def main():
os.makedirs(a.out_dir, exist_ok=True)
device = torch.device('cuda')
# setup generator
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
# load base or custom network
pkl_name = osp.splitext(a.model)[0]
if '.pkl' in a.model.lower():
custom = False
print(' .. Gs from pkl ..', basename(a.model))
else:
custom = True
print(' .. Gs custom ..', basename(a.model))
with dnnlib.util.open_url(pkl_name + '.pkl') as f:
Gs = legacy.load_network_pkl(f,
custom=custom, **Gs_kwargs)['G_ema'].to(
device) # type: ignore
dlat_shape = (1, Gs.num_ws, Gs.w_dim) # [1,18,512]
# read saved latents
if a.dlatents is not None and osp.isfile(a.dlatents):
key_dlatents = load_latents(a.dlatents)
if len(key_dlatents.shape) == 2:
key_dlatents = np.expand_dims(key_dlatents, 0)
elif a.dlatents is not None and osp.isdir(a.dlatents):
# if a.dlatents.endswith('/') or a.dlatents.endswith('\\'): a.dlatents = a.dlatents[:-1]
key_dlatents = []
npy_list = file_list(a.dlatents, 'npy')
for npy in npy_list:
key_dlatent = load_latents(npy)
if len(key_dlatent.shape) == 2:
key_dlatent = np.expand_dims(key_dlatent, 0)
key_dlatents.append(key_dlatent)
key_dlatents = np.concatenate(key_dlatents) # [frm,18,512]
else:
print(' No input dlatents found')
exit()
key_dlatents = key_dlatents[:, np.newaxis] # [frm,1,18,512]
print(' key dlatents', key_dlatents.shape)
# replace higher layers with single (style) latent
if a.style_dlat is not None:
print(' styling with dlatent', a.style_dlat)
style_dlatent = load_latents(a.style_dlat)
while len(style_dlatent.shape) < 4:
style_dlatent = np.expand_dims(style_dlatent, 0)
# try replacing 5 by other value, less than Gs.num_ws
key_dlatents[:, :, range(5, Gs.num_ws
), :] = style_dlatent[:, :,
range(5, Gs.num_ws), :]
frames = key_dlatents.shape[0] * a.fstep
dlatents = latent_anima(dlat_shape,
frames,
a.fstep,
key_latents=key_dlatents,
cubic=a.cubic,
verbose=True) # [frm,1,512]
print(' dlatents', dlatents.shape)
frame_count = dlatents.shape[0]
dlatents = torch.from_numpy(dlatents).to(device)
# distort image by tweaking initial const layer
if a.digress > 0:
try:
init_res = Gs.init_res
except Exception:
init_res = (4, 4) # default initial layer size
dconst = a.digress * latent_anima([1, Gs.z_dim, *init_res],
frame_count,
a.fstep,
cubic=True,
verbose=False)
else:
dconst = np.zeros([frame_count, 1, 1, 1, 1])
dconst = torch.from_numpy(dconst).to(device)
# generate images from latent timeline
pbar = ProgressBar(frame_count)
for i in range(frame_count):
# generate multi-latent result
if custom:
output = Gs.synthesis(dlatents[i],
None,
dconst[i],
noise_mode='const')
else:
output = Gs.synthesis(dlatents[i], noise_mode='const')
output = (output.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(
torch.uint8).cpu().numpy()
ext = 'png' if output.shape[3] == 4 else 'jpg'
filename = osp.join(a.out_dir, "%06d.%s" % (i, ext))
imsave(filename, output[0])
pbar.upd()
def main():
os.makedirs(a.out_dir, exist_ok=True)
# setup generator
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_multi.G_main'
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.impl = a.ops
# load model with arguments
sess = tflib.init_tf({'allow_soft_placement': True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try:
_, _, network = network
except:
pass
for k in list(network.static_kwargs.keys()):
Gs_kwargs[k] = network.static_kwargs[k]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..', basename(a.model))
Gs = network
else: # reconstruct network
print(' .. Gs custom ..', basename(a.model))
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
z_dim = Gs.input_shape[1]
dz_dim = 512 # dlatent_size
try:
dl_dim = 2 * (int(np.floor(np.log2(Gs_kwargs.resolution))) - 1)
except:
print(' Resave model, no resolution kwarg found!')
exit(1)
dlat_shape = (1, dl_dim, dz_dim) # [1,18,512]
# read saved latents
if a.dlatents is not None and osp.isfile(a.dlatents):
key_dlatents = load_latents(a.dlatents)
if len(key_dlatents.shape) == 2:
key_dlatents = np.expand_dims(key_dlatents, 0)
elif a.dlatents is not None and osp.isdir(a.dlatents):
# if a.dlatents.endswith('/') or a.dlatents.endswith('\\'): a.dlatents = a.dlatents[:-1]
key_dlatents = []
npy_list = file_list(a.dlatents, 'npy')
for npy in npy_list:
key_dlatent = load_latents(npy)
if len(key_dlatent.shape) == 2:
key_dlatent = np.expand_dims(key_dlatent, 0)
key_dlatents.append(key_dlatent)
key_dlatents = np.concatenate(key_dlatents) # [frm,18,512]
else:
print(' No input dlatents found')
exit()
key_dlatents = key_dlatents[:, np.newaxis] # [frm,1,18,512]
print(' key dlatents', key_dlatents.shape)
# replace higher layers with single (style) latent
if a.style_npy_file is not None:
print(' styling with latent', a.style_npy_file)
style_dlatent = load_latents(a.style_npy_file)
while len(style_dlatent.shape) < 4:
style_dlatent = np.expand_dims(style_dlatent, 0)
# try replacing 5 by other value, less than dl_dim
key_dlatents[:, :,
range(5, dl_dim), :] = style_dlatent[:, :,
range(5, dl_dim), :]
frames = key_dlatents.shape[0] * a.fstep
dlatents = latent_anima(dlat_shape,
frames,
a.fstep,
key_latents=key_dlatents,
cubic=a.cubic,
verbose=True) # [frm,1,512]
print(' dlatents', dlatents.shape)
frame_count = dlatents.shape[0]
# truncation trick
dlatent_avg = Gs.get_var('dlatent_avg') # (512,)
tr_range = range(0, 8)
dlatents[:, :, tr_range, :] = dlatent_avg + (dlatents[:, :, tr_range, :] -
dlatent_avg) * a.trunc
# distort image by tweaking initial const layer
if a.digress > 0:
try:
latent_size = Gs.static_kwargs['latent_size']
except:
latent_size = 512 # default latent size
try:
init_res = Gs.static_kwargs['init_res']
except:
init_res = (4, 4) # default initial layer size
dconst = a.digress * latent_anima([1, latent_size, *init_res],
frames,
a.fstep,
cubic=True,
verbose=False)
else:
dconst = np.zeros([frame_count, 1, 1, 1, 1])
# generate images from latent timeline
pbar = ProgressBar(frame_count)
for i in range(frame_count):
if a.digress is True:
tf.get_default_session().run(tf.assign(wvars[0], wts[i]))
# generate multi-latent result
if Gs.num_inputs == 2:
output = Gs.components.synthesis.run(dlatents[i],
randomize_noise=False,
output_transform=fmt,
minibatch_size=1)
else:
output = Gs.components.synthesis.run(dlatents[i], [None],
dconst[i],
randomize_noise=False,
output_transform=fmt,
minibatch_size=1)
ext = 'png' if output.shape[3] == 4 else 'jpg'
filename = osp.join(a.out_dir, "%06d.%s" % (i, ext))
imsave(filename, output[0])
pbar.upd()
def main():
if a.vector_dir is not None:
if a.vector_dir.endswith('/') or a.vector_dir.endswith('\\'): a.vector_dir = a.vector_dir[:-1]
os.makedirs(osp.join(a.out_dir, 'ttt'), exist_ok=True)
global Gs, use_d
# parse filename to model parameters
mparams = basename(a.model).split('-')
res = int(mparams[1])
cfg = mparams[2]
# setup generator
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_custom.G_main'
Gs_kwargs.verbose = False
Gs_kwargs.resolution = res
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.latent_size = a.latent_size
if cfg.lower() == 'f':
Gs_kwargs.synthesis_func = 'G_synthesis_stylegan2'
elif cfg.lower() == 'e':
Gs_kwargs.synthesis_func = 'G_synthesis_stylegan2'
Gs_kwargs.fmap_base = 8 << 10
else:
print(' old modes [A-D] not implemented'); exit()
# check initial model resolution
if len(mparams) > 3:
if 'x' in mparams[3].lower():
init_res = [int(x) for x in mparams[3].lower().split('x')]
Gs_kwargs.init_res = list(reversed(init_res)) # [H,W] !!! custom res
# load model, check channels
sess = tflib.init_tf({'allow_soft_placement':True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try: _, _, Gs = network
except: Gs = network
Gs_kwargs.num_channels = Gs.output_shape[1]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..')
else:
print(' .. Gs custom ..')
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
# load directions
if a.vector_dir is not None:
directions = []
vector_list = file_list(a.vector_dir, 'npy')
for v in vector_list:
direction = load_latents(v)
if len(direction.shape) == 2: direction = np.expand_dims(direction, 0)
directions.append(direction)
directions = np.concatenate(directions)[:, np.newaxis] # [frm,1,18,512]
else:
print(' No vectors found'); exit()
if len(direction[0].shape) > 1 and direction[0].shape[0] > 1:
use_d = True
print(' directions', directions.shape, 'using d' if use_d else 'using w')
# latent direction range
lrange = [-0.5, 0.5]
# load saved latents
if a.npy_file is not None:
base_latent = load_latents(a.npy_file)
else:
print(' No NPY input given, making random')
z_dim = Gs.input_shape[1]
shape = (1, z_dim)
base_latent = np.random.randn(*shape)
if use_d:
base_latent = Gs.components.mapping.run(base_latent, None) # [frm,18,512]
for i, direction in enumerate(directions):
make_loop(base_latent, direction, lrange, a.fstep*2, a.fstep*2 * i)
def main():
if a.vector_dir is not None:
if a.vector_dir.endswith('/') or a.vector_dir.endswith('\\'):
a.vector_dir = a.vector_dir[:-1]
os.makedirs(a.out_dir, exist_ok=True)
device = torch.device('cuda')
global Gs, use_d, custom
# setup generator
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
# load base or custom network
pkl_name = osp.splitext(a.model)[0]
if '.pkl' in a.model.lower():
custom = False
print(' .. Gs from pkl ..', basename(a.model))
else:
custom = True
print(' .. Gs custom ..', basename(a.model))
with dnnlib.util.open_url(pkl_name + '.pkl') as f:
Gs = legacy.load_network_pkl(f,
custom=custom, **Gs_kwargs)['G_ema'].to(
device) # type: ignore
# load directions
if a.vector_dir is not None:
directions = []
vector_list = file_list(a.vector_dir, 'npy')
for v in vector_list:
direction = load_latents(v)
if len(direction.shape) == 2:
direction = np.expand_dims(direction, 0)
directions.append(direction)
directions = np.concatenate(directions)[:,
np.newaxis] # [frm,1,18,512]
else:
print(' No vectors found')
exit()
if len(direction[0].shape) > 1 and direction[0].shape[0] > 1:
use_d = True
print(' directions', directions.shape, 'using d' if use_d else 'using w')
directions = torch.from_numpy(directions).to(device)
# latent direction range
lrange = [-0.5, 0.5]
# load saved latents
if a.base_lat is not None:
base_latent = load_latents(a.base_lat)
base_latent = torch.from_numpy(base_latent).to(device)
else:
print(' No NPY input given, making random')
base_latent = np.random.randn(1, Gs.z_dim)
if use_d:
base_latent = Gs.mapping(base_latent, None) # [frm,18,512]
pbar = ProgressBar(len(directions))
for i, direction in enumerate(directions):
make_loop(base_latent, direction, lrange, a.fstep * 2, a.fstep * 2 * i)
pbar.upd()
def main():
os.makedirs(a.out_dir, exist_ok=True)
# parse filename to model parameters
mparams = basename(a.model).split('-')
res = int(mparams[1])
cfg = mparams[2]
# setup generator
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_custom.G_main'
Gs_kwargs.verbose = False
Gs_kwargs.resolution = res
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.latent_size = a.latent_size
if cfg.lower() == 'f':
Gs_kwargs.synthesis_func = 'G_synthesis_stylegan2'
elif cfg.lower() == 'e':
Gs_kwargs.synthesis_func = 'G_synthesis_stylegan2'
Gs_kwargs.fmap_base = 8 << 10
else:
print(' old modes [A-D] not implemented')
exit()
# check initial model resolution
if len(mparams) > 3:
if 'x' in mparams[3].lower():
init_res = [int(x) for x in mparams[3].lower().split('x')]
Gs_kwargs.init_res = list(reversed(init_res)) # [H,W]
# load model, check channels
sess = tflib.init_tf({'allow_soft_placement': True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try:
_, _, Gs = network
except:
Gs = network
Gs_kwargs.num_channels = Gs.output_shape[1]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..')
else:
print(' .. Gs custom ..')
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
z_dim = Gs.input_shape[1]
dz_dim = a.dlatent_size # 512
dl_dim = 2 * (int(np.floor(np.log2(res))) - 1)
dlat_shape = (1, dl_dim, dz_dim) # [1,18,512]
# read saved latents
if a.dlatents is not None and osp.isfile(a.dlatents):
key_dlatents = load_latents(a.dlatents)
if len(key_dlatents.shape) == 2:
key_dlatents = np.expand_dims(key_dlatents, 0)
elif a.dlatents is not None and osp.isdir(a.dlatents):
# if a.dlatents.endswith('/') or a.dlatents.endswith('\\'): a.dlatents = a.dlatents[:-1]
key_dlatents = []
npy_list = file_list(a.dlatents, 'npy')
for npy in npy_list:
key_dlatent = load_latents(npy)
if len(key_dlatent.shape) == 2:
key_dlatent = np.expand_dims(key_dlatent, 0)
key_dlatents.append(key_dlatent)
key_dlatents = np.concatenate(key_dlatents) # [frm,18,512]
else:
print(' No input dlatents found')
exit()
key_dlatents = key_dlatents[:, np.newaxis] # [frm,1,18,512]
print(' key dlatents', key_dlatents.shape)
# replace higher layers with single (style) latent
if a.style_npy_file is not None:
print(' styling with latent', a.style_npy_file)
style_dlatent = load_latents(a.style_npy_file)
while len(style_dlatent.shape) < 4:
style_dlatent = np.expand_dims(style_dlatent, 0)
# try other values < dl_dim besides 5
key_dlatents[:, :,
range(5, dl_dim), :] = style_dlatent[:, :,
range(5, dl_dim), :]
frames = key_dlatents.shape[0] * a.fstep
dlatents = latent_anima(dlat_shape,
frames,
a.fstep,
key_latents=key_dlatents,
cubic=a.cubic,
verbose=True) # [frm,1,512]
print(' dlatents', dlatents.shape)
# truncation trick
dlatent_avg = Gs.get_var('dlatent_avg') # (512,)
tr_range = range(0, 8)
dlatents[:, :, tr_range, :] = dlatent_avg + (dlatents[:, :, tr_range, :] -
dlatent_avg) * a.trunc
# loop for graph frame by frame
frame_count = dlatents.shape[0]
pbar = ProgressBar(frame_count)
for i in range(frame_count):
dlatent = dlatents[i]
output = Gs.components.synthesis.run(dlatent,
randomize_noise=False,
output_transform=fmt,
minibatch_size=1)
ext = 'png' if output.shape[3] == 4 else 'jpg'
filename = osp.join(a.out_dir, "%05d.%s" % (i, ext))
imsave(filename, output[0])
pbar.upd()
def main():
if a.vector_dir is not None:
if a.vector_dir.endswith('/') or a.vector_dir.endswith('\\'):
a.vector_dir = a.vector_dir[:-1]
os.makedirs(osp.join(a.out_dir, 'ttt'), exist_ok=True)
global Gs, use_d
# setup generator
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.func_name = 'training.stylegan2_multi.G_main'
Gs_kwargs.verbose = a.verbose
Gs_kwargs.size = a.size
Gs_kwargs.scale_type = a.scale_type
Gs_kwargs.impl = a.ops
# load model with arguments
sess = tflib.init_tf({'allow_soft_placement': True})
pkl_name = osp.splitext(a.model)[0]
with open(pkl_name + '.pkl', 'rb') as file:
network = pickle.load(file, encoding='latin1')
try:
_, _, network = network
except:
pass
for k in list(network.static_kwargs.keys()):
Gs_kwargs[k] = network.static_kwargs[k]
# reload custom network, if needed
if '.pkl' in a.model.lower():
print(' .. Gs from pkl ..', basename(a.model))
Gs = network
else: # reconstruct network
print(' .. Gs custom ..', basename(a.model))
Gs = tflib.Network('Gs', **Gs_kwargs)
Gs.copy_vars_from(network)
# load directions
if a.vector_dir is not None:
directions = []
vector_list = file_list(a.vector_dir, 'npy')
for v in vector_list:
direction = load_latents(v)
if len(direction.shape) == 2:
direction = np.expand_dims(direction, 0)
directions.append(direction)
directions = np.concatenate(directions)[:,
np.newaxis] # [frm,1,18,512]
else:
print(' No vectors found')
exit()
if len(direction[0].shape) > 1 and direction[0].shape[0] > 1:
use_d = True
print(' directions', directions.shape, 'using d' if use_d else 'using w')
# latent direction range
lrange = [-0.5, 0.5]
# load saved latents
if a.npy_file is not None:
base_latent = load_latents(a.npy_file)
else:
print(' No NPY input given, making random')
z_dim = Gs.input_shape[1]
shape = (1, z_dim)
base_latent = np.random.randn(*shape)
if use_d:
base_latent = Gs.components.mapping.run(base_latent,
None) # [frm,18,512]
for i, direction in enumerate(directions):
make_loop(base_latent, direction, lrange, a.fstep * 2, a.fstep * 2 * i)
