Coordinating Bits and Priors: Compact and Faithful Decoding for Generative Image Compression

Generative image compression methods have recently shown strong perceptual quality at ultra-low bitrates. Pretrained generative models provide strong natural-image priors for ultra-low-rate image compression, but they are designed for image synthesis and generation rather than reconstructing a particular source from a decoded bitstream. This motivates two considerations: different stages of the generative prior contribute unequally to reconstruction, and the decoded evidence needs to be preserved as synthesis progresses toward pixels. We propose CFIC, a compact and faithful generative image compression framework built around these observations. A training-free analysis reveals that reconstruction importance varies substantially across stages, motivating asymmetric compaction of the one-step diffusion prior rather than uniform shrinking. Multi-scale evidence recovered from the same quantized representation is then coordinated with corresponding synthesis stages, allowing source-specific information to complement the generative prior without additional side information. Finally, we alleviate the decoding bottleneck inherited from the pretrained model by preserving this evidence hierarchically along the path to pixels. Experimental results demonstrate that our proposed method achieves state-of-the-art perceptual quality with a compact model size and lower computational cost.