Transition Models: Rethinking the Generative Learning Objective
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.
68ba5465736018af705e8e71
Zidong Wang
claimed_verified
2025-09-05T08:01:39.870Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64b7aa374df206a3ed1947d2/Ostk72ehOR6yUX-PhUvyQ.jpeg
wzd
user
GoodEnough
68ba5465736018af705e8e72
Yiyuan Zhang
68ba5465736018af705e8e73
Xiaoyu Yue
claimed_verified
2025-09-16T09:43:37.805Z
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/avatars/2a6065269f1980213625a9cfd8d42fbd.svg
Xiaoyu Yue
user
YueXY233
68ba5465736018af705e8e74
Xiangyu Yue
68ba5465736018af705e8e75
Yangguang Li
68ba5465736018af705e8e76
Wanli Ouyang
68ba5465736018af705e8e77
Lei Bai
Transition Models: Rethinking the Generative Learning Objective
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.
A novel generative paradigm, Transition Models (TiM), addresses the trade-off between computational cost and output quality in generative modeling by using a continuous-time dynamics equation.
iterative diffusion models continuous-time dynamics equation Transition Models (TiM) PF-ODEs direct endpoint prediction state-of-the-art performance SD3.5 FLUX.1 monotonic quality improvement native-resolution strategy