Spaces:
Runtime error
Runtime error
| from __future__ import annotations | |
| import os | |
| import pathlib | |
| import pickle | |
| import sys | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from huggingface_hub import hf_hub_download | |
| current_dir = pathlib.Path(__file__).parent | |
| submodule_dir = current_dir / 'stylegan3' | |
| sys.path.insert(0, submodule_dir.as_posix()) | |
| HF_TOKEN = os.environ['HF_TOKEN'] | |
| class Model: | |
| MODEL_NAME_DICT = { | |
| 'AFHQ-Cat-512': 'stylegan2-afhqcat-512x512.pkl', | |
| 'AFHQ-Dog-512': 'stylegan2-afhqdog-512x512.pkl', | |
| 'AFHQv2-512': 'stylegan2-afhqv2-512x512.pkl', | |
| 'AFHQ-Wild-512': 'stylegan2-afhqwild-512x512.pkl', | |
| 'BreCaHAD-512': 'stylegan2-brecahad-512x512.pkl', | |
| 'CelebA-HQ-256': 'stylegan2-celebahq-256x256.pkl', | |
| 'CIFAR-10': 'stylegan2-cifar10-32x32.pkl', | |
| 'FFHQ-256': 'stylegan2-ffhq-256x256.pkl', | |
| 'FFHQ-512': 'stylegan2-ffhq-512x512.pkl', | |
| 'FFHQ-1024': 'stylegan2-ffhq-1024x1024.pkl', | |
| 'FFHQ-U-256': 'stylegan2-ffhqu-256x256.pkl', | |
| 'FFHQ-U-1024': 'stylegan2-ffhqu-1024x1024.pkl', | |
| 'LSUN-Dog-256': 'stylegan2-lsundog-256x256.pkl', | |
| 'MetFaces-1024': 'stylegan2-metfaces-1024x1024.pkl', | |
| 'MetFaces-U-1024': 'stylegan2-metfacesu-1024x1024.pkl', | |
| } | |
| def __init__(self, device: str | torch.device): | |
| self.device = torch.device(device) | |
| self._download_all_models() | |
| self.model_name = 'FFHQ-1024' | |
| self.model = self._load_model(self.model_name) | |
| def _load_model(self, model_name: str) -> nn.Module: | |
| file_name = self.MODEL_NAME_DICT[model_name] | |
| path = hf_hub_download('hysts/StyleGAN2', | |
| f'models/{file_name}', | |
| use_auth_token=HF_TOKEN) | |
| with open(path, 'rb') as f: | |
| model = pickle.load(f)['G_ema'] | |
| model.eval() | |
| model.to(self.device) | |
| 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_NAME_DICT.keys(): | |
| 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, self.model.z_dim) | |
| return torch.from_numpy(z).float().to(self.device) | |
| def postprocess(self, tensor: torch.Tensor) -> np.ndarray: | |
| tensor = (tensor.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to( | |
| torch.uint8) | |
| return tensor.cpu().numpy() | |
| def make_label_tensor(self, class_index: int) -> torch.Tensor: | |
| class_index = round(class_index) | |
| class_index = min(max(0, class_index), self.model.c_dim - 1) | |
| class_index = torch.tensor(class_index, dtype=torch.long) | |
| label = torch.zeros([1, self.model.c_dim], device=self.device) | |
| if class_index >= 0: | |
| label[:, class_index] = 1 | |
| return label | |
| def generate(self, z: torch.Tensor, label: torch.Tensor, | |
| truncation_psi: float) -> torch.Tensor: | |
| return self.model(z, label, truncation_psi=truncation_psi) | |
| def generate_image(self, seed: int, truncation_psi: float, | |
| class_index: int) -> np.ndarray: | |
| z = self.generate_z(seed) | |
| label = self.make_label_tensor(class_index) | |
| out = self.generate(z, label, truncation_psi) | |
| out = self.postprocess(out) | |
| return out[0] | |
| def set_model_and_generate_image(self, model_name: str, seed: int, | |
| truncation_psi: float, | |
| class_index: int) -> np.ndarray: | |
| self.set_model(model_name) | |
| return self.generate_image(seed, truncation_psi, class_index) | |