Source code for modelzoo.vision.pytorch.dit.layers.vae.VAEEncoder

# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/vae.py


import torch
import torch.nn as nn

from modelzoo.vision.pytorch.dit.layers.vae.DownEncoderBlock2D import (
    DownEncoderBlock2D,
)
from modelzoo.vision.pytorch.dit.layers.vae.UNetMidBlock2D import UNetMidBlock2D


[docs]class Encoder(nn.Module):
[docs] def __init__( self, in_channels=3, out_channels=3, down_block_types=("DownEncoderBlock2D",), block_out_channels=(64,), layers_per_block=2, norm_num_groups=32, act_fn="silu", double_z=True, ): super().__init__() self.layers_per_block = layers_per_block self.conv_in = torch.nn.Conv2d( in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1, ) self.mid_block = None self.down_blocks = nn.ModuleList([]) # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): assert ( down_block_type == "DownEncoderBlock2D" ), f"Support for {down_block_type} not added" input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = DownEncoderBlock2D( num_layers=self.layers_per_block, in_channels=input_channel, out_channels=output_channel, add_downsample=not is_final_block, resnet_eps=1e-6, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, downsample_padding=0, resnet_time_scale_shift="default", ) self.down_blocks.append(down_block) # mid self.mid_block = UNetMidBlock2D( in_channels=block_out_channels[-1], resnet_eps=1e-6, resnet_act_fn=act_fn, output_scale_factor=1, resnet_time_scale_shift="default", attn_num_head_channels=None, resnet_groups=norm_num_groups, temb_channels=None, ) # out self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6, ) self.conv_act = nn.SiLU() conv_out_channels = 2 * out_channels if double_z else out_channels self.conv_out = nn.Conv2d( block_out_channels[-1], conv_out_channels, 3, padding=1 )
def forward(self, x): sample = x sample = self.conv_in(sample) # down for down_block in self.down_blocks: sample = down_block(sample) # middle sample = self.mid_block(sample) # post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) return sample