- The paper introduces the DiffC algorithm that leverages pretrained diffusion models for efficient lossy image compression, overcoming reverse-channel coding challenges.
- It achieves under 10-second compression and decompression with competitive rate-distortion tradeoffs compared to state-of-the-art generative methods.
- Empirical evaluations across various Stable Diffusion models, including Flux-dev, reveal tradeoffs in fidelity and bitrate while outlining a roadmap for future research.
Lossy Compression with Pretrained Diffusion Models
The paper presents an empirical investigation into the application of pretrained diffusion models for lossy image compression, focusing on the implementation and evaluation of the DiffC algorithm. The paper leverages various versions of the Stable Diffusion model, including Stable Diffusion 1.5, 2.1, XL, and the Flux-dev model, showcasing the capability of these models to serve as efficient image compressors without additional training.
Background and Motivation
The motivation behind this paper stems from the substantial computational and financial resources allocated to training state-of-the-art diffusion models, which have primarily found applications in generative tasks such as image creation. The exploration of diffusion models as image priors for alternative tasks, such as image restoration and generative 3D modeling, has led to the proposition of leveraging these models for data compression. Previous work, particularly by Ho et al. (2020) and Theis et al. (2022), laid the foundation for such an endeavor, detailing the potential use of pretrained diffusion models in compression tasks. However, practical implementations were hindered by challenges in reverse-channel coding, which this paper aims to address.
Methodology
The authors introduce several key innovations to enable a practical implementation of the DiffC algorithm, overcoming prior obstacles. Notably, they employ workarounds for efficient reverse-channel coding, essential for transforming the DiffC algorithm from a theoretical construct into a functional tool. Their implementation allows for compression and decompression operations to be completed in under 10 seconds using Stable Diffusion, achieving competitive results against state-of-the-art generative models at ultra-low bitrates.
The DiffC method operates by using diffusion models as entropy models, communicating progressive noisy samples of an image in a manner akin to denoising diffusion probabilistic models (DDPMs). This approach allows for the transmission of images at rates approaching the model's negative log-likelihood estimate, illustrating impressive efficiency and fidelity, particularly in the context of generative compression.
Results and Discussion
The paper provides thorough quantitative and qualitative evaluations of the diffusion models' performance in compression tasks. Key findings highlight that the implemented DiffC algorithm achieves impressive performance comparable to other generative methods like HiFiC, MS-ILLM, and PerCo. Notably, the method demonstrates a place on the rate/distortion/perception Pareto frontier, balancing low distortion and high perceptual quality, particularly at ultra-low bit-rates.
The authors acknowledge the limitations imposed by the latent-space restricted fidelity inherent in models like Stable Diffusion and Flux-dev, noting particularly the difference in maximum achievable fidelity across these models. Flux-dev, with its higher fidelity VAE, allows for higher bitrate compression while maintaining competitive distortion metrics.
Implications and Future Directions
This research not only expands the utility of pretrained diffusion models beyond generative tasks but also provides a framework for future developments in AI-driven image compression. As diffusion models continue to evolve, exploring their applications in communication-efficient and high-fidelity image compression remains a fertile ground for further inquiry.
The paper's insights regarding the robustness of DiffC’s rate-distortion performance across various diffusion models suggest potential scalability to more advanced models in the future. Moreover, the discussion on practical implementations of reverse-channel coding, alongside suggestions for further work on inference speed, rectified flow models, and handling of out-of-distribution image sizes, outlines a comprehensive agenda for subsequent research.
In conclusion, this paper presents a significant step toward utilizing diffusion models for practical compression tasks, demonstrating the potential for extending the applications of such models in AI technology even further.