Simplified Matching Distillation enables few-step diffusion without GANs or fake-score models
RubenEVillegas · x · 2026-10-19
raywzy1's team introduces Simplified Matching Distillation (SMD), a much simpler on-policy distillation method for few-step diffusion that needs no auxiliary fake-score model, no adversarial training, and no generator modification.
Highlights: - Photorealistic appearance, high-quality dynamics, memory-efficient training, and mode-covering diverse outputs; - Validated across image, video, joint audio–video, and 3D generation at scales up to 33B parameters, including editing and multimodal conditioning; - Supports multi-teacher capability merging (analogous to OPD in LLMs), cross-model capability transfer, and bidirectional teacher-to-causal distillation; - Full release: paper, code, models, and data are all public.
Related event: SMD: Simplified Matching Distillation for Few-Step Diffusion(2 posts)→
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