Improve model card with metadata and links

This PR improves the model card by adding essential metadata to ensure discoverability on the Hugging Face Hub. Specifically, it adds the `unconditional-image-generation` pipeline tag and specifies `diffusers` as the library name.

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+pipeline_tag: unconditional-image-generation
+library_name: diffusers
+---
+
+# Unified Continuous Generative Models
+
+The model was presented in the paper [Unified Continuous Generative Models](https://huggingface.co/papers/2505.07447).
+
+# Paper Abstract
+
+Recent advances in continuous generative models, including multi-step approaches like diffusion and flow-matching (typically requiring 8-1000 sampling steps) and few-step methods such as consistency models (typically 1-8 steps), have demonstrated impressive generative performance. However, existing work often treats these approaches as distinct paradigms, resulting in separate training and sampling methodologies. We introduce a unified framework for training, sampling, and analyzing these models. Our implementation, the Unified Continuous Generative Models Trainer and Sampler (UCGM-{T,S}), achieves state-of-the-art (SOTA) performance. For example, on ImageNet 256x256 using a 675M diffusion transformer, UCGM-T trains a multi-step model achieving 1.30 FID in 20 steps and a few-step model reaching 1.42 FID in just 2 steps. Additionally, applying UCGM-S to a pre-trained model (previously 1.26 FID at 250 steps) improves performance to 1.06 FID in only 40 steps. Code is available at: https://github.com/LINs-lab/UCGM.
+
+# Code
+
+The code for this model is available on Github: https://github.com/LINs-lab/UCGM