Diffusion Models and Image Generation

Diffusion models are an alternative approach to generating images compared to generative adversarial networks (GANs). While GANs create alternate images and rely on a discriminator to determine their authenticity, diffusion models progressively add noise to known training data, training the system to generate images from noise. Diffusion models have a broader range of image generation capabilities, including new image generation, image-to-image transformation, infilling, style transfer, super resolution, and outpainting. Popular diffusion models include DALL-E, DALL-E 2, Mid-Journey, and Stable Diffusion. Each model has its own unique methods for generating images and access methods, such as APIs or Discord bots. Although diffusion models may have some noticeable faults in generated images, they offer a more versatile and context-driven approach compared to GANs.