Kati8/ICLR_2023_2025 huggingface dataset:
authors: Kevin Frans
authors: Danijar Hafner
authors: Sergey Levine
authors: Pieter Abbeel
reviews: 
Comments: The paper introduces a shortcut design (self-consistency objective) in flow models for efficient and high-quality sampling. Unlike complex methods, this shortcut leverages existing networks and requires a single training phase.
The shortcut design involves incorporating an additional step size condition into the generative models, enabling them to learn the objective while considering the step size. This approach showcases improved performance in image and policy generation domains.
Strengths: The paper introduces a shortcut design (self-consistency objective) in flow models to maintain consistency in intermediate sampling trajectories. This shortcut involves adding a self-consistency objective during training without significant additional burden. Empirically, it consistently outperforms other end-to-end methods in terms of generation quality.
Weaknesses: Limited comparison with one-step generator GAN is provided. In Appendix Table 2, StyleGAN-X demonstrates the best performance (2.3). Does this suggest that GAN is currently the optimal choice for one-step sampling?
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 4
Clarity: 3
reviews: 
Comments: This paper proposes shortcut modes, an end-to-end training method that could produce high-quality images in either single or multiple sampling steps. The shortcut models take an additional input of the desired step size, compared to diffusion or consistency models.
Strengths: * The paper is well written in the context of reducing sampling steps of diffusion models.
* The proposed method is novel and clearly presented.
* The experiments are comprehensive and convincing.
Weaknesses: * Lacks explicit comparison of training compute against other methods.
* The issue with CFG could hinder future practical use.
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 4
Clarity: 4