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The `get_i2v_mask` method had a hardcoded `device="cuda"` default, which crashes on non-CUDA accelerators (Ascend NPU, etc.) with "Torch not compiled with CUDA enabled" when called without an explicit device argument. Change the default to None and resolve via `self._execution_device`, matching the pattern used across other pipeline methods. Verified on Ascend 910B NPU: torch.zeros(device="cuda") crashes, fix with device-agnostic resolution creates tensors on the correct device.
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Hi @li-lizhe, thanks for the PR! It does not appear to link an issue it fixes. If this PR addresses an existing issue, please add a closing keyword (e.g. Please note that PRs without a linked issue are likely to be automatically closed 10 days after this notice. Once the PR links an issue (or gets the |
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Description: Fix the
get_i2v_maskmethod'sdevice="cuda"default parameter which causes a crash on non-CUDA accelerators (Ascend NPU, Intel XPU, Apple MPS, etc.) when called without an explicit device argument.Change:
devicefrom"cuda"toNonedevice = device or self._execution_deviceat the function topprepare_reference_image_latents,encode_image, and other pipeline methodsVerification on Ascend 910B NPU (torch 2.14.0a0 + torch_npu):
torch.zeros(1, device="cuda")crashes withAssertionError: Torch not compiled with CUDA enableddevice = device or self._execution_deviceresolves tonpu:0, tensor created successfully on NPU