Single-step generative models can match multi-step diffusion models' quality without gradual transformations—simplicity in training objectives and architecture doesn't sacrifice performance.
This paper challenges the assumption that gradual noise-to-data transformations are necessary for good generative models. The authors build a minimalist single-step generative model using Implicit Maximum Likelihood Estimation (IMLE) and a simple convolutional network, achieving competitive results (FID 2.56 on ImageNet 256) without iterative denoising or transformers.