Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion
  Transformers via In-Context Reflection
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.
67d926523acb37a1cfa74cf8
Shufan Li
admin_assigned
2025-03-19T13:47:36.651Z
6310531914aa81e1044363ed
/avatars/ae7767e591cb7199ea2f62d2db89fc7f.svg
Shufan Li
user
jacklishufan
67d926523acb37a1cfa74cf9
Konstantinos Kallidromitis
67d926523acb37a1cfa74cfa
Akash Gokul
admin_assigned
2025-03-19T13:47:47.689Z
63093856b105f8675bd1c783
/avatars/505a7b99893330a36a136d13fa99e05e.svg
Akash Gokul
user
akashgokul
67d926523acb37a1cfa74cfb
Arsh Koneru
admin_assigned
2025-03-19T13:47:55.709Z
65cdc744b4a3a21113eed76a
/avatars/f1dceb512382cafbde81e3b8e28b10cf.svg
Arsh Koneru
user
arshkon
67d926523acb37a1cfa74cfc
Yusuke Kato
67d926523acb37a1cfa74cfd
Kazuki Kozuka
67d926523acb37a1cfa74cfe
Aditya Grover
admin_assigned
2025-03-19T13:48:22.431Z
65d38c1e5d5749637491679b
/avatars/010a6588f382d6d4394216549a2385e3.svg
Aditya Grover
user
adityagrover
Reflect-DiT: Inference-Time Scaling for Text-to-Image Diffusion
  Transformers via In-Context Reflection
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.
Reflect-DiT enhances text-to-image diffusion models by enabling in-context reflection, improving performance and efficiency compared to best-of-N sampling.
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