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Learn to implement and train diffusion models in PyTorch, covering unconditional and conditional versions, classifier-free guidance, and exponential moving average. Gain hands-on experience with state-of-the-art generative AI techniques.
Comprehensive explanation of diffusion models, covering theory, architecture, math derivation, algorithms, improvements, and results. Includes comparisons to other methods and references to state-of-the-art applications.
Implement Vector Quantized Generative Adversarial Networks (VQGAN) using PyTorch, covering encoder, decoder, codebook, discriminator, and transformer stages for high-resolution image synthesis.
Explore score-based generative models and their connection to diffusion models. Gain intuition on DDPM, DDIM, and EDM through mathematical explanations and visualizations.
Dive into Flow Matching's simplified approach to diffusion models through intuitive explanations, mathematical derivations, and hands-on PyTorch implementation for advanced AI image generation.
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