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| import gradio as gr | |
| from PIL import Image | |
| import torch | |
| model2 = torch.hub.load( | |
| "AK391/animegan2-pytorch:main", | |
| "generator", | |
| pretrained=True, | |
| progress=False | |
| ) | |
| model1 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1") | |
| face2paint = torch.hub.load( | |
| 'AK391/animegan2-pytorch:main', 'face2paint', | |
| size=512,side_by_side=False | |
| ) | |
| def inference(img, ver): | |
| if ver == 'version 2 (πΊ robustness,π» stylization)': | |
| out = face2paint(model2, img) | |
| else: | |
| out = face2paint(model1, img) | |
| return out | |
| title = "Portrait of your Pet" | |
| description = "Demo for Pet Portrait. To use it, simply upload your image, or click one of the examples to load them." | |
| article = "Github Repo Pytorch " | |
| examples=[['groot.jpeg','version 2 (πΊ robustness,π» stylization)'],['gongyoo.jpeg','version 2 (πΊ robustness,π» stylization)']] | |
| demo = gr.Interface( | |
| fn=inference, | |
| inputs=[gr.inputs.Image(type="pil"),gr.inputs.Radio(['version 2 (πΊ robustness,π» stylization)'], type="value", default='version 2 (πΊ robustness,π» stylization)', label='version')], | |
| outputs=gr.outputs.Image(type="pil"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples) | |
| demo.launch() | |