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#
# Copyright 2024 HunyuanVideo
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
from diffusers.utils import BaseOutput
from diffusers.utils.torch_utils import randn_tensor
from opensora.acceleration.checkpoint import auto_grad_checkpoint, checkpoint
from opensora.models.hunyuan_vae.unet_causal_3d_blocks import (
CausalConv3d,
DownEncoderBlockCausal3D,
UNetMidBlockCausal3D,
UpDecoderBlockCausal3D,
prepare_causal_attention_mask,
)
@dataclass
class DecoderOutput(BaseOutput):
r"""
Output of decoding method.
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model.
"""
sample: torch.FloatTensor
class EncoderCausal3D(nn.Module):
r"""
The `EncoderCausal3D` layer of a variational autoencoder that encodes its input into a latent representation.
"""
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
block_out_channels: Tuple[int, ...] = (64,),
layers_per_block: int = 2,
norm_num_groups: int = 32,
act_fn: str = "silu",
double_z: bool = True,
mid_block_add_attention=True,
time_compression_ratio: int = 4,
spatial_compression_ratio: int = 8,
dropout: float = 0.0,
):
super().__init__()
self.layers_per_block = layers_per_block
self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1)
self.mid_block = None
self.down_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i, _ in enumerate(block_out_channels):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio))
num_time_downsample_layers = int(np.log2(time_compression_ratio))
if time_compression_ratio == 4:
add_spatial_downsample = bool(i < num_spatial_downsample_layers)
add_time_downsample = bool(
i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block
)
elif time_compression_ratio == 8:
add_spatial_downsample = bool(i < num_spatial_downsample_layers)
add_time_downsample = bool(i < num_spatial_downsample_layers)
else:
raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}.")
downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
downsample_stride_T = (2,) if add_time_downsample else (1,)
downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)
down_block = DownEncoderBlockCausal3D(
num_layers=self.layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
dropout=dropout,
add_downsample=bool(add_spatial_downsample or add_time_downsample),
downsample_stride=downsample_stride,
resnet_eps=1e-6,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlockCausal3D(
in_channels=block_out_channels[-1],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
output_scale_factor=1,
attention_head_dim=block_out_channels[-1],
resnet_groups=norm_num_groups,
add_attention=mid_block_add_attention,
)
# out
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)
self.conv_act = nn.SiLU()
conv_out_channels = 2 * out_channels if double_z else out_channels
self.conv_out = CausalConv3d(block_out_channels[-1], conv_out_channels, kernel_size=3)
def prepare_attention_mask(self, hidden_states: torch.Tensor) -> torch.Tensor:
B, C, T, H, W = hidden_states.shape
attention_mask = prepare_causal_attention_mask(
T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B
)
return attention_mask
def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
r"""The forward method of the `EncoderCausal3D` class."""
assert len(sample.shape) == 5, "The input tensor should have 5 dimensions"
sample = self.conv_in(sample)
# down
for down_block in self.down_blocks:
sample = down_block(sample)
# middle
if self.mid_block.add_attention:
attention_mask = self.prepare_attention_mask(sample)
else:
attention_mask = None
sample = auto_grad_checkpoint(self.mid_block, sample, attention_mask)
# post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
return sample
class DecoderCausal3D(nn.Module):
r"""
The `DecoderCausal3D` layer of a variational autoencoder that decodes its latent representation into an output sample.
"""
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
block_out_channels: Tuple[int, ...] = (64,),
layers_per_block: int = 2,
norm_num_groups: int = 32,
act_fn: str = "silu",
mid_block_add_attention=True,
time_compression_ratio: int = 4,
spatial_compression_ratio: int = 8,
dropout: float = 0.0,
):
super().__init__()
self.layers_per_block = layers_per_block
self.conv_in = CausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1)
self.mid_block = None
self.up_blocks = nn.ModuleList([])
# mid
self.mid_block = UNetMidBlockCausal3D(
in_channels=block_out_channels[-1],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
output_scale_factor=1,
attention_head_dim=block_out_channels[-1],
resnet_groups=norm_num_groups,
add_attention=mid_block_add_attention,
)
# up
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, _ in enumerate(block_out_channels):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio))
num_time_upsample_layers = int(np.log2(time_compression_ratio))
if time_compression_ratio == 4:
add_spatial_upsample = bool(i < num_spatial_upsample_layers)
add_time_upsample = bool(
i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block
)
elif time_compression_ratio == 8:
add_spatial_upsample = bool(i < num_spatial_upsample_layers)
add_time_upsample = bool(i < num_spatial_upsample_layers)
else:
raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}.")
upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1)
upsample_scale_factor_T = (2,) if add_time_upsample else (1,)
upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW)
up_block = UpDecoderBlockCausal3D(
num_layers=self.layers_per_block + 1,
in_channels=prev_output_channel,
out_channels=output_channel,
resolution_idx=None,
dropout=dropout,
add_upsample=bool(add_spatial_upsample or add_time_upsample),
upsample_scale_factor=upsample_scale_factor,
resnet_eps=1e-6,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
self.conv_act = nn.SiLU()
self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3)
def post_process(self, sample: torch.Tensor) -> torch.Tensor:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
return sample
def prepare_attention_mask(self, hidden_states: torch.Tensor) -> torch.Tensor:
B, C, T, H, W = hidden_states.shape
attention_mask = prepare_causal_attention_mask(
T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B
)
return attention_mask
def forward(
self,
sample: torch.FloatTensor,
) -> torch.FloatTensor:
r"""The forward method of the `DecoderCausal3D` class."""
assert len(sample.shape) == 5, "The input tensor should have 5 dimensions."
sample = self.conv_in(sample)
upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
# middle
if self.mid_block.add_attention:
attention_mask = self.prepare_attention_mask(sample)
else:
attention_mask = None
sample = auto_grad_checkpoint(self.mid_block, sample, attention_mask)
sample = sample.to(upscale_dtype)
# up
for up_block in self.up_blocks:
sample = up_block(sample)
# post-process
if getattr(self, "grad_checkpointing", False):
sample = checkpoint(self.post_process, sample, use_reentrant=True)
else:
sample = self.post_process(sample)
sample = self.conv_out(sample)
return sample
class DiagonalGaussianDistribution(object):
def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
if parameters.ndim == 3:
dim = 2 # (B, L, C)
elif parameters.ndim == 5 or parameters.ndim == 4:
dim = 1 # (B, C, T, H ,W) / (B, C, H, W)
else:
raise NotImplementedError
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters, 2, dim=dim)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(
self.mean, device=self.parameters.device, dtype=self.parameters.dtype
)
def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
# make sure sample is on the same device as the parameters and has same dtype
sample = randn_tensor(
self.mean.shape,
generator=generator,
device=self.parameters.device,
dtype=self.parameters.dtype,
)
x = self.mean + self.std * sample
return x
def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
if self.deterministic:
return torch.Tensor([0.0])
else:
reduce_dim = list(range(1, self.mean.ndim))
if other is None:
return 0.5 * torch.sum(
torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
dim=reduce_dim,
)
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,
dim=reduce_dim,
)
def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor:
if self.deterministic:
return torch.Tensor([0.0])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims,
)
def mode(self) -> torch.Tensor:
return self.mean
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