text-to-image-generation

This is the repository of Pseudo Lab's Text-to-Image Generation (feat. Diffusion) team.

💡 Our aim is to review papers and code related to image generation and text-to-image generation models, approach them theoretically, and conduct various experiments by fine-tuning diffusion based models.

About Us - Pseudo Lab

About Us - Text-to-Image Generation (feat. Diffusion) Team

참여 방법: 매주 수요일 오후 9시, 가짜연구소 Discord Room-DH 로 입장!

Contributor

• 조상우 [Sangwoo Jo] | Github | Linkedin |
• 문광수 [Kwangsu Mun] | Github | Linkedin |
• 김지수 [Jisu Kim] | Github | Linkedin |
• 박범수 [Beomsoo Park] | Github | Linkedin
• 지승환 [Seunghwan Ji] | Github | Linkedin
• 고동근 [Donggeun Sean Ko] | Github | Linkedin |
• 조남경 [Namkyeong Cho] | Github | Linkedin

Schedule

idx	Date	Presenter	Paper / Code
1	2023.03.29	조상우 [Sangwoo Jo]	Auto-Encoding Variational Bayes (ICLR 2014) Generative Adversarial Networks (NIPS 2014)
2	2023.04.05	문광수 [Kwangsu Mun] 김지수 [Jisu Kim]	Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (ICCV 2017) A Style-Based Generator Architecture for Generative Adversarial Networks (CVPR 2019)
3	2023.04.12	박범수 [Beomsoo Park] 지승환 [Seunghwan Ji]	Denoising Diffusion Probabilistic Models (NeurIPS 2020) Denoising Diffusion Implicit Models (ICLR 2021)
4	2023.05.10	고동근 [Donggeun Sean Ko]	Diffusion Models Beat GANs in Image Synthesis (NeurIPS 2021) Zero-Shot Text-to-Image Generation (PMLR 2021)
5	2023.05.17	조남경 [Namkyeong Cho] 조상우 [Sangwoo Jo]	High-Resolution Image Synthesis with Latent Diffusion Models (CVPR 2022) DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (CVPR 2023)
6	2023.05.24	문광수 [Kwangsu Mun] 김지수 [Jisu Kim]	An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion Adding Conditional Control to Text-to-Image Diffusion Models
7	2023.05.31	박범수 [Beomsoo Park] 지승환 [Seunghwan Ji]	LoRA: Low-Rank Adaptation of Large Language Models Multi-Concept Customization of Text-to-Image Diffusion (CVPR 2023)
8	2023.08.30	고동근 [Donggeun Sean Ko] 조상우 [Sangwoo Jo]	Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting (CVPR 2023)

Jupyter Book Update Procedure

1. Clone the repo on your local computer

git clone https://github.com/Pseudo-Lab/text-to-image-generation.git

2. Install required packages

pip install jupyter-book==0.15.1
pip install ghp-import==2.1.0

3. Change the contents in book/docs folder with the following format and update _toc.yml file accordingly

   • reference: https://github.com/Pseudo-Lab/SegCrew-Book
   • default template: https://github.com/Pseudo-Lab/Jupyter-Book-Template

• 3.1. Add information section on top of the markdown page

- **Title:** {논문 제목}, {학회/학술지명}

- **Reference**
    - Paper:  [{논문 링크}]({논문 링크})
    - Code: [{code 링크}]({code 링크})
    - Review: [{review 링크}]({review 링크})
    
- **Author:** {리뷰 작성자 기입}

- **Edited by:** {리뷰 편집자 기입}

- **Last updated on {최종 update 날짜 e.g. Apr. 12, 2023}**

• 3-2. Use the following template when displaying images

:::{figure-md} 
<img src="{주소}" alt="{tag명}" class="bg-primary mb-1" width="{800px}">

{제목} \  (source: {출처})
:::

• 3-3. Update _toc.yml file accordingly

format: jb-book
root: intro
parts:
- caption: Paper/Code Review
  chapters:
  - file: docs/review/vae
  - file: docs/review/gan

4. Build the book using Jupyter Book command

jupyter-book build ./book

5. Sync your local and remote repositories

cd text-to-image-generation
git add .
git commit -m "adding my first book!"
git push

6. Publish your Jupyter Book with Github Pages

ghp-import -n -p -f book/_build/html -m "initial publishing"