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# Adapted from OpenSora
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# OpenSora: https://github.com/hpcaitech/Open-Sora
# --------------------------------------------------------
import os
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.models import AutoencoderKL, AutoencoderKLTemporalDecoder
from einops import rearrange
from transformers import PretrainedConfig, PreTrainedModel
from .utils import load_checkpoint
class DiagonalGaussianDistribution(object):
def __init__(
self,
parameters,
deterministic=False,
):
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
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).to(device=self.parameters.device, dtype=self.mean.dtype)
def sample(self):
# torch.randn: standard normal distribution
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device, dtype=self.mean.dtype)
return x
def kl(self, other=None):
if self.deterministic:
return torch.Tensor([0.0])
else:
if other is None: # SCH: assumes other is a standard normal distribution
return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3, 4])
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=[1, 2, 3, 4],
)
def nll(self, sample, dims=[1, 2, 3, 4]):
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):
return self.mean
def cast_tuple(t, length=1):
return t if isinstance(t, tuple) else ((t,) * length)
def divisible_by(num, den):
return (num % den) == 0
def is_odd(n):
return not divisible_by(n, 2)
def pad_at_dim(t, pad, dim=-1):
dims_from_right = (-dim - 1) if dim < 0 else (t.ndim - dim - 1)
zeros = (0, 0) * dims_from_right
return F.pad(t, (*zeros, *pad), mode="constant")
def exists(v):
return v is not None
class CausalConv3d(nn.Module):
def __init__(
self,
chan_in,
chan_out,
kernel_size: Union[int, Tuple[int, int, int]],
pad_mode="constant",
strides=None, # allow custom stride
**kwargs,
):
super().__init__()
kernel_size = cast_tuple(kernel_size, 3)
time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
assert is_odd(height_kernel_size) and is_odd(width_kernel_size)
dilation = kwargs.pop("dilation", 1)
stride = strides[0] if strides is not None else kwargs.pop("stride", 1)
self.pad_mode = pad_mode
time_pad = dilation * (time_kernel_size - 1) + (1 - stride)
height_pad = height_kernel_size // 2
width_pad = width_kernel_size // 2
self.time_pad = time_pad
self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0)
stride = strides if strides is not None else (stride, 1, 1)
dilation = (dilation, 1, 1)
self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
def forward(self, x):
x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
x = self.conv(x)
return x
class ResBlock(nn.Module):
def __init__(
self,
in_channels, # SCH: added
filters,
conv_fn,
activation_fn=nn.SiLU,
use_conv_shortcut=False,
num_groups=32,
):
super().__init__()
self.in_channels = in_channels
self.filters = filters
self.activate = activation_fn()
self.use_conv_shortcut = use_conv_shortcut
# SCH: MAGVIT uses GroupNorm by default
self.norm1 = nn.GroupNorm(num_groups, in_channels)
self.conv1 = conv_fn(in_channels, self.filters, kernel_size=(3, 3, 3), bias=False)
self.norm2 = nn.GroupNorm(num_groups, self.filters)
self.conv2 = conv_fn(self.filters, self.filters, kernel_size=(3, 3, 3), bias=False)
if in_channels != filters:
if self.use_conv_shortcut:
self.conv3 = conv_fn(in_channels, self.filters, kernel_size=(3, 3, 3), bias=False)
else:
self.conv3 = conv_fn(in_channels, self.filters, kernel_size=(1, 1, 1), bias=False)
def forward(self, x):
residual = x
x = self.norm1(x)
x = self.activate(x)
x = self.conv1(x)
x = self.norm2(x)
x = self.activate(x)
x = self.conv2(x)
if self.in_channels != self.filters: # SCH: ResBlock X->Y
residual = self.conv3(residual)
return x + residual
def get_activation_fn(activation):
if activation == "relu":
activation_fn = nn.ReLU
elif activation == "swish":
activation_fn = nn.SiLU
else:
raise NotImplementedError
return activation_fn
class Encoder(nn.Module):
"""Encoder Blocks."""
def __init__(
self,
in_out_channels=4,
latent_embed_dim=512, # num channels for latent vector
filters=128,
num_res_blocks=4,
channel_multipliers=(1, 2, 2, 4),
temporal_downsample=(False, True, True),
num_groups=32, # for nn.GroupNorm
activation_fn="swish",
):
super().__init__()
self.filters = filters
self.num_res_blocks = num_res_blocks
self.num_blocks = len(channel_multipliers)
self.channel_multipliers = channel_multipliers
self.temporal_downsample = temporal_downsample
self.num_groups = num_groups
self.embedding_dim = latent_embed_dim
self.activation_fn = get_activation_fn(activation_fn)
self.activate = self.activation_fn()
self.conv_fn = CausalConv3d
self.block_args = dict(
conv_fn=self.conv_fn,
activation_fn=self.activation_fn,
use_conv_shortcut=False,
num_groups=self.num_groups,
)
# first layer conv
self.conv_in = self.conv_fn(
in_out_channels,
filters,
kernel_size=(3, 3, 3),
bias=False,
)
# ResBlocks and conv downsample
self.block_res_blocks = nn.ModuleList([])
self.conv_blocks = nn.ModuleList([])
filters = self.filters
prev_filters = filters # record for in_channels
for i in range(self.num_blocks):
filters = self.filters * self.channel_multipliers[i]
block_items = nn.ModuleList([])
for _ in range(self.num_res_blocks):
block_items.append(ResBlock(prev_filters, filters, **self.block_args))
prev_filters = filters # update in_channels
self.block_res_blocks.append(block_items)
if i < self.num_blocks - 1:
if self.temporal_downsample[i]:
t_stride = 2 if self.temporal_downsample[i] else 1
s_stride = 1
self.conv_blocks.append(
self.conv_fn(
prev_filters, filters, kernel_size=(3, 3, 3), strides=(t_stride, s_stride, s_stride)
)
)
prev_filters = filters # update in_channels
else:
# if no t downsample, don't add since this does nothing for pipeline models
self.conv_blocks.append(nn.Identity(prev_filters)) # Identity
prev_filters = filters # update in_channels
# last layer res block
self.res_blocks = nn.ModuleList([])
for _ in range(self.num_res_blocks):
self.res_blocks.append(ResBlock(prev_filters, filters, **self.block_args))
prev_filters = filters # update in_channels
# MAGVIT uses Group Normalization
self.norm1 = nn.GroupNorm(self.num_groups, prev_filters)
self.conv2 = self.conv_fn(prev_filters, self.embedding_dim, kernel_size=(1, 1, 1), padding="same")
def forward(self, x):
x = self.conv_in(x)
for i in range(self.num_blocks):
for j in range(self.num_res_blocks):
x = self.block_res_blocks[i][j](x)
if i < self.num_blocks - 1:
x = self.conv_blocks[i](x)
for i in range(self.num_res_blocks):
x = self.res_blocks[i](x)
x = self.norm1(x)
x = self.activate(x)
x = self.conv2(x)
return x
class Decoder(nn.Module):
"""Decoder Blocks."""
def __init__(
self,
in_out_channels=4,
latent_embed_dim=512,
filters=128,
num_res_blocks=4,
channel_multipliers=(1, 2, 2, 4),
temporal_downsample=(False, True, True),
num_groups=32, # for nn.GroupNorm
activation_fn="swish",
):
super().__init__()
self.filters = filters
self.num_res_blocks = num_res_blocks
self.num_blocks = len(channel_multipliers)
self.channel_multipliers = channel_multipliers
self.temporal_downsample = temporal_downsample
self.num_groups = num_groups
self.embedding_dim = latent_embed_dim
self.s_stride = 1
self.activation_fn = get_activation_fn(activation_fn)
self.activate = self.activation_fn()
self.conv_fn = CausalConv3d
self.block_args = dict(
conv_fn=self.conv_fn,
activation_fn=self.activation_fn,
use_conv_shortcut=False,
num_groups=self.num_groups,
)
filters = self.filters * self.channel_multipliers[-1]
prev_filters = filters
# last conv
self.conv1 = self.conv_fn(self.embedding_dim, filters, kernel_size=(3, 3, 3), bias=True)
# last layer res block
self.res_blocks = nn.ModuleList([])
for _ in range(self.num_res_blocks):
self.res_blocks.append(ResBlock(filters, filters, **self.block_args))
# ResBlocks and conv upsample
self.block_res_blocks = nn.ModuleList([])
self.num_blocks = len(self.channel_multipliers)
self.conv_blocks = nn.ModuleList([])
# reverse to keep track of the in_channels, but append also in a reverse direction
for i in reversed(range(self.num_blocks)):
filters = self.filters * self.channel_multipliers[i]
# resblock handling
block_items = nn.ModuleList([])
for _ in range(self.num_res_blocks):
block_items.append(ResBlock(prev_filters, filters, **self.block_args))
prev_filters = filters # SCH: update in_channels
self.block_res_blocks.insert(0, block_items) # SCH: append in front
# conv blocks with upsampling
if i > 0:
if self.temporal_downsample[i - 1]:
t_stride = 2 if self.temporal_downsample[i - 1] else 1
# SCH: T-Causal Conv 3x3x3, f -> (t_stride * 2 * 2) * f, depth to space t_stride x 2 x 2
self.conv_blocks.insert(
0,
self.conv_fn(
prev_filters, prev_filters * t_stride * self.s_stride * self.s_stride, kernel_size=(3, 3, 3)
),
)
else:
self.conv_blocks.insert(
0,
nn.Identity(prev_filters),
)
self.norm1 = nn.GroupNorm(self.num_groups, prev_filters)
self.conv_out = self.conv_fn(filters, in_out_channels, 3)
def forward(self, x):
x = self.conv1(x)
for i in range(self.num_res_blocks):
x = self.res_blocks[i](x)
for i in reversed(range(self.num_blocks)):
for j in range(self.num_res_blocks):
x = self.block_res_blocks[i][j](x)
if i > 0:
t_stride = 2 if self.temporal_downsample[i - 1] else 1
x = self.conv_blocks[i - 1](x)
x = rearrange(
x,
"B (C ts hs ws) T H W -> B C (T ts) (H hs) (W ws)",
ts=t_stride,
hs=self.s_stride,
ws=self.s_stride,
)
x = self.norm1(x)
x = self.activate(x)
x = self.conv_out(x)
return x
class VAE_Temporal(nn.Module):
def __init__(
self,
in_out_channels=4,
latent_embed_dim=4,
embed_dim=4,
filters=128,
num_res_blocks=4,
channel_multipliers=(1, 2, 2, 4),
temporal_downsample=(True, True, False),
num_groups=32, # for nn.GroupNorm
activation_fn="swish",
):
super().__init__()
self.time_downsample_factor = 2 ** sum(temporal_downsample)
# self.time_padding = self.time_downsample_factor - 1
self.patch_size = (self.time_downsample_factor, 1, 1)
self.out_channels = in_out_channels
# NOTE: following MAGVIT, conv in bias=False in encoder first conv
self.encoder = Encoder(
in_out_channels=in_out_channels,
latent_embed_dim=latent_embed_dim * 2,
filters=filters,
num_res_blocks=num_res_blocks,
channel_multipliers=channel_multipliers,
temporal_downsample=temporal_downsample,
num_groups=num_groups, # for nn.GroupNorm
activation_fn=activation_fn,
)
self.quant_conv = CausalConv3d(2 * latent_embed_dim, 2 * embed_dim, 1)
self.post_quant_conv = CausalConv3d(embed_dim, latent_embed_dim, 1)
self.decoder = Decoder(
in_out_channels=in_out_channels,
latent_embed_dim=latent_embed_dim,
filters=filters,
num_res_blocks=num_res_blocks,
channel_multipliers=channel_multipliers,
temporal_downsample=temporal_downsample,
num_groups=num_groups, # for nn.GroupNorm
activation_fn=activation_fn,
)
def get_latent_size(self, input_size):
latent_size = []
for i in range(3):
if input_size[i] is None:
lsize = None
elif i == 0:
time_padding = (
0
if (input_size[i] % self.time_downsample_factor == 0)
else self.time_downsample_factor - input_size[i] % self.time_downsample_factor
)
lsize = (input_size[i] + time_padding) // self.patch_size[i]
else:
lsize = input_size[i] // self.patch_size[i]
latent_size.append(lsize)
return latent_size
def encode(self, x):
time_padding = (
0
if (x.shape[2] % self.time_downsample_factor == 0)
else self.time_downsample_factor - x.shape[2] % self.time_downsample_factor
)
x = pad_at_dim(x, (time_padding, 0), dim=2)
encoded_feature = self.encoder(x)
moments = self.quant_conv(encoded_feature).to(x.dtype)
posterior = DiagonalGaussianDistribution(moments)
return posterior
def decode(self, z, num_frames=None):
time_padding = (
0
if (num_frames % self.time_downsample_factor == 0)
else self.time_downsample_factor - num_frames % self.time_downsample_factor
)
z = self.post_quant_conv(z)
x = self.decoder(z)
x = x[:, :, time_padding:]
return x
def forward(self, x, sample_posterior=True):
posterior = self.encode(x)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
recon_video = self.decode(z, num_frames=x.shape[2])
return recon_video, posterior, z
def VAE_Temporal_SD(from_pretrained=None, **kwargs):
model = VAE_Temporal(
in_out_channels=4,
latent_embed_dim=4,
embed_dim=4,
filters=128,
num_res_blocks=4,
channel_multipliers=(1, 2, 2, 4),
temporal_downsample=(False, True, True),
**kwargs,
)
if from_pretrained is not None:
load_checkpoint(model, from_pretrained)
return model
class VideoAutoencoderKL(nn.Module):
def __init__(
self, from_pretrained=None, micro_batch_size=None, cache_dir=None, local_files_only=False, subfolder=None
):
super().__init__()
self.module = AutoencoderKL.from_pretrained(
from_pretrained,
cache_dir=cache_dir,
local_files_only=local_files_only,
subfolder=subfolder,
)
self.out_channels = self.module.config.latent_channels
self.patch_size = (1, 8, 8)
self.micro_batch_size = micro_batch_size
def encode(self, x):
# x: (B, C, T, H, W)
B = x.shape[0]
x = rearrange(x, "B C T H W -> (B T) C H W")
if self.micro_batch_size is None:
x = self.module.encode(x).latent_dist.sample().mul_(0.18215)
else:
# NOTE: cannot be used for training
bs = self.micro_batch_size
x_out = []
for i in range(0, x.shape[0], bs):
x_bs = x[i : i + bs]
x_bs = self.module.encode(x_bs).latent_dist.sample().mul_(0.18215)
x_out.append(x_bs)
x = torch.cat(x_out, dim=0)
x = rearrange(x, "(B T) C H W -> B C T H W", B=B)
return x
def decode(self, x, **kwargs):
# x: (B, C, T, H, W)
B = x.shape[0]
x = rearrange(x, "B C T H W -> (B T) C H W")
if self.micro_batch_size is None:
x = self.module.decode(x / 0.18215).sample
else:
# NOTE: cannot be used for training
bs = self.micro_batch_size
x_out = []
for i in range(0, x.shape[0], bs):
x_bs = x[i : i + bs]
x_bs = self.module.decode(x_bs / 0.18215).sample
x_out.append(x_bs)
x = torch.cat(x_out, dim=0)
x = rearrange(x, "(B T) C H W -> B C T H W", B=B)
return x
def get_latent_size(self, input_size):
latent_size = []
for i in range(3):
# assert (
# input_size[i] is None or input_size[i] % self.patch_size[i] == 0
# ), "Input size must be divisible by patch size"
latent_size.append(input_size[i] // self.patch_size[i] if input_size[i] is not None else None)
return latent_size
@property
def device(self):
return next(self.parameters()).device
@property
def dtype(self):
return next(self.parameters()).dtype
class VideoAutoencoderKLTemporalDecoder(nn.Module):
def __init__(self, from_pretrained=None, cache_dir=None, local_files_only=False):
super().__init__()
self.module = AutoencoderKLTemporalDecoder.from_pretrained(
from_pretrained, cache_dir=cache_dir, local_files_only=local_files_only
)
self.out_channels = self.module.config.latent_channels
self.patch_size = (1, 8, 8)
def encode(self, x):
raise NotImplementedError
def decode(self, x, **kwargs):
B, _, T = x.shape[:3]
x = rearrange(x, "B C T H W -> (B T) C H W")
x = self.module.decode(x / 0.18215, num_frames=T).sample
x = rearrange(x, "(B T) C H W -> B C T H W", B=B)
return x
def get_latent_size(self, input_size):
latent_size = []
for i in range(3):
# assert (
# input_size[i] is None or input_size[i] % self.patch_size[i] == 0
# ), "Input size must be divisible by patch size"
latent_size.append(input_size[i] // self.patch_size[i] if input_size[i] is not None else None)
return latent_size
@property
def device(self):
return next(self.parameters()).device
@property
def dtype(self):
return next(self.parameters()).dtype
class VideoAutoencoderPipelineConfig(PretrainedConfig):
model_type = "VideoAutoencoderPipeline"
def __init__(
self,
vae_2d=None,
vae_temporal=None,
from_pretrained=None,
freeze_vae_2d=False,
cal_loss=False,
micro_frame_size=None,
shift=0.0,
scale=1.0,
**kwargs,
):
self.vae_2d = vae_2d
self.vae_temporal = vae_temporal
self.from_pretrained = from_pretrained
self.freeze_vae_2d = freeze_vae_2d
self.cal_loss = cal_loss
self.micro_frame_size = micro_frame_size
self.shift = shift
self.scale = scale
super().__init__(**kwargs)
class VideoAutoencoderPipeline(PreTrainedModel):
config_class = VideoAutoencoderPipelineConfig
def __init__(self, config: VideoAutoencoderPipelineConfig):
super().__init__(config=config)
self.spatial_vae = VideoAutoencoderKL(
from_pretrained="PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",
local_files_only=False,
micro_batch_size=4,
subfolder="vae",
)
self.temporal_vae = VAE_Temporal_SD(from_pretrained=None)
self.cal_loss = config.cal_loss
self.micro_frame_size = config.micro_frame_size
self.micro_z_frame_size = self.temporal_vae.get_latent_size([config.micro_frame_size, None, None])[0]
if config.freeze_vae_2d:
for param in self.spatial_vae.parameters():
param.requires_grad = False
self.out_channels = self.temporal_vae.out_channels
# normalization parameters
scale = torch.tensor(config.scale)
shift = torch.tensor(config.shift)
if len(scale.shape) > 0:
scale = scale[None, :, None, None, None]
if len(shift.shape) > 0:
shift = shift[None, :, None, None, None]
self.register_buffer("scale", scale)
self.register_buffer("shift", shift)
def encode(self, x):
x_z = self.spatial_vae.encode(x)
if self.micro_frame_size is None:
posterior = self.temporal_vae.encode(x_z)
z = posterior.sample()
else:
z_list = []
for i in range(0, x_z.shape[2], self.micro_frame_size):
x_z_bs = x_z[:, :, i : i + self.micro_frame_size]
posterior = self.temporal_vae.encode(x_z_bs)
z_list.append(posterior.sample())
z = torch.cat(z_list, dim=2)
if self.cal_loss:
return z, posterior, x_z
else:
return (z - self.shift) / self.scale
def decode(self, z, num_frames=None):
if not self.cal_loss:
z = z * self.scale.to(z.dtype) + self.shift.to(z.dtype)
if self.micro_frame_size is None:
x_z = self.temporal_vae.decode(z, num_frames=num_frames)
x = self.spatial_vae.decode(x_z)
else:
x_z_list = []
for i in range(0, z.size(2), self.micro_z_frame_size):
z_bs = z[:, :, i : i + self.micro_z_frame_size]
x_z_bs = self.temporal_vae.decode(z_bs, num_frames=min(self.micro_frame_size, num_frames))
x_z_list.append(x_z_bs)
num_frames -= self.micro_frame_size
x_z = torch.cat(x_z_list, dim=2)
x = self.spatial_vae.decode(x_z)
if self.cal_loss:
return x, x_z
else:
return x
def forward(self, x):
assert self.cal_loss, "This method is only available when cal_loss is True"
z, posterior, x_z = self.encode(x)
x_rec, x_z_rec = self.decode(z, num_frames=x_z.shape[2])
return x_rec, x_z_rec, z, posterior, x_z
def get_latent_size(self, input_size):
if self.micro_frame_size is None or input_size[0] is None:
return self.temporal_vae.get_latent_size(self.spatial_vae.get_latent_size(input_size))
else:
sub_input_size = [self.micro_frame_size, input_size[1], input_size[2]]
sub_latent_size = self.temporal_vae.get_latent_size(self.spatial_vae.get_latent_size(sub_input_size))
sub_latent_size[0] = sub_latent_size[0] * (input_size[0] // self.micro_frame_size)
remain_temporal_size = [input_size[0] % self.micro_frame_size, None, None]
if remain_temporal_size[0] > 0:
remain_size = self.temporal_vae.get_latent_size(remain_temporal_size)
sub_latent_size[0] += remain_size[0]
return sub_latent_size
def get_temporal_last_layer(self):
return self.temporal_vae.decoder.conv_out.conv.weight
@property
def device(self):
return next(self.parameters()).device
@property
def dtype(self):
return next(self.parameters()).dtype
def OpenSoraVAE_V1_2(
micro_batch_size=4,
micro_frame_size=17,
from_pretrained=None,
local_files_only=False,
freeze_vae_2d=False,
cal_loss=False,
):
vae_2d = dict(
type="VideoAutoencoderKL",
from_pretrained="PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",
subfolder="vae",
micro_batch_size=micro_batch_size,
local_files_only=local_files_only,
)
vae_temporal = dict(
type="VAE_Temporal_SD",
from_pretrained=None,
)
shift = (-0.10, 0.34, 0.27, 0.98)
scale = (3.85, 2.32, 2.33, 3.06)
kwargs = dict(
vae_2d=vae_2d,
vae_temporal=vae_temporal,
freeze_vae_2d=freeze_vae_2d,
cal_loss=cal_loss,
micro_frame_size=micro_frame_size,
shift=shift,
scale=scale,
)
if from_pretrained is not None and not os.path.isdir(from_pretrained):
model = VideoAutoencoderPipeline.from_pretrained(from_pretrained, **kwargs)
else:
config = VideoAutoencoderPipelineConfig(**kwargs)
model = VideoAutoencoderPipeline(config)
if from_pretrained:
load_checkpoint(model, from_pretrained)
return model
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