639 lines
23 KiB
Python
639 lines
23 KiB
Python
# Copyright 2022 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, Union
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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 ..configuration_utils import ConfigMixin, register_to_config
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from ..modeling_utils import ModelMixin
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from ..utils import BaseOutput
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from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
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@dataclass
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class DecoderOutput(BaseOutput):
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"""
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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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Decoded output sample of the model. Output of the last layer of the model.
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"""
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sample: torch.FloatTensor
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@dataclass
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class VQEncoderOutput(BaseOutput):
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"""
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Output of VQModel encoding method.
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Args:
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latents (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Encoded output sample of the model. Output of the last layer of the model.
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"""
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latents: torch.FloatTensor
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@dataclass
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class AutoencoderKLOutput(BaseOutput):
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"""
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Output of AutoencoderKL encoding method.
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Args:
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latent_dist (`DiagonalGaussianDistribution`):
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Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
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`DiagonalGaussianDistribution` allows for sampling latents from the distribution.
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"""
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latent_dist: "DiagonalGaussianDistribution"
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class Encoder(nn.Module):
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def __init__(
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self,
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in_channels=3,
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out_channels=3,
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down_block_types=("DownEncoderBlock2D",),
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block_out_channels=(64,),
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layers_per_block=2,
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norm_num_groups=32,
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act_fn="silu",
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double_z=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 = torch.nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
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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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is_final_block = i == len(block_out_channels) - 1
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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,
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in_channels=input_channel,
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out_channels=output_channel,
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add_downsample=not is_final_block,
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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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attn_num_head_channels=None,
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temb_channels=None,
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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 = UNetMidBlock2D(
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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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attn_num_head_channels=None,
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resnet_groups=norm_num_groups,
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temb_channels=None,
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)
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# out
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self.conv_norm_out = nn.GroupNorm(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 = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1)
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def forward(self, x):
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sample = x
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sample = self.conv_in(sample)
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# down
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for down_block in self.down_blocks:
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sample = down_block(sample)
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# middle
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sample = self.mid_block(sample)
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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)
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return sample
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class Decoder(nn.Module):
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def __init__(
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self,
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in_channels=3,
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out_channels=3,
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up_block_types=("UpDecoderBlock2D",),
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block_out_channels=(64,),
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layers_per_block=2,
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norm_num_groups=32,
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act_fn="silu",
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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 = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)
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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 = UNetMidBlock2D(
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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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attn_num_head_channels=None,
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resnet_groups=norm_num_groups,
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temb_channels=None,
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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 + 1,
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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_upsample=not is_final_block,
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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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attn_num_head_channels=None,
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temb_channels=None,
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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 = nn.GroupNorm(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 = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
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def forward(self, z):
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sample = z
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sample = self.conv_in(sample)
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# middle
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sample = self.mid_block(sample)
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# up
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for up_block in self.up_blocks:
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sample = up_block(sample)
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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)
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return sample
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class VectorQuantizer(nn.Module):
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"""
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Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
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multiplications and allows for post-hoc remapping of indices.
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"""
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# NOTE: due to a bug the beta term was applied to the wrong term. for
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# backwards compatibility we use the buggy version by default, but you can
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# specify legacy=False to fix it.
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def __init__(
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self, n_e, vq_embed_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=True
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):
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super().__init__()
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self.n_e = n_e
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self.vq_embed_dim = vq_embed_dim
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self.beta = beta
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self.legacy = legacy
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self.embedding = nn.Embedding(self.n_e, self.vq_embed_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed + 1
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print(
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f"Remapping {self.n_e} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices."
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)
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else:
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self.re_embed = n_e
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self.sane_index_shape = sane_index_shape
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def remap_to_used(self, inds):
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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match = (inds[:, :, None] == used[None, None, ...]).long()
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new = match.argmax(-1)
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unknown = match.sum(2) < 1
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if self.unknown_index == "random":
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new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
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else:
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new[unknown] = self.unknown_index
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return new.reshape(ishape)
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def unmap_to_all(self, inds):
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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if self.re_embed > self.used.shape[0]: # extra token
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inds[inds >= self.used.shape[0]] = 0 # simply set to zero
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back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
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return back.reshape(ishape)
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def forward(self, z):
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# reshape z -> (batch, height, width, channel) and flatten
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z = z.permute(0, 2, 3, 1).contiguous()
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z_flattened = z.view(-1, self.vq_embed_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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min_encoding_indices = torch.argmin(torch.cdist(z_flattened, self.embedding.weight), dim=1)
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z_q = self.embedding(min_encoding_indices).view(z.shape)
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perplexity = None
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min_encodings = None
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# compute loss for embedding
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if not self.legacy:
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loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)
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else:
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loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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if self.remap is not None:
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min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
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min_encoding_indices = self.remap_to_used(min_encoding_indices)
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min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
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if self.sane_index_shape:
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min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])
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return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
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def get_codebook_entry(self, indices, shape):
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# shape specifying (batch, height, width, channel)
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if self.remap is not None:
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indices = indices.reshape(shape[0], -1) # add batch axis
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indices = self.unmap_to_all(indices)
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indices = indices.reshape(-1) # flatten again
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# get quantized latent vectors
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z_q = self.embedding(indices)
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if shape is not None:
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z_q = z_q.view(shape)
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q
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class DiagonalGaussianDistribution(object):
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def __init__(self, parameters, deterministic=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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device = self.parameters.device
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sample_device = "cpu" if device.type == "mps" else device
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sample = torch.randn(self.mean.shape, generator=generator, device=sample_device)
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# make sure sample is on the same device as the parameters and has same dtype
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sample = sample.to(device=device, dtype=self.parameters.dtype)
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x = self.mean + self.std * sample
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return x
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def kl(self, other=None):
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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(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3])
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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=[1, 2, 3],
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)
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def nll(self, sample, dims=[1, 2, 3]):
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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(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims)
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def mode(self):
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return self.mean
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class VQModel(ModelMixin, ConfigMixin):
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r"""VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray
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Kavukcuoglu.
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This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
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implements for all the model (such as downloading or saving, etc.)
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Parameters:
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in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
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out_channels (int, *optional*, defaults to 3): Number of channels in the output.
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down_block_types (`Tuple[str]`, *optional*, defaults to :
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obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
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up_block_types (`Tuple[str]`, *optional*, defaults to :
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obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
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block_out_channels (`Tuple[int]`, *optional*, defaults to :
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obj:`(64,)`): Tuple of block output channels.
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act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
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latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
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sample_size (`int`, *optional*, defaults to `32`): TODO
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num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.
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vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.
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"""
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@register_to_config
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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] = ("DownEncoderBlock2D",),
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up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
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block_out_channels: Tuple[int] = (64,),
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layers_per_block: int = 1,
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act_fn: str = "silu",
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latent_channels: int = 3,
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sample_size: int = 32,
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num_vq_embeddings: int = 256,
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norm_num_groups: int = 32,
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vq_embed_dim: Optional[int] = None,
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):
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super().__init__()
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# pass init params to Encoder
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self.encoder = Encoder(
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in_channels=in_channels,
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out_channels=latent_channels,
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down_block_types=down_block_types,
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block_out_channels=block_out_channels,
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layers_per_block=layers_per_block,
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act_fn=act_fn,
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norm_num_groups=norm_num_groups,
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double_z=False,
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)
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vq_embed_dim = vq_embed_dim if vq_embed_dim is not None else latent_channels
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self.quant_conv = torch.nn.Conv2d(latent_channels, vq_embed_dim, 1)
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self.quantize = VectorQuantizer(num_vq_embeddings, vq_embed_dim, beta=0.25, remap=None, sane_index_shape=False)
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self.post_quant_conv = torch.nn.Conv2d(vq_embed_dim, latent_channels, 1)
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# pass init params to Decoder
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self.decoder = Decoder(
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in_channels=latent_channels,
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out_channels=out_channels,
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up_block_types=up_block_types,
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block_out_channels=block_out_channels,
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layers_per_block=layers_per_block,
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act_fn=act_fn,
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norm_num_groups=norm_num_groups,
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)
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def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> VQEncoderOutput:
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h = self.encoder(x)
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h = self.quant_conv(h)
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if not return_dict:
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return (h,)
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return VQEncoderOutput(latents=h)
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def decode(
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self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True
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) -> Union[DecoderOutput, torch.FloatTensor]:
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# also go through quantization layer
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if not force_not_quantize:
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quant, emb_loss, info = self.quantize(h)
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else:
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quant = h
|
|
quant = self.post_quant_conv(quant)
|
|
dec = self.decoder(quant)
|
|
|
|
if not return_dict:
|
|
return (dec,)
|
|
|
|
return DecoderOutput(sample=dec)
|
|
|
|
def forward(self, sample: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
|
|
r"""
|
|
Args:
|
|
sample (`torch.FloatTensor`): Input sample.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
|
|
"""
|
|
x = sample
|
|
h = self.encode(x).latents
|
|
dec = self.decode(h).sample
|
|
|
|
if not return_dict:
|
|
return (dec,)
|
|
|
|
return DecoderOutput(sample=dec)
|
|
|
|
|
|
class AutoencoderKL(ModelMixin, ConfigMixin):
|
|
r"""Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma
|
|
and Max Welling.
|
|
|
|
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
|
|
implements for all the model (such as downloading or saving, etc.)
|
|
|
|
Parameters:
|
|
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
|
|
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
|
|
down_block_types (`Tuple[str]`, *optional*, defaults to :
|
|
obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
|
|
up_block_types (`Tuple[str]`, *optional*, defaults to :
|
|
obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
|
|
block_out_channels (`Tuple[int]`, *optional*, defaults to :
|
|
obj:`(64,)`): Tuple of block output channels.
|
|
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
|
|
latent_channels (`int`, *optional*, defaults to `4`): Number of channels in the latent space.
|
|
sample_size (`int`, *optional*, defaults to `32`): TODO
|
|
"""
|
|
|
|
@register_to_config
|
|
def __init__(
|
|
self,
|
|
in_channels: int = 3,
|
|
out_channels: int = 3,
|
|
down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
|
|
up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
|
|
block_out_channels: Tuple[int] = (64,),
|
|
layers_per_block: int = 1,
|
|
act_fn: str = "silu",
|
|
latent_channels: int = 4,
|
|
norm_num_groups: int = 32,
|
|
sample_size: int = 32,
|
|
):
|
|
super().__init__()
|
|
|
|
# pass init params to Encoder
|
|
self.encoder = Encoder(
|
|
in_channels=in_channels,
|
|
out_channels=latent_channels,
|
|
down_block_types=down_block_types,
|
|
block_out_channels=block_out_channels,
|
|
layers_per_block=layers_per_block,
|
|
act_fn=act_fn,
|
|
norm_num_groups=norm_num_groups,
|
|
double_z=True,
|
|
)
|
|
|
|
# pass init params to Decoder
|
|
self.decoder = Decoder(
|
|
in_channels=latent_channels,
|
|
out_channels=out_channels,
|
|
up_block_types=up_block_types,
|
|
block_out_channels=block_out_channels,
|
|
layers_per_block=layers_per_block,
|
|
norm_num_groups=norm_num_groups,
|
|
act_fn=act_fn,
|
|
)
|
|
|
|
self.quant_conv = torch.nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
|
|
self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
|
|
self.use_slicing = False
|
|
|
|
def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
|
|
h = self.encoder(x)
|
|
moments = self.quant_conv(h)
|
|
posterior = DiagonalGaussianDistribution(moments)
|
|
|
|
if not return_dict:
|
|
return (posterior,)
|
|
|
|
return AutoencoderKLOutput(latent_dist=posterior)
|
|
|
|
def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
|
|
z = self.post_quant_conv(z)
|
|
dec = self.decoder(z)
|
|
|
|
if not return_dict:
|
|
return (dec,)
|
|
|
|
return DecoderOutput(sample=dec)
|
|
|
|
def enable_slicing(self):
|
|
r"""
|
|
Enable sliced VAE decoding.
|
|
|
|
When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
|
|
steps. This is useful to save some memory and allow larger batch sizes.
|
|
"""
|
|
self.use_slicing = True
|
|
|
|
def disable_slicing(self):
|
|
r"""
|
|
Disable sliced VAE decoding. If `enable_slicing` was previously invoked, this method will go back to computing
|
|
decoding in one step.
|
|
"""
|
|
self.use_slicing = False
|
|
|
|
def decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
|
|
if self.use_slicing and z.shape[0] > 1:
|
|
decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
|
|
decoded = torch.cat(decoded_slices)
|
|
else:
|
|
decoded = self._decode(z).sample
|
|
|
|
if not return_dict:
|
|
return (decoded,)
|
|
|
|
return DecoderOutput(sample=decoded)
|
|
|
|
def forward(
|
|
self,
|
|
sample: torch.FloatTensor,
|
|
sample_posterior: bool = False,
|
|
return_dict: bool = True,
|
|
generator: Optional[torch.Generator] = None,
|
|
) -> Union[DecoderOutput, torch.FloatTensor]:
|
|
r"""
|
|
Args:
|
|
sample (`torch.FloatTensor`): Input sample.
|
|
sample_posterior (`bool`, *optional*, defaults to `False`):
|
|
Whether to sample from the posterior.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
|
|
"""
|
|
x = sample
|
|
posterior = self.encode(x).latent_dist
|
|
if sample_posterior:
|
|
z = posterior.sample(generator=generator)
|
|
else:
|
|
z = posterior.mode()
|
|
dec = self.decode(z).sample
|
|
|
|
if not return_dict:
|
|
return (dec,)
|
|
|
|
return DecoderOutput(sample=dec)
|