fix(wan): use device-agnostic default for get_i2v_mask

src/diffusers/modular_pipelines/wan_animate_2/encoders.py

@@ -81,11 +81,19 @@ def clip_visual_encode(image_encoder, tensor, device, dtype):
     return out.hidden_states[-2]
 
 
-def get_i2v_mask(lat_t, lat_h, lat_w, mask_len=1, device="cuda"):
+def get_i2v_mask(lat_t, lat_h, lat_w, mask_len=1, device=None):
     """Create an i2v mask in latent space.
 
     mask_len is in PIXEL space. Returns [4, lat_t, lat_h, lat_w] (no batch dim).
+
+    Args:
+        device: device on which the mask is allocated. Must be passed explicitly by the caller.
     """
+    if device is None:
+        raise ValueError(
+            "`device` must be specified when calling `get_i2v_mask`. It used to default to 'cuda', which "
+            "silently allocated the mask on CUDA and broke every non-CUDA accelerator (NPU/XPU/MPS/CPU)."
+        )
     msk = torch.zeros(1, (lat_t - 1) * 4 + 1, lat_h, lat_w, device=device)
     msk[:, :mask_len] = 1
     msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)

src/diffusers/pipelines/wan/pipeline_wan_animate.py

@@ -465,8 +465,10 @@ def get_i2v_mask(
         mask_len: int = 1,
         mask_pixel_values: torch.Tensor | None = None,
         dtype: torch.dtype | None = None,
-        device: str | torch.device = "cuda",
+        device: str | torch.device | None = None,
     ) -> torch.Tensor:
+        device = device or self._execution_device
+
         # mask_pixel_values shape (if supplied): [B, C = 1, T, latent_h, latent_w]
         if mask_pixel_values is None:
             mask_lat_size = torch.zeros(