SMD simplifies few-step diffusion distillation: no score model, no adversarial training, validated up to 33B
sainingxie · x · 2026-10-20
- SMD (Simplified Matching Distillation) is a new distillation method for few-step diffusion that needs no auxiliary score model, no adversarial training, and no generator modification.
- Claims: photorealistic appearance, high-quality dynamics, memory-efficient training, and mode-covering diverse outputs.
- Validated across image, video, joint audio–video, and 3D generation, at model scales up to 33B parameters.
- Extras: multi-teacher capability merging (akin to OPD in LLMs), cross-model capability transfer, and bidirectional teacher-to-causal distillation.
- Unlike MeanFlow it avoids unstable JVP, and unlike DMD it avoids heavy memory overhead — peers call it a potential game changer for few-step distillation. Paper, code, models, and data are fully released.
Related event: SMD: Simplified Matching Distillation for Few-Step Diffusion(2 posts)→
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