Consistency Models
Consistency models enable one-step high-quality sampling, surpassing diffusion models with FID 3.55 (CIFAR-10) and 6.20 (ImageNet 64x64).
Key Findings
Methodology
This paper introduces consistency models that learn to map any point along the PF ODE trajectory to its initial data point, enabling single-step sampling. The models are trained via self-consistency constraints, either by distilling pre-trained diffusion models or independently. The training employs numerical ODE solvers or direct training without pretraining, enforcing the boundary condition for stability. Multi-step sampling further refines sample quality, supporting zero-shot editing tasks. Experiments on CIFAR-10, ImageNet-64, and LSUN demonstrate state-of-the-art results, with FID scores of 3.55 and 6.20, outperforming existing distillation techniques and non-adversarial models.
Key Results
- On CIFAR-10, the model achieves a one-step FID of 3.55, surpassing previous methods and approaching GAN quality. On ImageNet 64x64, the one-step FID reaches 6.20, significantly better than multi-step diffusion sampling. Multi-step sampling with few steps yields high-quality images while reducing computational costs. The model also excels in zero-shot image editing tasks like inpainting, super-resolution, and colorization, demonstrating versatility and efficiency.
- Compared to existing distillation approaches like progressive distillation, the proposed consistency models outperform in few-step sampling without relying on pre-trained diffusion models. As standalone models, they match or exceed the performance of GANs and other non-adversarial single-step generators, showing strong generalization and practical potential.
Significance
This work addresses the critical bottleneck of slow inference in diffusion models, enabling rapid, high-quality generation suitable for real-time applications. Its zero-shot editing capabilities extend the utility to practical tasks such as image restoration and enhancement. The models' training flexibility and theoretical guarantees pave the way for broader adoption in diverse domains, fostering advancements in unsupervised and conditional generation, and bridging the gap between research and industry needs.
Technical Contribution
The paper introduces a novel consistency framework that leverages PFODE trajectory self-consistency, enabling one-step sampling with theoretical convergence guarantees. It innovates by training models either via distillation or independently, avoiding adversarial training. The multi-step sampling strategy balances efficiency and quality, supported by rigorous analysis. The approach provides a new paradigm for high-fidelity, fast generative modeling, with extensive empirical validation across multiple datasets.
Novelty
This is the first work to propose a consistency model that directly maps noise to data in a single step, bypassing the iterative nature of traditional diffusion models. Unlike GANs or VAEs, it relies on trajectory self-consistency, offering mathematical guarantees and flexible training options. The model's ability to distill pre-trained diffusion models or train independently marks a significant departure from prior methods, representing a fundamental innovation in generative modeling.
Limitations
- The model's performance in extremely high-dimensional or complex multi-modal data remains to be fully validated, especially at ultra-high resolutions. Numerical ODE solver stability and efficiency pose challenges for large-scale deployment. Zero-shot editing effectiveness may decline under severe modifications. Additionally, the current training strategies may require further optimization for robustness and scalability, particularly in real-world scenarios with noisy or incomplete data.
Future Work
Future directions include extending the models to multi-modal and multi-task settings, improving scalability to higher resolutions, and enhancing robustness. Developing more efficient numerical solvers and training algorithms will reduce computational costs. Exploring interpretability and controllability of the models can facilitate practical deployment. Further research into zero-shot editing, especially for complex transformations, will broaden application scope, ultimately aiming for universally adaptable, real-time generative systems.
AI Executive Summary
Diffusion models have revolutionized generative tasks but suffer from slow iterative sampling, limiting their practical deployment. This paper introduces a novel class called consistency models, designed to generate high-quality samples in a single step. Building upon the probability flow ODE framework, these models learn to map any point along the diffusion trajectory back to the data distribution's initial point, ensuring self-consistency. This approach allows for direct, one-step sampling from noise, drastically reducing inference time while maintaining or surpassing state-of-the-art sample quality.
The core innovation lies in training the models via self-consistency constraints, either through distillation of pre-trained diffusion models or independently, without adversarial training. The models can also perform multi-step refinement, balancing compute and quality, and support zero-shot image editing tasks such as inpainting, super-resolution, and colorization. Extensive experiments on CIFAR-10, ImageNet-64, and LSUN datasets demonstrate that the models achieve FID scores of 3.55 and 6.20 respectively, outperforming existing distillation techniques and non-adversarial models.
This work significantly advances the field by addressing the speed bottleneck of diffusion models, making high-fidelity generation feasible in real-time applications. The models' flexibility, theoretical guarantees, and practical performance open new avenues for efficient, versatile generative systems across industry and academia. Future research will focus on scaling to higher resolutions, multi-modal tasks, and further improving editing capabilities, pushing the boundaries of what generative models can achieve in real-world scenarios.
Deep Dive
Plain Language Accessible to non-experts
想象你在一个工厂里，工厂每天用不同的机器制造各种产品。以前，要制造一个新产品，你得逐步调节每个机器，从原料到成品，花费很多时间。现在，这个工厂发明了一种新方法，只用一次操作，就能把原料变成成品，速度快得多。这就像你只需按一下按钮，机器就能直接制造出你想要的产品，而不用逐步调试。这个新方法学会了工厂的生产轨迹，知道每一步怎么走，确保每次都能一次性完成。这样，工厂既快又准，还能根据需要随时调整产品。这种新技术，让工厂的生产变得更高效、更灵活，就像论文里的一致性模型一样，能直接从噪声到清晰的图像，省时省力，效果还更好。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的拼图游戏，之前你得一块块拼，花很长时间才能完成。现在，有个聪明的朋友告诉你，只要告诉他你想要的拼图样子，他就能一下子帮你拼好！这个朋友学会了拼图的秘密路径，知道每一步怎么走，能在你只看一眼的情况下，立刻拼出完整的图案。它还可以帮你修补破碎的拼图，或者让模糊的图片变得清晰。它的秘诀，是学会了拼图的全部路径，知道每个碎片该放在哪里。这样，你用很少的时间，就能得到漂亮的图片，甚至还能随意改变它，就像用魔法一样。这就是论文里的新模型，能一次性把噪声变成漂亮的图像，比以前慢慢拼还快，还能帮你修图，真是太酷了！
Abstract
Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either by distilling pre-trained diffusion models, or as standalone generative models altogether. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained in isolation, consistency models become a new family of generative models that can outperform existing one-step, non-adversarial generative models on standard benchmarks such as CIFAR-10, ImageNet 64x64 and LSUN 256x256.