TRAINING A NEURAL NETWORK FOR IMAGE STYLING

Improving the visualization of projects and portfolios of designers and architects can be achieved by enhancing the illustrativeness and stylization of images using artificial intelligence. The use of neural networks for content generation significantly speeds up the work of designers.

Among all the neural networks for image generation, MidJourney shows the best results. After analyzing the licenses and subscription costs of services as well as the models they employ, the Stable Diffusion deep learning neural network was chosen. The Stable Diffusion neural network is open-source, unlike DALL-E and Midjourney, allowing for unlimited content generation. The stylized images were generated in Stable Diffusion using Dreambooth based on the Google Collab platform. The creation of a custom model was conducted in two stages. The first stage involved preparing images for training the Stable Diffusion neural network. The second stage was the direct training of the neural network based on the Google Collab platform. Kobzar's graphic drawings served as the training dataset. Initially, 77 drawings with the same theme were selected for model training. 30 of these were used to train the model after corrections in Adobe Photoshop and Topaz Photo AI. Adjustments included cropping, background removal, printing raster, noise reduction, sharpening, and scaling images. The originality of the work lies in the fact that the trained model was used to create stylized creative images, utilizing excerpts from the poet's poems describing nature and events in a very realistic way. The generated images have successfully passed the Turing test, indicating a realistic reproduction of the style of Taras Shevchenko's drawings and the utilization of the author's poetic text as a prompt. The use of neural networks for generating and styling images as virtual assistants for designers and architects speeds up the creative process and enables the creation of works of any complexity.