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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
app_dir = pathlib.Path(__file__).parent
submodule_dir = app_dir / "StyleGAN-Human"
sys.path.insert(0, submodule_dir.as_posix())
class Model:
def __init__(self):
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model = self.load_model("stylegan_human_v2_1024.pkl")
def load_model(self, file_name: str) -> nn.Module:
path = hf_hub_download("public-data/StyleGAN-Human", f"models/{file_name}")
with open(path, "rb") as f:
model = pickle.load(f)["G_ema"]
model.eval()
model.to(self.device)
with torch.inference_mode():
z = torch.zeros((1, model.z_dim)).to(self.device)
label = torch.zeros([1, model.c_dim], device=self.device)
model(z, label, force_fp32=True)
return model
def generate_z(self, z_dim: int, seed: int) -> torch.Tensor:
return torch.from_numpy(np.random.RandomState(seed).randn(1, z_dim)).to(self.device).float()
@torch.inference_mode()
def generate_single_image(self, seed: int, truncation_psi: float) -> np.ndarray:
seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
z = self.generate_z(self.model.z_dim, seed)
label = torch.zeros([1, self.model.c_dim], device=self.device)
out = self.model(z, label, truncation_psi=truncation_psi, force_fp32=True)
out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
return out[0].cpu().numpy()
@torch.inference_mode()
def generate_interpolated_images(
self, seed0: int, psi0: float, seed1: int, psi1: float, num_intermediate: int
) -> list[np.ndarray]:
seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max))
seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max))
z0 = self.generate_z(self.model.z_dim, seed0)
z1 = self.generate_z(self.model.z_dim, seed1)
vec = z1 - z0
dvec = vec / (num_intermediate + 1)
zs = [z0 + dvec * i for i in range(num_intermediate + 2)]
dpsi = (psi1 - psi0) / (num_intermediate + 1)
psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)]
label = torch.zeros([1, self.model.c_dim], device=self.device)
res = []
for z, psi in zip(zs, psis):
out = self.model(z, label, truncation_psi=psi, force_fp32=True)
out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
out = out[0].cpu().numpy()
res.append(out)
return res
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