forked from dvschultz/stylegan2-ada-pytorch
-
Notifications
You must be signed in to change notification settings - Fork 1
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
adding Philip Bizimis updates to cff
Showing
2 changed files
with
171 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,137 @@ | ||
import os | ||
import subprocess | ||
import argparse | ||
import torch | ||
from torchvision import utils | ||
import legacy | ||
import dnnlib | ||
# import PIL.Image | ||
|
||
def generate_images(z, label, truncation_psi, noise_mode, direction, file_name): | ||
img1 = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode) | ||
img2 = G(z + direction, label, truncation_psi=truncation_psi, noise_mode=noise_mode) | ||
img3 = G(z - direction, label, truncation_psi=truncation_psi, noise_mode=noise_mode) | ||
return torch.cat([img3, img1, img2], 0) | ||
|
||
def generate_image(z, label, truncation_psi, noise_mode, direction, file_name): | ||
img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode) | ||
return img | ||
|
||
def line_interpolate(zs, steps): | ||
out = [] | ||
for i in range(len(zs)-1): | ||
for index in range(steps): | ||
fraction = index/float(steps) | ||
out.append(zs[i+1]*fraction + zs[i]*(1-fraction)) | ||
return out | ||
|
||
if __name__ == "__main__": | ||
torch.set_grad_enabled(False) | ||
parser = argparse.ArgumentParser(description="Apply closed form factorization") | ||
parser.add_argument("-i", "--index", type=str, default="all", help="index of eigenvector") | ||
parser.add_argument("-s", "--samples", type=int, default=1, help="number of samples") | ||
parser.add_argument( | ||
"-d", | ||
"--degree", | ||
type=float, | ||
default=5, | ||
help="scalar factors for moving latent vectors along eigenvector", | ||
) | ||
parser.add_argument("--out_prefix", type=str, default="factor", help="filename prefix to result samples",) | ||
parser.add_argument("--ckpt", type=str, required=True, help="stylegan2-ada-pytorch checkpoints") | ||
parser.add_argument("--truncation", type=float, default=0.7, help="truncation factor") | ||
parser.add_argument( "factor", type=str, help="name of the closed form factorization result factor file") | ||
parser.add_argument("--vid_increment", type=float, default=0.1, help="increment degree for interpolation video") | ||
|
||
vid_parser = parser.add_mutually_exclusive_group(required=False) | ||
vid_parser.add_argument('--video', dest='vid', action='store_true') | ||
vid_parser.add_argument('--no-video', dest='vid', action='store_false') | ||
vid_parser.set_defaults(vid=False) | ||
|
||
args = parser.parse_args() | ||
device = torch.device('cuda') | ||
eigvec = torch.load(args.factor)["eigvec"].to(device) | ||
index = args.index | ||
if index != "all": | ||
try: | ||
index = int(index) | ||
if index > len(eigvec) - 1: | ||
raise IndexError("Index out of range; i > " + str(len(eigvec))) | ||
except ValueError: | ||
raise ValueError("Index must be 'all' or an int.") from None | ||
with dnnlib.util.open_url(args.ckpt) as f: | ||
G = legacy.load_network_pkl(f)['G_ema'].to(device) | ||
|
||
image_grid = [] | ||
file_name = f"sample-{args.samples}_index-{index}_degree-{args.degree}.png" | ||
|
||
latents = torch.randn([args.samples, G.z_dim]).cuda() | ||
label = torch.zeros([1, G.c_dim], device=device) | ||
noise_mode = "const" # default | ||
truncation_psi = args.truncation | ||
|
||
for i,l in enumerate(latents): | ||
z = l.unsqueeze(0) | ||
if index == "all": | ||
image_grid_eigvec = [] | ||
file_name = f"sample-{i}_index-{index}_degree-{args.degree}.png" | ||
for j in range(len(eigvec)): | ||
current_eigvec = eigvec[:, j].unsqueeze(0) | ||
direction = args.degree * current_eigvec | ||
image_group = generate_images(z, label, truncation_psi, noise_mode, direction, file_name) | ||
image_grid_eigvec.append(image_group) | ||
grid = utils.save_image( | ||
torch.cat(image_grid_eigvec, 0), | ||
file_name, | ||
nrow = 3, | ||
normalize=True, | ||
value_range=(-1, 1) | ||
) | ||
else: | ||
fn = f"sample-{i}.png" | ||
direction = args.degree * eigvec[:, index].unsqueeze(0) | ||
image_group = generate_images(z, label, truncation_psi, noise_mode, direction, fn) | ||
image_grid.append(image_group) | ||
if len(image_grid) > 0: | ||
grid = utils.save_image( | ||
torch.cat(image_grid, 0), | ||
file_name, | ||
nrow = 3, | ||
normalize=True, | ||
value_range=(-1, 1) | ||
) | ||
|
||
if(args.vid): | ||
print('processing videos; this may take a while...') | ||
count = 0 | ||
for l in latents: | ||
fname = f"{args.out_prefix}_index-{args.index}_degree-{args.degree}_index-{count}" | ||
if not os.path.exists(fname): | ||
os.makedirs(fname) | ||
|
||
if not os.path.exists(fname + '/frames'): | ||
os.makedirs(fname + '/frames') | ||
|
||
zs = line_interpolate([l-direction, l+direction], int((args.degree*2)/args.vid_increment)) | ||
|
||
fcount = 0 | ||
for z in zs: | ||
# generate latent | ||
img = generate_image(z, label, truncation_psi, noise_mode, direction, file_name) | ||
|
||
# generate latent | ||
grid = utils.save_image( | ||
img, | ||
f"{fname}/frames/{fname}_{fcount:04}.png", | ||
normalize=True, | ||
value_range=(-1, 1), | ||
nrow=1, | ||
) | ||
|
||
fcount+=1 | ||
|
||
|
||
cmd=f"ffmpeg -y -r 24 -i {fname}/frames/{fname}_%04d.png -vcodec libx264 -pix_fmt yuv420p {fname}/{fname}.mp4" | ||
subprocess.call(cmd, shell=True) | ||
|
||
count+=1 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,34 @@ | ||
import argparse | ||
import torch | ||
import dnnlib | ||
import legacy | ||
import pickle | ||
|
||
if __name__ == "__main__": | ||
parser = argparse.ArgumentParser( | ||
description="Extract factor/eigenvectors of latent spaces using closed form factorization" | ||
) | ||
parser.add_argument( | ||
"--out", type=str, default="factor.pt", help="name of the result factor file" | ||
) | ||
parser.add_argument("--ckpt", type=str, help="name of the model checkpoint") | ||
args = parser.parse_args() | ||
|
||
device = torch.device('cuda') | ||
with dnnlib.util.open_url(args.ckpt) as f: | ||
G = pickle.load(f)['G_ema'].to(device) # type: ignore | ||
|
||
modulate = { | ||
k[0]: k[1] | ||
for k in G.named_parameters() | ||
if "affine" in k[0] and "torgb" not in k[0] and "weight" in k[0] | ||
} | ||
|
||
weight_mat = [] | ||
for k, v in modulate.items(): | ||
weight_mat.append(v) | ||
|
||
W = torch.cat(weight_mat, 0) | ||
eigvec = torch.svd(W).V.to("cpu") | ||
|
||
torch.save({"ckpt": args.ckpt, "eigvec": eigvec}, args.out) |