Diffusion Models

Diffusion models are a class of generative machine learning models that create data by gradually adding noise to an initial distribution and then learning to reverse this process. They work by training a neural network to denoise data through a series of steps, starting from pure noise and ending with a clean sample. This approach has become highly popular for generating high-quality images, audio, and other types of data.

Also known as: Denoising Diffusion Probabilistic Models, DDPM, Score-Based Generative Models, Stable Diffusion, Diffusion Probabilistic Models

Why learn Diffusion Models?

Developers should learn diffusion models when working on generative AI tasks, such as image synthesis, text-to-image generation, or data augmentation, as they often produce more detailed and diverse outputs compared to earlier models like GANs. They are particularly useful in creative applications, medical imaging, and scientific simulations where high-fidelity generation is critical. Their stable training process and scalability make them a go-to choice for state-of-the-art generative systems.

Learning Resources

• docs: Denoising Diffusion Probabilistic Models Paper
• course: Hugging Face Diffusion Models Course
• tutorial: Lilian Weng's Blog on Diffusion Models
• tutorial: Stable Diffusion with Diffusers Tutorial
• video: YouTube: Diffusion Models Explained