arXiv:2606.10450v1 Announce Type: cross Abstract: DiffC provides a principled way to reuse pre-trained diffusion models for lossy compression, but its encoding and decoding procedures remain slow because they require many discretized forward and reverse steps. We study whether few-step generative models -- Rectified Flow, Consistency Trajectory Models (CTM), and MeanFlow -- can be cast as codecs within the same reverse channel coding (RCC) framework. The main challenge is that RCC requires posterior and shared distribution parameters, whereas these models do not explicitly parameterize interme
The continuous development and refinement of generative AI models necessitate efficient methods for their deployment and application, especially in resource-constrained environments.
Improving the efficiency of generative models, particularly in terms of encoding and decoding speed for lossy compression, unlocks broader applications and reduces computational overhead.
New approaches to integrating few-step generative models into compression frameworks promise faster and more efficient use of advanced AI for data handling, potentially accelerating their adoption in practical systems.
- · AI developers
- · Cloud computing providers
- · Data storage industry
- · Generative AI applications
- · Traditional data compression methods
- · High-latency AI applications
Increased efficiency in generative AI model deployment and reduced computational resource requirements.
Broader adoption of sophisticated AI models in fields where computational cost or speed was previously a barrier.
Enhanced real-time AI processing capabilities leading to new intelligent systems for data communication and content generation.
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Read at arXiv cs.LG