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# DiT: https://github.com/facebookresearch/DiT/blob/main/models.py
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------
import torch
import torch.nn as nn
import math
from timm.models.vision_transformer import Attention, Mlp
def modulate(x, shift, scale):
return x * (1 + scale) + shift
#################################################################################
# Embedding Layers for Timesteps and conditions #
#################################################################################
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
device=t.device
)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(next(self.mlp.parameters()).dtype)
t_emb = self.mlp(t_freq)
return t_emb
class LabelEmbedder(nn.Module):
"""
Embeds conditions into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, in_size, hidden_size, dropout_prob=0.1, conditions_shape=(1, 1, 4096)):
super().__init__()
self.linear = nn.Linear(in_size, hidden_size)
self.dropout_prob = dropout_prob
if dropout_prob > 0:
self.uncondition = nn.Parameter(torch.empty(conditions_shape[1:]))
def token_drop(self, conditions, force_drop_ids=None):
"""
Drops conditions to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(conditions.shape[0], device=conditions.device) < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
conditions = torch.where(
drop_ids.unsqueeze(1).unsqueeze(1).expand(conditions.shape[0], *self.uncondition.shape),
self.uncondition,
conditions,
)
return conditions
def forward(self, conditions, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
conditions = self.token_drop(conditions, force_drop_ids)
embeddings = self.linear(conditions)
return embeddings
#################################################################################
# Embedding Layers for Actions and #
#################################################################################
class ActionEmbedder(nn.Module):
def __init__(self, action_size, hidden_size):
super().__init__()
self.linear = nn.Linear(action_size, hidden_size)
def forward(self, x):
x = self.linear(x)
return x
# Action_History is not used now
class HistoryEmbedder(nn.Module):
def __init__(self, action_size, hidden_size):
super().__init__()
self.linear = nn.Linear(action_size, hidden_size)
def forward(self, x):
x = self.linear(x)
return x
#################################################################################
# Core DiT Model #
#################################################################################
class DiTBlock(nn.Module):
"""
A DiT block with self-attention conditioning.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
def forward(self, x):
x = x + self.attn(self.norm1(x))
x = x + self.mlp(self.norm2(x))
return x
class FinalLayer(nn.Module):
"""
The final layer of DiT.
"""
def __init__(self, hidden_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, out_channels, bias=True)
def forward(self, x):
x = self.norm_final(x)
x = self.linear(x)
return x
class DiT(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
in_channels=7,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
token_size=4096,
future_action_window_size=1,
past_action_window_size=0,
learn_sigma=False,
n_conditon_token=64,
):
super().__init__()
assert past_action_window_size == 0, "Error: action_history is not used now"
self.num_cond_tokens = n_conditon_token
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.class_dropout_prob = class_dropout_prob
self.num_heads = num_heads
self.past_action_window_size = past_action_window_size
self.future_action_window_size = future_action_window_size
# Action history is not used now.
self.history_embedder = HistoryEmbedder(action_size=in_channels, hidden_size=token_size)
self.x_embedder = ActionEmbedder(action_size=in_channels, hidden_size=token_size)
self.t_embedder = TimestepEmbedder(token_size)
conditions_shape = (1, n_conditon_token, token_size)
self.z_embedder = LabelEmbedder(
in_size=token_size,
hidden_size=token_size,
dropout_prob=class_dropout_prob,
conditions_shape=conditions_shape,
)
scale = token_size**-0.5
# Learnable positional embeddings
# 1+64, one for the conditional token, and one for the current action prediction
self.positional_embedding = nn.Parameter(
scale
* torch.randn(self.num_cond_tokens + future_action_window_size + past_action_window_size + 1, token_size)
)
self.blocks = nn.ModuleList([DiTBlock(token_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth)])
self.final_layer = FinalLayer(token_size, self.out_channels)
self.initialize_weights()
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# # Initialize token_embed like nn.Linear
nn.init.normal_(self.x_embedder.linear.weight, std=0.02)
nn.init.constant_(self.x_embedder.linear.bias, 0)
nn.init.normal_(self.history_embedder.linear.weight, std=0.02)
nn.init.constant_(self.history_embedder.linear.bias, 0)
# Initialize label embedding table:
if self.class_dropout_prob > 0:
nn.init.normal_(self.z_embedder.uncondition, std=0.02)
nn.init.normal_(self.z_embedder.linear.weight, std=0.02)
nn.init.constant_(self.z_embedder.linear.bias, 0)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def forward(self, x, t, z):
"""
Forward pass of DiT.
history: (B, H, D) tensor of action history # not used now
x: (B, T, D) tensor of predicting action inputs
t: (B,) tensor of diffusion timesteps
z: [B, num_cond_tokens, D] -- condition token
"""
x = self.x_embedder(x) # (N, T, D)
t = self.t_embedder(t) # (N, D)
z = self.z_embedder(z, self.training) # [N, num_cond_tokens, D]
c = t.unsqueeze(1) + z # (N, 64, D)
x = torch.cat((c, x), dim=1) # (N, T+64, D)
x = x + self.positional_embedding # (N, T+64, D)
for block in self.blocks:
x = block(x) # (N, T+64, D)
x = self.final_layer(x) # (N, T+64, out_channels)
return x[:, self.num_cond_tokens :, :] # (N, T, C)
def forward_with_cfg(self, x, t, z, cfg_scale):
"""
Forward pass of Diffusion, but also batches the unconditional forward pass for classifier-free guidance.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
half = x[: len(x) // 2]
combined = torch.cat([half, half], dim=0).to(next(self.x_embedder.parameters()).dtype)
model_out = self.forward(combined, t, z)
# eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
eps, rest = model_out[:, :, : self.in_channels], model_out[:, :, self.in_channels :]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = torch.cat([half_eps, half_eps], dim=0)
# return torch.cat([eps, rest], dim=1)
return torch.cat([eps, rest], dim=2)
# Cross-Attention DiT Implementation
class CrossAttention(nn.Module):
"""
Cross-attention module that supports both self-attention and cross-attention.
"""
def __init__(self, hidden_size, num_heads, qkv_bias=True, attn_drop=0.0, proj_drop=0.0):
super().__init__()
assert hidden_size % num_heads == 0
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.scale = self.head_dim**-0.5
self.q = nn.Linear(hidden_size, hidden_size, bias=qkv_bias)
self.kv = nn.Linear(hidden_size, hidden_size * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(hidden_size, hidden_size)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, context=None):
"""
Args:
x: query tensor [B, N, C]
context: key/value tensor [B, M, C]. If None, performs self-attention
"""
B, N, C = x.shape
# Query from x
q = self.q(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
# Key and Value from context (or x if self-attention)
if context is None:
context = x
M = context.shape[1]
kv = self.kv(context).reshape(B, M, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv.unbind(0)
# Attention computation
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class DiTBlockCrossAttn(nn.Module):
"""
A DiT block with only cross-attention + MLP.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
super().__init__()
# Cross-attention components
self.norm_attn = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.cross_attn = CrossAttention(hidden_size, num_heads=num_heads, **block_kwargs)
# MLP components
self.norm_mlp = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
def forward(self, x, encoder_features=None):
"""
Args:
x: input tensor [B, N, C] (action-related tokens)
encoder_features: encoder features [B, M, C] (e.g., vision-language features)
"""
# Cross-attention (if encoder features provided) or self-attention (if None)
if encoder_features is not None:
# Cross-attention: Query from x, Key/Value from encoder_features
x = x + self.cross_attn(self.norm_attn(x), context=encoder_features)
else:
# Self-attention: Query, Key, Value all from x (for backward compatibility)
x = x + self.cross_attn(self.norm_attn(x), context=None)
# MLP
x = x + self.mlp(self.norm_mlp(x))
return x
class DiTBlockSelfAttn(nn.Module):
"""
A DiT block with only self-attention + MLP.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
super().__init__()
# Self-attention components (same as original DiTBlock)
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
# MLP components (same as original DiTBlock)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
def forward(self, x, encoder_features=None):
"""
Args:
x: input tensor [B, N, C] (action-related tokens)
encoder_features: encoder features [B, M, C] (not used in self-attention, for interface compatibility)
"""
# Self-attention (identical to original DiTBlock)
x = x + self.attn(self.norm1(x))
# MLP (identical to original DiTBlock)
x = x + self.mlp(self.norm2(x))
return x
class DiTCrossAttn(nn.Module):
"""
Diffusion model with a Transformer backbone supporting cross-attention.
"""
def __init__(
self,
in_channels=7,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
token_size=4096,
future_action_window_size=1,
past_action_window_size=0,
learn_sigma=False,
n_conditon_token=64,
):
super().__init__()
assert past_action_window_size == 0, "Error: action_history is not used now"
self.num_cond_tokens = n_conditon_token
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.class_dropout_prob = class_dropout_prob
self.num_heads = num_heads
self.past_action_window_size = past_action_window_size
self.future_action_window_size = future_action_window_size
# Action history is not used now.
self.history_embedder = HistoryEmbedder(action_size=in_channels, hidden_size=token_size)
self.x_embedder = ActionEmbedder(action_size=in_channels, hidden_size=token_size)
self.t_embedder = TimestepEmbedder(token_size)
conditions_shape = (1, n_conditon_token, token_size)
self.z_embedder = LabelEmbedder(
in_size=token_size,
hidden_size=token_size,
dropout_prob=class_dropout_prob,
conditions_shape=conditions_shape,
)
scale = token_size**-0.5
# Learnable positional embeddings
actual_action_length = future_action_window_size + past_action_window_size + 1
self.positional_embedding = nn.Parameter(
scale * torch.randn(self.num_cond_tokens + actual_action_length, token_size)
)
# Alternating cross-attention and self-attention blocks
self.blocks = nn.ModuleList()
for layer_idx in range(depth):
if layer_idx % 2 == 0: # Even layers (0, 2, 4, ...): Cross-Attention
block = DiTBlockCrossAttn(token_size, num_heads, mlp_ratio=mlp_ratio)
else: # Odd layers (1, 3, 5, ...): Self-Attention
block = DiTBlockSelfAttn(token_size, num_heads, mlp_ratio=mlp_ratio)
self.blocks.append(block)
self.final_layer = FinalLayer(token_size, self.out_channels)
self.initialize_weights()
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize embedders
nn.init.normal_(self.x_embedder.linear.weight, std=0.02)
nn.init.constant_(self.x_embedder.linear.bias, 0)
nn.init.normal_(self.history_embedder.linear.weight, std=0.02)
nn.init.constant_(self.history_embedder.linear.bias, 0)
# Initialize label embedding table:
if self.class_dropout_prob > 0:
nn.init.normal_(self.z_embedder.uncondition, std=0.02)
nn.init.normal_(self.z_embedder.linear.weight, std=0.02)
nn.init.constant_(self.z_embedder.linear.bias, 0)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def forward(self, x, t, z, encoder_features=None):
"""
Forward pass of DiT with cross-attention.
Args:
x: (B, T, D) tensor of predicting action inputs
t: (B,) tensor of diffusion timesteps
z: [B, num_cond_tokens, D] -- condition token
encoder_features: [B, M, D] -- encoder features for cross-attention (e.g., vision-language features)
"""
x = self.x_embedder(x) # (N, T, D)
t = self.t_embedder(t) # (N, D)
z = self.z_embedder(z, self.training) # [N, num_cond_tokens, D]
c = t.unsqueeze(1) + z # (N, 64, D)
x = torch.cat((c, x), dim=1) # (N, T+64, D)
x = x + self.positional_embedding # (N, T+64, D)
# Pass through cross-attention blocks
for block in self.blocks:
x = block(x, encoder_features=encoder_features) # (N, T+64, D)
x = self.final_layer(x) # (N, T+64, out_channels)
return x[:, self.num_cond_tokens :, :] # (N, T, C)
def forward_with_cfg(self, x, t, z, cfg_scale, encoder_features=None):
"""
Forward pass with classifier-free guidance for cross-attention DiT.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
half = x[: len(x) // 2]
combined = torch.cat([half, half], dim=0).to(next(self.x_embedder.parameters()).dtype)
# Handle encoder features for CFG: conditional + unconditional
if encoder_features is not None:
# First half: conditional (with encoder features)
# Second half: unconditional (without encoder features, set to None)
encoder_features_combined = torch.cat([encoder_features, encoder_features], dim=0)
# Note: For true CFG, you might want to pass None for the second half:
# But this would require modifying the forward pass to handle mixed batches
else:
encoder_features_combined = None
model_out = self.forward(combined, t, z, encoder_features=encoder_features_combined)
eps, rest = model_out[:, :, : self.in_channels], model_out[:, :, self.in_channels :]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = torch.cat([half_eps, half_eps], dim=0)
return torch.cat([eps, rest], dim=2)
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