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| from __future__ import annotations | |
| 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 / "stylegan_xl" | |
| sys.path.insert(0, submodule_dir.as_posix()) | |
| class Model: | |
| MODEL_NAMES = [ | |
| "imagenet16", | |
| "imagenet32", | |
| "imagenet64", | |
| "imagenet128", | |
| "cifar10", | |
| "ffhq256", | |
| "pokemon256", | |
| ] | |
| def __init__(self): | |
| self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| self._download_all_models() | |
| self.model_name = self.MODEL_NAMES[3] | |
| self.model = self._load_model(self.model_name) | |
| def _load_model(self, model_name: str) -> nn.Module: | |
| path = hf_hub_download("public-data/StyleGAN-XL", f"models/{model_name}.pkl") | |
| 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_NAMES: | |
| self._load_model(name) | |
| def make_transform(translate: tuple[float, float], angle: float) -> np.ndarray: | |
| mat = np.eye(3) | |
| sin = np.sin(angle / 360 * np.pi * 2) | |
| cos = np.cos(angle / 360 * np.pi * 2) | |
| mat[0][0] = cos | |
| mat[0][1] = sin | |
| mat[0][2] = translate[0] | |
| mat[1][0] = -sin | |
| mat[1][1] = cos | |
| mat[1][2] = translate[1] | |
| return mat | |
| 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 set_transform(self, tx: float, ty: float, angle: float) -> None: | |
| mat = self.make_transform((tx, ty), angle) | |
| mat = np.linalg.inv(mat) | |
| self.model.synthesis.input.transform.copy_(torch.from_numpy(mat)) | |
| 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, tx: float, ty: float, angle: float | |
| ) -> np.ndarray: | |
| self.set_transform(tx, ty, angle) | |
| 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, tx: float, ty: float, angle: float | |
| ) -> np.ndarray: | |
| self.set_model(model_name) | |
| return self.generate_image(seed, truncation_psi, class_index, tx, ty, angle) | |