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This repo is the official implementation of "Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition".
TL;DR: We introduce a dataset creation pipeline, Gen4Gen, to compose personal concept into realistic scenes with complex compositions, accompanied by detailed text descriptions.
Please prepare your personal images and put them under data/s0_source_images. Our personal images are from Unsplash. The structure of 🗂 data/s0_source_images looks like this:
🗂 Structure of data/s0_source_images
../data/s0_source_images
└── cat_dog_houseplant_3objs
├── cat
│ └── sergey-semin-agQhOHQipoE-unsplash.jpg
├── dog
│ ├── Copy of 5.jpeg
│ └── Copy of 6.jpeg
└── houseplant
├── Copy of 1.png
├── Copy of 2.png
├── Copy of 3.png
└── Copy of 5.png
└── [folder_of_other_scenes]
├── [object_name_1]
│ ├── [image_name_1.jpg]
│ ├── ...
│ └── [image_name_n.jpeg]
...
└── [oject_name_n]
🗺️ Environments
Library versions
Framework and environment
pytorch: 1.13.1
cuda: 11.7
torchvision: 0.14.1
🧨 Go-to library for diffusion models
diffusers: 0.21.4
OpenAI API for LLM-Guide Object Composition
openai: 0.28.1
Programming language
python: 3.8.5
Graphics card
[Gen4Gen pipeline] GPU: NVIDIA A100-SXM4-40GB x 1 or NVIDIA GeForce RTX 4090-24GB x 1
For Step1, Object Association and Foreground Segmentation, requiring around 2.2GB memory footprint
For Step3, Background Repainting, requring around 17GB memory footprint
If you have any general questions or need support, please feel free to contact: Chun-Hsiao Yeh, Ta-Ying Cheng and He-Yen Hsieh. Also, we encourage you to open an issue in the GitHub repository. By doing so, you not only receive support but also contribute to the collective knowledge base for others who may have similar inquiries.
❤️ Citation
If you find the codebase and MyCanvas dataset valuable and utilize it in your work, we kindly request that you consider giving our GitHub repository a ⭐ and citing our paper.
@misc{yeh2024gen4gen,
author = {Chun-Hsiao Yeh and
Ta-Ying Cheng and
He-Yen Hsieh and
David Chuan-En Lin and
Yi Ma and
Andrew Markham and
Niki Trigoni and
H.T. Kung and
Yubei Chen},
title = {Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition},
year = {2024},
eprint = {2402.15504},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
About
🏞️ Official implementation of "Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition"