Welcome to PseudoDiffusers!!

This is the repository of PseudoDiffusers team.

:bulb: Our aim is to review papers and code related to computer vision generation models, approach them theoretically, and conduct various experiments by fine-tuning diffusion based models.

About Us - PseudoLab

About Us - PseudoDiffusers

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

Publications

DiffInject: Revisiting Debias via Synthetic Data Generation using Diffusion-based Style Injection
Donggeun Ko*, Sangwoo Jo*, Dongjun Lee, Namjun Park, Jaekwang KIM
CVPR 2024 Workshop
PDF

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 |
• 송건학 [Geonhak Song] | Github | Linkedin |
• 한동현 [Donghyun Han] | GitHub | Linkedin |
• 이창환 [ChangHwan Lee] | Github | Linkedin |
• 유경민 [Kyeongmin Yu] | Github | Linkdedin |
• 이정인 [Jeongin Lee] | Github | Linkedin |
• 김현수 [Hyunsoo Kim] | 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 (ICLR 2023) Adding Conditional Control to Text-to-Image Diffusion Models (ICCV 2023)
7	2023.05.31	Beomsoo Park Seunghwan Ji	LoRA: Low-Rank Adaptation of Large Language Models (ICLR 2022) 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 (NeurIPS 2022) 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 (ICLR 2024 Spotlight)
11	2023.09.20	HyoungSeo Cho Sangwoo Jo	HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models (CVPR 2024) T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models (AAAI 2024)
12	2023.09.27	Sehwan Park Junhyoung Lee	GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models (PMLR 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 (TMLR 2023) 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 Spotlight) ConceptLab: Creative Generation using Diffusion Prior Constraints (ACM Transactions on Graphics 2024)
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 (ICLR 2023)
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 (NeurIPS 2022)
19	2024.03.13	Geonhak Song	Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation (CVPR Workshop 2023) DreaMoving: A Human Video Generation Framework based on Diffusion Models (CVPR Workshop 2023)
20	2024.03.20	Junhyoung Lee	Muse: Text-To-Image Generation via Masked Generative Transformers (ICML 2023)
21	2024.03.27	Seunghwan Ji	Scaling up GANs for Text-to-Image Synthesis (CVPR 2023)
22	2024.04.03	Sangwoo Jo	Consistency Models (ICML 2023)
23	2024.04.24	Donghyun Han	Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
24	2024.05.01	Jeonghwa Yoo	DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion (ICCV 2023)
25	2024.05.08	Sehwan Park	LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models (TMLR 2024)
26	2024.05.15	Kyeongmin Yu	AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning (ICLR 2024 Spotlight)
27	2024.05.22	Jeongin Lee	NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ECCV 2020 Oral)
28	2024.05.29	Hyunsoo Kim	3D Gaussian Splatting for Real-Time Radiance Field Rendering (SIGGRAPH 2023)
29	2024.06.12	Donggeun Sean Ko	DiffInject: Revisiting Debias via Synthetic Data Generation using Diffusion-based Style Injection (CVPR Workshop 2024)
30	2024.06.26	Jeonghwa Yoo Kyeongmin Yu	Point-E: A System for Generating 3D Point Clouds from Complex Prompts Shap-E: Generating Conditional 3D Implicit Function
31	2024.07.03	Geonhak Song	DreamFusion: Text-to-3D using 2D Diffusion (ICLR 2023)
32	2024.07.17	Sangwoo Jo Junhyoung Lee	Magic3D: High-Resolution Text-to-3D Content Creation (CVPR 2023) Scalable Diffusion Models with Transformers (ICCV 2023)
33	2024.07.24	Jeongin Lee Hyunsoo Kim	DreamBooth3D: Subject-Driven Text-to-3D Generation (ICCV 2023) Style Aligned Image Generation via Shared Attention (CVPR 2024)
34	2024.09.18	Sangwoo Jo	One-step Image Translation with Text-to-Image Models
35	2024.09.25	Joongwon Lee	One-step Diffusion with Distribution Matching Distillation (CVPR 2024)
36	2024.10.02	Donghyun Han	LCM-LoRA: A Universal Stable-Diffusion Acceleration Module
37	2024.10.09	Kyeongmin Yu	IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
38	2024.10.30	Jeongin Lee	Zero-1-to-3: Zero-shot One Image to 3D Object (ICCV 2023)
39	2024.11.06	Geonhak Song	MVDream: Multi-view Diffusion for 3D Generation (ICLR 2024)
40	2024.11.13	Kyeongmin Yu	ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation (NeurIPS 2023)
41	2024.11.27	Jeonghwa Yoo Sangwoo Jo	One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization (NeurIPS 2023) Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
42	2024.12.04	Joongwon Lee Donghyun Han	LRM: Large Reconstruction Model for Single Image to 3D (ICLR 2024 Oral) LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation (ECCV 2024 Oral)
43	2024.12.11	Kyeongmin Yu	DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation (ICLR 2024 Oral)
44	2024.12.18	Geonhak Song Donggeun Sean Ko	CAT3D: Create Anything in 3D with Multi-View Diffusion Models (NeurIPS 2024 Oral) Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning (CVPR Workshop 2024)

Jupyter Book Update Procedure

1. Clone the repo on your local computer

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

1. Install required packages

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

1. 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

1. Build the book using Jupyter Book command

jupyter-book build ./book

1. Sync your local and remote repositories

cd pseudodiffusers
git add .
git commit -m "adding my first book!"
git push

1. Publish your Jupyter Book with Github Pages

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