|
--- |
|
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. |
|
|
|
 |
|
|
|
|
|
## 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() |
|
``` |
|
|
|
<!--  --> |
|
<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() |
|
``` |
|
|
|
<!--  --> |
|
<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 |
|
|
|
``` |
|
|
