CPT: Controllable and Editable Design Variations with Language Models

• URL: http://arxiv.org/abs/2604.04380v1
• Date: Mon, 06 Apr 2026 03:04:54 GMT
• Title: CPT: Controllable and Editable Design Variations with Language Models
• Abstract summary: We present a system for generating editable design variations using a decoder-only language model, the Creative Pre-trained Transformer (CPT)<n>CPT is trained to predict visual style attributes in design templates.<n>In experiments, our approach generates contextual color and font variations for existing templates and shows promise in adjusting layouts while maintaining design principles.
• Score: 4.267349946267472
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 documents rather than pixel-only images, allowing users to iterate and personalize within a design editor. In experiments, our approach generates contextual color and font variations for existing templates and shows promise in adjusting layouts while maintaining design principles.

Related papers

• PSDesigner: Automated Graphic Design with a Human-Like Creative Workflow [80.29423400249944]
  PSDesigner is an automated graphic design system that emulates the creative workflow of human designers.<n>PSDesigner collects theme-related assets based on user instructions, and autonomously infers and executes tool calls to manipulate design files.<n>To endow the system with strong tool-use capabilities, we construct a design dataset, CreativePSD, which contains a large amount of high-quality PSD design files.
  arXiv  Detail & Related papers  (2026-03-26T17:59:51Z)
• UTDesign: A Unified Framework for Stylized Text Editing and Generation in Graphic Design Images [25.895852456869463]
  UTDesign is a unified framework for high-precision stylized text editing and conditional text generation in design images.<n>Our framework supports both English and Chinese scripts.<n>It achieves state-of-the-art performance among open-source methods in terms of stylistic consistency and text accuracy.
  arXiv  Detail & Related papers  (2025-12-23T16:13:55Z)
• Rethinking Layered Graphic Design Generation with a Top-Down Approach [76.33538798060326]
  Graphic design is crucial for conveying ideas and messages. Designers usually organize their work into objects, backgrounds, and vectorized text layers to simplify editing.<n>With the rise of GenAI methods, an endless supply of high-quality graphic designs in pixel format has become more accessible.<n>Despite this, non-layered designs still inspire human designers, influencing their choices in layouts and text styles, ultimately guiding the creation of layered designs.<n>Motivated by this observation, we propose Accordion, a graphic design generation framework taking the first attempt to convert AI-generated designs into editable layered designs.
  arXiv  Detail & Related papers  (2025-07-08T02:26:08Z)
• Calligrapher: Freestyle Text Image Customization [72.71919410487881]
  Calligrapher is a novel diffusion-based framework that integrates advanced text customization with artistic typography.<n>By automating high-quality, visually consistent typography, Calligrapher surpasses traditional models.
  arXiv  Detail & Related papers  (2025-06-30T17:59:06Z)
• CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generation [13.354283356097563]
  CreatiPoster is a framework that generates editable, multilayer compositions from optional natural-language instructions or assets.<n>To further research, we release a copyright-free corpus of 100,000 multi-layer designs.
  arXiv  Detail & Related papers  (2025-06-12T16:54:39Z)
• POSTA: A Go-to Framework for Customized Artistic Poster Generation [87.16343612086959]
  POSTA is a modular framework for customized artistic poster generation.<n>Background Diffusion creates a themed background based on user input.<n>Design MLLM then generates layout and typography elements that align with and complement the background style.<n>ArtText Diffusion applies additional stylization to key text elements.
  arXiv  Detail & Related papers  (2025-03-19T05:22:38Z)
• GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts [53.568057283934714]
  We propose a Vision-Language Model (VLM)-based framework that generates content-aware text logo layouts.<n>We introduce two model techniques that reduce the computational cost for processing multiple glyph images simultaneously.<n>To support instruction tuning of our model, we construct two extensive text logo datasets that are five times larger than existing public datasets.
  arXiv  Detail & Related papers  (2024-11-18T10:04:10Z)
• MetaDesigner: Advancing Artistic Typography Through AI-Driven, User-Centric, and Multilingual WordArt Synthesis [65.78359025027457]
  MetaDesigner introduces a transformative framework for artistic typography, powered by Large Language Models (LLMs)<n>Its foundation is a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively orchestrate the creation of customizable WordArt.
  arXiv  Detail & Related papers  (2024-06-28T11:58:26Z)
• PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generation [6.855409699832414]
  PosterLlama is a network designed for generating visually and textually coherent layouts. Our evaluations demonstrate that PosterLlama outperforms existing methods in producing authentic and content-aware layouts. It supports an unparalleled range of conditions, including but not limited to unconditional layout generation, element conditional layout generation, layout completion, among others, serving as a highly versatile user manipulation tool.
  arXiv  Detail & Related papers  (2024-04-01T08:46:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.