April 5, 2024Open Access

Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation

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Score identity distillation successfully compresses pretrained diffusion models into a one-step generator, matching or exceeding the original teacher synthesis quality.
Rapid frechet inception distance reduction occurs exponentially fast during distillation, outperforming competing one-step and few-step baselines across four benchmark datasets.
Evaluation across four benchmark datasets assessed training on generator-synthesized images by reformulating forward diffusion processes as semi-implicit distributions.
Highlights potential to redefine efficiency benchmarks in diffusion-based generation, eliminating the need for real training data or slow reverse diffusion iterations.

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Implication

Evaluation across four benchmark datasets demonstrates superior one-step generation quality in pretrained diffusion models, highlighting enhanced distillation efficiency.

Cite This Study

Zhou et al. (2024) studied this question.

synapsesocial.com/papers/68e70547b6db64358767f1b6 https://doi.org/10.48550/arxiv.2404.04057

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Implication

Evaluation across four benchmark datasets demonstrates superior one-step generation quality in pretrained diffusion models, highlighting enhanced distillation efficiency.

Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation

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Discussion

Implication

Cite This Study

Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation

Key Points

Discussion

Implication

Cite This Study

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