metadata
license: apache-2.0
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
Introduction 📖
This repo, named CSGO, contains the official PyTorch implementation of our paper CSGO: Content-Style Composition in Text-to-Image Generation. 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) 💖.
Detail ✨
We currently release two model weights.
| Mode | content token | style token | Other |
|---|---|---|---|
| csgo.bin | 4 | 16 | - |
| csgo_4_32.bin | 4 | 32 | Deepspeed zero2 |
| csgo_4_32_v2.bin | 4 | 32 | Deepspeed zero2+more(coming soon) |
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 🔥
Getting Started 🏁
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,
)
Demos
🔥 For more results, visit our homepage 🔥
Content-Style Composition
Cycle Translation
Text-Driven Style Synthesis
Text Editing-Driven Style Synthesis
Star History
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},
}