Improve model card: add metadata, paper links, and key results

This PR improves the model card for Flow-OPD. It adds the `text-to-image` pipeline tag to the metadata to ensure discoverability and replaces the placeholder template with comprehensive information about the model, including:
- Links to the [paper](https://huggingface.co/papers/2605.08063), [GitHub repository](https://github.com/CostaliyA/Flow-OPD), and [project page](https://costaliya.github.io/Flow-OPD/).
- Key evaluation results demonstrating improvements in GenEval and OCR accuracy compared to the base Stable Diffusion 3.5 Medium model.
- A summary of the methodology (On-Policy Distillation for Flow Matching).
- The official BibTeX citation.

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README.md +36 -182

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 ---
 base_model: stabilityai/stable-diffusion-3.5-medium
 library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-## Uses
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-### Framework versions
-- PEFT 0.10.0

 ---
 base_model: stabilityai/stable-diffusion-3.5-medium
 library_name: peft
+pipeline_tag: text-to-image
 ---
+# Flow-OPD: On-Policy Distillation for Flow Matching Models
+[**Project Page**](https://costaliya.github.io/Flow-OPD/) | [**Paper**](https://huggingface.co/papers/2605.08063) | [**Code**](https://github.com/CostaliyA/Flow-OPD)
+Flow-OPD is the first unified post-training framework that integrates On-Policy Distillation (OPD) into Flow Matching (FM) models. This repository contains the student model weights (LoRA adapter) aligned using the Flow-OPD methodology, built upon Stable Diffusion 3.5 Medium.
+## Model Description
+Flow-OPD addresses two critical bottlenecks in multi-task alignment for text-to-image models: reward sparsity and gradient interference. It adopts a two-stage strategy:
+1. **Cold Start Initialization**: Establishes a robust initial policy through SFT or Model Merging.
+2. **Multi-Teacher On-Policy Distillation**: Consolidates heterogeneous expertise into a single student through dense trajectory-level supervision from domain-specialized teachers (GenEval, OCR, DeQA, PickScore).
+The framework also introduces **Manifold Anchor Regularization (MAR)**, which uses a task-agnostic teacher to anchor generation to a high-quality manifold, effectively mitigating aesthetic degradation.
+## Key Results
+Built upon **Stable Diffusion 3.5 Medium**, Flow-OPD achieves significant performance gains over the base model:
+| Model | GenEval | OCR Acc. | DeQA | PickScore | Average |
+|---|---|---|---|---|---|
+| SD-3.5-M (base) | 0.63 | 0.59 | 4.07 | 21.64 | 0.72 |
+| **Flow-OPD (Merge Init)** | **0.92** | **0.94** | **4.35** | **23.08** | **0.90** |
+- ✨ **+18pt** average improvement over the base model.
+- 📝 **0.94** OCR accuracy (improved from 0.59).
+- 🚀 **0.92** GenEval score (improved from 0.63).
+## Method Highlights
+- **On-Policy Sampling (SDE)**: Stochastic exploration via SDE for diverse trajectory sampling.
+- **Dense Trajectory Supervision**: Replaces sparse scalar rewards with dense vector field supervision from multiple teachers.
+- **MAR Regularization**: Anchors generation to a high-quality aesthetic manifold.
+## Citation
+```bibtex
+@misc{fang2026flowopdonpolicydistillationflow,
+      title={Flow-OPD: On-Policy Distillation for Flow Matching Models},
+      author={Zhen Fang and Wenxuan Huang and Yu Zeng and Yiming Zhao and Shuang Chen and Kaituo Feng and Yunlong Lin and Lin Chen and Zehui Chen and Shaosheng Cao and Feng Zhao},
+      year={2026},
+      eprint={2605.08063},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2605.08063},
+}
+```
+## Acknowledgements
+This repository is based on [flow-grpo](https://github.com/yifan123/flow_grpo). We thank the authors for their valuable contributions to the AIGC community.