Improve model card with metadata, links, and usage for Transition Models (TiM)

This PR significantly enhances the model card for the Transition Models (TiM) by:

- Adding the `license` to the metadata (Apache-2.0).
- Including the `pipeline_tag: text-to-image` to ensure discoverability on the Hugging Face Hub.
- Providing a concise description of the model based on the paper's abstract and highlights from the GitHub README.
- Including a "Quickstart" section with instructions for setting up the environment, downloading the model, and running text-to-image generation, directly extracted from the GitHub README to facilitate immediate usage.

README.md

@@ -1 +1,58 @@
-arxiv.org/abs/2509.04394
+---
+license: apache-2.0
+pipeline_tag: text-to-image
+---
+
+# Transition Models: Rethinking the Generative Learning Objective
+
+This repository contains the Transition Models (TiM) presented in the paper [Transition Models: Rethinking the Generative Learning Objective](https://arxiv.org/abs/2509.04394).
+
+TiM is a novel generative model designed for flexible photorealistic text-to-image generation. It achieves state-of-the-art performance with high efficiency by learning arbitrary state-to-state transitions, unifying few-step and many-step generation within a single model.
+
+*   **Paper**: [https://arxiv.org/abs/2509.04394](https://arxiv.org/abs/2509.04394)
+*   **Code**: [https://github.com/WZDTHU/TiM](https://github.com/WZDTHU/TiM)
+
+## Highlights
+
+*   Our Transition Models (TiM) are trained to master arbitrary state-to-state transitions. This approach allows TiM to learn the entire solution manifold of the generative process, unifying the few-step and many-step regimes within a single, powerful model.
+    ![Figure](https://github.com/WZDTHU/TiM/raw/main/assets/illustration.png)
+*   Despite having only 865M parameters, TiM achieves state-to-art performance, surpassing leading models such as SD3.5 (8B parameters) and FLUX.1 (12B parameters) across all evaluated step counts on GenEval benchmark. Importantly, unlike previous few-step generators, TiM demonstrates monotonic quality improvement as the sampling budget increases.
+    ![Figure](https://github.com/WZDTHU/TiM/raw/main/assets/nfe_demo.png)
+*   Additionally, when employing our native-resolution strategy, TiM delivers exceptional fidelity at resolutions up to `4096x4096`.
+    ![Figure](https://github.com/WZDTHU/TiM/raw/main/assets/tim_demo.png)
+
+## Quickstart
+
+### 1. Setup
+
+First, clone the repo:
+```bash
+git clone https://github.com/WZDTHU/TiM.git && cd TiM
+```
+
+#### 1.1 Environment Setup
+
+```bash
+conda create -n tim_env python=3.10
+pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu118
+pip install flash-attn
+pip install -r requirements.txt
+pip install -e .
+```
+
+#### 1.2 Model Download
+
+Download the Text-to-Image model:
+```bash
+mkdir checkpoints
+wget -c "https://huggingface.co/GoodEnough/TiM-T2I/resolve/main/t2i_model.bin" -O checkpoints/t2i_model.bin
+```
+
+### 2. Sampling (Text-to-Image Generation)
+
+We provide the sampling scripts on three benchmarks. You can specify the sampling steps, resolutions, and CFG scale in the corresponding scripts.
+
+Sampling with TiM-T2I model on GenEval benchmark:
+```bash
+bash scripts/sample/t2i/sample_t2i_geneval.sh
+```