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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import functools | |
| import os | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from huggingface_hub import hf_hub_download | |
| from model import Model | |
| TITLE = 'MobileStyleGAN' | |
| DESCRIPTION = 'This is an unofficial demo for https://github.com/bes-dev/MobileStyleGAN.pytorch.' | |
| SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/MobileStyleGAN/resolve/main/samples' | |
| ARTICLE = f'''## Generated images | |
| ### FFHQ | |
| - size: 1024x1024 | |
| - seed: 0-99 | |
| - truncation: 1.0 | |
|  | |
| ''' | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor: | |
| return torch.from_numpy(np.random.RandomState(seed).randn( | |
| 1, z_dim)).to(device).float() | |
| def generate_image(seed: int, truncation_psi: float, generator: str, | |
| model: nn.Module, device: torch.device) -> np.ndarray: | |
| seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max)) | |
| z = generate_z(model.mapping_net.style_dim, seed, device) | |
| out = model(z, truncation_psi=truncation_psi, generator=generator) | |
| out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) | |
| return out[0].cpu().numpy() | |
| def load_model(device: torch.device) -> nn.Module: | |
| path = hf_hub_download('hysts/MobileStyleGAN', | |
| 'models/mobilestylegan_ffhq_v2.pth', | |
| use_auth_token=HF_TOKEN) | |
| ckpt = torch.load(path) | |
| model = Model() | |
| model.load_state_dict(ckpt['state_dict'], strict=False) | |
| model.eval() | |
| model.to(device) | |
| with torch.inference_mode(): | |
| z = torch.zeros((1, model.mapping_net.style_dim)).to(device) | |
| model(z) | |
| return model | |
| device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') | |
| model = load_model(device) | |
| func = functools.partial(generate_image, model=model, device=device) | |
| gr.Interface( | |
| fn=func, | |
| inputs=[ | |
| gr.Slider(label='Seed', | |
| minimum=0, | |
| maximum=100000, | |
| step=1, | |
| value=0, | |
| randomize=True), | |
| gr.Slider(label='Truncation psi', | |
| minimum=0, | |
| maximum=2, | |
| step=0.05, | |
| value=1.0), | |
| gr.Radio(label='Generator', | |
| choices=['student', 'teacher'], | |
| type='value', | |
| value='student'), | |
| ], | |
| outputs=gr.Image(label='Output', type='numpy'), | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| article=ARTICLE, | |
| ).queue().launch(show_api=False) | |