IGD: Instructional Graphic Design with Multimodal Layer Generation
IGD integrates multimodal understanding and parameter prediction with diffusion models, enabling editable multi-scenario graphic design from natural language instructions.
Key Findings
Methodology
IGD combines multimodal large language models (MLLM) with diffusion models through a standardized design platform. It predicts attributes, sequences, and layouts using MLLM, while the diffusion model generates image content. End-to-end training optimizes content coherence and editing flexibility. The approach employs Stable Diffusion for image synthesis, Qwen2.5 for language understanding, and Penpot for data standardization. The pipeline involves instruction parsing, attribute prediction, content generation, and layout arrangement, ensuring multi-scenario adaptability and content editability.
Key Results
- On a dataset of 90,000 samples, IGD achieved an OCR text accuracy of 86.83%, outperforming SD3.5 by over 20%. It generated design files with high layout coherence, text readability, and editing flexibility, suitable for complex multi-scenario tasks such as posters, advertisements, and UI mockups. The model supported both Chinese and English instructions, demonstrating robust multilingual capability.
- Quantitative metrics showed superior performance: Rali (layer alignment) below 0.055, Rove (text overlap) below 0.025, and FID of 56.35, surpassing baseline diffusion and layout models. Ablation studies confirmed the importance of end-to-end training and standardized data formats. Cross-scenario tests validated its versatility across advertising, publishing, and interface design.
- Further analysis indicated that end-to-end training enhanced content consistency and editability. The multimodal comprehension mechanism effectively handled complex instructions, and the parameter prediction strategy reduced computational costs compared to pure diffusion models. These results highlight IGD’s potential as a comprehensive automated graphic design tool.
Significance
This work addresses key limitations of existing layout and diffusion-based methods, offering a unified framework that supports multi-scenario, editable, and instruction-guided graphic design. It significantly advances the automation of creative workflows, reducing manual effort and enhancing design diversity. The integration of multimodal understanding with content generation paves the way for intelligent design assistants capable of handling complex, multi-element tasks. Its scalability and adaptability open new horizons for industrial applications such as marketing, publishing, and digital interfaces, fostering a new era of AI-driven creative tools.
Technical Contribution
The paper introduces a novel architecture combining MLLM-based understanding with parameter prediction and diffusion-based content generation. It proposes a standardized multi-scenario file format compatible with Penpot, enabling flexible, editable designs. The end-to-end training strategy aligns content attributes and layout predictions, improving coherence. The multi-modal comprehension module enhances instruction understanding, while the content generation module ensures high-quality, editable outputs. These innovations collectively enable scalable, versatile, and user-friendly graphic design automation, setting a new benchmark in the field.
Novelty
IGD is the first framework to unify instruction-guided, multi-modal, and editable graphic design generation within an end-to-end model. Unlike prior works limited to static layout or image synthesis, it integrates understanding and creation, supporting diverse scenarios and languages. Its key innovation lies in the parameter prediction and rendering paradigm, which replaces traditional static assets with flexible, editable layers, marking a significant departure from existing diffusion-only or layout-only approaches. This comprehensive approach sets a new standard for intelligent graphic design.
Limitations
- Despite its strengths, IGD struggles with highly complex scenes involving intricate details or dense element arrangements, leading to occasional inaccuracies in layout or content coherence.
- Training requires large annotated datasets and significant computational resources, which may limit real-time deployment or adaptation to niche domains without further optimization.
- The model’s performance on stylized or highly artistic designs remains limited, necessitating future research into style-aware generation and finer control mechanisms.
Future Work
Future directions include enhancing the model’s ability to handle complex and stylized designs, reducing training costs through semi-supervised learning, and improving real-time inference efficiency. Integrating user feedback for interactive editing, expanding multi-language support, and exploring multi-modal self-supervised learning are also promising avenues. Additionally, extending the framework to 3D content and dynamic media could further broaden its industrial impact, fostering more intelligent and versatile creative AI tools.
AI Executive Summary
In recent years, AI-driven graphic design has made significant strides, yet existing methods face notable limitations. Traditional layout algorithms rely heavily on predefined elements, requiring extensive manual effort and lacking creativity. Diffusion models, while capable of generating high-quality images, produce static, non-editable outputs with poor text legibility, hindering practical application. To overcome these challenges, Qu et al. introduce IGD, a novel framework that unifies understanding and content generation within an end-to-end architecture.
IGD leverages a standardized multi-scenario design format, built upon the Penpot platform, enabling flexible, editable layers. Its core combines a multimodal large language model (MLLM) for semantic understanding, attribute prediction, and layout sequencing, with a diffusion model for image content creation. This synergy allows the system to generate multi-layered graphic files solely from natural language instructions, supporting both Chinese and English. The architecture supports instruction-guided customization, content editing, and multi-scenario adaptability.
Experimental results on a dataset of 90,000 samples demonstrate that IGD surpasses existing methods in text accuracy, layout coherence, and visual quality. The OCR-based text accuracy reaches 86.83%, and the generated design files exhibit high consistency and flexibility, suitable for complex tasks like posters, advertisements, and UI mockups. The model’s scalability and robustness suggest broad industrial applications, from marketing to digital content creation.
This work marks a significant step toward fully automated, intelligent graphic design. By integrating understanding and generation, IGD reduces manual effort, enhances creativity, and paves the way for future interactive design tools. Despite current limitations in handling highly stylized or extremely detailed scenes, ongoing research aims to improve efficiency, expand multi-modal capabilities, and extend applications to 3D and dynamic media, promising a transformative impact on creative industries.
Deep Dive
Abstract
Graphic design visually conveys information and data by creating and combining text, images and graphics. Two-stage methods that rely primarily on layout generation lack creativity and intelligence, making graphic design still labor-intensive. Existing diffusion-based methods generate non-editable graphic design files at image level with poor legibility in visual text rendering, which prevents them from achieving satisfactory and practical automated graphic design. In this paper, we propose Instructional Graphic Designer (IGD) to swiftly generate multimodal layers with editable flexibility with only natural language instructions. IGD adopts a new paradigm that leverages parametric rendering and image asset generation. First, we develop a design platform and establish a standardized format for multi-scenario design files, thus laying the foundation for scaling up data. Second, IGD utilizes the multimodal understanding and reasoning capabilities of MLLM to accomplish attribute prediction, sequencing and layout of layers. It also employs a diffusion model to generate image content for assets. By enabling end-to-end training, IGD architecturally supports scalability and extensibility in complex graphic design tasks. The superior experimental results demonstrate that IGD offers a new solution for graphic design.