FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models [[Arxiv]](https://arxiv.org/abs/2312.03517) [[Project]](https://jungwon-lee.github.io/ProjectFRDiff) [[Colab]](https://colab.research.google.com/drive/1nG15sCcIS-XaZKDvGugBvg4eKF3qftoA#scrollTo=zvAneljsaaU)

Junhyuk So*, Jungwon Lee* and Eunhyeok Park

This repository is official code of FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models

Overview

In our work, we introduce an advanced acceleration technique that leverages the temporal redundancy inherent in diffusion models. Reusing feature maps with high temporal similarity opens up a new opportunity to save computation resources without compromising output quality. To realize the practical benefits of this intuition, we conduct an extensive analysis and propose a novel method, FRDiff. FRDiff is designed to harness the advantages of both reduced NFE and feature reuse, achieving a Pareto frontier that balances fidelity and latency trade-offs in various generative tasks.

Experiments

Stable Diffusion

Requirements

pip install torch transformers accelerate
pip install diffusers==0.26.3

Sampling

To enjoy FRDiff on Stable Diffusion (SDXL), run following script.

cd Stable-Diffusion
python sample.py --num_steps 50 --interval 2 --prompt "a photo of an astronaut on a moon"

DiT

Requirements

conda env create --file DiT/environment.yml

AutoFR Training & Sampling

To run AutoFR training, use following script.

cd DiT
python sample.py --lr 5e-3 --wgt 1e-3

The trained keyframeset and generated sample results will be saved in directory.

To use Uniform Keyframeset, uncomment line 137-139 in DiT/model.py

Citation

@article{so2023frdiff,
         title={FRDiff: Feature Reuse for Exquisite Zero-shot Acceleration of Diffusion Models},
         author={So, Junhyuk and Lee, Jungwon and Park, Eunhyeok},
         journal={arXiv preprint arXiv:2312.03517},
         year={2023}
}