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Text-to-face is a subset of text-to-image that require more complexarchitecture due to their more detailed production. In this paper, we presentan encoder-decoder model called Cycle Text2Face. Cycle Text2Face is a newinitiative in the encoder part, it uses a sentence transformer and GAN togenerate the image described by the text. The Cycle is completed by reproducingthe text of the face in the decoder part of the model. Evaluating the modelusing the CelebA dataset, leads to better results than previous GAN-basedmodels. In measuring the quality of the generate face, in addition tosatisfying the human audience, we obtain an FID score of 3.458. This model,with high-speed processing, provides quality face images in the short time.

cycle text2face: cycle text-to-face gan via transformers