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c5cfae9 acccad2 c5cfae9 | 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 | # Copyright 2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
def extract_patch_tokens_min_windows(
images: torch.Tensor,
model: nn.Module,
processor,
window_size: int = 224,
device: str | torch.device = "cuda",
) -> torch.Tensor:
r"""
Tile each image with a minimal window set and return averaged DINO patch tokens.
Args:
images (`torch.Tensor`): Batch of RGB images `(B, C, H, W)`.
model: DINO vision transformer.
processor: Hugging Face image processor for DINO.
window_size (`int`): Sliding-window size in pixels.
device: Device for intermediate tensors.
Returns:
`torch.Tensor` of shape `(B, H//patch, W//patch, hidden_size)`.
"""
batch_size, _, height, width = images.shape
hidden_size = model.config.hidden_size
patch_size = model.config.patch_size
token_avgs = []
for batch_idx in range(batch_size):
image = images[batch_idx]
if image.max() <= 1.0:
image_np = (image.permute(1, 2, 0).cpu().numpy() * 255).clip(0, 255).astype("uint8")
else:
image_np = image.permute(1, 2, 0).cpu().numpy().clip(0, 255).astype("uint8")
token_sum = torch.zeros((height // patch_size, width // patch_size, hidden_size), device=device)
token_count = torch.zeros((height // patch_size, width // patch_size, 1), device=device)
num_y = (height + window_size - 1) // window_size
num_x = (width + window_size - 1) // window_size
y_positions = [index * window_size for index in range(num_y - 1)] + [height - window_size]
x_positions = [index * window_size for index in range(num_x - 1)] + [width - window_size]
for y in y_positions:
for x in x_positions:
patch = image_np[y : y + window_size, x : x + window_size, :]
inputs = processor(images=patch, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model(**inputs)
patch_tokens = outputs.last_hidden_state[:, 1:, :]
patch_tokens = patch_tokens.reshape(
1, window_size // patch_size, window_size // patch_size, hidden_size
).squeeze(0)
y0, x0 = y // patch_size, x // patch_size
y1, x1 = y0 + window_size // patch_size, x0 + window_size // patch_size
token_sum[y0:y1, x0:x1, :] += patch_tokens
token_count[y0:y1, x0:x1, 0] += 1
token_avgs.append(token_sum / token_count)
return torch.stack(token_avgs, dim=0)
class LayerNorm2d(nn.Module):
def __init__(self, channels: int) -> None:
super().__init__()
self.norm = nn.LayerNorm([channels])
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.permute(0, 2, 3, 1)
x = self.norm(x)
return x.permute(0, 3, 1, 2)
class IMAA(nn.Module):
r"""
Intrinsic Map-Aware Attention (IMAA) gating module.
Produces per-map attention biases from DINO patch tokens and learnable map embeddings.
"""
def __init__(
self,
dino_model: Optional[nn.Module] = None,
processor=None,
num_maps: int = 5,
map_embedding_dim: int = 256,
common_dim: int = 128,
conv_channels: Optional[list[int]] = None,
dino_patch_dim: int = 768,
) -> None:
super().__init__()
conv_channels = conv_channels or [128, 64]
self.dino = dino_model
self.processor = processor
if self.dino is not None:
self.dino.eval()
for param in self.dino.parameters():
param.requires_grad = False
self.num_maps = num_maps
self.map_embedding_dim = map_embedding_dim
self.common_dim = common_dim
self.dino_patch_dim = dino_patch_dim
self.map_embedding = nn.Parameter(torch.randn(num_maps, map_embedding_dim))
self.dino_proj = nn.Conv2d(dino_patch_dim, common_dim, kernel_size=1)
self.map_proj = nn.Linear(map_embedding_dim, common_dim)
self.fusion_layer = nn.Sequential(
nn.Conv2d(common_dim * 2, common_dim, 1),
LayerNorm2d(common_dim),
nn.ReLU(),
nn.Conv2d(common_dim, common_dim, 3, padding=1),
)
conv_layers: list[nn.Module] = []
in_channels = common_dim
for out_channels in conv_channels:
conv_layers.extend([nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), nn.ReLU()])
in_channels = out_channels
conv_layers.append(nn.Conv2d(in_channels, 1, kernel_size=1))
self.conv_head = nn.Sequential(*conv_layers)
def forward(
self,
image: Optional[torch.Tensor] = None,
patch_tokens: Optional[torch.Tensor] = None,
output_size: Optional[Tuple[int, int]] = None,
map_ids: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if patch_tokens is None:
if self.dino is None or image is None:
raise ValueError("Either `patch_tokens` or (`image` and a frozen DINO model) must be provided.")
patch_tokens = extract_patch_tokens_min_windows(
image, self.dino, self.processor, window_size=224, device=image.device
)
dino_feat_map = patch_tokens.permute(0, 3, 1, 2)
dino_proj = self.dino_proj(dino_feat_map)
map_emb = self.map_embedding[map_ids]
map_proj = self.map_proj(map_emb).unsqueeze(-1).unsqueeze(-1).expand(-1, -1, dino_proj.size(2), dino_proj.size(3))
fused_map = self.fusion_layer(torch.cat([dino_proj, map_proj], dim=1))
raw_gating_map = self.conv_head(fused_map)
aligned_map = (
F.interpolate(raw_gating_map, size=output_size, mode="bilinear", align_corners=False)
if output_size is not None
else raw_gating_map
)
return torch.sigmoid(aligned_map)
def build_attn_mask(
w_gating: torch.Tensor,
text_len: int,
img_len: int,
lam: float,
) -> torch.Tensor:
r"""
Build an additive attention mask from IMAA gating weights.
Args:
w_gating (`torch.Tensor`): Gating map `[B, 1, H, W]` or flattened `[B, img_len]`.
text_len (`int`): Number of text tokens prepended to image tokens.
img_len (`int`): Expected number of image tokens.
lam (`float`): Mask scaling factor.
Returns:
Attention bias tensor shaped for SD3 joint attention.
"""
batch_size = w_gating.shape[0]
total_len = text_len + img_len
if w_gating.dim() == 4:
w_gating = w_gating.view(batch_size, -1)
gating = lam * w_gating
actual_img_len = gating.shape[1]
if actual_img_len != img_len:
if actual_img_len > img_len:
gating = gating[:, :img_len]
else:
padding = torch.zeros(batch_size, img_len - actual_img_len, device=gating.device, dtype=gating.dtype)
gating = torch.cat([gating, padding], dim=1)
col_bias = torch.zeros(batch_size, total_len, device=w_gating.device, dtype=w_gating.dtype)
col_bias[:, text_len:] = gating
return col_bias.view(batch_size, 1, 1, total_len)
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