sangyun884/fast-ode
Official PyTorch implementation for the paper Minimizing Trajectory Curvature of ODE-based Generative Models, ICML 2023
This project helps machine learning researchers create high-quality images from scratch using generative models more efficiently. It takes datasets of images (like CIFAR-10 or FFHQ) as input, and outputs new, distinct images that resemble the original dataset, but are generated faster than previous methods. This is designed for researchers or practitioners working on advanced image generation tasks who need to optimize model performance and speed.
No commits in the last 6 months.
Use this if you are a machine learning researcher focused on generative models and need to produce synthetic images with reduced sampling time while maintaining image quality.
Not ideal if you are looking for a plug-and-play image generation tool without diving into model training and optimization.
Stars
91
Forks
6
Language
Python
License
—
Category
Last pushed
Feb 14, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/sangyun884/fast-ode"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yang-song/score_sde_pytorch
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential...
ermongroup/ncsnv2
The official PyTorch implementation for NCSNv2 (NeurIPS 2020)
yang-song/score_sde
Official code for Score-Based Generative Modeling through Stochastic Differential Equations...
amazon-science/unconditional-time-series-diffusion
Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict,...
AI4HealthUOL/SSSD-ECG
Repository for the paper: 'Diffusion-based Conditional ECG Generation with Structured State Space Models'