- AI BEST SEARCH
- AI Glossary & Keyword Index [AI BEST SEARCH]
- Diffusion Model
Diffusion Model
A diffusion model is a type of generative AI that produces meaningful data—images, audio, video, and more—by progressively denoising random noise. Originally rooted in concepts from physics and statistical mechanics around "diffusion processes," it has in recent years achieved groundbreaking results especially in the field of image generation AI. The basic mechanism of a diffusion model consists of two stages: 1. Noise addition (forward process): Progressively adds noise to real images or data until they become completely random noise 2. Denoising (reverse process): The model learns to progressively recover information from that noise, reconstructing data resembling the original By repeating this process, the model becomes able to generate high-quality, natural-looking data from scratch. Techniques such as U-Net, schedulers, and self-attention mechanisms are used in the model, allowing fine-grained control over generation quality. Representative AI systems based on diffusion models include: • Stable Diffusion (Stability AI) • DALL·E 2 (OpenAI) • Midjourney • Imagen (Google Research) • Applications to audio and video (e.g., MusicLM, VideoDiffusion) are also underway Primary application areas: • Text-to-image generation • Image restoration and extension (inpainting and outpainting) • Photo style transfer and compositing • Automatic music and video generation Compared to traditional GANs (Generative Adversarial Networks), diffusion models are more stable to train and produce higher-quality outputs, and are rapidly establishing themselves as the dominant technology in generative AI.
