Core Concepts
Multistep Consistency Models offer a trade-off between sampling speed and quality, bridging the gap between Consistency and Diffusion Models.
Abstract
Abstract
Diffusion models are easy to train but require many steps for sample generation.
Consistency models are harder to train but generate samples in a single step.
Introduction
Diffusion models dominate generative models but have expensive sampling procedures.
Consistency models reduce sampling time significantly but struggle with image quality.
Multistep Consistency Models
Unification of Consistency Models and TRACT to balance sampling speed and quality.
Achieve impressive results on Imagenet datasets with increased sample budget.
Background: Diffusion Models
Diffusion models involve a destruction process with noise addition.
Sampling from these models requires denoising equations.
Consistency Models
Consistency models aim to learn a direct mapping from noise to data.
Consistency Training and Distillation improve performance compared to earlier works.
Data Extraction
Not applicable
Quotations
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Further Questions
How do Multistep Consistency Models compare to other state-of-the-art generative models?
What are the implications of the trade-off between sampling speed and quality in practical applications?
How can the findings of this study be applied to other domains beyond image generation?