PDMD: Projected Distribution Matching Distillation for Video Diffusion Models

arXiv:2609.35768 · cs.CV, cs.LG · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Project page: https://pdmd2026.github.io

Terminology

• Distillation — 47× in this paper · explained in 2 episodes
• Distribution Matching — 20× in this paper · explained in 2 episodes
• Alignment — 17× in this paper · explained in 1 episode
• Distribution Matching Distillation — 15× in this paper
• Consistency — 12× in this paper · explained in 3 episodes
• Diffusion Model — 11× in this paper · explained in 8 episodes
• Diffusion Models — 10× in this paper · explained in 6 episodes
• Projected Distribution Matching Distillation — 6× in this paper
• Ablation — 5× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Mode Collapse — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Alternation — 1× in this paper · explained in 1 episode
• Copula — 1× in this paper · explained in 2 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond
• Seedance 1.0: Exploring the Boundaries of Video Generation Models
• Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation
• VBench: Comprehensive Benchmark Suite for Video Generative Models
• Distribution Matching Distillation without Fake Score Network
• SDXL-Lightning: Progressive Adversarial Diffusion Distillation
• Hyper-Bagel: A Unified Acceleration Framework for Multimodal Understanding and Generation
• MagicDistillation: Weak-to-Strong Video Distillation for Large-Scale Few-Step Synthesis
• Wan: Open and Advanced Large-Scale Video Generative Models
• Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound
• Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
• One-step Diffusion Models with $f$-Divergence Distribution Matching
• VideoGen-Eval: Agent-based System for Video Generation Evaluation
• Adaptive Video Distillation: Mitigating Oversaturation and Temporal Collapse in Few-Step Generation
• CoDMD: Copula-aware Distribution Matching Distillation for Fast Video Generation

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