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CSGO

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----
-license: apache-2.0
----
-<div align="center">
-[//]: # (<h1>CSGO: Content-Style Composition in Text-to-Image Generation</h1>)
-[//]: # ()
-[//]: # ([**Peng Xing**]&#40;https://github.com/xingp-ng&#41;<sup>12*</sup> · [**Haofan Wang**]&#40;https://haofanwang.github.io/&#41;<sup>1*</sup> · [**Yanpeng Sun**]&#40;https://scholar.google.com.hk/citations?user=a3FI8c4AAAAJ&hl=zh-CN&oi=ao/&#41;<sup>2</sup> · [**Qixun Wang**]&#40;https://github.com/wangqixun&#41;<sup>1</sup> · [**Xu Bai**]&#40;https://huggingface.co/baymin0220&#41;<sup>1</sup> · [**Hao Ai**]&#40;https://github.com/aihao2000&#41;<sup>13</sup> · [**Renyuan Huang**]&#40;https://github.com/DannHuang&#41;<sup>14</sup> · [**Zechao Li**]&#40;https://zechao-li.github.io/&#41;<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/2408.16766'><img src='https://img.shields.io/badge/Technique-Report-red'></a>
-[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-App-red)](https://huggingface.co/spaces/xingpng/CSGO/)
-[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/spaces/InstantX/CSGO)
-</div>
-[//]: # (##  Updates 🔥)
-[//]: # ()
-[//]: # ([//]: # &#40;- **`2024/07/19`**: ✨ We support 🎞️ portrait video editing &#40;aka v2v&#41;! More to see [here]&#40;assets/docs/changelog/2024-07-19.md&#41;.&#41;)
-[//]: # ()
-[//]: # ([//]: # &#40;- **`2024/07/17`**: 🍎 We support macOS with Apple Silicon, modified from [jeethu]&#40;https://github.com/jeethu&#41;'s PR [#143]&#40;https://github.com/KwaiVGI/LivePortrait/pull/143&#41;.&#41;)
-[//]: # ()
-[//]: # ([//]: # &#40;- **`2024/07/10`**: 💪 We support audio and video concatenating, driving video auto-cropping, and template making to protect privacy. More to see [here]&#40;assets/docs/changelog/2024-07-10.md&#41;.&#41;)
-[//]: # ()
-[//]: # ([//]: # &#40;- **`2024/07/09`**: 🤗 We released the [HuggingFace Space]&#40;https://huggingface.co/spaces/KwaiVGI/liveportrait&#41;, thanks to the HF team and [Gradio]&#40;https://github.com/gradio-app/gradio&#41;!&#41;)
-[//]: # ([//]: # &#40;Continuous updates, stay tuned!&#41;)
-[//]: # (- **`2024/08/30`**: 😊 We released the initial version of the inference code.)
-[//]: # (- **`2024/08/30`**: 😊 We released the technical report on [arXiv]&#40;https://arxiv.org/pdf/2408.16766&#41;)
-[//]: # (- **`2024/07/15`**: 🔥 We released the [homepage]&#40;https://csgo-gen.github.io&#41;.)
-[//]: # ()
-[//]: # (##   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) 💖.
-## 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 |
-## 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.
-tips:If you expect to load Controlnet directly using ControlNetPipeline as in CSGO, do the following:
-```bash
-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 🚀
-```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_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
-<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
-[![Star History Chart](https://api.star-history.com/svg?repos=instantX-research/CSGO&type=Date)](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},
-}
 ```

+---
+license: apache-2.0
+---
+<div align="center">
+[//]: # (<h1>CSGO: Content-Style Composition in Text-to-Image Generation</h1>)
+[//]: # ()
+[//]: # ([**Peng Xing**]&#40;https://github.com/xingp-ng&#41;<sup>12*</sup> · [**Haofan Wang**]&#40;https://haofanwang.github.io/&#41;<sup>1*</sup> · [**Yanpeng Sun**]&#40;https://scholar.google.com.hk/citations?user=a3FI8c4AAAAJ&hl=zh-CN&oi=ao/&#41;<sup>2</sup> · [**Qixun Wang**]&#40;https://github.com/wangqixun&#41;<sup>1</sup> · [**Xu Bai**]&#40;https://huggingface.co/baymin0220&#41;<sup>1</sup> · [**Hao Ai**]&#40;https://github.com/aihao2000&#41;<sup>13</sup> · [**Renyuan Huang**]&#40;https://github.com/DannHuang&#41;<sup>14</sup> · [**Zechao Li**]&#40;https://zechao-li.github.io/&#41;<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/2408.16766'><img src='https://img.shields.io/badge/Technique-Report-red'></a>
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-App-red)](https://huggingface.co/spaces/xingpng/CSGO/)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue)](https://huggingface.co/spaces/InstantX/CSGO)
+</div>
+[//]: # (##  Updates 🔥)
+[//]: # ()
+[//]: # ([//]: # &#40;- **`2024/07/19`**: ✨ We support 🎞️ portrait video editing &#40;aka v2v&#41;! More to see [here]&#40;assets/docs/changelog/2024-07-19.md&#41;.&#41;)
+[//]: # ()
+[//]: # ([//]: # &#40;- **`2024/07/17`**: 🍎 We support macOS with Apple Silicon, modified from [jeethu]&#40;https://github.com/jeethu&#41;'s PR [#143]&#40;https://github.com/KwaiVGI/LivePortrait/pull/143&#41;.&#41;)
+[//]: # ()
+[//]: # ([//]: # &#40;- **`2024/07/10`**: 💪 We support audio and video concatenating, driving video auto-cropping, and template making to protect privacy. More to see [here]&#40;assets/docs/changelog/2024-07-10.md&#41;.&#41;)
+[//]: # ()
+[//]: # ([//]: # &#40;- **`2024/07/09`**: 🤗 We released the [HuggingFace Space]&#40;https://huggingface.co/spaces/KwaiVGI/liveportrait&#41;, thanks to the HF team and [Gradio]&#40;https://github.com/gradio-app/gradio&#41;!&#41;)
+[//]: # ([//]: # &#40;Continuous updates, stay tuned!&#41;)
+[//]: # (- **`2024/08/30`**: 😊 We released the initial version of the inference code.)
+[//]: # (- **`2024/08/30`**: 😊 We released the technical report on [arXiv]&#40;https://arxiv.org/pdf/2408.16766&#41;)
+[//]: # (- **`2024/07/15`**: 🔥 We released the [homepage]&#40;https://csgo-gen.github.io&#41;.)
+[//]: # ()
+[//]: # (##   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) 💖.
+## 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 |
+## 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/InstantX/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.
+tips:If you expect to load Controlnet directly using ControlNetPipeline as in CSGO, do the following:
+```bash
+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 🚀
+```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_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
+<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
+[![Star History Chart](https://api.star-history.com/svg?repos=instantX-research/CSGO&type=Date)](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},
+}
 ```