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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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"AFHQv2-512-R": "stylegan3-r-afhqv2-512x512.pkl", |
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"FFHQ-1024-R": "stylegan3-r-ffhq-1024x1024.pkl", |
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"FFHQ-U-256-R": "stylegan3-r-ffhqu-256x256.pkl", |
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"FFHQ-U-1024-R": "stylegan3-r-ffhqu-1024x1024.pkl", |
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"MetFaces-1024-R": "stylegan3-r-metfaces-1024x1024.pkl", |
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"MetFaces-U-1024-R": "stylegan3-r-metfacesu-1024x1024.pkl", |
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"AFHQv2-512-T": "stylegan3-t-afhqv2-512x512.pkl", |
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"FFHQ-1024-T": "stylegan3-t-ffhq-1024x1024.pkl", |
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"FFHQ-U-256-T": "stylegan3-t-ffhqu-256x256.pkl", |
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"FFHQ-U-1024-T": "stylegan3-t-ffhqu-1024x1024.pkl", |
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"MetFaces-1024-T": "stylegan3-t-metfaces-1024x1024.pkl", |
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"MetFaces-U-1024-T": "stylegan3-t-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-R" |
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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/StyleGAN3", 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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@staticmethod |
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def make_transform(translate: tuple[float, float], angle: float) -> np.ndarray: |
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mat = np.eye(3) |
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sin = np.sin(angle / 360 * np.pi * 2) |
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cos = np.cos(angle / 360 * np.pi * 2) |
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mat[0][0] = cos |
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mat[0][1] = sin |
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mat[0][2] = translate[0] |
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mat[1][0] = -sin |
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mat[1][1] = cos |
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mat[1][2] = translate[1] |
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return mat |
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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 set_transform(self, tx: float, ty: float, angle: float) -> None: |
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mat = self.make_transform((tx, ty), angle) |
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mat = np.linalg.inv(mat) |
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self.model.synthesis.input.transform.copy_(torch.from_numpy(mat)) |
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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, tx: float, ty: float, angle: float) -> np.ndarray: |
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self.set_transform(tx, ty, angle) |
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z = self.generate_z(seed) |
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label = torch.zeros([1, self.model.c_dim], device=self.device) |
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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, tx: float, ty: float, angle: float |
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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, tx, ty, angle) |
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