Build autoregressive image generators using post-training quantization that bridges continuous VAE tokens with discrete vocabulary modeling. Achieves state-of-the-art visual quality via dimension-wise token prediction without training instability.
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Build autoregressive image generators using post-training quantization that bridges continuous VAE tokens with discrete vocabulary modeling. Achieves state-of-the-art visual quality via dimension-wise token prediction without training instability.
Core Concept
Visual generation faces a tradeoff: discrete tokens enable simple modeling via cross-entropy loss but lose visual information; continuous tokens preserve quality but require complex distribution modeling. TokenBridge solves this by applying post-training quantization to pretrained VAE features, obtaining discrete tokens while maintaining visual fidelity. The method then uses dimension-wise factorization to predict high-dimensional token spaces efficiently in an autoregressive manner.
Architecture Overview
Post-Training Quantization: Non-uniform quantization of continuous VAE features based on standard normal distribution properties
Dimension-Wise Factorization: Decomposes large token vocabulary (B^C combinations) into sequential per-dimension predictions
Spatial-Dimension Autoregression: Combines spatial generation order with channel-wise token prediction
FFT-Guided Generation Order: Prioritizes low-frequency (structural) information early in generation
Lightweight Autoregressive Head: Single MLP conditioned on previously generated channels
Implementation Steps
Step 1: Post-Training Quantization of VAE Features
Apply dimension-wise non-uniform quantization to pretrained continuous VAE latents without retraining the VAE.
import torch
import numpy as np
from scipy import stats
defpost_training_quantization(
continuous_features,
num_levels=256,
assume_gaussian=True):
"""
Quantize continuous VAE features to discrete tokens using
non-uniform quantization. Assumes Gaussian distribution in latent space.
continuous_features: shape (batch, channels, height, width)
"""
batch, channels, h, w = continuous_features.shape
quantized = torch.zeros_like(continuous_features, dtype=torch.long)
for c inrange(channels):
channel_data = continuous_features[:, c, :, :].flatten()
if assume_gaussian:
# Compute quantile levels based on standard normal
quantiles = np.linspace(, , num_levels + )
q_vals = stats.norm.ppf(quantiles[:-])
mean = channel_data.mean()
std = channel_data.std() +
normalized = (channel_data - mean) / std
tokens = torch.searchsorted(
torch.tensor(q_vals, dtype=torch.float32),
normalized
)
:
min_val = channel_data.()
max_val = channel_data.()
normalized = (channel_data - min_val) / (max_val - min_val + )
tokens = (normalized * (num_levels - )).long()
quantized[:, c, :, :] = tokens.reshape(batch, h, w)
quantized
0
1
1
1
1
# Exclude 0 and 1
# Normalize channel data to standard normal
1e-8
# Assign tokens based on quantile membership
else
# Uniform quantization fallback
min
max
1e-8
1
return
Step 2: Build Dimension-Wise Autoregressive Head
Create an efficient MLP-based head that predicts one channel at a time, conditioning on previously generated channels.
import torch.nn as nn
classDimensionWiseAutoregressiveHead(nn.Module):
"""
Predicts discrete tokens one channel (dimension) at a time.
Conditions each prediction on all previously generated channels.
"""def__init__(
self,
hidden_dim=512,
num_channels=16,
num_token_levels=256,
spatial_size=32):
super().__init__()
self.hidden_dim = hidden_dim
self.num_channels = num_channels
self.num_token_levels = num_token_levels
self.spatial_size = spatial_size
# Embedding for previously generated channel tokensself.token_embedding = nn.Embedding(num_token_levels, hidden_dim // 4)
# Position embedding for spatial locationsself.spatial_embedding = nn.Parameter(
torch.randn(spatial_size * spatial_size, hidden_dim // 4)
)
# MLP layers for predicting next channelself.mlp = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, num_token_levels)
)
defforward(self, previous_channels, spatial_context):
"""
previous_channels: (batch, num_channels_generated, spatial_size, spatial_size)
spatial_context: (batch, hidden_dim // 2, spatial_size, spatial_size)
Returns logits: (batch, spatial_size, spatial_size, num_token_levels)
"""
batch_size = previous_channels.shape[0]
spatial_h, spatial_w = spatial_context.shape[-2:]
total_spatial = spatial_h * spatial_w
# Embed previous channel tokens
prev_embeds = self.token_embedding(previous_channels)
# Global average pool previous channels
channel_context = prev_embeds.mean(dim=(2, 3)) # (batch, hidden_dim // 4)# Expand spatial context and combine
spatial_flat = spatial_context.permute(0, 2, 3, 1).reshape(
batch_size, total_spatial, -1
)
# Broadcast channel context and concatenate
channel_context_expanded = channel_context.unsqueeze(1).expand(
batch_size, total_spatial, -1
)
combined = torch.cat([spatial_flat, channel_context_expanded], dim=-1)
# Predict tokens for this dimension
logits = self.mlp(combined) # (batch, spatial, num_levels)
logits = logits.reshape(batch_size, spatial_h, spatial_w, -1)
return logits
Step 3: Determine Generation Order via FFT Analysis
Compute generation order by analyzing frequency content; low-frequency (structural) dimensions should be generated first.
defcompute_generation_order_fft(latent_samples, num_channels):
"""
Analyze frequency distribution across channels using FFT.
Return channel indices ordered from low-frequency to high-frequency.
latent_samples: (num_samples, channels, height, width)
"""
frequency_energy = []
for c inrange(num_channels):
channel_data = latent_samples[:, c, :, :].cpu().numpy()
# Compute 2D FFT per sample and average
ffts = np.abs(np.fft.fft2(channel_data, axes=(1, 2)))
# Shift zero-frequency to center
ffts_shifted = np.fft.fftshift(ffts, axes=(1, 2))
# Compute energy: sum of low-frequency components (center region)
h, w = ffts_shifted.shape[1:]
center_h, center_w = h // 3, w // 3
low_freq_region = ffts_shifted[
:,
h // 2 - center_h:h // 2 + center_h,
w // 2 - center_w:w // 2 + center_w
]
low_freq_energy = low_freq_region.sum()
frequency_energy.append((c, low_freq_energy))
# Sort by frequency energy (descending) - generate low-freq first
frequency_energy.sort(key=lambda x: x[1], reverse=True)
generation_order = [idx for idx, _ in frequency_energy]
return generation_order
Step 4: Autoregressive Generation Loop
Generate discrete tokens spatially and dimension-wise, using the order determined by FFT analysis.
defautoregressive_generate(
model,
batch_size,
spatial_size=32,
num_channels=16,
num_levels=256,
generation_order=None):
"""
Generate discrete tokens autoregressively: iterate through
spatial locations and dimensions in specified order.
Returns quantized token grid ready for VAE decoding.
"""if generation_order isNone:
generation_order = list(range(num_channels))
# Initialize token grid
generated_tokens = torch.zeros(
batch_size, num_channels, spatial_size, spatial_size,
dtype=torch.long
)
# Get spatial context (e.g., from image encoder or as learned embeddings)
spatial_context = torch.randn(
batch_size, 256, spatial_size, spatial_size
)
# Iterate through channels in generation orderfor channel_idx in generation_order:
# Prepare previously generated channels
prev_channels = generated_tokens[:, :channel_idx, :, :]
# Get logits for this channel
logits = model.ar_head(prev_channels, spatial_context)
# logits shape: (batch, spatial_size, spatial_size, num_levels)# Sample tokens (or use argmax for deterministic generation)
sampled = torch.argmax(logits, dim=-1) # Greedy
generated_tokens[:, channel_idx, :, :] = sampled
return generated_tokens
Step 5: Decode to Continuous Image via VAE
De-quantize discrete tokens back to continuous features and decode through VAE decoder.
defdequantize_tokens(quantized_tokens, vae_decoder, num_levels=256):
"""
Convert discrete tokens back to continuous values and decode via VAE.
Uses inverse of the non-uniform quantization scheme.
"""
batch, channels, h, w = quantized_tokens.shape
continuous_features = torch.zeros_like(quantized_tokens, dtype=torch.float32)
for c inrange(channels):
# Map tokens back to continuous range
token_vals = quantized_tokens[:, c, :, :].float()
# Inverse quantization: assume tokens map to quantile positions
quantiles = np.linspace(0, 1, num_levels)
q_vals = stats.norm.ppf(quantiles) # Map to standard normal# Interpolate: token i corresponds to quantile at q_vals[i]
continuous_vals = torch.zeros_like(token_vals)
for i inrange(batch):
for j inrange(h):
for k inrange(w):
t = token_vals[i, j, k].long()
t_clamped = torch.clamp(t, 0, len(q_vals) - 1)
continuous_vals[i, j, k] = q_vals[int(t_clamped)]
continuous_features[:, c, :, :] = continuous_vals
# Decode through VAE decoder
images = vae_decoder(continuous_features)
return images
Practical Guidance
When to Use:
Building autoregressive image generators that need both quality and training stability
Projects requiring real-time or near-real-time generation with discrete token modeling
Scenarios with limited computational resources where continuous token models are too slow
Systems needing confidence-guided generation (sampling from discrete probabilities)
When NOT to Use:
Conditional generation with extreme conditioning signals (discrete token vocab may be insufficient)
Very high-resolution generation (>1024×1024) where spatial autoregression becomes prohibitively slow
