Synthetic Image Generation using StackGAN

The generation of artificially created images which resemble actual photos is known as synthetic image generation. Due to the many potential uses of synthetic image generation, it has drawn a lot of interest. Conversational chatbots that produce contextual visuals based on user input are one example of such an application. This ability is very beneficial in situations like drawing criminals from their descriptions. The field still faces difficulties in producing high-quality images that are aesthetically identical to actual photographs. In order to solve this issue, this paper offers the use of a cutting-edge Generative Adversarial Network (GAN) model, StackGAN, to produce high-quality photos. This main goal of this work is to investigate how well StackGAN can create realistic graphics from inputs that include both images and text embedding from a single dataset. It also seeks to contribute to the development of synthetic image generation techniques and offer a method for producing visually accurate artificial images by overcoming the drawbacks of prior GAN structures. The improved performance of StackGAN is demonstrated in creating high-quality photos with amazing visual fidelity through rigorous experimentation and evaluation.