Comfyui Storydiffusion

🚀 Image and Video Generation Tools Introduction

This README provides an overview of several innovative tools in the field of image and video generation, including their features and relevant citation information.

✨ Features

StoryDiffusion

StoryDiffusion is an autoregressive model for long-range image and video generation. It achieves high-quality output through a consistent self-attention mechanism.

IP-Adapter

IP-Adapter is a text-compatible image prompt adapter that enhances the generation capabilities of text-to-image diffusion models.

MS-Diffusion

MS-Diffusion is a multi-subject zero-shot image personalization method. It enables multi-subject image generation by incorporating layout guidance.

PhotoMaker

PhotoMaker generates highly personalized realistic human photos through stacked ID embedding technology.

Kolors

Kolors is an effective training method for diffusion models, focusing on improving photorealistic text-to-image synthesis.

📚 Documentation

Tool List

1. StoryDiffusion: An autoregressive model for long-range image and video generation.
2. IP-Adapter: An adapter that enhances the performance of text-to-image diffusion models.
3. MS-Diffusion: A multi-subject zero-shot image personalization method.
4. PhotoMaker: Generates realistic human photos via stacked ID embedding.
5. Kolors: A diffusion model training method focused on photorealistic text-to-image synthesis.

Citation Information

StoryDiffusion

• Author: Zhou Yupeng, Zhou Daquan, Cheng Ming-Ming, Feng Jiashi, Hou Qibin
• Publication: arXiv preprint arXiv:2405.01434
• Year: 2024

IP-Adapter

• Author: Ye Hu, Zhang Jun, Liu Sibo, Han Xiao, Yang Wei
• Publication: arXiv preprint arxiv:2308.06721
• Year: 2023

MS-Diffusion

• Author: Wang X., Fu Siming, Huang Qihan, He Wanggui, Jiang Hao
• Publication: arXiv eprint 2406.07209
• Year: 2024

PhotoMaker

• Author: Li Zhen, Cao Mingdeng, Wang Xintao, Qi Zhongang, Cheng Ming-Ming, Shan Ying
• Publication: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• Year: 2024

Kolors

• Author: Kolors Team
• Publication: arXiv preprint
• Year: 2024

BibTeX Citations

@article{zhou2024storydiffusion,
  title={StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation},
  author={Zhou, Yupeng and Zhou, Daquan and Cheng, Ming-Ming and Feng, Jiashi and Hou, Qibin},
  journal={arXiv preprint arXiv:2405.01434},
  year={2024}
}

@article{ye2023ip-adapter,
  title={IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models},
  author={Ye, Hu and Zhang, Jun and Liu, Sibo and Han, Xiao and Yang, Wei},
  booktitle={arXiv preprint arxiv:2308.06721},
  year={2023}
}

@misc{wang2024msdiffusion,
  title={MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance}, 
  author={X. Wang and Siming Fu and Qihan Huang and Wanggui He and Hao Jiang},
  year={2024},
  eprint={2406.07209},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@inproceedings{li2023photomaker,
  title={PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding},
  author={Li, Zhen and Cao, Mingdeng and Wang, Xintao and Qi, Zhongang and Cheng, Ming-Ming and Shan, Ying},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}

@article{kolors,
  title={Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis},
  author={Kolors Team},
  journal={arXiv preprint},
  year={2024}
}