International Journal of Science, Engineering and TechnologyAuthors: Shaikh Iqra Arif, Professor Bhosale Sachin Bajirao, Professor Bangar Abhishek Popat
Abstract: Diffusion models have become an important approach for generating images from text descriptions, random noise, and other conditions. They work by learning how to gradually remove noise from data until a meaningful image is formed. This review studies five publications from Springer and IEEE related to diffusion-based image generation: a broad survey of image-generation applications, a survey of lightweight diffusion models, the original Latent Diffusion Models paper, a survey of diffusion models in computer vision, and a review of text-to-image generative models. The selected works are compared in terms of their focus, main contribution, practical value, and limitations. Together, they show that image- generation research has progressed in three connected directions: improving visual quality and controllability, reducing computation, and better measuring how closely generated images follow user prompts. The review also identifies open issues involving evaluation, computational cost, dataset bias, privacy, copyright, and responsible use. This is a literature-based review; it does not report new experiments or claim that the five papers were tested under identical conditions.
