ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval

Diffusion models show promising generation capability for a variety of data. Despite their high generation quality, the inference for diffusion models is still time-consuming due to the numerous sampling iterations required. To accelerate the inference, we propose REDI, a simple yet learning-free Retrieval-based Diffusion sampling framework. From a precomputed knowledge base, REDI retrieves a trajectory similar to the partially generated trajectory at an early stage of generation, skips a large portion of intermediate steps, and continues sampling from a later step in the retrieved trajectory. We theoretically prove that the generation performance of REDI is guaranteed. Our experiments demonstrate that REDI improves the model inference efficiency by 2× speedup. Furthermore, REDI is able to generalize well in zero-shot cross-domain image genreation such as image stylization. The code and demo for REDI is available at https://github.com/ zkx06111/ReDiffusion .