Tony1109/DesignAsCode-planner

DesignAsCode Semantic Planner

The Semantic Planner for the DesignAsCode pipeline. Given a natural-language design request, it generates a structured design plan — including layout reasoning, layer grouping, image generation prompts, and text element specifications.

Model Details

Training Data

Trained on ~10k examples sampled from the DesignAsCode Training Data, which contains 19,479 design samples distilled from the Crello dataset using GPT-4o and GPT-o3. No additional data was used.

Training Format

• —Input: prompt — natural-language design request
• —Output: layout_thought + grouping + image_generator + generate_text

See the training data repo for field details.

Training Configuration

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_path = "Tony1109/DesignAsCode-planner"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto"
)

For full pipeline usage (plan → implement → reflection), see the project repo and Quick Start.

Outputs

The model generates semi-structured text with XML tags:

• —<layout_thought>...</layout_thought> — detailed layout reasoning
• —<grouping>...</grouping> — JSON array grouping related layers with thematic labels
• —<image_generator>...</image_generator> — JSON array of per-layer image generation prompts
• —<generate_text>...</generate_text> — JSON array of text element specifications (font, size, alignment, etc.)

Ethical Considerations

• —Designs should be reviewed by humans before production use.
• —May reflect biases present in the training data.
• —Generated content should be checked for copyright compliance.

Citation

@article{liu2026designascode,
  title     = {DesignAsCode: Bridging Structural Editability and 
               Visual Fidelity in Graphic Design Generation},
  author    = {Liu, Ziyuan and Sun, Shizhao and Huang, Danqing 
               and Shi, Yingdong and Zhang, Meisheng and Li, Ji 
               and Yu, Jingsong and Bian, Jiang},
  journal   = {arXiv preprint arXiv:2602.17690},
  year      = {2026},
  url       = {https://arxiv.org/abs/2602.17690}
}