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Self-Distilled-StyleGAN
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#!/usr/bin/env python

from __future__ import annotations

import argparse
import functools
import os
import pickle
import sys

sys.path.insert(0, 'stylegan3')

import gradio as gr
import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download

ORIGINAL_REPO_URL = 'https://github.com/self-distilled-stylegan/self-distilled-internet-photos'
TITLE = 'Self-Distilled StyleGAN'
DESCRIPTION = f'This is a demo for models provided in {ORIGINAL_REPO_URL}.'
SAMPLE_IMAGE_DIR = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/samples'
ARTICLE = f'''## Generated images
- truncation: 0.7
### Dogs
- size: 1024x1024
- seed: 0-99
![Dogs]({SAMPLE_IMAGE_DIR}/dogs.jpg)
### Elephants
- size: 512x512
- seed: 0-99
![Elephants]({SAMPLE_IMAGE_DIR}/elephants.jpg)
### Horses
- size: 256x256
- seed: 0-99
![Horses]({SAMPLE_IMAGE_DIR}/horses.jpg)
### Bicycles
- size: 256x256
- seed: 0-99
![Bicycles]({SAMPLE_IMAGE_DIR}/bicycles.jpg)
### Lions
- size: 512x512
- seed: 0-99
![Lions]({SAMPLE_IMAGE_DIR}/lions.jpg)
### Giraffes
- size: 512x512
- seed: 0-99
![Giraffes]({SAMPLE_IMAGE_DIR}/giraffes.jpg)
### Parrots
- size: 512x512
- seed: 0-99
![Parrots]({SAMPLE_IMAGE_DIR}/parrots.jpg)
'''

TOKEN = os.environ['TOKEN']


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--theme', type=str)
    parser.add_argument('--live', action='store_true')
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    parser.add_argument('--allow-flagging', type=str, default='never')
    parser.add_argument('--allow-screenshot', action='store_true')
    return parser.parse_args()


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()


@torch.inference_mode()
def generate_image(model_name: str, seed: int, truncation_psi: float,
                   model_dict: dict[str, nn.Module],
                   device: torch.device) -> np.ndarray:
    model = model_dict[model_name]
    seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))

    z = generate_z(model.z_dim, seed, device)
    label = torch.zeros([1, model.c_dim], device=device)

    out = model(z, label, truncation_psi=truncation_psi)
    out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
    return out[0].cpu().numpy()


def load_model(model_name: str, device: torch.device) -> nn.Module:
    path = hf_hub_download('hysts/Self-Distilled-StyleGAN',
                           f'models/{model_name}_pytorch.pkl',
                           use_auth_token=TOKEN)
    with open(path, 'rb') as f:
        model = pickle.load(f)['G_ema']
    model.eval()
    model.to(device)
    with torch.inference_mode():
        z = torch.zeros((1, model.z_dim)).to(device)
        label = torch.zeros([1, model.c_dim], device=device)
        model(z, label)
    return model


def main():
    gr.close_all()

    args = parse_args()
    device = torch.device(args.device)

    model_names = [
        'dogs_1024',
        'elephants_512',
        'horses_256',
        'bicycles_256',
        'lions_512',
        'giraffes_512',
        'parrots_512',
    ]

    model_dict = {name: load_model(name, device) for name in model_names}

    func = functools.partial(generate_image,
                             model_dict=model_dict,
                             device=device)
    func = functools.update_wrapper(func, generate_image)

    gr.Interface(
        func,
        [
            gr.inputs.Radio(
                model_names, type='value', default='dogs_1024', label='Model'),
            gr.inputs.Number(default=0, label='Seed'),
            gr.inputs.Slider(
                0, 2, step=0.05, default=0.7, label='Truncation psi'),
        ],
        gr.outputs.Image(type='numpy', label='Output'),
        title=TITLE,
        description=DESCRIPTION,
        article=ARTICLE,
        theme=args.theme,
        allow_screenshot=args.allow_screenshot,
        allow_flagging=args.allow_flagging,
        live=args.live,
    ).launch(
        enable_queue=args.enable_queue,
        server_port=args.port,
        share=args.share,
    )


if __name__ == '__main__':
    main()