Add model card and metadata for FIRM-SD3.5

README.md

@@ -1,3 +1,45 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- lora
+- image-generation
+- reinforcement-learning
+- reward-modeling
+- firm
+---
+
+# FIRM-SD3.5
+
+This repository contains the LoRA weights for **FIRM-SD3.5**, an enhanced text-to-image generation model developed using the FIRM (Faithful Image Reward Modeling) framework.
+
+The model was introduced in the paper [Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation](https://huggingface.co/papers/2603.12247).
+
+- **Project Page:** [https://firm-reward.github.io/](https://firm-reward.github.io/)
+- **GitHub Repository:** [https://github.com/VisionXLab/FIRM-Reward](https://github.com/VisionXLab/FIRM-Reward)
+- **Paper:** [arXiv:2603.12247](https://huggingface.co/papers/2603.12247)
+
+## Model Description
+
+Reinforcement learning (RL) for visual generation relies heavily on the faithfulness of the reward model used as a critic. FIRM addresses common issues like hallucinations and noisy scoring through:
+1. **Tailored Data Pipelines:** Using specialized curation for editing (execution and consistency) and generation (instruction following).
+2. **Robust Reward Models:** Training specialized reward models (like FIRM-Gen-8B) on high-quality scoring datasets.
+3. **"Base-and-Bonus" Reward Strategy:** A novel strategy to balance competing objectives, such as Quality-Modulated Alignment (QMA) for generation.
+
+The resulting **FIRM-SD3.5** model demonstrates significant breakthroughs in fidelity and instruction adherence compared to existing general models by mitigating hallucinations.
+
+## Citation
+
+```bibtex
+@article{zhao2026trust,
+  title={Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation},
+  author={Zhao, Xiangyu and Zhang, Peiyuan and Lin, Junming and Liang, Tianhao and Duan, Yuchen and Ding, Shengyuan and Tian, Changyao and Zang, Yuhang and Yan, Junchi and Yang, Xue},
+  journal={arXiv preprint arXiv:2603.12247},
+  year={2026}
+}
+```
+
+## Acknowledgements
+
+This project was developed by the VisionXLab and builds upon several open-source projects including [flow-grpo](https://github.com/yifan123/flow_grpo), [DiffusionNFT](https://github.com/NVlabs/DiffusionNFT), and [Edit-R1](https://github.com/PKU-YuanGroup/Edit-R1).