759 lines
28 KiB
Python
759 lines
28 KiB
Python
# Copyright 2023 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 typing import Any, Dict, Optional, Tuple, Union
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import torch
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from torch import nn
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from einops import rearrange
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from diffusers.utils import logging
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from diffusers.models.attention_processor import Attention
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from .modeling_resnet import (
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Downsample2D, ResnetBlock2D, CausalResnetBlock3D, Upsample2D,
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TemporalDownsample2x, TemporalUpsample2x,
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CausalDownsample2x, CausalTemporalDownsample2x,
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CausalUpsample2x, CausalTemporalUpsample2x,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def get_input_layer(
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in_channels: int,
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out_channels: int,
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norm_num_groups: int,
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layer_type: str,
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norm_type: str = 'group',
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affine: bool = True,
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):
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if layer_type == 'conv':
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input_layer = nn.Conv3d(
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in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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)
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elif layer_type == 'pixel_shuffle':
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input_layer = nn.Sequential(
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nn.PixelUnshuffle(2),
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nn.Conv2d(in_channels * 4, out_channels, kernel_size=1),
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)
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else:
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raise NotImplementedError(f"Not support input layer {layer_type}")
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return input_layer
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def get_output_layer(
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in_channels: int,
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out_channels: int,
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norm_num_groups: int,
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layer_type: str,
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norm_type: str = 'group',
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affine: bool = True,
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):
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if layer_type == 'norm_act_conv':
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output_layer = nn.Sequential(
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nn.GroupNorm(num_channels=in_channels, num_groups=norm_num_groups, eps=1e-6, affine=affine),
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nn.SiLU(),
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nn.Conv3d(in_channels, out_channels, 3, stride=1, padding=1),
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)
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elif layer_type == 'pixel_shuffle':
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output_layer = nn.Sequential(
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nn.Conv2d(in_channels, out_channels * 4, kernel_size=1),
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nn.PixelShuffle(2),
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)
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else:
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raise NotImplementedError(f"Not support output layer {layer_type}")
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return output_layer
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def get_down_block(
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down_block_type: str,
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num_layers: int,
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in_channels: int,
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out_channels: int = None,
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temb_channels: int = None,
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add_spatial_downsample: bool = None,
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add_temporal_downsample: bool = None,
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resnet_eps: float = 1e-6,
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resnet_act_fn: str = 'silu',
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resnet_groups: Optional[int] = None,
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downsample_padding: Optional[int] = None,
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resnet_time_scale_shift: str = "default",
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attention_head_dim: Optional[int] = None,
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dropout: float = 0.0,
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norm_affline: bool = True,
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norm_layer: str = 'layer',
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):
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if down_block_type == "DownEncoderBlock2D":
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return DownEncoderBlock2D(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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dropout=dropout,
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add_spatial_downsample=add_spatial_downsample,
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add_temporal_downsample=add_temporal_downsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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downsample_padding=downsample_padding,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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elif down_block_type == "DownEncoderBlockCausal3D":
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return DownEncoderBlockCausal3D(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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dropout=dropout,
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add_spatial_downsample=add_spatial_downsample,
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add_temporal_downsample=add_temporal_downsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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downsample_padding=downsample_padding,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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raise ValueError(f"{down_block_type} does not exist.")
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def get_up_block(
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up_block_type: str,
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num_layers: int,
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in_channels: int,
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out_channels: int,
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prev_output_channel: int = None,
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temb_channels: int = None,
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add_spatial_upsample: bool = None,
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add_temporal_upsample: bool = None,
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resnet_eps: float = 1e-6,
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resnet_act_fn: str = 'silu',
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resolution_idx: Optional[int] = None,
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resnet_groups: Optional[int] = None,
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resnet_time_scale_shift: str = "default",
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attention_head_dim: Optional[int] = None,
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dropout: float = 0.0,
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interpolate: bool = True,
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norm_affline: bool = True,
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norm_layer: str = 'layer',
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) -> nn.Module:
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if up_block_type == "UpDecoderBlock2D":
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return UpDecoderBlock2D(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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resolution_idx=resolution_idx,
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dropout=dropout,
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add_spatial_upsample=add_spatial_upsample,
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add_temporal_upsample=add_temporal_upsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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resnet_time_scale_shift=resnet_time_scale_shift,
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temb_channels=temb_channels,
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interpolate=interpolate,
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)
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elif up_block_type == "UpDecoderBlockCausal3D":
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return UpDecoderBlockCausal3D(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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resolution_idx=resolution_idx,
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dropout=dropout,
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add_spatial_upsample=add_spatial_upsample,
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add_temporal_upsample=add_temporal_upsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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resnet_time_scale_shift=resnet_time_scale_shift,
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temb_channels=temb_channels,
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interpolate=interpolate,
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)
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raise ValueError(f"{up_block_type} does not exist.")
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class UNetMidBlock2D(nn.Module):
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"""
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A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.
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Args:
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in_channels (`int`): The number of input channels.
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temb_channels (`int`): The number of temporal embedding channels.
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dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
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num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
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resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
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resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
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The type of normalization to apply to the time embeddings. This can help to improve the performance of the
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model on tasks with long-range temporal dependencies.
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resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
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resnet_groups (`int`, *optional*, defaults to 32):
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The number of groups to use in the group normalization layers of the resnet blocks.
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attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
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resnet_pre_norm (`bool`, *optional*, defaults to `True`):
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Whether to use pre-normalization for the resnet blocks.
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add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
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attention_head_dim (`int`, *optional*, defaults to 1):
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Dimension of a single attention head. The number of attention heads is determined based on this value and
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the number of input channels.
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output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
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Returns:
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`torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
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in_channels, height, width)`.
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"""
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def __init__(
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self,
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in_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", # default, spatial
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resnet_act_fn: str = "swish",
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resnet_groups: int = 32,
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attn_groups: Optional[int] = None,
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resnet_pre_norm: bool = True,
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add_attention: bool = True,
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attention_head_dim: int = 1,
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output_scale_factor: float = 1.0,
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):
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super().__init__()
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resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
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self.add_attention = add_attention
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if attn_groups is None:
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attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None
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# there is always at least one resnet
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resnets = [
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ResnetBlock2D(
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in_channels=in_channels,
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out_channels=in_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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attentions = []
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if attention_head_dim is None:
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logger.warn(
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f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
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)
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attention_head_dim = in_channels
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for _ in range(num_layers):
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if self.add_attention:
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# Spatial attention
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attentions.append(
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Attention(
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in_channels,
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heads=in_channels // attention_head_dim,
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dim_head=attention_head_dim,
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rescale_output_factor=output_scale_factor,
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eps=resnet_eps,
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norm_num_groups=attn_groups,
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spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
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residual_connection=True,
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bias=True,
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upcast_softmax=True,
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_from_deprecated_attn_block=True,
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)
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)
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else:
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attentions.append(None)
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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=in_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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self.attentions = nn.ModuleList(attentions)
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self.resnets = nn.ModuleList(resnets)
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def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
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hidden_states = self.resnets[0](hidden_states, temb)
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t = hidden_states.shape[2]
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for attn, resnet in zip(self.attentions, self.resnets[1:]):
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if attn is not None:
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hidden_states = rearrange(hidden_states, 'b c t h w -> b t c h w')
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hidden_states = rearrange(hidden_states, 'b t c h w -> (b t) c h w')
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hidden_states = attn(hidden_states, temb=temb)
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hidden_states = rearrange(hidden_states, '(b t) c h w -> b t c h w', t=t)
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hidden_states = rearrange(hidden_states, 'b t c h w -> b c t h w')
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hidden_states = resnet(hidden_states, temb)
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return hidden_states
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class CausalUNetMidBlock2D(nn.Module):
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"""
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A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.
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Args:
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in_channels (`int`): The number of input channels.
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temb_channels (`int`): The number of temporal embedding channels.
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dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
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num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
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resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
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resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
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The type of normalization to apply to the time embeddings. This can help to improve the performance of the
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model on tasks with long-range temporal dependencies.
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resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
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resnet_groups (`int`, *optional*, defaults to 32):
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The number of groups to use in the group normalization layers of the resnet blocks.
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attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
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resnet_pre_norm (`bool`, *optional*, defaults to `True`):
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Whether to use pre-normalization for the resnet blocks.
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add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
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attention_head_dim (`int`, *optional*, defaults to 1):
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Dimension of a single attention head. The number of attention heads is determined based on this value and
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the number of input channels.
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output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
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Returns:
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`torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
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in_channels, height, width)`.
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"""
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def __init__(
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self,
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in_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", # default, spatial
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resnet_act_fn: str = "swish",
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resnet_groups: int = 32,
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attn_groups: Optional[int] = None,
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resnet_pre_norm: bool = True,
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add_attention: bool = True,
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attention_head_dim: int = 1,
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output_scale_factor: float = 1.0,
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):
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super().__init__()
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resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
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self.add_attention = add_attention
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if attn_groups is None:
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attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None
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# there is always at least one resnet
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resnets = [
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CausalResnetBlock3D(
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in_channels=in_channels,
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out_channels=in_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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attentions = []
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if attention_head_dim is None:
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logger.warn(
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f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
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)
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attention_head_dim = in_channels
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for _ in range(num_layers):
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if self.add_attention:
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# Spatial attention
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attentions.append(
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Attention(
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in_channels,
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heads=in_channels // attention_head_dim,
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dim_head=attention_head_dim,
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rescale_output_factor=output_scale_factor,
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eps=resnet_eps,
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norm_num_groups=attn_groups,
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spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
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residual_connection=True,
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bias=True,
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upcast_softmax=True,
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_from_deprecated_attn_block=True,
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)
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)
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else:
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attentions.append(None)
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resnets.append(
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CausalResnetBlock3D(
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in_channels=in_channels,
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out_channels=in_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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self.attentions = nn.ModuleList(attentions)
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self.resnets = nn.ModuleList(resnets)
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def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None,
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is_init_image=True, temporal_chunk=False) -> torch.FloatTensor:
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hidden_states = self.resnets[0](hidden_states, temb, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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t = hidden_states.shape[2]
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for attn, resnet in zip(self.attentions, self.resnets[1:]):
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if attn is not None:
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hidden_states = rearrange(hidden_states, 'b c t h w -> b t c h w')
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hidden_states = rearrange(hidden_states, 'b t c h w -> (b t) c h w')
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hidden_states = attn(hidden_states, temb=temb)
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hidden_states = rearrange(hidden_states, '(b t) c h w -> b t c h w', t=t)
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hidden_states = rearrange(hidden_states, 'b t c h w -> b c t h w')
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hidden_states = resnet(hidden_states, temb, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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return hidden_states
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class DownEncoderBlockCausal3D(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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dropout: float = 0.0,
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num_layers: int = 1,
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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_spatial_downsample: bool = True,
|
|
add_temporal_downsample: bool = False,
|
|
downsample_padding: int = 1,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
|
|
for i in range(num_layers):
|
|
in_channels = in_channels if i == 0 else out_channels
|
|
resnets.append(
|
|
CausalResnetBlock3D(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=None,
|
|
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,
|
|
)
|
|
)
|
|
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
if add_spatial_downsample:
|
|
self.downsamplers = nn.ModuleList(
|
|
[
|
|
CausalDownsample2x(
|
|
out_channels, use_conv=True, out_channels=out_channels,
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.downsamplers = None
|
|
|
|
if add_temporal_downsample:
|
|
self.temporal_downsamplers = nn.ModuleList(
|
|
[
|
|
CausalTemporalDownsample2x(
|
|
out_channels, use_conv=True, out_channels=out_channels,
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.temporal_downsamplers = None
|
|
|
|
def forward(self, hidden_states: torch.FloatTensor, is_init_image=True, temporal_chunk=False) -> torch.FloatTensor:
|
|
for resnet in self.resnets:
|
|
hidden_states = resnet(hidden_states, temb=None, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
|
|
|
|
if self.temporal_downsamplers is not None:
|
|
for temporal_downsampler in self.temporal_downsamplers:
|
|
hidden_states = temporal_downsampler(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class DownEncoderBlock2D(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
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_spatial_downsample: bool = True,
|
|
add_temporal_downsample: bool = False,
|
|
downsample_padding: int = 1,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
|
|
for i in range(num_layers):
|
|
in_channels = in_channels if i == 0 else out_channels
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=None,
|
|
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,
|
|
)
|
|
)
|
|
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
if add_spatial_downsample:
|
|
self.downsamplers = nn.ModuleList(
|
|
[
|
|
Downsample2D(
|
|
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.downsamplers = None
|
|
|
|
if add_temporal_downsample:
|
|
self.temporal_downsamplers = nn.ModuleList(
|
|
[
|
|
TemporalDownsample2x(
|
|
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding,
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.temporal_downsamplers = None
|
|
|
|
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
|
for resnet in self.resnets:
|
|
hidden_states = resnet(hidden_states, temb=None)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states)
|
|
|
|
if self.temporal_downsamplers is not None:
|
|
for temporal_downsampler in self.temporal_downsamplers:
|
|
hidden_states = temporal_downsampler(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class UpDecoderBlock2D(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_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", # default, spatial
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
output_scale_factor: float = 1.0,
|
|
add_spatial_upsample: bool = True,
|
|
add_temporal_upsample: bool = False,
|
|
temb_channels: Optional[int] = None,
|
|
interpolate: bool = True,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
|
|
for i in range(num_layers):
|
|
input_channels = in_channels if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=input_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,
|
|
)
|
|
)
|
|
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
if add_spatial_upsample:
|
|
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels, interpolate=interpolate)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
if add_temporal_upsample:
|
|
self.temporal_upsamplers = nn.ModuleList([TemporalUpsample2x(out_channels, use_conv=True, out_channels=out_channels, interpolate=interpolate)])
|
|
else:
|
|
self.temporal_upsamplers = None
|
|
|
|
self.resolution_idx = resolution_idx
|
|
|
|
def forward(
|
|
self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0, is_image: bool = False,
|
|
) -> torch.FloatTensor:
|
|
for resnet in self.resnets:
|
|
hidden_states = resnet(hidden_states, temb=temb, scale=scale)
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states)
|
|
|
|
if self.temporal_upsamplers is not None:
|
|
for temporal_upsampler in self.temporal_upsamplers:
|
|
hidden_states = temporal_upsampler(hidden_states, is_image=is_image)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class UpDecoderBlockCausal3D(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_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", # default, spatial
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
output_scale_factor: float = 1.0,
|
|
add_spatial_upsample: bool = True,
|
|
add_temporal_upsample: bool = False,
|
|
temb_channels: Optional[int] = None,
|
|
interpolate: bool = True,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
|
|
for i in range(num_layers):
|
|
input_channels = in_channels if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
CausalResnetBlock3D(
|
|
in_channels=input_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,
|
|
)
|
|
)
|
|
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
if add_spatial_upsample:
|
|
self.upsamplers = nn.ModuleList([CausalUpsample2x(out_channels, use_conv=True, out_channels=out_channels, interpolate=interpolate)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
if add_temporal_upsample:
|
|
self.temporal_upsamplers = nn.ModuleList([CausalTemporalUpsample2x(out_channels, use_conv=True, out_channels=out_channels, interpolate=interpolate)])
|
|
else:
|
|
self.temporal_upsamplers = None
|
|
|
|
self.resolution_idx = resolution_idx
|
|
|
|
def forward(
|
|
self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None,
|
|
is_init_image=True, temporal_chunk=False,
|
|
) -> torch.FloatTensor:
|
|
for resnet in self.resnets:
|
|
hidden_states = resnet(hidden_states, temb=temb, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
|
|
|
|
if self.temporal_upsamplers is not None:
|
|
for temporal_upsampler in self.temporal_upsamplers:
|
|
hidden_states = temporal_upsampler(hidden_states, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
|
|
|
|
return hidden_states
|
|
|