Improve model card: add library_name, citation and sample usage

Hi! I'm Niels from the community science team at Hugging Face.

I've opened this PR to enhance the model card for OneReward-ComfyUI. Specifically, I have:
- Added `library_name: diffusers` to the metadata to enable better integration and discoverability.
- Included a sample usage code snippet from the official GitHub repository (note that this requires the custom pipeline from their source code).
- Added the BibTeX citation from the paper.

Please let me know if you have any questions!

README.md

@@ -1,20 +1,73 @@
 ---
-license: cc-by-nc-4.0
 base_model:
 - black-forest-labs/FLUX.1-Fill-dev
 - bytedance-research/OneReward
 language:
 - en
+license: cc-by-nc-4.0
 pipeline_tag: image-to-image
+library_name: diffusers
 ---
+
 # OneReward - ComfyUI
 
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2508.21066) [![GitHub Repo](https://img.shields.io/badge/GitHub-Repo-green?logo=github)](https://github.com/bytedance/OneReward) [![GitHub Pages](https://img.shields.io/badge/GitHub-Project-blue?logo=github)](https://one-reward.github.io/)
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-b31b1b.svg)](https://arxiv.org/abs/2508.21066) [![GitHub Repo](https://img.shields.io/badge/GitHub-Repo-green?logo=github)](https://github.com/bytedance/OneReward) [![GitHub Pages](https://img.shields.io/badge/GitHub-Project-blue?logo=github)](https://one-reward.github.io/)
 <br>
 
 This repo contains the checkpoint from [OneReward](https://huggingface.co/bytedance-research/OneReward) processed into a single model suitable for ComfyUI use.
 
-**OneReward** is a novel RLHF methodology for the visual domain by employing Qwen2.5-VL as a generative reward model to enhance multitask reinforcement learning, significantly improving the policy model’s generation ability across multiple subtask. Building on OneReward, **FLUX.1-Fill-dev-OneReward** -  based on FLUX Fill [dev], outperforms closed-source FLUX Fill [Pro] in inpainting and outpainting tasks, serving as a powerful new baseline for future research in unified image editing.
+**OneReward** is a novel RLHF methodology for the visual domain by employing Qwen2.5-VL as a generative reward model to enhance multitask reinforcement learning, significantly improving the policy model’s generation ability across multiple subtask. Building on OneReward, **FLUX.1-Fill-dev-OneReward** - based on FLUX Fill [dev], outperforms closed-source FLUX Fill [Pro] in inpainting and outpainting tasks, serving as a powerful new baseline for future research in unified image editing.
+
+For more details and examples see original model repo: [**OneReward**](https://huggingface.co/bytedance-research/OneReward)
+
+## Sample Usage
+
+The following code snippet illustrates how to use the model with the `diffusers` library. Note that this requires the custom `FluxFillCFGPipeline` defined in the [official source code](https://github.com/bytedance/OneReward/blob/main/src/pipeline_flux_fill_with_cfg.py).
+
+```python
+import torch
+from diffusers.utils import load_image
+from diffusers import FluxTransformer2DModel
+
+# Note: pipeline_flux_fill_with_cfg.py must be available in your local environment
+from src.pipeline_flux_fill_with_cfg import FluxFillCFGPipeline
+
+transformer_onereward = FluxTransformer2DModel.from_pretrained(
+    "bytedance-research/OneReward",
+    subfolder="flux.1-fill-dev-OneReward-transformer",
+    torch_dtype=torch.bfloat16
+)
+
+pipe = FluxFillCFGPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Fill-dev", 
+    transformer=transformer_onereward,
+    torch_dtype=torch.bfloat16).to("cuda")
+
+# Example: Image Fill
+image = load_image('assets/image.png')
+mask = load_image('assets/mask_fill.png')
+image = pipe(
+    prompt='the words "ByteDance", and in the next line "OneReward"',
+    negative_prompt="nsfw",
+    image=image,
+    mask_image=mask,
+    height=image.height,
+    width=image.width,
+    guidance_scale=1.0,
+    true_cfg=4.0,
+    num_inference_steps=50,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+image.save(f"image_fill.jpg")
+```
 
+## Citation
 
-For more details and examples see original model repo: [**OneReward**](https://huggingface.co/bytedance-research/OneReward)
+```bibtex
+@article{gong2025onereward,
+  title={OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning},
+  author={Gong, Yuan and Wang, Xionghui and Wu, Jie and Wang, Shiyin and Wang, Yitong and Wu, Xinglong},
+  journal={arXiv preprint arXiv:2508.21066},
+  year={2025}
+}
+```