fix: Float8Tensor as_strided workaround

src/scope/core/pipelines/wan2_1/utils.py

@@ -131,3 +131,22 @@ def load_state_dict(weights_path: str) -> dict:
         )
 
     return state_dict
+
+
+def _is_float8_tensor(tensor: torch.Tensor) -> bool:
+    """Check if a tensor is a Float8Tensor (from torchao) that lacks as_strided support."""
+    return type(tensor).__name__ == "Float8Tensor"
+
+
+def safe_unflatten(tensor: torch.Tensor, dim: int, sizes) -> torch.Tensor:
+    """unflatten that works with Float8Tensor by casting to float if needed.
+
+    Float8Tensor (torchao) does not implement aten.as_strided which is used
+    internally by unflatten/view/reshape.  We cast to the tensor's nominal
+    dtype, perform the operation, and return the plain tensor.  The surrounding
+    code already expects real-valued tensors for subsequent math, so this is
+    safe.
+    """
+    if _is_float8_tensor(tensor):
+        tensor = tensor.to(torch.float32)
+    return tensor.unflatten(dim, sizes)

src/scope/core/pipelines/wan2_1/vace/models/causal_vace_model.py

@@ -7,6 +7,8 @@
 import torch
 import torch.nn as nn
 
+from scope.core.pipelines.wan2_1.utils import safe_unflatten
+
 from .attention_blocks import (
     create_base_attention_block_class,
     create_vace_attention_block_class,
@@ -403,10 +405,10 @@ def _forward_inference(
                 self.causal_wan_model.freq_dim, t.flatten()
             ).type_as(x)
         )
-        e0 = (
-            self.causal_wan_model.time_projection(e)
-            .unflatten(1, (6, self.dim))
-            .unflatten(dim=0, sizes=t.shape)
+        e0 = safe_unflatten(
+            safe_unflatten(self.causal_wan_model.time_projection(e), 1, (6, self.dim)),
+            0,
+            t.shape,
         )
 
         # Context
@@ -510,7 +512,7 @@ def custom_forward(*inputs, **kwargs):
             )
 
         x = self.causal_wan_model.head(
-            x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2)
+            x, safe_unflatten(e, 0, t.shape).unsqueeze(2)
         )
         x = self.causal_wan_model.unpatchify(x, grid_sizes)
         return torch.stack(x)