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import gradio as gr |
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import numpy as np |
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from huggingface_hub import hf_hub_url, cached_download |
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import PIL |
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import onnx |
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import onnxruntime |
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config_file_url = hf_hub_url("Jacopo/ToonClip", filename="model.onnx") |
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model_file = cached_download(config_file_url) |
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onnx_model = onnx.load(model_file) |
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onnx.checker.check_model(onnx_model) |
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opts = onnxruntime.SessionOptions() |
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opts.intra_op_num_threads = 16 |
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ort_session = onnxruntime.InferenceSession(model_file, sess_options=opts) |
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input_name = ort_session.get_inputs()[0].name |
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output_name = ort_session.get_outputs()[0].name |
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def normalize(x, mean=(0., 0., 0.), std=(1.0, 1.0, 1.0)): |
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x = np.asarray(x, dtype=np.float32) |
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if len(x.shape) == 4: |
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for dim in range(3): |
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x[:, dim, :, :] = (x[:, dim, :, :] - mean[dim]) / std[dim] |
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if len(x.shape) == 3: |
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for dim in range(3): |
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x[dim, :, :] = (x[dim, :, :] - mean[dim]) / std[dim] |
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return x |
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def denormalize(x, mean=(0., 0., 0.), std=(1.0, 1.0, 1.0)): |
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x = np.asarray(x, dtype=np.float32) |
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if len(x.shape) == 4: |
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for dim in range(3): |
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x[:, dim, :, :] = (x[:, dim, :, :] * std[dim]) + mean[dim] |
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if len(x.shape) == 3: |
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for dim in range(3): |
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x[dim, :, :] = (x[dim, :, :] * std[dim]) + mean[dim] |
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return x |
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def nogan(input_img): |
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i = np.asarray(input_img) |
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i = i.astype("float32") |
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i = np.transpose(i, (2, 0, 1)) |
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i = np.expand_dims(i, 0) |
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i = i / 255.0 |
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i = normalize(i, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) |
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ort_outs = ort_session.run([output_name], {input_name: i}) |
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output = ort_outs |
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output = output[0][0] |
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output = denormalize(output, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) |
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output = output * 255.0 |
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output = output.astype('uint8') |
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output = np.transpose(output, (1, 2, 0)) |
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output_image = PIL.Image.fromarray(output, 'RGB') |
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return output_image |
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title = "ToonClip Comics Hero Demo" |
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description = """ |
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Gradio demo for ToonClip, a UNet++ network with MobileNet v3 backbone optimized for mobile frameworks and trained with VGG Perceptual Feature Loss using PyTorch Lighting. |
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To use it, simply upload an image with a face or choose an example from the list below. |
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""" |
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article = """ |
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<style> |
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.boxes{ |
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width:50%; |
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float:left; |
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} |
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#mainDiv{ |
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width:50%; |
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margin:auto; |
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} |
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img{ |
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max-width:100%; |
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} |
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</style> |
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<p style='text-align: center'>The \"ToonClip\" model was trained by <a href='https://twitter.com/JacopoMangia' target='_blank'>Jacopo Mangiavacchi</a> and available at <a href='https://github.com/jacopomangiavacchi/ComicsHeroMobileUNet' target='_blank'>Github Repo ComicsHeroMobileUNet</a></p> |
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<p style='text-align: center'>The \"Comics Hero dataset\" used to train this model was produced by <a href='https://linktr.ee/Norod78' target='_blank'>Doron Adler</a> and available at <a href='https://github.com/Norod/U-2-Net-StyleTransfer' target='_blank'>Github Repo Comics hero U2Net</a></p> |
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<p style='text-align: center'>The \"ToonClip\" iOS mobile app using a CoreML version of this model is available on Apple App Store at <a href='https://apps.apple.com/us/app/toonclip/id1536285338' target='_blank'>ToonClip</a></p> |
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<p style='text-align: center'>Blog post on <a href='https://medium.com/@JMangia/optimize-a-face-to-cartoon-style-transfer-model-trained-quickly-on-small-style-dataset-and-50594126e792' target='_blank'>Medium</a></p> |
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<br> |
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<p style='text-align: center'>Example images from untrained FFHQ validation set: </p> |
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<p> |
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<div id='mainDiv'> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i01.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o01.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i02.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o02.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i03.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o03.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i04.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o04.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i05.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o05.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i06.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o06.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i07.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o07.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i08.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o08.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i09.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o09.png' alt='Output01'/> |
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</div> |
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<div id='divOne' class='boxes'> |
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<img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/i10.jpeg' alt='Example01'/> |
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</div> |
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<div id='divTwo' class='boxes'> |
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<img <img src='https://hf.space/gradioiframe/Jacopo/ToonClip/file/o10.png' alt='Output01'/> |
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</div> |
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</div> |
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</p> |
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""" |
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examples=[['i01.jpeg'], ['i02.jpeg'], ['i03.jpeg'], ['i04.jpeg'], ['i05.jpeg'], ['i06.jpeg'], ['i07.jpeg'], ['i08.jpeg'], ['i09.jpeg'], ['i10.jpeg']] |
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iface = gr.Interface( |
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nogan, |
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gr.inputs.Image(type="pil", shape=(1024, 1024)), |
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gr.outputs.Image(type="pil"), |
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title=title, |
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description=description, |
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article=article, |
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examples=examples) |
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iface.launch() |
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