text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
norm_type (`str`, *optional*, defaults to `"group"`):
Type of normalization layer to use. Can be one of `"group"` or `"spatial"`.
""" | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
@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_b... | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
# 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,
... | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
# 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,
act_fn=act_fn,
... | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
@apply_forward_hook
def decode(
self, h: torch.Tensor, force_not_quantize: bool = False, return_dict: bool = True, shape=None
) -> Union[DecoderOutput, torch.Tensor]:
# also go through quantization layer
if not force_not_quantize:
quant, commit_loss, _ = self.quantize(h)
... | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
def forward(
self, sample: torch.Tensor, return_dict: bool = True
) -> Union[DecoderOutput, Tuple[torch.Tensor, ...]]:
r"""
The [`VQModel`] forward method.
Args:
sample (`torch.Tensor`): Input sample.
return_dict (`bool`, *optional*, defaults to `True`):
... | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
class ResBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
norm_type: str = "batch_norm",
act_fn: str = "relu6",
) -> None:
super().__init__()
self.norm_type = norm_type
self.nonlinearity = get_activation(act_fn) if act_f... | 1,214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
if self.norm_type == "rms_norm":
# move channel to the last dimension so we apply RMSnorm across channel dimension
hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1)
else:
hidden_states = self.norm(hidden_states)
return hidden_states + residual | 1,214 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
class EfficientViTBlock(nn.Module):
def __init__(
self,
in_channels: int,
mult: float = 1.0,
attention_head_dim: int = 32,
qkv_multiscales: Tuple[int, ...] = (5,),
norm_type: str = "batch_norm",
) -> None:
super().__init__()
self.attn = SanaMultis... | 1,215 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
class DCDownBlock2d(nn.Module):
def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None:
super().__init__()
self.downsample = downsample
self.factor = 2
self.stride = 1 if downsample else 2
self.group_size = in_channel... | 1,216 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
if self.shortcut:
y = F.pixel_unshuffle(hidden_states, self.factor)
y = y.unflatten(1, (-1, self.group_size))
y = y.mean(dim=2)
hidden_states = x + y
else:
hidden_states = x
return hidden_states | 1,216 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
class DCUpBlock2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
interpolate: bool = False,
shortcut: bool = True,
interpolation_mode: str = "nearest",
) -> None:
super().__init__()
self.interpolate = interpolate
self... | 1,217 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
if self.shortcut:
y = hidden_states.repeat_interleave(self.repeats, dim=1)
y = F.pixel_shuffle(y, self.factor)
hidden_states = x + y
else:
hidden_states = x
return hidden_states | 1,217 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
class Encoder(nn.Module):
def __init__(
self,
in_channels: int,
latent_channels: int,
attention_head_dim: int = 32,
block_type: Union[str, Tuple[str]] = "ResBlock",
block_out_channels: Tuple[int] = (128, 256, 512, 512, 1024, 1024),
layers_per_block: Tuple[int]... | 1,218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
if layers_per_block[0] > 0:
self.conv_in = nn.Conv2d(
in_channels,
block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1],
kernel_size=3,
stride=1,
padding=1,
)
else:
self.con... | 1,218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
for _ in range(num_layers):
block = get_block(
block_type[i],
out_channel,
out_channel,
attention_head_dim=attention_head_dim,
norm_type="rms_norm",
act_fn="silu",
... | 1,218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
self.conv_out = nn.Conv2d(block_out_channels[-1], latent_channels, 3, 1, 1)
self.out_shortcut = out_shortcut
if out_shortcut:
self.out_shortcut_average_group_size = block_out_channels[-1] // latent_channels
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_... | 1,218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
class Decoder(nn.Module):
def __init__(
self,
in_channels: int,
latent_channels: int,
attention_head_dim: int = 32,
block_type: Union[str, Tuple[str]] = "ResBlock",
block_out_channels: Tuple[int] = (128, 256, 512, 512, 1024, 1024),
layers_per_block: Tuple[int]... | 1,219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
self.conv_in = nn.Conv2d(latent_channels, block_out_channels[-1], 3, 1, 1)
self.in_shortcut = in_shortcut
if in_shortcut:
self.in_shortcut_repeats = block_out_channels[-1] // latent_channels
up_blocks = []
for i, (out_channel, num_layers) in reversed(list(enumerate(zip(bloc... | 1,219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
for _ in range(num_layers):
block = get_block(
block_type[i],
out_channel,
out_channel,
attention_head_dim=attention_head_dim,
norm_type=norm_type[i],
act_fn=act_fn[i],
... | 1,219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
if layers_per_block[0] > 0:
self.conv_out = nn.Conv2d(channels, in_channels, 3, 1, 1)
else:
self.conv_out = DCUpBlock2d(
channels, in_channels, interpolate=upsample_block_type == "interpolate", shortcut=False
)
def forward(self, hidden_states: torch.Tenso... | 1,219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
class AutoencoderDC(ModelMixin, ConfigMixin, FromOriginalModelMixin):
r"""
An Autoencoder model introduced in [DCAE](https://arxiv.org/abs/2410.10733) and used in
[SANA](https://arxiv.org/abs/2410.10629).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic metho... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
Args:
in_channels (`int`, defaults to `3`):
The number of input channels in samples.
latent_channels (`int`, defaults to `32`):
The number of channels in the latent space representation.
encoder_block_types (`Union[str, Tuple[str]]`, defaults to `"ResBlock"`):
... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
decoder_layers_per_block (`Tuple[int]`, defaults to `(3, 3, 3, 3, 3, 3)`):
The number of layers per block in the decoder.
encoder_qkv_multiscales (`Tuple[Tuple[int, ...], ...]`, defaults to `((), (), (), (5,), (5,), (5,))`):
Multi-scale configurations for the encoder's QKV (query-key-val... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
decoder_act_fns (`Union[str, Tuple[str]]`, defaults to `"silu"`):
The activation function(s) to use in the decoder.
scaling_factor (`float`, defaults to `1.0`):
The multiplicative inverse of the root mean square of the latent features. This is used to scale the latent
space t... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
_supports_gradient_checkpointing = False | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
@register_to_config
def __init__(
self,
in_channels: int = 3,
latent_channels: int = 32,
attention_head_dim: int = 32,
encoder_block_types: Union[str, Tuple[str]] = "ResBlock",
decoder_block_types: Union[str, Tuple[str]] = "ResBlock",
encoder_block_out_channel... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
scaling_factor: float = 1.0,
) -> None:
super().__init__() | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
self.encoder = Encoder(
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,
... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
self.spatial_compression_ratio = 2 ** (len(encoder_block_out_channels) - 1)
self.temporal_compression_ratio = 1
# When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
# to perform decoding of a single video latent at a time.
self.us... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
self.tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
self.tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
def enable_tiling(
self,
tile_sample_min_height: Optional[int] = None,
tile_sample_min_width: Optiona... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
Args:
tile_sample_min_height (`int`, *optional*):
The minimum height required for a sample to be separated into tiles across the height dimension.
tile_sample_min_width (`int`, *optional*):
The minimum width required for a sample to be separated into tiles across ... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
self.tile_latent_min_height = se... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
def disable_tiling(self) -> None:
r"""
Disable tiled AE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_tiling = False
def enable_slicing(self) -> None:
r"""
Enable sliced AE decodi... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
return self.tiled_encode(x, return_dict=False)[0]
encoded = self.encoder(x)
return encoded
@apply_forward_hook
def encode(self, x: torch.Tensor, return_dict: bool = True) -> Union[... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
Returns:
The latent representations of the encoded videos. If `return_dict` is True, a
[`~models.vae.EncoderOutput`] is returned, otherwise a plain `tuple` is returned.
"""
if self.use_slicing and x.shape[0] > 1:
encoded_slices = [self._encode(x_slice) for x_s... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
@apply_forward_hook
def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
r"""
Decode a batch of images.
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, defaults to `True`):
... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[2], b.shape[2], blend_extent)
for y in range(blend_extent):
b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
r... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
tile_latent_stride_width = self... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
# Split x into overlapping tiles and encode them separately.
# The tiles have an overlap to avoid seams between tiles.
rows = []
for i in range(0, x.shape[2], self.tile_sample_stride_height):
row = []
for j in range(0, x.shape[3], self.tile_sample_stride_width):
... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = self.blend_v(rows[i - 1][j]... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
encoded = torch.cat(result_rows, dim=2)[:, :, :latent_height, :latent_width]
if not return_dict:
return (encoded,)
return EncoderOutput(latent=encoded)
def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
batch_size, num_chann... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
# Split z into overlapping tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
rows = []
for i in range(0, height, tile_latent_stride_height):
row = []
for j in range(0, width, tile_latent_stride_width):
tile = z[:, ... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
result_rows = []
for i, row in enumerate(rows):
result_row = []
for j, tile in enumerate(row):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
tile = se... | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
def forward(self, sample: torch.Tensor, return_dict: bool = True) -> torch.Tensor:
encoded = self.encode(sample, return_dict=False)[0]
decoded = self.decode(encoded, return_dict=False)[0]
if not return_dict:
return (decoded,)
return DecoderOutput(sample=decoded) | 1,220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py |
class EncoderOutput(BaseOutput):
r"""
Output of encoding method.
Args:
latent (`torch.Tensor` of shape `(batch_size, num_channels, latent_height, latent_width)`):
The encoded latent.
"""
latent: torch.Tensor | 1,221 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class DecoderOutput(BaseOutput):
r"""
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model.
"""
sample: torch.Tensor
commit_loss: Optional[torch.FloatTensor]... | 1,222 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class Encoder(nn.Module):
r"""
The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation. | 1,223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, defaults to 3):
The number of output channels.
down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
The typ... | 1,223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
double_z (`bool`, *optional*, defaults to `True`):
Whether to double the number of output channels for the last block.
""" | 1,223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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",
d... | 1,223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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... | 1,223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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... | 1,223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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
... | 1,223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
return sample | 1,223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class Decoder(nn.Module):
r"""
The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample. | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, defaults to 3):
The number of output channels.
up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
The types o... | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
The normalization type to use. Can be either `"group"` or `"spatial"`.
""" | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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_... | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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_... | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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_ep... | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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.C... | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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,
)
s... | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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)
... | 1,224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class UpSample(nn.Module):
r"""
The `UpSample` layer of a variational autoencoder that upsamples its input.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, defaults to 3):
The number of output chann... | 1,225 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class MaskConditionEncoder(nn.Module):
"""
used in AsymmetricAutoencoderKL
"""
def __init__(
self,
in_ch: int,
out_ch: int = 192,
res_ch: int = 768,
stride: int = 16,
) -> None:
super().__init__()
channels = []
while stride > 1:
... | 1,226 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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_, ke... | 1,226 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class MaskConditionDecoder(nn.Module):
r"""The `MaskConditionDecoder` should be used in combination with [`AsymmetricAutoencoderKL`] to enhance the model's
decoder with a conditioner on the mask and masked image. | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, defaults to 3):
The number of output channels.
up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
The types o... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
The normalization type to use. Can be either `"group"` or `"spatial"`.
""" | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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_... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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_... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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_ep... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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.C... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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(
cre... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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 = sa... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
sample = sample.to(upscale_dtype) | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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,
... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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 = sa... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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_ + sampl... | 1,227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class VectorQuantizer(nn.Module):
"""
Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
multiplications and allows for post-hoc remapping of indices.
"""
# NOTE: due to a bug the beta term was applied to the wrong term. for
# backwards comp... | 1,228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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.unknow... | 1,228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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 =... | 1,228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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 # si... | 1,228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
# 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... | 1,228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
def get_codebook_entry(self, indices: torch.LongTensor, shape: Tuple[int, ...]) -> torch.Tensor:
# 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)
... | 1,228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class DiagonalGaussianDistribution(object):
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 = dete... | 1,229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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,
... | 1,229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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,
... | 1,229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class EncoderTiny(nn.Module):
r"""
The `EncoderTiny` layer is a simpler version of the `Encoder` layer.
Args:
in_channels (`int`):
The number of input channels.
out_channels (`int`):
The number of output channels.
num_blocks (`Tuple[int, ...]`):
E... | 1,230 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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(
... | 1,230 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
def forward(self, x: torch.Tensor) -> torch.Tensor:
r"""The forward method of the `EncoderTiny` class."""
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
... | 1,230 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class DecoderTiny(nn.Module):
r"""
The `DecoderTiny` layer is a simpler version of the `Decoder` layer.
Args:
in_channels (`int`):
The number of input channels.
out_channels (`int`):
The number of output channels.
num_blocks (`Tuple[int, ...]`):
E... | 1,231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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 = [
nn.C... | 1,231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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,
... | 1,231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
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)
# s... | 1,231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py |
class AutoencoderTinyOutput(BaseOutput):
"""
Output of AutoencoderTiny encoding method.
Args:
latents (`torch.Tensor`): Encoded outputs of the `Encoder`.
"""
latents: torch.Tensor | 1,232 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
class AutoencoderTiny(ModelMixin, ConfigMixin):
r"""
A tiny distilled VAE model for encoding images into latents and decoding latent representations into images.
[`AutoencoderTiny`] is a wrapper around the original implementation of `TAESD`.
This model inherits from [`ModelMixin`]. Check the superclas... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
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.
encoder_block_out_channels (`Tuple[int]`, *optional*, defaults to `(64, 64, 64, 64)`):
Tuple of integ... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
Number of channels in the latent representation. The latent space acts as a compressed representation of
the input image.
upsampling_scaling_factor (`int`, *optional*, defaults to 2):
Scaling factor for upsampling in the decoder. It determines the size of the output image during the
... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
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