What Is Diffusion Model?
A diffusion model is a generative AI architecture that creates data (typically images) by learning to gradually remove noise from random static through a step-by-step denoising process, producing high-quality outputs guided by text prompts or other conditioning signals.
How Diffusion Model Works
Diffusion models work in two phases. During training, the model learns to reverse a noise-adding process: real images are progressively corrupted with Gaussian noise until they become pure static, and the model learns to reverse each step. During generation, the model starts with random noise and iteratively denoises it, guided by a text prompt encoded by a separate model (like CLIP). Latent diffusion models (used in Stable Diffusion) perform this process in a compressed latent space for efficiency. Diffusion models surpassed GANs in image quality around 2022 and now power most major image generation tools including Midjourney, DALL-E 3, and Stable Diffusion.
Real-World Examples
Stable Diffusion generating a photorealistic landscape from the prompt 'misty mountain sunrise, 8K photography'
DALL-E 3 creating custom illustrations by iteratively denoising random static guided by text descriptions
A diffusion-based video model generating smooth video clips by denoising sequences of frames
Diffusion Model on Vincony
Vincony provides access to multiple diffusion-based image generation models, letting users compare outputs from different text-to-image systems.
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