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---
license: apache-2.0
---
<div align="center">
[//]: # (<h1>CSGO: Content-Style Composition in Text-to-Image Generation</h1>)
[//]: # ()
[//]: # ([**Peng Xing**](https://github.com/xingp-ng)<sup>12*</sup> · [**Haofan Wang**](https://haofanwang.github.io/)<sup>1*</sup> · [**Yanpeng Sun**](https://scholar.google.com.hk/citations?user=a3FI8c4AAAAJ&hl=zh-CN&oi=ao/)<sup>2</sup> · [**Qixun Wang**](https://github.com/wangqixun)<sup>1</sup> · [**Xu Bai**](https://huggingface.co/baymin0220)<sup>1</sup> · [**Hao Ai**](https://github.com/aihao2000)<sup>13</sup> · [**Renyuan Huang**](https://github.com/DannHuang)<sup>14</sup> · [**Zechao Li**](https://zechao-li.github.io/)<sup>2✉</sup>)
[//]: # ()
[//]: # (<sup>1</sup>InstantX Team · <sup>2</sup>Nanjing University of Science and Technology · <sup>3</sup>Beihang University · <sup>4</sup>Peking University)
[//]: # (<sup>*</sup>equal contributions, <sup>✉</sup>corresponding authors)
<a href='https://csgo-gen.github.io/'><img src='https://img.shields.io/badge/Project-Page-green'></a>
<a href='https://arxiv.org/abs/2404.02733'><img src='https://img.shields.io/badge/Technique-Report-red'></a>
[](https://huggingface.co/spaces/InstantX/InstantStyle)
[](https://modelscope.cn/studios/instantx/InstantStyle/summary)
</div>
[//]: # (## Updates 🔥)
[//]: # ()
[//]: # ([//]: # (- **`2024/07/19`**: ✨ We support 🎞️ portrait video editing (aka v2v)! More to see [here](assets/docs/changelog/2024-07-19.md).))
[//]: # ()
[//]: # ([//]: # (- **`2024/07/17`**: 🍎 We support macOS with Apple Silicon, modified from [jeethu](https://github.com/jeethu)'s PR [#143](https://github.com/KwaiVGI/LivePortrait/pull/143).))
[//]: # ()
[//]: # ([//]: # (- **`2024/07/10`**: 💪 We support audio and video concatenating, driving video auto-cropping, and template making to protect privacy. More to see [here](assets/docs/changelog/2024-07-10.md).))
[//]: # ()
[//]: # ([//]: # (- **`2024/07/09`**: 🤗 We released the [HuggingFace Space](https://huggingface.co/spaces/KwaiVGI/liveportrait), thanks to the HF team and [Gradio](https://github.com/gradio-app/gradio)!))
[//]: # ([//]: # (Continuous updates, stay tuned!))
[//]: # (- **`2024/08/30`**: 😊 We released the initial version of the inference code.)
[//]: # (- **`2024/08/30`**: 😊 We released the technical report on [arXiv](https://arxiv.org/pdf/2408.16766))
[//]: # (- **`2024/07/15`**: 🔥 We released the [homepage](https://csgo-gen.github.io).)
[//]: # ()
[//]: # (## Plan 💪)
[//]: # (- [x] technical report)
[//]: # (- [x] inference code)
[//]: # (- [ ] pre-trained weight)
[//]: # (- [ ] IMAGStyle dataset)
[//]: # (- [ ] training code)
## Introduction 📖
This repo, named **CSGO**, contains the official PyTorch implementation of our paper [CSGO: Content-Style Composition in Text-to-Image Generation](https://arxiv.org/pdf/).
We are actively updating and improving this repository. If you find any bugs or have suggestions, welcome to raise issues or submit pull requests (PR) 💖.
## Pipeline 💻
<p align="center">
<img src="assets/image3_1.jpg">
</p>
## Capabilities 🚅
🔥 Our CSGO achieves **image-driven style transfer, text-driven stylized synthesis, and text editing-driven stylized synthesis**.
🔥 For more results, visit our <a href="https://csgo-gen.github.io"><strong>homepage</strong></a> 🔥
<p align="center">
<img src="assets/vis.jpg">
</p>
## Getting Started 🏁
### 1. Clone the code and prepare the environment
```bash
git clone https://github.com/instantX-research/CSGO
cd CSGO
# create env using conda
conda create -n CSGO python=3.9
conda activate CSGO
# install dependencies with pip
# for Linux and Windows users
pip install -r requirements.txt
```
### 2. Download pretrained weights(coming soon)
The easiest way to download the pretrained weights is from HuggingFace:
```bash
# first, ensure git-lfs is installed, see: https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage
git lfs install
# clone and move the weights
git clone https://huggingface.co/InstanX/CSGO CSGO
```
Our method is fully compatible with [SDXL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), [VAE](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix), [ControlNet](https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic), and [Image Encoder](https://huggingface.co/h94/IP-Adapter/tree/main/sdxl_models/image_encoder).
Please download them and place them in the ./base_models folder.
### 3. Inference 🚀
```python
import torch
from ip_adapter.utils import resize_content
import numpy as np
from ip_adapter.utils import BLOCKS as BLOCKS
from ip_adapter.utils import controlnet_BLOCKS as controlnet_BLOCKS
from PIL import Image
from diffusers import (
AutoencoderKL,
ControlNetModel,
StableDiffusionXLControlNetPipeline,
)
from ip_adapter import CSGO
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
base_model_path = "./base_models/stable-diffusion-xl-base-1.0"
image_encoder_path = "./base_models/IP-Adapter/sdxl_models/image_encoder"
csgo_ckpt = "./CSGO/csgo.bin"
pretrained_vae_name_or_path ='./base_models/sdxl-vae-fp16-fix'
controlnet_path = "./base_models/TTPLanet_SDXL_Controlnet_Tile_Realistic"
weight_dtype = torch.float16
vae = AutoencoderKL.from_pretrained(pretrained_vae_name_or_path,torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16,use_safetensors=True)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
base_model_path,
controlnet=controlnet,
torch_dtype=torch.float16,
add_watermarker=False,
vae=vae
)
pipe.enable_vae_tiling()
target_content_blocks = BLOCKS['content']
target_style_blocks = BLOCKS['style']
controlnet_target_content_blocks = controlnet_BLOCKS['content']
controlnet_target_style_blocks = controlnet_BLOCKS['style']
csgo = CSGO(pipe, image_encoder_path, csgo_ckpt, device, num_content_tokens=4,num_style_tokens=32,
target_content_blocks=target_content_blocks, target_style_blocks=target_style_blocks,controlnet=False,controlnet_adapter=True,
controlnet_target_content_blocks=controlnet_target_content_blocks,
controlnet_target_style_blocks=controlnet_target_style_blocks,
content_model_resampler=True,
style_model_resampler=True,
load_controlnet=False,
)
style_name = 'img_1.png'
content_name = 'img_0.png'
style_image = Image.open("../assets/{}".format(style_name)).convert('RGB')
content_image = Image.open('../assets/{}'.format(content_name)).convert('RGB')
caption ='a small house with a sheep statue on top of it'
num_sample=4
#image-driven style transfer
images = csgo.generate(pil_content_image= content_image, pil_style_image=style_image,
prompt=caption,
negative_prompt= "text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
content_scale=1.0,
style_scale=1.0,
guidance_scale=10,
num_images_per_prompt=num_sample,
num_samples=1,
num_inference_steps=50,
seed=42,
image=content_image.convert('RGB'),
controlnet_conditioning_scale=0.6,
)
#text editing-driven stylized synthesis
caption='a small house'
images = csgo.generate(pil_content_image= content_image, pil_style_image=style_image,
prompt=caption,
negative_prompt= "text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
content_scale=1.0,
style_scale=1.0,
guidance_scale=10,
num_images_per_prompt=num_sample,
num_samples=1,
num_inference_steps=50,
seed=42,
image=content_image.convert('RGB'),
controlnet_conditioning_scale=0.4,
)
#text-driven stylized synthesis
caption='a cat'
#If the content image still interferes with the generated results, set the content image to an empty image.
# content_image =Image.fromarray(np.zeros((content_image.size[0],content_image.size[1], 3), dtype=np.uint8)).convert('RGB')
images = csgo.generate(pil_content_image= content_image, pil_style_image=style_image,
prompt=caption,
negative_prompt= "text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
content_scale=1.0,
style_scale=1.0,
guidance_scale=10,
num_images_per_prompt=num_sample,
num_samples=1,
num_inference_steps=50,
seed=42,
image=content_image.convert('RGB'),
controlnet_conditioning_scale=0.01,
)
```
## Demos
<p align="center">
<br>
🔥 For more results, visit our <a href="https://csgo-gen.github.io"><strong>homepage</strong></a> 🔥
</p>
### Content-Style Composition
<p align="center">
<img src="assets/page1.png">
</p>
<p align="center">
<img src="assets/page4.png">
</p>
### Cycle Translation
<p align="center">
<img src="assets/page8.png">
</p>
### Text-Driven Style Synthesis
<p align="center">
<img src="assets/page10.png">
</p>
### Text Editing-Driven Style Synthesis
<p align="center">
<img src="assets/page11.jpg">
</p>
## Star History
[](https://star-history.com/#instantX-research/CSGO&Date)
## Acknowledgements
This project is developed by InstantX Team, all copyright reserved.
## Citation 💖
If you find CSGO useful for your research, welcome to 🌟 this repo and cite our work using the following BibTeX:
```bibtex
@article{xing2024csgo,
title={CSGO: Content-Style Composition in Text-to-Image Generation},
author={Peng Xing and Haofan Wang and Yanpeng Sun and Qixun Wang and Xu Bai and Hao Ai and Renyuan Huang and Zechao Li},
year={2024},
journal = {arXiv 2408.16766},
}
``` |