Enhance model card for VGT: Add metadata, paper, code, and usage

This PR significantly enhances the model card for 'Visual Generation Tuning (VGT)' by:
- Adding the `pipeline_tag: text-to-image` to improve discoverability on the Hugging Face Hub.
- Including a detailed overview of the model and its key highlights.
- Providing a direct link to the official paper: [Visual Generation Tuning](https://huggingface.co/papers/2511.23469).
- Linking to the GitHub repository: https://github.com/hustvl/VGT.
- Adding comprehensive installation instructions and a sample inference code snippet directly from the GitHub README to guide users on how to use the model.
- Ensuring the academic citation information is present.

This update aims to make the model card more informative, user-friendly, and discoverable.

README.md

@@ -1,3 +1,105 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: text-to-image
+---
+
+# VGT: Visual Generation Tuning
+
+**_Unleashing Visual Generation Capabilities from Any Pretrained VLM_**
+
+VGT (Visual Generation Tuning) is a novel paradigm designed to stimulate the underlying capabilities of visual generation within any Vision-Language Model (VLM). By performing efficient visual generation tuning on well-pretrained VLMs, VGT significantly mitigates alignment costs and accelerates the convergence of autoregressive modeling in the continuous space (20x speedup). It aims to transform any pretrained VLM into a powerful image generator, achieving state-of-the-art outcomes in visual generation tasks with dramatically faster convergence and extreme data efficiency.
+
+**GenEval 0.83 | DPG-Bench 81.28 | 20× Faster Convergence**
+
+<div align="center">
+<img src="https://github.com/hustvl/VGT/raw/main/asserts/case_show.png" alt="VGT Generated Images">
+</div>
+
+## ✨ Highlights
+
+-   **🎯 Novel Paradigm**: Transform ANY pretrained Vision-Language Model into a powerful image generator through efficient visual generation tuning.
+-   **⚡ 20× Speedup**: Achieve dramatically faster convergence compared to vanilla VAE-based autoregressive models.
+-   **📊 SOTA Performance**: GenEval **0.83** and DPG-Bench **81.28** with minimal training data.
+-   **🚀 Extreme Data Efficiency**: Reach GenEval 0.55 in just 10K iterations, 0.60 in 30K iterations.
+-   **🔄 Parallel Inference**: QueryAR mechanism enables 16× parallel decoding while maintaining high-quality generation.
+-   **🎨 Superior Reconstruction**: 26.67 PSNR and 0.50 rFID at 28× compression ratio, outperforming specialized VAEs.
+
+## Paper
+
+The model was presented in the paper:
+[**Visual Generation Tuning**](https://huggingface.co/papers/2511.23469)
+
+## Code
+
+The official implementation and code are available on the [GitHub repository](https://github.com/hustvl/VGT).
+
+## 🚀 Getting Started
+
+### Installation
+
+To get started, clone the repository and install the required dependencies:
+
+```bash
+# Clone the repository
+git clone https://github.com/hustvl/VGT.git
+cd VGT
+
+# Install dependencies
+conda create -n vgt python=3.10
+conda activate vgt
+pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
+pip install mmengine xtuner tqdm timm
+pip install diffusers transformers==4.57.1
+pip install flash-attn --no-build-isolation
+```
+
+### Pretrained Models
+
+Pretrained VGT-tuned models based on Qwen2.5-VL and InternVL3 (448px) are available for download:
+
+| Model | Base Model | GenEval | DPG-Bench | Download |
+|:------|:-----------|:-------:|:---------:|:--------:|
+| VGT-InternVL3-1.6B-Pretrain | InternVL3-1.6B | 0.58 | 73.05 | [🤗 HuggingFace](https://huggingface.co/hustvl/vgt_internvl3_1_6B_pretrain) |
+| VGT-InternVL3-1.6B-SFT | InternVL3-1.6B | 0.83 | 76.33 | [🤗 HuggingFace](https://huggingface.co/hustvl/vgt_internvl3_1_6B_sft) |
+| VGT-Qwen2.5-VL-2B-Pretrain | Qwen2.5-VL-2B | 0.63 | 78.02 | [🤗 HuggingFace](https://huggingface.co/hustvl/vgt_qwen25vl_2B_pretrain) |
+| VGT-Qwen2.5-VL-2B-SFT | Qwen2.5-VL-2B | 0.83 | 81.28 | [🤗 HuggingFace](https://huggingface.co/hustvl/vgt_qwen25vl_2B_sft) |
+
+### Inference (Sample Usage)
+
+Download the SFT model checkpoints and generate images from text prompts:
+
+```bash
+# Ensure you are in the 'VGT' directory
+cd VGT
+
+# Create a directory for checkpoints
+mkdir -p ckpts/hustvl
+
+# Download the sft model checkpoints
+hf download hustvl/vgt_qwen25vl_2B_sft --repo-type model --local-dir ckpts/hustvl/vgt_qwen25vl_2B_sft
+hf download hustvl/vgt_internvl3_1_6B_sft --repo-type model --local-dir ckpts/hustvl/vgt_internvl3_1_6B_sft
+
+# Set Python path and run the inference script for InternVL3-1.6B
+export PYTHONPATH=./:$PYTHONPATH
+python scripts/sample_text_list_vgt_intervl3_0.6B.py
+```
+
+*Note: VGT-Qwen2.5-VL-2B performs better in face generation, while VGT-InternVL3-1.6B excels in generating landscapes, light and shadow, and animals. You can explore these differences yourself.*
+
+## 📝 Citation
+
+If you find our work useful, please cite our paper:
+
+```bibtex
+@misc{guo2025vgt,
+      title={Visual Generation Tuning}, 
+      author={Jiahao Guo and Sinan Du and Jingfeng Yao and Wenyu Liu and Bo Li and Haoxiang Cao and Kun Gai and Chun Yuan and Kai Wu and Xinggang Wang},
+      year={2025},
+      eprint={2511.23469},
+      archivePrefix={arXiv},
+}
+```
+
+## 📄 License
+
+This project is released under the MIT License. See the [LICENSE](https://github.com/hustvl/VGT/blob/main/LICENSE) file for details.