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c549f81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 | """ScribeFont neural architecture: 2D Vision Calligrapher Style Encoder + Cross-Attention Bézier Glyph Decoder."""
from __future__ import annotations
from typing import Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedConfig, PreTrainedModel
class ScribeFontConfig(PreTrainedConfig):
model_type = "scribefont"
def __init__(
self,
vocab_size: int = 62,
num_writers: int = 12,
num_points: int = 16,
style_dim: int = 128,
hidden_dim: int = 256,
num_layers: int = 4,
num_heads: int = 8,
dropout: float = 0.05,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.num_writers = num_writers
self.num_points = num_points
self.style_dim = style_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.num_heads = num_heads
self.dropout = dropout
class ConvResidualBlock(nn.Module):
def __init__(self, channels: int) -> None:
super().__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.norm1 = nn.GroupNorm(4, channels)
self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.norm2 = nn.GroupNorm(4, channels)
def forward(self, x: torch.Tensor) -> torch.Tensor:
h = F.gelu(self.norm1(self.conv1(x)))
h = self.norm2(self.conv2(h))
return F.gelu(x + h)
class ScribeFontForGeneration(PreTrainedModel):
config_class = ScribeFontConfig
def __init__(self, config: ScribeFontConfig) -> None:
super().__init__(config)
self.config = config
# 1. 2D Reference Sheet Vision Encoder (4 reference glyphs x 16x16 + 1 target cell x 16x16)
self.ref_conv_stem = nn.Sequential(
nn.Conv2d(4, 64, kernel_size=3, padding=1),
nn.GroupNorm(8, 64),
nn.GELU(),
ConvResidualBlock(64),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1), # -> [B, 128, 8, 8]
nn.GroupNorm(8, 128),
nn.GELU(),
ConvResidualBlock(128),
)
self.target_conv_stem = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, padding=1),
nn.GroupNorm(4, 32),
nn.GELU(),
nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1), # -> [B, 64, 8, 8]
nn.GroupNorm(8, 64),
nn.GELU(),
)
# Fuse visual patch features + 8 biometric summary features into 128-d Calligrapher Style Vector
self.style_projector = nn.Sequential(
nn.Linear(128 * 4 + 64 + 8, config.hidden_dim),
nn.LayerNorm(config.hidden_dim),
nn.GELU(),
nn.Linear(config.hidden_dim, config.style_dim),
nn.LayerNorm(config.style_dim),
nn.GELU(),
)
# Predict 8 normalized biometric style dimensions directly from visual + summary features
self.style_reg_head = nn.Sequential(
nn.Linear(config.style_dim, config.style_dim),
nn.GELU(),
nn.Linear(config.style_dim, 8),
nn.Sigmoid(),
)
# Writer archetype identification head (12 writer styles)
self.writer_head = nn.Linear(config.style_dim, config.num_writers)
# Missing / blank / smudged glyph cell detector head (calibrated p_missing)
self.missing_head = nn.Sequential(
nn.Linear(64 + config.style_dim, 128),
nn.LayerNorm(128),
nn.GELU(),
nn.Linear(128, 1),
)
# 2. Unicode Character & Point-Index Transformer Decoder
self.char_emb = nn.Embedding(config.vocab_size, config.hidden_dim)
self.point_emb = nn.Embedding(config.num_points, config.hidden_dim)
self.base_coord_proj = nn.Linear(2, config.hidden_dim)
self.style_to_dec = nn.Linear(config.style_dim + 8, config.hidden_dim)
encoder_layer = nn.TransformerEncoderLayer(
d_model=config.hidden_dim,
nhead=config.num_heads,
dim_feedforward=config.hidden_dim * 2,
dropout=config.dropout,
activation="gelu",
batch_first=True,
)
self.glyph_transformer = nn.TransformerEncoder(encoder_layer, num_layers=config.num_layers)
# Predicts residual coordinate adjustments [B, 16, 2] around canonical skeleton
self.contour_head = nn.Sequential(
nn.Linear(config.hidden_dim, config.hidden_dim // 2),
nn.GELU(),
nn.Linear(config.hidden_dim // 2, 2),
)
# Predicts 5 normalized glyph metrics: [advance_width, lsb, slant_deg, stroke_width, kern_av]
self.metrics_head = nn.Sequential(
nn.Linear(config.hidden_dim + config.style_dim + 8, 128),
nn.GELU(),
nn.Linear(128, 5),
nn.Sigmoid(),
)
self.post_init()
def forward(
self,
char_ids: torch.Tensor,
base_skeleton: torch.Tensor,
ref_patches: torch.Tensor,
target_patch: torch.Tensor,
style_feats: torch.Tensor,
is_missing_labels: torch.Tensor | None = None,
contour_labels: torch.Tensor | None = None,
metrics_labels: torch.Tensor | None = None,
style_true_norm: torch.Tensor | None = None,
writer_labels: torch.Tensor | None = None,
) -> dict[str, torch.Tensor]:
B = char_ids.shape[0]
ref_2d = ref_patches.view(B, 4, 16, 16)
tgt_2d = target_patch.view(B, 1, 16, 16)
ref_conv = self.ref_conv_stem(ref_2d) # [B, 128, 8, 8]
ref_pool = F.adaptive_avg_pool2d(ref_conv, (2, 2)).reshape(B, 128 * 4)
tgt_conv = self.target_conv_stem(tgt_2d) # [B, 64, 8, 8]
tgt_pool = F.adaptive_avg_pool2d(tgt_conv, (1, 1)).reshape(B, 64)
style_in = torch.cat([ref_pool, tgt_pool, style_feats], dim=-1)
style_emb = self.style_projector(style_in) # [B, style_dim]
pred_style_norm = self.style_reg_head(style_emb) # [B, 8]
writer_logits = self.writer_head(style_emb) # [B, num_writers]
missing_in = torch.cat([tgt_pool, style_emb], dim=-1)
missing_logits = self.missing_head(missing_in).squeeze(-1) # [B]
# Build 16-point token sequence for the target glyph
pt_ids = torch.arange(self.config.num_points, device=char_ids.device).unsqueeze(0).expand(B, -1)
c_tok = self.char_emb(char_ids).unsqueeze(1) # [B, 1, H]
p_tok = self.point_emb(pt_ids) # [B, 16, H]
b_tok = self.base_coord_proj(base_skeleton) # [B, 16, H]
s_tok = self.style_to_dec(torch.cat([style_emb, pred_style_norm], dim=-1)).unsqueeze(1) # [B, 1, H]
seq = p_tok + b_tok + c_tok + s_tok # [B, 16, H]
dec_out = self.glyph_transformer(seq) # [B, 16, H]
delta_coords = torch.tanh(self.contour_head(dec_out)) * 0.35
contour_preds = torch.clamp(base_skeleton + delta_coords, 0.01, 0.99)
glyph_pooled = dec_out.mean(dim=1) # [B, H]
metrics_in = torch.cat([glyph_pooled, style_emb, pred_style_norm], dim=-1)
metrics_preds = self.metrics_head(metrics_in) # [B, 5]
out: dict[str, torch.Tensor] = {
"missing_logits": missing_logits,
"contour_preds": contour_preds,
"metrics_preds": metrics_preds,
"pred_style_norm": pred_style_norm,
"writer_logits": writer_logits,
"style_emb": style_emb,
}
if is_missing_labels is not None and contour_labels is not None and metrics_labels is not None:
loss_missing = F.binary_cross_entropy_with_logits(missing_logits, is_missing_labels.float())
# Mask contour & metrics loss to valid (non-missing) glyphs or supervise full style synthesis
loss_contour = F.smooth_l1_loss(contour_preds, contour_labels, beta=0.01)
# Edge tangent preservation loss (keeps Bézier segments smooth and faithful)
pred_edges = torch.roll(contour_preds, -1, dims=1) - contour_preds
true_edges = torch.roll(contour_labels, -1, dims=1) - contour_labels
loss_edges = F.smooth_l1_loss(pred_edges, true_edges, beta=0.01)
loss_metrics = F.smooth_l1_loss(metrics_preds, metrics_labels, beta=0.01)
total_loss = 1.5 * loss_missing + 12.0 * loss_contour + 6.0 * loss_edges + 4.0 * loss_metrics
if style_true_norm is not None:
loss_style = F.smooth_l1_loss(pred_style_norm, style_true_norm, beta=0.01)
total_loss = total_loss + 3.0 * loss_style
if writer_labels is not None:
loss_writer = F.cross_entropy(writer_logits, writer_labels)
total_loss = total_loss + 0.5 * loss_writer
out["loss"] = total_loss
return out
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