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import torch
from torch.utils.checkpoint import checkpoint
from diffusers.models.autoencoders.autoencoder_dc import Encoder, Decoder, AutoencoderDC
class MyEncoder(Encoder):
def __init__(
self,
in_channels,
latent_channels,
attention_head_dim = 32,
block_type = "ResBlock",
block_out_channels = ...,
layers_per_block = ...,
qkv_multiscales = ...,
downsample_block_type = "pixel_unshuffle",
out_shortcut = True
):
super().__init__(
in_channels, latent_channels, attention_head_dim, block_type, block_out_channels,
layers_per_block, qkv_multiscales, downsample_block_type, out_shortcut
)
def forward(self, hidden_states: torch.Tensor, use_checkpoint=False) -> torch.Tensor:
hidden_states = self.conv_in(hidden_states)
for down_block in self.down_blocks:
if use_checkpoint:
hidden_states = checkpoint(self.ckpt_wrapper(down_block), hidden_states)
else:
hidden_states = down_block(hidden_states)
if self.out_shortcut:
x = hidden_states.unflatten(1, (-1, self.out_shortcut_average_group_size))
x = x.mean(dim=2)
hidden_states = self.conv_out(hidden_states) + x
else:
hidden_states = self.conv_out(hidden_states)
return hidden_states
def ckpt_wrapper(self, module):
def ckpt_forward(*inputs):
outputs = module(*inputs)
return outputs
return ckpt_forward
class MyDecoder(Decoder):
def __init__(
self,
in_channels,
latent_channels,
attention_head_dim = 32,
block_type = "ResBlock",
block_out_channels = ...,
layers_per_block = ...,
qkv_multiscales = ...,
norm_type = "rms_norm",
act_fn = "silu",
upsample_block_type = "pixel_shuffle",
in_shortcut = True
):
super().__init__(
in_channels, latent_channels, attention_head_dim, block_type, block_out_channels,
layers_per_block, qkv_multiscales, norm_type, act_fn, upsample_block_type, in_shortcut
)
def forward(self, hidden_states: torch.Tensor, use_checkpoint=False) -> torch.Tensor:
if self.in_shortcut:
x = hidden_states.repeat_interleave(
self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats
)
hidden_states = self.conv_in(hidden_states) + x
else:
hidden_states = self.conv_in(hidden_states)
for up_block in reversed(self.up_blocks):
if use_checkpoint:
hidden_states = checkpoint(self.ckpt_wrapper(up_block), hidden_states)
else:
hidden_states = up_block(hidden_states)
hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1)
hidden_states = self.conv_act(hidden_states)
hidden_states = self.conv_out(hidden_states)
return hidden_states
def ckpt_wrapper(self, module):
def ckpt_forward(*inputs):
outputs = module(*inputs)
return outputs
return ckpt_forward
class MyAutoencoderDC(AutoencoderDC):
def __init__(
self,
in_channels = 3,
latent_channels = 32,
attention_head_dim = 32,
encoder_block_types = "ResBlock",
decoder_block_types = "ResBlock",
encoder_block_out_channels = ...,
decoder_block_out_channels = ...,
encoder_layers_per_block = ...,
decoder_layers_per_block = ...,
encoder_qkv_multiscales = ...,
decoder_qkv_multiscales = ...,
upsample_block_type = "pixel_shuffle",
downsample_block_type = "pixel_unshuffle",
decoder_norm_types = "rms_norm",
decoder_act_fns = "silu",
scaling_factor = 1,
bn_momentum = 0.1,
):
super().__init__(
in_channels, latent_channels, attention_head_dim, encoder_block_types,
decoder_block_types, encoder_block_out_channels, decoder_block_out_channels,
encoder_layers_per_block, decoder_layers_per_block, encoder_qkv_multiscales,
decoder_qkv_multiscales, upsample_block_type, downsample_block_type,
decoder_norm_types, decoder_act_fns, scaling_factor
)
self.encoder = MyEncoder(
in_channels=in_channels,
latent_channels=latent_channels,
attention_head_dim=attention_head_dim,
block_type=encoder_block_types,
block_out_channels=encoder_block_out_channels,
layers_per_block=encoder_layers_per_block,
qkv_multiscales=encoder_qkv_multiscales,
downsample_block_type=downsample_block_type,
)
self.decoder = MyDecoder(
in_channels=in_channels,
latent_channels=latent_channels,
attention_head_dim=attention_head_dim,
block_type=decoder_block_types,
block_out_channels=decoder_block_out_channels,
layers_per_block=decoder_layers_per_block,
qkv_multiscales=decoder_qkv_multiscales,
norm_type=decoder_norm_types,
act_fn=decoder_act_fns,
upsample_block_type=upsample_block_type,
)
self.bn = torch.nn.BatchNorm2d(
latent_channels, eps=1e-4, momentum=bn_momentum, affine=False, track_running_stats=True
)
self.bn.reset_running_stats()
self.init_bn()
def init_bn(self):
# self.bn.running_mean = torch.zeros_like(self.bn.running_mean).to(torch.float64)
# self.bn.running_var = torch.ones_like(self.bn.running_var).to(torch.float64) / self.config.scaling_factor ** 2
self.bn.running_mean = torch.zeros_like(self.bn.running_mean)
self.bn.running_var = torch.ones_like(self.bn.running_var) / self.config.scaling_factor ** 2
print(self.config.scaling_factor, self.bn.running_var.flatten())
@property
def mean(self):
mean = self.bn.running_mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
return mean
@property
def std(self):
std = self.bn.running_var.sqrt().unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
return std
def forward(self, x: torch.Tensor, use_checkpoint=False) -> torch.Tensor:
z = self.encoder(x, use_checkpoint)
latent = self.bn(z)
recon = self.decoder(z, use_checkpoint)
posterior = None
return posterior, latent, recon
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