AnyInsertion

Wensong Song · Hong Jiang · Zongxing Yang · Ruijie Quan · Yi Yang


Zhejiang University   |   Harvard University   |   Nanyang Technological University

News

• [2025.5.9] Release new AnyInsertion v1 text- and mask-prompt dataset on HuggingFace.
• [2025.5.7] Release AnyInsertion v1 text prompt dataset on HuggingFace.
• [2025.4.24] Release AnyInsertion v1 mask prompt dataset on HuggingFace.

Summary

This is the dataset proposed in our paper Insert Anything: Image Insertion via In-Context Editing in DiT

AnyInsertion dataset consists of training and testing subsets. The training set includes 136,385 samples across two prompt types: 58,188 mask-prompt image pairs and 78,197 text-prompt image pairs;the test set includes 158 data pairs: 120 mask-prompt pairs and 38 text-prompt pairs.

AnyInsertion dataset covers diverse categories including human subjects, daily necessities, garments, furniture, and various objects.

Directory

data/
├── text_prompt/
│   ├── train/
│   │   ├── accessory/
│   │   │   ├── ref_image/      # Reference image containing the element to be inserted
│   │   │   ├── ref_mask/       # The mask corresponding to the inserted element
│   │   │   ├── tar_image/      # Ground truth
│   │   │   └── src_image/      # Source images
│   │   │       ├── add/        # Source image with the inserted element from Ground Truth removed
│   │   │       └── replace/    # Source image where the inserted element in Ground Truth is replaced
│   │   ├── object/
│   │   │   ├── ref_image/      
│   │   │   ├── ref_mask/      
│   │   │   ├── tar_image/      
│   │   │   └── src_image/      
│   │   │       ├── add/       
│   │   │       └── replace/    
│   │   └── person/
│   │       ├── ref_image/     
│   │       ├── ref_mask/       
│   │       ├── tar_image/     
│   │       └── src_image/    
│   │           ├── add/       
│   │           └── replace/  
│   └── test/
│       ├── garment/
│       │   ├── ref_image/      
│       │   ├── ref_mask/      
│       │   ├── tar_image/      
│       │   └── src_image/      
│       └── object/
│           ├── ref_image/     
│           ├── ref_mask/       
│           ├── tar_image/     
│           └── src_image/      
│
├── mask_prompt/
│   ├── train/
│   │   ├── accessory/
│   │   │   ├── ref_image/     
│   │   │   ├── ref_mask/      
│   │   │   ├── tar_image/     
│   │   │   ├── tar_mask/      # The mask corresponding to the edited area of target image
│   │   ├── object/
│   │   │   ├── ref_image/    
│   │   │   ├── ref_mask/      
│   │   │   ├── tar_image/     
│   │   │   ├── tar_mask/      
│   │   └── person/
│   │       ├── ref_image/     
│   │       ├── ref_mask/     
│   │       ├── tar_image/    
│   │       ├── tar_mask/   
│   └── test/
│       ├── garment/
│       │   ├── ref_image/   
│       │   ├── ref_mask/    
│       │   ├── tar_image/    
│       │   ├── tar_mask/    
│       ├── object/
│       │   ├── ref_image/    
│       │   ├── ref_mask/    
│       │   ├── tar_image/     
│       │   ├── tar_mask/    
│       └── person/
│           ├── ref_image/   
│           ├── ref_mask/   
│           ├── tar_image/  
│           ├── tar_mask/    

    

Example

 

       

Ref_image

 

 

       

Ref_mask

 

 

       

Tar_image

 

 

       

Tar_mask

 

 

       

Add

 

 

       

Replace

 

Text Prompt

Add Prompt: Add [label from tar_image (in label.json) ]

Replace Prompt: Replace [label from src_image (in src_image/replace/replace_label.json) ] with [label from tar_image (in label.json) ]

Citation

@article{song2025insert,
  title={Insert Anything: Image Insertion via In-Context Editing in DiT},
  author={Song, Wensong and Jiang, Hong and Yang, Zongxing and Quan, Ruijie and Yang, Yi},
  journal={arXiv preprint arXiv:2504.15009},
  year={2025}
}