Skip to content

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

License

Notifications You must be signed in to change notification settings

Pseudo-Lab/pseudodiffusers

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 

Repository files navigation

Welcome to PseudoDiffusers!!

This is the repository of PseudoDiffusers team.

💡 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

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
19 2024.03.13 Geonhak Song Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation
DreaMoving: A Human Video Generation Framework based on Diffusion Models
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
25 2024.05.08 Sehwan Park LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models (CVPR2024)
26 2024.05.15 Kyeongmin Yu AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning (ICLR 2024)
27 2024.05.22 Jeongin Lee NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (CVPR 2020)
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 (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

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

  • 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"