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import numpy as np
import jax
from jax import random
import jax.numpy as jnp
import flax.linen as nn
from typing import Any, Tuple, List
import h5py
from . import ops
from stylegan2 import utils
URLS = {'afhqcat': 'https://www.dropbox.com/s/lv1r0bwvg5ta51f/stylegan2_generator_afhqcat.h5?dl=1',
'afhqdog': 'https://www.dropbox.com/s/px6ply9hv0vdwen/stylegan2_generator_afhqdog.h5?dl=1',
'afhqwild': 'https://www.dropbox.com/s/p1slbtmzhcnw9q8/stylegan2_generator_afhqwild.h5?dl=1',
'brecahad': 'https://www.dropbox.com/s/28uykhj0ku6hwg2/stylegan2_generator_brecahad.h5?dl=1',
'car': 'https://www.dropbox.com/s/67o834b6xfg9x1q/stylegan2_generator_car.h5?dl=1',
'cat': 'https://www.dropbox.com/s/cu9egc4e74e1nig/stylegan2_generator_cat.h5?dl=1',
'church': 'https://www.dropbox.com/s/kwvokfwbrhsn58m/stylegan2_generator_church.h5?dl=1',
'cifar10': 'https://www.dropbox.com/s/h1kmymjzfwwkftk/stylegan2_generator_cifar10.h5?dl=1',
'ffhq': 'https://www.dropbox.com/s/e8de1peq7p8gq9d/stylegan2_generator_ffhq.h5?dl=1',
'horse': 'https://www.dropbox.com/s/3e5bimv2d41bc13/stylegan2_generator_horse.h5?dl=1',
'metfaces': 'https://www.dropbox.com/s/75klr5k6mgm7qdy/stylegan2_generator_metfaces.h5?dl=1'}
RESOLUTION = {'metfaces': 1024,
'ffhq': 1024,
'church': 256,
'cat': 256,
'horse': 256,
'car': 512,
'brecahad': 512,
'afhqwild': 512,
'afhqdog': 512,
'afhqcat': 512,
'cifar10': 32}
C_DIM = {'metfaces': 0,
'ffhq': 0,
'church': 0,
'cat': 0,
'horse': 0,
'car': 0,
'brecahad': 0,
'afhqwild': 0,
'afhqdog': 0,
'afhqcat': 0,
'cifar10': 10}
NUM_MAPPING_LAYERS = {'metfaces': 8,
'ffhq': 8,
'church': 8,
'cat': 8,
'horse': 8,
'car': 8,
'brecahad': 8,
'afhqwild': 8,
'afhqdog': 8,
'afhqcat': 8,
'cifar10': 2}
class MappingNetwork(nn.Module):
"""
Mapping Network.
Attributes:
z_dim (int): Input latent (Z) dimensionality.
c_dim (int): Conditioning label (C) dimensionality, 0 = no label.
w_dim (int): Intermediate latent (W) dimensionality.
embed_features (int): Label embedding dimensionality, None = same as w_dim.
layer_features (int): Number of intermediate features in the mapping layers, None = same as w_dim.
num_ws (int): Number of intermediate latents to output, None = do not broadcast.
num_layers (int): Number of mapping layers.
pretrained (str): Which pretrained model to use, None for random initialization.
param_dict (h5py.Group): Parameter dict with pretrained parameters. If not None, 'pretrained' will be ignored.
ckpt_dir (str): Directory to which the pretrained weights are downloaded. If None, a temp directory will be used.
activation (str): Activation function: 'relu', 'lrelu', etc.
lr_multiplier (float): Learning rate multiplier for the mapping layers.
w_avg_beta (float): Decay for tracking the moving average of W during training, None = do not track.
dtype (str): Data type.
rng (jax.random.PRNGKey): PRNG for initialization.
"""
# Dimensionality
z_dim: int=512
c_dim: int=0
w_dim: int=512
embed_features: int=None
layer_features: int=512
# Layers
num_ws: int=18
num_layers: int=8
# Pretrained
pretrained: str=None
param_dict: h5py.Group=None
ckpt_dir: str=None
# Internal details
activation: str='leaky_relu'
lr_multiplier: float=0.01
w_avg_beta: float=0.995
dtype: str='float32'
rng: Any=random.PRNGKey(0)
def setup(self):
self.embed_features_ = self.embed_features
self.c_dim_ = self.c_dim
self.layer_features_ = self.layer_features
self.num_layers_ = self.num_layers
self.param_dict_ = self.param_dict
if self.pretrained is not None and self.param_dict is None:
assert self.pretrained in URLS.keys(), f'Pretrained model not available: {self.pretrained}'
ckpt_file = utils.download(self.ckpt_dir, URLS[self.pretrained])
self.param_dict_ = h5py.File(ckpt_file, 'r')['mapping_network']
self.c_dim_ = C_DIM[self.pretrained]
self.num_layers_ = NUM_MAPPING_LAYERS[self.pretrained]
if self.embed_features_ is None:
self.embed_features_ = self.w_dim
if self.c_dim_ == 0:
self.embed_features_ = 0
if self.layer_features_ is None:
self.layer_features_ = self.w_dim
if self.param_dict_ is not None and 'w_avg' in self.param_dict_:
self.w_avg = self.variable('moving_stats', 'w_avg', lambda *_ : jnp.array(self.param_dict_['w_avg']), [self.w_dim])
else:
self.w_avg = self.variable('moving_stats', 'w_avg', jnp.zeros, [self.w_dim])
@nn.compact
def __call__(self, z, c=None, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False, train=True):
"""
Run Mapping Network.
Args:
z (tensor): Input noise, shape [N, z_dim].
c (tensor): Input labels, shape [N, c_dim].
truncation_psi (float): Controls truncation (trading off variation for quality). If 1, truncation is disabled.
truncation_cutoff (int): Controls truncation. None = disable.
skip_w_avg_update (bool): If True, updates the exponential moving average of W.
train (bool): Training mode.
Returns:
(tensor): Intermediate latent W.
"""
init_rng = self.rng
# Embed, normalize, and concat inputs.
x = None
if self.z_dim > 0:
x = ops.normalize_2nd_moment(z.astype(jnp.float32))
if self.c_dim_ > 0:
# Conditioning label
y = ops.LinearLayer(in_features=self.c_dim_,
out_features=self.embed_features_,
use_bias=True,
lr_multiplier=self.lr_multiplier,
activation='linear',
param_dict=self.param_dict_,
layer_name='label_embedding',
dtype=self.dtype,
rng=init_rng)(c.astype(jnp.float32))
y = ops.normalize_2nd_moment(y)
x = jnp.concatenate((x, y), axis=1) if x is not None else y
# Main layers.
for i in range(self.num_layers_):
init_rng, init_key = random.split(init_rng)
x = ops.LinearLayer(in_features=x.shape[1],
out_features=self.layer_features_,
use_bias=True,
lr_multiplier=self.lr_multiplier,
activation=self.activation,
param_dict=self.param_dict_,
layer_name=f'fc{i}',
dtype=self.dtype,
rng=init_key)(x)
# Update moving average of W.
if self.w_avg_beta is not None and train and not skip_w_avg_update:
self.w_avg.value = self.w_avg_beta * self.w_avg.value + (1 - self.w_avg_beta) * jnp.mean(x, axis=0)
# Broadcast.
if self.num_ws is not None:
x = jnp.repeat(jnp.expand_dims(x, axis=-2), repeats=self.num_ws, axis=-2)
# Apply truncation.
if truncation_psi != 1:
assert self.w_avg_beta is not None
if self.num_ws is None or truncation_cutoff is None:
x = truncation_psi * x + (1 - truncation_psi) * self.w_avg.value
else:
x[:, :truncation_cutoff] = truncation_psi * x[:, :truncation_cutoff] + (1 - truncation_psi) * self.w_avg.value
return x
class SynthesisLayer(nn.Module):
"""
Synthesis Layer.
Attributes:
fmaps (int): Number of output channels of the modulated convolution.
kernel (int): Kernel size of the modulated convolution.
layer_idx (int): Layer index. Used to access the latent code for a specific layer.
res (int): Resolution (log2) of the current layer.
lr_multiplier (float): Learning rate multiplier.
up (bool): If True, upsample the spatial resolution.
activation (str): Activation function: 'relu', 'lrelu', etc.
use_noise (bool): If True, add spatial-specific noise.
resample_kernel (Tuple): Kernel that is used for FIR filter.
fused_modconv (bool): If True, Perform modulation, convolution, and demodulation as a single fused operation.
param_dict (h5py.Group): Parameter dict with pretrained parameters. If not None, 'pretrained' will be ignored.
clip_conv (float): Clip the output of convolution layers to [-clip_conv, +clip_conv], None = disable clipping.
dtype (str): Data dtype.
rng (jax.random.PRNGKey): PRNG for initialization.
"""
fmaps: int
kernel: int
layer_idx: int
res: int
lr_multiplier: float=1
up: bool=False
activation: str='leaky_relu'
use_noise: bool=True
resample_kernel: Tuple=(1, 3, 3, 1)
fused_modconv: bool=False
param_dict: h5py.Group=None
clip_conv: float=None
dtype: str='float32'
rng: Any=random.PRNGKey(0)
def setup(self):
if self.param_dict is not None:
noise_const = jnp.array(self.param_dict['noise_const'], dtype=self.dtype)
else:
noise_const = random.normal(self.rng, shape=(1, 2 ** self.res, 2 ** self.res, 1), dtype=self.dtype)
self.noise_const = self.variable('noise_consts', 'noise_const', lambda *_: noise_const)
@nn.compact
def __call__(self, x, dlatents, noise_mode='random', rng=random.PRNGKey(0)):
"""
Run Synthesis Layer.
Args:
x (tensor): Input tensor of the shape [N, H, W, C].
dlatents (tensor): Intermediate latents (W) of shape [N, num_ws, w_dim].
noise_mode (str): Noise type.
- 'const': Constant noise.
- 'random': Random noise.
- 'none': No noise.
rng (jax.random.PRNGKey): PRNG for spatialwise noise.
Returns:
(tensor): Output tensor of shape [N, H', W', fmaps].
"""
assert noise_mode in ['const', 'random', 'none']
linear_rng, conv_rng = random.split(self.rng)
# Affine transformation to obtain style variable.
s = ops.LinearLayer(in_features=dlatents[:, self.layer_idx].shape[1],
out_features=x.shape[3],
use_bias=True,
bias_init=1,
lr_multiplier=self.lr_multiplier,
param_dict=self.param_dict,
layer_name='affine',
dtype=self.dtype,
rng=linear_rng)(dlatents[:, self.layer_idx])
# Noise variables.
if self.param_dict is None:
noise_strength = jnp.zeros(())
else:
noise_strength = jnp.array(self.param_dict['noise_strength'])
noise_strength = self.param(name='noise_strength', init_fn=lambda *_ : noise_strength)
# Weight and bias for convolution operation.
w_shape = [self.kernel, self.kernel, x.shape[3], self.fmaps]
w, b = ops.get_weight(w_shape, self.lr_multiplier, True, self.param_dict, 'conv', conv_rng)
w = self.param(name='weight', init_fn=lambda *_ : w)
b = self.param(name='bias', init_fn=lambda *_ : b)
w = ops.equalize_lr_weight(w, self.lr_multiplier)
b = ops.equalize_lr_bias(b, self.lr_multiplier)
x = ops.modulated_conv2d_layer(x=x,
w=w,
s=s,
fmaps=self.fmaps,
kernel=self.kernel,
up=self.up,
resample_kernel=self.resample_kernel,
fused_modconv=self.fused_modconv)
if self.use_noise and noise_mode != 'none':
if noise_mode == 'const':
noise = self.noise_const.value
elif noise_mode == 'random':
noise = random.normal(rng, shape=(x.shape[0], x.shape[1], x.shape[2], 1), dtype=self.dtype)
x += noise * noise_strength.astype(self.dtype)
x += b.astype(x.dtype)
x = ops.apply_activation(x, activation=self.activation)
if self.clip_conv is not None:
x = jnp.clip(x, -self.clip_conv, self.clip_conv)
return x
class ToRGBLayer(nn.Module):
"""
To RGB Layer.
Attributes:
fmaps (int): Number of output channels of the modulated convolution.
layer_idx (int): Layer index. Used to access the latent code for a specific layer.
kernel (int): Kernel size of the modulated convolution.
lr_multiplier (float): Learning rate multiplier.
fused_modconv (bool): If True, Perform modulation, convolution, and demodulation as a single fused operation.
param_dict (h5py.Group): Parameter dict with pretrained parameters. If not None, 'pretrained' will be ignored.
clip_conv (float): Clip the output of convolution layers to [-clip_conv, +clip_conv], None = disable clipping.
dtype (str): Data dtype.
rng (jax.random.PRNGKey): PRNG for initialization.
"""
fmaps: int
layer_idx: int
kernel: int=1
lr_multiplier: float=1
fused_modconv: bool=False
param_dict: h5py.Group=None
clip_conv: float=None
dtype: str='float32'
rng: Any=random.PRNGKey(0)
@nn.compact
def __call__(self, x, y, dlatents):
"""
Run To RGB Layer.
Args:
x (tensor): Input tensor of shape [N, H, W, C].
y (tensor): Image of shape [N, H', W', fmaps].
dlatents (tensor): Intermediate latents (W) of shape [N, num_ws, w_dim].
Returns:
(tensor): Output tensor of shape [N, H', W', fmaps].
"""
# Affine transformation to obtain style variable.
s = ops.LinearLayer(in_features=dlatents[:, self.layer_idx].shape[1],
out_features=x.shape[3],
use_bias=True,
bias_init=1,
lr_multiplier=self.lr_multiplier,
param_dict=self.param_dict,
layer_name='affine',
dtype=self.dtype,
rng=self.rng)(dlatents[:, self.layer_idx])
# Weight and bias for convolution operation.
w_shape = [self.kernel, self.kernel, x.shape[3], self.fmaps]
w, b = ops.get_weight(w_shape, self.lr_multiplier, True, self.param_dict, 'conv', self.rng)
w = self.param(name='weight', init_fn=lambda *_ : w)
b = self.param(name='bias', init_fn=lambda *_ : b)
w = ops.equalize_lr_weight(w, self.lr_multiplier)
b = ops.equalize_lr_bias(b, self.lr_multiplier)
x = ops.modulated_conv2d_layer(x, w, s, fmaps=self.fmaps, kernel=self.kernel, demodulate=False, fused_modconv=self.fused_modconv)
x += b.astype(x.dtype)
x = ops.apply_activation(x, activation='linear')
if self.clip_conv is not None:
x = jnp.clip(x, -self.clip_conv, self.clip_conv)
if y is not None:
x += y.astype(jnp.float32)
return x
class SynthesisBlock(nn.Module):
"""
Synthesis Block.
Attributes:
fmaps (int): Number of output channels of the modulated convolution.
res (int): Resolution (log2) of the current block.
num_layers (int): Number of layers in the current block.
num_channels (int): Number of output color channels.
lr_multiplier (float): Learning rate multiplier.
activation (str): Activation function: 'relu', 'lrelu', etc.
use_noise (bool): If True, add spatial-specific noise.
resample_kernel (Tuple): Kernel that is used for FIR filter.
fused_modconv (bool): If True, Perform modulation, convolution, and demodulation as a single fused operation.
param_dict (h5py.Group): Parameter dict with pretrained parameters. If not None, 'pretrained' will be ignored.
clip_conv (float): Clip the output of convolution layers to [-clip_conv, +clip_conv], None = disable clipping.
dtype (str): Data dtype.
rng (jax.random.PRNGKey): PRNG for initialization.
"""
fmaps: int
res: int
num_layers: int=2
num_channels: int=3
lr_multiplier: float=1
activation: str='leaky_relu'
use_noise: bool=True
resample_kernel: Tuple=(1, 3, 3, 1)
fused_modconv: bool=False
param_dict: h5py.Group=None
clip_conv: float=None
dtype: str='float32'
rng: Any=random.PRNGKey(0)
@nn.compact
def __call__(self, x, y, dlatents_in, noise_mode='random', rng=random.PRNGKey(0)):
"""
Run Synthesis Block.
Args:
x (tensor): Input tensor of shape [N, H, W, C].
y (tensor): Image of shape [N, H', W', fmaps].
dlatents (tensor): Intermediate latents (W) of shape [N, num_ws, w_dim].
noise_mode (str): Noise type.
- 'const': Constant noise.
- 'random': Random noise.
- 'none': No noise.
rng (jax.random.PRNGKey): PRNG for spatialwise noise.
Returns:
(tensor): Output tensor of shape [N, H', W', fmaps].
"""
x = x.astype(self.dtype)
init_rng = self.rng
for i in range(self.num_layers):
init_rng, init_key = random.split(init_rng)
x = SynthesisLayer(fmaps=self.fmaps,
kernel=3,
layer_idx=self.res * 2 - (5 - i) if self.res > 2 else 0,
res=self.res,
lr_multiplier=self.lr_multiplier,
up=i == 0 and self.res != 2,
activation=self.activation,
use_noise=self.use_noise,
resample_kernel=self.resample_kernel,
fused_modconv=self.fused_modconv,
param_dict=self.param_dict[f'layer{i}'] if self.param_dict is not None else None,
dtype=self.dtype,
rng=init_key)(x, dlatents_in, noise_mode, rng)
if self.num_layers == 2:
k = ops.setup_filter(self.resample_kernel)
y = ops.upsample2d(y, f=k, up=2)
init_rng, init_key = random.split(init_rng)
y = ToRGBLayer(fmaps=self.num_channels,
layer_idx=self.res * 2 - 3,
lr_multiplier=self.lr_multiplier,
param_dict=self.param_dict['torgb'] if self.param_dict is not None else None,
dtype=self.dtype,
rng=init_key)(x, y, dlatents_in)
return x, y
class SynthesisNetwork(nn.Module):
"""
Synthesis Network.
Attributes:
resolution (int): Output resolution.
num_channels (int): Number of output color channels.
w_dim (int): Input latent (Z) dimensionality.
fmap_base (int): Overall multiplier for the number of feature maps.
fmap_decay (int): Log2 feature map reduction when doubling the resolution.
fmap_min (int): Minimum number of feature maps in any layer.
fmap_max (int): Maximum number of feature maps in any layer.
fmap_const (int): Number of feature maps in the constant input layer. None = default.
pretrained (str): Which pretrained model to use, None for random initialization.
param_dict (h5py.Group): Parameter dict with pretrained parameters. If not None, 'pretrained' will be ignored.
ckpt_dir (str): Directory to which the pretrained weights are downloaded. If None, a temp directory will be used.
activation (str): Activation function: 'relu', 'lrelu', etc.
use_noise (bool): If True, add spatial-specific noise.
resample_kernel (Tuple): Kernel that is used for FIR filter.
fused_modconv (bool): If True, Perform modulation, convolution, and demodulation as a single fused operation.
num_fp16_res (int): Use float16 for the 'num_fp16_res' highest resolutions.
clip_conv (float): Clip the output of convolution layers to [-clip_conv, +clip_conv], None = disable clipping.
dtype (str): Data type.
rng (jax.random.PRNGKey): PRNG for initialization.
"""
# Dimensionality
resolution: int=1024
num_channels: int=3
w_dim: int=512
# Capacity
fmap_base: int=16384
fmap_decay: int=1
fmap_min: int=1
fmap_max: int=512
fmap_const: int=None
# Pretraining
pretrained: str=None
param_dict: h5py.Group=None
ckpt_dir: str=None
# Internal details
activation: str='leaky_relu'
use_noise: bool=True
resample_kernel: Tuple=(1, 3, 3, 1)
fused_modconv: bool=False
num_fp16_res: int=0
clip_conv: float=None
dtype: str='float32'
rng: Any=random.PRNGKey(0)
def setup(self):
self.resolution_ = self.resolution
self.param_dict_ = self.param_dict
if self.pretrained is not None and self.param_dict is None:
assert self.pretrained in URLS.keys(), f'Pretrained model not available: {self.pretrained}'
ckpt_file = utils.download(self.ckpt_dir, URLS[self.pretrained])
self.param_dict_ = h5py.File(ckpt_file, 'r')['synthesis_network']
self.resolution_ = RESOLUTION[self.pretrained]
@nn.compact
def __call__(self, dlatents_in, noise_mode='random', rng=random.PRNGKey(0)):
"""
Run Synthesis Network.
Args:
dlatents_in (tensor): Intermediate latents (W) of shape [N, num_ws, w_dim].
noise_mode (str): Noise type.
- 'const': Constant noise.
- 'random': Random noise.
- 'none': No noise.
rng (jax.random.PRNGKey): PRNG for spatialwise noise.
Returns:
(tensor): Image of shape [N, H, W, num_channels].
"""
resolution_log2 = int(np.log2(self.resolution_))
assert self.resolution_ == 2 ** resolution_log2 and self.resolution_ >= 4
def nf(stage): return np.clip(int(self.fmap_base / (2.0 ** (stage * self.fmap_decay))), self.fmap_min, self.fmap_max)
num_layers = resolution_log2 * 2 - 2
fmaps = self.fmap_const if self.fmap_const is not None else nf(1)
if self.param_dict_ is None:
const = random.normal(self.rng, (1, 4, 4, fmaps), dtype=self.dtype)
else:
const = jnp.array(self.param_dict_['const'], dtype=self.dtype)
x = self.param(name='const', init_fn=lambda *_ : const)
x = jnp.repeat(x, repeats=dlatents_in.shape[0], axis=0)
y = None
dlatents_in = dlatents_in.astype(jnp.float32)
init_rng = self.rng
for res in range(2, resolution_log2 + 1):
init_rng, init_key = random.split(init_rng)
x, y = SynthesisBlock(fmaps=nf(res - 1),
res=res,
num_layers=1 if res == 2 else 2,
num_channels=self.num_channels,
activation=self.activation,
use_noise=self.use_noise,
resample_kernel=self.resample_kernel,
fused_modconv=self.fused_modconv,
param_dict=self.param_dict_[f'block_{2 ** res}x{2 ** res}'] if self.param_dict_ is not None else None,
clip_conv=self.clip_conv,
dtype=self.dtype if res > resolution_log2 - self.num_fp16_res else 'float32',
rng=init_key)(x, y, dlatents_in, noise_mode, rng)
return y
class Generator(nn.Module):
"""
Generator.
Attributes:
resolution (int): Output resolution.
num_channels (int): Number of output color channels.
z_dim (int): Input latent (Z) dimensionality.
c_dim (int): Conditioning label (C) dimensionality, 0 = no label.
w_dim (int): Intermediate latent (W) dimensionality.
mapping_layer_features (int): Number of intermediate features in the mapping layers, None = same as w_dim.
mapping_embed_features (int): Label embedding dimensionality, None = same as w_dim.
num_ws (int): Number of intermediate latents to output, None = do not broadcast.
num_mapping_layers (int): Number of mapping layers.
fmap_base (int): Overall multiplier for the number of feature maps.
fmap_decay (int): Log2 feature map reduction when doubling the resolution.
fmap_min (int): Minimum number of feature maps in any layer.
fmap_max (int): Maximum number of feature maps in any layer.
fmap_const (int): Number of feature maps in the constant input layer. None = default.
pretrained (str): Which pretrained model to use, None for random initialization.
ckpt_dir (str): Directory to which the pretrained weights are downloaded. If None, a temp directory will be used.
use_noise (bool): If True, add spatial-specific noise.
activation (str): Activation function: 'relu', 'lrelu', etc.
w_avg_beta (float): Decay for tracking the moving average of W during training, None = do not track.
mapping_lr_multiplier (float): Learning rate multiplier for the mapping network.
resample_kernel (Tuple): Kernel that is used for FIR filter.
fused_modconv (bool): If True, Perform modulation, convolution, and demodulation as a single fused operation.
num_fp16_res (int): Use float16 for the 'num_fp16_res' highest resolutions.
clip_conv (float): Clip the output of convolution layers to [-clip_conv, +clip_conv], None = disable clipping.
dtype (str): Data type.
rng (jax.random.PRNGKey): PRNG for initialization.
"""
# Dimensionality
resolution: int=1024
num_channels: int=3
z_dim: int=512
c_dim: int=0
w_dim: int=512
mapping_layer_features: int=512
mapping_embed_features: int=None
# Layers
num_ws: int=18
num_mapping_layers: int=8
# Capacity
fmap_base: int=16384
fmap_decay: int=1
fmap_min: int=1
fmap_max: int=512
fmap_const: int=None
# Pretraining
pretrained: str=None
ckpt_dir: str=None
# Internal details
use_noise: bool=True
activation: str='leaky_relu'
w_avg_beta: float=0.995
mapping_lr_multiplier: float=0.01
resample_kernel: Tuple=(1, 3, 3, 1)
fused_modconv: bool=False
num_fp16_res: int=0
clip_conv: float=None
dtype: str='float32'
rng: Any=random.PRNGKey(0)
def setup(self):
self.resolution_ = self.resolution
self.c_dim_ = self.c_dim
self.num_mapping_layers_ = self.num_mapping_layers
if self.pretrained is not None:
assert self.pretrained in URLS.keys(), f'Pretrained model not available: {self.pretrained}'
ckpt_file = utils.download(self.ckpt_dir, URLS[self.pretrained])
self.param_dict = h5py.File(ckpt_file, 'r')
self.resolution_ = RESOLUTION[self.pretrained]
self.c_dim_ = C_DIM[self.pretrained]
self.num_mapping_layers_ = NUM_MAPPING_LAYERS[self.pretrained]
else:
self.param_dict = None
self.init_rng_mapping, self.init_rng_synthesis = random.split(self.rng)
@nn.compact
def __call__(self, z, c=None, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False, train=True, noise_mode='random', rng=random.PRNGKey(0)):
"""
Run Generator.
Args:
z (tensor): Input noise, shape [N, z_dim].
c (tensor): Input labels, shape [N, c_dim].
truncation_psi (float): Controls truncation (trading off variation for quality). If 1, truncation is disabled.
truncation_cutoff (int): Controls truncation. None = disable.
skip_w_avg_update (bool): If True, updates the exponential moving average of W.
train (bool): Training mode.
noise_mode (str): Noise type.
- 'const': Constant noise.
- 'random': Random noise.
- 'none': No noise.
rng (jax.random.PRNGKey): PRNG for spatialwise noise.
Returns:
(tensor): Image of shape [N, H, W, num_channels].
"""
dlatents_in = MappingNetwork(z_dim=self.z_dim,
c_dim=self.c_dim_,
w_dim=self.w_dim,
num_ws=self.num_ws,
num_layers=self.num_mapping_layers_,
embed_features=self.mapping_embed_features,
layer_features=self.mapping_layer_features,
activation=self.activation,
lr_multiplier=self.mapping_lr_multiplier,
w_avg_beta=self.w_avg_beta,
param_dict=self.param_dict['mapping_network'] if self.param_dict is not None else None,
dtype=self.dtype,
rng=self.init_rng_mapping,
name='mapping_network')(z, c, truncation_psi, truncation_cutoff, skip_w_avg_update, train)
x = SynthesisNetwork(resolution=self.resolution_,
num_channels=self.num_channels,
w_dim=self.w_dim,
fmap_base=self.fmap_base,
fmap_decay=self.fmap_decay,
fmap_min=self.fmap_min,
fmap_max=self.fmap_max,
fmap_const=self.fmap_const,
param_dict=self.param_dict['synthesis_network'] if self.param_dict is not None else None,
activation=self.activation,
use_noise=self.use_noise,
resample_kernel=self.resample_kernel,
fused_modconv=self.fused_modconv,
num_fp16_res=self.num_fp16_res,
clip_conv=self.clip_conv,
dtype=self.dtype,
rng=self.init_rng_synthesis,
name='synthesis_network')(dlatents_in, noise_mode, rng)
return x
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