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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)
@staticmethod
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))
@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, 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)
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