Ditto: Accelerating Diffusion Model via Temporal Value Similarity

TL;DR

Ditto introduces a novel algorithm and hardware accelerator that leverage temporal value similarity in diffusion models to significantly reduce computation and energy costs in image generation tasks.

Contribution

The paper proposes the Ditto algorithm and hardware, exploiting temporal similarity and quantization to accelerate diffusion models with reduced energy consumption.

Findings

Achieves up to 1.5x speedup over existing accelerators.

Saves 17.74% energy compared to other hardware.

Effectively reduces computation by leveraging temporal value similarity.

Abstract

Diffusion models achieve superior performance in image generation tasks. However, it incurs significant computation overheads due to its iterative structure. To address these overheads, we analyze this iterative structure and observe that adjacent time steps in diffusion models exhibit high value similarity, leading to narrower differences between consecutive time steps. We adapt these characteristics to a quantized diffusion model and reveal that the majority of these differences can be represented with reduced bit-width, and even zero. Based on our observations, we propose the Ditto algorithm, a difference processing algorithm that leverages temporal similarity with quantization to enhance the efficiency of diffusion models. By exploiting the narrower differences and the distributive property of layer operations, it performs full bit-width operations for the initial time step and processes subsequent steps with temporal differences. In addition, Ditto execution flow optimization is designed to mitigate the memory overhead of temporal difference processing, further boosting the efficiency of the Ditto algorithm. We also design the Ditto hardware, a specialized hardware accelerator, fully exploiting the dynamic characteristics of the proposed algorithm. As a result, the Ditto hardware achieves up to 1.5x speedup and 17.74% energy saving compared to other accelerators.