FlowCodec: One-Step Flow Prior for Generative Image Compression

• URL: http://arxiv.org/abs/2606.21030v1
• Date: Fri, 19 Jun 2026 01:44:15 GMT
• Title: FlowCodec: One-Step Flow Prior for Generative Image Compression
• Abstract summary: FlowCodec is a framework that plugs pretrained large-scale text-to-image priors into ultra-low-bitrate compression.<n>It can support multiples while keeping the number of trainable parameters below 0.54% of the generative backbone.<n> Experiments show that FlowCodec preserves high visual quality at parameters below 0.05 bits per pixel.
• Score: 18.82368921159262
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Diffusion-based image compression methods, leveraging powerful generative priors, have demonstrated remarkable perceptual quality at ultra-low bitrates. However, adapting modern generative models to image compression often relies on carefully engineered conditioning or auxiliary branches, together with substantial retraining, and these costs grow as the models scale. This motivates an open question: Can stronger generative priors be integrated into compression through a simpler, more extensible design? To answer this, we propose FlowCodec, a streamlined framework that plugs pretrained large-scale text-to-image priors (e.g., Qwen-image-2512 and FLUX.1-dev) into ultra-low-bitrate codecs. FlowCodec decomposes the pipeline into two decoupled stages: (1) Latent Compression, which maps clean latents to bitrate-constrained noisy latents; and (2) Latent Transport, which leverages the pretrained prior to refine the noisy latents toward the clean ones in a single step. Notably, FlowCodec requires neither additional conditioning signals nor auxiliary networks. Furthermore, with lightweight adaptation, it can flexibly support multiple bitrates while keeping the number of trainable parameters below 0.54% of the generative backbone. Experiments show that FlowCodec preserves high visual quality at bitrates below 0.05 bits per pixel. The Qwen-image variant significantly outperforms existing methods in terms of LPIPS and DISTS, while both variants deliver higher PSNR and clearly faster encoding than existing one-step diffusion-based methods, with the FLUX variant also maintaining competitive decoding speed.

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