Abstract: Text-to-image generation is a type of generative modelling where a machine learning model is trained to generate realistic images from textual descriptions. This involves encoding textual descriptions into a latent space representation and then decoding the latent representation into an image. The goal is to generate images that are not only visually realistic but also semantically coherent with the input text. Text-to-image generation has many applications, such as creating virtual environments, generating product images for e-commerce, and aiding in creative tasks such as graphic design and art. However, it is still an active research area with many challenges, such as handling the high dimensionality of images, capturing fine-grained details, and ensuring that generated images are diverse and plausible.

Keywords: Generative Adversarial Networks (GANs), Image Synthesis, Image to Image translation, AI Glide.

PDF | DOI: 10.17148/IARJSET.2023.10526

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