This article will explain the essence, principle, and applications of diffusion models, helping you to understand diffusion models in one go.
Diffusion Models
The essence of diffusion models
Definition of diffusion models:Diffusion Models are a new, advanced generative model used to generate data similar to the training data, capable of generating various high-resolution images.
The core idea of diffusion models:Diffusion Models are a generative model inspired by non-equilibrium thermodynamics. The core idea is to gradually add noise to data by simulating the diffusion process and subsequently learn to reverse this process to construct the desired data samples from the noise.
The essence of diffusion models:Utilizing Markov chains to define diffusion steps, gradually “destroying” the data into pure noise through state transitions at each step. Then, by training a neural network to approximate the true reverse diffusion process, diffusion models can gradually restore the original data from pure noise.
The principle of diffusion models
Two, the principle of diffusion models
The working principle of diffusion models:Diffusion Models damage the training data by continuously adding Gaussian noise and then restore the data by learning the reverse denoising process. After training, we can input randomly sampled noise into the model, generating data through the learned denoising process.
The working principle of diffusion models
Diffusion models are latent variable models that use Markov chains to map to latent space. This mapping allows the model to capture the inherent structure and patterns of the data in latent space.
The latent space of diffusion models
Markov Chain:A memoryless random process that has the Markov property, meaning the future depends only on the present and not on the past.
Markov chain
Learn more about Markov chains:Mathematical Foundations of Artificial Intelligence – Markov Chain
The components of diffusion models:Diffusion models primarily consist of forward diffusion and reverse diffusion.
Forward diffusion and reverse diffusion
Forward diffusion:the process of gradually adding noise to the original data until it becomes pure noise.
Although this process itself cannot directly generate images, it is crucial for understanding the working principle of diffusion models and constructing the target for training samples (ground truth, GT).
Forward diffusion
Reverse diffusion:the reverse operation of forward diffusion, starting from pure noise and gradually removing noise to restore the original data.
This process relies on the model learning how to recover the original data patterns from noise. The model learns the reverse diffusion process through a neural network, enabling it to generate samples similar to the original data.
Three, applications of diffusion models
Stable Diffusion Model:Stable Diffusion is the most advanced pattern among diffusion models.It employs a more stable, controllable, and efficient method to generate high-quality images.
Stable Diffusion was launched in August 2022 by researchers from CompVis, Stability AI, and LAION, with core technology derived from AI video editing technology startup Runway’s chief research scientist Patrick Esser and Robin Rombach from the University of Munich’s machine vision learning group.
DALL-E 2: developed by OpenAI, known for its highly detailed and creative images based on text descriptions.
It uses advanced diffusion technology to generate both imaginative and realistic images, making it a popular tool in creative and artistic applications.
