title: Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion
    Transformers via In-Context Reflection
summary: The predominant approach to advancing text-to-image generation has been
  training-time scaling, where larger models are trained on more data using
  greater computational resources. While effective, this approach is
  computationally expensive, leading to growing interest in inference-time
  scaling to improve performance. Currently, inference-time scaling for
  text-to-image diffusion models is largely limited to best-of-N sampling, where
  multiple images are generated per prompt and a selection model chooses the best
  output. Inspired by the recent success of reasoning models like DeepSeek-R1 in
  the language domain, we introduce an alternative to naive best-of-N sampling by
  equipping text-to-image Diffusion Transformers with in-context reflection
  capabilities. We propose Reflect-DiT, a method that enables Diffusion
  Transformers to refine their generations using in-context examples of
  previously generated images alongside textual feedback describing necessary
  improvements. Instead of passively relying on random sampling and hoping for a
  better result in a future generation, Reflect-DiT explicitly tailors its
  generations to address specific aspects requiring enhancement. Experimental
  results demonstrate that Reflect-DiT improves performance on the GenEval
  benchmark (+0.19) using SANA-1.0-1.6B as a base model. Additionally, it
  achieves a new state-of-the-art score of 0.81 on GenEval while generating only
  20 samples per prompt, surpassing the previous best score of 0.80, which was
  obtained using a significantly larger model (SANA-1.5-4.8B) with 2048 samples
  under the best-of-N approach.
paper_authors._id: 67d926523acb37a1cfa74cf8
paper_authors.name: Shufan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:47:36.651Z
paper_authors._id: 6310531914aa81e1044363ed
paper_authors.avatarUrl: /avatars/ae7767e591cb7199ea2f62d2db89fc7f.svg
paper_authors.fullname: Shufan Li
paper_authors.type: user
paper_authors.user: jacklishufan
paper_authors._id: 67d926523acb37a1cfa74cf9
paper_authors.name: Konstantinos Kallidromitis
paper_authors._id: 67d926523acb37a1cfa74cfa
paper_authors.name: Akash Gokul
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:47:47.689Z
paper_authors._id: 63093856b105f8675bd1c783
paper_authors.avatarUrl: /avatars/505a7b99893330a36a136d13fa99e05e.svg
paper_authors.fullname: Akash Gokul
paper_authors.type: user
paper_authors.user: akashgokul
paper_authors._id: 67d926523acb37a1cfa74cfb
paper_authors.name: Arsh Koneru
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:47:55.709Z
paper_authors._id: 65cdc744b4a3a21113eed76a
paper_authors.avatarUrl: /avatars/f1dceb512382cafbde81e3b8e28b10cf.svg
paper_authors.fullname: Arsh Koneru
paper_authors.type: user
paper_authors.user: arshkon
paper_authors._id: 67d926523acb37a1cfa74cfc
paper_authors.name: Yusuke Kato
paper_authors._id: 67d926523acb37a1cfa74cfd
paper_authors.name: Kazuki Kozuka
paper_authors._id: 67d926523acb37a1cfa74cfe
paper_authors.name: Aditya Grover
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:48:22.431Z
paper_authors._id: 65d38c1e5d5749637491679b
paper_authors.avatarUrl: /avatars/010a6588f382d6d4394216549a2385e3.svg
paper_authors.fullname: Aditya Grover
paper_authors.type: user
paper_authors.user: adityagrover
paper_title: Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion
    Transformers via In-Context Reflection
paper_summary: The predominant approach to advancing text-to-image generation has been
  training-time scaling, where larger models are trained on more data using
  greater computational resources. While effective, this approach is
  computationally expensive, leading to growing interest in inference-time
  scaling to improve performance. Currently, inference-time scaling for
  text-to-image diffusion models is largely limited to best-of-N sampling, where
  multiple images are generated per prompt and a selection model chooses the best
  output. Inspired by the recent success of reasoning models like DeepSeek-R1 in
  the language domain, we introduce an alternative to naive best-of-N sampling by
  equipping text-to-image Diffusion Transformers with in-context reflection
  capabilities. We propose Reflect-DiT, a method that enables Diffusion
  Transformers to refine their generations using in-context examples of
  previously generated images alongside textual feedback describing necessary
  improvements. Instead of passively relying on random sampling and hoping for a
  better result in a future generation, Reflect-DiT explicitly tailors its
  generations to address specific aspects requiring enhancement. Experimental
  results demonstrate that Reflect-DiT improves performance on the GenEval
  benchmark (+0.19) using SANA-1.0-1.6B as a base model. Additionally, it
  achieves a new state-of-the-art score of 0.81 on GenEval while generating only
  20 samples per prompt, surpassing the previous best score of 0.80, which was
  obtained using a significantly larger model (SANA-1.5-4.8B) with 2048 samples
  under the best-of-N approach.
paper_ai_summary: Reflect-DiT enhances text-to-image diffusion models by enabling in-context reflection, improving performance and efficiency compared to best-of-N sampling.
paper_ai_keywords: text-to-image generation diffusion models best-of-N sampling reasoning models in-context reflection Diffusion Transformers GenEval SANA-1.0-1.6B SANA-1.5-4.8B