---
title: VAREdit
canonical_url: "https://www.modelscope.ai/models/HiDream-ai/VAREdit"
md_url: "https://www.modelscope.ai/models/HiDream-ai/VAREdit.md"
repository: HiDream-ai/VAREdit
last_updated: 2026-06-03
license: mit
pipeline_tag: image-to-image
tasks:
  - image-to-image
base_model:
  - FoundationVision/Infinity
base_model_relation: finetune
parameters: 2.8B
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 154
stars: 0
tags:
  - image-editing
  - HiDream.ai
---

# VAREdit

> VAREdit - An open-source model by HiDream-ai on ModelScope. VAREdit: Visual Autoregressive Modeling for Instruction-Guided Image Editing

HiDream-ai/VAREdit is a 2.8B-parameter image-to-image model on ModelScope. licensed under mit. derived from FoundationVision/Infinity.

- **Repository**: HiDream-ai/VAREdit
- **License**: mit
- **Tasks**: image-to-image
- **Parameters**: 2.8B
- **Base model**: FoundationVision/Infinity
- **Tags**: image-editing, HiDream.ai
- **Downloads**: 154
- **Stars**: 0
- **Last updated**: 2026-06-03

Source: https://www.modelscope.ai/models/HiDream-ai/VAREdit

---

# VAREdit: Visual Autoregressive Modeling for Instruction-Guided Image Editing

[📄 Paper](https://huggingface.co/papers/2508.15772)

![VAREdit Demo](assets/demo.jpg)

[VAREdit](https://github.com/HiDream-ai/VAREdit) is an advanced image editing model built on the [Infinity](https://huggingface.co/FoundationVision/infinity) models, designed for high-quality instruction-based image editing.


Try our online demos: [🤗VAREdit-8B-1024](https://huggingface.co/spaces/HiDream-ai/VAREdit-8B-1024) and [🤗VAREdit-8B-512](https://huggingface.co/spaces/HiDream-ai/VAREdit-8B-512).

## 🌟 Key Features

-   **Strong Instruction Follow**: Follows instructions more accurately due to the autoregressive nature of the model.
-   **Efficient Inference**: Optimized for fast generation with less than 1 seconds for 8B model.
-   **Flexible Resolution**: Supports 512×512 and 1024×1024 image resolutions
![VAREdit Demo](assets/framework.jpg)

## 📊 Model Variants

| Model Variant | Resolutions | HuggingFace Model | Time (H800) | VRAM (GB) |
|:--------------|:------------|:---------------------------------------------------------------------------------|:----------|:----------|
| VAREdit-8B-512 | 512×512 | [VAREdit-8B-512](https://huggingface.co/HiDream-ai/VAREdit) | ~0.7s | 50.41 |
| VAREdit-8B-1024 | 1024×1024 | [VAREdit-8B-1024](https://huggingface.co/HiDream-ai/VAREdit) | ~1.99s | 50.41 |

## 🚀 Quick Start

### Prerequisites

Before starting, ensure you have:
- Python 3.8+
- CUDA-compatible GPU with sufficient VRAM (8GB+ for 2B model, 24GB+ for 8B model)
- Required dependencies installed

### Installation

1.  **Clone the repository**
```bash
git clone https://github.com/HiDream-ai/VAREdit.git
cd VAREdit
```

2.  **Install dependencies**
```bash
pip install -r requirements.txt
```

3.  **Download model checkpoints**

Download the VAREdit model checkpoints:
```bash
# Download from HuggingFace
git lfs install
git clone https://huggingface.co/HiDream-ai/VAREdit
```

### Basic Usage

```python
from infer import load_model, generate_image

model_components = load_model(
    pretrain_root="HiDream-ai/VAREdit",
    model_path="HiDream-ai/VAREdit/8B-1024.pth",
    model_size="8B",
    image_size=1024
)

# Generate edited image
edited_image = generate_image(
    model_components,
    src_img_path="assets/test.jpg",
    instruction="Add glasses to this girl and change hair color to red",
    cfg=3.0,  # Classifier-free guidance scale
    tau=0.1,  # Temperature parameter
    seed=42  # Optional random seed
)
```

## 📝 Detailed Configuration

### Model Sampling Parameters

| Parameter | Description | Default |
|:----------|:------------|:--------|
| `cfg` | Classifier-free guidance scale | 3.0 |
| `tau` | Temperature for sampling | 0.1 |
| `seed` | Random seed for reproducibility | -1 (random) |

## 📂 Project Structure

```
VAREdit/
├── infer.py              # Main inference script
├── infinity/             # Core model implementations
│   ├── models/          # Model architectures
│   ├── dataset/         # Data processing utilities
│   └── utils/           # Helper functions
├── tools/               # Additional tools and scripts
│   └── run_infinity.py  # Model execution utilities
├── assets/              # Demo images and resources
└── README.md           # This file
```

## 📊 Performance Benchmarks
| **Method** | **Size** | **EMU-Edit Bal.** | **PIE-Bench Bal.** | **Time (A800)** |
|:---|:---:|:---:|:---:|:---:|
| InstructPix2Pix | 1.1B | 2.923 | 4.034 | 3.5s |
| UltraEdit | 7.7B | 4.541 | 5.580 | 2.6s |
| OmniGen | 3.8B | 4.674 | 3.492 | 16.5s |
| AnySD | 2.9B | 3.129 | 3.326 | 3.4s |
| EditAR | 0.8B | 3.305 | 4.707 | 45.5s |
| ACE++ | 16.9B | 2.076 | 2.574 | 5.7s |
| ICEdit | 17.0B | 4.785 | 4.933 | 8.4s |
| **VAREdit** (256px) | 2.2B | 5.565 | 6.684 | 0.5s |
| **VAREdit** (512px) | 2.2B | 5.662 | 6.996 | 0.7s |
| **VAREdit** (512px) | 8.4B | 7.792 | 8.105 | 1.2s |
| **VAREdit** (1024px) | 8.4B | 7.379 | 7.688 | 3.9s |

**Note**: The released 8B models are trained longer and on more data, so the performances are better than that in the paper.

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 📚 Citation

If you use VAREdit in your research, please cite:

```bibtex
@article{varedit2025,
  title={Visual Autoregressive Modeling for Instruction-Guided Image Editing},
  author={Mao, Qingyang and Cai, Qi and Li, Yehao and Pan, Yingwei and Cheng, Mingyue and Yao, Ting and Liu, Qi and Mei, Tao},
  journal={arXiv preprint},
  year={2025}
}
```

## 🙏 Acknowledgments

- Built on the [Infinity](https://huggingface.co/FoundationVision/infinity) models

**Note**: This project is under active development. Features and code may change.