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# 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: int, | |
layers_per_block: int = 2, | |
num_attention_heads: int = 8, | |
attention_bias: bool = False, | |
cross_attention_dim: Optional[int] = None, | |
activation_fn: str = "geglu", | |
norm_num_groups: int = 32, | |
max_seq_length: int = 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): | |
def __init__( | |
self, | |
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), | |
motion_layers_per_block: int = 2, | |
motion_mid_block_layers_per_block: int = 1, | |
motion_num_attention_heads: int = 8, | |
motion_norm_num_groups: int = 32, | |
motion_max_seq_length: int = 32, | |
use_motion_mid_block: bool = 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 | |
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: int = 32, | |
norm_eps: float = 1e-5, | |
cross_attention_dim: int = 1280, | |
use_linear_projection: bool = False, | |
num_attention_heads: Union[int, Tuple[int, ...]] = 8, | |
motion_max_seq_length: int = 32, | |
motion_num_attention_heads: int = 8, | |
use_motion_mid_block: int = True, | |
encoder_hid_dim: Optional[int] = None, | |
encoder_hid_dim_type: Optional[str] = None, | |
): | |
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, | |
) | |
if encoder_hid_dim_type is None: | |
self.encoder_hid_proj = None | |
# 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 | |
) | |
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) -> None: | |
"""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 | |
def load_motion_modules(self, motion_adapter: Optional[MotionAdapter]) -> None: | |
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, | |
) -> None: | |
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, | |
) | |
# 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]]): | |
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) | |
else: | |
module.set_processor(processor.pop(f"{name}.processor")) | |
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: Optional[int] = None, dim: int = 0) -> None: | |
""" | |
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) -> None: | |
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) -> None: | |
""" | |
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) | |
def _set_gradient_checkpointing(self, module, value: bool = False) -> None: | |
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: float, s2: float, b1: float, b2: float) -> None: | |
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) -> None: | |
"""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, | |
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = 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[torch.Tensor]]: | |
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) | |
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": | |
if "image_embeds" not in added_cond_kwargs: | |
raise ValueError( | |
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" | |
) | |
image_embeds = added_cond_kwargs.get("image_embeds") | |
image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype) | |
encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1) | |
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, | |
temb=emb, | |
encoder_hidden_states=encoder_hidden_states, | |
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) | |