Diffusion Models
Cutting-edge technology for image generation. Works by adding noise to an image and then learning to reverse the process to create new samples.
Key Concepts
Forward Process
The process of adding noise to an image.
Reverse Process
The process of removing noise from an image.
Denoising Autoencoder
A type of neural network that is used to remove noise from an image.
Detailed Explanation
Diffusion models are a type of generative model that are used to generate high-quality images. They work by adding noise to an image and then learning to reverse the process to create new samples.
Diffusion models are composed of two main parts: a forward process and a reverse process. The forward process adds noise to an image, and the reverse process removes noise from an image. The reverse process is learned by a neural network, which is trained to predict the noise that was added to the image.
Real-World Examples & Use Cases
Image Generation
Diffusion models are used to generate high-quality images.
Image Denoising
Diffusion models are used to remove noise from images.
Image Inpainting
Diffusion models are used to fill in missing parts of an image.