FutureXiang/edm2
Minimal multi-gpu implementation of EDM2: "Analyzing and Improving the Training Dynamics of Diffusion Models"
This project helps machine learning researchers efficiently train and evaluate diffusion models for generating images. You provide a dataset of images, and it trains different configurations of diffusion models. The output is a trained model capable of generating new images, along with metrics like FID scores to assess generation quality. This is for researchers experimenting with cutting-edge image generation techniques.
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Use this if you are an AI researcher wanting to implement and compare advanced diffusion model training techniques on image datasets like CIFAR-100.
Not ideal if you need to train diffusion models on very large-scale datasets like ImageNet-1k, as it would be computationally prohibitive.
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Python
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Last pushed
Mar 05, 2024
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