Diffusion Models: The Next Frontier in Image Generation
Diffusion models have emerged as a powerful alternative to GANs for image synthesis. These models generate images by starting from random noise and iteratively refining the image through a denoising process. The process is guided by learned data distributions, allowing for the generation of highly detailed and photorealistic images. Unlike GANs, diffusion models are less prone to mode collapse, a common issue where the model generates limited variations of images. Their stability and quality make them suitable for applications in high-resolution art generation and scientific visualization.