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#!/usr/bin/env python
from __future__ import annotations
import functools
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
def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor:
return torch.from_numpy(np.random.RandomState(seed).randn(
1, z_dim)).to(device).float()
@torch.inference_mode()
def generate_interpolated_images(seed0: int, psi0: float, seed1: int,
psi1: float, num_intermediate: int,
model: nn.Module,
device: torch.device) -> 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, device)
z1 = generate_z(model.z_dim, seed1, 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
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = load_model('stylegan_human_v2_1024.pkl', device)
fn = functools.partial(generate_interpolated_images,
model=model,
device=device)
gr.Interface(
fn=fn,
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', type='numpy'),
title=TITLE,
description=DESCRIPTION,
).queue(max_size=10).launch()
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