title: Rewards Are Enough for Fast Photo-Realistic Text-to-image Generation
summary: 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.
paper_authors._id: 67d8fbf641d31cc626e4d7b9
paper_authors.name: Yihong Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T08:07:30.236Z
paper_authors._id: 65f7e6856bd4bac5b6a4ecc3
paper_authors.avatarUrl: /avatars/9c0c48310fb5e99c19700316a0ab531e.svg
paper_authors.fullname: Yihong Luo
paper_authors.type: user
paper_authors.user: Luo-Yihong
paper_authors._id: 67d8fbf641d31cc626e4d7ba
paper_authors.name: Tianyang Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-18T09:33:01.489Z
paper_authors._id: 636a40faa6f948c4f0c62ae5
paper_authors.avatarUrl: /avatars/30c35b194ba84d6e274df30e91a8cc45.svg
paper_authors.fullname: Tianyang Hu
paper_authors.type: user
paper_authors.user: whatlegequ
paper_authors._id: 67d8fbf641d31cc626e4d7bb
paper_authors.name: Weijian Luo
paper_authors._id: 67d8fbf641d31cc626e4d7bc
paper_authors.name: Kenji Kawaguchi
paper_authors._id: 67d8fbf641d31cc626e4d7bd
paper_authors.name: Jing Tang
paper_title: Rewards Are Enough for Fast Photo-Realistic Text-to-image Generation
paper_summary: 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.
paper_ai_summary: R0, a novel text-to-image generation method, prioritizes reward maximization over diffusion losses, demonstrating superior performance in complex condition scenarios.
paper_ai_keywords: reward-enhanced diffusion distillation text-to-image models diffusion losses conditional generation compositional rewards generator parameterization regularization techniques