Uni Edit BAGEL

--- base_model: - ByteDance-Seed/BAGEL-7B-MoT datasets: - Uni-Edit/Train-Data library_name: transformers pipeline_tag: any-to-any license: apache-2.0 --- <p align="left"> <img src="https://github.com/zhengdian1/Uni-Edit/blob/main/assets/logo.jpg?raw=true" alt="Uni-Edit" width="480"/> </p> # 🥯 Uni-Edit: Intelligent Editing Is A General Task For Unified Model Tuning [**Project Page**](https://zhengdian1.github.io/Uni-Edit-proj/) | [**GitHub Repository**](https://github.com/zhengdian1/Uni-Edit) | [**Paper**](https://arxiv.org/pdf/2605.21487) # 👀 Intro <div align="center"> <img src="https://github.com/zhengdian1/Uni-Edit/blob/main/assets/teaser.webp?raw=true" alt="Uni-Edit Teaser" width="80%"> </div> We introduce **Uni-Edit**, an intelligent image editing task that serves as the **first general task for Unified Multimodal Model (UMM) tuning**. Unlike conventional mixed multi-task training that suffers from inherent task conflicts and requires complex multi-stage pipelines, Uni-Edit breaks this paradigm. It achieves true mutual reinforcement by **improving image understanding, generation, and editing capabilities simultaneously using only one task, one training stage, and one dataset.** To overcome the limitations of simplistic existing editing data, we propose the **first automated and scalable data synthesis pipeline** for intelligent editing. By transforming diverse VQA data into complex instructions with embedded questions and nested logic, we build **Uni-Edit-148k**, a dedicated dataset pairing reasoning-intensive instructions with high-quality edited images. Extensive experiments on BAGEL and Janus-Pro demonstrate that tuning solely on Uni-Edit achieves **comprehensive enhancements across all three multimodal capabilities** without requiring any massive data mixing, balancing tricks, or auxiliary operations. ## 🎥 Demo Refer to our website [[🌐Project Page]](https://zhengdian1.github.io/Uni-Edit-proj/) ## 🚀 Training and Inference For detailed instructions on setup, training, inference, evaluation, data construction, please refer to the [official GitHub repository](https://github.com/zhengdian1/Uni-Edit). **⚠️ IMPORTANT: Custom Architecture** Because this is a custom architecture, you **CANNOT** load it directly via `AutoModel.from_pretrained()`. To run the provided inference code, you **MUST** physically merge these shards into a single...