Spaces:
Running
Running
File size: 1,903 Bytes
88298b8 b2db920 88298b8 b2db920 88298b8 b2db920 88298b8 b2db920 6a22c0b 69c1172 6a22c0b b2db920 6a22c0b 69c1172 6a22c0b 69c1172 |
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 |
#!/usr/bin/env python
from __future__ import annotations
import pickle
import sys
import gradio as gr
import numpy as np
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
sys.path.insert(0, "StyleGAN-Human")
TITLE = "StyleGAN-Human"
DESCRIPTION = "https://github.com/stylegan-human/StyleGAN-Human"
def load_model(file_name: str, device: torch.device) -> 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(device)
with torch.inference_mode():
z = torch.zeros((1, model.z_dim)).to(device)
label = torch.zeros([1, model.c_dim], device=device)
model(z, label, force_fp32=True)
return model
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = load_model("stylegan_human_v2_1024.pkl", device)
def generate_z(z_dim: int, seed: int) -> torch.Tensor:
return torch.from_numpy(np.random.RandomState(seed).randn(1, z_dim)).float()
@torch.inference_mode()
def generate_image(seed: int, truncation_psi: float) -> np.ndarray:
seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
z = generate_z(model.z_dim, seed)
z = z.to(device)
label = torch.zeros([1, model.c_dim], device=device)
out = 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()
demo = gr.Interface(
fn=generate_image,
inputs=[
gr.Slider(label="Seed", minimum=0, maximum=100000, step=1, value=0),
gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7),
],
outputs=gr.Image(label="Output"),
title=TITLE,
description=DESCRIPTION,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
|