Edm
Visit Tooledm is an open-source PyTorch implementation for elucidating the design space of diffusion-based generative models. It provides state-of-the-art FID scores and faster sampling for generative AI research.
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edm is an open-source PyTorch implementation for elucidating the design space of diffusion-based generative models. It provides state-of-the-art FID scores and faster sampling for generative AI research.
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About
edm is the official PyTorch implementation of the NeurIPS 2022 paper "Elucidating the Design Space of Diffusion-Based Generative Models." This open-source tool provides a clear framework for understanding and experimenting with diffusion models, separating concrete design choices in sampling and training processes, as well as score network preconditioning. It introduces improvements that lead to state-of-the-art FID scores for CIFAR-10, FFHQ, AFHQv2, and ImageNet, with significantly faster sampling times. The project includes pre-trained models, tools for generating images, calculating FrΓ©chet Inception Distance (FID), and preparing custom datasets. It supports both Linux and Windows, recommending Linux for performance, and requires high-end NVIDIA GPUs for optimal use.
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