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Diffusion Models

Diffusion models are a type of generative AI that create new data, like images or audio, by gradually adding noise to existing data and then learning to reverse this process. They achieve this by training a neural network to predict and remove the added noise, effectively "denoising" the data to reconstruct or generate new content.

Forward Diffusion Process​

  • The process starts with a sample of data (e.g., an image).
  • Over many steps, a small amount of random noise (usually Gaussian noise) is gradually added to the sample.
  • With each step, the data becomes increasingly noisy and loses its structure.

Reverse Diffusion Process (Denoising)​

  • A neural network is trained to learn the reverse process, i.e., to predict and remove the noise added in the forward diffusion process.
  • The neural network takes the noisy data and predicts the noise that was added at that step.
  • By iteratively removing the predicted noise, the neural network can reconstruct the original data or generate new data that is similar to the training data.

Applications​

  • Diffusion models are powerful for tasks like image and audio generation, text-to-image synthesis, and other computer vision tasks.
  • They are particularly adept at generating high-quality images with fine details and realistic textures.

Key Concepts​

  • Generative Model: Diffusion models are a type of generative model, meaning they are used to create new data samples.
  • Markov Chain: The forward and reverse diffusion processes are often formulated as Markov chains, meaning that each step depends only on the previous step.
  • Denoising: The core principle of diffusion models is to learn how to remove noise from data.
  • U-Net Architecture: Many diffusion model implementations utilize a U-Net architecture, a type of neural network commonly used for image segmentation and denoising tasks.