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Zero
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import torch
import torch.nn as nn
from typing import Optional
from diffusers.models.transformer_temporal import TransformerTemporalModelOutput
from diffusers.models.attention import BasicTransformerBlock
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
from diffusers.models.resnet import AlphaBlender
from cameractrl.models.attention import TemporalPoseCondTransformerBlock
class TransformerSpatioTemporalModelPoseCond(nn.Module):
"""
A Transformer model for video-like data.
Parameters:
num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
in_channels (`int`, *optional*):
The number of channels in the input and output (specify if the input is **continuous**).
out_channels (`int`, *optional*):
The number of channels in the output (specify if the input is **continuous**).
num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
"""
def __init__(
self,
num_attention_heads: int = 16,
attention_head_dim: int = 88,
in_channels: int = 320,
out_channels: Optional[int] = None,
num_layers: int = 1,
cross_attention_dim: Optional[int] = None,
):
super().__init__()
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
inner_dim = num_attention_heads * attention_head_dim
self.inner_dim = inner_dim
# 2. Define input layers
self.in_channels = in_channels
self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6)
self.proj_in = nn.Linear(in_channels, inner_dim)
# 3. Define transformers blocks
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim,
cross_attention_dim=cross_attention_dim,
)
for d in range(num_layers)
]
)
time_mix_inner_dim = inner_dim
self.temporal_transformer_blocks = nn.ModuleList(
[
TemporalPoseCondTransformerBlock(
inner_dim,
time_mix_inner_dim,
num_attention_heads,
attention_head_dim,
cross_attention_dim=cross_attention_dim,
)
for _ in range(num_layers)
]
)
time_embed_dim = in_channels * 4
self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)
self.time_proj = Timesteps(in_channels, True, 0)
self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images")
# 4. Define output layers
self.out_channels = in_channels if out_channels is None else out_channels
# TODO: should use out_channels for continuous projections
self.proj_out = nn.Linear(inner_dim, in_channels)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor, # [bs * frame, c, h, w]
encoder_hidden_states: Optional[torch.Tensor] = None, # [bs * frame, 1, c]
image_only_indicator: Optional[torch.Tensor] = None, # [bs, frame]
pose_feature: Optional[torch.Tensor] = None, # [bs, c, frame, h, w]
return_dict: bool = True,
):
"""
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
Input hidden_states.
num_frames (`int`):
The number of frames to be processed per batch. This is used to reshape the hidden states.
encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
self-attention.
image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*):
A tensor indicating whether the input contains only images. 1 indicates that the input contains only
images, 0 indicates that the input contains video frames.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a
plain tuple.
Returns:
[`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
returned, otherwise a `tuple` where the first element is the sample tensor.
"""
# 1. Input
batch_frames, _, height, width = hidden_states.shape
num_frames = image_only_indicator.shape[-1]
batch_size = batch_frames // num_frames
time_context = encoder_hidden_states # [bs * frame, 1, c]
time_context_first_timestep = time_context[None, :].reshape(
batch_size, num_frames, -1, time_context.shape[-1]
)[:, 0] # [bs, frame, c]
time_context = time_context_first_timestep[:, None].broadcast_to(
batch_size, height * width, time_context.shape[-2], time_context.shape[-1]
) # [bs, h*w, 1, c]
time_context = time_context.reshape(batch_size * height * width, -1, time_context.shape[-1]) # [bs * h * w, 1, c]
residual = hidden_states
hidden_states = self.norm(hidden_states) # [bs * frame, c, h, w]
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim) # [bs * frame, h * w, c]
hidden_states = self.proj_in(hidden_states) # [bs * frame, h * w, c]
num_frames_emb = torch.arange(num_frames, device=hidden_states.device)
num_frames_emb = num_frames_emb.repeat(batch_size, 1) # [bs, frame]
num_frames_emb = num_frames_emb.reshape(-1) # [bs * frame]
t_emb = self.time_proj(num_frames_emb) # [bs * frame, c]
# `Timesteps` does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=hidden_states.dtype)
emb = self.time_pos_embed(t_emb)
emb = emb[:, None, :] # [bs * frame, 1, c]
# 2. Blocks
for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
if self.training and self.gradient_checkpointing:
hidden_states = torch.utils.checkpoint.checkpoint(
block,
hidden_states,
None,
encoder_hidden_states,
None,
use_reentrant=False,
)
else:
hidden_states = block(
hidden_states, # [bs * frame, h * w, c]
encoder_hidden_states=encoder_hidden_states, # [bs * frame, 1, c]
) # [bs * frame, h * w, c]
hidden_states_mix = hidden_states
hidden_states_mix = hidden_states_mix + emb
hidden_states_mix = temporal_block(
hidden_states_mix, # [bs * frame, h * w, c]
num_frames=num_frames,
encoder_hidden_states=time_context, # [bs * h * w, 1, c]
pose_feature=pose_feature
)
hidden_states = self.time_mixer(
x_spatial=hidden_states,
x_temporal=hidden_states_mix,
image_only_indicator=image_only_indicator,
)
# 3. Output
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
output = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=output)
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