2178 lines
97 KiB
Python
2178 lines
97 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import Any, Dict, Optional, Tuple, Union
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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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from ...configuration_utils import ConfigMixin, FrozenDict, register_to_config
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from ...loaders import FromOriginalModelMixin, PeftAdapterMixin, UNet2DConditionLoadersMixin
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from ...utils import BaseOutput, deprecate, logging
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from ...utils.torch_utils import apply_freeu
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from ..attention import BasicTransformerBlock
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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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FusedAttnProcessor2_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 ..resnet import Downsample2D, ResnetBlock2D, Upsample2D
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from ..transformers.dual_transformer_2d import DualTransformer2DModel
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from ..transformers.transformer_2d import Transformer2DModel
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from .unet_2d_blocks import UNetMidBlock2DCrossAttn
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from .unet_2d_condition import UNet2DConditionModel
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class UNetMotionOutput(BaseOutput):
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"""
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The output of [`UNetMotionOutput`].
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Args:
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sample (`torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`):
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The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
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"""
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sample: torch.Tensor
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class AnimateDiffTransformer3D(nn.Module):
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"""
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A Transformer model for video-like data.
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Parameters:
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num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
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attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
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in_channels (`int`, *optional*):
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The number of channels in the input and output (specify if the input is **continuous**).
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num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
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dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
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cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
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attention_bias (`bool`, *optional*):
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Configure if the `TransformerBlock` attention should contain a bias parameter.
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sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
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This is fixed during training since it is used to learn a number of position embeddings.
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activation_fn (`str`, *optional*, defaults to `"geglu"`):
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Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported
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activation functions.
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norm_elementwise_affine (`bool`, *optional*):
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Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization.
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double_self_attention (`bool`, *optional*):
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Configure if each `TransformerBlock` should contain two self-attention layers.
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positional_embeddings: (`str`, *optional*):
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The type of positional embeddings to apply to the sequence input before passing use.
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num_positional_embeddings: (`int`, *optional*):
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The maximum length of the sequence over which to apply positional embeddings.
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"""
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def __init__(
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self,
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num_attention_heads: int = 16,
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attention_head_dim: int = 88,
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in_channels: Optional[int] = None,
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out_channels: Optional[int] = None,
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num_layers: int = 1,
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dropout: float = 0.0,
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norm_num_groups: int = 32,
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cross_attention_dim: Optional[int] = None,
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attention_bias: bool = False,
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sample_size: Optional[int] = None,
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activation_fn: str = "geglu",
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norm_elementwise_affine: bool = True,
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double_self_attention: bool = True,
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positional_embeddings: Optional[str] = None,
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num_positional_embeddings: Optional[int] = None,
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):
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super().__init__()
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self.num_attention_heads = num_attention_heads
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self.attention_head_dim = attention_head_dim
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inner_dim = num_attention_heads * attention_head_dim
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self.in_channels = in_channels
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self.norm = nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
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self.proj_in = nn.Linear(in_channels, inner_dim)
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# 3. Define transformers blocks
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self.transformer_blocks = nn.ModuleList(
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[
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BasicTransformerBlock(
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inner_dim,
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num_attention_heads,
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attention_head_dim,
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dropout=dropout,
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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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double_self_attention=double_self_attention,
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norm_elementwise_affine=norm_elementwise_affine,
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positional_embeddings=positional_embeddings,
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num_positional_embeddings=num_positional_embeddings,
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)
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for _ in range(num_layers)
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]
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)
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self.proj_out = nn.Linear(inner_dim, in_channels)
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def forward(
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self,
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.LongTensor] = None,
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timestep: Optional[torch.LongTensor] = None,
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class_labels: Optional[torch.LongTensor] = None,
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num_frames: int = 1,
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cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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) -> torch.Tensor:
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"""
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The [`AnimateDiffTransformer3D`] forward method.
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Args:
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hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.Tensor` of shape `(batch size, channel, height, width)` if continuous):
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Input hidden_states.
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encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
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Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
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self-attention.
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timestep ( `torch.LongTensor`, *optional*):
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Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
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class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
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Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
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`AdaLayerZeroNorm`.
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num_frames (`int`, *optional*, defaults to 1):
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The number of frames to be processed per batch. This is used to reshape the hidden states.
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cross_attention_kwargs (`dict`, *optional*):
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A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
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`self.processor` in
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[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
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Returns:
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torch.Tensor:
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The output tensor.
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"""
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# 1. Input
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batch_frames, channel, height, width = hidden_states.shape
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batch_size = batch_frames // num_frames
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residual = hidden_states
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hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width)
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hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
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hidden_states = self.norm(hidden_states)
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hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel)
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hidden_states = self.proj_in(input=hidden_states)
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# 2. Blocks
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for block in self.transformer_blocks:
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hidden_states = block(
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hidden_states=hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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timestep=timestep,
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cross_attention_kwargs=cross_attention_kwargs,
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class_labels=class_labels,
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)
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# 3. Output
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hidden_states = self.proj_out(input=hidden_states)
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hidden_states = (
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hidden_states[None, None, :]
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.reshape(batch_size, height, width, num_frames, channel)
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.permute(0, 3, 4, 1, 2)
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.contiguous()
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)
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hidden_states = hidden_states.reshape(batch_frames, channel, height, width)
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output = hidden_states + residual
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return output
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class DownBlockMotion(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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temb_channels: int,
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dropout: float = 0.0,
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num_layers: int = 1,
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resnet_eps: float = 1e-6,
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resnet_time_scale_shift: str = "default",
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resnet_act_fn: str = "swish",
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resnet_groups: int = 32,
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resnet_pre_norm: bool = True,
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output_scale_factor: float = 1.0,
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add_downsample: bool = True,
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downsample_padding: int = 1,
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temporal_num_attention_heads: Union[int, Tuple[int]] = 1,
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temporal_cross_attention_dim: Optional[int] = None,
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temporal_max_seq_length: int = 32,
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temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
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temporal_double_self_attention: bool = True,
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):
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super().__init__()
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resnets = []
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motion_modules = []
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# support for variable transformer layers per temporal block
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if isinstance(temporal_transformer_layers_per_block, int):
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temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
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elif len(temporal_transformer_layers_per_block) != num_layers:
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raise ValueError(
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f"`temporal_transformer_layers_per_block` must be an integer or a tuple of integers of length {num_layers}"
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)
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# support for variable number of attention head per temporal layers
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if isinstance(temporal_num_attention_heads, int):
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temporal_num_attention_heads = (temporal_num_attention_heads,) * num_layers
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elif len(temporal_num_attention_heads) != num_layers:
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raise ValueError(
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f"`temporal_num_attention_heads` must be an integer or a tuple of integers of length {num_layers}"
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)
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for i in range(num_layers):
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in_channels = in_channels if i == 0 else out_channels
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resnets.append(
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ResnetBlock2D(
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in_channels=in_channels,
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out_channels=out_channels,
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temb_channels=temb_channels,
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eps=resnet_eps,
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groups=resnet_groups,
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dropout=dropout,
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time_embedding_norm=resnet_time_scale_shift,
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non_linearity=resnet_act_fn,
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output_scale_factor=output_scale_factor,
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pre_norm=resnet_pre_norm,
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)
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)
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motion_modules.append(
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AnimateDiffTransformer3D(
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num_attention_heads=temporal_num_attention_heads[i],
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in_channels=out_channels,
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num_layers=temporal_transformer_layers_per_block[i],
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norm_num_groups=resnet_groups,
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cross_attention_dim=temporal_cross_attention_dim,
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attention_bias=False,
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activation_fn="geglu",
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positional_embeddings="sinusoidal",
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num_positional_embeddings=temporal_max_seq_length,
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attention_head_dim=out_channels // temporal_num_attention_heads[i],
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double_self_attention=temporal_double_self_attention,
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)
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)
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self.resnets = nn.ModuleList(resnets)
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self.motion_modules = nn.ModuleList(motion_modules)
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if add_downsample:
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self.downsamplers = nn.ModuleList(
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[
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Downsample2D(
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out_channels,
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use_conv=True,
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out_channels=out_channels,
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padding=downsample_padding,
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name="op",
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)
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]
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)
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else:
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self.downsamplers = None
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self.gradient_checkpointing = False
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def forward(
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self,
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hidden_states: torch.Tensor,
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temb: Optional[torch.Tensor] = None,
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num_frames: int = 1,
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*args,
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**kwargs,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
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if len(args) > 0 or kwargs.get("scale", None) is not None:
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deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
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deprecate("scale", "1.0.0", deprecation_message)
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output_states = ()
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blocks = zip(self.resnets, self.motion_modules)
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for resnet, motion_module in blocks:
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if torch.is_grad_enabled() and self.gradient_checkpointing:
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hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb)
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else:
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hidden_states = resnet(input_tensor=hidden_states, temb=temb)
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hidden_states = motion_module(hidden_states, num_frames=num_frames)
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output_states = output_states + (hidden_states,)
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if self.downsamplers is not None:
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for downsampler in self.downsamplers:
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hidden_states = downsampler(hidden_states=hidden_states)
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output_states = output_states + (hidden_states,)
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return hidden_states, output_states
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class CrossAttnDownBlockMotion(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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temb_channels: int,
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dropout: float = 0.0,
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num_layers: int = 1,
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transformer_layers_per_block: Union[int, Tuple[int]] = 1,
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resnet_eps: float = 1e-6,
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resnet_time_scale_shift: str = "default",
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resnet_act_fn: str = "swish",
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resnet_groups: int = 32,
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resnet_pre_norm: bool = True,
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num_attention_heads: int = 1,
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cross_attention_dim: int = 1280,
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output_scale_factor: float = 1.0,
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downsample_padding: int = 1,
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add_downsample: bool = True,
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dual_cross_attention: bool = False,
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use_linear_projection: bool = False,
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only_cross_attention: bool = False,
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upcast_attention: bool = False,
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attention_type: str = "default",
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temporal_cross_attention_dim: Optional[int] = None,
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temporal_num_attention_heads: int = 8,
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temporal_max_seq_length: int = 32,
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temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
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temporal_double_self_attention: bool = True,
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):
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super().__init__()
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resnets = []
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attentions = []
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motion_modules = []
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self.has_cross_attention = True
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self.num_attention_heads = num_attention_heads
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# support for variable transformer layers per block
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if isinstance(transformer_layers_per_block, int):
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transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
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elif len(transformer_layers_per_block) != num_layers:
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raise ValueError(
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f"transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
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)
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# support for variable transformer layers per temporal block
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if isinstance(temporal_transformer_layers_per_block, int):
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temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
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elif len(temporal_transformer_layers_per_block) != num_layers:
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raise ValueError(
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f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
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)
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for i in range(num_layers):
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in_channels = in_channels if i == 0 else out_channels
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resnets.append(
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ResnetBlock2D(
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in_channels=in_channels,
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out_channels=out_channels,
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temb_channels=temb_channels,
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eps=resnet_eps,
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groups=resnet_groups,
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dropout=dropout,
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time_embedding_norm=resnet_time_scale_shift,
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non_linearity=resnet_act_fn,
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output_scale_factor=output_scale_factor,
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pre_norm=resnet_pre_norm,
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)
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)
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if not dual_cross_attention:
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attentions.append(
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Transformer2DModel(
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num_attention_heads,
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out_channels // num_attention_heads,
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in_channels=out_channels,
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num_layers=transformer_layers_per_block[i],
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cross_attention_dim=cross_attention_dim,
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norm_num_groups=resnet_groups,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention,
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upcast_attention=upcast_attention,
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attention_type=attention_type,
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)
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)
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else:
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attentions.append(
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DualTransformer2DModel(
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num_attention_heads,
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out_channels // num_attention_heads,
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in_channels=out_channels,
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num_layers=1,
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cross_attention_dim=cross_attention_dim,
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norm_num_groups=resnet_groups,
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)
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)
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motion_modules.append(
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AnimateDiffTransformer3D(
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num_attention_heads=temporal_num_attention_heads,
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in_channels=out_channels,
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num_layers=temporal_transformer_layers_per_block[i],
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norm_num_groups=resnet_groups,
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cross_attention_dim=temporal_cross_attention_dim,
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attention_bias=False,
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activation_fn="geglu",
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positional_embeddings="sinusoidal",
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num_positional_embeddings=temporal_max_seq_length,
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attention_head_dim=out_channels // temporal_num_attention_heads,
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double_self_attention=temporal_double_self_attention,
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)
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)
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self.attentions = nn.ModuleList(attentions)
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self.resnets = nn.ModuleList(resnets)
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self.motion_modules = nn.ModuleList(motion_modules)
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if add_downsample:
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self.downsamplers = nn.ModuleList(
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[
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Downsample2D(
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out_channels,
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use_conv=True,
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out_channels=out_channels,
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padding=downsample_padding,
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name="op",
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)
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]
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)
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else:
|
|
self.downsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
temb: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
num_frames: int = 1,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
additional_residuals: Optional[torch.Tensor] = None,
|
|
):
|
|
if cross_attention_kwargs is not None:
|
|
if cross_attention_kwargs.get("scale", None) is not None:
|
|
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
|
|
|
|
output_states = ()
|
|
|
|
blocks = list(zip(self.resnets, self.attentions, self.motion_modules))
|
|
for i, (resnet, attn, motion_module) in enumerate(blocks):
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb)
|
|
else:
|
|
hidden_states = resnet(input_tensor=hidden_states, temb=temb)
|
|
|
|
hidden_states = attn(
|
|
hidden_states=hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
hidden_states = motion_module(hidden_states, num_frames=num_frames)
|
|
|
|
# apply additional residuals to the output of the last pair of resnet and attention blocks
|
|
if i == len(blocks) - 1 and additional_residuals is not None:
|
|
hidden_states = hidden_states + additional_residuals
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states=hidden_states)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
class CrossAttnUpBlockMotion(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
prev_output_channel: int,
|
|
temb_channels: int,
|
|
resolution_idx: Optional[int] = None,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
num_attention_heads: int = 1,
|
|
cross_attention_dim: int = 1280,
|
|
output_scale_factor: float = 1.0,
|
|
add_upsample: bool = True,
|
|
dual_cross_attention: bool = False,
|
|
use_linear_projection: bool = False,
|
|
only_cross_attention: bool = False,
|
|
upcast_attention: bool = False,
|
|
attention_type: str = "default",
|
|
temporal_cross_attention_dim: Optional[int] = None,
|
|
temporal_num_attention_heads: int = 8,
|
|
temporal_max_seq_length: int = 32,
|
|
temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
attentions = []
|
|
motion_modules = []
|
|
|
|
self.has_cross_attention = True
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
# support for variable transformer layers per block
|
|
if isinstance(transformer_layers_per_block, int):
|
|
transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
|
|
elif len(transformer_layers_per_block) != num_layers:
|
|
raise ValueError(
|
|
f"transformer_layers_per_block must be an integer or a list of integers of length {num_layers}, got {len(transformer_layers_per_block)}"
|
|
)
|
|
|
|
# support for variable transformer layers per temporal block
|
|
if isinstance(temporal_transformer_layers_per_block, int):
|
|
temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
|
|
elif len(temporal_transformer_layers_per_block) != num_layers:
|
|
raise ValueError(
|
|
f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}, got {len(temporal_transformer_layers_per_block)}"
|
|
)
|
|
|
|
for i in range(num_layers):
|
|
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=resnet_in_channels + res_skip_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
|
|
if not dual_cross_attention:
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
num_attention_heads,
|
|
out_channels // num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=transformer_layers_per_block[i],
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
attention_type=attention_type,
|
|
)
|
|
)
|
|
else:
|
|
attentions.append(
|
|
DualTransformer2DModel(
|
|
num_attention_heads,
|
|
out_channels // num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=1,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
)
|
|
)
|
|
motion_modules.append(
|
|
AnimateDiffTransformer3D(
|
|
num_attention_heads=temporal_num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=temporal_transformer_layers_per_block[i],
|
|
norm_num_groups=resnet_groups,
|
|
cross_attention_dim=temporal_cross_attention_dim,
|
|
attention_bias=False,
|
|
activation_fn="geglu",
|
|
positional_embeddings="sinusoidal",
|
|
num_positional_embeddings=temporal_max_seq_length,
|
|
attention_head_dim=out_channels // temporal_num_attention_heads,
|
|
)
|
|
)
|
|
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
self.motion_modules = nn.ModuleList(motion_modules)
|
|
|
|
if add_upsample:
|
|
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
self.resolution_idx = resolution_idx
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
|
|
temb: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
upsample_size: Optional[int] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
num_frames: int = 1,
|
|
) -> torch.Tensor:
|
|
if cross_attention_kwargs is not None:
|
|
if cross_attention_kwargs.get("scale", None) is not None:
|
|
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
|
|
|
|
is_freeu_enabled = (
|
|
getattr(self, "s1", None)
|
|
and getattr(self, "s2", None)
|
|
and getattr(self, "b1", None)
|
|
and getattr(self, "b2", None)
|
|
)
|
|
|
|
blocks = zip(self.resnets, self.attentions, self.motion_modules)
|
|
for resnet, attn, motion_module in blocks:
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
|
|
# FreeU: Only operate on the first two stages
|
|
if is_freeu_enabled:
|
|
hidden_states, res_hidden_states = apply_freeu(
|
|
self.resolution_idx,
|
|
hidden_states,
|
|
res_hidden_states,
|
|
s1=self.s1,
|
|
s2=self.s2,
|
|
b1=self.b1,
|
|
b2=self.b2,
|
|
)
|
|
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb)
|
|
else:
|
|
hidden_states = resnet(input_tensor=hidden_states, temb=temb)
|
|
|
|
hidden_states = attn(
|
|
hidden_states=hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
hidden_states = motion_module(hidden_states, num_frames=num_frames)
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states=hidden_states, output_size=upsample_size)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class UpBlockMotion(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
prev_output_channel: int,
|
|
out_channels: int,
|
|
temb_channels: int,
|
|
resolution_idx: Optional[int] = None,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
output_scale_factor: float = 1.0,
|
|
add_upsample: bool = True,
|
|
temporal_cross_attention_dim: Optional[int] = None,
|
|
temporal_num_attention_heads: int = 8,
|
|
temporal_max_seq_length: int = 32,
|
|
temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
motion_modules = []
|
|
|
|
# support for variable transformer layers per temporal block
|
|
if isinstance(temporal_transformer_layers_per_block, int):
|
|
temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
|
|
elif len(temporal_transformer_layers_per_block) != num_layers:
|
|
raise ValueError(
|
|
f"temporal_transformer_layers_per_block must be an integer or a list of integers of length {num_layers}"
|
|
)
|
|
|
|
for i in range(num_layers):
|
|
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=resnet_in_channels + res_skip_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
|
|
motion_modules.append(
|
|
AnimateDiffTransformer3D(
|
|
num_attention_heads=temporal_num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=temporal_transformer_layers_per_block[i],
|
|
norm_num_groups=resnet_groups,
|
|
cross_attention_dim=temporal_cross_attention_dim,
|
|
attention_bias=False,
|
|
activation_fn="geglu",
|
|
positional_embeddings="sinusoidal",
|
|
num_positional_embeddings=temporal_max_seq_length,
|
|
attention_head_dim=out_channels // temporal_num_attention_heads,
|
|
)
|
|
)
|
|
|
|
self.resnets = nn.ModuleList(resnets)
|
|
self.motion_modules = nn.ModuleList(motion_modules)
|
|
|
|
if add_upsample:
|
|
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
self.resolution_idx = resolution_idx
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
|
|
temb: Optional[torch.Tensor] = None,
|
|
upsample_size=None,
|
|
num_frames: int = 1,
|
|
*args,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
if len(args) > 0 or kwargs.get("scale", None) is not None:
|
|
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
|
|
deprecate("scale", "1.0.0", deprecation_message)
|
|
|
|
is_freeu_enabled = (
|
|
getattr(self, "s1", None)
|
|
and getattr(self, "s2", None)
|
|
and getattr(self, "b1", None)
|
|
and getattr(self, "b2", None)
|
|
)
|
|
|
|
blocks = zip(self.resnets, self.motion_modules)
|
|
|
|
for resnet, motion_module in blocks:
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
|
|
# FreeU: Only operate on the first two stages
|
|
if is_freeu_enabled:
|
|
hidden_states, res_hidden_states = apply_freeu(
|
|
self.resolution_idx,
|
|
hidden_states,
|
|
res_hidden_states,
|
|
s1=self.s1,
|
|
s2=self.s2,
|
|
b1=self.b1,
|
|
b2=self.b2,
|
|
)
|
|
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb)
|
|
else:
|
|
hidden_states = resnet(input_tensor=hidden_states, temb=temb)
|
|
|
|
hidden_states = motion_module(hidden_states, num_frames=num_frames)
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states=hidden_states, output_size=upsample_size)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class UNetMidBlockCrossAttnMotion(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
num_attention_heads: int = 1,
|
|
output_scale_factor: float = 1.0,
|
|
cross_attention_dim: int = 1280,
|
|
dual_cross_attention: bool = False,
|
|
use_linear_projection: bool = False,
|
|
upcast_attention: bool = False,
|
|
attention_type: str = "default",
|
|
temporal_num_attention_heads: int = 1,
|
|
temporal_cross_attention_dim: Optional[int] = None,
|
|
temporal_max_seq_length: int = 32,
|
|
temporal_transformer_layers_per_block: Union[int, Tuple[int]] = 1,
|
|
):
|
|
super().__init__()
|
|
|
|
self.has_cross_attention = True
|
|
self.num_attention_heads = num_attention_heads
|
|
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
|
|
|
|
# support for variable transformer layers per block
|
|
if isinstance(transformer_layers_per_block, int):
|
|
transformer_layers_per_block = (transformer_layers_per_block,) * num_layers
|
|
elif len(transformer_layers_per_block) != num_layers:
|
|
raise ValueError(
|
|
f"`transformer_layers_per_block` should be an integer or a list of integers of length {num_layers}."
|
|
)
|
|
|
|
# support for variable transformer layers per temporal block
|
|
if isinstance(temporal_transformer_layers_per_block, int):
|
|
temporal_transformer_layers_per_block = (temporal_transformer_layers_per_block,) * num_layers
|
|
elif len(temporal_transformer_layers_per_block) != num_layers:
|
|
raise ValueError(
|
|
f"`temporal_transformer_layers_per_block` should be an integer or a list of integers of length {num_layers}."
|
|
)
|
|
|
|
# there is always at least one resnet
|
|
resnets = [
|
|
ResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
]
|
|
attentions = []
|
|
motion_modules = []
|
|
|
|
for i in range(num_layers):
|
|
if not dual_cross_attention:
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
num_attention_heads,
|
|
in_channels // num_attention_heads,
|
|
in_channels=in_channels,
|
|
num_layers=transformer_layers_per_block[i],
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
upcast_attention=upcast_attention,
|
|
attention_type=attention_type,
|
|
)
|
|
)
|
|
else:
|
|
attentions.append(
|
|
DualTransformer2DModel(
|
|
num_attention_heads,
|
|
in_channels // num_attention_heads,
|
|
in_channels=in_channels,
|
|
num_layers=1,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
)
|
|
)
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
motion_modules.append(
|
|
AnimateDiffTransformer3D(
|
|
num_attention_heads=temporal_num_attention_heads,
|
|
attention_head_dim=in_channels // temporal_num_attention_heads,
|
|
in_channels=in_channels,
|
|
num_layers=temporal_transformer_layers_per_block[i],
|
|
norm_num_groups=resnet_groups,
|
|
cross_attention_dim=temporal_cross_attention_dim,
|
|
attention_bias=False,
|
|
positional_embeddings="sinusoidal",
|
|
num_positional_embeddings=temporal_max_seq_length,
|
|
activation_fn="geglu",
|
|
)
|
|
)
|
|
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
self.motion_modules = nn.ModuleList(motion_modules)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
temb: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
num_frames: int = 1,
|
|
) -> torch.Tensor:
|
|
if cross_attention_kwargs is not None:
|
|
if cross_attention_kwargs.get("scale", None) is not None:
|
|
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
|
|
|
|
hidden_states = self.resnets[0](input_tensor=hidden_states, temb=temb)
|
|
|
|
blocks = zip(self.attentions, self.resnets[1:], self.motion_modules)
|
|
for attn, resnet, motion_module in blocks:
|
|
hidden_states = attn(
|
|
hidden_states=hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
hidden_states = self._gradient_checkpointing_func(
|
|
motion_module, hidden_states, None, None, None, num_frames, None
|
|
)
|
|
hidden_states = self._gradient_checkpointing_func(resnet, hidden_states, temb)
|
|
else:
|
|
hidden_states = motion_module(hidden_states, None, None, None, num_frames, None)
|
|
hidden_states = resnet(input_tensor=hidden_states, temb=temb)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class MotionModules(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
layers_per_block: int = 2,
|
|
transformer_layers_per_block: Union[int, Tuple[int]] = 8,
|
|
num_attention_heads: Union[int, Tuple[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([])
|
|
|
|
if isinstance(transformer_layers_per_block, int):
|
|
transformer_layers_per_block = (transformer_layers_per_block,) * layers_per_block
|
|
elif len(transformer_layers_per_block) != layers_per_block:
|
|
raise ValueError(
|
|
f"The number of transformer layers per block must match the number of layers per block, "
|
|
f"got {layers_per_block} and {len(transformer_layers_per_block)}"
|
|
)
|
|
|
|
for i in range(layers_per_block):
|
|
self.motion_modules.append(
|
|
AnimateDiffTransformer3D(
|
|
in_channels=in_channels,
|
|
num_layers=transformer_layers_per_block[i],
|
|
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, FromOriginalModelMixin):
|
|
@register_to_config
|
|
def __init__(
|
|
self,
|
|
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
|
|
motion_layers_per_block: Union[int, Tuple[int]] = 2,
|
|
motion_transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]] = 1,
|
|
motion_mid_block_layers_per_block: int = 1,
|
|
motion_transformer_layers_per_mid_block: Union[int, Tuple[int]] = 1,
|
|
motion_num_attention_heads: Union[int, Tuple[int]] = 8,
|
|
motion_norm_num_groups: int = 32,
|
|
motion_max_seq_length: int = 32,
|
|
use_motion_mid_block: bool = True,
|
|
conv_in_channels: Optional[int] = None,
|
|
):
|
|
"""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` or `Tuple[int]`, *optional*, defaults to 2):
|
|
The number of motion layers per UNet block.
|
|
motion_transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple[int]]`, *optional*, defaults to 1):
|
|
The number of transformer layers to use in each motion layer in each block.
|
|
motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1):
|
|
The number of motion layers in the middle UNet block.
|
|
motion_transformer_layers_per_mid_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
|
|
The number of transformer layers to use in each motion layer in the middle block.
|
|
motion_num_attention_heads (`int` or `Tuple[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 = []
|
|
|
|
if isinstance(motion_layers_per_block, int):
|
|
motion_layers_per_block = (motion_layers_per_block,) * len(block_out_channels)
|
|
elif len(motion_layers_per_block) != len(block_out_channels):
|
|
raise ValueError(
|
|
f"The number of motion layers per block must match the number of blocks, "
|
|
f"got {len(block_out_channels)} and {len(motion_layers_per_block)}"
|
|
)
|
|
|
|
if isinstance(motion_transformer_layers_per_block, int):
|
|
motion_transformer_layers_per_block = (motion_transformer_layers_per_block,) * len(block_out_channels)
|
|
|
|
if isinstance(motion_transformer_layers_per_mid_block, int):
|
|
motion_transformer_layers_per_mid_block = (
|
|
motion_transformer_layers_per_mid_block,
|
|
) * motion_mid_block_layers_per_block
|
|
elif len(motion_transformer_layers_per_mid_block) != motion_mid_block_layers_per_block:
|
|
raise ValueError(
|
|
f"The number of layers per mid block ({motion_mid_block_layers_per_block}) "
|
|
f"must match the length of motion_transformer_layers_per_mid_block ({len(motion_transformer_layers_per_mid_block)})"
|
|
)
|
|
|
|
if isinstance(motion_num_attention_heads, int):
|
|
motion_num_attention_heads = (motion_num_attention_heads,) * len(block_out_channels)
|
|
elif len(motion_num_attention_heads) != len(block_out_channels):
|
|
raise ValueError(
|
|
f"The length of the attention head number tuple in the motion module must match the "
|
|
f"number of block, got {len(motion_num_attention_heads)} and {len(block_out_channels)}"
|
|
)
|
|
|
|
if conv_in_channels:
|
|
# input
|
|
self.conv_in = nn.Conv2d(conv_in_channels, block_out_channels[0], kernel_size=3, padding=1)
|
|
else:
|
|
self.conv_in = None
|
|
|
|
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[i],
|
|
max_seq_length=motion_max_seq_length,
|
|
layers_per_block=motion_layers_per_block[i],
|
|
transformer_layers_per_block=motion_transformer_layers_per_block[i],
|
|
)
|
|
)
|
|
|
|
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[-1],
|
|
max_seq_length=motion_max_seq_length,
|
|
layers_per_block=motion_mid_block_layers_per_block,
|
|
transformer_layers_per_block=motion_transformer_layers_per_mid_block,
|
|
)
|
|
else:
|
|
self.mid_block = None
|
|
|
|
reversed_block_out_channels = list(reversed(block_out_channels))
|
|
output_channel = reversed_block_out_channels[0]
|
|
|
|
reversed_motion_layers_per_block = list(reversed(motion_layers_per_block))
|
|
reversed_motion_transformer_layers_per_block = list(reversed(motion_transformer_layers_per_block))
|
|
reversed_motion_num_attention_heads = list(reversed(motion_num_attention_heads))
|
|
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=reversed_motion_num_attention_heads[i],
|
|
max_seq_length=motion_max_seq_length,
|
|
layers_per_block=reversed_motion_layers_per_block[i] + 1,
|
|
transformer_layers_per_block=reversed_motion_transformer_layers_per_block[i],
|
|
)
|
|
)
|
|
|
|
self.down_blocks = nn.ModuleList(down_blocks)
|
|
self.up_blocks = nn.ModuleList(up_blocks)
|
|
|
|
def forward(self, sample):
|
|
pass
|
|
|
|
|
|
class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
|
|
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
|
|
_skip_layerwise_casting_patterns = ["norm"]
|
|
|
|
@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: Union[int, Tuple[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,
|
|
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
|
|
reverse_transformer_layers_per_block: Optional[Union[int, Tuple[int], Tuple[Tuple]]] = None,
|
|
temporal_transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
|
|
reverse_temporal_transformer_layers_per_block: Optional[Union[int, Tuple[int], Tuple[Tuple]]] = None,
|
|
transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = None,
|
|
temporal_transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]] = 1,
|
|
use_linear_projection: bool = False,
|
|
num_attention_heads: Union[int, Tuple[int, ...]] = 8,
|
|
motion_max_seq_length: int = 32,
|
|
motion_num_attention_heads: Union[int, Tuple[int, ...]] = 8,
|
|
reverse_motion_num_attention_heads: Optional[Union[int, Tuple[int, ...], Tuple[Tuple[int, ...], ...]]] = None,
|
|
use_motion_mid_block: bool = True,
|
|
mid_block_layers: int = 1,
|
|
encoder_hid_dim: Optional[int] = None,
|
|
encoder_hid_dim_type: Optional[str] = None,
|
|
addition_embed_type: Optional[str] = None,
|
|
addition_time_embed_dim: Optional[int] = None,
|
|
projection_class_embeddings_input_dim: Optional[int] = None,
|
|
time_cond_proj_dim: Optional[int] = 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}."
|
|
)
|
|
|
|
if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
|
|
raise ValueError(
|
|
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}."
|
|
)
|
|
|
|
if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
|
|
raise ValueError(
|
|
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}."
|
|
)
|
|
|
|
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:
|
|
if isinstance(layer_number_per_block, list):
|
|
raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
|
|
|
|
if (
|
|
isinstance(temporal_transformer_layers_per_block, list)
|
|
and reverse_temporal_transformer_layers_per_block is None
|
|
):
|
|
for layer_number_per_block in temporal_transformer_layers_per_block:
|
|
if isinstance(layer_number_per_block, list):
|
|
raise ValueError(
|
|
"Must provide 'reverse_temporal_transformer_layers_per_block` if using asymmetrical motion module in UNet."
|
|
)
|
|
|
|
# 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, cond_proj_dim=time_cond_proj_dim
|
|
)
|
|
|
|
if encoder_hid_dim_type is None:
|
|
self.encoder_hid_proj = None
|
|
|
|
if addition_embed_type == "text_time":
|
|
self.add_time_proj = Timesteps(addition_time_embed_dim, True, 0)
|
|
self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
|
|
|
# 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)
|
|
|
|
if isinstance(cross_attention_dim, int):
|
|
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
|
|
|
|
if isinstance(layers_per_block, int):
|
|
layers_per_block = [layers_per_block] * len(down_block_types)
|
|
|
|
if isinstance(transformer_layers_per_block, int):
|
|
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
|
|
|
if isinstance(reverse_transformer_layers_per_block, int):
|
|
reverse_transformer_layers_per_block = [reverse_transformer_layers_per_block] * len(down_block_types)
|
|
|
|
if isinstance(temporal_transformer_layers_per_block, int):
|
|
temporal_transformer_layers_per_block = [temporal_transformer_layers_per_block] * len(down_block_types)
|
|
|
|
if isinstance(reverse_temporal_transformer_layers_per_block, int):
|
|
reverse_temporal_transformer_layers_per_block = [reverse_temporal_transformer_layers_per_block] * len(
|
|
down_block_types
|
|
)
|
|
|
|
if isinstance(motion_num_attention_heads, int):
|
|
motion_num_attention_heads = (motion_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
|
|
|
|
if down_block_type == "CrossAttnDownBlockMotion":
|
|
down_block = CrossAttnDownBlockMotion(
|
|
in_channels=input_channel,
|
|
out_channels=output_channel,
|
|
temb_channels=time_embed_dim,
|
|
num_layers=layers_per_block[i],
|
|
transformer_layers_per_block=transformer_layers_per_block[i],
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
resnet_groups=norm_num_groups,
|
|
num_attention_heads=num_attention_heads[i],
|
|
cross_attention_dim=cross_attention_dim[i],
|
|
downsample_padding=downsample_padding,
|
|
add_downsample=not is_final_block,
|
|
use_linear_projection=use_linear_projection,
|
|
temporal_num_attention_heads=motion_num_attention_heads[i],
|
|
temporal_max_seq_length=motion_max_seq_length,
|
|
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
|
|
)
|
|
elif down_block_type == "DownBlockMotion":
|
|
down_block = DownBlockMotion(
|
|
in_channels=input_channel,
|
|
out_channels=output_channel,
|
|
temb_channels=time_embed_dim,
|
|
num_layers=layers_per_block[i],
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
resnet_groups=norm_num_groups,
|
|
add_downsample=not is_final_block,
|
|
downsample_padding=downsample_padding,
|
|
temporal_num_attention_heads=motion_num_attention_heads[i],
|
|
temporal_max_seq_length=motion_max_seq_length,
|
|
temporal_transformer_layers_per_block=temporal_transformer_layers_per_block[i],
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
"Invalid `down_block_type` encountered. Must be one of `CrossAttnDownBlockMotion` or `DownBlockMotion`"
|
|
)
|
|
|
|
self.down_blocks.append(down_block)
|
|
|
|
# mid
|
|
if transformer_layers_per_mid_block is None:
|
|
transformer_layers_per_mid_block = (
|
|
transformer_layers_per_block[-1] if isinstance(transformer_layers_per_block[-1], int) else 1
|
|
)
|
|
|
|
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[-1],
|
|
num_attention_heads=num_attention_heads[-1],
|
|
resnet_groups=norm_num_groups,
|
|
dual_cross_attention=False,
|
|
use_linear_projection=use_linear_projection,
|
|
num_layers=mid_block_layers,
|
|
temporal_num_attention_heads=motion_num_attention_heads[-1],
|
|
temporal_max_seq_length=motion_max_seq_length,
|
|
transformer_layers_per_block=transformer_layers_per_mid_block,
|
|
temporal_transformer_layers_per_block=temporal_transformer_layers_per_mid_block,
|
|
)
|
|
|
|
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[-1],
|
|
num_attention_heads=num_attention_heads[-1],
|
|
resnet_groups=norm_num_groups,
|
|
dual_cross_attention=False,
|
|
use_linear_projection=use_linear_projection,
|
|
num_layers=mid_block_layers,
|
|
transformer_layers_per_block=transformer_layers_per_mid_block,
|
|
)
|
|
|
|
# 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))
|
|
reversed_layers_per_block = list(reversed(layers_per_block))
|
|
reversed_cross_attention_dim = list(reversed(cross_attention_dim))
|
|
reversed_motion_num_attention_heads = list(reversed(motion_num_attention_heads))
|
|
|
|
if reverse_transformer_layers_per_block is None:
|
|
reverse_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
|
|
|
|
if reverse_temporal_transformer_layers_per_block is None:
|
|
reverse_temporal_transformer_layers_per_block = list(reversed(temporal_transformer_layers_per_block))
|
|
|
|
output_channel = reversed_block_out_channels[0]
|
|
for i, up_block_type in enumerate(up_block_types):
|
|
is_final_block = i == len(block_out_channels) - 1
|
|
|
|
prev_output_channel = output_channel
|
|
output_channel = reversed_block_out_channels[i]
|
|
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
|
|
|
|
# add upsample block for all BUT final layer
|
|
if not is_final_block:
|
|
add_upsample = True
|
|
self.num_upsamplers += 1
|
|
else:
|
|
add_upsample = False
|
|
|
|
if up_block_type == "CrossAttnUpBlockMotion":
|
|
up_block = CrossAttnUpBlockMotion(
|
|
in_channels=input_channel,
|
|
out_channels=output_channel,
|
|
prev_output_channel=prev_output_channel,
|
|
temb_channels=time_embed_dim,
|
|
resolution_idx=i,
|
|
num_layers=reversed_layers_per_block[i] + 1,
|
|
transformer_layers_per_block=reverse_transformer_layers_per_block[i],
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
resnet_groups=norm_num_groups,
|
|
num_attention_heads=reversed_num_attention_heads[i],
|
|
cross_attention_dim=reversed_cross_attention_dim[i],
|
|
add_upsample=add_upsample,
|
|
use_linear_projection=use_linear_projection,
|
|
temporal_num_attention_heads=reversed_motion_num_attention_heads[i],
|
|
temporal_max_seq_length=motion_max_seq_length,
|
|
temporal_transformer_layers_per_block=reverse_temporal_transformer_layers_per_block[i],
|
|
)
|
|
elif up_block_type == "UpBlockMotion":
|
|
up_block = UpBlockMotion(
|
|
in_channels=input_channel,
|
|
prev_output_channel=prev_output_channel,
|
|
out_channels=output_channel,
|
|
temb_channels=time_embed_dim,
|
|
resolution_idx=i,
|
|
num_layers=reversed_layers_per_block[i] + 1,
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
resnet_groups=norm_num_groups,
|
|
add_upsample=add_upsample,
|
|
temporal_num_attention_heads=reversed_motion_num_attention_heads[i],
|
|
temporal_max_seq_length=motion_max_seq_length,
|
|
temporal_transformer_layers_per_block=reverse_temporal_transformer_layers_per_block[i],
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
"Invalid `up_block_type` encountered. Must be one of `CrossAttnUpBlockMotion` or `UpBlockMotion`"
|
|
)
|
|
|
|
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
|
|
|
|
if has_motion_adapter:
|
|
motion_adapter.to(device=unet.device)
|
|
|
|
# check compatibility of number of blocks
|
|
if len(unet.config["down_block_types"]) != len(motion_adapter.config["block_out_channels"]):
|
|
raise ValueError("Incompatible Motion Adapter, got different number of blocks")
|
|
|
|
# check layers compatibility for each block
|
|
if isinstance(unet.config["layers_per_block"], int):
|
|
expanded_layers_per_block = [unet.config["layers_per_block"]] * len(unet.config["down_block_types"])
|
|
else:
|
|
expanded_layers_per_block = list(unet.config["layers_per_block"])
|
|
if isinstance(motion_adapter.config["motion_layers_per_block"], int):
|
|
expanded_adapter_layers_per_block = [motion_adapter.config["motion_layers_per_block"]] * len(
|
|
motion_adapter.config["block_out_channels"]
|
|
)
|
|
else:
|
|
expanded_adapter_layers_per_block = list(motion_adapter.config["motion_layers_per_block"])
|
|
if expanded_layers_per_block != expanded_adapter_layers_per_block:
|
|
raise ValueError("Incompatible Motion Adapter, got different number of layers per block")
|
|
|
|
# based on https://github.com/guoyww/AnimateDiff/blob/895f3220c06318ea0760131ec70408b466c49333/animatediff/models/unet.py#L459
|
|
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"]
|
|
config["layers_per_block"] = motion_adapter.config["motion_layers_per_block"]
|
|
config["temporal_transformer_layers_per_mid_block"] = motion_adapter.config[
|
|
"motion_transformer_layers_per_mid_block"
|
|
]
|
|
config["temporal_transformer_layers_per_block"] = motion_adapter.config[
|
|
"motion_transformer_layers_per_block"
|
|
]
|
|
config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"]
|
|
|
|
# For PIA UNets we need to set the number input channels to 9
|
|
if motion_adapter.config["conv_in_channels"]:
|
|
config["in_channels"] = motion_adapter.config["conv_in_channels"]
|
|
|
|
# Need this for backwards compatibility with UNet2DConditionModel checkpoints
|
|
if not config.get("num_attention_heads"):
|
|
config["num_attention_heads"] = config["attention_head_dim"]
|
|
|
|
expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)
|
|
config = FrozenDict({k: config.get(k) for k in config if k in expected_kwargs or k in optional_kwargs})
|
|
config["_class_name"] = cls.__name__
|
|
model = cls.from_config(config)
|
|
|
|
if not load_weights:
|
|
return model
|
|
|
|
# Logic for loading PIA UNets which allow the first 4 channels to be any UNet2DConditionModel conv_in weight
|
|
# while the last 5 channels must be PIA conv_in weights.
|
|
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)
|
|
|
|
# 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.unets.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()
|
|
|
|
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.unets.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)
|
|
|
|
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)
|
|
|
|
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.unets.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)
|
|
|
|
# Copied from diffusers.models.unets.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://huggingface.co/papers/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.unets.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)
|
|
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
|
|
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)
|
|
|
|
self.set_attn_processor(FusedAttnProcessor2_0())
|
|
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
|
|
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[UNetMotionOutput, 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_motion_model.UNetMotionOutput`] instead of a plain
|
|
tuple.
|
|
|
|
Returns:
|
|
[`~models.unets.unet_motion_model.UNetMotionOutput`] or `tuple`:
|
|
If `return_dict` is True, an [`~models.unets.unet_motion_model.UNetMotionOutput`] 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"
|
|
is_npu = sample.device.type == "npu"
|
|
if isinstance(timestep, float):
|
|
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
|
|
else:
|
|
dtype = torch.int32 if (is_mps or is_npu) 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)
|
|
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(num_frames, dim=0, output_size=emb.shape[0] * num_frames)
|
|
|
|
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(num_frames, dim=0, output_size=image_embed.shape[0] * num_frames)
|
|
for image_embed in image_embeds
|
|
]
|
|
encoder_hidden_states = (encoder_hidden_states, image_embeds)
|
|
|
|
# 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 UNetMotionOutput(sample=sample)
|