Merge branch 'master' into worksplit-multigpu

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])
+        y = self.ffn(self.norm2(x) * (1 + repeat_e(e[4], x)) + repeat_e(e[3], x))
-        x = x + y * e[5]
+        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/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):

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"