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