Spaces:
Runtime error
Runtime error
| from huggingface_hub import hf_hub_url, hf_hub_download | |
| import gradio as gr | |
| import numpy as np | |
| import requests | |
| import torch | |
| from torchvision import transforms | |
| from torch.autograd import Variable | |
| from PIL import Image | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| path_to_model = hf_hub_download(repo_id="opetrova/face-frontalization", filename="generator_v0.pt") | |
| # Download network.py into the current directory | |
| network_url = hf_hub_url(repo_id="opetrova/face-frontalization", filename="network.py") | |
| r = requests.get(network_url, allow_redirects=True) | |
| open('network.py', 'wb').write(r.content) | |
| saved_model = torch.load(path_to_model, map_location=torch.device('cpu')) | |
| def frontalize(image): | |
| # Convert the test image to a [1, 3, 128, 128]-shaped torch tensor | |
| # (as required by the frontalization model) | |
| preprocess = transforms.Compose((transforms.ToPILImage(), | |
| transforms.Resize(size = (128, 128)), | |
| transforms.ToTensor())) | |
| input_tensor = torch.unsqueeze(preprocess(image), 0) | |
| # Use the saved model to generate an output (whose values go between -1 and 1, | |
| # and this will need to get fixed before the output is displayed) | |
| generated_image = saved_model(Variable(input_tensor.type('torch.FloatTensor'))) | |
| generated_image = generated_image.detach().squeeze().permute(1, 2, 0).numpy() | |
| generated_image = (generated_image + 1.0) / 2.0 | |
| return generated_image | |
| iface = gr.Interface(frontalize, gr.inputs.Image(type="numpy"), "image", | |
| title='Face Frontalization', | |
| description='PyTorch implementation of a supervised GAN (see <a href="https://blog.scaleway.com/gpu-instances-using-deep-learning-to-obtain-frontal-rendering-of-facial-images/">blog post</a>)', | |
| examples=["amos.png", "clarissa.png"], | |
| ) | |
| iface.launch() |