# 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): @register_to_config 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 @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: 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 ) @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) -> 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, ) @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: 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, _remove_lora=True) 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)