Unified Continuous Generative Models

• URL: http://arxiv.org/abs/2505.07447v2
• Date: Tue, 20 May 2025 12:27:53 GMT
• Title: Unified Continuous Generative Models
• Authors: Peng Sun, Yi Jiang, Tao Lin,
• Abstract summary: We introduce a unified framework for training, sampling, and analyzing continuous generative models.<n>Our implementation achieves state-of-the-art (SOTA) performance.
• Score: 12.358393766570732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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