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# Modified from Meta DiT
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DiT: https://github.com/facebookresearch/DiT/tree/main
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------
import numpy as np
import torch
import torch.nn as nn
import torch.utils.checkpoint
from einops import rearrange
from timm.models.vision_transformer import Mlp
from opensora.acceleration.checkpoint import auto_grad_checkpoint
from opensora.models.layers.blocks import (
Attention,
CaptionEmbedder,
FinalLayer,
LabelEmbedder,
PatchEmbed3D,
TimestepEmbedder,
approx_gelu,
get_1d_sincos_pos_embed,
get_2d_sincos_pos_embed,
get_layernorm,
modulate,
)
from opensora.registry import MODELS
from opensora.utils.ckpt_utils import load_checkpoint
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,
enable_flashattn=False,
enable_layernorm_kernel=False,
):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.enable_flashattn = enable_flashattn
mlp_hidden_dim = int(hidden_size * mlp_ratio)
self.norm1 = get_layernorm(hidden_size, eps=1e-6, affine=False, use_kernel=enable_layernorm_kernel)
self.attn = Attention(
hidden_size,
num_heads=num_heads,
qkv_bias=True,
enable_flashattn=enable_flashattn,
)
self.norm2 = get_layernorm(hidden_size, eps=1e-6, affine=False, use_kernel=enable_layernorm_kernel)
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
@MODELS.register_module()
class DiT(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=(16, 32, 32),
in_channels=4,
patch_size=(1, 2, 2),
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
learn_sigma=True,
condition="text",
no_temporal_pos_emb=False,
caption_channels=512,
model_max_length=77,
dtype=torch.float32,
enable_flashattn=False,
enable_layernorm_kernel=False,
):
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.hidden_size = hidden_size
self.patch_size = patch_size
self.input_size = input_size
num_patches = np.prod([input_size[i] // patch_size[i] for i in range(3)])
self.num_patches = num_patches
self.num_temporal = input_size[0] // patch_size[0]
self.num_spatial = num_patches // self.num_temporal
self.num_heads = num_heads
self.dtype = dtype
self.use_text_encoder = not condition.startswith("label")
if enable_flashattn:
assert dtype in [
torch.float16,
torch.bfloat16,
], f"Flash attention only supports float16 and bfloat16, but got {self.dtype}"
self.no_temporal_pos_emb = no_temporal_pos_emb
self.mlp_ratio = mlp_ratio
self.depth = depth
self.register_buffer("pos_embed_spatial", self.get_spatial_pos_embed())
self.register_buffer("pos_embed_temporal", self.get_temporal_pos_embed())
self.x_embedder = PatchEmbed3D(patch_size, in_channels, embed_dim=hidden_size)
if not self.use_text_encoder:
num_classes = int(condition.split("_")[-1])
self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
else:
self.y_embedder = CaptionEmbedder(
in_channels=caption_channels,
hidden_size=hidden_size,
uncond_prob=class_dropout_prob,
act_layer=approx_gelu,
token_num=1, # pooled token
)
self.t_embedder = TimestepEmbedder(hidden_size)
self.blocks = nn.ModuleList(
[
DiTBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
enable_flashattn=enable_flashattn,
enable_layernorm_kernel=enable_layernorm_kernel,
)
for _ in range(depth)
]
)
self.final_layer = FinalLayer(hidden_size, np.prod(self.patch_size), self.out_channels)
self.initialize_weights()
self.enable_flashattn = enable_flashattn
self.enable_layernorm_kernel = enable_layernorm_kernel
def get_spatial_pos_embed(self):
pos_embed = get_2d_sincos_pos_embed(
self.hidden_size,
self.input_size[1] // self.patch_size[1],
)
pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).requires_grad_(False)
return pos_embed
def get_temporal_pos_embed(self):
pos_embed = get_1d_sincos_pos_embed(
self.hidden_size,
self.input_size[0] // self.patch_size[0],
)
pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).requires_grad_(False)
return pos_embed
def unpatchify(self, x):
c = self.out_channels
t, h, w = [self.input_size[i] // self.patch_size[i] for i in range(3)]
pt, ph, pw = self.patch_size
x = x.reshape(shape=(x.shape[0], t, h, w, pt, ph, pw, c))
x = rearrange(x, "n t h w r p q c -> n c t r h p w q")
imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))
return imgs
def forward(self, x, t, y):
"""
Forward pass of DiT.
x: (B, C, T, H, W) tensor of inputs
t: (B,) tensor of diffusion timesteps
y: list of text
"""
# origin inputs should be float32, cast to specified dtype
x = x.to(self.dtype)
# embedding
x = self.x_embedder(x) # (B, N, D)
x = rearrange(x, "b (t s) d -> b t s d", t=self.num_temporal, s=self.num_spatial)
x = x + self.pos_embed_spatial
if not self.no_temporal_pos_emb:
x = rearrange(x, "b t s d -> b s t d")
x = x + self.pos_embed_temporal
x = rearrange(x, "b s t d -> b (t s) d")
else:
x = rearrange(x, "b t s d -> b (t s) d")
t = self.t_embedder(t, dtype=x.dtype) # (N, D)
y = self.y_embedder(y, self.training) # (N, D)
if self.use_text_encoder:
y = y.squeeze(1).squeeze(1)
condition = t + y
# blocks
for _, block in enumerate(self.blocks):
c = condition
x = auto_grad_checkpoint(block, x, c) # (B, N, D)
# final process
x = self.final_layer(x, condition) # (B, N, num_patches * out_channels)
x = self.unpatchify(x) # (B, out_channels, T, H, W)
# cast to float32 for better accuracy
x = x.to(torch.float32)
return x
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
if module.weight.requires_grad_:
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# 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 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)
# Zero-out text embedding layers:
if self.use_text_encoder:
nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)
@MODELS.register_module("DiT-XL/2")
def DiT_XL_2(from_pretrained=None, **kwargs):
model = DiT(
depth=28,
hidden_size=1152,
patch_size=(1, 2, 2),
num_heads=16,
**kwargs,
)
if from_pretrained is not None:
load_checkpoint(model, from_pretrained)
return model
@MODELS.register_module("DiT-XL/2x2")
def DiT_XL_2x2(from_pretrained=None, **kwargs):
model = DiT(
depth=28,
hidden_size=1152,
patch_size=(2, 2, 2),
num_heads=16,
**kwargs,
)
if from_pretrained is not None:
load_checkpoint(model, from_pretrained)
return model
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