Rewards Are Enough for Fast Photo-Realistic Text-to-image Generation
Aligning generated images to complicated text prompts and human preferences
is a central challenge in Artificial Intelligence-Generated Content (AIGC).
With reward-enhanced diffusion distillation emerging as a promising approach
that boosts controllability and fidelity of text-to-image models, we identify a
fundamental paradigm shift: as conditions become more specific and reward
signals stronger, the rewards themselves become the dominant force in
generation. In contrast, the diffusion losses serve as an overly expensive form
of regularization. To thoroughly validate our hypothesis, we introduce R0, a
novel conditional generation approach via regularized reward maximization.
Instead of relying on tricky diffusion distillation losses, R0 proposes a new
perspective that treats image generations as an optimization problem in data
space which aims to search for valid images that have high compositional
rewards. By innovative designs of the generator parameterization and proper
regularization techniques, we train state-of-the-art few-step text-to-image
generative models with R0 at scales. Our results challenge the conventional
wisdom of diffusion post-training and conditional generation by demonstrating
that rewards play a dominant role in scenarios with complex conditions. We hope
our findings can contribute to further research into human-centric and
reward-centric generation paradigms across the broader field of AIGC. Code is
available at https://github.com/Luo-Yihong/R0.
67d8fbf641d31cc626e4d7b9
Yihong Luo
claimed_verified
2025-03-18T08:07:30.236Z
65f7e6856bd4bac5b6a4ecc3
/avatars/9c0c48310fb5e99c19700316a0ab531e.svg
Yihong Luo
user
Luo-Yihong
67d8fbf641d31cc626e4d7ba
Tianyang Hu
admin_assigned
2025-03-18T09:33:01.489Z
636a40faa6f948c4f0c62ae5
/avatars/30c35b194ba84d6e274df30e91a8cc45.svg
Tianyang Hu
user
whatlegequ
67d8fbf641d31cc626e4d7bb
Weijian Luo
67d8fbf641d31cc626e4d7bc
Kenji Kawaguchi
67d8fbf641d31cc626e4d7bd
Jing Tang
Rewards Are Enough for Fast Photo-Realistic Text-to-image Generation
Aligning generated images to complicated text prompts and human preferences
is a central challenge in Artificial Intelligence-Generated Content (AIGC).
With reward-enhanced diffusion distillation emerging as a promising approach
that boosts controllability and fidelity of text-to-image models, we identify a
fundamental paradigm shift: as conditions become more specific and reward
signals stronger, the rewards themselves become the dominant force in
generation. In contrast, the diffusion losses serve as an overly expensive form
of regularization. To thoroughly validate our hypothesis, we introduce R0, a
novel conditional generation approach via regularized reward maximization.
Instead of relying on tricky diffusion distillation losses, R0 proposes a new
perspective that treats image generations as an optimization problem in data
space which aims to search for valid images that have high compositional
rewards. By innovative designs of the generator parameterization and proper
regularization techniques, we train state-of-the-art few-step text-to-image
generative models with R0 at scales. Our results challenge the conventional
wisdom of diffusion post-training and conditional generation by demonstrating
that rewards play a dominant role in scenarios with complex conditions. We hope
our findings can contribute to further research into human-centric and
reward-centric generation paradigms across the broader field of AIGC. Code is
available at https://github.com/Luo-Yihong/R0.
R0, a novel text-to-image generation method, prioritizes reward maximization over diffusion losses, demonstrating superior performance in complex condition scenarios.
reward-enhanced diffusion distillation text-to-image models diffusion losses conditional generation compositional rewards generator parameterization regularization techniques