Project Page | GitHub Repository | Paper

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.

Refer to our website [🌐Project Page]

For detailed instructions on setup, training, inference, evaluation, data construction, please refer to the official GitHub repository.

⚠️ 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 ema.safetensors file on your local machine.

Run the Python script in the code where you downloaded the repository. (Note: You need at least 54GB of free system RAM to perform this merge).

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