Diffusion Based Image Generation

Diffusion-based image generation leverages the principles of diffusion probabilistic models to create high-quality images from various inputs, such as text prompts, sketches, or other images, aiming to achieve greater control and realism than previous methods. Current research focuses on improving model efficiency, enhancing control over aspects like lighting, object placement, and style, and addressing challenges such as sample replication and generalization to unseen data, often employing architectures like U-Nets and latent diffusion models. This rapidly advancing field has significant implications for various applications, including robotics, autonomous driving, art creation, and medical image analysis, by enabling the generation of diverse and highly realistic images tailored to specific needs.