fix: prevent autocast crash in WAN model addcmul ops

Wraps torch.addcmul calls in WAN attention blocks with autocast-disabled
context to prevent 'Unexpected floating ScalarType in at::autocast::prioritize'
RuntimeError. This occurs when upstream nodes (e.g. SAM3) leave CUDA autocast
enabled - PyTorch 2.8's autocast promote dispatch for addcmul hits an unhandled
dtype in the prioritize function.

Uses torch.is_autocast_enabled(device_type) (non-deprecated API) and only
applies the workaround when autocast is actually active (zero overhead otherwise).

Author: deepme987

comfy/ldm/wan/model.py

@@ -174,6 +174,17 @@ def repeat_e(e, x):
         return torch.repeat_interleave(e, repeats + 1, dim=1)[:, :x.size(1)]
 
 
+def _addcmul(x, y, z):
+    """torch.addcmul wrapper that disables autocast to prevent
+    'Unexpected floating ScalarType in at::autocast::prioritize' when
+    upstream nodes (e.g. SAM3) leave CUDA autocast enabled."""
+    device_type = x.device.type
+    if torch.is_autocast_enabled(device_type):
+        with torch.autocast(device_type=device_type, enabled=False):
+            return torch.addcmul(x, y, z)
+    return torch.addcmul(x, y, z)
+
+
 class WanAttentionBlock(nn.Module):
 
     def __init__(self,
@@ -242,10 +253,10 @@ def forward(
         # self-attention
         x = x.contiguous() # otherwise implicit in LayerNorm
         y = self.self_attn(
-            torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
+            _addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
             freqs, transformer_options=transformer_options)
 
-        x = torch.addcmul(x, y, repeat_e(e[2], x))
+        x = _addcmul(x, y, repeat_e(e[2], x))
         del y
 
         # cross-attention & ffn
@@ -255,8 +266,8 @@ def forward(
             for p in patches["attn2_patch"]:
                 x = p({"x": x, "transformer_options": transformer_options})
 
-        y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
-        x = torch.addcmul(x, y, repeat_e(e[5], x))
+        y = self.ffn(_addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
+        x = _addcmul(x, y, repeat_e(e[5], x))
         return x
 
 
@@ -371,7 +382,7 @@ def forward(self, x, e):
         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(torch.addcmul(repeat_e(e[0], x), self.norm(x), 1 + repeat_e(e[1], x))))
+        x = (self.head(_addcmul(repeat_e(e[0], x), self.norm(x), 1 + repeat_e(e[1], x))))
         return x
 
 
@@ -1453,17 +1464,17 @@ def forward(
 
         # self-attention
         y = self.self_attn(
-            torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
+            _addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
             freqs, transformer_options=transformer_options)
 
-        x = torch.addcmul(x, y, repeat_e(e[2], x))
+        x = _addcmul(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, transformer_options=transformer_options)
         if audio is not None:
             x = self.audio_cross_attn_wrapper(x, audio, transformer_options=transformer_options)
-        y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
-        x = torch.addcmul(x, y, repeat_e(e[5], x))
+        y = self.ffn(_addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
+        x = _addcmul(x, y, repeat_e(e[5], x))
         return x
 
 class DummyAdapterLayer(nn.Module):