From Reflection to Perfection: Scaling Inference-Time Optimization for
  Text-to-Image Diffusion Models via Reflection Tuning
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.
680845d997f32b8ffc13569c
Le Zhuo
claimed_verified
2025-09-13T15:05:16.162Z
6358a167f56b03ec9147074d
/avatars/e54ea7bf0c240cf76d538296efb3976c.svg
Le Zhuo
user
JackyZhuo
680845d997f32b8ffc13569d
Liangbing Zhao
680845d997f32b8ffc13569e
Sayak Paul
admin_assigned
2025-04-23T13:55:10.090Z
5f7fbd813e94f16a85448745
https://cdn-avatars.huggingface.co/v1/production/uploads/1649681653581-5f7fbd813e94f16a85448745.jpeg
Sayak Paul
user
sayakpaul
680845d997f32b8ffc13569f
Yue Liao
680845d997f32b8ffc1356a0
Renrui Zhang
680845d997f32b8ffc1356a1
Yi Xin
680845d997f32b8ffc1356a2
Peng Gao
admin_assigned
2025-04-23T13:54:51.574Z
67b299cc6f6dc4376d9e6c76
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/UniMpmfOUlyiSOrf47wuT.png
Peng Gao
user
cosumosu25
680845d997f32b8ffc1356a3
Mohamed Elhoseiny
admin_assigned
2025-04-23T13:54:43.898Z
64a27d39649b0d08ca0a4ca6
/avatars/e4446a875506c10de9ae28411dc6416d.svg
Mohamed Elhoseiny
user
mhelhoseiny
680845d997f32b8ffc1356a4
Hongsheng Li
admin_assigned
2025-04-23T13:54:37.416Z
65c04e9c27a5fdca81abcbd9
/avatars/12a155683c824fa23da4a9e2bed4f64e.svg
Hongsheng LI
user
hsli-cuhk
From Reflection to Perfection: Scaling Inference-Time Optimization for
  Text-to-Image Diffusion Models via Reflection Tuning
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.
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.
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