Atenrev/diffusion_continual_learning
PyTorch implementation of various distillation approaches for continual learning of Diffusion Models.
This project helps machine learning practitioners who work with generative AI models to prevent 'catastrophic forgetting' when updating their models with new data. It takes an existing diffusion model and new datasets, then trains the model incrementally to retain its previous knowledge while learning new patterns. The outcome is a continually updated diffusion model that can generate high-quality images reflecting all learned data, without losing the ability to generate from earlier training.
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Use this if you need to continually update your image-generating diffusion models with new data over time without having to retrain from scratch or sacrifice performance on previously learned tasks.
Not ideal if you are a non-technical user or are looking for a pre-trained, ready-to-use image generation tool rather than a framework for developing and evaluating continual learning strategies.
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Python
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Last pushed
Mar 04, 2025
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