Yandex Launches YandexART 2.0 With Advanced Text Generation on Images

Yandex has introduced YandexART 2.0, a powerful upgrade to its visual neural network that adds text-generation capabilities and offers significant design tools for businesses and individual users.

Yandex has introduced YandexART 2.0, a powerful upgrade to its visual neural network that adds text-generation capabilities and offers significant design tools for businesses and individual users.

YandexART 2.0 allows companies and creators to design logos, banners, and advertising materials with text generated directly on images. This release offers enhanced features, such as applying multiple styles to a single image, arranging elements naturally, and incorporating detailed prompts more effectively. Businesses can now create brand assets, product labels, and other marketing visuals with greater ease and customization.

The technology has already seen practical applications. Text.ru, a content service, uses YandexART to create visuals for web and blog materials. Another service, presentsimple.ai, leverages both YandexART and YandexGPT to automate slide presentation creation, combining content generation and visual design in a streamlined workflow. In Yandex Direct, 11% of advertisers are using YandexART-generated images, achieving 10-15% higher engagement in their campaigns.

Advanced Model Design

YandexART 2.0 combines convolutional and transformer neural network architectures. The convolutional component mimics human visual perception by recognizing edges, shapes, and textures in images, while the transformer model excels at handling more detailed text prompts. This hybrid architecture allows YandexART to understand and render complex image requests, such as a photorealistic can featuring an anime character.

The system was trained on hundreds of millions of images with detailed descriptions, improved with Yandex’s proprietary VLM model. It now also includes an extensive dataset of text-inclusive images, allowing YandexART 2.0 to generate Latin-text captions on images. The training focused on four parameters—relevance, aesthetics, defect minimization, and complexity. According to Yandex, YandexART 2.0 performed better than Midjourney V6.1 in complexity (66%) and aesthetics (58%) and closely matched it in relevance to user prompts.

Available on Yandex Cloud for Business Integration

YandexART 2.0 is now available through Yandex Cloud, with businesses able to access it via API integration. This provides companies a platform for faster, high-quality image generation, allowing for the creation of social media graphics, article illustrations, branding visuals, and more. Users can test the tool in demo mode or integrate it directly into their applications to fine-tune prompts.

User-Friendly Features for Everyday Creativity

Individual users can access YandexART 2.0 through the Pro option on Yandex’s virtual assistant, Alice. This option lets users generate customized images and refine them in real time by adjusting prompts in a conversational chat format. The feature is available across mobile and desktop platforms, providing an accessible tool for creating profile images, social media posts, app icons, and other personal or creative visuals.

YandexART’s quick image generation (within seconds) allows users to easily complete everyday tasks, such as designing social media content or creating unique prints for clothing.

About YandexART Technology

YandexART, a diffusion-based neural network, specializes in rendering images and animations based on text prompts. Trained on a dataset of 850 million image-description pairs, it understands Russian cultural context and uses a unique text-recognition algorithm. YandexART’s reach spans across Yandex services, such as Yandex Business, Direct, Browser, and Market, and is available to companies through Yandex Cloud.

YandexART 2.0 promises to be a powerful tool for both creative users and business-oriented applications, expanding possibilities for quick, high-quality, and customized visual content creation.

Dan Taylor is the founder and editor of International Search Insights (ISI), the publication behind ReadIsi, and a Partner and Head of Innovation (Search & AI) at SALT.agency. He has worked in search for more than a decade, in-house and agency-side, and is regarded as one of the West’s leading voices on search engines beyond Google, including Yandex, Baidu, Naver and Cốc Cốc. On ReadIsi, Dan covers the engines and AI answer platforms that English-language coverage tends to miss: Baidu and ERNIE, Yandex and Alice AI, Naver, Cốc Cốc, Shenma, Sogou, WeChat and Weibo search, plus DeepSeek and Alibaba’s Qwen. He also keeps dated records of documented changes, such as the Yandex, Baidu and Naver algorithm update histories and the DeepSeek, ERNIE and Qwen model update histories. His approach is to take learnings from global search engines and apply logic with a high evidence threshold. A claim needs a source, patterns seen in one market are treated as a hypothesis until they are checked in another, and where the evidence is thin he says so. For brands, that means advice built on what can be shown, with measurement that separates being cited from being chosen. Dan is credited with coining the term “edge SEO” and won the inaugural TechSEO Boost research competition in 2018 for his work on CDN and edge changes for SEO. In 2026 he was credited with coining “Infinite Tail” to describe how keyword research is changing with AI search. He is the author of AI Search Marketing For The Travel Sector, writes for Search Engine Journal, Search Engine Land and Travel Marketing Guy, and has advised technology, SaaS, travel and enterprise ecommerce brands, including Cloudflare, GitLab and Airbnb. He has given multiple talks and panels at the International Search Summit in Barcelona, and has also spoken at Optimization Moscow, brightonSEO, Google Search Central Live Deep Dive Asia in Bangkok, Athens SEO and the Asia SEO Virtual Summit 2026, where his talk looked at how Baidu, Naver, DeepSeek, Tencent, Cốc Cốc and Yandex built their own AI answers.