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 (🔺 robustness,🔻 stylization)': out = face2paint(model2, img) else: out = face2paint(model1, img) return out title = "AnimeGANv2" description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below." article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>" examples=[['groot.jpeg','version 2 (🔺 robustness,🔻 stylization)'],['gongyoo.jpeg','version 1 (🔺 stylization, 🔻 robustness)']] demo = gr.Interface( fn=inference, inputs=[gr.inputs.Image(type="pil"),gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version')], outputs=gr.outputs.Image(type="pil"), title=title, description=description, article=article, examples=examples) demo.launch()