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from __future__ import annotations |
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import pathlib |
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import pickle |
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import sys |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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from huggingface_hub import hf_hub_download |
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current_dir = pathlib.Path(__file__).parent |
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submodule_dir = current_dir / "stylegan3" |
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sys.path.insert(0, submodule_dir.as_posix()) |
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class Model: |
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MODEL_NAME_DICT = { |
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"AFHQ-Cat-512": "stylegan2-afhqcat-512x512.pkl", |
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"AFHQ-Dog-512": "stylegan2-afhqdog-512x512.pkl", |
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"AFHQv2-512": "stylegan2-afhqv2-512x512.pkl", |
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"AFHQ-Wild-512": "stylegan2-afhqwild-512x512.pkl", |
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"BreCaHAD-512": "stylegan2-brecahad-512x512.pkl", |
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"CelebA-HQ-256": "stylegan2-celebahq-256x256.pkl", |
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"CIFAR-10": "stylegan2-cifar10-32x32.pkl", |
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"FFHQ-256": "stylegan2-ffhq-256x256.pkl", |
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"FFHQ-512": "stylegan2-ffhq-512x512.pkl", |
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"FFHQ-1024": "stylegan2-ffhq-1024x1024.pkl", |
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"FFHQ-U-256": "stylegan2-ffhqu-256x256.pkl", |
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"FFHQ-U-1024": "stylegan2-ffhqu-1024x1024.pkl", |
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"LSUN-Dog-256": "stylegan2-lsundog-256x256.pkl", |
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"MetFaces-1024": "stylegan2-metfaces-1024x1024.pkl", |
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"MetFaces-U-1024": "stylegan2-metfacesu-1024x1024.pkl", |
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} |
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def __init__(self): |
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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self._download_all_models() |
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self.model_name = "FFHQ-1024" |
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self.model = self._load_model(self.model_name) |
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def _load_model(self, model_name: str) -> nn.Module: |
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file_name = self.MODEL_NAME_DICT[model_name] |
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path = hf_hub_download("hysts/StyleGAN2", f"models/{file_name}") |
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with open(path, "rb") as f: |
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model = pickle.load(f)["G_ema"] |
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model.eval() |
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model.to(self.device) |
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return model |
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def set_model(self, model_name: str) -> None: |
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if model_name == self.model_name: |
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return |
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self.model_name = model_name |
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self.model = self._load_model(model_name) |
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def _download_all_models(self): |
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for name in self.MODEL_NAME_DICT.keys(): |
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self._load_model(name) |
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def generate_z(self, seed: int) -> torch.Tensor: |
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seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max)) |
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z = np.random.RandomState(seed).randn(1, self.model.z_dim) |
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return torch.from_numpy(z).float().to(self.device) |
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def postprocess(self, tensor: torch.Tensor) -> np.ndarray: |
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tensor = (tensor.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) |
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return tensor.cpu().numpy() |
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def make_label_tensor(self, class_index: int) -> torch.Tensor: |
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class_index = round(class_index) |
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class_index = min(max(0, class_index), self.model.c_dim - 1) |
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class_index = torch.tensor(class_index, dtype=torch.long) |
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label = torch.zeros([1, self.model.c_dim], device=self.device) |
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if class_index >= 0: |
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label[:, class_index] = 1 |
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return label |
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@torch.inference_mode() |
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def generate(self, z: torch.Tensor, label: torch.Tensor, truncation_psi: float) -> torch.Tensor: |
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return self.model(z, label, truncation_psi=truncation_psi) |
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def generate_image(self, seed: int, truncation_psi: float, class_index: int) -> np.ndarray: |
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z = self.generate_z(seed) |
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label = self.make_label_tensor(class_index) |
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out = self.generate(z, label, truncation_psi) |
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out = self.postprocess(out) |
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return out[0] |
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def set_model_and_generate_image( |
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self, model_name: str, seed: int, truncation_psi: float, class_index: int |
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) -> np.ndarray: |
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self.set_model(model_name) |
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return self.generate_image(seed, truncation_psi, class_index) |
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