r"""

r"""

def __init__(

self,

in_channels: int = 3,

out_channels: int = 3,

down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",),

block_out_channels: Tuple[int, ...] = (64,),

layers_per_block: int = 2,

norm_num_groups: int = 32,

act_fn: str = "silu",

double_z: bool = True,

):

super().__init__()

self.layers_per_block = layers_per_block

self.conv_in = 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):

input_channel = output_channel

output_channel = block_out_channels[i]

is_final_block = i == len(block_out_channels) - 1

down_block = get_down_block(

down_block_type,

num_layers=self.layers_per_block,

in_channels=input_channel,

out_channels=output_channel,

add_downsample=not is_final_block,

resnet_eps=1e-6,

downsample_padding=0,

resnet_act_fn=act_fn,

resnet_groups=norm_num_groups,

attention_head_dim=output_channel,

temb_channels=None,

)

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",

attention_head_dim=block_out_channels[-1],

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)

self.gradient_checkpointing = False

def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:

r"""The forward method of the `Encoder` class."""

sample = self.conv_in(sample)

if self.training and self.gradient_checkpointing:

def create_custom_forward(module):

def custom_forward(*inputs):

return module(*inputs)

return custom_forward

# down

if is_torch_version(">=", "1.11.0"):

for down_block in self.down_blocks:

sample = torch.utils.checkpoint.checkpoint(

create_custom_forward(down_block), sample, use_reentrant=False

)

# middle

sample = torch.utils.checkpoint.checkpoint(

create_custom_forward(self.mid_block), sample, use_reentrant=False

)

else:

for down_block in self.down_blocks:

sample = torch.utils.checkpoint.checkpoint(create_custom_forward(down_block), sample)

# middle

sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample)

else:

# 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)

r"""

def __init__(

self,

in_channels: int = 3,

out_channels: int = 3,

up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),

block_out_channels: Tuple[int, ...] = (64,),

layers_per_block: int = 2,

norm_num_groups: int = 32,

act_fn: str = "silu",

norm_type: str = "group", # group, spatial

):

super().__init__()

self.layers_per_block = layers_per_block

self.conv_in = nn.Conv2d(

in_channels,

block_out_channels[-1],

kernel_size=3,

stride=1,

padding=1,

)

self.mid_block = None

self.up_blocks = nn.ModuleList([])

temb_channels = in_channels if norm_type == "spatial" else None

# 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" if norm_type == "group" else norm_type,

attention_head_dim=block_out_channels[-1],

resnet_groups=norm_num_groups,

temb_channels=temb_channels,

)

# up

reversed_block_out_channels = list(reversed(block_out_channels))

output_channel = reversed_block_out_channels[0]

for i, up_block_type in enumerate(up_block_types):

prev_output_channel = output_channel

output_channel = reversed_block_out_channels[i]

is_final_block = i == len(block_out_channels) - 1

up_block = get_up_block(

up_block_type,

num_layers=self.layers_per_block + 1,

in_channels=prev_output_channel,

out_channels=output_channel,

prev_output_channel=None,

add_upsample=not is_final_block,

resnet_eps=1e-6,

resnet_act_fn=act_fn,

resnet_groups=norm_num_groups,

attention_head_dim=output_channel,

temb_channels=temb_channels,

resnet_time_scale_shift=norm_type,

)

self.up_blocks.append(up_block)

prev_output_channel = output_channel

# out

if norm_type == "spatial":

self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)

else:

self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)

self.conv_act = nn.SiLU()

self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)

self.gradient_checkpointing = False

def forward(

self,

sample: torch.FloatTensor,

latent_embeds: Optional[torch.FloatTensor] = None,

) -> torch.FloatTensor:

r"""The forward method of the `Decoder` class."""

sample = self.conv_in(sample)

upscale_dtype = next(iter(self.up_blocks.parameters())).dtype

if self.training and self.gradient_checkpointing:

def create_custom_forward(module):

def custom_forward(*inputs):

return module(*inputs)

return custom_forward

if is_torch_version(">=", "1.11.0"):

# middle

sample = torch.utils.checkpoint.checkpoint(

create_custom_forward(self.mid_block),

sample,

latent_embeds,

use_reentrant=False,

)

sample = sample.to(upscale_dtype)

# up

for up_block in self.up_blocks:

sample = torch.utils.checkpoint.checkpoint(

create_custom_forward(up_block),

sample,

latent_embeds,

use_reentrant=False,

)

else:

# middle

sample = torch.utils.checkpoint.checkpoint(

create_custom_forward(self.mid_block), sample, latent_embeds

)

sample = sample.to(upscale_dtype)

# up

for up_block in self.up_blocks:

sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds)

else:

# middle

sample = self.mid_block(sample, latent_embeds)

sample = sample.to(upscale_dtype)

# up

for up_block in self.up_blocks:

sample = up_block(sample, latent_embeds)

# post-process

if latent_embeds is None:

sample = self.conv_norm_out(sample)

else:

sample = self.conv_norm_out(sample, latent_embeds)

sample = self.conv_act(sample)

sample = self.conv_out(sample)

r"""

def __init__(

self,

in_channels: int,

out_channels: int,

) -> None:

super().__init__()

self.in_channels = in_channels

self.out_channels = out_channels

self.deconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1)

def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:

r"""The forward method of the `UpSample` class."""

x = torch.relu(x)

x = self.deconv(x)

"""

def __init__(

self,

in_ch: int,

out_ch: int = 192,

res_ch: int = 768,

stride: int = 16,

) -> None:

super().__init__()

channels = []

while stride > 1:

stride = stride // 2

in_ch_ = out_ch * 2

if out_ch > res_ch:

out_ch = res_ch

if stride == 1:

in_ch_ = res_ch

channels.append((in_ch_, out_ch))

out_ch *= 2

out_channels = []

for _in_ch, _out_ch in channels:

out_channels.append(_out_ch)

out_channels.append(channels[-1][0])

layers = []

in_ch_ = in_ch

for l in range(len(out_channels)):

out_ch_ = out_channels[l]

if l == 0 or l == 1:

layers.append(nn.Conv2d(in_ch_, out_ch_, kernel_size=3, stride=1, padding=1))

else:

layers.append(nn.Conv2d(in_ch_, out_ch_, kernel_size=4, stride=2, padding=1))

in_ch_ = out_ch_

self.layers = nn.Sequential(*layers)

def forward(self, x: torch.FloatTensor, mask=None) -> torch.FloatTensor:

r"""The forward method of the `MaskConditionEncoder` class."""

out = {}

for l in range(len(self.layers)):

layer = self.layers[l]

x = layer(x)

out[str(tuple(x.shape))] = x

x = torch.relu(x)

r"""The `MaskConditionDecoder` should be used in combination with [`AsymmetricAutoencoderKL`] to enhance the model's

def __init__(

self,

in_channels: int = 3,

out_channels: int = 3,

up_block_types: Tuple[str, ...] = ("UpDecoderBlock2D",),

block_out_channels: Tuple[int, ...] = (64,),

layers_per_block: int = 2,

norm_num_groups: int = 32,

act_fn: str = "silu",

norm_type: str = "group", # group, spatial

):

super().__init__()

self.layers_per_block = layers_per_block

self.conv_in = nn.Conv2d(

in_channels,

block_out_channels[-1],

kernel_size=3,

stride=1,

padding=1,

)

self.mid_block = None

self.up_blocks = nn.ModuleList([])

temb_channels = in_channels if norm_type == "spatial" else None

# 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" if norm_type == "group" else norm_type,

attention_head_dim=block_out_channels[-1],

resnet_groups=norm_num_groups,

temb_channels=temb_channels,

)

# up

reversed_block_out_channels = list(reversed(block_out_channels))

output_channel = reversed_block_out_channels[0]

for i, up_block_type in enumerate(up_block_types):

prev_output_channel = output_channel

output_channel = reversed_block_out_channels[i]

is_final_block = i == len(block_out_channels) - 1

up_block = get_up_block(

up_block_type,

num_layers=self.layers_per_block + 1,

in_channels=prev_output_channel,

out_channels=output_channel,

prev_output_channel=None,

add_upsample=not is_final_block,

resnet_eps=1e-6,

resnet_act_fn=act_fn,

resnet_groups=norm_num_groups,

attention_head_dim=output_channel,

temb_channels=temb_channels,

resnet_time_scale_shift=norm_type,

)

self.up_blocks.append(up_block)

prev_output_channel = output_channel

# condition encoder

self.condition_encoder = MaskConditionEncoder(

in_ch=out_channels,

out_ch=block_out_channels[0],

res_ch=block_out_channels[-1],

)

# out

if norm_type == "spatial":

self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels)

else:

self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)

self.conv_act = nn.SiLU()

self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)

self.gradient_checkpointing = False

def forward(

self,

z: torch.FloatTensor,

image: Optional[torch.FloatTensor] = None,

mask: Optional[torch.FloatTensor] = None,

latent_embeds: Optional[torch.FloatTensor] = None,

) -> torch.FloatTensor:

r"""The forward method of the `MaskConditionDecoder` class."""

sample = z

sample = self.conv_in(sample)

upscale_dtype = next(iter(self.up_blocks.parameters())).dtype

if self.training and self.gradient_checkpointing:

def create_custom_forward(module):

def custom_forward(*inputs):

return module(*inputs)

return custom_forward

if is_torch_version(">=", "1.11.0"):

# middle

sample = torch.utils.checkpoint.checkpoint(

create_custom_forward(self.mid_block),

sample,

latent_embeds,

use_reentrant=False,

)

sample = sample.to(upscale_dtype)

# condition encoder

if image is not None and mask is not None:

masked_image = (1 - mask) * image

im_x = torch.utils.checkpoint.checkpoint(

create_custom_forward(self.condition_encoder),

masked_image,

mask,

use_reentrant=False,

)

# up

for up_block in self.up_blocks:

if image is not None and mask is not None:

sample_ = im_x[str(tuple(sample.shape))]

mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest")

sample = sample * mask_ + sample_ * (1 - mask_)

sample = torch.utils.checkpoint.checkpoint(

create_custom_forward(up_block),

sample,

latent_embeds,

use_reentrant=False,

)

if image is not None and mask is not None:

sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)

else:

# middle

sample = torch.utils.checkpoint.checkpoint(

create_custom_forward(self.mid_block), sample, latent_embeds

)

sample = sample.to(upscale_dtype)

# condition encoder

if image is not None and mask is not None:

masked_image = (1 - mask) * image

im_x = torch.utils.checkpoint.checkpoint(

create_custom_forward(self.condition_encoder),

masked_image,

mask,

)

# up

for up_block in self.up_blocks:

if image is not None and mask is not None:

sample_ = im_x[str(tuple(sample.shape))]

mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest")

sample = sample * mask_ + sample_ * (1 - mask_)

sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds)

if image is not None and mask is not None:

sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)

else:

# middle

sample = self.mid_block(sample, latent_embeds)

sample = sample.to(upscale_dtype)

# condition encoder

if image is not None and mask is not None:

masked_image = (1 - mask) * image

im_x = self.condition_encoder(masked_image, mask)

# up

for up_block in self.up_blocks:

if image is not None and mask is not None:

sample_ = im_x[str(tuple(sample.shape))]

mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest")

sample = sample * mask_ + sample_ * (1 - mask_)

sample = up_block(sample, latent_embeds)

if image is not None and mask is not None:

sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)

# post-process

if latent_embeds is None:

sample = self.conv_norm_out(sample)

else:

sample = self.conv_norm_out(sample, latent_embeds)

sample = self.conv_act(sample)

sample = self.conv_out(sample)

"""

# NOTE: due to a bug the beta term was applied to the wrong term. for

# backwards compatibility we use the buggy version by default, but you can

# specify legacy=False to fix it.

def __init__(

self,

n_e: int,

vq_embed_dim: int,

beta: float,

remap=None,

unknown_index: str = "random",

sane_index_shape: bool = False,

legacy: bool = True,

):

super().__init__()

self.n_e = n_e

self.vq_embed_dim = vq_embed_dim

self.beta = beta

self.legacy = legacy

self.embedding = nn.Embedding(self.n_e, self.vq_embed_dim)

self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)

self.remap = remap

if self.remap is not None:

self.register_buffer("used", torch.tensor(np.load(self.remap)))

self.used: torch.Tensor

self.re_embed = self.used.shape[0]

self.unknown_index = unknown_index # "random" or "extra" or integer

if self.unknown_index == "extra":

self.unknown_index = self.re_embed

self.re_embed = self.re_embed + 1

print(

f"Remapping {self.n_e} indices to {self.re_embed} indices. "

f"Using {self.unknown_index} for unknown indices."

)

else:

self.re_embed = n_e

self.sane_index_shape = sane_index_shape

def remap_to_used(self, inds: torch.LongTensor) -> torch.LongTensor:

ishape = inds.shape

assert len(ishape) > 1

inds = inds.reshape(ishape[0], -1)

used = self.used.to(inds)

match = (inds[:, :, None] == used[None, None, ...]).long()

new = match.argmax(-1)

unknown = match.sum(2) < 1

if self.unknown_index == "random":

new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)

else:

new[unknown] = self.unknown_index

return new.reshape(ishape)

def unmap_to_all(self, inds: torch.LongTensor) -> torch.LongTensor:

ishape = inds.shape

assert len(ishape) > 1

inds = inds.reshape(ishape[0], -1)

used = self.used.to(inds)

if self.re_embed > self.used.shape[0]: # extra token

inds[inds >= self.used.shape[0]] = 0 # simply set to zero

back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)

return back.reshape(ishape)

def forward(self, z: torch.FloatTensor) -> Tuple[torch.FloatTensor, torch.FloatTensor, Tuple]:

# reshape z -> (batch, height, width, channel) and flatten

z = z.permute(0, 2, 3, 1).contiguous()

z_flattened = z.view(-1, self.vq_embed_dim)

# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z

min_encoding_indices = torch.argmin(torch.cdist(z_flattened, self.embedding.weight), dim=1)

z_q = self.embedding(min_encoding_indices).view(z.shape)

perplexity = None

min_encodings = None

# compute loss for embedding

if not self.legacy:

loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)

else:

loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)

# preserve gradients

z_q: torch.FloatTensor = z + (z_q - z).detach()

# reshape back to match original input shape

z_q = z_q.permute(0, 3, 1, 2).contiguous()

if self.remap is not None:

min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis

min_encoding_indices = self.remap_to_used(min_encoding_indices)

min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten

if self.sane_index_shape:

min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])

return z_q, loss, (perplexity, min_encodings, min_encoding_indices)

def get_codebook_entry(self, indices: torch.LongTensor, shape: Tuple[int, ...]) -> torch.FloatTensor:

# shape specifying (batch, height, width, channel)

if self.remap is not None:

indices = indices.reshape(shape[0], -1) # add batch axis

indices = self.unmap_to_all(indices)

indices = indices.reshape(-1) # flatten again

# get quantized latent vectors

z_q: torch.FloatTensor = self.embedding(indices)

if shape is not None:

z_q = z_q.view(shape)

# reshape back to match original input shape

z_q = z_q.permute(0, 3, 1, 2).contiguous()

def __init__(self, parameters: torch.Tensor, deterministic: bool = False):

self.parameters = parameters

self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)

self.logvar = torch.clamp(self.logvar, -30.0, 20.0)

self.deterministic = deterministic

self.std = torch.exp(0.5 * self.logvar)

self.var = torch.exp(self.logvar)

if self.deterministic:

self.var = self.std = torch.zeros_like(

self.mean, device=self.parameters.device, dtype=self.parameters.dtype

)

def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:

# make sure sample is on the same device as the parameters and has same dtype

sample = randn_tensor(

self.mean.shape,

generator=generator,

device=self.parameters.device,

dtype=self.parameters.dtype,

)

x = self.mean + self.std * sample

return x

def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:

if self.deterministic:

return torch.Tensor([0.0])

else:

if other is None:

return 0.5 * torch.sum(

torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,

dim=[1, 2, 3],

)

else:

return 0.5 * torch.sum(

torch.pow(self.mean - other.mean, 2) / other.var

+ self.var / other.var

- 1.0

- self.logvar

+ other.logvar,

dim=[1, 2, 3],

)

def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor:

if self.deterministic:

return torch.Tensor([0.0])

logtwopi = np.log(2.0 * np.pi)

return 0.5 * torch.sum(

logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,

dim=dims,

)

def mode(self) -> torch.Tensor:

r"""

def __init__(

self,

in_channels: int,

out_channels: int,

num_blocks: Tuple[int, ...],

block_out_channels: Tuple[int, ...],

act_fn: str,

):

super().__init__()

layers = []

for i, num_block in enumerate(num_blocks):

num_channels = block_out_channels[i]

if i == 0:

layers.append(nn.Conv2d(in_channels, num_channels, kernel_size=3, padding=1))

else:

layers.append(

nn.Conv2d(

num_channels,

num_channels,

kernel_size=3,

padding=1,

stride=2,

bias=False,

)

)

for _ in range(num_block):

layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn))

layers.append(nn.Conv2d(block_out_channels[-1], out_channels, kernel_size=3, padding=1))

self.layers = nn.Sequential(*layers)

self.gradient_checkpointing = False

def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:

r"""The forward method of the `EncoderTiny` class."""

if self.training and self.gradient_checkpointing:

def create_custom_forward(module):

def custom_forward(*inputs):

return module(*inputs)

return custom_forward

if is_torch_version(">=", "1.11.0"):

x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x, use_reentrant=False)

else:

x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x)

else:

# scale image from [-1, 1] to [0, 1] to match TAESD convention

x = self.layers(x.add(1).div(2))

r"""

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,

):

super().__init__()

layers = [

nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=1),

get_activation(act_fn),

]

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):

layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn))

if not is_final_block:

layers.append(nn.Upsample(scale_factor=upsampling_scaling_factor))

conv_out_channel = num_channels if not is_final_block else out_channels

layers.append(

nn.Conv2d(

num_channels,

conv_out_channel,

kernel_size=3,

padding=1,

bias=is_final_block,

)

)

self.layers = nn.Sequential(*layers)

self.gradient_checkpointing = False

def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:

r"""The forward method of the `DecoderTiny` class."""

# Clamp.

x = torch.tanh(x / 3) * 3

if self.training and self.gradient_checkpointing:

def create_custom_forward(module):

def custom_forward(*inputs):

return module(*inputs)

return custom_forward

if is_torch_version(">=", "1.11.0"):

x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x, use_reentrant=False)

else:

x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.layers), x)

else:

x = self.layers(x)

# scale image from [0, 1] to [-1, 1] to match diffusers convention