ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling

TL;DR - ROMS-IMLE is a minimalist, single-step image generator that pairs Implicit Maximum Likelihood Estimation with a moderately sized convolutional network. It challenges the assumption that high-quality generation requires iterative denoising or transformer architectures.

• Avoids variational inference, adversarial training, numerical integration, and iterative denoising.
• Generates samples in one step for fast, parameter-efficient inference.
• Achieves an FID of 2.56 on ImageNet 256 while maintaining good precision and recall.
• Suggests gradual noise-to-data transformations are not essential for competitive image generation.