ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
This addresses the computational bottleneck in diffusion model inference for applications requiring fast generation, though it is an incremental improvement on existing acceleration methods.
The paper tackles the slow inference speed of diffusion models by proposing ReDi, a retrieval-based sampling framework that skips intermediate steps using precomputed trajectories, achieving a 2x speedup while maintaining generation quality and demonstrating zero-shot cross-domain generalization.
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 2x speedup. Furthermore, ReDi is able to generalize well in zero-shot cross-domain image generation such as image stylization.