CSGO

🚀 CSGO: Content-Style Composition in Text-to-Image Generation

This repository contains the official PyTorch implementation of our paper, focusing on content-style composition in text-to-image generation, offering multiple model weights and achieving various stylized synthesis capabilities.

🚀 Quick Start

1. Clone the code and prepare the environment

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:

# 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/InstantX/CSGO

Our method is fully compatible with SDXL, VAE, ControlNet, and Image Encoder. Please download them and place them in the ./base_models folder.

tips:If you expect to load Controlnet directly using ControlNetPipeline as in CSGO, do the following:

git clone https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic
mv TTPLanet_SDXL_Controlnet_Tile_Realistic/TTPLANET_Controlnet_Tile_realistic_v2_fp16.safetensors TTPLanet_SDXL_Controlnet_Tile_Realistic/diffusion_pytorch_model.safetensors

3. Inference 🚀

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_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,

                              )

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,
                          )

✨ Features

• Multiple Model Weights: We currently release two model weights, csgo.bin, csgo_4_32.bin, and csgo_4_32_v2.bin, with different configurations of content and style tokens and additional features.
• Stylized Synthesis Capabilities: Our CSGO achieves image-driven style transfer, text-driven stylized synthesis, and text editing-driven stylized synthesis.

📦 Installation

The installation process mainly includes cloning the code, preparing the environment, and downloading the pretrained weights (coming soon). Please refer to the "Quick Start" section for detailed steps.

💻 Usage Examples

Basic Usage

The inference code in the "Quick Start" section demonstrates the basic usage of CSGO, including image-driven style transfer, text editing-driven stylized synthesis, and text-driven stylized synthesis.

📚 Documentation

Detail

We currently release two model weights.

Pipeline

Capabilities

🔥 Our CSGO achieves image-driven style transfer, text-driven stylized synthesis, and text editing-driven stylized synthesis.

🔥 For more results, visit our homepage 🔥

Demos

🔥 For more results, visit our homepage 🔥

Content-Style Composition

Cycle Translation

Text-Driven Style Synthesis

Text Editing-Driven Style Synthesis

🔧 Technical Details

The project is an official PyTorch implementation of our paper CSGO: Content-Style Composition in Text-to-Image Generation. It is fully compatible with SDXL, VAE, ControlNet, and Image Encoder.

📄 License

This project is under the Apache-2.0 license.

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:

@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},
}

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