Add pipeline tag and improve model card

This PR improves the model card by:
- Adding the `unconditional-image-generation` pipeline tag to the metadata for better discoverability.
- Adding a link to the paper on Hugging Face Papers.
- Adding the official GitHub repository link.
- Adding a BibTeX citation section.

README.md

@@ -1,18 +1,33 @@
 ---
 license: mit
+pipeline_tag: unconditional-image-generation
 ---
 
 # Adversarial Flow Models
 
-This repository contains the official checkpoins of the paper [Adversarial Flow Models](https://www.arxiv.org/abs/2511.22475).
+This repository contains the official checkpoints for the paper [Adversarial Flow Models](https://huggingface.co/papers/2511.22475).
 
-Adversarial Flow Models is a class of generative models that unifies Adversarial Models and Flow Models. This repo contains the pretrained ImageNet-256px models in the paper experiment section.
+Adversarial Flow Models is a class of generative models that unifies Adversarial Models and Flow Models. This repository contains the pre-trained ImageNet-256px models described in the paper.
+
+- **GitHub Repository**: [ByteDance-Seed/Adversarial-Flow-Models](https://github.com/ByteDance-Seed/Adversarial-Flow-Models)
+- **Paper**: [Adversarial Flow Models](https://huggingface.co/papers/2511.22475)
 
 ## Usage
 
-Code and instructions are available in the [ByteDance-Seed/Adversarial-Flow-Models](https://github.com/ByteDance-Seed/Adversarial-Flow-Models) repository.
+Code and instructions for generation and training are available in the [official GitHub repository](https://github.com/ByteDance-Seed/Adversarial-Flow-Models).
 
-## Content 
-* `models/` contains pre-trained ImageNet256px checkpoints.
+## Repository Content 
+* `models/` contains pre-trained ImageNet-256px checkpoints.
 * `eval/` contains pre-generated 50k samples for evaluations following ADM npz format.
-* `misc/` contains VAE and other checkpoints used in training.
+* `misc/` contains VAE and other checkpoints used in training.
+
+## Citation
+
+```bibtex
+@article{lin2025adversarial,
+  title={Adversarial Flow Models},
+  author={Lin, Shanchuan and Yang, Ceyuan and Lin, Zhijie and Chen, Hao and Fan, Haoqi},
+  journal={arXiv preprint arXiv:2511.22475},
+  year={2025}
+}
+```