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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import functools | |
| 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 | |
| DESCRIPTION = "# [MobileStyleGAN](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 | |
|  | |
| """ | |
| 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("public-data/MobileStyleGAN", "models/mobilestylegan_ffhq_v2.pth") | |
| 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) | |
| fn = functools.partial(generate_image, model=model, device=device) | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| seed = gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, value=0, randomize=True) | |
| psi = gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=1.0) | |
| generator = gr.Radio(label="Generator", choices=["student", "teacher"], type="value", value="student") | |
| run_button = gr.Button("Run") | |
| with gr.Column(): | |
| result = gr.Image(label="Output", type="numpy") | |
| with gr.Row(): | |
| gr.Markdown(ARTICLE) | |
| run_button.click( | |
| fn=fn, | |
| inputs=[seed, psi, generator], | |
| outputs=result, | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=10).launch() | |