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.

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