Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression

TL;DR

SODEC is a single-step diffusion-based image compression model that significantly reduces decoding latency and improves fidelity by leveraging rich latent representations and fidelity guidance, outperforming existing methods.

Contribution

The paper introduces SODEC, a novel single-step diffusion model for image compression that combines a pre-trained VAE, fidelity guidance, and rate annealing for superior performance.

Findings

Decoding speed improved by over 20 times.

Achieves better rate-distortion-perception performance.

Outperforms existing diffusion-based compression methods.

Abstract

Diffusion-based image compression has demonstrated impressive perceptual performance. However, it suffers from two critical drawbacks: (1) excessive decoding latency due to multi-step sampling, and (2) poor fidelity resulting from over-reliance on generative priors. To address these issues, we propose SODEC, a novel single-step diffusion image compression model. We argue that in image compression, a sufficiently informative latent renders multi-step refinement unnecessary. Based on this insight, we leverage a pre-trained VAE-based model to produce latents with rich information, and replace the iterative denoising process with a single-step decoding. Meanwhile, to improve fidelity, we introduce the fidelity guidance module, encouraging output that is faithful to the original image. Furthermore, we design the rate annealing training strategy to enable effective training under extremely low bitrates. Extensive experiments show that SODEC significantly outperforms existing methods, achieving superior rate-distortion-perception performance. Moreover, compared to previous diffusion-based compression models, SODEC improves decoding speed by more than 20$\times$. Code is released at: https://github.com/zhengchen1999/SODEC.