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#!/usr/bin/env python
from __future__ import annotations
import argparse
import functools
import os
import sys
sys.path.insert(0, 'StyleSwin')
import gradio as gr
import huggingface_hub
import numpy as np
import PIL.Image
import torch
import torch.nn as nn
from models.generator import Generator
TOKEN = os.environ['TOKEN']
MODEL_REPO = 'hysts/StyleSwin'
MODEL_NAMES = [
'CelebAHQ_256',
'FFHQ_256',
'LSUNChurch_256',
'CelebAHQ_1024',
'FFHQ_1024',
]
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 load_model(model_name: str, device: torch.device) -> nn.Module:
size = int(model_name.split('_')[1])
channel_multiplier = 1 if size == 1024 else 2
model = Generator(size,
style_dim=512,
n_mlp=8,
channel_multiplier=channel_multiplier)
ckpt_path = huggingface_hub.hf_hub_download(MODEL_REPO,
f'models/{model_name}.pt',
use_auth_token=TOKEN)
ckpt = torch.load(ckpt_path)
model.load_state_dict(ckpt['g_ema'])
model.to(device)
model.eval()
return model
def generate_z(seed: int, device: torch.device) -> torch.Tensor:
return torch.from_numpy(np.random.RandomState(seed).randn(
1, 512)).to(device).float()
def postprocess(tensors: torch.Tensor) -> torch.Tensor:
assert tensors.dim() == 4
tensors = tensors.cpu()
std = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None]
mean = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None]
tensors = tensors * std + mean
tensors = (tensors * 255).clamp(0, 255).to(torch.uint8)
return tensors
@torch.inference_mode()
def generate_image(model_name: str, seed: int, model_dict: dict,
device: torch.device) -> PIL.Image.Image:
model = model_dict[model_name]
seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
z = generate_z(seed, device)
out, _ = model(z)
out = postprocess(out)
out = out.numpy()[0].transpose(1, 2, 0)
return PIL.Image.fromarray(out, 'RGB')
def main():
gr.close_all()
args = parse_args()
device = torch.device(args.device)
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)
repo_url = 'https://github.com/microsoft/StyleSwin'
title = 'microsoft/StyleSwin'
description = f'A demo for {repo_url}'
article = None
gr.Interface(
func,
[
gr.inputs.Radio(MODEL_NAMES,
type='value',
default='FFHQ_256',
label='model',
optional=False),
gr.inputs.Slider(0, 2147483647, step=1, default=0, label='Seed'),
],
gr.outputs.Image(type='pil', label='Output'),
theme=args.theme,
title=title,
description=description,
article=article,
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()
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