BackgroundRemovalModel
+
 @comfytype(io_type="CLIP_VISION")
 class ClipVision(ComfyTypeIO):
     if TYPE_CHECKING:
@@ -2257,6 +2263,7 @@ __all__ = [
     "ModelPatch",
     "ClipVision",
     "ClipVisionOutput",
+    "BackgroundRemoval",
     "AudioEncoder",
     "AudioEncoderOutput",
     "StyleModel",

comfy_api_nodes/nodes_bytedance.py

@@ -1271,7 +1271,7 @@ PRICE_BADGE_VIDEO = IO.PriceBadge(
 )
 
 
-def _seedance2_text_inputs(resolutions: list[str]):
+def _seedance2_text_inputs(resolutions: list[str], default_ratio: str = "16:9"):
     return [
         IO.String.Input(
             "prompt",
@@ -1287,6 +1287,7 @@ def _seedance2_text_inputs(resolutions: list[str]):
         IO.Combo.Input(
             "ratio",
             options=["16:9", "4:3", "1:1", "3:4", "9:16", "21:9", "adaptive"],
+            default=default_ratio,
             tooltip="Aspect ratio of the output video.",
         ),
         IO.Int.Input(
@@ -1420,8 +1421,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
-                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p"])),
-                        IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs(["480p", "720p"])),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0",
+                            _seedance2_text_inputs(["480p", "720p", "1080p"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Fast",
+                            _seedance2_text_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
                     ],
                     tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),
@@ -1588,9 +1595,9 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
 
 
-def _seedance2_reference_inputs(resolutions: list[str]):
+def _seedance2_reference_inputs(resolutions: list[str], default_ratio: str = "16:9"):
     return [
-        *_seedance2_text_inputs(resolutions),
+        *_seedance2_text_inputs(resolutions, default_ratio=default_ratio),
         IO.Autogrow.Input(
             "reference_images",
             template=IO.Autogrow.TemplateNames(
@@ -1668,8 +1675,14 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
-                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_reference_inputs(["480p", "720p", "1080p"])),
-                        IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_reference_inputs(["480p", "720p"])),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0",
+                            _seedance2_reference_inputs(["480p", "720p", "1080p"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Fast",
+                            _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
                     ],
                     tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),

comfy_api_nodes/nodes_gemini.py

@@ -83,13 +83,16 @@ class GeminiImageModel(str, Enum):
 
 async def create_image_parts(
     cls: type[IO.ComfyNode],
-    images: Input.Image,
+    images: Input.Image | list[Input.Image],
     image_limit: int = 0,
 ) -> list[GeminiPart]:
     image_parts: list[GeminiPart] = []
     if image_limit < 0:
         raise ValueError("image_limit must be greater than or equal to 0 when creating Gemini image parts.")
-    total_images = get_number_of_images(images)
+
+    # Accept either a single (possibly-batched) tensor or a list of them; share URL budget across all.
+    images_list: list[Input.Image] = images if isinstance(images, list) else [images]
+    total_images = sum(get_number_of_images(img) for img in images_list)
     if total_images <= 0:
         raise ValueError("No images provided to create_image_parts; at least one image is required.")
 
@@ -98,10 +101,18 @@ async def create_image_parts(
 
     # Number of images we'll send as URLs (fileData)
     num_url_images = min(effective_max, 10)  # Vertex API max number of image links
+    upload_kwargs: dict = {"wait_label": "Uploading reference images"}
+    if effective_max > num_url_images:
+        # Split path (e.g. 11+ images): suppress per-image counter to avoid a confusing dual-fraction label.
+        upload_kwargs = {
+            "wait_label": f"Uploading reference images ({num_url_images}+)",
+            "show_batch_index": False,
+        }
     reference_images_urls = await upload_images_to_comfyapi(
         cls,
-        images,
+        images_list,
         max_images=num_url_images,
+        **upload_kwargs,
     )
     for reference_image_url in reference_images_urls:
         image_parts.append(
@@ -112,15 +123,22 @@ async def create_image_parts(
                 )
             )
         )
-    for idx in range(num_url_images, effective_max):
-        image_parts.append(
-            GeminiPart(
-                inlineData=GeminiInlineData(
-                    mimeType=GeminiMimeType.image_png,
-                    data=tensor_to_base64_string(images[idx]),
+    if effective_max > num_url_images:
+        flat: list[torch.Tensor] = []
+        for tensor in images_list:
+            if len(tensor.shape) == 4:
+                flat.extend(tensor[i] for i in range(tensor.shape[0]))
+            else:
+                flat.append(tensor)
+        for idx in range(num_url_images, effective_max):
+            image_parts.append(
+                GeminiPart(
+                    inlineData=GeminiInlineData(
+                        mimeType=GeminiMimeType.image_png,
+                        data=tensor_to_base64_string(flat[idx]),
+                    )
                 )
             )
-        )
     return image_parts
 
 
@@ -891,10 +909,6 @@ class GeminiNanoBanana2(IO.ComfyNode):
                         "9:16",
                         "16:9",
                         "21:9",
-                        # "1:4",
-                        # "4:1",
-                        # "8:1",
-                        # "1:8",
                     ],
                     default="auto",
                     tooltip="If set to 'auto', matches your input image's aspect ratio; "
@@ -902,12 +916,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "resolution",
-                    options=[
-                        # "512px",
-                        "1K",
-                        "2K",
-                        "4K",
-                    ],
+                    options=["1K", "2K", "4K"],
                     tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
                 ),
                 IO.Combo.Input(
@@ -956,6 +965,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=GEMINI_IMAGE_2_PRICE_BADGE,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -1016,6 +1026,197 @@ class GeminiNanoBanana2(IO.ComfyNode):
         )
 
 
+def _nano_banana_2_v2_model_inputs():
+    return [
+        IO.Combo.Input(
+            "aspect_ratio",
+            options=[
+                "auto",
+                "1:1",
+                "2:3",
+                "3:2",
+                "3:4",
+                "4:3",
+                "4:5",
+                "5:4",
+                "9:16",
+                "16:9",
+                "21:9",
+                "1:4",
+                "4:1",
+                "8:1",
+                "1:8",
+            ],
+            default="auto",
+            tooltip="If set to 'auto', matches your input image's aspect ratio; "
+            "if no image is provided, a 16:9 square is usually generated.",
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=["1K", "2K", "4K"],
+            tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
+        ),
+        IO.Combo.Input(
+            "thinking_level",
+            options=["MINIMAL", "HIGH"],
+        ),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, 15)],
+                min=0,
+            ),
+            tooltip="Optional reference image(s). Up to 14 images total.",
+        ),
+        IO.Custom("GEMINI_INPUT_FILES").Input(
+            "files",
+            optional=True,
+            tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+        ),
+    ]
+
+
+class GeminiNanoBanana2V2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNanoBanana2V2",
+            display_name="Nano Banana 2",
+            category="api node/image/Gemini",
+            description="Generate or edit images synchronously via Google Vertex API.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text prompt describing the image to generate or the edits to apply. "
+                    "Include any constraints, styles, or details the model should follow.",
+                    default="",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Nano Banana 2 (Gemini 3.1 Flash Image)",
+                            _nano_banana_2_v2_model_inputs(),
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When the seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Combo.Input(
+                    "response_modalities",
+                    options=["IMAGE", "IMAGE+TEXT"],
+                    advanced=True,
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(),
+                IO.Image.Output(
+                    display_name="thought_image",
+                    tooltip="First image from the model's thinking process. "
+                    "Only available with thinking_level HIGH and IMAGE+TEXT modality.",
+                ),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
+                expr="""
+                (
+                  $r := $lookup(widgets, "model.resolution");
+                  $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
+                  {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        response_modalities: str,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_choice = model["model"]
+        if model_choice == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model_id = "gemini-3.1-flash-image-preview"
+        else:
+            model_id = model_choice
+
+        images = model.get("images") or {}
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        if images:
+            image_tensors: list[Input.Image] = [t for t in images.values() if t is not None]
+            if image_tensors:
+                if sum(get_number_of_images(t) for t in image_tensors) > 14:
+                    raise ValueError("The current maximum number of supported images is 14.")
+                parts.extend(await create_image_parts(cls, image_tensors))
+        files = model.get("files")
+        if files is not None:
+            parts.extend(files)
+
+        image_config = GeminiImageConfig(imageSize=model["resolution"])
+        if model["aspect_ratio"] != "auto":
+            image_config.aspectRatio = model["aspect_ratio"]
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model_id}", method="POST"),
+            data=GeminiImageGenerateContentRequest(
+                contents=[
+                    GeminiContent(role=GeminiRole.user, parts=parts),
+                ],
+                generationConfig=GeminiImageGenerationConfig(
+                    responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
+                    imageConfig=image_config,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+            price_extractor=calculate_tokens_price,
+        )
+        return IO.NodeOutput(
+            await get_image_from_response(response),
+            get_text_from_response(response),
+            await get_image_from_response(response, thought=True),
+        )
+
+
 class GeminiExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1024,6 +1225,7 @@ class GeminiExtension(ComfyExtension):
             GeminiImage,
             GeminiImage2,
             GeminiNanoBanana2,
+            GeminiNanoBanana2V2,
             GeminiInputFiles,
         ]
 

comfy_api_nodes/nodes_kling.py

@@ -2787,11 +2787,15 @@ class MotionControl(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["mode", "model"]),
                 expr="""
                 (
-                  $prices := {"std": 0.07, "pro": 0.112};
-                  {"type":"usd","usd": $lookup($prices, widgets.mode), "format":{"suffix":"/second"}}
+                  $prices := {
+                    "kling-v3": {"std": 0.126, "pro": 0.168},
+                    "kling-v2-6": {"std": 0.07, "pro": 0.112}
+                  };
+                  $modelPrices := $lookup($prices, widgets.model);
+                  {"type":"usd","usd": $lookup($modelPrices, widgets.mode), "format":{"suffix":"/second"}}
                 )
                 """,
             ),

comfy_extras/nodes_advanced_samplers.py

@@ -92,7 +92,7 @@ class SamplerEulerCFGpp(io.ComfyNode):
         return io.Schema(
             node_id="SamplerEulerCFGpp",
             display_name="SamplerEulerCFG++",
-            category="_for_testing",  # "sampling/custom_sampling/samplers"
+            category="experimental",  # "sampling/custom_sampling/samplers"
             inputs=[
                 io.Combo.Input("version", options=["regular", "alternative"], advanced=True),
             ],

comfy_extras/nodes_ar_video.py

@@ -2,6 +2,7 @@
 ComfyUI nodes for autoregressive video generation (Causal Forcing, Self-Forcing, etc.).
   - EmptyARVideoLatent: create 5D [B, C, T, H, W] video latent tensors
   - SamplerARVideo: SAMPLER for the block-by-block autoregressive denoising loop
+  - ARVideoI2V: image-to-video conditioning for AR models (seeds KV cache with start image)
 """
 
 import torch
@@ -9,6 +10,7 @@ from typing_extensions import override
 
 import comfy.model_management
 import comfy.samplers
+import comfy.utils
 from comfy_api.latest import ComfyExtension, io
 
 
@@ -71,12 +73,62 @@ class SamplerARVideo(io.ComfyNode):
         return io.NodeOutput(comfy.samplers.ksampler("ar_video", extra_options))
 
 
+class ARVideoI2V(io.ComfyNode):
+    """Image-to-video setup for AR video models (Causal Forcing, Self-Forcing).
+
+    VAE-encodes the start image and stores it in the model's transformer_options
+    so that sample_ar_video can seed the KV cache before denoising.
+    Uses the same T2V model checkpoint -- no separate I2V architecture needed.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ARVideoI2V",
+            category="conditioning/video_models",
+            inputs=[
+                io.Model.Input("model"),
+                io.Vae.Input("vae"),
+                io.Image.Input("start_image"),
+                io.Int.Input("width", default=832, min=16, max=8192, step=16),
+                io.Int.Input("height", default=480, min=16, max=8192, step=16),
+                io.Int.Input("length", default=81, min=1, max=1024, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[
+                io.Model.Output(display_name="MODEL"),
+                io.Latent.Output(display_name="LATENT"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, vae, start_image, width, height, length, batch_size) -> io.NodeOutput:
+        start_image = comfy.utils.common_upscale(
+            start_image[:1].movedim(-1, 1), width, height, "bilinear", "center"
+        ).movedim(1, -1)
+
+        initial_latent = vae.encode(start_image[:, :, :, :3])
+
+        m = model.clone()
+        to = m.model_options.setdefault("transformer_options", {})
+        ar_cfg = to.setdefault("ar_config", {})
+        ar_cfg["initial_latent"] = initial_latent
+
+        lat_t = ((length - 1) // 4) + 1
+        latent = torch.zeros(
+            [batch_size, 16, lat_t, height // 8, width // 8],
+            device=comfy.model_management.intermediate_device(),
+        )
+        return io.NodeOutput(m, {"samples": latent})
+
+
 class ARVideoExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             EmptyARVideoLatent,
             SamplerARVideo,
+            ARVideoI2V,
         ]
 
 

comfy_extras/nodes_attention_multiply.py

@@ -25,7 +25,7 @@ class UNetSelfAttentionMultiply(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="UNetSelfAttentionMultiply",
-            category="_for_testing/attention_experiments",
+            category="experimental/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
@@ -48,7 +48,7 @@ class UNetCrossAttentionMultiply(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="UNetCrossAttentionMultiply",
-            category="_for_testing/attention_experiments",
+            category="experimental/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
@@ -72,7 +72,7 @@ class CLIPAttentionMultiply(io.ComfyNode):
         return io.Schema(
             node_id="CLIPAttentionMultiply",
             search_aliases=["clip attention scale", "text encoder attention"],
-            category="_for_testing/attention_experiments",
+            category="experimental/attention_experiments",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
@@ -106,7 +106,7 @@ class UNetTemporalAttentionMultiply(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="UNetTemporalAttentionMultiply",
-            category="_for_testing/attention_experiments",
+            category="experimental/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),

comfy_extras/nodes_audio_encoder.py

@@ -10,6 +10,7 @@ class AudioEncoderLoader(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="AudioEncoderLoader",
+            display_name="Load Audio Encoder",
             category="loaders",
             inputs=[
                 io.Combo.Input(

comfy_extras/nodes_bg_removal.py

@@ -0,0 +1,60 @@
+import folder_paths
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO
+from comfy.bg_removal_model import load
+
+
+class LoadBackgroundRemovalModel(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        files = folder_paths.get_filename_list("background_removal")
+        return IO.Schema(
+            node_id="LoadBackgroundRemovalModel",
+            display_name="Load Background Removal Model",
+            category="loaders",
+            inputs=[
+                IO.Combo.Input("bg_removal_name", options=sorted(files), tooltip="The model used to remove backgrounds from images"),
+            ],
+            outputs=[
+                IO.BackgroundRemoval.Output("bg_model")
+            ]
+        )
+    @classmethod
+    def execute(cls, bg_removal_name):
+        path = folder_paths.get_full_path_or_raise("background_removal", bg_removal_name)
+        bg = load(path)
+        if bg is None:
+            raise RuntimeError("ERROR: background model file is invalid and does not contain a valid background removal model.")
+        return IO.NodeOutput(bg)
+
+class RemoveBackground(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="RemoveBackground",
+            display_name="Remove Background",
+            category="image/background removal",
+            inputs=[
+                IO.Image.Input("image", tooltip="Input image to remove the background from"),
+                IO.BackgroundRemoval.Input("bg_removal_model", tooltip="Background removal model used to generate the mask")
+            ],
+            outputs=[
+                IO.Mask.Output("mask", tooltip="Generated foreground mask")
+            ]
+        )
+    @classmethod
+    def execute(cls, image, bg_removal_model):
+        mask = bg_removal_model.encode_image(image)
+        return IO.NodeOutput(mask)
+
+class BackgroundRemovalExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            LoadBackgroundRemovalModel,
+            RemoveBackground
+        ]
+
+
+async def comfy_entrypoint() -> BackgroundRemovalExtension:
+    return BackgroundRemovalExtension()

comfy_extras/nodes_camera_trajectory.py

@@ -153,7 +153,7 @@ class WanCameraEmbedding(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanCameraEmbedding",
-            category="camera",
+            category="conditioning/video_models",
             inputs=[
                 io.Combo.Input(
                     "camera_pose",

comfy_extras/nodes_compositing.py

@@ -203,7 +203,7 @@ class JoinImageWithAlpha(io.ComfyNode):
     @classmethod
     def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
         batch_size = max(len(image), len(alpha))
-        alpha = 1.0 - resize_mask(alpha, image.shape[1:])
+        alpha = 1.0 - resize_mask(alpha.to(image), image.shape[1:])
         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))

comfy_extras/nodes_cond.py

@@ -8,7 +8,7 @@ class CLIPTextEncodeControlnet(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CLIPTextEncodeControlnet",
-            category="_for_testing/conditioning",
+            category="experimental/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Conditioning.Input("conditioning"),
@@ -35,7 +35,7 @@ class T5TokenizerOptions(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="T5TokenizerOptions",
-            category="_for_testing/conditioning",
+            category="experimental/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),

comfy_extras/nodes_context_windows.py

@@ -10,7 +10,7 @@ class ContextWindowsManualNode(io.ComfyNode):
         return io.Schema(
             node_id="ContextWindowsManual",
             display_name="Context Windows (Manual)",
-            category="context",
+            category="model_patches",
             description="Manually set context windows.",
             inputs=[
                 io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),

comfy_extras/nodes_custom_sampler.py

@@ -984,7 +984,7 @@ class AddNoise(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="AddNoise",
-            category="_for_testing/custom_sampling/noise",
+            category="experimental/custom_sampling/noise",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
@@ -1034,7 +1034,7 @@ class ManualSigmas(io.ComfyNode):
         return io.Schema(
             node_id="ManualSigmas",
             search_aliases=["custom noise schedule", "define sigmas"],
-            category="_for_testing/custom_sampling",
+            category="experimental/custom_sampling",
             is_experimental=True,
             inputs=[
                 io.String.Input("sigmas", default="1, 0.5", multiline=False)

comfy_extras/nodes_differential_diffusion.py

@@ -13,7 +13,7 @@ class DifferentialDiffusion(io.ComfyNode):
             node_id="DifferentialDiffusion",
             search_aliases=["inpaint gradient", "variable denoise strength"],
             display_name="Differential Diffusion",
-            category="_for_testing",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input(

comfy_extras/nodes_flux.py

@@ -102,7 +102,7 @@ class FluxDisableGuidance(io.ComfyNode):
     append = execute  # TODO: remove
 
 
-PREFERED_KONTEXT_RESOLUTIONS = [
+PREFERRED_KONTEXT_RESOLUTIONS = [
     (672, 1568),
     (688, 1504),
     (720, 1456),
@@ -143,7 +143,7 @@ class FluxKontextImageScale(io.ComfyNode):
         width = image.shape[2]
         height = image.shape[1]
         aspect_ratio = width / height
-        _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
+        _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_KONTEXT_RESOLUTIONS)
         image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
         return io.NodeOutput(image)
 

comfy_extras/nodes_fresca.py

@@ -60,7 +60,7 @@ class FreSca(io.ComfyNode):
             node_id="FreSca",
             search_aliases=["frequency guidance"],
             display_name="FreSca",
-            category="_for_testing",
+            category="experimental",
             description="Applies frequency-dependent scaling to the guidance",
             inputs=[
                 io.Model.Input("model"),

comfy_extras/nodes_hunyuan.py

@@ -131,6 +131,8 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanVideo15SuperResolution",
+            display_name="Hunyuan Video 1.5 Super Resolution",
+            category="conditioning/video_models",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -381,6 +383,8 @@ class HunyuanRefinerLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanRefinerLatent",
+            display_name="Hunyuan Latent Refiner",
+            category="conditioning/video_models",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),

comfy_extras/nodes_hunyuan3d.py

@@ -40,7 +40,7 @@ class Hunyuan3Dv2Conditioning(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Hunyuan3Dv2Conditioning",
-            category="conditioning/video_models",
+            category="conditioning/3d_models",
             inputs=[
                 IO.ClipVisionOutput.Input("clip_vision_output"),
             ],
@@ -65,7 +65,7 @@ class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Hunyuan3Dv2ConditioningMultiView",
-            category="conditioning/video_models",
+            category="conditioning/3d_models",
             inputs=[
                 IO.ClipVisionOutput.Input("front", optional=True),
                 IO.ClipVisionOutput.Input("left", optional=True),
@@ -424,6 +424,7 @@ class VoxelToMeshBasic(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VoxelToMeshBasic",
+            display_name="Voxel to Mesh (Basic)",
             category="3d",
             inputs=[
                 IO.Voxel.Input("voxel"),
@@ -453,6 +454,7 @@ class VoxelToMesh(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VoxelToMesh",
+            display_name="Voxel to Mesh",
             category="3d",
             inputs=[
                 IO.Voxel.Input("voxel"),

comfy_extras/nodes_hypernetwork.py

@@ -102,6 +102,7 @@ class HypernetworkLoader(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="HypernetworkLoader",
+            display_name="Load Hypernetwork",
             category="loaders",
             inputs=[
                 IO.Model.Input("model"),

comfy_extras/nodes_lora_extract.py

@@ -91,7 +91,7 @@ class LoraSave(io.ComfyNode):
             node_id="LoraSave",
             search_aliases=["export lora"],
             display_name="Extract and Save Lora",
-            category="_for_testing",
+            category="experimental",
             inputs=[
                 io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
                 io.Int.Input("rank", default=8, min=1, max=4096, step=1, advanced=True),

comfy_extras/nodes_lt.py

@@ -106,12 +106,12 @@ class LTXVImgToVideoInplace(io.ComfyNode):
         if bypass:
             return (latent,)
 
-        samples = latent["samples"]
+        samples = latent["samples"].clone()
         _, height_scale_factor, width_scale_factor = (
             vae.downscale_index_formula
         )
 
-        batch, _, latent_frames, latent_height, latent_width = samples.shape
+        _, _, _, latent_height, latent_width = samples.shape
         width = latent_width * width_scale_factor
         height = latent_height * height_scale_factor
 
@@ -124,11 +124,7 @@ class LTXVImgToVideoInplace(io.ComfyNode):
 
         samples[:, :, :t.shape[2]] = t
 
-        conditioning_latent_frames_mask = torch.ones(
-            (batch, 1, latent_frames, 1, 1),
-            dtype=torch.float32,
-            device=samples.device,
-        )
+        conditioning_latent_frames_mask = get_noise_mask(latent)
         conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength
 
         return io.NodeOutput({"samples": samples, "noise_mask": conditioning_latent_frames_mask})
@@ -236,7 +232,7 @@ class LTXVAddGuide(io.ComfyNode):
     def encode(cls, vae, latent_width, latent_height, images, scale_factors):
         time_scale_factor, width_scale_factor, height_scale_factor = scale_factors
         images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1]
-        pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="disabled").movedim(1, -1)
+        pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="center").movedim(1, -1)
         encode_pixels = pixels[:, :, :, :3]
         t = vae.encode(encode_pixels)
         return encode_pixels, t
@@ -594,7 +590,8 @@ class LTXVPreprocess(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVPreprocess",
-            category="image",
+            display_name="LTXV Preprocess",
+            category="video/preprocessors",
             inputs=[
                 io.Image.Input("image"),
                 io.Int.Input(

comfy_extras/nodes_mahiro.py

@@ -11,7 +11,7 @@ class Mahiro(io.ComfyNode):
         return io.Schema(
             node_id="Mahiro",
             display_name="Positive-Biased Guidance",
-            category="_for_testing",
+            category="experimental",
             description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
             inputs=[
                 io.Model.Input("model"),

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):
@@ -84,18 +95,23 @@ class ImageCompositeMasked(IO.ComfyNode):
             display_name="Image Composite Masked",
             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)
@@ -381,7 +397,6 @@ class GrowMask(IO.ComfyNode):
 
     expand_mask = execute  # TODO: remove
 
-
 class ThresholdMask(IO.ComfyNode):
     @classmethod
     def define_schema(cls):

comfy_extras/nodes_math.py

@@ -70,7 +70,7 @@ class MathExpressionNode(io.ComfyNode):
         return io.Schema(
             node_id="ComfyMathExpression",
             display_name="Math Expression",
-            category="math",
+            category="logic",
             search_aliases=[
                 "expression", "formula", "calculate", "calculator",
                 "eval", "math",

comfy_extras/nodes_number_convert.py

@@ -21,7 +21,7 @@ class NumberConvertNode(io.ComfyNode):
         return io.Schema(
             node_id="ComfyNumberConvert",
             display_name="Number Convert",
-            category="math",
+            category="utils",
             search_aliases=[
                 "int to float", "float to int", "number convert",
                 "int2float", "float2int", "cast", "parse number",

comfy_extras/nodes_perpneg.py

@@ -24,8 +24,8 @@ class PerpNeg(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PerpNeg",
-            display_name="Perp-Neg (DEPRECATED by PerpNegGuider)",
-            category="_for_testing",
+            display_name="Perp-Neg (DEPRECATED by Perp-Neg Guider)",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("empty_conditioning"),
@@ -127,7 +127,8 @@ class PerpNegGuider(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PerpNegGuider",
-            category="_for_testing",
+            display_name="Perp-Neg Guider",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("positive"),

comfy_extras/nodes_photomaker.py

@@ -123,7 +123,7 @@ class PhotoMakerLoader(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PhotoMakerLoader",
-            category="_for_testing/photomaker",
+            category="experimental/photomaker",
             inputs=[
                 io.Combo.Input("photomaker_model_name", options=folder_paths.get_filename_list("photomaker")),
             ],
@@ -149,7 +149,7 @@ class PhotoMakerEncode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PhotoMakerEncode",
-            category="_for_testing/photomaker",
+            category="experimental/photomaker",
             inputs=[
                 io.Photomaker.Input("photomaker"),
                 io.Image.Input("image"),

comfy_extras/nodes_post_processing.py

@@ -116,6 +116,7 @@ class Quantize(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageQuantize",
+            display_name="Quantize Image",
             category="image/postprocessing",
             inputs=[
                 io.Image.Input("image"),
@@ -181,6 +182,7 @@ class Sharpen(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageSharpen",
+            display_name="Sharpen Image",
             category="image/postprocessing",
             inputs=[
                 io.Image.Input("image"),
@@ -436,7 +438,7 @@ class ResizeImageMaskNode(io.ComfyNode):
             node_id="ResizeImageMaskNode",
             display_name="Resize Image/Mask",
             description="Resize an image or mask using various scaling methods.",
-            category="transform",
+            category="image/transform",
             search_aliases=["resize", "resize image", "resize mask", "scale", "scale image", "scale mask", "image resize", "change size", "dimensions", "shrink", "enlarge"],
             inputs=[
                 io.MatchType.Input("input", template=template),

comfy_extras/nodes_rtdetr.py

@@ -15,7 +15,7 @@ class RTDETR_detect(io.ComfyNode):
         return io.Schema(
             node_id="RTDETR_detect",
             display_name="RT-DETR Detect",
-            category="detection/",
+            category="detection",
             search_aliases=["bbox", "bounding box", "object detection", "coco"],
             inputs=[
                 io.Model.Input("model", display_name="model"),
@@ -71,7 +71,7 @@ class DrawBBoxes(io.ComfyNode):
         return io.Schema(
             node_id="DrawBBoxes",
             display_name="Draw BBoxes",
-            category="detection/",
+            category="detection",
             search_aliases=["bbox", "bounding box", "object detection", "rt_detr", "visualize detections", "coco"],
             inputs=[
                 io.Image.Input("image", optional=True),

comfy_extras/nodes_sag.py

@@ -113,7 +113,7 @@ class SelfAttentionGuidance(io.ComfyNode):
         return io.Schema(
             node_id="SelfAttentionGuidance",
             display_name="Self-Attention Guidance",
-            category="_for_testing",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01),

comfy_extras/nodes_sam3.py

@@ -93,7 +93,7 @@ class SAM3_Detect(io.ComfyNode):
         return io.Schema(
             node_id="SAM3_Detect",
             display_name="SAM3 Detect",
-            category="detection/",
+            category="detection",
             search_aliases=["sam3", "segment anything", "open vocabulary", "text detection", "segment"],
             inputs=[
                 io.Model.Input("model", display_name="model"),
@@ -265,15 +265,15 @@ class SAM3_VideoTrack(io.ComfyNode):
         return io.Schema(
             node_id="SAM3_VideoTrack",
             display_name="SAM3 Video Track",
-            category="detection/",
+            category="detection",
             search_aliases=["sam3", "video", "track", "propagate"],
             inputs=[
                 io.Image.Input("images", display_name="images", tooltip="Video frames as batched images"),
                 io.Model.Input("model", display_name="model"),
                 io.Mask.Input("initial_mask", display_name="initial_mask", optional=True, tooltip="Mask(s) for the first frame to track (one per object)"),
                 io.Conditioning.Input("conditioning", display_name="conditioning", optional=True, tooltip="Text conditioning for detecting new objects during tracking"),
-                io.Float.Input("detection_threshold", display_name="detection_threshold", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Score threshold for text-prompted detection"),
-                io.Int.Input("max_objects", display_name="max_objects", default=0, min=0, tooltip="Max tracked objects (0=unlimited). Initial masks count toward this limit."),
+                io.Float.Input("detection_threshold", display_name="detection_threshold", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Score threshold for text-prompted detection."),
+                io.Int.Input("max_objects", display_name="max_objects", default=4, min=0, max=64, tooltip="Max tracked objects. Initial masks count toward this limit. 0 uses the internal cap of 64."),
                 io.Int.Input("detect_interval", display_name="detect_interval", default=1, min=1, tooltip="Run detection every N frames (1=every frame). Higher values save compute."),
             ],
             outputs=[
@@ -290,8 +290,7 @@ class SAM3_VideoTrack(io.ComfyNode):
         dtype = model.model.get_dtype()
         sam3_model = model.model.diffusion_model
 
-        frames = images[..., :3].movedim(-1, 1)
-        frames_in = comfy.utils.common_upscale(frames, 1008, 1008, "bilinear", crop="disabled").to(device=device, dtype=dtype)
+        frames_in = images[..., :3].movedim(-1, 1)
 
         init_masks = None
         if initial_mask is not None:
@@ -308,7 +307,7 @@ class SAM3_VideoTrack(io.ComfyNode):
         result = sam3_model.forward_video(
             images=frames_in, initial_masks=init_masks, pbar=pbar, text_prompts=text_prompts,
             new_det_thresh=detection_threshold, max_objects=max_objects,
-            detect_interval=detect_interval)
+            detect_interval=detect_interval, target_device=device, target_dtype=dtype)
         result["orig_size"] = (H, W)
         return io.NodeOutput(result)
 
@@ -321,7 +320,7 @@ class SAM3_TrackPreview(io.ComfyNode):
         return io.Schema(
             node_id="SAM3_TrackPreview",
             display_name="SAM3 Track Preview",
-            category="detection/",
+            category="detection",
             inputs=[
                 SAM3TrackData.Input("track_data", display_name="track_data"),
                 io.Image.Input("images", display_name="images", optional=True),
@@ -449,14 +448,18 @@ class SAM3_TrackPreview(io.ComfyNode):
                     cx = (bool_masks * grid_x).sum(dim=(-1, -2)) // area
                     has = area > 1
                     scores = track_data.get("scores", [])
+                    label_scale = max(3, H // 240) # Scale font with resolutio
+                    size_caps = (area.float().sqrt() / 15).clamp_(min=1).long().tolist() #cap per-object so the number doesn't dwarf small masks
                     for obj_idx in range(N_obj):
                         if has[obj_idx]:
                             _cx, _cy = int(cx[obj_idx]), int(cy[obj_idx])
                             color = cls.COLORS[obj_idx % len(cls.COLORS)]
-                            SAM3_TrackPreview._draw_number_gpu(frame_gpu, obj_idx, _cx, _cy, color)
+                            obj_scale = min(label_scale, size_caps[obj_idx])
+                            score_scale = max(1, obj_scale * 2 // 3)
+                            SAM3_TrackPreview._draw_number_gpu(frame_gpu, obj_idx, _cx, _cy, color, scale=obj_scale)
                             if obj_idx < len(scores) and scores[obj_idx] < 1.0:
                                 SAM3_TrackPreview._draw_number_gpu(frame_gpu, int(scores[obj_idx] * 100),
-                                                                   _cx, _cy + 5 * 3 + 3, color, scale=2)
+                                                                   _cx, _cy + 5 * obj_scale + 3, color, scale=score_scale)
                     frame_cpu.copy_(frame_gpu.clamp_(0, 1).mul_(255).byte())
                 else:
                     frame_cpu.copy_(frame.clamp_(0, 1).mul_(255).byte())
@@ -475,7 +478,7 @@ class SAM3_TrackToMask(io.ComfyNode):
         return io.Schema(
             node_id="SAM3_TrackToMask",
             display_name="SAM3 Track to Mask",
-            category="detection/",
+            category="detection",
             inputs=[
                 SAM3TrackData.Input("track_data", display_name="track_data"),
                 io.String.Input("object_indices", display_name="object_indices", default="",
@@ -507,9 +510,10 @@ class SAM3_TrackToMask(io.ComfyNode):
         if not indices:
             return io.NodeOutput(torch.zeros(N, H, W, device=comfy.model_management.intermediate_device()))
 
-        selected = packed[:, indices]
-        binary = unpack_masks(selected)  # [N, len(indices), Hm, Wm] bool
-        union = binary.any(dim=1, keepdim=True).float()
+        union_packed = packed[:, indices[0]].clone()
+        for i in indices[1:]:
+            union_packed |= packed[:, i]
+        union = unpack_masks(union_packed).unsqueeze(1).float()  # [N, 1, Hm, Wm]
         mask_out = F.interpolate(union, size=(H, W), mode="bilinear", align_corners=False)[:, 0]
         return io.NodeOutput(mask_out)
 

comfy_extras/nodes_stable_cascade.py

@@ -119,7 +119,7 @@ class StableCascade_SuperResolutionControlnet(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_SuperResolutionControlnet",
-            category="_for_testing/stable_cascade",
+            category="experimental/stable_cascade",
             is_experimental=True,
             inputs=[
                 io.Image.Input("image"),

comfy_extras/nodes_textgen.py

@@ -26,7 +26,8 @@ class TextGenerate(io.ComfyNode):
 
         return io.Schema(
             node_id="TextGenerate",
-            category="textgen",
+            display_name="Generate Text",
+            category="text",
             search_aliases=["LLM", "gemma"],
             inputs=[
                 io.Clip.Input("clip"),
@@ -157,6 +158,7 @@ class TextGenerateLTX2Prompt(TextGenerate):
         parent_schema = super().define_schema()
         return io.Schema(
             node_id="TextGenerateLTX2Prompt",
+            display_name="Generate LTX2 Prompt",
             category=parent_schema.category,
             inputs=parent_schema.inputs,
             outputs=parent_schema.outputs,

comfy_extras/nodes_torch_compile.py

@@ -10,7 +10,7 @@ class TorchCompileModel(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="TorchCompileModel",
-            category="_for_testing",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Combo.Input(

comfy_extras/nodes_train.py

@@ -1361,7 +1361,7 @@ class SaveLoRA(io.ComfyNode):
             node_id="SaveLoRA",
             search_aliases=["export lora"],
             display_name="Save LoRA Weights",
-            category="loaders",
+            category="advanced/model_merging",
             is_experimental=True,
             is_output_node=True,
             inputs=[

comfy_extras/nodes_video_model.py

@@ -15,7 +15,7 @@ class ImageOnlyCheckpointLoader:
     RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE")
     FUNCTION = "load_checkpoint"
 
-    CATEGORY = "loaders/video_models"
+    CATEGORY = "loaders"
 
     def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
         ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)

custom_nodes/websocket_image_save.py

@@ -22,7 +22,7 @@ class SaveImageWebsocket:
 
     OUTPUT_NODE = True
 
-    CATEGORY = "api/image"
+    CATEGORY = "image"
 
     def save_images(self, images):
         pbar = comfy.utils.ProgressBar(images.shape[0])
@@ -42,3 +42,7 @@ class SaveImageWebsocket:
 NODE_CLASS_MAPPINGS = {
     "SaveImageWebsocket": SaveImageWebsocket,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SaveImageWebsocket": "Save Image (Websocket)",
+}

folder_paths.py

@@ -52,6 +52,8 @@ folder_names_and_paths["model_patches"] = ([os.path.join(models_dir, "model_patc
 
 folder_names_and_paths["audio_encoders"] = ([os.path.join(models_dir, "audio_encoders")], supported_pt_extensions)
 
+folder_names_and_paths["background_removal"] = ([os.path.join(models_dir, "background_removal")], supported_pt_extensions)
+
 folder_names_and_paths["frame_interpolation"] = ([os.path.join(models_dir, "frame_interpolation")], supported_pt_extensions)
 
 folder_names_and_paths["optical_flow"] = ([os.path.join(models_dir, "optical_flow")], supported_pt_extensions)

nodes.py

@@ -330,7 +330,7 @@ class VAEDecodeTiled:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8):
         if tile_size < overlap * 4:
@@ -377,7 +377,7 @@ class VAEEncodeTiled:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8):
         t = vae.encode_tiled(pixels, tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
@@ -493,7 +493,7 @@ class SaveLatent:
 
     OUTPUT_NODE = True
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
@@ -538,7 +538,7 @@ class LoadLatent:
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
         return {"required": {"latent": [sorted(files), ]}, }
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "load"
@@ -1443,7 +1443,7 @@ class LatentBlend:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "blend"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"):
 
@@ -2092,6 +2092,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "StyleModelLoader": "Load Style Model",
     "CLIPVisionLoader": "Load CLIP Vision",
     "UNETLoader": "Load Diffusion Model",
+    "unCLIPCheckpointLoader": "Load unCLIP Checkpoint",
+    "GLIGENLoader": "Load GLIGEN Model",
     # Conditioning
     "CLIPVisionEncode": "CLIP Vision Encode",
     "StyleModelApply": "Apply Style Model",
@@ -2140,7 +2142,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageSharpen": "Sharpen Image",
     "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     "GetImageSize": "Get Image Size",
-    # _for_testing
+    # experimental
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",
 }
@@ -2427,6 +2429,7 @@ async def init_builtin_extra_nodes():
         "nodes_number_convert.py",
         "nodes_painter.py",
         "nodes_curve.py",
+        "nodes_bg_removal.py",
         "nodes_rtdetr.py",
         "nodes_frame_interpolation.py",
         "nodes_sam3.py",

tests-unit/app_test/node_replace_manager_test.py

@@ -0,0 +1,90 @@
+"""Tests for NodeReplaceManager registration behavior."""
+import importlib
+import sys
+import types
+
+import pytest
+
+
+@pytest.fixture
+def NodeReplaceManager(monkeypatch):
+    """Provide NodeReplaceManager with `nodes` stubbed.
+
+    `app.node_replace_manager` does `import nodes` at module level, which pulls in
+    torch + the full ComfyUI graph. register() doesn't actually need it, so we
+    stub `nodes` per-test (via monkeypatch so it's torn down) and reload the
+    module so it picks up the stub instead of any cached real import.
+    """
+    fake_nodes = types.ModuleType("nodes")
+    fake_nodes.NODE_CLASS_MAPPINGS = {}
+    monkeypatch.setitem(sys.modules, "nodes", fake_nodes)
+    monkeypatch.delitem(sys.modules, "app.node_replace_manager", raising=False)
+    module = importlib.import_module("app.node_replace_manager")
+    yield module.NodeReplaceManager
+    # Drop the freshly-imported module so the next test (or a later real import
+    # of `nodes`) starts from a clean slate.
+    sys.modules.pop("app.node_replace_manager", None)
+
+
+class FakeNodeReplace:
+    """Lightweight stand-in for comfy_api.latest._io.NodeReplace."""
+    def __init__(self, new_node_id, old_node_id, old_widget_ids=None,
+                 input_mapping=None, output_mapping=None):
+        self.new_node_id = new_node_id
+        self.old_node_id = old_node_id
+        self.old_widget_ids = old_widget_ids
+        self.input_mapping = input_mapping
+        self.output_mapping = output_mapping
+
+
+def test_register_adds_replacement(NodeReplaceManager):
+    manager = NodeReplaceManager()
+    manager.register(FakeNodeReplace(new_node_id="NewNode", old_node_id="OldNode"))
+    assert manager.has_replacement("OldNode")
+    assert len(manager.get_replacement("OldNode")) == 1
+
+
+def test_register_allows_multiple_alternatives_for_same_old_node(NodeReplaceManager):
+    """Different new_node_ids for the same old_node_id should all be kept."""
+    manager = NodeReplaceManager()
+    manager.register(FakeNodeReplace(new_node_id="AltA", old_node_id="OldNode"))
+    manager.register(FakeNodeReplace(new_node_id="AltB", old_node_id="OldNode"))
+    replacements = manager.get_replacement("OldNode")
+    assert len(replacements) == 2
+    assert {r.new_node_id for r in replacements} == {"AltA", "AltB"}
+
+
+def test_register_is_idempotent_for_duplicate_pair(NodeReplaceManager):
+    """Re-registering the same (old_node_id, new_node_id) should be a no-op."""
+    manager = NodeReplaceManager()
+    manager.register(FakeNodeReplace(new_node_id="NewNode", old_node_id="OldNode"))
+    manager.register(FakeNodeReplace(new_node_id="NewNode", old_node_id="OldNode"))
+    manager.register(FakeNodeReplace(new_node_id="NewNode", old_node_id="OldNode"))
+    assert len(manager.get_replacement("OldNode")) == 1
+
+
+def test_register_idempotent_preserves_first_registration(NodeReplaceManager):
+    """First registration wins; later duplicates with different mappings are ignored."""
+    manager = NodeReplaceManager()
+    first = FakeNodeReplace(
+        new_node_id="NewNode", old_node_id="OldNode",
+        input_mapping=[{"new_id": "a", "old_id": "x"}],
+    )
+    second = FakeNodeReplace(
+        new_node_id="NewNode", old_node_id="OldNode",
+        input_mapping=[{"new_id": "b", "old_id": "y"}],
+    )
+    manager.register(first)
+    manager.register(second)
+    replacements = manager.get_replacement("OldNode")
+    assert len(replacements) == 1
+    assert replacements[0] is first
+
+
+def test_register_dedupe_does_not_affect_other_old_nodes(NodeReplaceManager):
+    manager = NodeReplaceManager()
+    manager.register(FakeNodeReplace(new_node_id="NewA", old_node_id="OldA"))
+    manager.register(FakeNodeReplace(new_node_id="NewA", old_node_id="OldA"))
+    manager.register(FakeNodeReplace(new_node_id="NewB", old_node_id="OldB"))
+    assert len(manager.get_replacement("OldA")) == 1
+    assert len(manager.get_replacement("OldB")) == 1

tests/execution/testing_nodes/testing-pack/api_test_nodes.py

@@ -21,7 +21,7 @@ class TestAsyncProgressUpdate(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "execute"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def execute(self, value, sleep_seconds):
         start = time.time()
@@ -51,7 +51,7 @@ class TestSyncProgressUpdate(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "execute"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     def execute(self, value, sleep_seconds):
         start = time.time()

tests/execution/testing_nodes/testing-pack/async_test_nodes.py

@@ -21,7 +21,7 @@ class TestAsyncValidation(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     @classmethod
     async def VALIDATE_INPUTS(cls, value, threshold):
@@ -53,7 +53,7 @@ class TestAsyncError(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "error_execution"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def error_execution(self, value, error_after):
         await asyncio.sleep(error_after)
@@ -74,7 +74,7 @@ class TestAsyncValidationError(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     @classmethod
     async def VALIDATE_INPUTS(cls, value, max_value):
@@ -105,7 +105,7 @@ class TestAsyncTimeout(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "timeout_execution"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def timeout_execution(self, value, timeout, operation_time):
         try:
@@ -129,7 +129,7 @@ class TestSyncError(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "sync_error"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     def sync_error(self, value):
         raise RuntimeError("Intentional sync execution error for testing")
@@ -150,7 +150,7 @@ class TestAsyncLazyCheck(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def check_lazy_status(self, condition, input1, input2):
         # Simulate async checking (e.g., querying remote service)
@@ -184,7 +184,7 @@ class TestDynamicAsyncGeneration(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "generate_async_workflow"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     def generate_async_workflow(self, image1, image2, num_async_nodes, sleep_duration):
         g = GraphBuilder()
@@ -229,7 +229,7 @@ class TestAsyncResourceUser(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "use_resource"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def use_resource(self, value, resource_id, duration):
         # Check if resource is already in use
@@ -265,7 +265,7 @@ class TestAsyncBatchProcessing(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process_batch"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def process_batch(self, images, process_time_per_item, unique_id):
         batch_size = images.shape[0]
@@ -305,7 +305,7 @@ class TestAsyncConcurrentLimit(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "limited_execution"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def limited_execution(self, value, duration, node_id):
         async with self._semaphore:

tests/execution/testing_nodes/testing-pack/specific_tests.py

@@ -409,7 +409,7 @@ class TestSleep(ComfyNodeABC):
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "sleep"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     async def sleep(self, value, seconds, unique_id):
         pbar = ProgressBar(seconds, node_id=unique_id)
@@ -440,7 +440,7 @@ class TestParallelSleep(ComfyNodeABC):
         }
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "parallel_sleep"
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
     OUTPUT_NODE = True
 
     def parallel_sleep(self, image1, image2, image3, sleep1, sleep2, sleep3, unique_id):
@@ -474,7 +474,7 @@ class TestOutputNodeWithSocketOutput:
         }
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process"
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
     OUTPUT_NODE = True
 
     def process(self, image, value):