Unified Continuous Generative Models
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.
6822c7d78fc623ca8c6b2578
Peng Sun
claimed_verified
2025-05-13T08:11:37.679Z
6305a4cf5054a1c5b82ae8f7
/avatars/7138103d5b86d8b0b6cdd71e1dd4f1eb.svg
Black Box
user
sp12138sp
6822c7d78fc623ca8c6b2579
Yi Jiang
6822c7d78fc623ca8c6b257a
Tao Lin
claimed_verified
2025-05-13T08:57:54.259Z
65296b8a6cdea40585bd81e2
/avatars/824452e8b8fd056eaf7549c46393c47b.svg
Tao LIN
user
tlin-taolin
Unified Continuous Generative Models
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.
A unified framework for continuous generative models improves training and sampling performance across multi-step and few-step methods.
continuous generative models diffusion flow-matching consistency models Unified Continuous Generative Models Trainer and Sampler (UCGM-T S) FID