Key Points
1. The paper introduces consistency models, a new family of generative models that support efficient, one-step generation by directly mapping noise to data.
2. These consistency models outperform existing distillation techniques for diffusion models, achieving state-of-the-art performance on image benchmarks such as CIFAR-10, ImageNet 64 ˆ 64, and LSUN 256 ˆ 256.
3. Consistency models can be trained either by distilling pre-trained diffusion models or as standalone generative models altogether, offering flexibility in their training approach.
4. The models are inspired by the theory of continuous-time diffusion models and are designed to support efficient single-step generation without sacrificing the advantages of iterative sampling or zero-shot data editing tasks.
5. They are trained based on the Probability Flow (PF) ODE, and their outputs are consistent for points on the same trajectory, allowing high-quality sample generation with only one network evaluation.
6. The paper demonstrates the efficacy of consistency models on several image datasets, including CIFAR-10, ImageNet 64 ˆ 64, and LSUN 256 ˆ 256, outperforming existing generative models and distillation techniques across multiple datasets.
7. Consistency models also enable zero-shot data editing tasks such as image denoising, interpolation, inpainting, colorization, super-resolution, and stroke-guided image editing, without explicit training on these tasks.
8. The paper presents theoretical justifications for consistency distillation and consistency training and provides insights into the factors affecting their performance, such as the choice of metric function, ODE solver, number of discretization steps, and schedule functions.
9. Overall, the paper demonstrates that consistency models are a promising new approach in the field of generative modeling, offering significant advancements in sample quality, training flexibility, and zero-shot data editing capabilities.
Summary
The research paper explores the concept of consistency models, a new family of generative models designed to efficiently generate high-quality samples by directly mapping noise to data. These models support fast one-step generation, while still allowing multi-step sampling and zero-shot data editing tasks without explicit training for these tasks. The paper proposes two methods for training consistency models, one involving numerical ODE solvers and a pre-trained diffusion model, and the other allowing the training of a consistency model in isolation. Through extensive experiments on several image datasets, it is demonstrated that consistency models outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving state-of-the-art results. The paper also showcases the ability of consistency models to perform zero-shot data editing tasks such as image inpainting, colorization, and super-resolution.
The efficiency and efficacy of the proposed consistency models are highlighted, and their potential for various applications including zero-shot data editing is emphasized. The paper provides theoretical justifications, experimental results, and practical insights into the training and application of consistency models, positioning them as a promising new family of generative models with diverse capabilities and potential for real-world applications.
Reference: https://arxiv.org/abs/2303.01469