| --- |
| 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. |
