Post
942
Caching is an essential technique used in diffusion inference serving for speeding up image/video generations. Diffusers just added support for another caching method: First Block Cache - a technique developed by
@chengzeyi
building upon the ideas of TeaCache.
The idea in short is: if the model predictions do not vary much over successive inference steps, we can skip certain steps where the prediction difference is small. To figure out whether an inference step will make a significant improvement to the overall velocity/noise prediction, we calculate the relative difference of the output of the first transformer block at timestep $t$ with $t-1$, and compare it against a selected threshold. If the difference is lower than the threshold, we skip the step. A higher threshold will lead to more steps being skipped. However, skipping many steps is bad because it can throw off the model predictions, and so we need to test and select the threshold based on level of quality-speed tradeoff for every model we use it with.
Diffusers usage with CogView4:
Below, you'll find the benchmarks and visualizations of the predicted output at different blocks of the Flux DiT.
Docs: https://huggingface.co/docs/diffusers/main/en/optimization/cache
PR: https://github.com/huggingface/diffusers/pull/11180
References:
- First Block Cache: https://github.com/chengzeyi/ParaAttention
- TeaCache: https://github.com/ali-vilab/TeaCache
The idea in short is: if the model predictions do not vary much over successive inference steps, we can skip certain steps where the prediction difference is small. To figure out whether an inference step will make a significant improvement to the overall velocity/noise prediction, we calculate the relative difference of the output of the first transformer block at timestep $t$ with $t-1$, and compare it against a selected threshold. If the difference is lower than the threshold, we skip the step. A higher threshold will lead to more steps being skipped. However, skipping many steps is bad because it can throw off the model predictions, and so we need to test and select the threshold based on level of quality-speed tradeoff for every model we use it with.
Diffusers usage with CogView4:
import torch
from diffusers import CogView4Pipeline
from diffusers.hooks import apply_first_block_cache, FirstBlockCacheConfig
pipe = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16)
pipe.to("cuda")
apply_first_block_cache(pipe.transformer, FirstBlockCacheConfig(threshold=0.2))
prompt = "A photo of an astronaut riding a horse on mars"
image = pipe(prompt, generator=torch.Generator().manual_seed(42)).images[0]
image.save("output.png")Below, you'll find the benchmarks and visualizations of the predicted output at different blocks of the Flux DiT.
Docs: https://huggingface.co/docs/diffusers/main/en/optimization/cache
PR: https://github.com/huggingface/diffusers/pull/11180
References:
- First Block Cache: https://github.com/chengzeyi/ParaAttention
- TeaCache: https://github.com/ali-vilab/TeaCache
