nopperl/lineart-vectorizer

1from os.path import basename, splitext2import spaces3import gradio as gr4from huggingface_hub import hf_hub_download5 6from onnx_inference import vectorize_image7 8 9MODEL_PATH = hf_hub_download("nopperl/marked-lineart-vectorizer", "model.onnx")10 11@spaces.GPU12def predict(input_image_path, threshold, stroke_width):13    output_filepath = splitext(basename(input_image_path))[0] + ".svg"14    for recons_img in vectorize_image(input_image_path, model=MODEL_PATH, output=output_filepath, threshold_ratio=threshold, stroke_width=stroke_width):15        yield recons_img16    yield output_filepath17 18 19interface = gr.Interface(20        predict,21        inputs=[gr.Image(sources="upload", type="filepath"), gr.Slider(minimum=0.1, maximum=0.9, value=0.1, label="threshold"), gr.Slider(minimum=0.1, maximum=4.0, value=0.512, label="stroke_width")],22        outputs=gr.Image(),23        description="Demo for a model that converts raster line-art images into vector images iteratively. The model is trained on black-and-white line-art images, hence it won't work with other images. Inference time will be quite slow due to a lack of GPU resources. More information at https://github.com/nopperl/marked-lineart-vectorization.",24        examples = [25            ["examples/01.png", 0.1, 0.512],26            ["examples/02.png", 0.1, 0.512]27        ],28        analytics_enabled=False,29        cache_examples=False30    )31interface.launch()32