text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
Parameters:
encoder_hidden_size (`int`, *optional*, defaults to 128):
Intermediate representation dimension for the encoder.
downsampling_ratios (`List[int]`, *optional*, defaults to `[2, 4, 4, 8, 8]`):
Ratios for downsampling in the encoder. These are used in reverse order for u... | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
""" | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
_supports_gradient_checkpointing = False
@register_to_config
def __init__(
self,
encoder_hidden_size=128,
downsampling_ratios=[2, 4, 4, 8, 8],
channel_multiples=[1, 2, 4, 8, 16],
decoder_channels=128,
decoder_input_channels=64,
audio_channels=2,
s... | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
self.decoder = OobleckDecoder(
channels=decoder_channels,
input_channels=decoder_input_channels,
audio_channels=audio_channels,
upsampling_ratios=self.upsampling_ratios,
channel_multiples=channel_multiples,
)
self.use_slicing = False
def ... | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
@apply_forward_hook
def encode(
self, x: torch.Tensor, return_dict: bool = True
) -> Union[AutoencoderOobleckOutput, Tuple[OobleckDiagonalGaussianDistribution]]:
"""
Encode a batch of images into latents.
Args:
x (`torch.Tensor`): Input batch of images.
r... | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
if not return_dict:
return (posterior,)
return AutoencoderOobleckOutput(latent_dist=posterior)
def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[OobleckDecoderOutput, torch.Tensor]:
dec = self.decoder(z)
if not return_dict:
return (dec,)
... | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
Returns:
[`~models.vae.OobleckDecoderOutput`] or `tuple`:
If return_dict is True, a [`~models.vae.OobleckDecoderOutput`] is returned, otherwise a plain `tuple`
is returned.
"""
if self.use_slicing and z.shape[0] > 1:
decoded_slices = [self._decode... | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
) -> Union[OobleckDecoderOutput, torch.Tensor]:
r"""
Args:
sample (`torch.Tensor`): Input sample.
... | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class LTXVideoCausalConv3d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int, int]] = 3,
stride: Union[int, Tuple[int, int, int]] = 1,
dilation: Union[int, Tuple[int, int, int]] = 1,
groups: int = 1,
... | 1,202 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
self.conv = nn.Conv3d(
in_channels,
out_channels,
self.kernel_size,
stride=stride,
dilation=dilation,
groups=groups,
padding=padding,
padding_mode=padding_mode,
)
def forward(self, hidden_states: torch.Tensor) -... | 1,202 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class LTXVideoResnetBlock3d(nn.Module):
r"""
A 3D ResNet block used in the LTXVideo model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
dropout (`float`, ... | 1,203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
dropout: float = 0.0,
eps: float = 1e-6,
elementwise_affine: bool = False,
non_linearity: str = "swish",
is_causal: bool = True,
inject_noise: bool = False,
timestep_... | 1,203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
self.norm2 = RMSNorm(out_channels, eps=1e-8, elementwise_affine=elementwise_affine)
self.dropout = nn.Dropout(dropout)
self.conv2 = LTXVideoCausalConv3d(
in_channels=out_channels, out_channels=out_channels, kernel_size=3, is_causal=is_causal
)
self.norm3 = None
self.... | 1,203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
self.scale_shift_table = None
if timestep_conditioning:
self.scale_shift_table = nn.Parameter(torch.randn(4, in_channels) / in_channels**0.5)
def forward(
self, inputs: torch.Tensor, temb: Optional[torch.Tensor] = None, generator: Optional[torch.Generator] = None
) -> torch.Tensor:
... | 1,203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if self.per_channel_scale1 is not None:
spatial_shape = hidden_states.shape[-2:]
spatial_noise = torch.randn(
spatial_shape, generator=generator, device=hidden_states.device, dtype=hidden_states.dtype
)[None]
hidden_states = hidden_states + (spatial_noise ... | 1,203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if self.per_channel_scale2 is not None:
spatial_shape = hidden_states.shape[-2:]
spatial_noise = torch.randn(
spatial_shape, generator=generator, device=hidden_states.device, dtype=hidden_states.dtype
)[None]
hidden_states = hidden_states + (spatial_noise ... | 1,203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class LTXVideoUpsampler3d(nn.Module):
def __init__(
self,
in_channels: int,
stride: Union[int, Tuple[int, int, int]] = 1,
is_causal: bool = True,
residual: bool = False,
upscale_factor: int = 1,
) -> None:
super().__init__()
self.stride = stride i... | 1,204 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if self.residual:
residual = hidden_states.reshape(
batch_size, -1, self.stride[0], self.stride[1], self.stride[2], num_frames, height, width
)
residual = residual.permute(0, 1, 5, 2, 6, 3, 7, 4).flatten(6, 7).flatten(4, 5).flatten(2, 3)
repeats = (self.st... | 1,204 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class LTXVideoDownBlock3D(nn.Module):
r"""
Down block used in the LTXVideo model. | 1,205 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
num_layers (`int`, defaults to `1`):
Number of resnet layers.
dropout (`float`, defaults to `0.0`):
... | 1,205 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
num_layers: int = 1,
dropout: float = 0.0,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
spatio_temporal_scale: bool = True,
is_causal: bool = True,
):
... | 1,205 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
self.downsamplers = None
if spatio_temporal_scale:
self.downsamplers = nn.ModuleList(
[
LTXVideoCausalConv3d(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=3,
... | 1,205 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
generator: Optional[torch.Generator] = None,
) -> torch.Tensor:
r"""Forward method of the `LTXDownBlock3D` class."""
for i, resnet in enumerate(self.resnets):
if torch.is... | 1,205 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if self.conv_out is not None:
hidden_states = self.conv_out(hidden_states, temb, generator)
return hidden_states | 1,205 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class LTXVideoMidBlock3d(nn.Module):
r"""
A middle block used in the LTXVideo model.
Args:
in_channels (`int`):
Number of input channels.
num_layers (`int`, defaults to `1`):
Number of resnet layers.
dropout (`float`, defaults to `0.0`):
Dropout r... | 1,206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def __init__(
self,
in_channels: int,
num_layers: int = 1,
dropout: float = 0.0,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
is_causal: bool = True,
inject_noise: bool = False,
timestep_conditioning: bool = False,
) -> None:
... | 1,206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
resnets = []
for _ in range(num_layers):
resnets.append(
LTXVideoResnetBlock3d(
in_channels=in_channels,
out_channels=in_channels,
dropout=dropout,
eps=resnet_eps,
non_linearity=resnet... | 1,206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if self.time_embedder is not None:
temb = self.time_embedder(
timestep=temb.flatten(),
resolution=None,
aspect_ratio=None,
batch_size=hidden_states.size(0),
hidden_dtype=hidden_states.dtype,
)
temb = temb... | 1,206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class LTXVideoUpBlock3d(nn.Module):
r"""
Up block used in the LTXVideo model. | 1,207 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
num_layers (`int`, defaults to `1`):
Number of resnet layers.
dropout (`float`, defaults to `0.0`):
... | 1,207 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
num_layers: int = 1,
dropout: float = 0.0,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
spatio_temporal_scale: bool = True,
is_causal: bool = True,
inject_... | 1,207 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
self.conv_in = None
if in_channels != out_channels:
self.conv_in = LTXVideoResnetBlock3d(
in_channels=in_channels,
out_channels=out_channels,
dropout=dropout,
eps=resnet_eps,
non_linearity=resnet_act_fn,
... | 1,207 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
resnets = []
for _ in range(num_layers):
resnets.append(
LTXVideoResnetBlock3d(
in_channels=out_channels,
out_channels=out_channels,
dropout=dropout,
eps=resnet_eps,
non_linearity=resn... | 1,207 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if self.time_embedder is not None:
temb = self.time_embedder(
timestep=temb.flatten(),
resolution=None,
aspect_ratio=None,
batch_size=hidden_states.size(0),
hidden_dtype=hidden_states.dtype,
)
temb = temb... | 1,207 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, temb, generator
)
else:
hidden_states = resnet(hidden_states, temb, generator)
return hidden_states | 1,207 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class LTXVideoEncoder3d(nn.Module):
r"""
The `LTXVideoEncoder3d` layer of a variational autoencoder that encodes input video samples to its latent
representation. | 1,208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Args:
in_channels (`int`, defaults to 3):
Number of input channels.
out_channels (`int`, defaults to 128):
Number of latent channels.
block_out_channels (`Tuple[int, ...]`, defaults to `(128, 256, 512, 512)`):
The number of output channels for each block.
... | 1,208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Whether this layer behaves causally (future frames depend only on past frames) or not.
""" | 1,208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def __init__(
self,
in_channels: int = 3,
out_channels: int = 128,
block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
spatio_temporal_scaling: Tuple[bool, ...] = (True, True, True, False),
layers_per_block: Tuple[int, ...] = (4, 3, 3, 3, 4),
patch_size: i... | 1,208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
# down blocks
num_block_out_channels = len(block_out_channels)
self.down_blocks = nn.ModuleList([])
for i in range(num_block_out_channels):
input_channel = output_channel
output_channel = block_out_channels[i + 1] if i + 1 < num_block_out_channels else block_out_channels[... | 1,208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
# out
self.norm_out = RMSNorm(out_channels, eps=1e-8, elementwise_affine=False)
self.conv_act = nn.SiLU()
self.conv_out = LTXVideoCausalConv3d(
in_channels=output_channel, out_channels=out_channels + 1, kernel_size=3, stride=1, is_causal=is_causal
)
self.gradient_che... | 1,208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
hidden_states = hidden_states.reshape(
batch_size, num_channels, post_patch_num_frames, p_t, post_patch_height, p, post_patch_width, p
)
# Thanks for driving me insane with the weird patching order :(
hidden_states = hidden_states.permute(0, 1, 3, 7, 5, 2, 4, 6).flatten(1, 4)
... | 1,208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
hidden_states = self.mid_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)
last_channel = hidden_states[:, -1:]
last_channel = last_channel.re... | 1,208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class LTXVideoDecoder3d(nn.Module):
r"""
The `LTXVideoDecoder3d` layer of a variational autoencoder that decodes its latent representation into an output
sample. | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Args:
in_channels (`int`, defaults to 128):
Number of latent channels.
out_channels (`int`, defaults to 3):
Number of output channels.
block_out_channels (`Tuple[int, ...]`, defaults to `(128, 256, 512, 512)`):
The number of output channels for each block.
... | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Whether this layer behaves causally (future frames depend only on past frames) or not.
timestep_conditioning (`bool`, defaults to `False`):
Whether to condition the model on timesteps.
""" | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def __init__(
self,
in_channels: int = 128,
out_channels: int = 3,
block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
spatio_temporal_scaling: Tuple[bool, ...] = (True, True, True, False),
layers_per_block: Tuple[int, ...] = (4, 3, 3, 3, 4),
patch_size: i... | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
block_out_channels = tuple(reversed(block_out_channels))
spatio_temporal_scaling = tuple(reversed(spatio_temporal_scaling))
layers_per_block = tuple(reversed(layers_per_block))
inject_noise = tuple(reversed(inject_noise))
upsample_residual = tuple(reversed(upsample_residual))
ups... | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
# up blocks
num_block_out_channels = len(block_out_channels)
self.up_blocks = nn.ModuleList([])
for i in range(num_block_out_channels):
input_channel = output_channel // upsample_factor[i]
output_channel = block_out_channels[i] // upsample_factor[i]
up_block ... | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
# out
self.norm_out = RMSNorm(out_channels, eps=1e-8, elementwise_affine=False)
self.conv_act = nn.SiLU()
self.conv_out = LTXVideoCausalConv3d(
in_channels=output_channel, out_channels=self.out_channels, kernel_size=3, stride=1, is_causal=is_causal
)
# timestep embed... | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def create_custom_forward(module):
def create_forward(*inputs):
return module(*inputs)
return create_forward
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block), hidden_states, temb
)
... | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if self.time_embedder is not None:
temb = self.time_embedder(
timestep=temb.flatten(),
resolution=None,
aspect_ratio=None,
batch_size=hidden_states.size(0),
hidden_dtype=hidden_states.dtype,
)
temb = temb... | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
batch_size, num_channels, num_frames, height, width = hidden_states.shape
hidden_states = hidden_states.reshape(batch_size, -1, p_t, p, p, num_frames, height, width)
hidden_states = hidden_states.permute(0, 1, 5, 2, 6, 4, 7, 3).flatten(6, 7).flatten(4, 5).flatten(2, 3)
return hidden_states | 1,209 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[LTX](https://huggingface.co/Lightricks/LTX-Video).
This model inherits from [`ModelMixin`]. Check the su... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Args:
in_channels (`int`, defaults to `3`):
Number of input channels.
out_channels (`int`, defaults to `3`):
Number of output channels.
latent_channels (`int`, defaults to `128`):
Number of latent channels.
block_out_channels (`Tuple[int, ...]`, defaul... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
scaling_factor (`float`, *optional*, defaults to `1.0`):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents a... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Whether the decoder should behave causally (future frames depend only on past frames) or not.
""" | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
_supports_gradient_checkpointing = True | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
latent_channels: int = 128,
block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
layers_per_block: T... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
encoder_causal: bool = True,
decoder_causal: bool = False,
) -> None:
super().__init__() | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
self.encoder = LTXVideoEncoder3d(
in_channels=in_channels,
out_channels=latent_channels,
block_out_channels=block_out_channels,
spatio_temporal_scaling=spatio_temporal_scaling,
layers_per_block=layers_per_block,
patch_size=patch_size,
p... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
upsample_residual=upsample_residual,
upsample_factor=upsample_factor,
) | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
latents_mean = torch.zeros((latent_channels,), requires_grad=False)
latents_std = torch.ones((latent_channels,), requires_grad=False)
self.register_buffer("latents_mean", latents_mean, persistent=True)
self.register_buffer("latents_std", latents_std, persistent=True)
self.spatial_compre... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
# When decoding spatially large video latents, the memory requirement is very high. By breaking the video latent
# frames spatially into smaller tiles and performing multiple forward passes for decoding, and then blending the
# intermediate tiles together, the memory requirement can be lowered.
... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
# This can be configured based on the amount of GPU memory available.
# `16` for sample frames and `2` for latent frames are sensible defaults for consumer GPUs.
# Setting it to higher values results in higher memory usage.
self.num_sample_frames_batch_size = 16
self.num_latent_frames_ba... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def enable_tiling(
self,
tile_sample_min_height: Optional[int] = None,
tile_sample_min_width: Optional[int] = None,
tile_sample_min_num_frames: Optional[int] = None,
tile_sample_stride_height: Optional[float] = None,
tile_sample_stride_width: Optional[float] = None,
... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.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 ... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
self.tile_sample_min_num_frames = tile_sample_min_num_frames or self.tile_sample_min_num_frames
self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
self.tile_sample_stride_wi... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def disable_tiling(self) -> None:
r"""
Disable tiled VAE 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 VAE deco... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if self.use_framewise_decoding and num_frames > self.tile_sample_min_num_frames:
return self._temporal_tiled_encode(x)
if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
return self.tiled_encode(x)
enc = self.encoder(x)
... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Returns:
The latent representations of the encoded videos. If `return_dict` is True, a
[`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
"""
if self.use_slicing and x.shape[0] > 1:
encoded_slices = [self._encode... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def _decode(
self, z: torch.Tensor, temb: Optional[torch.Tensor] = None, return_dict: bool = True
) -> Union[DecoderOutput, torch.Tensor]:
batch_size, num_channels, num_frames, height, width = z.shape
tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
@apply_forward_hook
def decode(
self, z: torch.Tensor, temb: Optional[torch.Tensor] = None, return_dict: bool = True
) -> Union[DecoderOutput, torch.Tensor]:
"""
Decode a batch of images.
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dic... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
returned.
"""
if self.use_slicing and z.shape[0] > 1:
if temb is not None:
decoded_... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[3], b.shape[3], blend_extent)
for y in range(blend_extent):
b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
y... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
for x in range(blend_extent):
b[:, :, x, :, :] = a[:, :, -blend_extent + x, :, :] * (1 - x / blend_extent) + b[:, :, x, :, :] * (
... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.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... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
row.append(time)
rows.append(row)
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
... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def tiled_decode(
self, z: torch.Tensor, temb: Optional[torch.Tensor], return_dict: bool = True
) -> Union[DecoderOutput, torch.Tensor]:
r"""
Decode a batch of images using a tiled decoder.
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict ... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.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... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
row.append(time)
rows.append(row)
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
... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
def _temporal_tiled_encode(self, x: torch.Tensor) -> AutoencoderKLOutput:
batch_size, num_channels, num_frames, height, width = x.shape
latent_num_frames = (num_frames - 1) // self.temporal_compression_ratio + 1
tile_latent_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compr... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
result_row = []
for i, tile in enumerate(row):
if i > 0:
tile = self.blend_t(row[i - 1], tile, blend_num_frames)
result_row.append(tile[:, :, :tile_latent_stride_num_frames, :, :])
else:
result_row.append(tile[:, :, : tile_latent_stride_num... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.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_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compression_ratio
tile_latent_stride_num_frame... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
row = []
for i in range(0, num_frames, tile_latent_stride_num_frames):
tile = z[:, :, i : i + tile_latent_min_num_frames + 1, :, :]
if self.use_tiling and (tile.shape[-1] > tile_latent_min_width or tile.shape[-2] > tile_latent_min_height):
decoded = self.tiled_decode(tile... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(
self,
sample: torch.Tensor,
temb: Optional[torch.Tensor] = None,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,... | 1,210 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py |
class AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):
r"""
Designing a Better Asymmetric VQGAN for StableDiffusion https://arxiv.org/abs/2306.04632 . A VAE model with KL loss
for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Che... | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.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.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample b... | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py |
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
norm_num_groups (`int`, *optional*, defaults to `32`... | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py |
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
""" | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py |
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str, ...] = ("DownEncoderBlock2D",),
down_block_out_channels: Tuple[int, ...] = (64,),
layers_per_down_block: int = 1,
up_block_types: Tuple[str, ...] ... | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.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=down_block_out_channels,
layers_per_block=layers_per_down_block,
act_fn=act_f... | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py |
self.use_slicing = False
self.use_tiling = False
self.register_to_config(block_out_channels=up_block_out_channels)
self.register_to_config(force_upcast=False)
@apply_forward_hook
def encode(self, x: torch.Tensor, return_dict: bool = True) -> Union[AutoencoderKLOutput, Tuple[torch.Tenso... | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py |
@apply_forward_hook
def decode(
self,
z: torch.Tensor,
generator: Optional[torch.Generator] = None,
image: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
decoded... | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py |
def forward(
self,
sample: torch.Tensor,
mask: Optional[torch.Tensor] = None,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
r"""
Args:
... | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py |
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec) | 1,211 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py |
class VQEncoderOutput(BaseOutput):
"""
Output of VQModel encoding method.
Args:
latents (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The encoded output sample from the last layer of the model.
"""
latents: torch.Tensor | 1,212 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
class VQModel(ModelMixin, ConfigMixin):
r"""
A VQ-VAE model for decoding latent representations.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving). | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.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.
down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
Tuple of downsample b... | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.
norm_num_groups (`int`, *optional*, defaults to `32`): Number of groups for normalization layers.
vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.
scaling_factor (`f... | 1,213 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py |
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