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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-night2day')
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'], ['sample1.jpg'], ['sample2.jpg']]).launch() |