Diffusion Models in Image Generation: A Comprehensive Survey
Abstract
Diffusion models are the primary approach for generative work in computer vision, used for creating new images from scratch, tweaking existing ones, or performing complex edits. This survey traces the evolution of AI image generation from early methods to the latest diffusion models. Beginning with the basics of Denoising Diffusion Probabilistic Models (DDPM)—how noise is gradually added in the forward pass and how the model learns to remove it step by step in the reverse process—this paper then covers Latent Diffusion Models (LDMs), which advance the process by operating in a compressed latent space using Variational Autoencoders (VAEs). This approach encodes images into lower- dimensional representations before adding noise and decoding them after denoising, dramatically reducing computational cost while preserving perceptual quality. The survey offers a complete look at diffusion models tracing their path from core fundamentals to cutting-edge advances and real-world tools for image generation..
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