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torch_unet_pseudo3d_condition.py
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torch_unet_pseudo3d_condition.py
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# Make-A-Video Latent Diffusion Models
# Copyright (C) 2023 Lopho <contact@lopho.org>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from typing import Tuple, Union
import torch
from torch import nn
import torch.nn as nn
from .torch_unet_pseudo3d_blocks import (
UNetMidBlockPseudo3DCrossAttn,
DownBlockPseudo3D,
CrossAttnDownBlockPseudo3D,
UpBlockPseudo3D,
CrossAttnUpBlockPseudo3D
)
from .torch_resnet_pseudo3d import ConvPseudo3D
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
from dataclasses import dataclass
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import UNet2DConditionLoadersMixin
from diffusers.utils import BaseOutput
from diffusers.models.modeling_utils import ModelMixin
@dataclass
class UNetPseudo3DConditionOutput(BaseOutput):
sample: torch.FloatTensor
class UNetPseudo3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
_supports_gradient_checkpointing = True
@register_to_config
def __init__(self,
in_channels: int = 9,
out_channels: int = 4,
flip_sin_to_cos: bool = True,
freq_shift: int = 0,
down_block_types: Tuple[str] = (
"CrossAttnDownBlockPseudo3D",
"CrossAttnDownBlockPseudo3D",
"CrossAttnDownBlockPseudo3D",
"DownBlockPseudo3D",
),
up_block_types: Tuple[str] = (
"UpBlockPseudo3D",
"CrossAttnUpBlockPseudo3D",
"CrossAttnUpBlockPseudo3D",
"CrossAttnUpBlockPseudo3D"
),
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
layers_per_block: Union[int, Tuple[int, ...]] = 2,
cross_attention_dim: Union[int, Tuple[int, ...]] = 768,
attention_head_dim: Union[int, Tuple[int, ...]] = 8,
**kwargs
) -> None:
super().__init__()
time_embed_dim = block_out_channels[0] * 4
if isinstance(attention_head_dim, int):
attention_head_dim = (attention_head_dim,) * len(down_block_types)
if isinstance(cross_attention_dim, int):
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
if isinstance(layers_per_block, int):
layers_per_block = (layers_per_block,) * len(down_block_types)
# input
self.conv_in = ConvPseudo3D(
in_channels,
block_out_channels[0],
kernel_size = 3,
padding = (1, 1)
)
# time
self.time_proj = Timesteps(
block_out_channels[0],
flip_sin_to_cos,
freq_shift
)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim
)
self.down_blocks = nn.ModuleList()
self.mid_block = None
self.up_blocks = nn.ModuleList()
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
if down_block_type in ['DownBlock2D', 'DownBlockPseudo3D']:
down_block = DownBlockPseudo3D(
num_layers = layers_per_block[i],
in_channels = input_channel,
out_channels = output_channel,
temb_channels = time_embed_dim,
add_downsample = not is_final_block
)
elif down_block_type in ['CrossAttnDownBlock2D', 'CrossAttnDownBlockPseudo3D']:
down_block = CrossAttnDownBlockPseudo3D(
num_layers = layers_per_block[i],
in_channels = input_channel,
out_channels = output_channel,
temb_channels = time_embed_dim,
add_downsample = not is_final_block,
cross_attention_dim = cross_attention_dim[i],
attn_num_head_channels = attention_head_dim[i]
)
else:
raise NotImplementedError(down_block_type)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlockPseudo3DCrossAttn(
in_channels = block_out_channels[-1],
temb_channels = time_embed_dim,
cross_attention_dim = cross_attention_dim[-1],
attn_num_head_channels = attention_head_dim[-1]
)
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_attention_head_dim = list(reversed(attention_head_dim))
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
reversed_layers_per_block = list(reversed(layers_per_block))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False
if up_block_type in ['UpBlock2D', 'UpBlockPseudo3D']:
up_block = UpBlockPseudo3D(
num_layers = reversed_layers_per_block[i] + 1,
in_channels = input_channel,
out_channels = output_channel,
prev_output_channel = prev_output_channel,
temb_channels = time_embed_dim,
add_upsample = add_upsample
)
elif up_block_type in ['CrossAttnUpBlock2D', 'CrossAttnUpBlockPseudo3D']:
up_block = CrossAttnUpBlockPseudo3D(
num_layers = reversed_layers_per_block[i] + 1,
in_channels = input_channel,
out_channels = output_channel,
prev_output_channel = prev_output_channel,
temb_channels = time_embed_dim,
add_upsample = add_upsample,
cross_attention_dim = reversed_cross_attention_dim[i],
attn_num_head_channels = reversed_attention_head_dim[i]
)
else:
raise NotImplementedError(up_block_type)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
self.conv_norm_out = nn.GroupNorm(
num_channels = block_out_channels[0],
num_groups = 32,
eps = 1e-5
)
self.conv_act = nn.SiLU()
self.conv_out = ConvPseudo3D(
block_out_channels[0],
out_channels,
3,
padding = 1
)
def forward(
self,
sample: torch.FloatTensor,
timesteps: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor
) -> Union[UNetPseudo3DConditionOutput, Tuple]:
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
forward_upsample_size = True
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
t_emb = t_emb.to(dtype = sample.dtype)
emb = self.time_embedding(t_emb)
sample = self.conv_in(sample)
# down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, 'attentions') and downsample_block.attentions is not None:
sample, res_samples = downsample_block(
hidden_states = sample,
temb = emb,
encoder_hidden_states = encoder_hidden_states,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# mid
sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)
# up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, 'attentions') and upsample_block.attentions is not None:
sample = upsample_block(
hidden_states = sample,
temb = emb,
res_hidden_states_tuple = res_samples,
encoder_hidden_states = encoder_hidden_states,
upsample_size = upsample_size
)
else:
sample = upsample_block(
hidden_states = sample,
temb = emb,
res_hidden_states_tuple = res_samples,
upsample_size = upsample_size
)
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
return UNetPseudo3DConditionOutput(sample = sample)