[New Article] How diffusion models work: the math from scratch
Diffusion models are a new class of state-of-the-art generative models that generate diverse high-resolution images.
theaisummer.com
#machinelearning #deeplearning #python #art #ai
For more details🧵⬇️
Diffusion models are a new class of state-of-the-art generative models that generate diverse high-resolution images.
theaisummer.com
#machinelearning #deeplearning #python #art #ai
For more details🧵⬇️
They have already attracted a lot of attention after OpenAI, Nvidia and Google managed to train large-scale models. Example architectures that are based on diffusion models are GLIDE, DALLE-2, Imagen, and the full open-source stable diffusion.
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In this new blog post, we will dig our way up from the basic principles. There are already a bunch of different diffusion-based architectures. We will focus on the most prominent one, which is the Denoising Diffusion Probabilistic Models (DDPMs).
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Once we understand the forward and reverse diffusion, we will dive into the math behind the training process. We will also explore two methodologies on how to scale these types of architectures, called cascade diffusion models and latent diffusion models.
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Finally, we will discuss score-based models and their similarities/differences with diffusion models, and we will explore an interesting work that aims to encapsulate both types of models under the same umbrella.
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