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7bef20f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | """Resolution-aware encoder for VibeToken.
Vision Transformer-based encoder with flexible patch sizes for variable-resolution inputs.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple
from einops import rearrange
from torch import Tensor, vmap
import numpy as np
from .blocks import ResidualAttentionBlock, _expand_token
from .embeddings import FuzzyEmbedding, to_2tuple
class ResolutionEncoder(nn.Module):
"""Vision Transformer encoder with flexible resolution support.
Encodes images into latent tokens using a ViT architecture with
support for variable input resolutions and patch sizes.
"""
# Model size configurations
MODEL_CONFIGS = {
"small": {"width": 512, "num_layers": 8, "num_heads": 8},
"base": {"width": 768, "num_layers": 12, "num_heads": 12},
"large": {"width": 1024, "num_layers": 24, "num_heads": 16},
}
def __init__(self, config):
"""Initialize ResolutionEncoder.
Args:
config: OmegaConf config with model parameters.
"""
super().__init__()
self.config = config
# Extract config values
vq_config = config.model.vq_model if hasattr(config.model, 'vq_model') else config.model
self.patch_size = getattr(vq_config, 'vit_enc_patch_size', 32)
self.model_size = getattr(vq_config, 'vit_enc_model_size', 'large')
self.num_latent_tokens = getattr(vq_config, 'num_latent_tokens', 256)
self.token_size = getattr(vq_config, 'token_size', 256)
self.is_legacy = getattr(vq_config, 'is_legacy', False)
# Handle VAE mode (doubles token size for mean+std)
quantize_mode = getattr(vq_config, 'quantize_mode', 'vq')
if quantize_mode == "vae":
self.token_size = self.token_size * 2
# Get model dimensions from config
model_cfg = self.MODEL_CONFIGS[self.model_size]
self.width = model_cfg["width"]
self.num_layers = model_cfg["num_layers"]
self.num_heads = model_cfg["num_heads"]
# Patch embedding
self.patch_embed = nn.Conv2d(
in_channels=3, out_channels=self.width,
kernel_size=self.patch_size, stride=self.patch_size, bias=True
)
# Embeddings
scale = self.width ** -0.5
self.class_embedding = nn.Parameter(scale * torch.randn(1, self.width))
self.positional_embedding = FuzzyEmbedding(1024, scale, self.width)
self.latent_token_positional_embedding = nn.Parameter(
scale * torch.randn(self.num_latent_tokens, self.width)
)
self.ln_pre = nn.LayerNorm(self.width)
# Transformer layers
self.transformer = nn.ModuleList([
ResidualAttentionBlock(self.width, self.num_heads, mlp_ratio=4.0)
for _ in range(self.num_layers)
])
# Output projection
self.ln_post = nn.LayerNorm(self.width)
self.conv_out = nn.Conv2d(self.width, self.token_size, kernel_size=1, bias=True)
# Cache for pseudo-inverse matrices
self.pinvs = {}
def _resize(self, x: Tensor, shape: Tuple[int, int]) -> Tensor:
"""Bilinear resize of 2D tensor."""
x_resized = F.interpolate(
x[None, None, ...], shape, mode="bilinear", antialias=False
)
return x_resized[0, 0, ...]
def _calculate_pinv(
self,
old_shape: Tuple[int, int],
new_shape: Tuple[int, int],
device: torch.device,
) -> Tensor:
"""Calculate pseudo-inverse of resize matrix for FlexiViT."""
mat = []
for i in range(np.prod(old_shape)):
basis_vec = torch.zeros(old_shape, device=device)
basis_vec[np.unravel_index(i, old_shape)] = 1.0
mat.append(self._resize(basis_vec, new_shape).reshape(-1))
resize_matrix = torch.stack(mat)
return torch.linalg.pinv(resize_matrix)
def resize_patch_embed(self, patch_embed: Tensor, new_patch_size: Tuple[int, int]) -> Tensor:
"""Resize patch embedding kernel to new patch size (FlexiViT).
Args:
patch_embed: Original weight tensor (out_ch, in_ch, H, W).
new_patch_size: Target (H, W) patch size.
Returns:
Resized weight tensor.
"""
base_size = to_2tuple(self.patch_size)
if base_size == new_patch_size:
return patch_embed
if new_patch_size not in self.pinvs:
self.pinvs[new_patch_size] = self._calculate_pinv(
base_size, new_patch_size, device=patch_embed.device
)
pinv = self.pinvs[new_patch_size]
def resample_patch_embed(pe: Tensor) -> Tensor:
h, w = new_patch_size
original_dtype = pe.dtype
resampled = pinv @ pe.float().reshape(-1)
return rearrange(resampled.to(original_dtype), "(h w) -> h w", h=h, w=w)
v_resample = vmap(vmap(resample_patch_embed, 0, 0), 1, 1)
return v_resample(patch_embed)
def apply_flexivit_patch_embed(self, x: Tensor, target_patch_size: Tuple[int, int]) -> Tensor:
"""Apply patch embedding with flexible patch size.
Args:
x: Input image tensor (B, 3, H, W).
target_patch_size: Target patch size (H, W).
Returns:
Patch embeddings (B, C, grid_H, grid_W).
"""
patch_size = to_2tuple(target_patch_size)
if patch_size == to_2tuple(self.patch_size):
weight = self.patch_embed.weight
else:
weight = self.resize_patch_embed(self.patch_embed.weight, patch_size)
return F.conv2d(x, weight, bias=self.patch_embed.bias, stride=patch_size)
def forward(
self,
pixel_values: torch.Tensor,
latent_tokens: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encode_patch_size: Optional[Tuple[int, int]] = None,
) -> torch.Tensor:
"""Encode images to latent tokens.
Args:
pixel_values: Input images (B, 3, H, W), values in [0, 1].
latent_tokens: Learnable latent tokens (num_latent, width).
attention_mask: Optional attention mask.
encode_patch_size: Optional custom patch size for encoding.
Returns:
Encoded latent features (B, token_size, 1, num_latent).
"""
batch_size, _, H, W = pixel_values.shape
# Determine patch size
if encode_patch_size is None:
target_patch_size = (self.patch_size, self.patch_size)
elif isinstance(encode_patch_size, int):
target_patch_size = (encode_patch_size, encode_patch_size)
else:
target_patch_size = encode_patch_size
# Apply flexible patch embedding
x = self.apply_flexivit_patch_embed(pixel_values, target_patch_size)
# Flatten spatial dimensions
x = x.reshape(x.shape[0], x.shape[1], -1)
x = x.permute(0, 2, 1) # (B, num_patches, width)
# Add class embedding
x = torch.cat([_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x], dim=1)
# Compute grid dimensions
grid_height = H // target_patch_size[0]
grid_width = W // target_patch_size[1]
# Add positional embeddings to latent tokens
num_latent = latent_tokens.shape[0]
latent_tokens = _expand_token(latent_tokens, x.shape[0]).to(x.dtype)
latent_tokens = latent_tokens + self.latent_token_positional_embedding.to(x.dtype)[:num_latent]
# Add positional embeddings to image patches
x = x + self.positional_embedding(grid_height, grid_width, train=False, dtype=x.dtype)
# Concatenate image patches and latent tokens
x = torch.cat([x, latent_tokens], dim=1)
# Pre-norm and reshape for transformer
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # (seq_len, B, width)
# Apply transformer layers
for layer in self.transformer:
x = layer(x, attention_mask=None)
x = x.permute(1, 0, 2) # (B, seq_len, width)
# Extract latent tokens
latent_tokens = x[:, 1 + grid_height * grid_width:]
latent_tokens = self.ln_post(latent_tokens)
# Reshape and project to token size
if self.is_legacy:
latent_tokens = latent_tokens.reshape(batch_size, self.width, num_latent, 1)
else:
latent_tokens = latent_tokens.reshape(batch_size, num_latent, self.width, 1).permute(0, 2, 1, 3)
latent_tokens = self.conv_out(latent_tokens)
latent_tokens = latent_tokens.reshape(batch_size, self.token_size, 1, num_latent)
return latent_tokens
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