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Research Article | Open Access
Volume 15 2023 | None
TRANSFORMING WORDS INTO VISUALS
MR.GARDASU ANIL KUMAR, SANKALAMADDI GOWTHAM REDDY, POOSIRINTYNAYAKULU HANISH, SOMARAPU HARISHITH, JARPULA SRAVAN KUMAR
Pages: 879-889
Abstract
Using state-of-the-art neural network methods, the machine learning text-to-image generator can convert written descriptions into related images. Utilizing deep learning architectures like Transformer models or Generative Adversarial Networks (GANS), the generator is trained to understand the meaning of input text and produce visuals that are both coherent and relevant to their context. Art, design, and content production are just a few of the many areas that benefit from this technology, which allows for the automatic synthesis of pictures from verbal stimuli. Generative models, of which Diffusion Models are a kind, generate new data that is statistically similar to the training data. First, diffusion models learn to damage training data by adding Gaussian noise in sequential steps. Then, they learn to recover the data by reversing the noising process.
Keywords
hugging face diffusers, deep learning, machine learning, steady diffusion, and natural language processing.
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