---
license: apache-2.0
---

# Virtual Artist (InstructG2I: Synthesizing Images from Multimodal Attributed Graphs - NeurIPs 2024)

<a href=''><img src='https://img.shields.io/badge/Project-Page-green'></a> 

## Introduction

We propose a graph context-conditioned diffusion model called **InstructG2I** to generate images from multimodal attributed graphs (MMAGs).
InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling by combining personalized page rank and re-ranking based on vision-language features. 
Then, a Graph-QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process of diffusion. 
Finally, we propose graph classifier-free guidance, enabling controllable generation by varying the strength of graph guidance and multiple connected edges to a node.

![arch](figs/intro.png)


## Installation
```bash
conda create --name instructg2i python==3.10
conda activate instructg2i

git clone https://github.com/PeterGriffinJin/InstructG2I.git
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -e .
```

## Quick Start

Generate a picture called *a mountain in the blue sky* under Claude Monet's style.

```python
import os
from PIL import Image
from instructg2i import InstructG2IPipeline, get_neighbor_transforms

text_prompt = 'a mountain in the blue sky'  # a man playing soccer, a man playing piano
neighbor_pic_dir = 'examples/monet_pictures'

neighbor_transforms = get_neighbor_transforms(resolution=256)
pipeline = InstructG2IPipeline.from_pretrained("PeterJinGo/VirtualArtist", neighbor_num=5, device='cuda:0')

neighbor_image = [neighbor_transforms(Image.open(f'{neighbor_pic_dir}/{n_file}').convert("RGB")) for n_file in os.listdir(neighbor_pic_dir)]
image_gen = pipeline(prompt=text_prompt, neighbor_image=neighbor_image, neighbor_mask=[1] * len(neighbor_image), num_inference_steps=100).images[0]
image_gen.show()
```

<!-- ![arch](figs/example.png) -->
<div align="center">
    <img src="figs/example.png" alt="arch" width="300" height="300" />
</div>

Generate a picture called *a house in the snow* combining the style of Claude Monet and my little brother.
```python
import os
from PIL import Image
from instructg2i import image_grid, InstructG2IMultiGuidePipeline, get_neighbor_transforms

# load the model
pipeline = InstructG2IMultiGuidePipeline.from_pretrained("PeterJinGo/VirtualArtist", neighbor_num=5, device='cuda:0')

# configuration
text_prompt = 'a house in the snow'  # a man playing soccer, a man playing piano
scale_as = [0, 3, 10]
scale_bs = [0, 5, 15]

# read the sampled neighbors
path1 = "examples/monet_pictures"
path2 = "examples/children_pictures"
neighbor_images = [[neighbor_transforms(Image.open(os.path.join(path1, n_file)).convert("RGB")) for n_file in os.listdir(path1)],
                   [neighbor_transforms(Image.open(os.path.join(path2, n_file)).convert("RGB")) for n_file in os.listdir(path2)]]
neighbor_masks = [[1,1,1,1,1],
                   [1,1,1,1,1]]

# generation
image_gens = []
neighbor_transforms = get_neighbor_transforms(resolution=256)
for scale_a in scale_as:
    for scale_b in scale_bs:
        graph_guidance_scales = [scale_a, scale_b]

        image_gen = pipeline(prompt=text_prompt, 
                                        neighbor_images=neighbor_images, 
                                        neighbor_masks=neighbor_masks, 
                                        graph_guidance_scales=graph_guidance_scales,
                                        num_inference_steps=100).images[0]
        image_gens.append(image_gen)
res_grid = image_grid(image_gens, len(scale_as), len(scale_bs))
res_grid.show()
```

<!-- ![arch](figs/example2.png) -->
<div align="center">
    <img src="figs/example2.png" alt="arch" width="400" height="400" />
</div>

## Download Models

### Image Encoder
Create an image_encoder folder by ```mkdir image_encoder```, then place the files downloaded [here](https://drive.google.com/drive/folders/1AtbN401MDSVLZlH5webITfskkIIjUPLZ?usp=sharing) into the folder.

### InstructG2I checkpoints

The virtual artist InstructG2I checkpoint which is trained on Artwork graphs can be downloaded [here](https://drive.google.com/drive/folders/1ntmPgZXmb-M-k5M0Cnh34p0fxoeKnnYC?usp=sharing) or [here](https://huggingface.co/PeterJinGo/VirtualArtist).

```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="PeterJinGo/VirtualArtist", local_dir=your_local_path)
```

The InstructG2I checkpoints for the Amazon graph and Goodreads graph can be found [here](https://drive.google.com/drive/folders/1rPhc-LFoyqDrqn6gigTogFB7cEpRLx72?usp=sharing).

## Citations
If you find InstructG2I useful for your research and applications, please cite using this BibTeX:
```bibtex
@article{jin2024instructg2i,
  title={InstructG2I: Synthesizing Images from Multimodal Attributed Graphs},
  author={Jin, Bowen and Pang, Ziqi and Guo, Bingjun and Wang, Yu-Xiong and You, Jiaxuan and Han, Jiawei},
  journal={arXiv preprint arXiv:2410.07157},
  year={2024}
}
```