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import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from einops import rearrange
from .configuration_stdit import STDiTConfig
from .layers import (
STDiTBlock,
CaptionEmbedder,
PatchEmbed3D,
T2IFinalLayer,
TimestepEmbedder,
)
from .utils import (
approx_gelu,
get_1d_sincos_pos_embed,
get_2d_sincos_pos_embed,
)
from transformers import PreTrainedModel
class STDiT(PreTrainedModel):
config_class = STDiTConfig
def __init__(
self,
config
):
super().__init__(config)
self.pred_sigma = config.pred_sigma
self.in_channels = config.in_channels
self.out_channels = config.in_channels * 2 if config.pred_sigma else config.in_channels
self.hidden_size = config.hidden_size
self.patch_size = config.patch_size
self.input_size = config.input_size
num_patches = np.prod([config.input_size[i] // config.patch_size[i] for i in range(3)])
self.num_patches = num_patches
self.num_temporal = config.input_size[0] // config.patch_size[0]
self.num_spatial = num_patches // self.num_temporal
self.num_heads = config.num_heads
self.no_temporal_pos_emb = config.no_temporal_pos_emb
self.depth = config.depth
self.mlp_ratio = config.mlp_ratio
self.enable_flash_attn = config.enable_flash_attn
self.enable_layernorm_kernel = config.enable_layernorm_kernel
self.space_scale = config.space_scale
self.time_scale = config.time_scale
self.register_buffer("pos_embed", self.get_spatial_pos_embed())
self.register_buffer("pos_embed_temporal", self.get_temporal_pos_embed())
self.x_embedder = PatchEmbed3D(config.patch_size, config.in_channels, config.hidden_size)
self.t_embedder = TimestepEmbedder(config.hidden_size)
self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(config.hidden_size, 6 * config.hidden_size, bias=True))
self.y_embedder = CaptionEmbedder(
in_channels=config.caption_channels,
hidden_size=config.hidden_size,
uncond_prob=config.class_dropout_prob,
act_layer=approx_gelu,
token_num=config.model_max_length,
)
drop_path = [x.item() for x in torch.linspace(0, config.drop_path, config.depth)]
self.blocks = nn.ModuleList(
[
STDiTBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=self.mlp_ratio,
drop_path=drop_path[i],
enable_flash_attn=self.enable_flash_attn,
enable_layernorm_kernel=self.enable_layernorm_kernel,
enable_sequence_parallelism=config.enable_sequence_parallelism,
d_t=self.num_temporal,
d_s=self.num_spatial,
)
for i in range(self.depth)
]
)
self.final_layer = T2IFinalLayer(config.hidden_size, np.prod(self.patch_size), self.out_channels)
# init model
self.initialize_weights()
self.initialize_temporal()
if config.freeze is not None:
assert config.freeze in ["not_temporal", "text"]
if config.freeze == "not_temporal":
self.freeze_not_temporal()
elif config.freeze == "text":
self.freeze_text()
# sequence parallel related configs
self.enable_sequence_parallelism = config.enable_sequence_parallelism
if config.enable_sequence_parallelism:
self.sp_rank = dist.get_rank(get_sequence_parallel_group())
else:
self.sp_rank = None
def forward(self, x, timestep, y, mask=None):
"""
Forward pass of STDiT.
Args:
x (torch.Tensor): latent representation of video; of shape [B, C, T, H, W]
timestep (torch.Tensor): diffusion time steps; of shape [B]
y (torch.Tensor): representation of prompts; of shape [B, 1, N_token, C]
mask (torch.Tensor): mask for selecting prompt tokens; of shape [B, N_token]
Returns:
x (torch.Tensor): output latent representation; of shape [B, C, T, H, W]
"""
x = x.to(self.final_layer.linear.weight.dtype)
timestep = timestep.to(self.final_layer.linear.weight.dtype)
y = y.to(self.final_layer.linear.weight.dtype)
# embedding
x = self.x_embedder(x) # [B, N, C]
x = rearrange(x, "B (T S) C -> B T S C", T=self.num_temporal, S=self.num_spatial)
x = x + self.pos_embed
x = rearrange(x, "B T S C -> B (T S) C")
# shard over the sequence dim if sp is enabled
if self.enable_sequence_parallelism:
x = split_forward_gather_backward(x, get_sequence_parallel_group(), dim=1, grad_scale="down")
t = self.t_embedder(timestep, dtype=x.dtype) # [B, C]
t0 = self.t_block(t) # [B, C]
y = self.y_embedder(y, self.training) # [B, 1, N_token, C]
if mask is not None:
if mask.shape[0] != y.shape[0]:
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
mask = mask.squeeze(1).squeeze(1)
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
y_lens = mask.sum(dim=1).tolist()
else:
y_lens = [y.shape[2]] * y.shape[0]
y = y.squeeze(1).view(1, -1, x.shape[-1])
# blocks
for i, block in enumerate(self.blocks):
if i == 0:
if self.enable_sequence_parallelism:
tpe = torch.chunk(
self.pos_embed_temporal, dist.get_world_size(get_sequence_parallel_group()), dim=1
)[self.sp_rank].contiguous()
else:
tpe = self.pos_embed_temporal
else:
tpe = None
x = block(x, y, t0, y_lens, tpe)
# x = auto_grad_checkpoint(block, x, y, t0, y_lens, tpe)
if self.enable_sequence_parallelism:
x = gather_forward_split_backward(x, get_sequence_parallel_group(), dim=1, grad_scale="up")
# x.shape: [B, N, C]
# final process
x = self.final_layer(x, t) # [B, N, C=T_p * H_p * W_p * C_out]
x = self.unpatchify(x) # [B, C_out, T, H, W]
# cast to float32 for better accuracy
x = x.to(torch.float32)
return x
def unpatchify(self, x):
"""
Args:
x (torch.Tensor): of shape [B, N, C]
Return:
x (torch.Tensor): of shape [B, C_out, T, H, W]
"""
N_t, N_h, N_w = [self.input_size[i] // self.patch_size[i] for i in range(3)]
T_p, H_p, W_p = self.patch_size
x = rearrange(
x,
"B (N_t N_h N_w) (T_p H_p W_p C_out) -> B C_out (N_t T_p) (N_h H_p) (N_w W_p)",
N_t=N_t,
N_h=N_h,
N_w=N_w,
T_p=T_p,
H_p=H_p,
W_p=W_p,
C_out=self.out_channels,
)
return x
def unpatchify_old(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 get_spatial_pos_embed(self, grid_size=None):
if grid_size is None:
grid_size = self.input_size[1:]
pos_embed = get_2d_sincos_pos_embed(
self.hidden_size,
(grid_size[0] // self.patch_size[1], grid_size[1] // self.patch_size[2]),
scale=self.space_scale,
)
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],
scale=self.time_scale,
)
pos_embed = torch.from_numpy(pos_embed).float().unsqueeze(0).requires_grad_(False)
return pos_embed
def freeze_not_temporal(self):
for n, p in self.named_parameters():
if "attn_temp" not in n:
p.requires_grad = False
def freeze_text(self):
for n, p in self.named_parameters():
if "cross_attn" in n:
p.requires_grad = False
def initialize_temporal(self):
for block in self.blocks:
nn.init.constant_(block.attn_temp.proj.weight, 0)
nn.init.constant_(block.attn_temp.proj.bias, 0)
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 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]))
# 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.normal_(self.t_block[1].weight, std=0.02)
# Initialize caption embedding MLP:
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)
# Zero-out adaLN modulation layers in PixArt blocks:
for block in self.blocks:
nn.init.constant_(block.cross_attn.proj.weight, 0)
nn.init.constant_(block.cross_attn.proj.bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
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