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Implement EasyCache and Invent LazyCache (#9496)

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* Attempting a universal implementation of EasyCache, starting with flux as test; I screwed up the math a bit, but when I set it just right it works.

* Fixed math to make threshold work as expected, refactored code to use EasyCacheHolder instead of a dict wrapped by object

* Use sigmas from transformer_options instead of timesteps to be compatible with a greater amount of models, make end_percent work

* Make log statement when not skipping useful, preparing for per-cond caching

* Added DIFFUSION_MODEL wrapper around forward function for wan model

* Add subsampling for heuristic inputs

* Add subsampling to output_prev (output_prev_subsampled now)

* Properly consider conds in EasyCache logic

* Created SuperEasyCache to test what happens if caching and reuse is moved outside the scope of conds, added PREDICT_NOISE wrapper to facilitate this test

* Change max reuse_threshold to 3.0

* Mark EasyCache/SuperEasyCache as experimental (beta)

* Make Lumina2 compatible with EasyCache

* Add EasyCache support for Qwen Image

* Fix missing comma, curse you Cursor

* Add EasyCache support to AceStep

* Add EasyCache support to Chroma

* Added EasyCache support to Cosmos Predict t2i

* Make EasyCache not crash with Cosmos Predict ImagToVideo latents, but does not work well at all

* Add EasyCache support to hidream

* Added EasyCache support to hunyuan video

* Added EasyCache support to hunyuan3d

* Added EasyCache support to LTXV (not very good, but does not crash)

* Implemented EasyCache for aura_flow

* Renamed SuperEasyCache to LazyCache, hardcoded subsample_factor to 8 on nodes

* Eatra logging when verbose is true for EasyCache
This commit is contained in:
Jedrzej Kosinski
2025-08-22 19:41:08 -07:00
committed by GitHub
parent fe31ad0276
commit fc247150fe
17 changed files with 639 additions and 7 deletions
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9
comfy/samplers.py
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@@ -953,7 +953,14 @@ class CFGGuider:
self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
def __call__(self, *args, **kwargs):
return self.predict_noise(*args, **kwargs)
return self.outer_predict_noise(*args, **kwargs)
def outer_predict_noise(self, x, timestep, model_options={}, seed=None):
return comfy.patcher_extension.WrapperExecutor.new_class_executor(
self.predict_noise,
self,
comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, self.model_options, is_model_options=True)
).execute(x, timestep, model_options, seed)
def predict_noise(self, x, timestep, model_options={}, seed=None):
return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)