smsharma/consistency-models
Implementation of Consistency Models (Song et al 2023) for few-step image generation in Jax.
This project helps machine learning practitioners and researchers generate high-quality images from scratch with very few computational steps. It takes in training data, such as image datasets like MNIST or CIFAR-10, and outputs a trained model capable of creating new, unique images efficiently. This is ideal for those exploring new generative model architectures or needing fast image synthesis.
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Use this if you are a machine learning researcher or engineer interested in experimenting with state-of-the-art, few-step image generation techniques from scratch.
Not ideal if you need to distill an existing, pre-trained diffusion model or require a continuous-time objective that is stable during training without a pre-trained initialization.
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19
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jun 11, 2023
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