# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from ..configuration_utils import ConfigMixin, register_to_config
from ..loaders import UNet2DConditionLoadersMixin
from ..utils import logging
from .attention_processor import (
ADDED_KV_ATTENTION_PROCESSORS,
CROSS_ATTENTION_PROCESSORS,
AttentionProcessor,
AttnAddedKVProcessor,
AttnProcessor,
)
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .transformer_temporal import TransformerTemporalModel
from .unet_2d_blocks import UNetMidBlock2DCrossAttn
from .unet_2d_condition import UNet2DConditionModel
from .unet_3d_blocks import (
CrossAttnDownBlockMotion,
CrossAttnUpBlockMotion,
DownBlockMotion,
UNetMidBlockCrossAttnMotion,
UpBlockMotion,
get_down_block,
get_up_block,
)
from .unet_3d_condition import UNet3DConditionOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class MotionModules(nn.Module):
def __init__(
self,
in_channels,
layers_per_block=2,
num_attention_heads=8,
attention_bias=False,
cross_attention_dim=None,
activation_fn="geglu",
norm_num_groups=32,
max_seq_length=32,
):
super().__init__()
self.motion_modules = nn.ModuleList([])
for i in range(layers_per_block):
self.motion_modules.append(
TransformerTemporalModel(
in_channels=in_channels,
norm_num_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
attention_bias=attention_bias,
num_attention_heads=num_attention_heads,
attention_head_dim=in_channels // num_attention_heads,
positional_embeddings="sinusoidal",
num_positional_embeddings=max_seq_length,
)
)
class MotionAdapter(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
block_out_channels=(320, 640, 1280, 1280),
motion_layers_per_block=2,
motion_mid_block_layers_per_block=1,
motion_num_attention_heads=8,
motion_norm_num_groups=32,
motion_max_seq_length=32,
use_motion_mid_block=True,
):
"""Container to store AnimateDiff Motion Modules
Args:
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each UNet block.
motion_layers_per_block (`int`, *optional*, defaults to 2):
The number of motion layers per UNet block.
motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1):
The number of motion layers in the middle UNet block.
motion_num_attention_heads (`int`, *optional*, defaults to 8):
The number of heads to use in each attention layer of the motion module.
motion_norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use in each group normalization layer of the motion module.
motion_max_seq_length (`int`, *optional*, defaults to 32):
The maximum sequence length to use in the motion module.
use_motion_mid_block (`bool`, *optional*, defaults to True):
Whether to use a motion module in the middle of the UNet.
"""
super().__init__()
down_blocks = []
up_blocks = []
for i, channel in enumerate(block_out_channels):
output_channel = block_out_channels[i]
down_blocks.append(
MotionModules(
in_channels=output_channel,
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=motion_num_attention_heads,
max_seq_length=motion_max_seq_length,
layers_per_block=motion_layers_per_block,
)
)
if use_motion_mid_block:
self.mid_block = MotionModules(
in_channels=block_out_channels[-1],
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=motion_num_attention_heads,
layers_per_block=motion_mid_block_layers_per_block,
max_seq_length=motion_max_seq_length,
)
else:
self.mid_block = None
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, channel in enumerate(reversed_block_out_channels):
output_channel = reversed_block_out_channels[i]
up_blocks.append(
MotionModules(
in_channels=output_channel,
norm_num_groups=motion_norm_num_groups,
cross_attention_dim=None,
activation_fn="geglu",
attention_bias=False,
num_attention_heads=motion_num_attention_heads,
max_seq_length=motion_max_seq_length,
layers_per_block=motion_layers_per_block + 1,
)
)
self.down_blocks = nn.ModuleList(down_blocks)
self.up_blocks = nn.ModuleList(up_blocks)
def forward(self, sample):
pass
class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a
sample shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
down_block_types: Tuple[str] = (
"CrossAttnDownBlockMotion",
"CrossAttnDownBlockMotion",
"CrossAttnDownBlockMotion",
"DownBlockMotion",
),
up_block_types: Tuple[str] = (
"UpBlockMotion",
"CrossAttnUpBlockMotion",
"CrossAttnUpBlockMotion",
"CrossAttnUpBlockMotion",
),
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
layers_per_block: int = 2,
downsample_padding: int = 1,
mid_block_scale_factor: float = 1,
act_fn: str = "silu",
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: int = 1280,
use_linear_projection: bool = False,
num_attention_heads: Optional[Union[int, Tuple[int]]] = 8,
motion_max_seq_length: Optional[int] = 32,
motion_num_attention_heads: int = 8,
use_motion_mid_block: int = True,
):
super().__init__()
self.sample_size = sample_size
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
)
if len(block_out_channels) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
)
# input
conv_in_kernel = 3
conv_out_kernel = 3
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)
# time
time_embed_dim = block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], True, 0)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
act_fn=act_fn,
)
# class embedding
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
# 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
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[i],
downsample_padding=downsample_padding,
use_linear_projection=use_linear_projection,
dual_cross_attention=False,
temporal_num_attention_heads=motion_num_attention_heads,
temporal_max_seq_length=motion_max_seq_length,
)
self.down_blocks.append(down_block)
# mid
if use_motion_mid_block:
self.mid_block = UNetMidBlockCrossAttnMotion(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=False,
temporal_num_attention_heads=motion_num_attention_heads,
temporal_max_seq_length=motion_max_seq_length,
)
else:
self.mid_block = UNetMidBlock2DCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=False,
)
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
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
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsample=add_upsample,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=reversed_num_attention_heads[i],
dual_cross_attention=False,
resolution_idx=i,
use_linear_projection=use_linear_projection,
temporal_num_attention_heads=motion_num_attention_heads,
temporal_max_seq_length=motion_max_seq_length,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
if norm_num_groups is not None:
self.conv_norm_out = nn.GroupNorm(
num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps
)
self.conv_act = nn.SiLU()
else:
self.conv_norm_out = None
self.conv_act = None
conv_out_padding = (conv_out_kernel - 1) // 2
self.conv_out = nn.Conv2d(
block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
)
@classmethod
def from_unet2d(
cls,
unet: UNet2DConditionModel,
motion_adapter: Optional[MotionAdapter] = None,
load_weights: bool = True,
):
has_motion_adapter = motion_adapter is not None
# based on https://github.com/guoyww/AnimateDiff/blob/895f3220c06318ea0760131ec70408b466c49333/animatediff/models/unet.py#L459
config = unet.config
config["_class_name"] = cls.__name__
down_blocks = []
for down_blocks_type in config["down_block_types"]:
if "CrossAttn" in down_blocks_type:
down_blocks.append("CrossAttnDownBlockMotion")
else:
down_blocks.append("DownBlockMotion")
config["down_block_types"] = down_blocks
up_blocks = []
for down_blocks_type in config["up_block_types"]:
if "CrossAttn" in down_blocks_type:
up_blocks.append("CrossAttnUpBlockMotion")
else:
up_blocks.append("UpBlockMotion")
config["up_block_types"] = up_blocks
if has_motion_adapter:
config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"]
config["motion_max_seq_length"] = motion_adapter.config["motion_max_seq_length"]
config["use_motion_mid_block"] = motion_adapter.config["use_motion_mid_block"]
# Need this for backwards compatibility with UNet2DConditionModel checkpoints
if not config.get("num_attention_heads"):
config["num_attention_heads"] = config["attention_head_dim"]
model = cls.from_config(config)
if not load_weights:
return model
model.conv_in.load_state_dict(unet.conv_in.state_dict())
model.time_proj.load_state_dict(unet.time_proj.state_dict())
model.time_embedding.load_state_dict(unet.time_embedding.state_dict())
for i, down_block in enumerate(unet.down_blocks):
model.down_blocks[i].resnets.load_state_dict(down_block.resnets.state_dict())
if hasattr(model.down_blocks[i], "attentions"):
model.down_blocks[i].attentions.load_state_dict(down_block.attentions.state_dict())
if model.down_blocks[i].downsamplers:
model.down_blocks[i].downsamplers.load_state_dict(down_block.downsamplers.state_dict())
for i, up_block in enumerate(unet.up_blocks):
model.up_blocks[i].resnets.load_state_dict(up_block.resnets.state_dict())
if hasattr(model.up_blocks[i], "attentions"):
model.up_blocks[i].attentions.load_state_dict(up_block.attentions.state_dict())
if model.up_blocks[i].upsamplers:
model.up_blocks[i].upsamplers.load_state_dict(up_block.upsamplers.state_dict())
model.mid_block.resnets.load_state_dict(unet.mid_block.resnets.state_dict())
model.mid_block.attentions.load_state_dict(unet.mid_block.attentions.state_dict())
if unet.conv_norm_out is not None:
model.conv_norm_out.load_state_dict(unet.conv_norm_out.state_dict())
if unet.conv_act is not None:
model.conv_act.load_state_dict(unet.conv_act.state_dict())
model.conv_out.load_state_dict(unet.conv_out.state_dict())
if has_motion_adapter:
model.load_motion_modules(motion_adapter)
# ensure that the Motion UNet is the same dtype as the UNet2DConditionModel
model.to(unet.dtype)
return model
def freeze_unet2d_params(self):
"""Freeze the weights of just the UNet2DConditionModel, and leave the motion modules
unfrozen for fine tuning.
"""
# Freeze everything
for param in self.parameters():
param.requires_grad = False
# Unfreeze Motion Modules
for down_block in self.down_blocks:
motion_modules = down_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True
for up_block in self.up_blocks:
motion_modules = up_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True
if hasattr(self.mid_block, "motion_modules"):
motion_modules = self.mid_block.motion_modules
for param in motion_modules.parameters():
param.requires_grad = True
return
def load_motion_modules(self, motion_adapter: Optional[MotionAdapter]):
for i, down_block in enumerate(motion_adapter.down_blocks):
self.down_blocks[i].motion_modules.load_state_dict(down_block.motion_modules.state_dict())
for i, up_block in enumerate(motion_adapter.up_blocks):
self.up_blocks[i].motion_modules.load_state_dict(up_block.motion_modules.state_dict())
# to support older motion modules that don't have a mid_block
if hasattr(self.mid_block, "motion_modules"):
self.mid_block.motion_modules.load_state_dict(motion_adapter.mid_block.motion_modules.state_dict())
def save_motion_modules(
self,
save_directory: str,
is_main_process: bool = True,
safe_serialization: bool = True,
variant: Optional[str] = None,
push_to_hub: bool = False,
**kwargs,
):
state_dict = self.state_dict()
# Extract all motion modules
motion_state_dict = {}
for k, v in state_dict.items():
if "motion_modules" in k:
motion_state_dict[k] = v
adapter = MotionAdapter(
block_out_channels=self.config["block_out_channels"],
motion_layers_per_block=self.config["layers_per_block"],
motion_norm_num_groups=self.config["norm_num_groups"],
motion_num_attention_heads=self.config["motion_num_attention_heads"],
motion_max_seq_length=self.config["motion_max_seq_length"],
use_motion_mid_block=self.config["use_motion_mid_block"],
)
adapter.load_state_dict(motion_state_dict)
adapter.save_pretrained(
save_directory=save_directory,
is_main_process=is_main_process,
safe_serialization=safe_serialization,
variant=variant,
push_to_hub=push_to_hub,
**kwargs,
)
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(
self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor, _remove_lora=_remove_lora)
else:
module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
# Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
def enable_forward_chunking(self, chunk_size=None, dim=0):
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
Parameters:
chunk_size (`int`, *optional*):
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=`dim`.
dim (`int`, *optional*, defaults to `0`):
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).
"""
if dim not in [0, 1]:
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
# By default chunk size is 1
chunk_size = chunk_size or 1
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
# Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
def disable_forward_chunking(self):
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, None, 0)
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnAddedKVProcessor()
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor, _remove_lora=True)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, CrossAttnUpBlockMotion, UpBlockMotion)):
module.gradient_checkpointing = value
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.enable_freeu
def enable_freeu(self, s1, s2, b1, b2):
r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stage blocks where they are being applied.
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
for i, upsample_block in enumerate(self.up_blocks):
setattr(upsample_block, "s1", s1)
setattr(upsample_block, "s2", s2)
setattr(upsample_block, "b1", b1)
setattr(upsample_block, "b2", b2)
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.disable_freeu
def disable_freeu(self):
"""Disables the FreeU mechanism."""
freeu_keys = {"s1", "s2", "b1", "b2"}
for i, upsample_block in enumerate(self.up_blocks):
for k in freeu_keys:
if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
setattr(upsample_block, k, None)
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet3DConditionOutput, Tuple]:
r"""
The [`UNetMotionModel`] forward method.
Args:
sample (`torch.FloatTensor`):
The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.
timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
encoder_hidden_states (`torch.FloatTensor`):
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
through the `self.time_embedding` layer to obtain the timestep embeddings.
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
negative values to the attention scores corresponding to "discard" tokens.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
A tuple of tensors that if specified are added to the residuals of down unet blocks.
mid_block_additional_residual: (`torch.Tensor`, *optional*):
A tensor that if specified is added to the residual of the middle unet block.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
tuple.
Returns:
[`~models.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unet_3d_condition.UNet3DConditionOutput`] is returned, otherwise
a `tuple` is returned where the first element is the sample tensor.
"""
# 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:]):
logger.info("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# prepare attention_mask
if attention_mask is not None:
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
attention_mask = attention_mask.unsqueeze(1)
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
num_frames = sample.shape[2]
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# 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=self.dtype)
emb = self.time_embedding(t_emb, timestep_cond)
emb = emb.repeat_interleave(repeats=num_frames, dim=0)
encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
# 2. pre-process
sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample, # 32 320 64 64
temb=emb,
encoder_hidden_states=encoder_hidden_states, # 32 77 768
attention_mask=attention_mask,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
new_down_block_res_samples = ()
for down_block_res_sample, down_block_additional_residual in zip(
down_block_res_samples, down_block_additional_residuals
):
down_block_res_sample = down_block_res_sample + down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
down_block_res_samples = new_down_block_res_samples
# 4. mid
if self.mid_block is not None:
# To support older versions of motion modules that don't have a mid_block
if hasattr(self.mid_block, "motion_modules"):
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
cross_attention_kwargs=cross_attention_kwargs,
)
if mid_block_additional_residual is not None:
sample = sample + mid_block_additional_residual
# 5. 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, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states,
upsample_size=upsample_size,
attention_mask=attention_mask,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
upsample_size=upsample_size,
num_frames=num_frames,
)
# 6. post-process
if self.conv_norm_out:
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
# reshape to (batch, channel, framerate, width, height)
sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)
if not return_dict:
return (sample,)
return UNet3DConditionOutput(sample=sample)