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Song and colleagues: consistency models support one-step and few-step generation

Contribution grade

Fast generative modeling and diffusion distillation · Fast generative modeling and diffusion distillation

Reference score60,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

A boundary-constrainedtrajectory-endpoint map supports one/fewstep generation, teacher distillation anddirect-data training interface; originalfiniteexperiments andlaterCTargumentlimits separated.

Scientific scope

A boundary-constrainedtrajectory-endpoint map supports one/fewstep generation, teacher distillation anddirect-data training interface; originalfiniteexperiments andlaterCTargumentlimits separated. The target is arXiv 2303.01469v2. Consistency distillation and direct training have different assumptions and costs. The endpoint is smoothed and approximate; the theoretical bounds require the paper’s regularity, boundary and discretization conditions and do not guarantee successful optimization. A later Song–Dhariwal point-mass counterexample exposes a problem in the unequal-teacher moving-average justification for original direct training. Its corrected justification and improved benchmark scores are credited separately. Editing examples may use many steps. WRS has not replayed the image benchmarks or certified every theorem.

Evidence

Source-linked, scoped account of the original work. The published scope states the scientific assumptions and remaining verification limits. WRS has not independently certified the full proof, experimental result or system benchmark.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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