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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_NAMES = [
'dogs_1024',
'elephants_512',
'horses_256',
'bicycles_256',
'lions_512',
'giraffes_512',
'parrots_512',
]
def __init__(self, device: str | torch.device):
self.device = torch.device(device)
self._download_all_models()
self._download_all_cluster_centers()
self.model_name = self.MODEL_NAMES[0]
self.model = self._load_model(self.model_name)
self.cluster_centers = self._load_cluster_centers(self.model_name)
def _load_model(self, model_name: str) -> nn.Module:
path = hf_hub_download('hysts/Self-Distilled-StyleGAN',
f'models/{model_name}_pytorch.pkl',
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 _load_cluster_centers(self, model_name: str) -> torch.Tensor:
path = hf_hub_download('hysts/Self-Distilled-StyleGAN',
f'cluster_centers/{model_name}.npy',
use_auth_token=HF_TOKEN)
centers = np.load(path)
centers = torch.from_numpy(centers).float().to(self.device)
return centers
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)
self.cluster_centers = self._load_cluster_centers(model_name)
def _download_all_models(self):
for name in self.MODEL_NAMES:
self._load_model(name)
def _download_all_cluster_centers(self):
for name in self.MODEL_NAMES:
self._load_cluster_centers(name)
def generate_z(self, seed: int) -> torch.Tensor:
seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
return torch.from_numpy(
np.random.RandomState(seed).randn(1, self.model.z_dim)).float().to(
self.device)
def compute_w(self, z: torch.Tensor) -> torch.Tensor:
label = torch.zeros((1, self.model.c_dim), device=self.device)
w = self.model.mapping(z, label)
return w
def find_nearest_cluster_center(self, w: torch.Tensor) -> int:
# Here, Euclidean distance is used instead of LPIPS distance
dist2 = ((self.cluster_centers - w)**2).sum(dim=1)
return torch.argmin(dist2).item()
@staticmethod
def truncate_w(w_center: torch.Tensor, w: torch.Tensor,
psi: float) -> torch.Tensor:
if psi == 1:
return w
return w_center.lerp(w, psi)
@torch.inference_mode()
def synthesize(self, w: torch.Tensor) -> torch.Tensor:
return self.model.synthesis(w)
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 generate_image(self, seed: int, truncation_psi: float,
multimodal_truncation: bool) -> np.ndarray:
z = self.generate_z(seed)
w = self.compute_w(z)
if multimodal_truncation:
cluster_index = self.find_nearest_cluster_center(w[:, 0])
w0 = self.cluster_centers[cluster_index]
else:
w0 = self.model.mapping.w_avg
new_w = self.truncate_w(w0, w, truncation_psi)
out = self.synthesize(new_w)
out = self.postprocess(out)
return out[0]
def set_model_and_generate_image(
self, model_name: str, seed: int, truncation_psi: float,
multimodal_truncation: bool) -> np.ndarray:
self.set_model(model_name)
return self.generate_image(seed, truncation_psi, multimodal_truncation)
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