File size: 3,765 Bytes
c98f2aa a67f43b 9006c6b a67f43b e1b5cb0 a67f43b e1b5cb0 a67f43b 9006c6b a67f43b 9006c6b a67f43b 9006c6b c98f2aa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
license: apache-2.0
tags:
- yahoo-open-source-software-incubator
---
# Salient Object-Aware Background Generation using Text-Guided Diffusion Models [![Paper](assets/arxiv.svg)](https://arxiv.org/pdf/2404.10157.pdf)
This repository accompanies our paper, [Salient Object-Aware Background Generation using Text-Guided Diffusion Models](https://arxiv.org/abs/2404.10157), which has been accepted for publication in [CVPR 2024 Generative Models for Computer Vision](https://generative-vision.github.io/workshop-CVPR-24/) workshop.
The paper addresses an issue we call "object expansion" when generating backgrounds for salient objects using inpainting diffusion models. We show that models such as [Stable Inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) can sometimes arbitrarily expand or distort the salient object, which is undesirable in applications where the object's identity should be preserved, such as e-commerce ads. We provide some examples of object expansion as follows:
<div align="center">
<img src="assets/fig.jpg">
</div>
# Inference
### Load pipeline
```py
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("yahoo-inc/photo-background-generation")
pipeline = pipeline.to('cuda')
```
### Load an image and extract its background and foreground
```py
from PIL import Image, ImageOps
import requests
from io import BytesIO
from transparent_background import Remover
def resize_with_padding(img, expected_size):
img.thumbnail((expected_size[0], expected_size[1]))
# print(img.size)
delta_width = expected_size[0] - img.size[0]
delta_height = expected_size[1] - img.size[1]
pad_width = delta_width // 2
pad_height = delta_height // 2
padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
return ImageOps.expand(img, padding)
seed = 0
image_url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/1/16/Granja_comary_Cisne_-_Escalavrado_e_Dedo_De_Deus_ao_fundo_-Teres%C3%B3polis.jpg/2560px-Granja_comary_Cisne_-_Escalavrado_e_Dedo_De_Deus_ao_fundo_-Teres%C3%B3polis.jpg'
response = requests.get(image_url)
img = Image.open(BytesIO(response.content))
img = resize_with_padding(img, (512, 512))
# Load background detection model
remover = Remover() # default setting
remover = Remover(mode='base') # nightly release checkpoint
# Get foreground mask
fg_mask = remover.process(img, type='map') # default setting - transparent background
```
### Background generation
```py
seed = 13
mask = ImageOps.invert(fg_mask)
img = resize_with_padding(img, (512, 512))
generator = torch.Generator(device='cuda').manual_seed(seed)
prompt = 'A dark swan in a bedroom'
cond_scale = 1.0
with torch.autocast("cuda"):
controlnet_image = pipeline(
prompt=prompt, image=img, mask_image=mask, control_image=mask, num_images_per_prompt=1, generator=generator, num_inference_steps=20, guess_mode=False, controlnet_conditioning_scale=cond_scale
).images[0]
controlnet_image
```
## Citations
If you found our work useful, please consider citing our paper:
```bibtex
@misc{eshratifar2024salient,
title={Salient Object-Aware Background Generation using Text-Guided Diffusion Models},
author={Amir Erfan Eshratifar and Joao V. B. Soares and Kapil Thadani and Shaunak Mishra and Mikhail Kuznetsov and Yueh-Ning Ku and Paloma de Juan},
year={2024},
eprint={2404.10157},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Maintainers
- Erfan Eshratifar: erfan.eshratifar@yahooinc.com
- Joao Soares: jvbsoares@yahooinc.com
## License
This project is licensed under the terms of the [Apache 2.0](LICENSE) open source license. Please refer to [LICENSE](LICENSE) for the full terms. |