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Official code for VINCIE: Unlocking In-context Image Editing from Video

VINCIE 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# VINCIE: Unlocking In-context Image Editing from Video
<p align="center">
  <a href="https://vincie2025.github.io/">
    <img
      src="https://img.shields.io/badge/VINCIE-Website-0A66C2?logo=safari&logoColor=white"
      alt="VINCIE Website"
    />
  </a>
  <a href="https://arxiv.org/abs/2506.10941">
    <img
      src="https://img.shields.io/badge/VINCIE-Paper-red?logo=arxiv&logoColor=red"
      alt="VINCIE Paper on ArXiv"
    />
  </a>
  <a href="https://github.com/ByteDance-Seed/VINCIE">
            <img 
              alt="Github" src="https://img.shields.io/badge/VINCIE-Codebase-536af5?color=536af5&logo=github"
              alt="VINCIE Codebase"
            />
  </a>
  <a href="https://huggingface.co/collections/ByteDance-Seed/vincie-6864cc2e3116d82e4a83a17c">
    <img 
        src="https://img.shields.io/badge/VINCIE-Models-yellow?logo=huggingface&logoColor=yellow" 
        alt="VINCIE Models"
    />
  </a>
  <a href="https://huggingface.co/datasets/leigangqu/VINCIE-10M">
    <img 
        src="https://img.shields.io/badge/VINCIE (10M)-Dataset-yellow?logo=huggingface&logoColor=yellow" 
        alt="VINCIE-10M Dataset"
    />
  </a>
  <a href="https://huggingface.co/datasets/leigangqu/MSE-Bench">
    <img 
        src="https://img.shields.io/badge/MSE-Benchmark-yellow?logo=huggingface&logoColor=yellow" 
        alt="MSE-Bench"
    />
  </a>
   
</p>

> [Leigang Qu](https://leigang-qu.github.io/), [Feng Cheng](https://klauscc.github.io/), [Ziyan Yang](https://ziyanyang.github.io/), [Qi Zhao](https://kevinz8866.github.io/), [Shanchuan Lin](https://scholar.google.com/citations?user=EDWUw7gAAAAJ&hl=en), [Yichun Shi](https://seasonsh.github.io/), [Yicong Li](https://yl3800.github.io/), [Wenjie Wang](https://wenjiewwj.github.io/), [Tat-Seng Chua](https://www.chuatatseng.com/), [Lu Jiang](http://www.lujiang.info/index.html)
> 
> In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pipelines and expert models (*e.g.*, segmentation and inpainting) to curate training data. In this work, we explore whether an in-context image editing model can be learned directly from videos. We introduce a scalable approach to annotate videos as interleaved multimodal sequences. To effectively learn from this data, we design a block-causal diffusion transformer trained on three proxy tasks: next-image prediction, current segmentation prediction, and next-segmentation prediction. Additionally, we propose a novel multi-turn image editing benchmark to advance research in this area. Extensive experiments demonstrate that our model exhibits strong in-context image editing capabilities and achieves state-of-the-art results on two multi-turn image editing benchmarks. Despite being trained exclusively on videos, our model also shows promising abilities in multi-concept composition, story generation, and chain-of-editing applications.

## News
- **19 Mar, 2026:** Released the [Evaluation Code](https://github.com/ByteDance-Seed/VINCIE/tree/main/evaluation) on MSE-Bench. 
- **15 Mar, 2026:** Released the  [Generated Images on MSE-Bench](https://huggingface.co/datasets/leigangqu/MSE-Bench-results), including VINCIE-3B, VINCIE-7B, Nano Banana, Qwen-Image-Edit, FLUX.1-Kontext-dev, Bagel, Step1X-Edit, Omnigen 2, Omnigen, ICEdit, UltraEdit, HQEdit, Magicbrush, and InstructPix2Pix. 
- **15 Mar, 2026:** Released the Multi-turn Session image Editing Benchmark ([MSE-Bench](https://huggingface.co/datasets/leigangqu/MSE-Bench)). 
- **6 Jan, 2026:** Released the [VINCIE-7B checkpoint](https://huggingface.co/ByteDance-Seed/VINCIE-7B) (full attention).
- **6 Sep, 2025:** Released the [VINCIE-3B checkpoint](https://huggingface.co/ByteDance-Seed/VINCIE-3B) (full attention).
- **25 Aug, 2025:** Released the official [website](https://vincie2025.github.io/) and the inference code.
- **23 Aug, 2025:** Released the [VINCIE-10M dataset](https://huggingface.co/datasets/leigangqu/VINCIE-10M). 
- **12 Jun, 2025:** Released the [VINCIE technical report](https://arxiv.org/abs/2506.10941) .

## Quick Start

1️⃣  Set up environment
```bash
git clone https://github.com/ByteDance-Seed/VINCIE
cd VINCIE
conda create -n vincie python=3.10 -y
conda activate vincie
pip install -r requirements.txt
pip install flash_attn==2.6.3 --no-build-isolation
```

2️⃣  Download pretrained checkpoint
```bash
# VINCIE-3B
python download_ckpt.py --model 3B

# VINCIE-7B
python download_ckpt.py --model 7B
```

Checkpoints are saved to:
- `ckpt/VINCIE-3B/` — `dit.pth`, `vae.pth`, `llm14b/`
- `ckpt/VINCIE-7B/` — `ema.pth`, `ema_vae.pth`, `flan-ul2/`

## Inference for Multi-turn Image Editing

```bash
turn1="Lower the pineapple beside her face, and change it to a smaller one."
turn2="Add a crown to the woman's head."
turn3="Change the woman's expression so that she is laughing."
turn4="Change the background to a pastel gradient of blue and lavender."
turn5="Add a colorful bird hovering above the crown."
```

### VINCIE-3B

```bash
torchrun --nproc-per-node=1 main.py configs/generate.yaml \
    generation.positive_prompt.image_path='["assets/woman_pineapple.png"]' \
    generation.positive_prompt.prompts="[\"$turn1\", \"$turn2\", \"$turn3\", \"$turn4\", \"$turn5\"]" \
    generation.output.dir=output/woman_pineapple_3b \
    generation.batch_size=1 \
    generation.resolution=256 \
    diffusion.timesteps.sampling.steps=50 \
    diffusion.cfg.scale=7.5 \
    dit.checkpoint=./ckpt/VINCIE-3B/dit.pth
```

### VINCIE-7B

```bash
torchrun --nproc-per-node=1 main.py configs/generate_7b.yaml \
    generation.positive_prompt.image_path='["assets/woman_pineapple.png"]' \
    generation.positive_prompt.prompts="[\"$turn1\", \"$turn2\", \"$turn3\", \"$turn4\", \"$turn5\"]" \
    generation.output.dir=output/woman_pineapple_7b \
    generation.batch_size=1 \
    generation.resolution=720 \
    diffusion.timesteps.sampling.steps=50 \
    diffusion.cfg.scale=10 \
    dit.checkpoint=./ckpt/VINCIE-7B/ema.pth
```

For multi-GPU inference, set `--nproc-per-node` to the number of GPUs.

## Inference for Multi-concept Composition

```bash
p1="<IMG1>: "; p2="<IMG2>: "; p3="<IMG3>: "; p4="<IMG4>: "; p5="<IMG5>: "
p6="Based on <IMG0>, <IMG1>, <IMG2>, <IMG3>, <IMG4>, and <IMG5>, A smiling multi-generational family including the father in <IMG0>, mother in <IMG1>, son in <IMG2>, daughter in <IMG3>, dog in <IMG4>, and cat in <IMG5>, poses for a portrait amidst the sunlit trees and ferns of a forest. Output <IMG6>: "
img0="./assets/father.png"; img1="./assets/mother.png"; img2="./assets/son.png"
img3="./assets/daughter.png"; img4="./assets/dog1.png"; img5="./assets/cat.png"
```

### VINCIE-3B

```bash
torchrun --nproc-per-node=1 main.py configs/generate.yaml \
    generation.pad_img_placehoder=False \
    generation.positive_prompt.image_path="[\"$img0\", \"$img1\", \"$img2\", \"$img3\", \"$img4\", \"$img5\"]" \
    generation.positive_prompt.prompts="[\"$p1\", \"$p2\", \"$p3\", \"$p4\", \"$p5\", \"$p6\"]" \
    generation.output.dir=output/family_3b \
    dit.checkpoint=./ckpt/VINCIE-3B/dit.pth
```

### VINCIE-7B

```bash
torchrun --nproc-per-node=1 main.py configs/generate_7b.yaml \
    generation.pad_img_placehoder=False \
    generation.positive_prompt.image_path="[\"$img0\", \"$img1\", \"$img2\", \"$img3\", \"$img4\", \"$img5\"]" \
    generation.positive_prompt.prompts="[\"$p1\", \"$p2\", \"$p3\", \"$p4\", \"$p5\", \"$p6\"]" \
    generation.output.dir=output/family_7b \
    dit.checkpoint=./ckpt/VINCIE-7B/ema.pth
```

## Evaluation

To evaluate multi-turn image editing performance on the **MSE-Bench** benchmark:

1. Install dependencies:
```bash
cd evaluation
pip install -r evaluation/requirements.txt
```

2. Set your OpenAI-compatible API key:
```bash
export OPENAI_API_KEY="<YOUR_KEY>"
```

3. Run evaluation:
```bash
model_name="vincie_7b"
python3 compute_score.py \
  --model_name "$model_name" \
  --api_model gpt-5-nano \
  --num_workers 32 \
  --res_path ./tmp_data/results/"$model_name".json
```

This evaluates prompt-following and consistency using a VLM. Results are saved to the specified path. See `evaluation/README.md` for details.

## Citation

```bibtex
@article{qu2025vincie,
  title   = {VINCIE: Unlocking In-context Image Editing from Video},
  author  = {Qu, Leigang and Cheng, Feng and Yang, Ziyan and Zhao, Qi and Lin, Shanchuan and Shi, Yichun and Li, Yicong and Wang, Wenjie and Chua, Tat-Seng and Jiang, Lu},
  journal = {arXiv preprint arXiv:2506.10941},
  year    = {2025}
}
```

## License
This project is licensed under the [Apache-2.0 License](LICENSE), subject to any intellectual property rights in the model owned by ByteDance. The text encoder of the model is adapted from [Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) and your use of that model must comply with its license.

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