yang-song/score_flow
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
This project offers a method for training advanced image generation models that create highly realistic images from random noise. It takes training image datasets like CIFAR-10 or ImageNet 32x32 and produces a trained model capable of generating new, high-quality images, along with metrics to evaluate their realism. Machine learning researchers and generative AI practitioners would use this for developing state-of-the-art image synthesis techniques.
153 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher focused on advancing image generation technologies and need to train highly efficient and accurate score-based diffusion models.
Not ideal if you are an application developer looking for a pre-built image generation API or if your primary goal is to deploy existing models for content creation rather than research into model training.
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Language
Python
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Last pushed
Dec 09, 2021
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