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import functools |
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import unittest |
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import torch |
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from diffusers import CogVideoXTransformer3DModel, DiffusionPipeline |
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from para_attn.first_block_cache import utils |
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def apply_cache_on_transformer( |
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transformer: CogVideoXTransformer3DModel, |
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*, |
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residual_diff_threshold=0.04, |
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): |
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cached_transformer_blocks = torch.nn.ModuleList( |
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[ |
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utils.CachedTransformerBlocks( |
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transformer.transformer_blocks, |
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transformer=transformer, |
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residual_diff_threshold=residual_diff_threshold, |
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) |
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] |
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) |
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original_forward = transformer.forward |
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@functools.wraps(transformer.__class__.forward) |
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def new_forward( |
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self, |
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*args, |
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**kwargs, |
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): |
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with unittest.mock.patch.object( |
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self, |
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"transformer_blocks", |
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cached_transformer_blocks, |
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): |
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return original_forward( |
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*args, |
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**kwargs, |
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) |
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transformer.forward = new_forward.__get__(transformer) |
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return transformer |
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def apply_cache_on_pipe( |
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pipe: DiffusionPipeline, |
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*, |
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shallow_patch: bool = False, |
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**kwargs, |
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): |
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original_call = pipe.__class__.__call__ |
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if not getattr(original_call, "_is_cached", False): |
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@functools.wraps(original_call) |
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def new_call(self, *args, **kwargs): |
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with utils.cache_context(utils.create_cache_context()): |
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return original_call(self, *args, **kwargs) |
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pipe.__class__.__call__ = new_call |
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new_call._is_cached = True |
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if not shallow_patch: |
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apply_cache_on_transformer(pipe.transformer, **kwargs) |
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pipe._is_cached = True |
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return pipe |
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