Few-Step Generative Models Adapted for Fast Lossy Compression Without Retraining
Researchers have developed a method to adapt fast generative models (Rectified Flow, Consistency Trajectory Models, and MeanFlow) for lossy image compression using a reverse channel coding framework. The approach allows these models to be used as codecs without requiring retraining, addressing the speed limitations of traditional diffusion-based compression. The work demonstrates faster encoding and decoding times with improved image quality at low bit rates, potentially making generative compression more practical.
A new study presents a framework for converting few-step generative models into efficient lossy compression codecs. The researchers adapted three fast generative models—Rectified Flow, Consistency Trajectory Models (CTM), and MeanFlow—to work within a reverse channel coding (RCC) framework originally designed for diffusion models. The main technical challenge was that these models do not explicitly parameterize the intermediate conditional distributions required by RCC; the authors solved this by deriving mathematical equivalences between velocity parameterization and diffusion-style denoising, and using local Gaussian approximations for CTM. Testing on low-resolution benchmarks showed the resulting codecs achieve faster encoding and decoding compared to existing methods while producing more realistic images at low bit rates. This work enables compression with pre-trained models without additional training, potentially making generative compression more accessible and efficient.
What's missing
The paper does not provide comparisons with state-of-the-art traditional compression standards (e.g., JPEG, WebP, H.265) or recent neural compression methods beyond DiffC. The evaluation is limited to low-resolution benchmarks; performance on high-resolution images and computational resource requirements (memory, GPU specifications) are not detailed. The practical applicability to real-world compression scenarios and user-perceived quality metrics beyond 'realism' are not discussed.
What different sources said
- arXiv cs.LGCenter
Few-step Generative Models as Lossy Compression
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