metadata

license: bsd-3-clause-clear

WAFFLE: Multi-Modal Model for Automated Front-End Development

We develope WAFFLE, a fine-tuning approach to train multi-modal LLM (MLLM) to generate HTML code from webpage screenshots or UI designs. WAFFLE uses a structure-aware attention mechanism to improve MLLMs' understanding of HTML's structure and a contrastive fine-tuning approach to align MLLMs' understanding of UI images and HTML code. Models fine-tuned with WAFFLE show up to 9.00 pp (percentage point) higher HTML match, 0.0982 higher CW-SSIM, 32.99 higher CLIP, and 27.12 pp higher LLEM on our new benchmark WebSight-Test and an existing benchmark Design2Code.

Updates:

10/24/2024: Our preprint avaiable at: preprint
10/24/2024: Our code (keep maintaining) avaiable at: code
10/24/2024: Our fine-tuned Waffle_VLM_WebSight (7B), using DoRA, is released at: lt-asset/Waffle_VLM_WebSight

Dependency

peft 0.11.1
transformers 4.41.1
pytorch 2.3.0
selenium
Python 3.10.14
deepspeed 0.14.1
datasets 2.19.1
beautifulsoup4 4.12.3
accelerate 0.30.1

Structure

vlm_websight contains the dataset class file, model class files, and training file for vlm_websight.
- eval_websight.py is the inference file
- dataset.py is the dataset class file
WebSight-Test is one of our test dataset

Quick Start

cd vlm_websight
# generate HTML/CSS code for UI image --image_path, save the code to --html_path
python quick_start.py --image_path ../WebSight-Test/test-495.png --html_path examples/example-495.html
# render the HTML/CSS code in --html_path, and save the rendered image to --image_path
python render_html.py --html_path examples/example-495.html --image_path examples/example-495.png

Citation

@misc{liang2024wafflemultimodalmodelautomated,
      title={WAFFLE: Multi-Modal Model for Automated Front-End Development}, 
      author={Shanchao Liang and Nan Jiang and Shangshu Qian and Lin Tan},
      year={2024},
      eprint={2410.18362},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2410.18362}, 
}