Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever
In recent times, there has been growing interest in using diffusion models or score-based generative models across different domains such as image and audio generation. Unlike traditional models like GANs, diffusion models don’t involve adversarial training but rather utilize iterative improvements to create top-notch outputs. Nonetheless, these iterations are computationally intensive and can limit real-time usage.
A group of researchers suggested ‘consistency models’ to tackle this restriction by offering fast yet reliable sampling. These models draw on the probability flow ODE concept from constant time diffusion models and figure out how to reconstruct the starting point given any other point during the process. With just a single input to the network, users obtain superior quality output.
There are two approaches to train consistency models – both do away with adversarial training while permitting adaptable architectures. Method #1 employs numerical ODE solvers along with an already trained diffusion model to produce consecutive points along a probability flow ODE pathway. By reducing differences between predicted values of neighboring points, the consistency model absorbs properties from its parent diffusion model. This results in faster, dependable sample creation sans losing excellence. Technique #2 excludes the requirement for previously trained diffusion models, making standalone consistency models possible.
Experiments showcased impressive results when applied to images over multiple difficult datasets including CIFAR-10, ImageNet 64×64, and LSUN 256×256. This opens up new possibilities for diffusion based models thanks to faster computational demands. In essence, consistency models are viable alternatives that excel at generating premium content quickly and efficiently. They may prove valuable across areas that necessitate instantaneous generative modelling capacities.