https://github.com/kingnobro/iconshop
(Siggraph Asia 2023) Code of "IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers"
https://github.com/kingnobro/iconshop
icon svg svg-generation text-to-svg vector-graphics vector-graphics-generation
Last synced: 6 months ago
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(Siggraph Asia 2023) Code of "IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers"
- Host: GitHub
- URL: https://github.com/kingnobro/iconshop
- Owner: kingnobro
- Created: 2024-04-22T11:34:27.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-19T14:16:06.000Z (9 months ago)
- Last Synced: 2025-01-19T15:24:49.703Z (9 months ago)
- Topics: icon, svg, svg-generation, text-to-svg, vector-graphics, vector-graphics-generation
- Language: Python
- Homepage: https://icon-shop.github.io/
- Size: 54 MB
- Stars: 40
- Watchers: 6
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers
# Setup
```
git clone https://github.com/kingnobro/IconShop.git
cd IconShop
```
## Environment
To set up our environment, please run:
```
conda env create -f environment.yml
conda activate iconshop
```
Please refer to [cairosvg](https://cairosvg.org/documentation/#installation)'s documentation for additional requirements of CairoSVG. For example:
- on Ubuntu: `sudo apt-get install libcairo2-dev`.
- on macOS: `brew install cairo libffi`.
## Dataset
We have cleaned up the [FIGR-8-SVG](https://github.com/marcdemers/FIGR-8-SVG) by command simplification, removing the black bounding box, and resizing the icons.
Download the labels and SVG files from [Google Drive](https://drive.google.com/drive/folders/1dXdrBqJDmEn8K8TeY2w3mvEtADZipPOc?usp=drive_link) and place them in the `./dataset` folder. You need to unzip the `FIGR-SVG-svgo.zip`.
The resulting file structure should be:
```
./dataset
├── FIGR-SVG-train.csv
├── FIGR-SVG-valid.csv
├── FIGR-SVG-test.csv
└── FIGR-SVG-svgo
├── 100000-200.svg
├── 1000003-200.svg
└── ...
```
There are some other useful files in the Google Drive link. Please check the `Readme.md` and download them as needed.
## Training
I use 8 RTX 3090 (or 2 A100) to train the model.
Feel free to adjust the batch size, number of epochs, and learning rate. The training will take several days, so I did not spend a lot of time optimizing these parameters.
```
bash scripts/train.sh
```
Use the following command to check the loss curve:
```
bash scripts/log.sh
```
## Sample
```bash
bash scripts/sample.sh
```
Download our [pretrained models](https://drive.google.com/drive/folders/1xF0AjYk-WvfNuv6z5xluNDC87ktke2rK?usp=sharing) and unzip it under `proj_log/FIGR_SVG`. This model is trained with 100 epochs.
> [!CAUTION]
> The pretrained model in this repository produces different results from those shown in the paper. This is primarily due to dataset differences:
> - The paper uses a higher-precision dataset with 100x100 bounding boxes
> - This repository uses the FIGR dataset with 200x200 bounding boxes
>
> To reproduce the paper's results:
> 1. Original model weights and GUI: [Download Link](https://drive.google.com/drive/folders/1ZS1dAIGz6t29uJ9i_7LTDvZbMRUKG0dp?usp=sharing)
> 2. Training dataset (zoom_svgs.zip): [Download Link](https://drive.google.com/drive/folders/1Iz39p31i8z9H0LrnSS3RJ2zwWA4T9Btv?usp=sharing)
>
> The provided GUI significantly simplifies the editing process. Manual implementation without the GUI requires extensive parameter tuning and preprocessing steps.
## Miscellaneous
- Our code is based on [SkexGen](https://github.com/samxuxiang/SkexGen) and [DeepSVG](https://github.com/alexandre01/deepsvg) (for read and write SVG data).
- Acknowledgement: We would like to express our sincere gratitude to OPPO for their generous support of our work.