| | --- |
| | base_model: |
| | - stabilityai/stable-diffusion-3.5-medium |
| | library_name: diffusers |
| | pipeline_tag: text-to-image |
| | --- |
| | |
| | # Model Card |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | This is a reproduced LoRA of SD3.5-Medium, post-trained with DiffusionNFT on multiple reward models, as presented in the paper [Diffusion Negative-aware FineTuning (DiffusionNFT)](https://huggingface.co/papers/2509.16117). |
| |
|
| | ### Paper Abstract |
| | Online reinforcement learning (RL) has been central to post-training language |
| | models, but its extension to diffusion models remains challenging due to |
| | intractable likelihoods. Recent works discretize the reverse sampling process |
| | to enable GRPO-style training, yet they inherit fundamental drawbacks, |
| | including solver restrictions, forward-reverse inconsistency, and complicated |
| | integration with classifier-free guidance (CFG). We introduce Diffusion |
| | Negative-aware FineTuning (DiffusionNFT), a new online RL paradigm that |
| | optimizes diffusion models directly on the forward process via flow matching. |
| | DiffusionNFT contrasts positive and negative generations to define an implicit |
| | policy improvement direction, naturally incorporating reinforcement signals |
| | into the supervised learning objective. This formulation enables training with |
| | arbitrary black-box solvers, eliminates the need for likelihood estimation, and |
| | requires only clean images rather than sampling trajectories for policy |
| | optimization. DiffusionNFT is up to 25times more efficient than FlowGRPO in |
| | head-to-head comparisons, while being CFG-free. For instance, DiffusionNFT |
| | improves the GenEval score from 0.24 to 0.98 within 1k steps, while FlowGRPO |
| | achieves 0.95 with over 5k steps and additional CFG employment. By leveraging |
| | multiple reward models, DiffusionNFT significantly boosts the performance of |
| | SD3.5-Medium in every benchmark tested. |
| |
|
| | ### Model Sources |
| |
|
| | <!-- Provide the basic links for the model. --> |
| |
|
| | - **Repository:** https://github.com/NVlabs/DiffusionNFT |
| | - **Paper:** https://huggingface.co/papers/2509.16117 |
| | - **Project Page:** https://research.nvidia.com/labs/dir/DiffusionNFT |
| |
|
| | ## Uses |
| |
|
| | Please refer to the evaluation script in GitHub. |
