Pseudo-Lab/pseudodiffusers
:bulb: PseudoDiffusers: paper/code review and experimental findings related to computer vision generation and diffusion-based models
PseudoDiffusers is a collaborative project for researchers and practitioners in computer vision. It focuses on reviewing, understanding, and experimenting with generative AI models, specifically diffusion models, to create and manipulate images and videos. The project helps stay updated on the latest advancements and apply them to practical image generation tasks.
No commits in the last 6 months.
Use this if you are a computer vision researcher or practitioner keen on understanding, fine-tuning, and experimenting with the latest diffusion-based models for generating or manipulating visual content.
Not ideal if you are looking for a ready-to-use, off-the-shelf tool for immediate image generation without delving into the underlying research and model architectures.
Stars
44
Forks
10
Language
HTML
License
MIT
Category
Last pushed
Jul 11, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/Pseudo-Lab/pseudodiffusers"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
jolibrain/joliGEN
Generative AI Image and Video Toolset with GANs and Diffusion for Real-World Applications
zhangmozhe/Deep-Exemplar-based-Video-Colorization
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".
naver-ai/StyleKeeper
Official Pytorch implementation of "StyleKeeper: Prevent Content Leakage using Negative Visual...
un1tz3r0/finetunepixelartdiffusion
Fine tune a pixelart diffusion model with isometric dataset.
lixiaowen-xw/DiffuEraser
DiffuEraser is a diffusion model for video inpainting, which performs great content completeness...