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from __future__ import annotations
import os
import pathlib
import sys
import huggingface_hub
import numpy as np
import PIL.Image
import torch
import torch.nn as nn
if os.environ.get('SYSTEM') == 'spaces':
os.system("sed -i '14,21d' StyleSwin/op/fused_act.py")
os.system("sed -i '12,19d' StyleSwin/op/upfirdn2d.py")
current_dir = pathlib.Path(__file__).parent
submodule_dir = current_dir / 'StyleSwin'
sys.path.insert(0, submodule_dir.as_posix())
from models.generator import Generator
HF_TOKEN = os.environ['HF_TOKEN']
class Model:
MODEL_NAMES = [
'CelebAHQ_256',
'FFHQ_256',
'LSUNChurch_256',
'CelebAHQ_1024',
'FFHQ_1024',
]
def __init__(self, device: str | torch.device):
self.device = torch.device(device)
self._download_all_models()
self.model_name = self.MODEL_NAMES[3]
self.model = self._load_model(self.model_name)
self.std = torch.FloatTensor([0.229, 0.224,
0.225])[None, :, None,
None].to(self.device)
self.mean = torch.FloatTensor([0.485, 0.456,
0.406])[None, :, None,
None].to(self.device)
def _load_model(self, model_name: str) -> 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('hysts/StyleSwin',
f'models/{model_name}.pt',
use_auth_token=HF_TOKEN)
ckpt = torch.load(ckpt_path)
model.load_state_dict(ckpt['g_ema'])
model.to(self.device)
model.eval()
return model
def set_model(self, model_name: str) -> None:
if model_name == self.model_name:
return
self.model_name = model_name
self.model = self._load_model(model_name)
def _download_all_models(self):
for name in self.MODEL_NAMES:
self._load_model(name)
def generate_z(self, seed: int) -> torch.Tensor:
seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
z = np.random.RandomState(seed).randn(1, 512)
return torch.from_numpy(z).float().to(self.device)
def postprocess(self, tensors: torch.Tensor) -> np.ndarray:
assert tensors.dim() == 4
tensors = tensors * self.std + self.mean
tensors = (tensors * 255).clamp(0, 255).to(torch.uint8)
return tensors.permute(0, 2, 3, 1).cpu().numpy()
@torch.inference_mode()
def generate_image(self, seed: int) -> np.ndarray:
z = self.generate_z(seed)
out, _ = self.model(z)
out = self.postprocess(out)
return out[0]
def set_model_and_generate_image(self, model_name: str,
seed: int) -> np.ndarray:
self.set_model(model_name)
return self.generate_image(seed)
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