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from typing import Optional, Tuple, Union
from diffusers import AutoencoderTiny
from diffusers.models.autoencoders.vae import (
EncoderTiny,
get_activation,
AutoencoderTinyBlock,
DecoderOutput
)
from diffusers.utils.accelerate_utils import apply_forward_hook
from diffusers.configuration_utils import register_to_config
import torch
import torch.nn as nn
class DecoderTinyWithPooledExits(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
num_blocks: Tuple[int, ...],
block_out_channels: Tuple[int, ...],
upsampling_scaling_factor: int,
act_fn: str,
upsample_fn: str,
):
super().__init__()
layers = []
self.ordered_layers = []
l = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=1)
self.ordered_layers.append(l)
layers.append(l)
l = get_activation(act_fn)
self.ordered_layers.append(l)
layers.append(l)
pooled_exits = []
for i, num_block in enumerate(num_blocks):
is_final_block = i == (len(num_blocks) - 1)
num_channels = block_out_channels[i]
for _ in range(num_block):
l = AutoencoderTinyBlock(num_channels, num_channels, act_fn)
layers.append(l)
self.ordered_layers.append(l)
if not is_final_block:
l = nn.Upsample(
scale_factor=upsampling_scaling_factor, mode=upsample_fn
)
layers.append(l)
self.ordered_layers.append(l)
conv_out_channel = num_channels if not is_final_block else out_channels
l = nn.Conv2d(
num_channels,
conv_out_channel,
kernel_size=3,
padding=1,
bias=is_final_block,
)
layers.append(l)
self.ordered_layers.append(l)
if not is_final_block:
p = nn.Conv2d(
conv_out_channel,
out_channels=3,
kernel_size=3,
padding=1,
bias=True,
)
p._is_pooled_exit = True
pooled_exits.append(p)
self.ordered_layers.append(p)
self.layers = nn.ModuleList(layers)
self.pooled_exits = nn.ModuleList(pooled_exits)
self.gradient_checkpointing = False
def forward(self, x: torch.Tensor, pooled_outputs=False) -> torch.Tensor:
r"""The forward method of the `DecoderTiny` class."""
# Clamp.
x = torch.tanh(x / 3) * 3
pooled_output_list = []
for layer in self.ordered_layers:
# see if is pooled exit
try:
if hasattr(layer, '_is_pooled_exit') and layer._is_pooled_exit:
if pooled_outputs:
pooled_output = layer(x)
pooled_output_list.append(pooled_output)
else:
if torch.is_grad_enabled() and self.gradient_checkpointing:
x = self._gradient_checkpointing_func(layer, x)
else:
x = layer(x)
except RuntimeError as e:
raise e
# scale image from [0, 1] to [-1, 1] to match diffusers convention
x = x.mul(2).sub(1)
if pooled_outputs:
return x, pooled_output_list
return x
class AutoencoderTinyWithPooledExits(AutoencoderTiny):
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
encoder_block_out_channels: Tuple[int, ...] = (64, 64, 64, 64),
decoder_block_out_channels: Tuple[int, ...] = (64, 64, 64, 64),
act_fn: str = "relu",
upsample_fn: str = "nearest",
latent_channels: int = 4,
upsampling_scaling_factor: int = 2,
num_encoder_blocks: Tuple[int, ...] = (1, 3, 3, 3),
num_decoder_blocks: Tuple[int, ...] = (3, 3, 3, 1),
latent_magnitude: int = 3,
latent_shift: float = 0.5,
force_upcast: bool = False,
scaling_factor: float = 1.0,
shift_factor: float = 0.0,
):
super(AutoencoderTiny, self).__init__()
if len(encoder_block_out_channels) != len(num_encoder_blocks):
raise ValueError(
"`encoder_block_out_channels` should have the same length as `num_encoder_blocks`."
)
if len(decoder_block_out_channels) != len(num_decoder_blocks):
raise ValueError(
"`decoder_block_out_channels` should have the same length as `num_decoder_blocks`."
)
self.encoder = EncoderTiny(
in_channels=in_channels,
out_channels=latent_channels,
num_blocks=num_encoder_blocks,
block_out_channels=encoder_block_out_channels,
act_fn=act_fn,
)
self.decoder = DecoderTinyWithPooledExits(
in_channels=latent_channels,
out_channels=out_channels,
num_blocks=num_decoder_blocks,
block_out_channels=decoder_block_out_channels,
upsampling_scaling_factor=upsampling_scaling_factor,
act_fn=act_fn,
upsample_fn=upsample_fn,
)
self.latent_magnitude = latent_magnitude
self.latent_shift = latent_shift
self.scaling_factor = scaling_factor
self.use_slicing = False
self.use_tiling = False
# only relevant if vae tiling is enabled
self.spatial_scale_factor = 2**out_channels
self.tile_overlap_factor = 0.125
self.tile_sample_min_size = 512
self.tile_latent_min_size = (
self.tile_sample_min_size // self.spatial_scale_factor
)
self.register_to_config(block_out_channels=decoder_block_out_channels)
self.register_to_config(force_upcast=False)
@apply_forward_hook
def decode_with_pooled_exits(
self, x: torch.Tensor, generator: Optional[torch.Generator] = None, return_dict: bool = False
) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
output, pooled_outputs = self.decoder(x, pooled_outputs=True)
if not return_dict:
return (output, pooled_outputs)
return DecoderOutput(sample=output)
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