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 @torch.inference_mode() 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)