No Cache Left Idle: Accelerating diffusion model via Extreme-slimming Caching

TL;DR

X-Slim is a novel cache-based acceleration framework for diffusion models that adaptively exploits redundancy across timesteps, blocks, and tokens, significantly reducing latency while maintaining high fidelity.

Contribution

It introduces a unified, training-free caching framework with a dual-threshold controller for adaptive redundancy exploitation in diffusion models.

Findings

Up to 4.97x latency reduction on FLUX.1-dev.

Achieves 3.13x acceleration and 2.42 FID improvement on DiT-XL/2.

Maintains minimal perceptual loss across diverse tasks.

Abstract

Diffusion models achieve remarkable generative quality, but computational overhead scales with step count, model depth, and sequence length. Feature caching is effective since adjacent timesteps yield highly similar features. However, an inherent trade-off remains: aggressive timestep reuse offers large speedups but can easily cross the critical line, hurting fidelity, while block- or token-level reuse is safer but yields limited computational savings. We present X-Slim (eXtreme-Slimming Caching), a training-free, cache-based accelerator that, to our knowledge, is the first unified framework to exploit cacheable redundancy across timesteps, structure (blocks), and space (tokens). Rather than simply mixing levels, X-Slim introduces a dual-threshold controller that turns caching into a push-then-polish process: it first pushes reuse at the timestep level up to an early-warning line, then switches to lightweight block- and token-level refresh to polish the remaining redundancy, and triggers full inference once the critical line is crossed to reset accumulated error. At each level, context-aware indicators decide when and where to cache. Across diverse tasks, X-Slim advances the speed-quality frontier. On FLUX.1-dev and HunyuanVideo, it reduces latency by up to 4.97x and 3.52x with minimal perceptual loss. On DiT-XL/2, it reaches 3.13x acceleration and improves FID by 2.42 over prior methods.