A Survey on Generative Models for Image Synthesis: GANs, Diffusion Models, and Beyond

Author(s)	Ms. Rashmi V, Ms. Radhika S K
Country	India
Abstract	Generative models have changed computer vision by enabling realistic and diverse image synthesis. This survey provides a comprehensive review of popular generative models; Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models. We examine their architectures, training methodologies, strengths, limitations, and applications. Key datasets and evaluation metrics are discussed, in addition to insights obtained from comparative analysis. Additionally, ethical considerations and research challenges such as training instability, mode collapse, and computational complexity are addressed and potential future directions are explored to guide research in efficient, interpretable, and safe generative modeling.
Keywords	Generative Models, Image Synthesis, GANs, Diffusion Models, Variational Autoencoders, Deep Learning, Computer Vision, Ethical AI
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-31
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.59218

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research