Recent advances in Artificial Intelligence Generated Content (AIGC) triggered an increasing need to transmit and compress the vast number of AI-generated images (AIGIs). However, there is a noticeable deficiency in research focused on compression methods for AIGIs. To address this critical gap, we advocate that Stable Diffusion serves as a natural cross-modal decoder by leveraging rich and scalable priors, and introduce a scalable cross-modal compression framework that incorporates multiple human-comprehensible modalities. As illustrated in Fig. 1(a), the proposed framework encodes images into a layered bitstream: a semantic prior that delivers high-level semantic information through text prompts; a structural prior that captures spatial details using edge or skeleton maps; and a texture prior that preserves local textures via a colormap. Utilizing Stable Diffusion as the backend, the decoder leverages multi-modal scalable priors to generate images with different levels of fidelity. Experiments show our method preserves realistic details and semantic fidelity at an extremely low bitrate (< 0.02 bpp), comparable with recent perceptual coding approaches and outperforming VVC. The R-D performance also demonstrate the scalability of our proposed multi-layered bitstream since image fidelity incrementally improves with structure and texture priors provided during decoding. Additionally, as illustrated in Fig. 1(b), our framework facilitates downstream editing applications such as Structure Manipulation, Texture Synthesis, and Object Erasing, without requiring full decoding, thereby paving a new direction for future research in AIGI compression.

Recent advances in generative compression methods have demonstrated remarkable progress in enhancing the perceptual quality of compressed data, especially in scenarios with low bitrates. However, their efficacy and applicability to achieve extreme compression ratios (< 0.05 bpp) remain constrained. In this work, we propose a simple yet effective coding framework by introducing vector quantization (VQ)–based generative models into the image compression domain. The main insight is that the codebook learned by the VQGAN model yields a strong expressive capacity, facilitating efficient compression of continuous information in the latent space while maintaining reconstruction quality. Specifically, an image can be represented as VQ-indices by finding the nearest codeword, which can be encoded using lossless compression methods into bitstreams. We propose clustering a pre-trained large-scale codebook into smaller codebooks through the K-means algorithm, yielding variable bitrates and different levels of reconstruction quality within the coding framework. Furthermore, we introduce a transformer to predict lost indices and restore images in unstable environments. Extensive qualitative and quantitative experiments on various benchmark datasets demonstrate that the proposed framework outperforms state-of-the-art codecs in terms of perceptual quality-oriented metrics and human perception at extremely low bitrates (≤ 0.04 bpp). Remarkably, even with the loss of up to 20% of indices, the images can be effectively restored with minimal perceptual loss.