Karlo

The ability to convert text into images using artificial intelligence has gained significant attention in recent years. Stable diffusion is one effective method for achieving this task, utilizing the power of deep neural networks to generate images from textual descriptions. The first step is to convert the textual description of an image into a numerical format that a neural network can process. Text embedding is a popular technique that converts each word in the text into a vector representation. After encoding, a deep neural network generates an initial image based on the encoded text. This image is usually noisy and lacks detail, but it serves as a starting point for the next step. The generated image is refined in several iterations to improve the quality. Diffusion steps are applied gradually, smoothing and removing noise while preserving important features such as edges and contours.

YandexART is a diffusion neural network by Yandex designed for image and video creation. This new neural network ranks as a global leader among generative models in terms of image generation quality. Integrated into Yandex services like Yandex Business and Shedevrum, it generates images and videos using the cascade diffusion method—initially creating images based on requests and progressively enhancing their resolution while infusing them with intricate details. The updated version of this neural network is already operational within the Shedevrum application, enhancing user experiences. YandexART fueling Shedevrum boasts an immense scale, with 5 billion parameters, and underwent training on an extensive dataset comprising 330 million pairs of images and corresponding text descriptions. Through the fusion of a refined dataset, a proprietary text encoder, and reinforcement learning, Shedevrum consistently delivers high-calibre content.