Language Enabled Image Originator

Language Enabled Image Originator

Akarsh J1, Dr. Devaraj Verma C 2

1Research Scholar, Department of CSE, Jain (Deemed-to-be) University

2Professor, Department of CSE, FET, Jain (Deemed-to-be) University.

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Abstract - Traditionally, forensic artists would painstakingly sketch a suspect's face from a witness's statement in order to create forensic photographs. There are restrictions on this procedure, though. First, it depends a great deal on the interpretation of the artist, which can bring falsehoods and prejudices. It can also take a lot of time, particularly if the drawing needs to be refined repeatedly.

Image generation is the process of creating new pictures that are comparable to the people in a certain dataset. Producing visually realistic pictures that fit the input's properties is the aim of image creation. information. Machine learning employs a number of pictures generating approaches, such as auto-regressive models, variational autoencoders (VAEs), generative adversarial networks (GANs), and stable diffusion models. These models are trained on an image dataset (for instance, 5,85B CLIP-filtered image-text pairings make up the large-scale research dataset LAION 5B) and are taught to produce new pictures that are comparable to the original data. An image generating model may be used to create a series of pictures for forensic sketching, with the witness's description serving as the basis for selecting the best image. This and the necessary face feature changes may be fed into the Image-to-Image Translation model. Until an adequate picture is produced, the picture-to-Image Translation model creates a fresh series of images with alterations.

Key Words:  variational autoencoders (VAEs), Generative adversarial networks (GANs),