DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

arXiv:2610.02188 · cs.CV, cs.AI · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-01

Updated: 2026-10-01

Project page: https://yzmblog.github.io/projects/DMAD

Terminology

• Distillation — 42× in this paper · explained in 2 episodes
• Distribution Matching — 23× in this paper · explained in 2 episodes
• Diffusion Model — 13× in this paper · explained in 8 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Distribution Matching Distillation — 10× in this paper
• Diffusion Models — 9× in this paper · explained in 6 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Alignment — 6× in this paper · explained in 1 episode
• Generative Model — 5× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Training — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Optimizing Few-Step Generation with Adaptive Matching Distillation
• SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation
• Mean Flows for One-step Generative Modeling
• AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
• Distribution Matching Distillation without Fake Score Network
• SDXL-Lightning: Progressive Adversarial Diffusion Distillation
• Diffusion Adversarial Post-Training for One-Step Video Generation
• Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation
• Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed
• LCM-LoRA: A Universal Stable-Diffusion Acceleration Module
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• Adversarial Diffusion Distillation
• Parallel Decoding Distillation for Fast Image and Video Generation
• Score-Based Generative Modeling through Stochastic Differential Equations
• Wan: Open and Advanced Large-Scale Video Generative Models
• Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
• SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation
• One-step Diffusion Models with $f$-Divergence Distribution Matching
• UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions
• VideoGen-Eval: Agent-based System for Video Generation Evaluation

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