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Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Keval Morabia
2025-09-02 19:58:33 +05:30
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examples/diffusers/README.md
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@@ -369,7 +369,7 @@ By following these steps, your PEFT LoRA model should be efficiently quantized u
Cache Diffusion methods, such as [DeepCache](https://arxiv.org/abs/2312.00858), [Block Caching](https://arxiv.org/abs/2312.03209) and [T-Gate](https://arxiv.org/abs/2404.02747), optimize performance by reusing cached outputs from previous steps instead of recalculating them. This **training-free** caching approach is compatible with a variety of models, like **DiT** and **UNet**, enabling considerable acceleration without compromising quality.
<div align="center">
<img src="./cache_diffusion/assets/sdxl_cache.png" width="900"/>
<img src="./cache_diffusion/assets/sdxl_cache.png" width="900" alt="SDXL Cache"/>
This diagram shows the default SDXL Cache compute graph in this example.
Significant speedup is achieve through skipping certain blocks at the specific steps.
</div>