Multistep Consistency Models combine Consistency Models and TRACT to improve sample quality with fewer steps. By increasing the number of steps, the models achieve competitive performance compared to standard diffusion models. The proposed method simplifies the modeling task and enhances performance significantly.
Diffusion models are effective but require many steps for sampling, while Consistency models offer faster sampling but sacrifice image quality. Multistep Consistency Models bridge this gap by interpolating between these two approaches. The results show improved FID scores on ImageNet datasets with consistency distillation.
The paper introduces a deterministic sampler named Adjusted DDIM (aDDIM) to correct integration errors in diffusion models, leading to improved sample quality. Experiments demonstrate that Multistep Consistency Models converge to diffusion model performance as the number of steps increases.
The study compares Multistep CT and CD with Progressive Distillation (PD) on ImageNet datasets, showing superior FID scores at low step counts. Annealing the step schedule is crucial for achieving optimal performance in multistep consistency training.
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by Jonathan Hee... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.06807.pdfDeeper Inquiries