Transition Models: Rethinking the Generative Learning Objective
Authors: name: Zidong Wang, name: Yiyuan Zhang, name: Xiaoyu Yue, name: Xiangyu Yue, name: Yangguang Li, name: Wanli Ouyang, name: Lei Bai

A fundamental dilemma in generative modeling persists: iterative diffusion
models achieve outstanding fidelity, but at a significant computational cost,
while efficient few-step alternatives are constrained by a hard quality
ceiling. This conflict between generation steps and output quality arises from
restrictive training objectives that focus exclusively on either infinitesimal
dynamics (PF-ODEs) or direct endpoint prediction. We address this challenge by
introducing an exact, continuous-time dynamics equation that analytically
defines state transitions across any finite time interval. This leads to a
novel generative paradigm, Transition Models (TiM), which adapt to
arbitrary-step transitions, seamlessly traversing the generative trajectory
from single leaps to fine-grained refinement with more steps. Despite having
only 865M parameters, TiM achieves state-of-the-art performance, surpassing
leading models such as SD3.5 (8B parameters) and FLUX.1 (12B parameters) across
all evaluated step counts. Importantly, unlike previous few-step generators,
TiM demonstrates monotonic quality improvement as the sampling budget
increases. Additionally, when employing our native-resolution strategy, TiM
delivers exceptional fidelity at resolutions up to 4096x4096.