ComfyUI version 0.3.47

comfy/ldm/wan/model.py

@@ -146,6 +146,15 @@ WAN_CROSSATTENTION_CLASSES = {
 }
 
 
+def repeat_e(e, x):
+    repeats = 1
+    if e.shape[1] > 1:
+        repeats = x.shape[1] // e.shape[1]
+    if repeats == 1:
+        return e
+    return torch.repeat_interleave(e, repeats, dim=1)
+
+
 class WanAttentionBlock(nn.Module):
 
     def __init__(self,
@@ -201,6 +210,7 @@ class WanAttentionBlock(nn.Module):
             freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
         # assert e.dtype == torch.float32
+
         if e.ndim < 4:
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
         else:
@@ -209,15 +219,15 @@ class WanAttentionBlock(nn.Module):
 
         # self-attention
         y = self.self_attn(
-            self.norm1(x) * (1 + e[1]) + e[0],
+            self.norm1(x) * (1 + repeat_e(e[1], x)) + repeat_e(e[0], x),
             freqs)
 
-        x = x + y * e[2]
+        x = x + y * repeat_e(e[2], x)
 
         # cross-attention & ffn
         x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len)
-        y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
-        x = x + y * e[5]
+        y = self.ffn(self.norm2(x) * (1 + repeat_e(e[4], x)) + repeat_e(e[3], x))
+        x = x + y * repeat_e(e[5], x)
         return x
 
 
@@ -331,7 +341,8 @@ class Head(nn.Module):
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
         else:
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2)
-        x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
+
+        x = (self.head(self.norm(x) * (1 + repeat_e(e[1], x)) + repeat_e(e[0], x)))
         return x
 
 

comfy/ldm/wan/vae2_2.py

@@ -136,7 +136,7 @@ class ResidualBlock(nn.Module):
             if in_dim != out_dim else nn.Identity())
 
     def forward(self, x, feat_cache=None, feat_idx=[0]):
-        h = self.shortcut(x)
+        old_x = x
         for layer in self.residual:
             if isinstance(layer, CausalConv3d) and feat_cache is not None:
                 idx = feat_idx[0]
@@ -156,7 +156,7 @@ class ResidualBlock(nn.Module):
                 feat_idx[0] += 1
             else:
                 x = layer(x)
-        return x + h
+        return x + self.shortcut(old_x)
 
 
 def patchify(x, patch_size):
@@ -327,7 +327,7 @@ class Down_ResidualBlock(nn.Module):
         self.downsamples = nn.Sequential(*downsamples)
 
     def forward(self, x, feat_cache=None, feat_idx=[0]):
-        x_copy = x.clone()
+        x_copy = x
         for module in self.downsamples:
             x = module(x, feat_cache, feat_idx)
 
@@ -369,7 +369,7 @@ class Up_ResidualBlock(nn.Module):
         self.upsamples = nn.Sequential(*upsamples)
 
     def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
-        x_main = x.clone()
+        x_main = x
         for module in self.upsamples:
             x_main = module(x_main, feat_cache, feat_idx)
         if self.avg_shortcut is not None:

comfy/model_base.py

@@ -1202,7 +1202,7 @@ class WAN22(BaseModel):
     def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
         if denoise_mask is None:
             return timestep
-        temp_ts = (torch.mean(denoise_mask[:, :, :, ::2, ::2], dim=1, keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1)
+        temp_ts = (torch.mean(denoise_mask[:, :, :, :, :], dim=(1, 3, 4), keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1)
         return temp_ts
 
     def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):

comfy/model_management.py

@@ -529,6 +529,8 @@ WINDOWS = any(platform.win32_ver())
 EXTRA_RESERVED_VRAM = 400 * 1024 * 1024
 if WINDOWS:
     EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue
+    if total_vram > (15 * 1024):  # more extra reserved vram on 16GB+ cards
+        EXTRA_RESERVED_VRAM += 100 * 1024 * 1024
 
 if args.reserve_vram is not None:
     EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.46"
+__version__ = "0.3.47"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.46"
+version = "0.3.47"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"