Enhance model card for UniREdit-Bagel: Add metadata, links, and usage

README.md

@@ -1,5 +1,117 @@
 ---
 license: apache-2.0
+pipeline_tag: image-to-image
+library_name: transformers
 ---
 
-[UniREditBench: A Unified Reasoning-based Image Editing Benchmark](https://arxiv.org/abs/2511.01295)
+# UniREdit-Bagel: A Unified Reasoning-based Image Editing Model
+
+This repository hosts **UniREdit-Bagel**, a model developed as part of the research presented in the paper:
+[UniREditBench: A Unified Reasoning-based Image Editing Benchmark](https://arxiv.org/abs/2511.01295)
+
+**Project Page**: [https://maplebb.github.io/UniREditBench/](https://maplebb.github.io/UniREditBench/)
+**Code Repository**: [https://github.com/Maplebb/UniREditBench](https://github.com/Maplebb/UniREditBench)
+
+<div align="center">
+    <img alt="image" src="https://github.com/Maplebb/UniREditBench/raw/main/docs/static/images/teaser.png" />
+</div>
+
+## Introduction
+We propose **UniREditBench**, a unified benchmark for reasoning-based image editing evaluation with broader evaluation dimension coverage and a robust evaluation pipeline. We also design an automated multi-scenario data synthesis pipeline and construct **UniREdit-Data-100K**, a large-scale synthetic dataset with high-quality chain-of-thought (CoT) reasoning annotations. We fine-tune Bagel on this dataset and develop **UniREdit-Bagel**, demonstrating substantial improvements in both in-domain and out-of-distribution settings.
+
+<div align="center">
+    <img alt="image" src="https://github.com/Maplebb/UniREditBench/raw/main/docs/static/images/radar.png" />
+</div>
+
+### ✨ Highlights:
+
+-   **Broader Scenario and Reasoning Dimension Coverage**: It contains 2,700 high-quality samples organized into 8 primary reasoning dimensions and 18 sub-categories, spanning both real-world and game-world image editing tasks.
+-   **Reliable Dual-Reference Evaluation**: For each sample assessment, we design both the textual reference and ground-truth (GT) image reference. This multi-modal reference enables vision-language model (VLM) evaluators to perform direct and fine-grained comparisons at both the textual and visual levels with the generated images, leading to more reliable evaluation.
+
+<div align="center">
+    <img alt="image" src="https://github.com/Maplebb/UniREditBench/raw/main/docs/static/images/motivation_tab.png" />
+</div>
+<div align="center">
+    <img alt="image" src="https://github.com/Maplebb/UniREditBench/raw/main/docs/static/images/motivation_fig.png" />
+</div>
+<div align="center">
+    <img alt="image" src="https://github.com/Maplebb/UniREditBench/raw/main/docs/static/images/testpoint_cases.png" />
+</div>
+
+## 🚀 Sample Usage
+
+To perform image editing with reasoning using UniREdit-Bagel, follow the steps below. This section is adapted from the [official GitHub repository](https://github.com/Maplebb/UniREditBench).
+
+### 1. Set Up Environment
+
+```bash
+conda create -n uniredit python=3.10 -y
+conda activate uniredit
+pip install -r requirements.txt
+pip install flash_attn==2.7.0.post1 --no-build-isolation
+```
+You can also install `flash_attn` via:
+```bash
+# for cuda11 torch2.5.x
+pip install "https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.0.post1/flash_attn-2.7.0.post1+cu11torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"
+
+# for cuda12 torch2.5.x
+pip install "https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.0.post1/flash_attn-2.7.0.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"
+```
+
+### 2. Benchmark and Checkpoint Preparation
+
+First, prepare the UniREditBench benchmark dataset:
+```bash
+huggingface-cli download --resume-download maplebb/UniREditBench  --local-dir ./UniREditBench
+cd UniREditBench
+unzip original_image.zip
+unzip reference_image.zip
+cd ..
+```
+Then, prepare the UniREdit-Bagel checkpoint:
+```bash
+huggingface-cli download --resume-download maplebb/UniREdit-Bagel  --local-dir ./ckpt
+
+pip install safetensors
+
+python merge_ckpt.py
+```
+*(Note: The `merge_ckpt.py` script is part of the UniREditBench GitHub repository and should be run from its root directory after cloning and downloading the checkpoint.)*
+
+### 3. Inference
+
+Once the environment and checkpoints are prepared, you can run inference:
+```bash
+GPUS=8
+model_path=./ckpt
+input_path=./UniREditBench
+output_path=./output_images
+
+# Image Editing with Reasoning
+torchrun \
+    --nnodes=1 \
+    --nproc_per_node=$GPUS \
+    gen_images_mp_uniredit.py \
+    --input_dir $input_path \
+    --output_dir $output_path \
+    --metadata_file ./UniREditBench/data.json \
+    --max_latent_size 64 \
+    --model-path $model_path \
+    --think
+```
+This command will generate edited images based on the instructions and save them to the specified `output_images` directory. The `--think` argument enables reasoning capabilities.
+
+## 📧 Contact
+If you have any comments or questions, please open a new issue on the [GitHub repository](https://github.com/Maplebb/UniREditBench) or feel free to contact [Feng Han](fhan25@m.fudan.edu.cn) and [Yibin Wang](https://codegoat24.github.io).
+
+## ��� Citation
+If you find our work helpful or inspiring, please consider citing it:
+```bibtex
+@article{han2025unireditbench,
+  title={UniREditBench: A Unified Reasoning-based Image Editing Benchmark},
+  author={Han, Feng and Wang, Yibin and Li, Chenglin and Liang, Zheming and Wang, Dianyi and Jiao, Yang and Wei, Zhipeng and Gong, Chao and Jin, Cheng and Chen, Jingjing and Wang, Jiaqi},
+  journal={arXiv preprint arXiv:2511.01295},
+  year={2025}
+}
+```