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| import gradio as gr | |
| import numpy as np | |
| from huggingface_hub import hf_hub_url, cached_download | |
| import PIL | |
| import onnx | |
| import onnxruntime | |
| config_file_url = hf_hub_url("Jacopo/ToonClip", filename="model.onnx") | |
| model_file = cached_download(config_file_url) | |
| onnx_model = onnx.load(model_file) | |
| onnx.checker.check_model(onnx_model) | |
| opts = onnxruntime.SessionOptions() | |
| opts.intra_op_num_threads = 16 | |
| ort_session = onnxruntime.InferenceSession(model_file, sess_options=opts) | |
| input_name = ort_session.get_inputs()[0].name | |
| output_name = ort_session.get_outputs()[0].name | |
| def normalize(x, mean=(0., 0., 0.), std=(1.0, 1.0, 1.0)): | |
| # x = (x - mean) / std | |
| x = np.asarray(x, dtype=np.float32) | |
| if len(x.shape) == 4: | |
| for dim in range(3): | |
| x[:, dim, :, :] = (x[:, dim, :, :] - mean[dim]) / std[dim] | |
| if len(x.shape) == 3: | |
| for dim in range(3): | |
| x[dim, :, :] = (x[dim, :, :] - mean[dim]) / std[dim] | |
| return x | |
| def denormalize(x, mean=(0., 0., 0.), std=(1.0, 1.0, 1.0)): | |
| # x = (x * std) + mean | |
| x = np.asarray(x, dtype=np.float32) | |
| if len(x.shape) == 4: | |
| for dim in range(3): | |
| x[:, dim, :, :] = (x[:, dim, :, :] * std[dim]) + mean[dim] | |
| if len(x.shape) == 3: | |
| for dim in range(3): | |
| x[dim, :, :] = (x[dim, :, :] * std[dim]) + mean[dim] | |
| return x | |
| def nogan(input_img): | |
| i = np.asarray(input_img) | |
| i = i.astype("float32") | |
| i = np.transpose(i, (2, 0, 1)) | |
| i = np.expand_dims(i, 0) | |
| i = i / 255.0 | |
| i = normalize(i, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) | |
| ort_outs = ort_session.run([output_name], {input_name: i}) | |
| output = ort_outs | |
| output = output[0][0] | |
| output = denormalize(output, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) | |
| output = output * 255.0 | |
| output = output.astype('uint8') | |
| output = np.transpose(output, (1, 2, 0)) | |
| output_image = PIL.Image.fromarray(output, 'RGB') | |
| return output_image | |
| title = "Zoom, Clip, Toon" | |
| description = """Image to Toon Using AI""" | |
| article = """ | |
| <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> | |
| <br> | |
| """ | |
| examples=[['1m_hires.jpeg'],['2m_hires.jpeg'],['3m_hires.jpeg'],['1f_hires.jpeg'],['2f_hires.jpeg'],['3f_hires.jpeg']] | |
| iface = gr.Interface( | |
| nogan, | |
| gr.inputs.Image(type="pil", shape=(1024, 1024)), | |
| gr.outputs.Image(type="pil"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples) | |
| iface.launch() |