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#!/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 (Interpolation)"
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_interpolated_images(
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 = generate_z(model.z_dim, seed0)
z1 = generate_z(model.z_dim, seed1)
z0 = z0.to(device)
z1 = z1.to(device)
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, model.c_dim], device=device)
res = []
for z, psi in zip(zs, psis):
out = 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
demo = gr.Interface(
fn=generate_interpolated_images,
inputs=[
gr.Slider(label="Seed 1", minimum=0, maximum=100000, step=1, value=0, randomize=True),
gr.Slider(label="Truncation psi 1", minimum=0, maximum=2, step=0.05, value=0.7),
gr.Slider(label="Seed 2", minimum=0, maximum=100000, step=1, value=1, randomize=True),
gr.Slider(label="Truncation psi 2", minimum=0, maximum=2, step=0.05, value=0.7),
gr.Slider(label="Number of Intermediate Frames", minimum=0, maximum=21, step=1, value=7),
],
outputs=gr.Gallery(label="Output Images"),
title=TITLE,
description=DESCRIPTION,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
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