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Fine-Tuning Diffusion Models via Intermediate Distribution Shaping

arXiv – CS AI|Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen, Dheeraj Nagaraj, Sanjay Shakkottai, Karthikeyan Shanmugam|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers present P-GRAFT, a new method for fine-tuning diffusion models by shaping distributions at intermediate noise levels, showing improved performance on text-to-image generation tasks. The framework achieved an 8.81% relative improvement over base Stable Diffusion v2 model on popular benchmarks.

Key Takeaways

→P-GRAFT introduces a novel approach to fine-tune diffusion models by targeting intermediate noise levels rather than just final outputs.
→The method unifies existing rejection sampling techniques under the GRAFT framework with KL regularized reward maximization.
→Applied to Stable Diffusion v2, the approach shows 8.81% relative improvement over baseline models on text-to-image benchmarks.
→Inverse noise correction algorithm improves pre-trained flow models without requiring explicit rewards.
→The framework demonstrates effectiveness across multiple domains including image generation, layout generation, and molecule generation.

#diffusion-models #machine-learning #text-to-image #stable-diffusion #fine-tuning #generative-ai #computer-vision #research

Read Original →via arXiv – CS AI

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