[feature] implement TeaCache

[sayakpaul]

Currently, we support PAB and FasterCache. More details: https://huggingface.co/docs/diffusers/main/en/optimization/cache.

It'd be cool to support Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model as it's quite popular in the community. Code is here:
https://github.com/ali-vilab/TeaCache

Before proceeding with a PR, please propose a design first in this thread. We can take it from there.

[LawJarp-A]

Hey @sayakpaul
I want to take this up, i have a few questions tho

• any preferred tests/datasets to test this on?
• it's model-specific due to different timestep embedding patterns across architectures. should I proto with CogVideoX first?

[LawJarp-A]

Additionally this is my thought process (I want to refine it still) @sayakpaul

Instead of monkey-patching model forward methods (like original TeaCache), use hooks:
-> Create a TeaCacheHook that uses new_forward() to replace entire transformer block forward methods. The hook intercepts the block before any computation starts, extracts timestep embeddings, makes the skip/compute decision, and either returns cached residuals or runs the full computation.
-> Each supported model gets an extractor class that knows how to pull timestep embeddings from that model's specific architecture. CogVideoX extracts from time_embedding(), FLUX from time_text_embed(),

State management
-> Use diffusers existing BaseState system to track accumulated distance, previous embeddings, and cached residuals across inference steps (need to refine this a bit)

Add TeaCacheConfig to the existing cache configuration system in CacheMixin. This ensures it works seamlessly

Note: I am exploring the repository more as I work on figuring out how best to integrate this. I took some of time to test the current caching play around with it. I think this can be integrated well. I'm open to any suggestions and corrections

Thanks!

[yiyixuxu]

hi @LawJarp-A
Thanks for your interest!
feel free to open a PR and we can start working on it from there

[sayakpaul]

Yes, we can prototype with a model of choice. In case the original repository already provides results for a fairly popular model like Flux, let's prototype with that instead. If not, we can start with CogVideoX, as you mentioned.

[LawJarp-A]

Awesome, will prototype on flux first then. Will keep you guys updated on the PR
@sayakpaul @yiyixuxu
Thanks :)

[LawJarp-A]

Working on this PR: #12652
As of now, writing it for just one model (FLUX), after testing for it, I want to write it with better strcuture to suuport future additions, something like below

 src/diffusers/hooks/teacache/
 ├── __init__.py 
 ├── base.py # TeaCacheConfig, TeaCacheHook base classes
 ├── extractors.py # All model-specific extractors
 ├── flux.py 
 ├── cogvideox.py 
and so on...

[sayakpaul]

Hey,

Thanks for your willingness to contribute. Could we try to follow the hooks-based implementation for this (we use it for FasterCache)?

[LawJarp-A]

I am using hook based only now. Is there any issue in the PR?
(Not completed yet, still a draft PR)
(Above structure was something I was thinking out loud for the future expansion)

[sayakpaul]

Cool, we will check the PR first.

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