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# Copyright (c) SenseTime Research. All rights reserved.
## interpolate between two z code
## score all middle latent code
# https://www.aiuai.cn/aifarm1929.html
import os
import re
from typing import List
from tqdm import tqdm
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
import click
import legacy
import random
from typing import List, Optional
def lerp(code1, code2, alpha):
return code1 * alpha + code2 * (1 - alpha)
# Taken and adapted from wikipedia's slerp article
# https://en.wikipedia.org/wiki/Slerp
def slerp(code1, code2, alpha, DOT_THRESHOLD=0.9995): # Spherical linear interpolation
code1_copy = np.copy(code1)
code2_copy = np.copy(code2)
code1 = code1 / np.linalg.norm(code1)
code2 = code2 / np.linalg.norm(code2)
dot = np.sum(code1 * code2)
if np.abs(dot) > DOT_THRESHOLD:
return lerp(code1_copy, code2_copy, alpha)
# Calculate initial angle between v0 and v1
theta_0 = np.arccos(dot)
sin_theta_0 = np.sin(theta_0)
# Angle at timestep t
theta_t = theta_0 * alpha
sin_theta_t = np.sin(theta_t)
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
s1 = sin_theta_t / sin_theta_0
code3 = s0 * code1_copy + s1 * code2_copy
return code3
def generate_image_from_z(G, z, noise_mode, truncation_psi, device):
label = torch.zeros([1, G.c_dim], device=device)
w = G.mapping(z, label,truncation_psi=truncation_psi)
img = G.synthesis(w, noise_mode=noise_mode,force_fp32 = True)
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
return img
def get_concat_h(im1, im2):
dst = PIL.Image.new('RGB', (im1.width + im2.width, im1.height))
dst.paste(im1, (0, 0))
dst.paste(im2, (im1.width, 0))
return dst
def make_latent_interp_animation(G, code1, code2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi,device, outdir,fps):
step_size = 1.0/num_interps
all_imgs = []
amounts = np.arange(0, 1, step_size)
for seed_idx, alpha in enumerate(tqdm(amounts)):
interpolated_latent_code = lerp(code1, code2, alpha)
image = generate_image_from_z(G,interpolated_latent_code, noise_mode, truncation_psi, device)
interp_latent_image = image.resize((512, 1024))
if not os.path.exists(os.path.join(outdir,'img')): os.makedirs(os.path.join(outdir,'img'), exist_ok=True)
if save_mid_image:
interp_latent_image.save(f'{outdir}/img/seed{seed_idx:04d}.png')
frame = get_concat_h(img2, interp_latent_image)
frame = get_concat_h(frame, img1)
all_imgs.append(frame)
save_name = os.path.join(outdir,'latent_space_traversal.gif')
all_imgs[0].save(save_name, save_all=True, append_images=all_imgs[1:], duration=1000/fps, loop=0)
"""
Create interpolated images between two given seeds using pretrained network pickle.
Examples:
\b
python interpolation.py --network=pretrained_models/stylegan_human_v2_1024.pkl --seeds=85,100 --outdir=outputs/inter_gifs
"""
@click.command()
@click.pass_context
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--seeds', type=legacy.num_range, help='List of 2 random seeds, e.g. 1,2')
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.8, show_default=True)
@click.option('--noise-mode', 'noise_mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
@click.option('--outdir', default= 'outputs/inter_gifs', help='Where to save the output images', type=str, required=True, metavar='DIR')
@click.option('--save_mid_image', default=True, type=bool, help='select True if you want to save all interpolated images')
@click.option('--fps', default= 15, help='FPS for GIF', type=int)
@click.option('--num_interps', default= 100, help='Number of interpolation images', type=int)
def main(
ctx: click.Context,
network_pkl: str,
seeds: Optional[List[int]],
truncation_psi: float,
noise_mode: str,
outdir: str,
save_mid_image: bool,
fps:int,
num_interps:int
):
device = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
dtype = torch.float32 if device.type == 'mps' else torch.float64
with dnnlib.util.open_url(network_pkl) as f:
G = legacy.load_network_pkl(f)['G_ema'].to(device, dtype=dtype) # type: ignore
outdir = os.path.join(outdir)
if not os.path.exists(outdir):
os.makedirs(outdir,exist_ok=True)
os.makedirs(os.path.join(outdir,'img'),exist_ok=True)
if len(seeds) > 2:
print("Receiving more than two seeds, only use the first two.")
seeds = seeds[0:2]
elif len(seeds) == 1:
print('Require two seeds, randomly generate two now.')
seeds = [seeds[0],random.randint(0,10000)]
z1 = torch.from_numpy(np.random.RandomState(seeds[0]).randn(1, G.z_dim)).to(device, dtype=dtype)
z2 = torch.from_numpy(np.random.RandomState(seeds[1]).randn(1, G.z_dim)).to(device, dtype=dtype)
img1 = generate_image_from_z(G, z1, noise_mode, truncation_psi, device)
img2 = generate_image_from_z(G, z2, noise_mode, truncation_psi, device)
img1.save(f'{outdir}/seed{seeds[0]:04d}.png')
img2.save(f'{outdir}/seed{seeds[1]:04d}.png')
make_latent_interp_animation(G, z1, z2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi, device, outdir, fps)
if __name__ == "__main__":
main()
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