| 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() |