Spaces:
Running
Running
File size: 2,925 Bytes
7df68a5 0aff08c 7df68a5 0aff08c 7df68a5 46438b0 7df68a5 46438b0 7df68a5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
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
|