| | 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( |
| | [ |
| | |
| | ToPILImage(), |
| | |
| | ] |
| | ) |
| |
|
| | generator = GeneratorUNet.from_pretrained("debisoft/stamen-wc-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() |
| |
|