Consistency models are generative models that generate high-quality data without adversarial training. They achieve this by learning from pre-trained diffusion models and utilizing metrics like LPIPS. However, the use of LPIPS introduces bias into the evaluation process. The OpenAI research team has developed innovative methods to improve consistency models, outperforming consistency distillation (CD) and mitigating the bias. They suggest using lognormal noise schedule and increasing discretization steps during training. These advancements result in impressive scores and highlight the potential of consistency models.
OpenAI Researchers Pioneer Advanced Consistency Models for High-Quality Data Sampling Without Adversarial Training
Consistency models are a type of generative models that can generate high-quality data in a single step without the need for adversarial training. These models achieve optimal sample quality by learning from pre-trained diffusion models and using metrics like LPIPS (learning Perceptual Image Patch Similarity). However, using distillation with these models limits their quality to that of the pre-trained diffusion model and introduces unwanted bias.
Consistency models have the advantage of generating high-quality samples without the need for multiple sampling steps like score-based diffusion models. They retain the benefits of diffusion models, such as the ability to trade computing power for multi-step sampling to improve sample quality. Additionally, they allow for a zero-shot strategy to alter data without prior exposure.
In their publication “Techniques for Training Consistency Models,” the OpenAI research team introduced innovative methods to enable consistency models to learn directly from data. These methods outperform consistency distillation (CD) in producing high-quality samples while addressing the limitations associated with LPIPS.
Prior studies have shown that CD tends to perform better than consistency training (CT) in training consistency models. However, CD limits the sample quality that consistency models can achieve by requiring the training of a unique diffusion model.
The researchers propose training models consistently by adding a lognormal noise schedule and increasing the total discretization steps regularly during training. These improvements enhance Contrastive Training (CT) to outperform Consistency Distillation (CD). The researchers also investigate the effects of weighting functions, noise embeddings, dropout, and propose eliminating the Exponential Moving Average (EMA) from the teacher network to address flaws in earlier analyses.
To mitigate assessment bias caused by LPIPS, the team uses pseudo-Huber losses from the robust statistics domain and explores the addition of more discretization steps to improve sample quality. These advancements enable Contrastive Training (CT) to achieve impressive Frechet Inception Distance (FID) scores of 2.51 and 3.25 for CIFAR-10 and ImageNet 64×64, respectively, in just one sampling step. These scores represent significant improvements compared to Consistency Distillation (CD).
The improved methods implemented for Contrastive Training (CT) overcome previous drawbacks and deliver results on par with state-of-the-art diffusion models and Generative Adversarial Networks (GANs). This highlights the considerable potential of consistency models as a standalone category within the generative model space.
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