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WanToDance: hardcoded `device='cuda'` in music encoder construction crashes model loading on Ascend NPU

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2/5
预计耗时
1-3 小时
新手友好度
75/100
Issue 类型
缺陷
描述清晰度
描述清楚
活跃度
活跃
技术栈
python, pytorch

调研方向

问题位于 diffsynth/models/wan_video_dit.py 第 462 行和 diffsynth/models/wantodance.py 第 156 行。从 WanToDanceMusicEncoderLayer 构造函数中移除硬编码的参数 device='cuda'。通过运行一个在非 CUDA 设备(如 CPU 或 NPU)上加载模型的测试来验证修复,确保不会发生断言错误。

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描述

Summary

WanModel.__init__ builds the WanToDance music encoder with a hardcoded CUDA device (diffsynth/models/wan_video_dit.py:462):

self.music_encoder.append(
    WanToDanceMusicEncoderLayer(
        d_model=latent_dim,
        nhead=nhead,
        dim_feedforward=ff_size,
        dropout=dropout,
        activation=activation,
        batch_first=True,
        rotary=rotary,
        device='cuda',
    )
)

WanToDanceMusicEncoderLayer forwards device to nn.MultiheadAttention(..., device=device) (diffsynth/models/wantodance.py:156), so the attention parameters are allocated on CUDA at construction time.

Impact

On any build without CUDA (Ascend NPU, CPU-only, ...) the model cannot be constructed at all, before the surrounding pipeline ever gets a chance to move it to the target device:

AssertionError: Torch not compiled with CUDA enabled

This block is gated by wantodance_enable_global / wantodance_enable_dynamicfps / wantodance_enable_unimodel, i.e. loading a WanToDance model on Ascend fails outright. Every sibling layer in the same block (nn.Linear, nn.Sequential) leaves the device to the caller's .to(device), so the hardcoded argument is also inconsistent with the rest of the file — and with diffsynth.core.device.npu_compatible_device, which the project already ships for NPU support.

Reproduction

# Ascend 910B4, torch 2.15.0.dev + torch_npu 2.15.0.dev
from wantodance import WanToDanceMusicEncoderLayer
WanToDanceMusicEncoderLayer(d_model=256, nhead=4, dim_feedforward=1024,
                            dropout=0.1, batch_first=True, rotary=None,
                            device='cuda')
# AssertionError: Torch not compiled with CUDA enabled

Proposed fix

Drop the hardcoded device='cuda'; the module is moved to the real device by the pipeline afterwards, exactly like its sibling layers. Verified on real Ascend 910B4 hardware (construction + forward pass on NPU); I will open a PR referencing this issue.

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