Pseudo-Lab/text-to-image-generation-feat-diffusion

:bulb: PseudoDiffusers: paper/code review and experimental findings related to computer vision generation and diffusion-based models

text-to-image-generation-feat-diffusion

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!

Contributors

• 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 |
• 김선훈 [SeonHoon Kim] | Github | Linkedin |
• 이준형 [Junhyoung Lee] | Github | Linkedin |
• 조형서 [Hyoungseo Cho] | Github | Linkedin |
• 유정화 [Jeonghwa Yoo] | Github | Linkedin |
• 박세환 [Sehwan Park] | Github | Linkedin |

Reviewed Papers

idx	Date	Presenter	Paper / Code
1	2023.03.29	Sangwoo Jo	Auto-Encoding Variational Bayes (ICLR 2014) Generative Adversarial Networks (NIPS 2014)
2	2023.04.05	Kwangsu Mun 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	Beomsoo Park Seunghwan Ji	Denoising Diffusion Probabilistic Models (NeurIPS 2020) Denoising Diffusion Implicit Models (ICLR 2021)
4	2023.05.10	Donggeun Sean Ko	Diffusion Models Beat GANs in Image Synthesis (NeurIPS 2021) Zero-Shot Text-to-Image Generation (ICML 2021)
5	2023.05.17	Namkyeong Cho 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	Kwangsu Mun 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	Beomsoo Park Seunghwan Ji	LoRA: Low-Rank Adaptation of Large Language Models Multi-Concept Customization of Text-to-Image Diffusion (CVPR 2023)
8	2023.08.30	Donggeun Sean Ko 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)
9	2023.09.06	SeonHoon Kim Seunghwan Ji	Hierarchical Text-Conditional Image Generation with CLIP Latents SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations (ICLR 2022)
10	2023.09.13	Namkyeong Cho Junhyoung Lee	DeepFloyd IF SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
11	2023.09.20	HyoungSeo Cho Sangwoo Jo	HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
12	2023.09.27	Sehwan Park Junhyoung Lee	GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models (ICML 2022) Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
13	2023.10.11	Jeonghwa Yoo SeonHoon Kim	Synthetic Data from Diffusion Models Improves ImageNet Classification Your Diffusion Model is Secretly a Zero-Shot Classifier (ICCV 2023)
14	2023.10.18	Seunghwan Ji	A Study on the Evaluation of Generative Models
15	2023.10.25	Sangwoo Jo HyoungSeo Cho	Progressive Distillation for Fast Sampling of Diffusion Models (ICLR 2022) ConceptLab: Creative Generation using Diffusion Prior Constraints
16	2023.11.01	SeonHoon Kim Jeonghwa Yoo	BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models (CVPR 2023) Make-A-Video: Text-to-Video Generation without Text-Video Data
17	2023.11.15	Sehwan Park Junhyoung Lee	Diffusion Models already have a Semantic Latent Space (ICLR 2023) Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models (CVPR 2023)
18	2023.11.29	Donggeun Sean Ko	Video Diffusion Models

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"