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# https://github.com/facebookresearch/DiT
# 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 numpy as np
import math
# from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
from timm.models.vision_transformer import PatchEmbed, Mlp
from einops import rearrange
from pdb import set_trace as st
# support flash attention and xformer acceleration
from vit.vision_transformer import MemEffAttention as Attention
# from torch.nn import LayerNorm
# from xformers import triton
# import xformers.triton
# from xformers.triton import FusedLayerNorm as LayerNorm
# from xformers.components.activations import build_activation, Activation
# from xformers.components.feedforward import fused_mlp
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
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)
t_emb = self.mlp(t_freq)
return t_emb
class LabelEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, num_classes, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding,
hidden_size)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
def token_drop(self, labels, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(labels.shape[0],
device=labels.device) < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
labels = torch.where(drop_ids, self.num_classes, labels)
return labels
def forward(self, labels, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
embeddings = self.embedding_table(labels)
return embeddings
class ClipProjector(nn.Module):
def __init__(self, transformer_width, embed_dim, tx_width, *args,
**kwargs) -> None:
super().__init__(*args, **kwargs)
'''a CLIP text encoder projector, adapted from CLIP.encode_text
'''
self.text_projection = nn.Parameter(
torch.empty(transformer_width, embed_dim))
nn.init.normal_(self.text_projection, std=tx_width**-0.5)
def forward(self, clip_text_x):
return clip_text_x @ self.text_projection
#################################################################################
# Core DiT Model #
#################################################################################
# class DiTBlock(nn.Module):
# """
# A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
# """
# def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
# super().__init__()
# nn.LayerNorm
# self.norm1 = LayerNorm(
# hidden_size,
# affine=False,
# # elementwise_affine=False,
# eps=1e-6)
# self.attn = Attention(hidden_size,
# num_heads=num_heads,
# qkv_bias=True,
# **block_kwargs)
# self.norm2 = LayerNorm(
# hidden_size,
# # elementwise_affine=False,
# 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)
# # self.mlp = fused_mlp.FusedMLP(
# # dim_model=hidden_size,
# # dropout=0,
# # activation=Activation.GeLU,
# # hidden_layer_multiplier=mlp_ratio,
# # )
# self.adaLN_modulation = nn.Sequential(
# nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
# def forward(self, x, c):
# shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
# c).chunk(6, dim=1)
# x = x + gate_msa.unsqueeze(1) * self.attn(
# modulate(self.norm1(x), shift_msa, scale_msa))
# x = x + gate_mlp.unsqueeze(1) * self.mlp(
# modulate(self.norm2(x), shift_mlp, scale_mlp))
# return x
class DiTBlock(nn.Module):
"""
A DiT block with adaptive layer norm zero (adaLN-Zero) 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)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
def forward(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(
modulate(self.norm1(x), shift_msa, scale_msa))
x = x + gate_mlp.unsqueeze(1) * self.mlp(
modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class DiTBlockRollOut(DiTBlock):
"""
A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4, **block_kwargs):
super().__init__(hidden_size * 3, num_heads, mlp_ratio, **block_kwargs)
def forward(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(
modulate(self.norm1(x), shift_msa, scale_msa))
x = x + gate_mlp.unsqueeze(1) * self.mlp(
modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class FinalLayer(nn.Module):
"""
The final layer of DiT, basically the decoder_pred in MAE with adaLN.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(
hidden_size,
# self.norm_final = LayerNorm(
hidden_size,
elementwise_affine=False,)
# affine=False,
# eps=1e-6)
self.linear = nn.Linear(hidden_size,
patch_size * patch_size * out_channels,
bias=True)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DiT(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=32,
patch_size=2,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
mixing_logit_init=-3,
mixed_prediction=True,
context_dim=False,
roll_out=False,
vit_blk=DiTBlock,
final_layer_blk=FinalLayer,
):
super().__init__()
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.embed_dim = hidden_size
# st()
self.x_embedder = PatchEmbed(input_size,
patch_size,
in_channels,
hidden_size,
bias=True)
self.t_embedder = TimestepEmbedder(hidden_size)
if num_classes > 0:
self.y_embedder = LabelEmbedder(num_classes, hidden_size,
class_dropout_prob)
else:
self.y_embedder = None
if context_dim is not None:
self.clip_text_proj = ClipProjector(context_dim,
hidden_size,
tx_width=depth)
else:
self.clip_text_proj = None
self.roll_out = roll_out
num_patches = self.x_embedder.num_patches # 14*14*3
# Will use fixed sin-cos embedding:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size),
requires_grad=False)
# if not self.roll_out:
self.blocks = nn.ModuleList([
vit_blk(hidden_size, num_heads, mlp_ratio=mlp_ratio)
for _ in range(depth)
])
# else:
# self.blocks = nn.ModuleList([
# DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) if idx % 2 == 0 else
# DiTBlockRollOut(hidden_size, num_heads, mlp_ratio=mlp_ratio)
# for idx in range(depth)
# ])
self.final_layer = final_layer_blk(hidden_size, patch_size,
self.out_channels)
self.initialize_weights()
self.mixed_prediction = mixed_prediction # This enables mixed prediction
if self.mixed_prediction:
if self.roll_out:
logit_ch = in_channels * 3
else:
logit_ch = in_channels
init = mixing_logit_init * torch.ones(
size=[1, logit_ch, 1, 1]) # hard coded for now
self.mixing_logit = torch.nn.Parameter(init, requires_grad=True)
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 (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(
self.pos_embed.shape[-1], int(self.x_embedder.num_patches**0.5))
# st()
self.pos_embed.data.copy_(
torch.from_numpy(pos_embed).float().unsqueeze(0))
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
# Initialize label embedding table:
if self.y_embedder is not None:
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
# 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)
# Zero-out adaLN modulation layers in DiT blocks:
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def unpatchify(self, x):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
# p = self.x_embedder.patch_size[0]
p = self.patch_size
h = w = int(x.shape[1]**0.5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
return imgs
# def forward(self, x, t, y=None, get_attr=''):
def forward(self,
x,
timesteps=None,
context=None,
y=None,
get_attr='',
**kwargs):
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
# t = timesteps
if get_attr != '': # not breaking the forward hooks
return getattr(self, get_attr)
t = self.t_embedder(timesteps) # (N, D)
if self.roll_out: # !
x = rearrange(x, 'b (n c) h w->(b n) c h w', n=3)
x = self.x_embedder(
x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
if self.roll_out: # ! roll-out in the L dim, not B dim. add condition to all tokens.
x = rearrange(x, '(b n) l c ->b (n l) c', n=3)
if self.y_embedder is not None:
assert y is not None
y = self.y_embedder(y, self.training) # (N, D)
c = t + y # (N, D)
elif context is not None:
assert context.ndim == 2
context = self.clip_text_proj(context)
if context.shape[0] < t.shape[
0]: # same caption context for different view input of the same ID
context = torch.repeat_interleave(context,
t.shape[0] //
context.shape[0],
dim=0)
# if context.ndim == 3: # compat version from SD
# context = context[:, 0, :]
c = t + context
else:
c = t # BS 1024
for blk_idx, block in enumerate(self.blocks):
# if self.roll_out:
# if blk_idx % 2 == 0: # with-in plane self attention
# x = rearrange(x, 'b (n l) c -> b l (n c) ', n=3)
# x = block(x, torch.repeat_interleave(c, 3, 0)) # (N, T, D)
# else: # global attention
# # x = rearrange(x, '(b n) l c -> b (n l) c ', n=3)
# x = rearrange(x, 'b l (n c) -> b (n l) c ', n=3)
# x = block(x, c) # (N, T, D)
# else:
x = block(x, c) # (N, T, D)
x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels)
if self.roll_out: # move n from L to B axis
x = rearrange(x, 'b (n l) c ->(b n) l c', n=3)
x = self.unpatchify(x) # (N, out_channels, H, W)
if self.roll_out: # move n from L to B axis
x = rearrange(x, '(b n) c h w -> b (n c) h w', n=3)
# x = rearrange(x, 'b n) c h w -> b (n c) h w', n=3)
return x
def forward_with_cfg(self, x, t, y, cfg_scale):
"""
Forward pass of DiT, 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)
model_out = self.forward(combined, t, y)
# For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the line following that.
# eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
eps, rest = model_out[:, :3], model_out[:, 3:]
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)
def forward_with_cfg_unconditional(self, x, t, y=None, cfg_scale=None):
"""
Forward pass of DiT, 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)
combined = x
model_out = self.forward(combined, t, y)
# For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the line following that.
# eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
# eps, rest = model_out[:, :3], model_out[:, 3:]
# 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)
# st()
return model_out
#################################################################################
# Sine/Cosine Positional Embedding Functions #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
def get_2d_sincos_pos_embed(embed_dim,
grid_size,
cls_token=False,
extra_tokens=0):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
if isinstance(grid_size, tuple):
grid_size_h, grid_size_w = grid_size
grid_h = np.arange(grid_size_h, dtype=np.float32)
grid_w = np.arange(grid_size_w, dtype=np.float32)
else:
grid_size_h = grid_size_w = grid_size
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size_h, grid_size_w])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate(
[np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2,
grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2,
grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
#################################################################################
# DiT Configs #
#################################################################################
def DiT_XL_2(**kwargs):
return DiT(depth=28,
hidden_size=1152,
patch_size=2,
num_heads=16,
**kwargs)
def DiT_XL_4(**kwargs):
return DiT(depth=28,
hidden_size=1152,
patch_size=4,
num_heads=16,
**kwargs)
def DiT_XL_8(**kwargs):
return DiT(depth=28,
hidden_size=1152,
patch_size=8,
num_heads=16,
**kwargs)
def DiT_L_2(**kwargs):
return DiT(depth=24,
hidden_size=1024,
patch_size=2,
num_heads=16,
**kwargs)
def DiT_L_4(**kwargs):
return DiT(depth=24,
hidden_size=1024,
patch_size=4,
num_heads=16,
**kwargs)
def DiT_L_8(**kwargs):
return DiT(depth=24,
hidden_size=1024,
patch_size=8,
num_heads=16,
**kwargs)
def DiT_B_2(**kwargs):
return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs)
def DiT_B_4(**kwargs):
return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs)
def DiT_B_8(**kwargs):
return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs)
def DiT_B_16(**kwargs): # ours cfg
return DiT(depth=12,
hidden_size=768,
patch_size=16,
num_heads=12,
**kwargs)
def DiT_S_2(**kwargs):
return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs)
def DiT_S_4(**kwargs):
return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs)
def DiT_S_8(**kwargs):
return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs)
DiT_models = {
'DiT-XL/2': DiT_XL_2,
'DiT-XL/4': DiT_XL_4,
'DiT-XL/8': DiT_XL_8,
'DiT-L/2': DiT_L_2,
'DiT-L/4': DiT_L_4,
'DiT-L/8': DiT_L_8,
'DiT-B/2': DiT_B_2,
'DiT-B/4': DiT_B_4,
'DiT-B/8': DiT_B_8,
'DiT-B/16': DiT_B_16,
'DiT-S/2': DiT_S_2,
'DiT-S/4': DiT_S_4,
'DiT-S/8': DiT_S_8,
}
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