byrkbrk/conditional-ddpm
A simple PyTorch implementation of conditional denoising diffusion probabilistic models (DDPM) on MNIST, Fashion-MNIST, and Sprite datasets
This tool helps machine learning engineers and researchers generate new, synthetic images based on a set of existing image examples. You provide a dataset of labeled images, and it outputs new images that resemble the originals but are entirely unique. This is useful for expanding datasets or creating novel visuals for testing.
No commits in the last 6 months.
Use this if you need to generate high-quality, diverse synthetic images conditionally based on specific categories or labels from a given image dataset.
Not ideal if you're looking for a user-friendly, drag-and-drop solution without any coding, or if your primary goal is image manipulation rather than generation.
Stars
88
Forks
9
Language
Python
License
—
Category
Last pushed
May 18, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/byrkbrk/conditional-ddpm"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
quantgirluk/aleatory
📦 Python library for Stochastic Processes Simulation and Visualisation
blei-lab/treeffuser
Treeffuser is an easy-to-use package for probabilistic prediction and probabilistic regression...
TuftsBCB/RegDiffusion
Diffusion model for gene regulatory network inference.
yuanchenyang/smalldiffusion
Simple and readable code for training and sampling from diffusion models
chairc/Integrated-Design-Diffusion-Model
IDDM (Industrial, landscape, animate, latent diffusion), support LDM, DDPM, DDIM, PLMS, webui...