title: From Reflection to Perfection: Scaling Inference-Time Optimization for
    Text-to-Image Diffusion Models via Reflection Tuning
summary: Recent text-to-image diffusion models achieve impressive visual quality
  through extensive scaling of training data and model parameters, yet they often
  struggle with complex scenes and fine-grained details. Inspired by the
  self-reflection capabilities emergent in large language models, we propose
  ReflectionFlow, an inference-time framework enabling diffusion models to
  iteratively reflect upon and refine their outputs. ReflectionFlow introduces
  three complementary inference-time scaling axes: (1) noise-level scaling to
  optimize latent initialization; (2) prompt-level scaling for precise semantic
  guidance; and most notably, (3) reflection-level scaling, which explicitly
  provides actionable reflections to iteratively assess and correct previous
  generations. To facilitate reflection-level scaling, we construct GenRef, a
  large-scale dataset comprising 1 million triplets, each containing a
  reflection, a flawed image, and an enhanced image. Leveraging this dataset, we
  efficiently perform reflection tuning on state-of-the-art diffusion
  transformer, FLUX.1-dev, by jointly modeling multimodal inputs within a unified
  framework. Experimental results show that ReflectionFlow significantly
  outperforms naive noise-level scaling methods, offering a scalable and
  compute-efficient solution toward higher-quality image synthesis on challenging
  tasks.
paper_authors._id: 680845d997f32b8ffc13569c
paper_authors.name: Le Zhuo
paper_authors._id: 680845d997f32b8ffc13569d
paper_authors.name: Liangbing Zhao
paper_authors._id: 680845d997f32b8ffc13569e
paper_authors.name: Sayak Paul
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:55:10.090Z
paper_authors._id: 5f7fbd813e94f16a85448745
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1649681653581-5f7fbd813e94f16a85448745.jpeg
paper_authors.fullname: Sayak Paul
paper_authors.type: user
paper_authors.user: sayakpaul
paper_authors._id: 680845d997f32b8ffc13569f
paper_authors.name: Yue Liao
paper_authors._id: 680845d997f32b8ffc1356a0
paper_authors.name: Renrui Zhang
paper_authors._id: 680845d997f32b8ffc1356a1
paper_authors.name: Yi Xin
paper_authors._id: 680845d997f32b8ffc1356a2
paper_authors.name: Peng Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:54:51.574Z
paper_authors._id: 67b299cc6f6dc4376d9e6c76
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/UniMpmfOUlyiSOrf47wuT.png
paper_authors.fullname: Peng Gao
paper_authors.type: user
paper_authors.user: cosumosu25
paper_authors._id: 680845d997f32b8ffc1356a3
paper_authors.name: Mohamed Elhoseiny
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:54:43.898Z
paper_authors._id: 64a27d39649b0d08ca0a4ca6
paper_authors.avatarUrl: /avatars/e4446a875506c10de9ae28411dc6416d.svg
paper_authors.fullname: Mohamed Elhoseiny
paper_authors.type: user
paper_authors.user: mhelhoseiny
paper_authors._id: 680845d997f32b8ffc1356a4
paper_authors.name: Hongsheng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:54:37.416Z
paper_authors._id: 65c04e9c27a5fdca81abcbd9
paper_authors.avatarUrl: /avatars/12a155683c824fa23da4a9e2bed4f64e.svg
paper_authors.fullname: Hongsheng LI
paper_authors.type: user
paper_authors.user: hsli-cuhk
paper_title: From Reflection to Perfection: Scaling Inference-Time Optimization for
    Text-to-Image Diffusion Models via Reflection Tuning
paper_summary: Recent text-to-image diffusion models achieve impressive visual quality
  through extensive scaling of training data and model parameters, yet they often
  struggle with complex scenes and fine-grained details. Inspired by the
  self-reflection capabilities emergent in large language models, we propose
  ReflectionFlow, an inference-time framework enabling diffusion models to
  iteratively reflect upon and refine their outputs. ReflectionFlow introduces
  three complementary inference-time scaling axes: (1) noise-level scaling to
  optimize latent initialization; (2) prompt-level scaling for precise semantic
  guidance; and most notably, (3) reflection-level scaling, which explicitly
  provides actionable reflections to iteratively assess and correct previous
  generations. To facilitate reflection-level scaling, we construct GenRef, a
  large-scale dataset comprising 1 million triplets, each containing a
  reflection, a flawed image, and an enhanced image. Leveraging this dataset, we
  efficiently perform reflection tuning on state-of-the-art diffusion
  transformer, FLUX.1-dev, by jointly modeling multimodal inputs within a unified
  framework. Experimental results show that ReflectionFlow significantly
  outperforms naive noise-level scaling methods, offering a scalable and
  compute-efficient solution toward higher-quality image synthesis on challenging
  tasks.
paper_ai_summary: ReflectionFlow enhances text-to-image diffusion models by enabling iterative refinement during inference, using noise-level, prompt-level, and reflection-level scaling to improve quality.
paper_ai_keywords: diffusion models visual quality training data model parameters self-reflection ReflectionFlow noise-level scaling prompt-level scaling reflection-level scaling GenRef multimodal inputs FLUX.1-dev image synthesis