NVlabs/edm

Health v2 · maintenance only

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Adoption not part of the score

1999 stars · 215 forks observed · 2026-09-23

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of the NeurIPS 2022 paper 'Elucidating the Design Space of Diffusion-Based Generative Models' (EDM) by NVIDIA Research. It provides training and sampling code for diffusion-based image generators, achieving state-of-the-art FID on CIFAR-10, FFHQ, AFHQ, and ImageNet-64 with fast sampling.

Use cases

• generate images with a diffusion model
• reproduce state-of-the-art CIFAR-10 FID results
• train a diffusion model on my own dataset
• improve sampling efficiency of pre-trained score networks
• study design choices of diffusion generative models
• sample images from pre-trained FFHQ or ImageNet-64 models

When to choose

• you need a research-grade diffusion model implementation with strong FID results
• you want fast deterministic sampling with few network evaluations
• you are experimenting with diffusion training, sampling, or score-network preconditioning
• you have NVIDIA GPUs and want to reproduce paper results

When to avoid

• you need a production-ready image generation service or API
• you lack high-end NVIDIA GPUs, especially for training
• you need a maintained general-purpose diffusion library with broad model support
• you need permissive licensing for commercial use without checking NVIDIA's license

Facets

library · maturity maintenance

machine-learning deep-learning image-processing deep-learning machine-learning artificial-intelligence image-processing windows python diffusion-models generative-models pytorch research-code image-generation score-networks neurips-2022 linux gpu docker

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