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| import gradio as gr | |
| from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet | |
| from PIL import Image | |
| from torchvision.utils import save_image | |
| from torchvision.transforms import Compose, Resize, ToTensor, Normalize, ToPILImage | |
| from diffusers.utils import load_image, make_image_grid | |
| transform = Compose( | |
| [ | |
| Resize((256, 256), Image.BICUBIC), | |
| ToTensor(), | |
| Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), | |
| ] | |
| ) | |
| transform2 = Compose( | |
| [ | |
| #Resize((256, 256), Image.BICUBIC), | |
| ToPILImage(), | |
| #Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), | |
| ] | |
| ) | |
| generator = GeneratorUNet.from_pretrained("debisoft/gimp-pred-men-gan") | |
| def greet(input): | |
| coord_zxy = input | |
| image = load_image("https://c.basemaps.cartocdn.com/rastertiles/voyager_labels_under" + coord_zxy + ".png") | |
| pixel_values = transform(image).unsqueeze(0) | |
| output = generator(pixel_values) | |
| return transform2(output[0]) | |
| iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="coord_zxy", value="/18/73237/95677")], outputs=[gr.Image(type="pil", width=256, label="Output Image")]) | |
| iface.queue(api_open=True); | |
| iface.launch() | |