DeepCache: Accelerating Diffusion Models for Free
DeepCache is an innovative approach introduced to accelerate diffusion models without the need for additional training or significant loss in quality. Developed by researchers at the Learning and Vision Lab, National University of Singapore, it leverages a clever concept from model architecture, particularly utilizing the U-Net's property to enhance speed while maintaining output quality.
Why Choose DeepCache?
- Training-Free: DeepCache eliminates the need for retraining models, which saves time and resources.
- Broad Compatibility: It works seamlessly with popular diffusion models like Stable Diffusion and DDPM, among others.
- Supports Various Pipelines: It is compatible with multiple model pipelines including Stable Video Diffusion, Stable Diffusion for Inpainting, and the Img2Img Pipeline.
- Sampling Algorithm Compatibility: Supports widely-used sampling algorithms such as DDIM and PLMS.
Key Updates
DeepCache has seen numerous advancements:
- A parallel inference technique on multiple GPUs through AsyncDiff was introduced.
- An improved version, Learning-to-Cache, provides better performance enhancements.
- Integration with platforms like Diffusers and Colab for wider accessibility.
- The release of code for newer models like Stable Video Diffusion and DDPM.
How DeepCache Works
DeepCache operates by reusing high-level features in the U-Net model architecture and updating lower-level features economically. This strategy results in significant speed-ups with minimal degradation in output quality. For example, it accelerates Stable Diffusion v1.5 by 2.3x with only a slight 0.05 reduction in CLIP Score.
Quick Start and Usage
You can quickly start using DeepCache by installing it via pip and integrating it with your existing pipelines. The steps involve importing the necessary modules, setting caching parameters, and using it with models like Stable Diffusion and its variants. A sample code snippet is provided to guide users through the setup process.
Experimental Code and Visualization
DeepCache supports a variety of setups, including Stable Diffusion XL, Stable Diffusion v1.5, and even video processing with Stable Video Diffusion. Visualization examples illustrate DeepCache's ability to maintain image quality while significantly reducing processing time.
Quantitative Results
DeepCache provides compelling quantitative improvements across different datasets, including ImageNet and models like the DDPM for tasks involving LSUN Church & Bedroom scenes.
Community and Contribution
Many developers have adapted DeepCache into plugins and other platforms, evidence of its practical utility and versatility. The project invites collaboration and is open to contributions.
For those interested in exploring DeepCache further, academic references and detailed quantitative results are available in the associated paper.