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

README.md

@@ -1,202 +1,56 @@
 ---
 base_model: stabilityai/stable-diffusion-3.5-medium
 library_name: peft
+pipeline_tag: text-to-image
 ---
 
-# Model Card for Model ID
+# Flow-OPD: On-Policy Distillation for Flow Matching Models
 
-<!-- Provide a quick summary of what the model is/does. -->
+[**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
 
-## Model Details
+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).
 
-### Model Description
+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.
 
-<!-- Provide a longer summary of what this model is. -->
+## 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** |
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- ✨ **+18pt** average improvement over the base model.
+- 📝 **0.94** OCR accuracy (improved from 0.59).
+- 🚀 **0.92** GenEval score (improved from 0.63).
 
-### Model Sources [optional]
+## Method Highlights
 
-<!-- Provide the basic links for the model. -->
+- **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.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** arxiv.org/abs/2605.08063
-- **Demo [optional]:** [More Information Needed]
+## Citation
 
-## Uses
+```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},
+}
+```
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Acknowledgements
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-[More Information Needed]
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-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
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-[More Information Needed]
-
-#### Hardware
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-[More Information Needed]
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-#### Software
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
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-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-[More Information Needed]
-### Framework versions
-
-- PEFT 0.10.0
+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.