Canva Debuts Craft Image Model Built by Its In-House Research Lab

Canva on October 22, 2026 unveiled Canva Craft, the first foundation model built and trained from scratch by Canva Research, the company’s in-house AI lab. The image generation model is available to all Canva users globally through Dream Lab and to developers through the company’s public API.

Canva said the launch makes it the world’s second most advanced AI lab for image generation, as measured by rankings from DesignArena, which the company described as the largest crowdsourced assessor of AI design.

A Two-Stage Architecture

Unlike conventional image generation models that generate pixels directly from a prompt, Craft uses a two-stage architecture that separates compositional planning from image rendering. In the first stage, an image planning model interprets the prompt and writes a detailed plan of the composition: which elements appear, their spatial relationships, relative scale, visual hierarchy, and any text that needs to appear in the image. In the second stage, a dedicated image rendering model translates the plan into a high-fidelity image at up to 2K resolution across more than 30 aspect ratios.

Canva said that by modelling composition and typography before rendering, Craft can generate complex layouts with legible text, precise object placement and consistent visual relationships, and that the architecture enables more granular control over individual elements, allowing targeted changes without unnecessarily altering everything else. The company said the same structure makes Craft outputs well suited to Magic Layers, its tool for transforming generated images into fully editable design elements, and provides the technical foundation for advanced editing and reference-image capabilities planned for future releases.

Users can write prompts in any language, and Craft can generate text in 55 languages. Users can choose from more than 30 aspect ratios or enter a custom one. A JSON boundary boxing feature converts generated images into editable layers within Magic Layers.

Training, Evaluation, and Efficiency

Craft’s training data includes millions of human-made templates from Canva’s content library, spanning professionally designed posters, social posts, presentations, infographics, menus and typography-rich layouts. Canva said most image models are trained primarily on photographs, and that the templates instead teach the model how layout, hierarchy, and typography fit together.

Following pre-training, Canva optimized Craft through supervised fine-tuning and reinforcement learning, incorporating feedback from its professional designers to improve instruction following, typography accuracy, and overall visual quality. For internal evaluations, Canva said its professional designers worked alongside dozens of researchers to assess more than 25,000 pairs of images in head-to-head comparisons with leading models, and that it sided with the designer when automated evaluations and human judgments differed.

Canva reported that Craft debuted as the fourth most advanced image generation model on DesignArena. The company said its outputs hold the top global rank for People & Portraits, out of 87 models, and for Landscapes & Nature, out of 85 models, along with second place for Abstract & Patterns and fourth for Products.

Canva reports that more than a quarter of a billion people use its platform, creating over 400 designs every second. The company said delivering frontier-level performance at that scale requires breakthroughs in both compute efficiency and inference speed. It said advanced model distillation reduced the number of inference steps Craft requires to a fraction of the original model, and that a custom inference engine optimized for the planning stage made Craft an order of magnitude faster than its first internal version. According to Canva, these advances underpin Craft-fast and Craft-ultrafast, two upcoming variants designed to deliver high-quality image generation at a fraction of the cost of leading frontier models.

Availability and Safety

To use Craft inside Canva, users open Dream Lab from the bottom left panel. Developers and product teams can build the model into their own tools, including marketing platforms, e-commerce sites, ad builders and print services, through Canva’s public API. Support through the Canva MCP Server, which connects Craft to AI assistants and automated workflows so agents can generate images ready to use inside a design, is listed as coming soon.

Craft is backed by Canva Shield, the company’s AI safety approach covering input and output moderation, privacy and safety controls, and reporting tools that let people flag potentially harmful or misleading content. Canva said that alongside visual quality, it evaluates brand safety, cultural context, and the potential for harmful content before releasing its models.

The Lab Behind the Model

One day earlier, on October 21, 2026, Canva published a first look inside Canva Research, its in-house research and development lab for visual and design AI. The lab is made up of more than 160 AI research scientists and engineers globally, within a broader AI unit of 400 people.

Canva’s AI research began in 2017 with machine learning for search and recommendations in its template library. It acquired Vienna-based AI lab Kaleido in 2021 and Leonardo in 2024, and in 2025 the company brought its AI research teams together as Canva Research. It later added MangoAI and Simtheory, which Canva said bring deep expertise in reinforcement learning and AI agents.

Canva reports more than 36 billion uses of its AI tools and more than one billion designs created with Canva every month. The company said its in-house models already power dozens of features across the platform, including Magic Layers, which is built on a learned decomposition model that understands the elements and layers in a composition, and Leonardo’s Lucid Origin image model. Canva also said its image-to-video work achieved 70% more video generations per GPU hour.

Canva said faster Craft models are already on the way and that it will keep working on quality, detail and control, naming fine print and technical drawings as areas for improvement.