Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers

TL;DR

DiffCR introduces a dynamic, differentiable token compression framework for diffusion transformers, optimizing computation across tokens, layers, and timesteps to enhance efficiency without sacrificing image generation quality.

Contribution

It proposes a novel adaptive inference method with differentiable compression ratios, enabling dynamic routing and compression in diffusion transformers for improved efficiency.

Findings

Achieves better quality-efficiency trade-offs than prior methods.

Effectively adapts compression ratios across tokens, layers, and timesteps.

Demonstrates superior performance on text-to-image and inpainting tasks.

Abstract

Diffusion Transformers (DiTs) have achieved state-of-the-art (SOTA) image generation quality but suffer from high latency and memory inefficiency, making them difficult to deploy on resource-constrained devices. One major efficiency bottleneck is that existing DiTs apply equal computation across all regions of an image. However, not all image tokens are equally important, and certain localized areas require more computation, such as objects. To address this, we propose DiffCR, a dynamic DiT inference framework with differentiable compression ratios, which automatically learns to dynamically route computation across layers and timesteps for each image token, resulting in efficient DiTs. Specifically, DiffCR integrates three features: (1) A token-level routing scheme where each DiT layer includes a router that is fine-tuned jointly with model weights to predict token importance scores. In this way, unimportant tokens bypass the entire layer's computation; (2) A layer-wise differentiable ratio mechanism where different DiT layers automatically learn varying compression ratios from a zero initialization, resulting in large compression ratios in redundant layers while others remain less compressed or even uncompressed; (3) A timestep-wise differentiable ratio mechanism where each denoising timestep learns its own compression ratio. The resulting pattern shows higher ratios for noisier timesteps and lower ratios as the image becomes clearer. Extensive experiments on text-to-image and inpainting tasks show that DiffCR effectively captures dynamism across token, layer, and timestep axes, achieving superior trade-offs between generation quality and efficiency compared to prior works. The project website is available at https://www.haoranyou.com/diffcr.