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from __future__ import annotations
import os
import pathlib
import sys
import huggingface_hub
import numpy as np
import torch
import torch.nn as nn
if os.getenv("SYSTEM") == "spaces":
os.system("sed -i '14,21d' StyleSwin/op/fused_act.py")
os.system("sed -i '12,19d' StyleSwin/op/upfirdn2d.py")
current_dir = pathlib.Path(__file__).parent
submodule_dir = current_dir / "StyleSwin"
sys.path.insert(0, submodule_dir.as_posix())
from models.generator import Generator
class Model:
MODEL_NAMES = [
"CelebAHQ_256",
"FFHQ_256",
"LSUNChurch_256",
"CelebAHQ_1024",
"FFHQ_1024",
]
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)
self.std = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None].to(self.device)
self.mean = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None].to(self.device)
def _load_model(self, model_name: str) -> nn.Module:
size = int(model_name.split("_")[1])
channel_multiplier = 1 if size == 1024 else 2
model = Generator(size, style_dim=512, n_mlp=8, channel_multiplier=channel_multiplier)
ckpt_path = huggingface_hub.hf_hub_download("public-data/StyleSwin", f"models/{model_name}.pt")
ckpt = torch.load(ckpt_path)
model.load_state_dict(ckpt["g_ema"])
model.to(self.device)
model.eval()
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)
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, 512)
return torch.from_numpy(z).float().to(self.device)
def postprocess(self, tensors: torch.Tensor) -> np.ndarray:
assert tensors.dim() == 4
tensors = tensors * self.std + self.mean
tensors = (tensors * 255).clamp(0, 255).to(torch.uint8)
return tensors.permute(0, 2, 3, 1).cpu().numpy()
@torch.inference_mode()
def generate_image(self, seed: int) -> np.ndarray:
z = self.generate_z(seed)
out, _ = self.model(z)
out = self.postprocess(out)
return out[0]
def set_model_and_generate_image(self, model_name: str, seed: int) -> np.ndarray:
self.set_model(model_name)
return self.generate_image(seed)
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