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import torch
import torch.nn as nn
# import numpy as np
# import math
# from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
from .dit_models import TimestepEmbedder, LabelEmbedder, DiTBlock, get_2d_sincos_pos_embed
class DiTwoEmbedder(nn.Module):
"""
Diffusion model with a Transformer backbone, performing directly on the ViT token latents rather than spatial latents.
"""
def __init__(
self,
input_size=224, # raw img input size
# patch_size=14, # dino version
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,
):
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 = 14 # dino-v2 patch sized fixed in this project
self.num_heads = num_heads
# 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
# num_patches = self.x_embedder.num_patches # 14*14*3
self.num_patches = (input_size // self.patch_size)**2
# Will use fixed sin-cos embedding:
self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches,
hidden_size),
requires_grad=False)
self.blocks = nn.ModuleList([
DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
for _ in range(depth)
])
# self.final_layer = FinalLayer(hidden_size, patch_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 (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1],
int(self.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 forward(self, x, t, y=None):
"""
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
"""
# ! no embedder operation
# x = self.x_embedder(
# x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
x = x + self.pos_embed
t = self.t_embedder(t) # (N, D)
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)
else:
c = t
for block in self.blocks:
x = block(x, c) # (N, T, D)
# x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels)
# x = self.unpatchify(x) # (N, out_channels, H, W)
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.
"""
combined = x
model_out = self.forward(combined, t, y)
return model_out
class DiTwoEmbedderLongSkipConnection(nn.Module):
def __init__(
self,
input_size=224, # raw img input size
patch_size=14, # dino version
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,
):
"""DiT with long skip-connections from U-ViT, CVPR 23'
"""
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.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
# num_patches = self.x_embedder.num_patches # 14*14*3
self.num_patches = (input_size // patch_size)**2
# Will use fixed sin-cos embedding:
self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches,
hidden_size),
requires_grad=False)
self.blocks = nn.ModuleList([
DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
for _ in range(depth)
])
# ! add long-skip-connections from U-ViT
self.in_blocks = nn.ModuleList([
DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
for _ in range(depth // 2)
])
self.mid_block = DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
self.out_blocks = nn.ModuleList([
DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio)
for _ in range(depth // 2)
])
# ! needed or to be replaced?
# self.final_layer = FinalLayer(hidden_size, patch_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 (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1],
int(self.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 forward(self, x, t, y=None):
"""
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
"""
# ! no embedder operation
# x = self.x_embedder(
# x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2
x = x + self.pos_embed
t = self.t_embedder(t) # (N, D)
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)
else:
c = t
# ! add long-skip-connections here
# for block in self.blocks:
# x = block(x, c) # (N, T, D)
skips = []
for blk in self.in_blocks:
x = blk(x)
skips.append(x)
x = self.mid_block(x)
for blk in self.out_blocks:
x = blk(x, skips.pop())
# ! the order of unpatchify and final_linear swaps in the baseline implementation
# x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels)
# x = self.unpatchify(x) # (N, out_channels, H, W)
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.
"""
combined = x
model_out = self.forward(combined, t, y)
return model_out
#################################################################################
# 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)
def DiT_woembed_S(**kwargs):
return DiTwoEmbedder(depth=12, hidden_size=384, num_heads=6, **kwargs)
def DiT_woembed_B(**kwargs):
return DiTwoEmbedder(depth=12, hidden_size=768, num_heads=12, **kwargs)
def DiT_woembed_L(**kwargs):
return DiTwoEmbedder(
depth=24,
hidden_size=1024,
num_heads=16,
**kwargs)
DiT_woembed_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,
'DiT-wo-S': DiT_woembed_S,
'DiT-wo-B': DiT_woembed_B,
'DiT-wo-L': DiT_woembed_L,
}
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