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import gradio as gr | |
from torchvision.transforms import Compose, Resize, ToTensor, Normalize | |
from PIL import Image | |
from torchvision.utils import save_image | |
from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet | |
transform = Compose( | |
[ | |
Resize((256, 256), Image.BICUBIC), | |
ToTensor(), | |
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), | |
] | |
) | |
model = GeneratorUNet.from_pretrained('huggan/pix2pix-map') | |
def predict_fn(img): | |
inp = transform(img).unsqueeze(0) | |
out = model(inp) | |
save_image(out, 'out.png', normalize=True) | |
return 'out.png' | |
gr.Interface(predict_fn, inputs=gr.inputs.Image(type='pil'), outputs='image', examples=[['sample.jpg'], ['sample2.jpg'], ['sample3.jpg']]).launch() |