Advances in Diffusion-Based Generative Compression

arXiv:2601.18932 · eess.IV, cs.IT, cs.LG, math.IT, stat.ML · Submitted 2026-01-26 · Read on arXiv

eess.IV, cs.IT, cs.LG, math.IT, stat.ML

Submitted: 2026-01-26

Updated: 2026-09-23

Terminology

• Diffusion Model — 53× in this paper · explained in 8 episodes
• Diffusion Models — 19× in this paper · explained in 6 episodes
• Flow Matching — 11× in this paper · explained in 8 episodes
• Quantization — 11× in this paper · explained in 12 episodes
• Inverse Problem — 10× in this paper · explained in 2 episodes
• Diffusion Process — 9× in this paper · explained in 2 episodes
• Generative Modeling — 8× in this paper · explained in 3 episodes
• Dithered Quantization — 5× in this paper
• Generative Model — 5× in this paper · explained in 2 episodes
• Two-Stage Architecture — 5× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Score Function — 3× in this paper · explained in 4 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Two-Stage Framework — 1× in this paper · explained in 2 episodes

Sources

• Improving and generalizing flow-based generative models with minibatch optimal transport
• Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
• Text + Sketch: Image Compression at Ultra Low Rates
• Lossy Compression with Gaussian Diffusion
• High-Fidelity Image Compression with Score-based Generative Models
• Optimally Controllable Perceptual Lossy Compression
• A Survey on Diffusion Models for Inverse Problems
• A coding theorem for the rate-distortion-perception function
• On the advantages of stochastic encoders

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