Unified Continuous Generative Models
Authors: name: Peng Sun, name: Yi Jiang, name: Tao Lin

Recent advances in continuous generative models, including multi-step
approaches like diffusion and flow-matching (typically requiring 8-1000
sampling steps) and few-step methods such as consistency models (typically 1-8
steps), have demonstrated impressive generative performance. However, existing
work often treats these approaches as distinct paradigms, resulting in separate
training and sampling methodologies. We introduce a unified framework for
training, sampling, and analyzing these models. Our implementation, the Unified
Continuous Generative Models Trainer and Sampler (UCGM-{T,S}), achieves
state-of-the-art (SOTA) performance. For example, on ImageNet 256x256 using a
675M diffusion transformer, UCGM-T trains a multi-step model achieving 1.30 FID
in 20 steps and a few-step model reaching 1.42 FID in just 2 steps.
Additionally, applying UCGM-S to a pre-trained model (previously 1.26 FID at
250 steps) improves performance to 1.06 FID in only 40 steps. Code is available
at: https://github.com/LINs-lab/UCGM.