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

Base Model	Qwen3-8B
Fine-tuning	Supervised Fine-Tuning (SFT)
Size	16 GB (fp16)
Context Window	8,192 tokens

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

Batch Size	1
Gradient Accumulation	2
Learning Rate	5e-5 (AdamW)
Epochs	2
Max Sequence Length	8,192 tokens
Precision	bfloat16
Loss	Completion-only (only on generated tokens)

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 QUICKSTART.md.

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{liu2025designascode,
  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},
  year      = {2025}
}