Improve model card and metadata

This PR improves the model card by:
- Adding descriptive tags (`flow-matching`, `image-generation`, etc.) to the metadata for better discoverability.
- Removing the inapplicable `code_eval` metric.
- Including the full list of authors.
- Providing structured setup and inference instructions based on the official GitHub repository.
- Linking the paper to its Hugging Face paper page.

README.md

@@ -1,18 +1,22 @@
 ---
-license: apache-2.0
 datasets:
 - CSU-JPG/VisPrompt5M
 - CSU-JPG/VPBench
 language:
 - en
-metrics:
-- code_eval
+license: apache-2.0
 pipeline_tag: image-to-image
+tags:
+- flow-matching
+- image-generation
+- image-editing
+- vision-centric
 ---
+
 <div align="center">
   <h2 align="center" style="margin-top: 0; margin-bottom: 15px;">
-    <span style="color:#0052CC">F</span><span style="color:#135FD0">l</span><span style="color:#266CD4">o</span><span style="color:#3979D7">w</span><span style="color:#4C86DB">I</span><span style="color:#6093DF">n</span><span style="color:#73A0E3">O</span><span style="color:#86ADE7">n</span><span style="color:#99BAEB">e</span>: Unifying Multimodal Generation as 
-    <span style="color:#0052CC">I</span><span style="color:#0958CE">m</span><span style="color:#125ED0">a</span><span style="color:#1B64D2">g</span><span style="color:#246AD4">e</span><span style="color:#2D70D6">-</span><span style="color:#3676D8">i</span><span style="color:#3F7CDA">n</span><span style="color:#4882DC">,</span>&nbsp;<span style="color:#5188DE">I</span><span style="color:#5A8EE0">m</span><span style="color:#6394E2">a</span><span style="color:#6C9AE4">g</span><span style="color:#75A0E6">e</span><span style="color:#7EA6E8">-</span><span style="color:#87ACEA">o</span><span style="color:#90B2EC">u</span><span style="color:#99B8EE">t</span> Flow Matching
+    <span style="color:#0052CC">F</span><span style="color:#135FD0">l</span><span style="color:#266CD4">o</span><span style="color:#3979D7">w</span><span style="color:#4C86DB">I</span><span style="color:#6093DF">n</span><span style="color:#73A0E3">O</span><span style="color:#86ADE7">n</span><span style="color:#99BAEB\">e</span>: Unifying Multimodal Generation as 
+    <span style="color:#0052CC">I</span><span style="color:#0958CE">m</span><span style="color:#125ED0">a</span><span style="color:#1B64D2">g</span><span style="color:#246AD4">e</span><span style="color:#2D70D6">-</span><span style="color:#3676D8">i</span><span style="color:#3F7CDA\">n</span><span style="color:#4882DC">,</span>&nbsp;<span style="color:#5188DE">I</span><span style="color:#5A8EE0\">m</span><span style="color:#6394E2\">a</span><span style="color:#6C9AE4\">g</span><span style="color:#75A0E6\">e</span><span style="color:#7EA6E8">-</span><span style="color:#87ACEA\">o</span><span style="color:#90B2EC\">u</span><span style="color:#99B8EE\">t</span> Flow Matching
   </h2>
   <p align="center" style="font-size: 15px;">
     <span style="color:#E74C3C; font-weight: bold;">TL;DR:</span> <strong>The first vision-centric image-in, image-out image generation model.</strong>
@@ -20,40 +24,59 @@ pipeline_tag: image-to-image
   <p align="center" style="font-size: 16px;">
     <a href="https://csu-jpg.github.io/FlowInOne.github.io/" style="text-decoration: none;">🌐 Homepage</a> | 
     <a href="https://github.com/CSU-JPG/FlowInOne" style="text-decoration: none;">💻 Code</a> | 
-    <a href="https://arxiv.org/pdf/2604.06757" style="text-decoration: none;">📄 Paper</a> | 
+    <a href="https://huggingface.co/papers/2604.06757" style="text-decoration: none;">📄 Paper</a> | 
     <a href="https://huggingface.co/datasets/CSU-JPG/VisPrompt5M" style="text-decoration: none;">📁 Dataset</a> | 
     <a href="https://huggingface.co/datasets/CSU-JPG/VPBench" style="text-decoration: none;">🌏 Benchmark</a> | 
     <a href="https://huggingface.co/CSU-JPG/FlowInOne" style="text-decoration: none;">🤗 Model</a>
 </p>
 </div>
 
+## Authors
+Junchao Yi, Rui Zhao, Jiahao Tang, Weixian Lei, Linjie Li, Qisheng Su, Zhengyuan Yang, Lijuan Wang, Xiaofeng Zhu, Alex Jinpeng Wang.
+
 ## About
-We present FlowInOne, a framework that reformulates multimodal generation as a **purely visual flow**, converting all inputs into visual prompts and enabling a clean **image-in, image-out** pipeline governed by a single flow matching model. 
+FlowInOne is a framework that reformulates multimodal generation as a **purely visual flow**, converting all inputs into visual prompts and enabling a clean **image-in, image-out** pipeline governed by a single flow matching model. 
+
 This vision-centric formulation naturally eliminates cross-modal alignment bottlenecks, noise scheduling, and task-specific architectural branches, **unifying text-to-image generation, layout-guided editing, and visual instruction following under one coherent paradigm**.
-Extensive experiments demonstrate that FlowInOne achieves **state-of-the-art performance across all unified generation tasks**, surpassing both open-source models and competitive commercial systems, establishing a new foundation for fully vision-centric generative modeling where perception and creation coexist within a single continuous visual space.
 
-## 🧪 Usage
-you can download the model weights and model preparation
+## 🚀 Setup
+
+```bash
+# Create conda environment
+conda create -n flowinone python=3.10 -y
+conda activate flowinone
+
+# Install required packages
+git clone https://github.com/CSU-JPG/FlowInOne.git
+cd FlowInOne/scripts
+sh setup.sh
+```
+
+## ✨ Usage
+
+### 1. Download Weights
+You can download the model weights and model preparation files using the following commands:
 ```bash
 # model weights
-wget -O /path/to/download https://huggingface.co/CSU-JPG/FlowInOne/resolve/main/flowinone_256px.pth
+wget -O checkpoints/flowinone_256px.pth https://huggingface.co/CSU-JPG/FlowInOne/resolve/main/flowinone_256px.pth
+
 # model preparation
-wget -O /path/to/download https://huggingface.co/CSU-JPG/FlowInOne/resolve/main/preparation.tar.gz
-# unzip
-tar -xzvf "preparation.tar.gz" -C "/path/to/preparation"
+wget https://huggingface.co/CSU-JPG/FlowInOne/resolve/main/preparation.tar.gz
+tar -xzvf "preparation.tar.gz"
 ```
-you can download the dataset examples 
+
+### 2. Inference
+Run inference with the provided script in the repository:
 ```bash
-wget -O /path/to/download https://huggingface.co/CSU-JPG/FlowInOne/resolve/main/flowinone_demo_dataset.tar.gz
-# unzip
-tar -xzvf "flowinone_demo_dataset.tar.gz" -C "/path/to/flowinone_demo_dataset"
+sh scripts/inference.sh
 ```
-Our training and inference scripts are now available on [GitHub](https://github.com/CSU-JPG/FlowInOne)!
+
+Our training and inference scripts are fully available on [GitHub](https://github.com/CSU-JPG/FlowInOne).
 
 ## Citation
 
 If you found our work useful, please consider citing:
-```
+```bibtex
 @article{yi2026flowinoneunifyingmultimodalgenerationimagein,
       title={FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching}, 
       author={Junchao Yi and Rui Zhao and Jiahao Tang and Weixian Lei and Linjie Li and Qisheng Su and Zhengyuan Yang and Lijuan Wang and Xiaofeng Zhu and Alex Jinpeng Wang},