# Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis
[](https://arxiv.org/abs/2412.08603)
[](https://style3d.github.io/design2garmentcode/)
[](https://www.youtube.com/xxx)
Feng Zhou,
Ruiyang Liu,
Chen Liu,
Gaofeng He,
Yong-Lu Li,
Xiaogang Jin,
Huamin Wang
Official implementation for Design2GarmentCode, a motility-agnostic sewing pattern generation framework that leverages fine-tuned Large Multimodal Models to generate parametric pattern-making programs from multi-modal design concepts.
## Installation
### 1. Clone the repository
```bash
git clone https://github.com/Style3D/design2garmentcode-impl.git
cd design2garmentcode-impl
```
### 2. Create the Conda environment
An `environment.yml` file is provided in the project root with all required Conda and PyPI dependencies (Python 3.9.19, Torch 2.4.0 + CUDA 12.1, etc.).
```bash
conda env create -f environment.yml
conda activate d2g
python -m pip install --upgrade pip # optional: upgrade pip
```
### 3. (Optional) Enable 3‑D simulation
If you need local cloth simulation and 3‑D visualization, follow the installation instructions for **GarmentCode Warp Simulator**:
---
### 4. Language‑Model API
`Design2GarmentCode` communicates with large multimodal models.
Follow the steps **in the given order**:
#### 1. **Provide API credentials for MMUA**
- **Environment variable (recommended)** – defaults to *ChatGPT‑4o*
```bash
export OPENAI_API_KEY="sk‑..."
```
- **Edit `system.json`** (project root) – manually specify `api_key`, `base_url`, and `model` if you prefer a file‑based approach.
#### 2. **Set up the parameter projector**:
- Download the base model [Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/tree/main) and place the modal to `lmm_utils/Qwen/Qwen2-VL-2B-Instruct/`.
- Download the fine-tuned weights file from [Google Drive](https://drive.google.com/file/d/1CL7OLUq6fYcwoDuLRkBxtKNxJ0_G73U-/view?usp=sharing), and place it in `lmm_utils/Qwen/qwen2vl_lora_mlp/`.
---
## Testing with GUI
Setting up the GUI with `python gui.py` where you will see the following interface (modified from GarmentCode)
Switching to the `PARSE DESIGN` tab, and input your design input, either text description, photograph or sketch, to the chatbox. The generated sewing pattern will appear on the right side after parsing.
Once a pattern is generated, you can modify the result by typing `modify: ` in the chatbox.
---
## Batch Inference
### 1. Text Guided Generation
Use `test_text_batch.py` to process a list of text descriptions from a JSON file.
```bash
python lmm_utils/test_text_batch.py \
--input assets/test_text/examples.json \
--output assets/test_text_result \
--sim
```
- `--input`: Path to your input JSON file containing multiple garment description texts.
- `--output`: Directory where the output `.json` files will be saved.
- `--sim`: Enable or disable physical simulation output.
Supports physical simulation (enabled by default in script).
---
### 2. Image Guided Generation
Use `test_picture_batch.py` to process all image files in a directory.
```bash
python lmm_utils/test_picture_batch.py \
--input assets/test_img/examples \
--output assets/test_image_result/examples \
--sim
```
- `--input`: Folder containing multiple image files.
- `--output`: Output folder where results will be saved.
- `--sim`: Enable or disable physical simulation output.
---
## Simulate 3D Garment
### 1. Generate from a pattern.json
After generating the pattern data, you can simulate the corresponding 3D output directly from the pattern's JSON file with
```bash
python test_garment_sim.py --pattern_spec $INPUT_JSON
```
Or run the simulation directly in the `3D View` GUI tab.
### Citation
If you find this work useful, please cite:
```bibtex
@inproceedings{zhou2025design2garmentcode,
title={Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis},
author={Zhou, Feng and Liu, Ruiyang and Liu, Chen and He, Gaofeng and Li, Yong-Lu and Jin, Xiaogang and Wang, Huamin},
booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
pages={23712--23722},
year={2025}
}
```