[Feature] Implement tiled VAE encoding/decoding for Wan model. #11414
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Hi @a-r-r-o-w @yiyixuxu, could you please help reviewing this patch? Thanks! |
c8ef
commented
Apr 24, 2025
| @@ -677,42 +677,7 @@ def __init__( | |||
| attn_scales: List[float] = [], | |||
| temperal_downsample: List[bool] = [False, True, True], | |||
| dropout: float = 0.0, | |||
| latents_mean: List[float] = [ | |||
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These function parameters are not being used. They have been removed in this patch but can be added back at any time if needed.
| batch_size = 2 | ||
| num_frames = 9 | ||
| num_channels = 3 | ||
| sizes = (640, 480) |
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Add another input because the (16, 16) tensor is too small for tiling operations.
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| self.assertLess( | ||
| (output_without_tiling.detach().cpu().numpy() - output_with_tiling.detach().cpu().numpy()).max(), | ||
| 0.5, |
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On my machine, this value is approximately 0.404, and IIRC the average absolute value of these arrays is less than 0.01, which makes me confident that the implementation is correct at some point.
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What does this PR do?
Implement tiled VAE encoding/decoding for Wan model.
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