Уровень 0 · материалов: 4
В кластер входят документы, описывающие возможности, техническую реализацию или влияние систем генерации изображений по текстовому описанию.
Общие признаки: создание изображений с помощью нейросетей, текст-в-изображение, DALL-E, Stable Diffusion, генеративное искусство
Группа выше: Генерация изображений нейросетями
Смысл: The main idea is to present DALL·E as a revolutionary step in AI that bridges the gap between natural language processing and visual generation, showcasing its versatility and potential societal impact.
OpenAI's DALL·E is a 12-billion parameter transformer model that generates complex and context-aware images from text, signaling a convergence of NLP and computer vision.
Смысл: The text serves as a practical demonstration of DALL-E 2's versatility and efficiency, arguing that AI image generation is reaching a point where it can replace traditional manual design for rapid prototyping and artistic creation.
The author showcases the impressive image generation and editing capabilities of DALL-E 2, suggesting it may soon replace professional designers.
Смысл: The text serves as both a practical tutorial on using Stable Diffusion's img2img capabilities for controlled artistic creation and a philosophical meditation on the rapid democratization and inevitable normalization of generative AI.
A step-by-step guide on using Stable Diffusion and GIMP to create a post-apocalyptic Seattle scene, paired with reflections on AI ethics and the nature of neural network models.
Смысл: The main idea is to present the technical achievement of building a high-capacity, Russian-language text-to-image generation system (ruDALL-E) and to share the architectural and computational methodologies used to achieve it.
Sber AI and partners developed ruDALL-E, a massive transformer-based model capable of generating images from Russian text, utilizing over 24,000 GPU-days of training.