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import math
from dataclasses import dataclass
import os
from typing import Dict, Optional, Tuple, Union
from einops import rearrange
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.models.autoencoders.vae import DecoderOutput, DiagonalGaussianDistribution
from diffusers.models.attention_processor import Attention
from diffusers.models.resnet import ResnetBlock2D
from diffusers.models.upsampling import Upsample2D
from diffusers.models.downsampling import Downsample2D
from diffusers.models.attention_processor import SpatialNorm
class TemporalConvBlock(nn.Module):
"""
Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from:
https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016
"""
def __init__(self, in_dim, out_dim=None, dropout=0.0, up_sample=False, down_sample=False, spa_stride=1):
super().__init__()
out_dim = out_dim or in_dim
self.in_dim = in_dim
self.out_dim = out_dim
spa_pad = int((spa_stride-1)*0.5)
temp_pad = 0
self.temp_pad = temp_pad
if down_sample:
self.conv1 = nn.Sequential(
nn.GroupNorm(32, in_dim),
nn.SiLU(),
nn.Conv3d(in_dim, out_dim, (2, spa_stride, spa_stride), stride=(2,1,1), padding=(0, spa_pad, spa_pad))
)
elif up_sample:
self.conv1 = nn.Sequential(
nn.GroupNorm(32, in_dim),
nn.SiLU(),
nn.Conv3d(in_dim, out_dim*2, (1, spa_stride, spa_stride), padding=(0, spa_pad, spa_pad))
)
else:
self.conv1 = nn.Sequential(
nn.GroupNorm(32, in_dim),
nn.SiLU(),
nn.Conv3d(in_dim, out_dim, (3, spa_stride, spa_stride), padding=(temp_pad, spa_pad, spa_pad))
)
self.conv2 = nn.Sequential(
nn.GroupNorm(32, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, spa_stride, spa_stride), padding=(temp_pad, spa_pad, spa_pad)),
)
self.conv3 = nn.Sequential(
nn.GroupNorm(32, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, spa_stride, spa_stride), padding=(temp_pad, spa_pad, spa_pad)),
)
self.conv4 = nn.Sequential(
nn.GroupNorm(32, out_dim),
nn.SiLU(),
nn.Conv3d(out_dim, in_dim, (3, spa_stride, spa_stride), padding=(temp_pad, spa_pad, spa_pad)),
)
# zero out the last layer params,so the conv block is identity
nn.init.zeros_(self.conv4[-1].weight)
nn.init.zeros_(self.conv4[-1].bias)
self.down_sample = down_sample
self.up_sample = up_sample
def forward(self, hidden_states):
identity = hidden_states
if self.down_sample:
identity = identity[:,:,::2]
elif self.up_sample:
hidden_states_new = torch.cat((hidden_states,hidden_states),dim=2)
hidden_states_new[:, :, 0::2] = hidden_states
hidden_states_new[:, :, 1::2] = hidden_states
identity = hidden_states_new
del hidden_states_new
if self.down_sample or self.up_sample:
hidden_states = self.conv1(hidden_states)
else:
hidden_states = torch.cat((hidden_states[:,:,0:1], hidden_states), dim=2)
hidden_states = torch.cat((hidden_states,hidden_states[:,:,-1:]), dim=2)
hidden_states = self.conv1(hidden_states)
if self.up_sample:
hidden_states = rearrange(hidden_states, 'b (d c) f h w -> b c (f d) h w', d=2)
hidden_states = torch.cat((hidden_states[:,:,0:1], hidden_states), dim=2)
hidden_states = torch.cat((hidden_states,hidden_states[:,:,-1:]), dim=2)
hidden_states = self.conv2(hidden_states)
hidden_states = torch.cat((hidden_states[:,:,0:1], hidden_states), dim=2)
hidden_states = torch.cat((hidden_states,hidden_states[:,:,-1:]), dim=2)
hidden_states = self.conv3(hidden_states)
hidden_states = torch.cat((hidden_states[:,:,0:1], hidden_states), dim=2)
hidden_states = torch.cat((hidden_states,hidden_states[:,:,-1:]), dim=2)
hidden_states = self.conv4(hidden_states)
hidden_states = identity + hidden_states
return hidden_states
class DownEncoderBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor=1.0,
add_downsample=True,
add_temp_downsample=False,
downsample_padding=1,
):
super().__init__()
resnets = []
temp_convs = []
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=None,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
temp_convs.append(
TemporalConvBlock(
out_channels,
out_channels,
dropout=0.1,
)
)
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
if add_temp_downsample:
self.temp_convs_down = TemporalConvBlock(
out_channels,
out_channels,
dropout=0.1,
down_sample=True,
spa_stride=3
)
self.add_temp_downsample = add_temp_downsample
if add_downsample:
self.downsamplers = nn.ModuleList(
[
Downsample2D(
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
)
]
)
else:
self.downsamplers = None
def _set_partial_grad(self):
for temp_conv in self.temp_convs:
temp_conv.requires_grad_(True)
if self.downsamplers:
for down_layer in self.downsamplers:
down_layer.requires_grad_(True)
def forward(self, hidden_states):
bz = hidden_states.shape[0]
for resnet, temp_conv in zip(self.resnets, self.temp_convs):
hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
hidden_states = resnet(hidden_states, temb=None)
hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
hidden_states = temp_conv(hidden_states)
if self.add_temp_downsample:
hidden_states = self.temp_convs_down(hidden_states)
if self.downsamplers is not None:
hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
for upsampler in self.downsamplers:
hidden_states = upsampler(hidden_states)
hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
return hidden_states
class UpDecoderBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default, spatial
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
output_scale_factor=1.0,
add_upsample=True,
add_temp_upsample=False,
temb_channels=None,
):
super().__init__()
self.add_upsample = add_upsample
resnets = []
temp_convs = []
for i in range(num_layers):
input_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=input_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
temp_convs.append(
TemporalConvBlock(
out_channels,
out_channels,
dropout=0.1,
)
)
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
self.add_temp_upsample = add_temp_upsample
if add_temp_upsample:
self.temp_conv_up = TemporalConvBlock(
out_channels,
out_channels,
dropout=0.1,
up_sample=True,
spa_stride=3
)
if self.add_upsample:
# self.upsamplers = nn.ModuleList([PSUpsample2D(out_channels, use_conv=True, use_pixel_shuffle=True, out_channels=out_channels)])
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
else:
self.upsamplers = None
def _set_partial_grad(self):
for temp_conv in self.temp_convs:
temp_conv.requires_grad_(True)
if self.add_upsample:
self.upsamplers.requires_grad_(True)
def forward(self, hidden_states):
bz = hidden_states.shape[0]
for resnet, temp_conv in zip(self.resnets, self.temp_convs):
hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
hidden_states = resnet(hidden_states, temb=None)
hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
hidden_states = temp_conv(hidden_states)
if self.add_temp_upsample:
hidden_states = self.temp_conv_up(hidden_states)
if self.upsamplers is not None:
hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
return hidden_states
class UNetMidBlock3DConv(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default, spatial
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
resnet_pre_norm: bool = True,
add_attention: bool = True,
attention_head_dim=1,
output_scale_factor=1.0,
):
super().__init__()
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
self.add_attention = add_attention
# there is always at least one resnet
resnets = [
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
]
temp_convs = [
TemporalConvBlock(
in_channels,
in_channels,
dropout=0.1,
)
]
attentions = []
if attention_head_dim is None:
attention_head_dim = in_channels
for _ in range(num_layers):
if self.add_attention:
attentions.append(
Attention(
in_channels,
heads=in_channels // attention_head_dim,
dim_head=attention_head_dim,
rescale_output_factor=output_scale_factor,
eps=resnet_eps,
norm_num_groups=resnet_groups if resnet_time_scale_shift == "default" else None,
spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
residual_connection=True,
bias=True,
upcast_softmax=True,
_from_deprecated_attn_block=True,
)
)
else:
attentions.append(None)
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
time_embedding_norm=resnet_time_scale_shift,
non_linearity=resnet_act_fn,
output_scale_factor=output_scale_factor,
pre_norm=resnet_pre_norm,
)
)
temp_convs.append(
TemporalConvBlock(
in_channels,
in_channels,
dropout=0.1,
)
)
self.resnets = nn.ModuleList(resnets)
self.temp_convs = nn.ModuleList(temp_convs)
self.attentions = nn.ModuleList(attentions)
def _set_partial_grad(self):
for temp_conv in self.temp_convs:
temp_conv.requires_grad_(True)
def forward(
self,
hidden_states,
):
bz = hidden_states.shape[0]
hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
hidden_states = self.resnets[0](hidden_states, temb=None)
hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
hidden_states = self.temp_convs[0](hidden_states)
hidden_states = rearrange(hidden_states, 'b c n h w -> (b n) c h w')
for attn, resnet, temp_conv in zip(
self.attentions, self.resnets[1:], self.temp_convs[1:]
):
hidden_states = attn(hidden_states)
hidden_states = resnet(hidden_states, temb=None)
hidden_states = rearrange(hidden_states, '(b n) c h w -> b c n h w', b=bz)
hidden_states = temp_conv(hidden_states)
return hidden_states
class Encoder3D(nn.Module):
def __init__(
self,
in_channels=3,
out_channels=3,
num_blocks=4,
blocks_temp_li=[False, False, False, False],
block_out_channels=(64,),
layers_per_block=2,
norm_num_groups=32,
act_fn="silu",
double_z=True,
):
super().__init__()
self.layers_per_block = layers_per_block
self.blocks_temp_li = blocks_temp_li
self.conv_in = nn.Conv2d(
in_channels,
block_out_channels[0],
kernel_size=3,
stride=1,
padding=1,
)
self.temp_conv_in = nn.Conv3d(
block_out_channels[0],
block_out_channels[0],
(3,1,1),
padding = (1, 0, 0)
)
self.mid_block = None
self.down_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i in range(num_blocks):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = DownEncoderBlock3D(
num_layers=self.layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
add_downsample=not is_final_block,
add_temp_downsample=blocks_temp_li[i],
resnet_eps=1e-6,
downsample_padding=0,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlock3DConv(
in_channels=block_out_channels[-1],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
output_scale_factor=1,
resnet_time_scale_shift="default",
attention_head_dim=block_out_channels[-1],
resnet_groups=norm_num_groups,
temb_channels=None,
)
# 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.temp_conv_out = nn.Conv3d(block_out_channels[-1], block_out_channels[-1], (3,1,1), padding = (1, 0, 0))
self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1)
nn.init.zeros_(self.temp_conv_in.weight)
nn.init.zeros_(self.temp_conv_in.bias)
nn.init.zeros_(self.temp_conv_out.weight)
nn.init.zeros_(self.temp_conv_out.bias)
self.gradient_checkpointing = False
def forward(self, x):
'''
x: [b, c, (tb f), h, w]
'''
bz = x.shape[0]
sample = rearrange(x, 'b c n h w -> (b n) c h w')
sample = self.conv_in(sample)
sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
temp_sample = sample
sample = self.temp_conv_in(sample)
sample = sample+temp_sample
# down
for b_id, down_block in enumerate(self.down_blocks):
sample = down_block(sample)
# middle
sample = self.mid_block(sample)
# post-process
sample = rearrange(sample, 'b c n h w -> (b n) c h w')
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
temp_sample = sample
sample = self.temp_conv_out(sample)
sample = sample+temp_sample
sample = rearrange(sample, 'b c n h w -> (b n) c h w')
sample = self.conv_out(sample)
sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
return sample
class Decoder3D(nn.Module):
def __init__(
self,
in_channels=4,
out_channels=3,
num_blocks=4,
blocks_temp_li=[False, False, False, False],
block_out_channels=(64,),
layers_per_block=2,
norm_num_groups=32,
act_fn="silu",
norm_type="group", # group, spatial
):
super().__init__()
self.layers_per_block = layers_per_block
self.blocks_temp_li = blocks_temp_li
self.conv_in = nn.Conv2d(
in_channels,
block_out_channels[-1],
kernel_size=3,
stride=1,
padding=1,
)
self.temp_conv_in = nn.Conv3d(
block_out_channels[-1],
block_out_channels[-1],
(3,1,1),
padding = (1, 0, 0)
)
self.mid_block = None
self.up_blocks = nn.ModuleList([])
temb_channels = in_channels if norm_type == "spatial" else None
# mid
self.mid_block = UNetMidBlock3DConv(
in_channels=block_out_channels[-1],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
output_scale_factor=1,
resnet_time_scale_shift="default" if norm_type == "group" else norm_type,
attention_head_dim=block_out_channels[-1],
resnet_groups=norm_num_groups,
temb_channels=temb_channels,
)
# up
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i in range(num_blocks):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
up_block = UpDecoderBlock3D(
num_layers=self.layers_per_block + 1,
in_channels=prev_output_channel,
out_channels=output_channel,
add_upsample=not is_final_block,
add_temp_upsample=blocks_temp_li[i],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
temb_channels=temb_channels,
resnet_time_scale_shift=norm_type,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
if norm_type == "spatial":
self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)
else:
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.temp_conv_out = nn.Conv3d(block_out_channels[0], block_out_channels[0], (3,1,1), padding = (1, 0, 0))
self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
nn.init.zeros_(self.temp_conv_in.weight)
nn.init.zeros_(self.temp_conv_in.bias)
nn.init.zeros_(self.temp_conv_out.weight)
nn.init.zeros_(self.temp_conv_out.bias)
self.gradient_checkpointing = False
def forward(self, z):
bz = z.shape[0]
sample = rearrange(z, 'b c n h w -> (b n) c h w')
sample = self.conv_in(sample)
sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
temp_sample = sample
sample = self.temp_conv_in(sample)
sample = sample+temp_sample
upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
# middle
sample = self.mid_block(sample)
sample = sample.to(upscale_dtype)
# up
for b_id, up_block in enumerate(self.up_blocks):
sample = up_block(sample)
# post-process
sample = rearrange(sample, 'b c n h w -> (b n) c h w')
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
temp_sample = sample
sample = self.temp_conv_out(sample)
sample = sample+temp_sample
sample = rearrange(sample, 'b c n h w -> (b n) c h w')
sample = self.conv_out(sample)
sample = rearrange(sample, '(b n) c h w -> b c n h w', b=bz)
return sample
class AllegroAutoencoderKL3D(ModelMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of block output channels.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `256`): Spatial Tiling Size.
tile_overlap (`tuple`, *optional*, defaults to `(120, 80`): Spatial overlapping size while tiling (height, width)
chunk_len (`int`, *optional*, defaults to `24`): Temporal Tiling Size.
t_over (`int`, *optional*, defaults to `8`): Temporal overlapping size while tiling
scaling_factor (`float`, *optional*, defaults to 0.13235):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
force_upcast (`bool`, *optional*, default to `True`):
If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
can be fine-tuned / trained to a lower range without loosing too much precision in which case
`force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
blocks_tempdown_li (`List`, *optional*, defaults to `[True, True, False, False]`): Each item indicates whether each TemporalBlock in the Encoder performs temporal downsampling.
blocks_tempup_li (`List`, *optional*, defaults to `[False, True, True, False]`): Each item indicates whether each TemporalBlock in the Decoder performs temporal upsampling.
load_mode (`str`, *optional*, defaults to `full`): Load mode for the model. Can be one of `full`, `encoder_only`, `decoder_only`. which corresponds to loading the full model state dicts, only the encoder state dicts, or only the decoder state dicts.
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_num: int = 4,
up_block_num: int = 4,
block_out_channels: Tuple[int] = (128,256,512,512),
layers_per_block: int = 2,
act_fn: str = "silu",
latent_channels: int = 4,
norm_num_groups: int = 32,
sample_size: int = 320,
tile_overlap: tuple = (120, 80),
force_upcast: bool = True,
chunk_len: int = 24,
t_over: int = 8,
scale_factor: float = 0.13235,
blocks_tempdown_li=[True, True, False, False],
blocks_tempup_li=[False, True, True, False],
load_mode = 'full',
):
super().__init__()
self.blocks_tempdown_li = blocks_tempdown_li
self.blocks_tempup_li = blocks_tempup_li
# pass init params to Encoder
self.load_mode = load_mode
if load_mode in ['full', 'encoder_only']:
self.encoder = Encoder3D(
in_channels=in_channels,
out_channels=latent_channels,
num_blocks=down_block_num,
blocks_temp_li=blocks_tempdown_li,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
double_z=True,
)
self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
if load_mode in ['full', 'decoder_only']:
# pass init params to Decoder
self.decoder = Decoder3D(
in_channels=latent_channels,
out_channels=out_channels,
num_blocks=up_block_num,
blocks_temp_li=blocks_tempup_li,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
norm_num_groups=norm_num_groups,
act_fn=act_fn,
)
self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)
# only relevant if vae tiling is enabled
sample_size = (
sample_size[0]
if isinstance(sample_size, (list, tuple))
else sample_size
)
self.tile_overlap = tile_overlap
self.vae_scale_factor=[4, 8, 8]
self.scale_factor = scale_factor
self.sample_size = sample_size
self.chunk_len = chunk_len
self.t_over = t_over
self.latent_chunk_len = self.chunk_len//4
self.latent_t_over = self.t_over//4
self.kernel = (self.chunk_len, self.sample_size, self.sample_size) #(24, 256, 256)
self.stride = (self.chunk_len - self.t_over, self.sample_size-self.tile_overlap[0], self.sample_size-self.tile_overlap[1]) # (16, 112, 192)
def encode(self, input_imgs: torch.Tensor, return_dict: bool = True, local_batch_size=1) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
KERNEL = self.kernel
STRIDE = self.stride
LOCAL_BS = local_batch_size
OUT_C = 8
B, C, N, H, W = input_imgs.shape
out_n = math.floor((N - KERNEL[0]) / STRIDE[0]) + 1
out_h = math.floor((H - KERNEL[1]) / STRIDE[1]) + 1
out_w = math.floor((W - KERNEL[2]) / STRIDE[2]) + 1
## cut video into overlapped small cubes and batch forward
num = 0
out_latent = torch.zeros((out_n*out_h*out_w, OUT_C, KERNEL[0]//4, KERNEL[1]//8, KERNEL[2]//8), device=input_imgs.device, dtype=input_imgs.dtype)
vae_batch_input = torch.zeros((LOCAL_BS, C, KERNEL[0], KERNEL[1], KERNEL[2]), device=input_imgs.device, dtype=input_imgs.dtype)
for i in range(out_n):
for j in range(out_h):
for k in range(out_w):
n_start, n_end = i * STRIDE[0], i * STRIDE[0] + KERNEL[0]
h_start, h_end = j * STRIDE[1], j * STRIDE[1] + KERNEL[1]
w_start, w_end = k * STRIDE[2], k * STRIDE[2] + KERNEL[2]
video_cube = input_imgs[:, :, n_start:n_end, h_start:h_end, w_start:w_end]
vae_batch_input[num%LOCAL_BS] = video_cube
if num%LOCAL_BS == LOCAL_BS-1 or num == out_n*out_h*out_w-1:
latent = self.encoder(vae_batch_input)
if num == out_n*out_h*out_w-1 and num%LOCAL_BS != LOCAL_BS-1:
out_latent[num-num%LOCAL_BS:] = latent[:num%LOCAL_BS+1]
else:
out_latent[num-LOCAL_BS+1:num+1] = latent
vae_batch_input = torch.zeros((LOCAL_BS, C, KERNEL[0], KERNEL[1], KERNEL[2]), device=input_imgs.device, dtype=input_imgs.dtype)
num+=1
## flatten the batched out latent to videos and supress the overlapped parts
B, C, N, H, W = input_imgs.shape
out_video_cube = torch.zeros((B, OUT_C, N//4, H//8, W//8), device=input_imgs.device, dtype=input_imgs.dtype)
OUT_KERNEL = KERNEL[0]//4, KERNEL[1]//8, KERNEL[2]//8
OUT_STRIDE = STRIDE[0]//4, STRIDE[1]//8, STRIDE[2]//8
OVERLAP = OUT_KERNEL[0]-OUT_STRIDE[0], OUT_KERNEL[1]-OUT_STRIDE[1], OUT_KERNEL[2]-OUT_STRIDE[2]
for i in range(out_n):
n_start, n_end = i * OUT_STRIDE[0], i * OUT_STRIDE[0] + OUT_KERNEL[0]
for j in range(out_h):
h_start, h_end = j * OUT_STRIDE[1], j * OUT_STRIDE[1] + OUT_KERNEL[1]
for k in range(out_w):
w_start, w_end = k * OUT_STRIDE[2], k * OUT_STRIDE[2] + OUT_KERNEL[2]
latent_mean_blend = prepare_for_blend((i, out_n, OVERLAP[0]), (j, out_h, OVERLAP[1]), (k, out_w, OVERLAP[2]), out_latent[i*out_h*out_w+j*out_w+k].unsqueeze(0))
out_video_cube[:, :, n_start:n_end, h_start:h_end, w_start:w_end] += latent_mean_blend
## final conv
out_video_cube = rearrange(out_video_cube, 'b c n h w -> (b n) c h w')
out_video_cube = self.quant_conv(out_video_cube)
out_video_cube = rearrange(out_video_cube, '(b n) c h w -> b c n h w', b=B)
posterior = DiagonalGaussianDistribution(out_video_cube)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def decode(self, input_latents: torch.Tensor, return_dict: bool = True, local_batch_size=1) -> Union[DecoderOutput, torch.Tensor]:
KERNEL = self.kernel
STRIDE = self.stride
LOCAL_BS = local_batch_size
OUT_C = 3
IN_KERNEL = KERNEL[0]//4, KERNEL[1]//8, KERNEL[2]//8
IN_STRIDE = STRIDE[0]//4, STRIDE[1]//8, STRIDE[2]//8
B, C, N, H, W = input_latents.shape
## post quant conv (a mapping)
input_latents = rearrange(input_latents, 'b c n h w -> (b n) c h w')
input_latents = self.post_quant_conv(input_latents)
input_latents = rearrange(input_latents, '(b n) c h w -> b c n h w', b=B)
## out tensor shape
out_n = math.floor((N - IN_KERNEL[0]) / IN_STRIDE[0]) + 1
out_h = math.floor((H - IN_KERNEL[1]) / IN_STRIDE[1]) + 1
out_w = math.floor((W - IN_KERNEL[2]) / IN_STRIDE[2]) + 1
## cut latent into overlapped small cubes and batch forward
num = 0
decoded_cube = torch.zeros((out_n*out_h*out_w, OUT_C, KERNEL[0], KERNEL[1], KERNEL[2]), device=input_latents.device, dtype=input_latents.dtype)
vae_batch_input = torch.zeros((LOCAL_BS, C, IN_KERNEL[0], IN_KERNEL[1], IN_KERNEL[2]), device=input_latents.device, dtype=input_latents.dtype)
for i in range(out_n):
for j in range(out_h):
for k in range(out_w):
n_start, n_end = i * IN_STRIDE[0], i * IN_STRIDE[0] + IN_KERNEL[0]
h_start, h_end = j * IN_STRIDE[1], j * IN_STRIDE[1] + IN_KERNEL[1]
w_start, w_end = k * IN_STRIDE[2], k * IN_STRIDE[2] + IN_KERNEL[2]
latent_cube = input_latents[:, :, n_start:n_end, h_start:h_end, w_start:w_end]
vae_batch_input[num%LOCAL_BS] = latent_cube
if num%LOCAL_BS == LOCAL_BS-1 or num == out_n*out_h*out_w-1:
latent = self.decoder(vae_batch_input)
if num == out_n*out_h*out_w-1 and num%LOCAL_BS != LOCAL_BS-1:
decoded_cube[num-num%LOCAL_BS:] = latent[:num%LOCAL_BS+1]
else:
decoded_cube[num-LOCAL_BS+1:num+1] = latent
vae_batch_input = torch.zeros((LOCAL_BS, C, IN_KERNEL[0], IN_KERNEL[1], IN_KERNEL[2]), device=input_latents.device, dtype=input_latents.dtype)
num+=1
B, C, N, H, W = input_latents.shape
out_video = torch.zeros((B, OUT_C, N*4, H*8, W*8), device=input_latents.device, dtype=input_latents.dtype)
OVERLAP = KERNEL[0]-STRIDE[0], KERNEL[1]-STRIDE[1], KERNEL[2]-STRIDE[2]
for i in range(out_n):
n_start, n_end = i * STRIDE[0], i * STRIDE[0] + KERNEL[0]
for j in range(out_h):
h_start, h_end = j * STRIDE[1], j * STRIDE[1] + KERNEL[1]
for k in range(out_w):
w_start, w_end = k * STRIDE[2], k * STRIDE[2] + KERNEL[2]
out_video_blend = prepare_for_blend((i, out_n, OVERLAP[0]), (j, out_h, OVERLAP[1]), (k, out_w, OVERLAP[2]), decoded_cube[i*out_h*out_w+j*out_w+k].unsqueeze(0))
out_video[:, :, n_start:n_end, h_start:h_end, w_start:w_end] += out_video_blend
out_video = rearrange(out_video, 'b c t h w -> b t c h w').contiguous()
decoded = out_video
if not return_dict:
return (decoded,)
return DecoderOutput(sample=decoded)
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
encoder_local_batch_size: int = 2,
decoder_local_batch_size: int = 2,
) -> Union[DecoderOutput, torch.Tensor]:
r"""
Args:
sample (`torch.Tensor`): Input sample.
sample_posterior (`bool`, *optional*, defaults to `False`):
Whether to sample from the posterior.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
generator (`torch.Generator`, *optional*):
PyTorch random number generator.
encoder_local_batch_size (`int`, *optional*, defaults to 2):
Local batch size for the encoder's batch inference.
decoder_local_batch_size (`int`, *optional*, defaults to 2):
Local batch size for the decoder's batch inference.
"""
x = sample
posterior = self.encode(x, local_batch_size=encoder_local_batch_size).latent_dist
if sample_posterior:
z = posterior.sample(generator=generator)
else:
z = posterior.mode()
dec = self.decode(z, local_batch_size=decoder_local_batch_size).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
kwargs["torch_type"] = torch.float32
return super().from_pretrained(pretrained_model_name_or_path, **kwargs)
def prepare_for_blend(n_param, h_param, w_param, x):
n, n_max, overlap_n = n_param
h, h_max, overlap_h = h_param
w, w_max, overlap_w = w_param
if overlap_n > 0:
if n > 0: # the head overlap part decays from 0 to 1
x[:,:,0:overlap_n,:,:] = x[:,:,0:overlap_n,:,:] * (torch.arange(0, overlap_n).float().to(x.device) / overlap_n).reshape(overlap_n,1,1)
if n < n_max-1: # the tail overlap part decays from 1 to 0
x[:,:,-overlap_n:,:,:] = x[:,:,-overlap_n:,:,:] * (1 - torch.arange(0, overlap_n).float().to(x.device) / overlap_n).reshape(overlap_n,1,1)
if h > 0:
x[:,:,:,0:overlap_h,:] = x[:,:,:,0:overlap_h,:] * (torch.arange(0, overlap_h).float().to(x.device) / overlap_h).reshape(overlap_h,1)
if h < h_max-1:
x[:,:,:,-overlap_h:,:] = x[:,:,:,-overlap_h:,:] * (1 - torch.arange(0, overlap_h).float().to(x.device) / overlap_h).reshape(overlap_h,1)
if w > 0:
x[:,:,:,:,0:overlap_w] = x[:,:,:,:,0:overlap_w] * (torch.arange(0, overlap_w).float().to(x.device) / overlap_w)
if w < w_max-1:
x[:,:,:,:,-overlap_w:] = x[:,:,:,:,-overlap_w:] * (1 - torch.arange(0, overlap_w).float().to(x.device) / overlap_w)
return x
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