[2604.04380] CPT: Controllable and Editable Design Variations with Language Models

Computer Science > Machine Learning arXiv:2604.04380 (cs) [Submitted on 6 Apr 2026] Title:CPT: Controllable and Editable Design Variations with Language Models Authors:Karthik Suresh, Amine Ben Khalifa, Li Zhang, Wei-ting Hsu, Fangzheng Wu, Vinay More, Asim Kadav View a PDF of the paper titled CPT: Controllable and Editable Design Variations with Language Models, by Karthik Suresh and 6 other authors View PDF HTML (experimental) Abstract:Designing visually diverse and high-quality designs remains a manual, time-consuming process, limiting scalability and personalization in creative workflows. We present a system for generating editable design variations using a decoder-only language model, the Creative Pre-trained Transformer (CPT), trained to predict visual style attributes in design templates. At the core of our approach is a new representation called Creative Markup Language (CML), a compact, machine-learning-friendly format that captures canvas-level structure, page layout, and element-level details (text, images, and vector graphics), including both content and style. We fine-tune CPT on a large corpus of design templates authored by professional designers, enabling it to learn meaningful, context-aware predictions for attributes such as color schemes and font choices. The model produces semantically structured and stylistically coherent outputs, preserving internal consistency across elements. Unlike generative image models, our system yields fully editable design do...