Multistep Consistency Models (AI summary)

Key Points

- The research paper introduces Multistep Consistency Models, which are a unification of Consistency Models and TRACT, aiming to close the performance gap between standard diffusion and few-step sampling.

- Multistep Consistency Models offer a trade-off between sample quality and speed, and they are shown to achieve performances comparable to standard diffusion in as little as eight steps.

- These models demonstrate improved image quality and sampling speed benefits, achieving notable results such as 1.4 FID on Imagenet 64 in 8 steps and 2.1 FID on Imagenet128 in 8 steps with consistency distillation.

- The paper addresses the limitations of diffusion models, which require many steps to generate samples, by proposing Multistep Consistency Models that can generate samples in as few as 4, 8, or 16 function evaluations for certain settings.

- The study presents a deterministic sampler for diffusion models named Adjusted DDIM (aDDIM), which corrects for integration errors that produce blurrier samples.

- Comparison with other methods, such as Progressive Distillation, indicates that Multistep Consistency Models perform favorably, achieving better performance at low step counts.

- The quantitative evaluation on ImageNet demonstrates that the performance of Multistep Consistency Models considerably improves as the number of steps increases, validating the hypothesis that more steps provide a beneficial trade-off between sample quality and speed.

- The research also presents a qualitative evaluation on Text-to-Image modeling, showcasing the minor differences between samples generated by Multistep Consistency Models and the original diffusion models, along with the benefits of precise details in the former.

- The paper provides insights into the training algorithm, deterministic sampler, performance evaluation, and offers comparisons with other related works to demonstrate the effectiveness of Multistep Consistency Models in addressing the limitations of standard diffusion models.

Summary

The research paper compares diffusion models and consistency models in terms of training ease and sample generation efficiency. The authors introduce Multistep Consistency Models, which unify Consistency Models and TRACT to achieve a trade-off between sampling speed and quality. The paper discusses the challenges faced by diffusion models in requiring many steps to generate samples, while consistency models are difficult to train but generate samples in a single step. The authors propose a unifying training algorithm for Multistep Consistency Models and demonstrate that these models work well in practice.

Performance of Multistep Consistency Models
The paper presents various findings and results, emphasizing that Multistep Consistency Models can achieve performance rivaling standard diffusion approaches with as few as 8 or sometimes 4 sampling steps. The experiments focus on ImageNet and show significant improvements in performance as the multistep count increases, providing a trade-off between sample quality and speed. The authors also compare their approach to Progressive Distillation and achieve better performance in certain scenarios.

Evaluation of Multistep Consistency Models
The research highlights the quantitative evaluation on ImageNet, demonstrating state-of-the-art FID scores with Multistep Consistency Models. Additionally, the paper includes a qualitative evaluation on text-to-image modeling, comparing samples from Multistep Consistency Models and standard diffusion models. The results show minor differences between the models, with certain details found to be more precise in the Multistep Consistency Models.

Overall, the paper provides a comprehensive comparison of diffusion models and consistency models, proposing a practical and effective approach in the form of Multistep Consistency Models. The findings showcase the potential of these models in achieving a balance between sample quality and generation speed, ultimately bridging the gap between standard diffusion and low-step diffusion-inspired approaches.

Reference; https://arxiv.org/abs/2403.06807