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A diffusion model has two main stages: forward diffusion, which adds noise to data, and reverse diffusion, which removes noise to generate new samples.
Diffusion models are generative models that learn to create new data by reversing a gradual noise-adding process. In the forward diffusion process, data is progressively corrupted with Gaussian noise until it resembles a simple distribution, typically a standard normal distribution. A neural network then learns the reverse process, removing noise step by step to reconstruct data similar to the training examples. This framework can be expressed through Markov chains, denoising diffusion probabilistic models, score-based models, or stochastic differential equations. Denoising Diffusion Probabilistic Models (DDPMs) train a network to predict the noise present in a partially corrupted sample. During generation, the model begins with random noise and repeatedly estimates and removes noise until a data sample is produced. Diffusion models are widely used in image generation, denoising, inpainting, super-resolution, and video generation, often with text encoders and cross-attention for text-conditioned outputs. They are also applied to text generation, summarization, sound synthesis, and reinforcement learning.
A diffusion model has two main stages: forward diffusion, which adds noise to data, and reverse diffusion, which removes noise to generate new samples.
The forward process gradually transforms a data distribution into a Gaussian distribution, while the learned reverse process reconstructs samples from random noise.
DDPMs commonly train a neural network to predict the Gaussian noise added at each time step, using a mean-squared error objective.
Diffusion models can use U-Nets or transformers as backbones and support conditional generation through text encoders and cross-attention.
Major applications include image generation, denoising, inpainting, super-resolution, video and sound generation, text generation, summarization, and reinforcement learning.
The gradual addition of noise to data until it approaches a simple Gaussian distribution.
The learned sequence of denoising steps that transforms random noise into a sample from the target data distribution.
A diffusion model that uses variational inference and a neural network to model the reverse denoising transitions.
A neural network that estimates the noise contained in a corrupted sample at a particular diffusion time step.
The gradient of the logarithm of a data probability density, indicating how the density changes around a data point.
A diffusion-model formulation that learns score functions at different noise levels and uses them for sampling.
An optimization approach that trains the model by maximizing a tractable lower bound on the data likelihood.
The main neural network architecture responsible for predicting denoising information, commonly a U-Net or transformer.
Random noise sampled from a normal distribution and progressively added during the forward diffusion process.
“Can you explain what "A diffusion model has two main stages: forward diffusion, which adds noise to data, and reverse diffusion, which removes noise to generate new samples." means in simple terms?”