CoD: A Diffusion Foundation Model for Image Compression

• URL: http://arxiv.org/abs/2511.18706v1
• Date: Mon, 24 Nov 2025 03:00:15 GMT
• Title: CoD: A Diffusion Foundation Model for Image Compression
• Authors: Zhaoyang Jia, Zihan Zheng, Naifu Xue, Jiahao Li, Bin Li, Zongyu Guo, Xiaoyi Zhang, Houqiang Li, Yan Lu,
• Abstract summary: Existing diffusion codecs typically build on text-to-image diffusion foundation models like Stable Diffusion.<n>textbfCoD can be trained from scratch to enable end-to-end optimization of both compression and generation.
• Score: 57.572664625372106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing diffusion codecs typically build on text-to-image diffusion foundation models like Stable Diffusion. However, text conditioning is suboptimal from a compression perspective, hindering the potential of downstream diffusion codecs, particularly at ultra-low bitrates. To address it, we introduce \textbf{CoD}, the first \textbf{Co}mpression-oriented \textbf{D}iffusion foundation model, trained from scratch to enable end-to-end optimization of both compression and generation. CoD is not a fixed codec but a general foundation model designed for various diffusion-based codecs. It offers several advantages: \textbf{High compression efficiency}, replacing Stable Diffusion with CoD in downstream codecs like DiffC achieves SOTA results, especially at ultra-low bitrates (e.g., 0.0039 bpp); \textbf{Low-cost and reproducible training}, 300$\times$ faster training than Stable Diffusion ($\sim$ 20 vs. $\sim$ 6,250 A100 GPU days) on entirely open image-only datasets; \textbf{Providing new insights}, e.g., We find pixel-space diffusion can achieve VTM-level PSNR with high perceptual quality and can outperform GAN-based codecs using fewer parameters. We hope CoD lays the foundation for future diffusion codec research. Codes will be released.

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