Add model card for TV-Edit

README.md

@@ -0,0 +1,62 @@
+---
+license: apache-2.0
+pipeline_tag: image-to-image
+tags:
+- image-editing
+- multi-modal
+---
+
+# Text-Vision Co-Instructed Image Editing (TV-Edit)
+
+This repository contains the weights for **TV-Edit**, a Textual-Visual instruction unified Editing framework. TV-Edit jointly models textual instructions as semantic intent and sparse visual instructions (such as points or drags) as spatial guidance to achieve precise and intent-faithful image manipulation.
+
+[**Project Page**](https://xiechenxi99.github.io/TVEdit/) | [**Paper**](https://huggingface.co/papers/2606.16767) | [**GitHub**](https://github.com/PolyU-VCLab/TVEdit)
+
+## Introduction
+TV-Edit is designed to unify the strengths of textual instructions and visual prompts. By integrating semantic intent and spatial constraints, TV-Edit leads to more precise spatial control, less instruction ambiguity, and stronger structural consistency than text-only or drag-based alternatives.
+
+## Installation
+
+To set up the environment, run the following commands:
+
+```shell
+## git clone the repository
+git clone https://github.com/PolyU-VCLab/TVEdit.git
+cd TVEdit
+
+# create an environment
+conda create -n TVEdit python=3.10
+conda activate TVEdit
+
+pip install --upgrade pip
+pip install torch==2.5.0+cu121 torchvision==0.20.0+cu121 --index-url https://download.pytorch.org/whl/cu121
+pip install transformers==4.52.4 pytorch-lightning==2.4.0 diffusers==0.35.1
+```
+
+## Usage
+
+You can run the interactive Gradio demo to test the model:
+
+1. Download the base model checkpoint: [Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit).
+2. Download the trained TV-Edit weights from this repository.
+3. [Optional] TV-Edit supports existing trained acceleration LoRA for 4-step editing: [Qwen-Image-Edit-4step](https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Edit-Lightning-4steps-V1.0-bf16.safetensors).
+4. Launch the Gradio demo:
+
+```shell
+python gradio_demo.py
+```
+
+After launching the demo, you can upload an image, draw point trajectories for spatial control, and enter textual instructions for semantic changes.
+
+## Citation
+
+If you find this work helpful, please cite:
+
+```bibtex
+@article{xie2026text-vision,
+  title={Text-Vision Co-Instructed Image Editing},
+  author={Xie, Chenxi and Wu, Yuhui and Yi, Qiaosi and Zhang, Lei},
+  journal={arXiv preprint arXiv:2606.16767},
+  year={2026},
+}
+```