OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates

Abstract

Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconstruct images by iteratively denoising from random noise, guided by compressed latent representations. While these approaches have achieved high reconstruction quality, their multi-step sampling process incurs substantial computational overhead. Moreover, they typically require training separate models for different compression bit-rates, leading to significant training and storage costs. To address these challenges, we propose a one-step diffusion codec across multiple bit-rates. termed OSCAR. Specifically, our method views compressed latents as noisy variants of the original latents, where the level of distortion depends on the bit-rate. This perspective allows them to be modeled as intermediate states along a diffusion trajectory. By establishing a mapping from the compression bit-rate to a pseudo diffusion timestep, we condition a single generative model to support reconstructions at multiple bit-rates. Meanwhile, we argue that the compressed latents retain rich structural information, thereby making one-step denoising feasible. Thus, OSCAR replaces iterative sampling with a single denoising pass, significantly improving inference efficiency. Extensive experiments demonstrate that OSCAR achieves superior performance in both quantitative and visual quality metrics.

Paper

OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates

Code

The code is available on GitHub: https://github.com/jp-guo/OSCAR

Citation

@article{guo2025oscar,
    title={OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates},
    author={Guo, Jinpei and Ji, Yifei and Chen, Zheng and Liu, Kai and Liu, Min and Rao, Wang and Li, Wenbo and Guo, Yong and Zhang, Yulun},
    journal={arXiv preprint arXiv:2505.16091},
    year={2025}
}