gmongaras/Latent_Diffusion_Model_Imagenet2012
A latent flow-based diffusion model trained on the 2012 ImageNet dataset from scratch.
This project helps machine learning researchers and practitioners experiment with and train custom image generation models using the ImageNet 2012 dataset. It takes image data and class labels as input and produces a trained model that can generate new images conditioned on specified classes. This is ideal for those focused on understanding and developing flow-based diffusion models for image synthesis.
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Use this if you need a lightweight, class-conditioned image generation model to train or fine-tune on a moderately sized dataset like ImageNet 2012, or to learn about diffusion model architectures.
Not ideal if you need a production-ready model for real-world applications or if you require text-to-image generation capabilities.
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Language
Python
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Last pushed
May 21, 2025
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