arXiv Machine Learning By Luke Budny, Yuhong Guo, Kevin Cheung

Quality-Aware Modulation for Diffusion Transformers

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arXiv:2606. 30934v1 Announce Type: new Abstract: Modern text-to-image diffusion models, such as diffusion transformers (DiT), rely on timestep or prompt embeddings to modulate the strength of the denoising process in each timestep.

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arXiv Machine Learning
Sep 24

Advances in Diffusion-Based Generative Compression

This article reviews recent diffusion‑based methods for generative lossy image compression, highlighting how these techniques encode a source into an embedding and use a diffusion model to iteratively refine the reconstruction during decoding. It discusses the role of auxiliary entropy models for transmitting the embedding, explores the use of diffusion models for information transmission via channel simulation, and frames the discussion within rate‑distortion‑perception theory, common randomness, and inverse‑problem connections. The review also identifies open challenges in the field.

By Yibo Yang, Stephan Mandt