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from typing import Any, Dict, Optional, Tuple, Union |
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|
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.utils.checkpoint |
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|
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from ...configuration_utils import ConfigMixin, FrozenDict, register_to_config |
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from ...loaders import UNet2DConditionLoadersMixin |
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from ...utils import logging |
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from ..attention_processor import ( |
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ADDED_KV_ATTENTION_PROCESSORS, |
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CROSS_ATTENTION_PROCESSORS, |
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Attention, |
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AttentionProcessor, |
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AttnAddedKVProcessor, |
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AttnProcessor, |
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AttnProcessor2_0, |
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IPAdapterAttnProcessor, |
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IPAdapterAttnProcessor2_0, |
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) |
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from ..embeddings import TimestepEmbedding, Timesteps |
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from ..modeling_utils import ModelMixin |
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from ..transformers.transformer_temporal import TransformerTemporalModel |
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from .unet_2d_blocks import UNetMidBlock2DCrossAttn |
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from .unet_2d_condition import UNet2DConditionModel |
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from .unet_3d_blocks import ( |
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CrossAttnDownBlockMotion, |
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CrossAttnUpBlockMotion, |
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DownBlockMotion, |
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UNetMidBlockCrossAttnMotion, |
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UpBlockMotion, |
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get_down_block, |
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get_up_block, |
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) |
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from .unet_3d_condition import UNet3DConditionOutput |
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logger = logging.get_logger(__name__) |
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class MotionModules(nn.Module): |
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def __init__( |
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self, |
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in_channels: int, |
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layers_per_block: int = 2, |
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num_attention_heads: int = 8, |
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attention_bias: bool = False, |
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cross_attention_dim: Optional[int] = None, |
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activation_fn: str = "geglu", |
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norm_num_groups: int = 32, |
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max_seq_length: int = 32, |
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): |
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super().__init__() |
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self.motion_modules = nn.ModuleList([]) |
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|
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for i in range(layers_per_block): |
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self.motion_modules.append( |
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TransformerTemporalModel( |
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in_channels=in_channels, |
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norm_num_groups=norm_num_groups, |
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cross_attention_dim=cross_attention_dim, |
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activation_fn=activation_fn, |
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attention_bias=attention_bias, |
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num_attention_heads=num_attention_heads, |
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attention_head_dim=in_channels // num_attention_heads, |
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positional_embeddings="sinusoidal", |
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num_positional_embeddings=max_seq_length, |
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) |
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) |
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|
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class MotionAdapter(ModelMixin, ConfigMixin): |
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@register_to_config |
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def __init__( |
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self, |
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block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), |
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motion_layers_per_block: int = 2, |
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motion_mid_block_layers_per_block: int = 1, |
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motion_num_attention_heads: int = 8, |
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motion_norm_num_groups: int = 32, |
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motion_max_seq_length: int = 32, |
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use_motion_mid_block: bool = True, |
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conv_in_channels: Optional[int] = None, |
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): |
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"""Container to store AnimateDiff Motion Modules |
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|
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Args: |
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block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): |
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The tuple of output channels for each UNet block. |
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motion_layers_per_block (`int`, *optional*, defaults to 2): |
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The number of motion layers per UNet block. |
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motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1): |
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The number of motion layers in the middle UNet block. |
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motion_num_attention_heads (`int`, *optional*, defaults to 8): |
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The number of heads to use in each attention layer of the motion module. |
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motion_norm_num_groups (`int`, *optional*, defaults to 32): |
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The number of groups to use in each group normalization layer of the motion module. |
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motion_max_seq_length (`int`, *optional*, defaults to 32): |
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The maximum sequence length to use in the motion module. |
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use_motion_mid_block (`bool`, *optional*, defaults to True): |
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Whether to use a motion module in the middle of the UNet. |
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""" |
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|
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super().__init__() |
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down_blocks = [] |
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up_blocks = [] |
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|
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if conv_in_channels: |
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|
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self.conv_in = nn.Conv2d(conv_in_channels, block_out_channels[0], kernel_size=3, padding=1) |
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else: |
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self.conv_in = None |
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|
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for i, channel in enumerate(block_out_channels): |
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output_channel = block_out_channels[i] |
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down_blocks.append( |
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MotionModules( |
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in_channels=output_channel, |
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norm_num_groups=motion_norm_num_groups, |
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cross_attention_dim=None, |
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activation_fn="geglu", |
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attention_bias=False, |
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num_attention_heads=motion_num_attention_heads, |
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max_seq_length=motion_max_seq_length, |
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layers_per_block=motion_layers_per_block, |
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) |
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) |
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if use_motion_mid_block: |
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self.mid_block = MotionModules( |
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in_channels=block_out_channels[-1], |
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norm_num_groups=motion_norm_num_groups, |
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cross_attention_dim=None, |
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activation_fn="geglu", |
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attention_bias=False, |
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num_attention_heads=motion_num_attention_heads, |
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layers_per_block=motion_mid_block_layers_per_block, |
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max_seq_length=motion_max_seq_length, |
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) |
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else: |
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self.mid_block = None |
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reversed_block_out_channels = list(reversed(block_out_channels)) |
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output_channel = reversed_block_out_channels[0] |
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for i, channel in enumerate(reversed_block_out_channels): |
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output_channel = reversed_block_out_channels[i] |
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up_blocks.append( |
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MotionModules( |
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in_channels=output_channel, |
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norm_num_groups=motion_norm_num_groups, |
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cross_attention_dim=None, |
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activation_fn="geglu", |
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attention_bias=False, |
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num_attention_heads=motion_num_attention_heads, |
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max_seq_length=motion_max_seq_length, |
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layers_per_block=motion_layers_per_block + 1, |
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) |
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) |
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self.down_blocks = nn.ModuleList(down_blocks) |
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self.up_blocks = nn.ModuleList(up_blocks) |
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|
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def forward(self, sample): |
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pass |
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class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): |
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r""" |
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A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a |
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sample shaped output. |
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This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented |
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for all models (such as downloading or saving). |
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""" |
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_supports_gradient_checkpointing = True |
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@register_to_config |
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def __init__( |
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self, |
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sample_size: Optional[int] = None, |
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in_channels: int = 4, |
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out_channels: int = 4, |
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down_block_types: Tuple[str, ...] = ( |
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"CrossAttnDownBlockMotion", |
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"CrossAttnDownBlockMotion", |
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"CrossAttnDownBlockMotion", |
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"DownBlockMotion", |
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), |
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up_block_types: Tuple[str, ...] = ( |
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"UpBlockMotion", |
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"CrossAttnUpBlockMotion", |
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"CrossAttnUpBlockMotion", |
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"CrossAttnUpBlockMotion", |
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), |
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block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), |
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layers_per_block: int = 2, |
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downsample_padding: int = 1, |
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mid_block_scale_factor: float = 1, |
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act_fn: str = "silu", |
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norm_num_groups: int = 32, |
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norm_eps: float = 1e-5, |
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cross_attention_dim: int = 1280, |
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transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, |
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reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, |
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use_linear_projection: bool = False, |
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num_attention_heads: Union[int, Tuple[int, ...]] = 8, |
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motion_max_seq_length: int = 32, |
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motion_num_attention_heads: int = 8, |
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use_motion_mid_block: int = True, |
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encoder_hid_dim: Optional[int] = None, |
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encoder_hid_dim_type: Optional[str] = None, |
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addition_embed_type: Optional[str] = None, |
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addition_time_embed_dim: Optional[int] = None, |
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projection_class_embeddings_input_dim: Optional[int] = None, |
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time_cond_proj_dim: Optional[int] = None, |
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): |
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super().__init__() |
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self.sample_size = sample_size |
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|
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if len(down_block_types) != len(up_block_types): |
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raise ValueError( |
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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}." |
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) |
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|
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if len(block_out_channels) != len(down_block_types): |
|
raise ValueError( |
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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}." |
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) |
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|
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if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): |
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raise ValueError( |
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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}." |
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) |
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|
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if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): |
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raise ValueError( |
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f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." |
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) |
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|
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if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): |
|
raise ValueError( |
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f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." |
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) |
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|
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if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None: |
|
for layer_number_per_block in transformer_layers_per_block: |
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if isinstance(layer_number_per_block, list): |
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raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.") |
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|
|
|
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conv_in_kernel = 3 |
|
conv_out_kernel = 3 |
|
conv_in_padding = (conv_in_kernel - 1) // 2 |
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self.conv_in = nn.Conv2d( |
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in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding |
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) |
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|
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time_embed_dim = block_out_channels[0] * 4 |
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self.time_proj = Timesteps(block_out_channels[0], True, 0) |
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timestep_input_dim = block_out_channels[0] |
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|
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self.time_embedding = TimestepEmbedding( |
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timestep_input_dim, time_embed_dim, act_fn=act_fn, cond_proj_dim=time_cond_proj_dim |
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) |
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|
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if encoder_hid_dim_type is None: |
|
self.encoder_hid_proj = None |
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|
|
if addition_embed_type == "text_time": |
|
self.add_time_proj = Timesteps(addition_time_embed_dim, True, 0) |
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self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) |
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|
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self.down_blocks = nn.ModuleList([]) |
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self.up_blocks = nn.ModuleList([]) |
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|
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if isinstance(num_attention_heads, int): |
|
num_attention_heads = (num_attention_heads,) * len(down_block_types) |
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|
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if isinstance(cross_attention_dim, int): |
|
cross_attention_dim = (cross_attention_dim,) * len(down_block_types) |
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|
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if isinstance(layers_per_block, int): |
|
layers_per_block = [layers_per_block] * len(down_block_types) |
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|
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if isinstance(transformer_layers_per_block, int): |
|
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) |
|
|
|
|
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output_channel = block_out_channels[0] |
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for i, down_block_type in enumerate(down_block_types): |
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input_channel = output_channel |
|
output_channel = block_out_channels[i] |
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is_final_block = i == len(block_out_channels) - 1 |
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|
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down_block = get_down_block( |
|
down_block_type, |
|
num_layers=layers_per_block[i], |
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in_channels=input_channel, |
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out_channels=output_channel, |
|
temb_channels=time_embed_dim, |
|
add_downsample=not is_final_block, |
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resnet_eps=norm_eps, |
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resnet_act_fn=act_fn, |
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resnet_groups=norm_num_groups, |
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cross_attention_dim=cross_attention_dim[i], |
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num_attention_heads=num_attention_heads[i], |
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downsample_padding=downsample_padding, |
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use_linear_projection=use_linear_projection, |
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dual_cross_attention=False, |
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temporal_num_attention_heads=motion_num_attention_heads, |
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temporal_max_seq_length=motion_max_seq_length, |
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transformer_layers_per_block=transformer_layers_per_block[i], |
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) |
|
self.down_blocks.append(down_block) |
|
|
|
|
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if use_motion_mid_block: |
|
self.mid_block = UNetMidBlockCrossAttnMotion( |
|
in_channels=block_out_channels[-1], |
|
temb_channels=time_embed_dim, |
|
resnet_eps=norm_eps, |
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resnet_act_fn=act_fn, |
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output_scale_factor=mid_block_scale_factor, |
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cross_attention_dim=cross_attention_dim[-1], |
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num_attention_heads=num_attention_heads[-1], |
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resnet_groups=norm_num_groups, |
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dual_cross_attention=False, |
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use_linear_projection=use_linear_projection, |
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temporal_num_attention_heads=motion_num_attention_heads, |
|
temporal_max_seq_length=motion_max_seq_length, |
|
transformer_layers_per_block=transformer_layers_per_block[-1], |
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) |
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|
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else: |
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self.mid_block = UNetMidBlock2DCrossAttn( |
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in_channels=block_out_channels[-1], |
|
temb_channels=time_embed_dim, |
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resnet_eps=norm_eps, |
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resnet_act_fn=act_fn, |
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output_scale_factor=mid_block_scale_factor, |
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cross_attention_dim=cross_attention_dim[-1], |
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num_attention_heads=num_attention_heads[-1], |
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resnet_groups=norm_num_groups, |
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dual_cross_attention=False, |
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use_linear_projection=use_linear_projection, |
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transformer_layers_per_block=transformer_layers_per_block[-1], |
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) |
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|
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self.num_upsamplers = 0 |
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|
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reversed_block_out_channels = list(reversed(block_out_channels)) |
|
reversed_num_attention_heads = list(reversed(num_attention_heads)) |
|
reversed_layers_per_block = list(reversed(layers_per_block)) |
|
reversed_cross_attention_dim = list(reversed(cross_attention_dim)) |
|
reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) |
|
|
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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 |
|
|
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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)] |
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|
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if not is_final_block: |
|
add_upsample = True |
|
self.num_upsamplers += 1 |
|
else: |
|
add_upsample = False |
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|
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up_block = get_up_block( |
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up_block_type, |
|
num_layers=reversed_layers_per_block[i] + 1, |
|
in_channels=input_channel, |
|
out_channels=output_channel, |
|
prev_output_channel=prev_output_channel, |
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temb_channels=time_embed_dim, |
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add_upsample=add_upsample, |
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resnet_eps=norm_eps, |
|
resnet_act_fn=act_fn, |
|
resnet_groups=norm_num_groups, |
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cross_attention_dim=reversed_cross_attention_dim[i], |
|
num_attention_heads=reversed_num_attention_heads[i], |
|
dual_cross_attention=False, |
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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, |
|
transformer_layers_per_block=reversed_transformer_layers_per_block[i], |
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) |
|
self.up_blocks.append(up_block) |
|
prev_output_channel = output_channel |
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|
|
|
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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 |
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) |
|
|
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@classmethod |
|
def from_unet2d( |
|
cls, |
|
unet: UNet2DConditionModel, |
|
motion_adapter: Optional[MotionAdapter] = None, |
|
load_weights: bool = True, |
|
): |
|
has_motion_adapter = motion_adapter is not None |
|
|
|
if has_motion_adapter: |
|
motion_adapter.to(device=unet.device) |
|
|
|
|
|
config = dict(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"] |
|
|
|
|
|
if motion_adapter.config["conv_in_channels"]: |
|
config["in_channels"] = motion_adapter.config["conv_in_channels"] |
|
|
|
|
|
if not config.get("num_attention_heads"): |
|
config["num_attention_heads"] = config["attention_head_dim"] |
|
|
|
config = FrozenDict(config) |
|
model = cls.from_config(config) |
|
|
|
if not load_weights: |
|
return model |
|
|
|
|
|
|
|
if has_motion_adapter and motion_adapter.config["conv_in_channels"]: |
|
model.conv_in = motion_adapter.conv_in |
|
updated_conv_in_weight = torch.cat( |
|
[unet.conv_in.weight, motion_adapter.conv_in.weight[:, 4:, :, :]], dim=1 |
|
) |
|
model.conv_in.load_state_dict({"weight": updated_conv_in_weight, "bias": unet.conv_in.bias}) |
|
else: |
|
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()) |
|
|
|
if any( |
|
isinstance(proc, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)) |
|
for proc in unet.attn_processors.values() |
|
): |
|
attn_procs = {} |
|
for name, processor in unet.attn_processors.items(): |
|
if name.endswith("attn1.processor"): |
|
attn_processor_class = ( |
|
AttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else AttnProcessor |
|
) |
|
attn_procs[name] = attn_processor_class() |
|
else: |
|
attn_processor_class = ( |
|
IPAdapterAttnProcessor2_0 |
|
if hasattr(F, "scaled_dot_product_attention") |
|
else IPAdapterAttnProcessor |
|
) |
|
attn_procs[name] = attn_processor_class( |
|
hidden_size=processor.hidden_size, |
|
cross_attention_dim=processor.cross_attention_dim, |
|
scale=processor.scale, |
|
num_tokens=processor.num_tokens, |
|
) |
|
for name, processor in model.attn_processors.items(): |
|
if name not in attn_procs: |
|
attn_procs[name] = processor.__class__() |
|
model.set_attn_processor(attn_procs) |
|
model.config.encoder_hid_dim_type = "ip_image_proj" |
|
model.encoder_hid_proj = unet.encoder_hid_proj |
|
|
|
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) |
|
|
|
|
|
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. |
|
""" |
|
|
|
for param in self.parameters(): |
|
param.requires_grad = False |
|
|
|
|
|
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()) |
|
|
|
|
|
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() |
|
|
|
|
|
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 |
|
|
|
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. |
|
""" |
|
|
|
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() |
|
|
|
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 |
|
|
|
|
|
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) |
|
|
|
|
|
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}") |
|
|
|
|
|
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) |
|
|
|
|
|
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) |
|
|
|
|
|
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 |
|
|
|
|
|
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) |
|
|
|
|
|
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 fuse_qkv_projections(self): |
|
""" |
|
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) |
|
are fused. For cross-attention modules, key and value projection matrices are fused. |
|
|
|
<Tip warning={true}> |
|
|
|
This API is 🧪 experimental. |
|
|
|
</Tip> |
|
""" |
|
self.original_attn_processors = None |
|
|
|
for _, attn_processor in self.attn_processors.items(): |
|
if "Added" in str(attn_processor.__class__.__name__): |
|
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") |
|
|
|
self.original_attn_processors = self.attn_processors |
|
|
|
for module in self.modules(): |
|
if isinstance(module, Attention): |
|
module.fuse_projections(fuse=True) |
|
|
|
|
|
def unfuse_qkv_projections(self): |
|
"""Disables the fused QKV projection if enabled. |
|
|
|
<Tip warning={true}> |
|
|
|
This API is 🧪 experimental. |
|
|
|
</Tip> |
|
|
|
""" |
|
if self.original_attn_processors is not None: |
|
self.set_attn_processor(self.original_attn_processors) |
|
|
|
def forward( |
|
self, |
|
sample: torch.Tensor, |
|
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.Tensor`): |
|
The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`. |
|
timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input. |
|
encoder_hidden_states (`torch.Tensor`): |
|
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.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plain |
|
tuple. |
|
|
|
Returns: |
|
[`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`: |
|
If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned, |
|
otherwise a `tuple` is returned where the first element is the sample tensor. |
|
""" |
|
|
|
|
|
|
|
|
|
default_overall_up_factor = 2**self.num_upsamplers |
|
|
|
|
|
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 |
|
|
|
|
|
if attention_mask is not None: |
|
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 |
|
attention_mask = attention_mask.unsqueeze(1) |
|
|
|
|
|
timesteps = timestep |
|
if not torch.is_tensor(timesteps): |
|
|
|
|
|
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) |
|
|
|
|
|
num_frames = sample.shape[2] |
|
timesteps = timesteps.expand(sample.shape[0]) |
|
|
|
t_emb = self.time_proj(timesteps) |
|
|
|
|
|
|
|
|
|
t_emb = t_emb.to(dtype=self.dtype) |
|
|
|
emb = self.time_embedding(t_emb, timestep_cond) |
|
aug_emb = None |
|
|
|
if self.config.addition_embed_type == "text_time": |
|
if "text_embeds" not in added_cond_kwargs: |
|
raise ValueError( |
|
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" |
|
) |
|
|
|
text_embeds = added_cond_kwargs.get("text_embeds") |
|
if "time_ids" not in added_cond_kwargs: |
|
raise ValueError( |
|
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" |
|
) |
|
time_ids = added_cond_kwargs.get("time_ids") |
|
time_embeds = self.add_time_proj(time_ids.flatten()) |
|
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) |
|
|
|
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) |
|
add_embeds = add_embeds.to(emb.dtype) |
|
aug_emb = self.add_embedding(add_embeds) |
|
|
|
emb = emb if aug_emb is None else emb + aug_emb |
|
emb = emb.repeat_interleave(repeats=num_frames, dim=0) |
|
encoder_hidden_states = encoder_hidden_states.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) |
|
image_embeds = [image_embed.repeat_interleave(repeats=num_frames, dim=0) for image_embed in image_embeds] |
|
encoder_hidden_states = (encoder_hidden_states, image_embeds) |
|
|
|
|
|
sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:]) |
|
sample = self.conv_in(sample) |
|
|
|
|
|
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 |
|
|
|
|
|
if self.mid_block is not None: |
|
|
|
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 |
|
|
|
|
|
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 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, |
|
) |
|
|
|
|
|
if self.conv_norm_out: |
|
sample = self.conv_norm_out(sample) |
|
sample = self.conv_act(sample) |
|
|
|
sample = self.conv_out(sample) |
|
|
|
|
|
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) |
|
|