Add pipeline tag and paper link

This PR improves the model card by:
- Adding the `pipeline_tag: unconditional-image-generation` to the metadata, which helps users discover the model under the relevant task category on the Hugging Face Hub.
- Explicitly linking to the paper [AlphaFlow: Understanding and Improving MeanFlow Models](https://huggingface.co/papers/2510.20771) within the model card content.
- Including the comparison image from the original GitHub repository to provide a visual overview of the model's capabilities.

README.md

@@ -2,7 +2,12 @@
 license: other
 license_name: snap-non-commercial
 license_link: LICENSE
+pipeline_tag: unconditional-image-generation
 ---
 
-This repo contains the checkpoints for official implementation of "AlphaFlow: Understanding and Improving MeanFlow Models".
-The code is available at [https://github.com/snap-research/alphaflow](https://github.com/snap-research/alphaflow)
+This repository contains the checkpoints for the official implementation of **AlphaFlow**, as described in the paper [AlphaFlow: Understanding and Improving MeanFlow Models](https://huggingface.co/papers/2510.20771).
+
+The code is available at [https://github.com/snap-research/alphaflow](https://github.com/snap-research/alphaflow).
+
+![Comparison image from AlphaFlow GitHub README](https://github.com/snap-research/alphaflow/raw/main/figures/teaser.jpg)
+*Uncurated* samples (seeds 1-8) from the DiT-XL model for MeanFlow [1] and $\alpha$-Flow (our proposed method) produced with 1 (upper) and 2 (lower) sampling steps for ImageNet-1K $256^2$.