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Adaptive Normalization Modules for ChronFlux
Includes AdaIN (Adaptive Instance Normalization) and AdaLN (Adaptive Layer Normalization)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
NIGHT_SKY_INTENSITY = -0.95
WARM_BIAS_RGB = [0.2, 0.0, -0.2]
class AdaIN(nn.Module):
"""
Adaptive Instance Normalization (AdaIN) for style transfer.
Used for generating pseudo-style targets during training with extensive augmentation.
Reference: Huang & Belongie, "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization"
"""
def __init__(self, eps=1e-5):
super().__init__()
self.eps = eps
def calc_mean_std(self, feat):
size = feat.size()
N, C = size[:2]
feat_view = feat.view(N, C, -1)
feat_std, feat_mean = torch.std_mean(feat_view, dim=2, unbiased=False)
feat_std = (feat_std + self.eps).view(N, C, 1, 1)
feat_mean = feat_mean.view(N, C, 1, 1)
return feat_mean, feat_std
def forward(self, content, style):
"""
Apply AdaIN: transfers style statistics to content
Args:
content: Content features [B, C, H, W]
style: Style features [B, C, H, W]
Returns:
Stylized content with style's mean and std
"""
size = content.size()
content_mean, content_std = self.calc_mean_std(content)
style_mean, style_std = self.calc_mean_std(style)
normalized = (content - content_mean) / content_std
stylized = normalized * style_std + style_mean
return stylized
class AdaINTransfer(nn.Module):
"""
Extended AdaIN for pseudo-style target generation with augmentation support.
Used specifically for training pipeline to create robust style targets.
"""
def __init__(self, eps=1e-5):
super().__init__()
self.adain = AdaIN(eps=eps)
self.register_buffer(
'warm_bias_template',
torch.tensor(WARM_BIAS_RGB, dtype=torch.float32).view(1, 3, 1, 1)
)
def adaptive_night_transfer(self, content, style, sky_mask=None):
"""
Transfer night style to content with optional sky masking
Args:
content: Day images [B, C, H, W]
style: Night images [B, C, H, W]
sky_mask: Binary mask for sky regions [B, 1, H, W]
Returns:
Night-styled content
"""
target = self.adain(content, style)
if sky_mask is not None:
target = target * (1 - sky_mask) + (NIGHT_SKY_INTENSITY * sky_mask)
return target.clamp(-0.95, 0.95)
def adaptive_sunset_transfer(self, content, style_night, sky_mask=None):
"""
Create sunset style by channel manipulation of night style
Args:
content: Day images [B, C, H, W]
style_night: Night images for base style [B, C, H, W]
sky_mask: Binary mask for sky regions [B, 1, H, W]
Returns:
Sunset-styled content with warm tones
"""
content_mean, content_std = self.adain.calc_mean_std(content)
style_mean, style_std = self.adain.calc_mean_std(style_night)
mu_swap = style_mean.clone()
mu_swap[:, 0] = style_mean[:, 2]
mu_swap[:, 2] = style_mean[:, 0]
std_swap = style_std.clone()
std_swap[:, 0] = style_std[:, 2]
std_swap[:, 2] = style_std[:, 0]
B = content.shape[0]
warm_bias = self.warm_bias_template.to(content.dtype).expand(B, 3, 1, 1)
mu_final = mu_swap * 0.7 + warm_bias * 0.3
std_final = std_swap * 1.4
target = (content - content_mean) / content_std * std_final + mu_final
if sky_mask is not None:
sky_color = mu_final.view(B, 3, 1, 1)
target = target * (1 - sky_mask) + (target * 0.4 + sky_color * 0.6) * sky_mask
return target.clamp(-0.95, 0.95)
class AdaLNModulation(nn.Module):
"""
Adaptive Layer Normalization (AdaLN) Modulation.
Generates scale, shift, and gate parameters for layer-wise adaptive normalization.
Provides better control over style consistency and layer-wise adjustments.
"""
def __init__(self, hidden_size, components=6):
super().__init__()
self.silu = nn.SiLU()
self.linear = nn.Linear(hidden_size, components * hidden_size, bias=True)
self.components = components
nn.init.zeros_(self.linear.weight)
nn.init.zeros_(self.linear.bias)
def forward(self, vec):
"""
Generate modulation parameters
Args:
vec: Conditioning vector [B, hidden_size]
Returns:
Tuple of (shift, scale, gate) parameters for each component
"""
chunk = self.linear(self.silu(vec))
return chunk.chunk(self.components, dim=1)
class AdaLNBlock(nn.Module):
"""
Base AdaLN normalization block with scale, shift, and gate.
Can be used as a building block for various architectures.
"""
def __init__(self, hidden_size, use_gate=True):
super().__init__()
self.norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.use_gate = use_gate
def forward(self, x, scale, shift, gate=None):
"""
Apply AdaLN: normalize, then scale and shift, optionally gate
Args:
x: Input features [B, L, D]
scale: Scale parameter [B, D]
shift: Shift parameter [B, D]
gate: Optional gate parameter [B, D]
Returns:
Modulated features
"""
x_norm = self.norm(x)
x_mod = x_norm * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
if self.use_gate and gate is not None:
x_mod = gate.unsqueeze(1) * x_mod
return x_mod
class AdaLNZero(nn.Module):
"""
AdaLN-Zero: AdaLN with zero-initialized output scaling.
Particularly useful for residual connections and stable training.
"""
def __init__(self, hidden_size):
super().__init__()
self.norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.scale = nn.Parameter(torch.zeros(hidden_size))
self.shift = nn.Parameter(torch.zeros(hidden_size))
def forward(self, x, modulation_scale, modulation_shift):
"""
Apply AdaLN with zero-initialization
Args:
x: Input features [B, L, D]
modulation_scale: Additional scale from conditioning [B, D]
modulation_shift: Additional shift from conditioning [B, D]
Returns:
Modulated features
"""
x_norm = self.norm(x)
x_mod = x_norm * (self.scale + modulation_scale.unsqueeze(1)) + (self.shift + modulation_shift.unsqueeze(1))
return x_mod
class StyleAdaLN(nn.Module):
"""
Style-aware AdaLN that combines style and conditioning vectors.
Particularly useful for style transfer with additional controls.
"""
def __init__(self, hidden_size, style_dim):
super().__init__()
self.style_proj = nn.Linear(style_dim, hidden_size)
self.adaln = AdaLNModulation(hidden_size, components=2)
self.norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
def forward(self, x, style_vec, cond_vec=None):
"""
Apply style-aware normalization
Args:
x: Input features [B, L, D]
style_vec: Style vector [B, style_dim]
cond_vec: Optional additional conditioning [B, D]
Returns:
Style-modulated features
"""
style_emb = self.style_proj(style_vec)
if cond_vec is not None:
combined = style_emb + cond_vec
else:
combined = style_emb
scale, shift = self.adaln(combined)
x_norm = self.norm(x)
x_mod = x_norm * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
return x_mod
__all__ = [
'AdaIN',
'AdaINTransfer',
'AdaLNModulation',
'AdaLNBlock',
'AdaLNZero',
'StyleAdaLN',
] |