414 lines
16 KiB
Python
414 lines
16 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 dataclasses import dataclass
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from typing import Optional, Tuple
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import numpy as np
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import torch
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import torch.nn as nn
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from diffusers.utils import BaseOutput, is_torch_version
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from diffusers.utils.torch_utils import randn_tensor
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from .modeling_block import (
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UNetMidBlock2D,
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CausalUNetMidBlock2D,
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get_down_block,
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get_up_block,
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get_input_layer,
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get_output_layer,
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)
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from .modeling_causal_conv import CausalConv3d, CausalGroupNorm
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@dataclass
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class DecoderOutput(BaseOutput):
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r"""
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Output of decoding method.
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Args:
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sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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The decoded output sample from the last layer of the model.
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"""
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sample: torch.FloatTensor
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class CausalVaeEncoder(nn.Module):
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r"""
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The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.
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Args:
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in_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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out_channels (`int`, *optional*, defaults to 3):
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The number of output channels.
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down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
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The types of down blocks to use. See `~diffusers.models.unet_2d_blocks.get_down_block` for available
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options.
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block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
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The number of output channels for each block.
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layers_per_block (`int`, *optional*, defaults to 2):
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The number of layers per block.
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norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups for normalization.
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act_fn (`str`, *optional*, defaults to `"silu"`):
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The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
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double_z (`bool`, *optional*, defaults to `True`):
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Whether to double the number of output channels for the last block.
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"""
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def __init__(
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self,
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in_channels: int = 3,
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out_channels: int = 3,
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down_block_types: Tuple[str, ...] = ("DownEncoderBlockCausal3D",),
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spatial_down_sample: Tuple[bool, ...] = (True,),
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temporal_down_sample: Tuple[bool, ...] = (False,),
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block_out_channels: Tuple[int, ...] = (64,),
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layers_per_block: Tuple[int, ...] = (2,),
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norm_num_groups: int = 32,
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act_fn: str = "silu",
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double_z: bool = True,
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block_dropout: Tuple[int, ...] = (0.0,),
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mid_block_add_attention=True,
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):
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super().__init__()
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self.layers_per_block = layers_per_block
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self.conv_in = CausalConv3d(
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in_channels,
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block_out_channels[0],
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kernel_size=3,
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stride=1,
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)
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self.mid_block = None
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self.down_blocks = nn.ModuleList([])
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# down
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output_channel = block_out_channels[0]
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for i, down_block_type in enumerate(down_block_types):
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input_channel = output_channel
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output_channel = block_out_channels[i]
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down_block = get_down_block(
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down_block_type,
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num_layers=self.layers_per_block[i],
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in_channels=input_channel,
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out_channels=output_channel,
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add_spatial_downsample=spatial_down_sample[i],
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add_temporal_downsample=temporal_down_sample[i],
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resnet_eps=1e-6,
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downsample_padding=0,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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attention_head_dim=output_channel,
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temb_channels=None,
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dropout=block_dropout[i],
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)
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self.down_blocks.append(down_block)
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# mid
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self.mid_block = CausalUNetMidBlock2D(
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in_channels=block_out_channels[-1],
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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output_scale_factor=1,
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resnet_time_scale_shift="default",
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attention_head_dim=block_out_channels[-1],
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resnet_groups=norm_num_groups,
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temb_channels=None,
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add_attention=mid_block_add_attention,
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dropout=block_dropout[-1],
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)
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# out
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self.conv_norm_out = CausalGroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)
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self.conv_act = nn.SiLU()
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conv_out_channels = 2 * out_channels if double_z else out_channels
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self.conv_out = CausalConv3d(block_out_channels[-1], conv_out_channels, kernel_size=3, stride=1)
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self.gradient_checkpointing = False
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def forward(self, sample: torch.FloatTensor, is_init_image=True, temporal_chunk=False) -> torch.FloatTensor:
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r"""The forward method of the `Encoder` class."""
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sample = self.conv_in(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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if self.training and self.gradient_checkpointing:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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return module(*inputs)
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return custom_forward
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# down
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if is_torch_version(">=", "1.11.0"):
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for down_block in self.down_blocks:
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sample = torch.utils.checkpoint.checkpoint(
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create_custom_forward(down_block), sample, is_init_image,
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temporal_chunk, use_reentrant=False
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)
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# middle
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sample = torch.utils.checkpoint.checkpoint(
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create_custom_forward(self.mid_block), sample, is_init_image,
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temporal_chunk, use_reentrant=False
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)
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else:
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for down_block in self.down_blocks:
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sample = torch.utils.checkpoint.checkpoint(create_custom_forward(down_block), sample, is_init_image, temporal_chunk)
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# middle
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sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample, is_init_image, temporal_chunk)
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else:
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# down
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for down_block in self.down_blocks:
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sample = down_block(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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# middle
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sample = self.mid_block(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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# post-process
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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sample = self.conv_out(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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return sample
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class CausalVaeDecoder(nn.Module):
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r"""
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The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.
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Args:
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in_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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out_channels (`int`, *optional*, defaults to 3):
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The number of output channels.
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up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
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The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.
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block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
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The number of output channels for each block.
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layers_per_block (`int`, *optional*, defaults to 2):
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The number of layers per block.
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norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups for normalization.
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act_fn (`str`, *optional*, defaults to `"silu"`):
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The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
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norm_type (`str`, *optional*, defaults to `"group"`):
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The normalization type to use. Can be either `"group"` or `"spatial"`.
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"""
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def __init__(
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self,
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in_channels: int = 3,
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out_channels: int = 3,
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up_block_types: Tuple[str, ...] = ("UpDecoderBlockCausal3D",),
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spatial_up_sample: Tuple[bool, ...] = (True,),
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temporal_up_sample: Tuple[bool, ...] = (False,),
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block_out_channels: Tuple[int, ...] = (64,),
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layers_per_block: Tuple[int, ...] = (2,),
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norm_num_groups: int = 32,
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act_fn: str = "silu",
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mid_block_add_attention=True,
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interpolate: bool = True,
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block_dropout: Tuple[int, ...] = (0.0,),
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):
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super().__init__()
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self.layers_per_block = layers_per_block
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self.conv_in = CausalConv3d(
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in_channels,
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block_out_channels[-1],
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kernel_size=3,
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stride=1,
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)
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self.mid_block = None
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self.up_blocks = nn.ModuleList([])
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# mid
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self.mid_block = CausalUNetMidBlock2D(
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in_channels=block_out_channels[-1],
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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output_scale_factor=1,
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resnet_time_scale_shift="default",
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attention_head_dim=block_out_channels[-1],
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resnet_groups=norm_num_groups,
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temb_channels=None,
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add_attention=mid_block_add_attention,
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dropout=block_dropout[-1],
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)
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# up
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reversed_block_out_channels = list(reversed(block_out_channels))
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output_channel = reversed_block_out_channels[0]
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for i, up_block_type in enumerate(up_block_types):
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prev_output_channel = output_channel
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output_channel = reversed_block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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up_block = get_up_block(
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up_block_type,
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num_layers=self.layers_per_block[i],
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in_channels=prev_output_channel,
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out_channels=output_channel,
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prev_output_channel=None,
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add_spatial_upsample=spatial_up_sample[i],
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add_temporal_upsample=temporal_up_sample[i],
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resnet_eps=1e-6,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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attention_head_dim=output_channel,
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temb_channels=None,
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resnet_time_scale_shift='default',
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interpolate=interpolate,
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dropout=block_dropout[i],
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)
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self.up_blocks.append(up_block)
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prev_output_channel = output_channel
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# out
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self.conv_norm_out = CausalGroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
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self.conv_act = nn.SiLU()
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self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3, stride=1)
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self.gradient_checkpointing = False
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def forward(
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self,
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sample: torch.FloatTensor,
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is_init_image=True,
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temporal_chunk=False,
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) -> torch.FloatTensor:
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r"""The forward method of the `Decoder` class."""
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sample = self.conv_in(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
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if self.training and self.gradient_checkpointing:
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def create_custom_forward(module):
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def custom_forward(*inputs):
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return module(*inputs)
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return custom_forward
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if is_torch_version(">=", "1.11.0"):
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# middle
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sample = torch.utils.checkpoint.checkpoint(
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create_custom_forward(self.mid_block),
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sample,
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is_init_image=is_init_image,
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temporal_chunk=temporal_chunk,
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use_reentrant=False,
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)
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sample = sample.to(upscale_dtype)
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# up
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for up_block in self.up_blocks:
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sample = torch.utils.checkpoint.checkpoint(
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create_custom_forward(up_block),
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sample,
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is_init_image=is_init_image,
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temporal_chunk=temporal_chunk,
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use_reentrant=False,
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)
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else:
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# middle
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sample = torch.utils.checkpoint.checkpoint(
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create_custom_forward(self.mid_block), sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk,
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)
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sample = sample.to(upscale_dtype)
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# up
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for up_block in self.up_blocks:
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sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample,
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is_init_image=is_init_image, temporal_chunk=temporal_chunk,)
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else:
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# middle
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sample = self.mid_block(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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sample = sample.to(upscale_dtype)
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# up
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for up_block in self.up_blocks:
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sample = up_block(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk,)
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# post-process
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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sample = self.conv_out(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
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return sample
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class DiagonalGaussianDistribution(object):
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def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
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self.parameters = parameters
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
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self.deterministic = deterministic
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self.std = torch.exp(0.5 * self.logvar)
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self.var = torch.exp(self.logvar)
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if self.deterministic:
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self.var = self.std = torch.zeros_like(
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self.mean, device=self.parameters.device, dtype=self.parameters.dtype
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)
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def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
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# make sure sample is on the same device as the parameters and has same dtype
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sample = randn_tensor(
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self.mean.shape,
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generator=generator,
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device=self.parameters.device,
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dtype=self.parameters.dtype,
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)
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x = self.mean + self.std * sample
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return x
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def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
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if self.deterministic:
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return torch.Tensor([0.0])
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else:
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if other is None:
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return 0.5 * torch.sum(
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torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
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dim=[2, 3, 4],
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)
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else:
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return 0.5 * torch.sum(
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torch.pow(self.mean - other.mean, 2) / other.var
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+ self.var / other.var
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- 1.0
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- self.logvar
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+ other.logvar,
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dim=[2, 3, 4],
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)
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def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor:
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if self.deterministic:
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return torch.Tensor([0.0])
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logtwopi = np.log(2.0 * np.pi)
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return 0.5 * torch.sum(
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logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
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dim=dims,
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)
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def mode(self) -> torch.Tensor:
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return self.mean |