text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
number of decoder blocks.
latent_magnitude (`float`, *optional*, defaults to 3.0):
Magnitude of the latent representation. This parameter scales the latent representation values to control
the extent of information preservation.
latent_shift (float, *optional*, defaults to 0.5):
... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.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. For this Autoencoder,
however, no such scaling factor was used, hence the value of 1.0 as the default.
f... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
encoder_block_out_channels: Tuple[int, ...] = (64, 64, 64, 64),
decoder_block_out_channels: Tuple[int, ...] = (64, 64, 64, 64),
act_fn: st... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
if len(encoder_block_out_channels) != len(num_encoder_blocks):
raise ValueError("`encoder_block_out_channels` should have the same length as `num_encoder_blocks`.")
if len(decoder_block_out_channels) != len(num_decoder_blocks):
raise ValueError("`decoder_block_out_channels` should have t... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
self.latent_magnitude = latent_magnitude
self.latent_shift = latent_shift
self.scaling_factor = scaling_factor
self.use_slicing = False
self.use_tiling = False
# only relevant if vae tiling is enabled
self.spatial_scale_factor = 2**out_channels
self.tile_overlap... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
def unscale_latents(self, x: torch.Tensor) -> torch.Tensor:
"""[0, 1] -> raw latents"""
return x.sub(self.latent_shift).mul(2 * self.latent_magnitude)
def enable_slicing(self) -> None:
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor ... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
def enable_tiling(self, use_tiling: bool = True) -> None:
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
process... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
steps. This is useful to keep memory use constant regardless of image size. To avoid tiling artifacts, the
tiles overlap and are blended together to form a smooth output.
Args:
... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
# mask for blending
blend_masks = torch.stack(
torch.meshgrid([torch.arange(tile_size / sf) / (blend_size / sf - 1)] * 2, indexing="ij")
)
blend_masks = blend_masks.clamp(0, 1).to(x.device) | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
# output array
out = torch.zeros(x.shape[0], 4, x.shape[-2] // sf, x.shape[-1] // sf, device=x.device)
for i in ti:
for j in tj:
tile_in = x[..., i : i + tile_size, j : j + tile_size]
# tile result
tile_out = out[..., i // sf : (i + tile_size) ... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
def _tiled_decode(self, x: torch.Tensor) -> torch.Tensor:
r"""Encode a batch of images using a tiled encoder.
When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
steps. This is useful to keep memory use constant regardless of image size. To... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
# tiles index (up/left)
ti = range(0, x.shape[-2], traverse_size)
tj = range(0, x.shape[-1], traverse_size)
# mask for blending
blend_masks = torch.stack(
torch.meshgrid([torch.arange(tile_size * sf) / (blend_size * sf - 1)] * 2, indexing="ij")
)
blend_masks ... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
# output array
out = torch.zeros(x.shape[0], 3, x.shape[-2] * sf, x.shape[-1] * sf, device=x.device)
for i in ti:
for j in tj:
tile_in = x[..., i : i + tile_size, j : j + tile_size]
# tile result
tile_out = out[..., i * sf : (i + tile_size) * s... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
@apply_forward_hook
def encode(self, x: torch.Tensor, return_dict: bool = True) -> Union[AutoencoderTinyOutput, Tuple[torch.Tensor]]:
if self.use_slicing and x.shape[0] > 1:
output = [
self._tiled_encode(x_slice) if self.use_tiling else self.encoder(x_slice) for x_slice in x.spli... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
@apply_forward_hook
def decode(
self, x: torch.Tensor, generator: Optional[torch.Generator] = None, return_dict: bool = True
) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
if self.use_slicing and x.shape[0] > 1:
output = [
self._tiled_decode(x_slice) if self.use_tili... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
def forward(
self,
sample: torch.Tensor,
return_dict: bool = True,
) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
r"""
Args:
sample (`torch.Tensor`): Input sample.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to... | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec) | 1,233 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py |
class AllegroTemporalConvLayer(nn.Module):
r"""
Temporal convolutional layer that can be used for video (sequence of images) input. Code adapted from:
https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/models/multi_modal/video_synthesis/unet_sd.py#L1016
"""... | 1,234 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
if down_sample:
self.conv1 = nn.Sequential(
nn.GroupNorm(norm_num_groups, in_dim),
nn.SiLU(),
nn.Conv3d(in_dim, out_dim, (2, stride, stride), stride=(2, 1, 1), padding=(0, pad_h, pad_w)),
)
elif up_sample:
self.conv1 = nn.Sequen... | 1,234 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
self.conv3 = nn.Sequential(
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, stride, stride), padding=(pad_t, pad_h, pad_h)),
)
self.conv4 = nn.Sequential(
nn.GroupNorm(norm_num_groups, out_... | 1,234 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
@staticmethod
def _pad_temporal_dim(hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = torch.cat((hidden_states[:, :, 0:1], hidden_states), dim=2)
hidden_states = torch.cat((hidden_states, hidden_states[:, :, -1:]), dim=2)
return hidden_states
def forward(self, hidden_states:... | 1,234 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
if self.up_sample:
hidden_states = hidden_states.unflatten(1, (2, -1)).permute(0, 2, 3, 1, 4, 5).flatten(2, 3)
hidden_states = self._pad_temporal_dim(hidden_states)
hidden_states = self.conv2(hidden_states)
hidden_states = self._pad_temporal_dim(hidden_states)
hidden_states... | 1,234 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
class AllegroDownBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default",
resnet_act_fn: str = "swish",
resnet_gr... | 1,235 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
for i in range(num_layers):
in_channels = in_channels if i == 0 else out_channels
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=out_channels,
temb_channels=None,
eps=resnet_eps,... | 1,235 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
if temporal_downsample:
self.temp_convs_down = AllegroTemporalConvLayer(
out_channels, out_channels, dropout=0.1, norm_num_groups=resnet_groups, down_sample=True, stride=3
)
self.add_temp_downsample = temporal_downsample
if spatial_downsample:
self.do... | 1,235 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
for resnet, temp_conv in zip(self.resnets, self.temp_convs):
hidden_states = resnet(hidden_states, temb=None)
hidden_states = temp_conv(hidden_states, batch_size=batch_size)
if self.add_temp_downsample:
hidden_states = self.temp_convs_down(hidden_states, batch_size=batch_siz... | 1,235 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
class AllegroUpBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default, spatial
resnet_act_fn: str = "swish",... | 1,236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
resnets.append(
ResnetBlock2D(
in_channels=input_channels,
out_channels=out_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
... | 1,236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
self.add_temp_upsample = temporal_upsample
if temporal_upsample:
self.temp_conv_up = AllegroTemporalConvLayer(
out_channels, out_channels, dropout=0.1, norm_num_groups=resnet_groups, up_sample=True, stride=3
)
if spatial_upsample:
self.upsamplers = nn... | 1,236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states)
hidden_states = hidden_states.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
return hidden_states | 1,236 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
class AllegroMidBlock3DConv(nn.Module):
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_time_scale_shift: str = "default", # default, spatial
resnet_act_fn: str = "s... | 1,237 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
# there is always at least one resnet
resnets = [
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
... | 1,237 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
for _ in range(num_layers):
if add_attention:
attentions.append(
Attention(
in_channels,
heads=in_channels // attention_head_dim,
dim_head=attention_head_dim,
rescale_output_fa... | 1,237 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
resnets.append(
ResnetBlock2D(
in_channels=in_channels,
out_channels=in_channels,
temb_channels=temb_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
... | 1,237 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
batch_size = hidden_states.shape[0]
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
hidden_states = self.resnets[0](hidden_states, temb=None)
hidden_states = self.temp_convs[0](hidden_states, batch_size=... | 1,237 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
class AllegroEncoder3D(nn.Module):
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str, ...] = (
"AllegroDownBlock3D",
"AllegroDownBlock3D",
"AllegroDownBlock3D",
"AllegroDownBlock3D",
),... | 1,238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
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
... | 1,238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
self.down_blocks.append(down_block)
# mid
self.mid_block = AllegroMidBlock3DConv(
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_d... | 1,238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
def forward(self, sample: torch.Tensor) -> torch.Tensor:
batch_size = sample.shape[0]
sample = sample.permute(0, 2, 1, 3, 4).flatten(0, 1)
sample = self.conv_in(sample)
sample = sample.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
residual = sample
sample = self... | 1,238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
# Mid block
sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample)
else:
# Down blocks
for down_block in self.down_blocks:
sample = down_block(sample)
# Mid block
sample = self.mid_block(sample)
... | 1,238 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
class AllegroDecoder3D(nn.Module):
def __init__(
self,
in_channels: int = 4,
out_channels: int = 3,
up_block_types: Tuple[str, ...] = (
"AllegroUpBlock3D",
"AllegroUpBlock3D",
"AllegroUpBlock3D",
"AllegroUpBlock3D",
),
t... | 1,239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
temb_channels = in_channels if norm_type == "spatial" else None
# mid
self.mid_block = AllegroMidBlock3DConv(
in_channels=block_out_channels[-1],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
output_scale_factor=1,
resnet_time_scale_shift="default" i... | 1,239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
if up_block_type == "AllegroUpBlock3D":
up_block = AllegroUpBlock3D(
num_layers=layers_per_block + 1,
in_channels=prev_output_channel,
out_channels=output_channel,
spatial_upsample=not is_final_block,
tem... | 1,239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.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.temp_conv_out... | 1,239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
# Mid block
sample = torch.utils.checkpoint.checkpoint(creat... | 1,239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
sample = sample.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
residual = sample
sample = self.temp_conv_out(sample)
sample = sample + residual
sample = sample.permute(0, 2, 1, 3, 4).flatten(0, 1)
sample = self.conv_out(sample)
sample = sample.unflatten(0, (batch... | 1,239 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
class AutoencoderKLAllegro(ModelMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used in
[Allegro](https://github.com/rhymes-ai/Allegro).
This model inherits from [`ModelMixin`]. Check the superclass documentation for i... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
Parameters:
in_channels (int, defaults to `3`):
Number of channels in the input image.
out_channels (int, defaults to `3`):
Number of channels in the output.
down_block_types (`Tuple[str, ...]`, defaults to `("AllegroDownBlock3D", "AllegroDownBlock3D", "AllegroDownBlock3D... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
latent_channels (`int`, defaults to `4`):
Number of channels in latents.
layers_per_block (`int`, defaults to `2`):
Number of resnet or attention or temporal convolution layers per down/up block.
act_fn (`str`, defaults to `"silu"`):
The activation function to use.
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Reso... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
_supports_gradient_checkpointing = True | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str, ...] = (
"AllegroDownBlock3D",
"AllegroDownBlock3D",
"AllegroDownBlock3D",
"AllegroDownBlock3D",
),
up_blo... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
) -> None:
super().__init__() | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
self.encoder = AllegroEncoder3D(
in_channels=in_channels,
out_channels=latent_channels,
down_block_types=down_block_types,
temporal_downsample_blocks=temporal_downsample_blocks,
block_out_channels=block_out_channels,
layers_per_block=layers_per_blo... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
# TODO(aryan): For the 1.0.0 refactor, `temporal_compression_ratio` can be inferred directly and we don't need
# to use a specific parameter here or in other VAEs.
self.use_slicing = False
self.use_tiling = False
self.spatial_compression_ratio = 2 ** (len(block_out_channels) - 1)
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
def enable_tiling(self) -> None:
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
def disable_slicing(self) -> None:
r"""
Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_slicing = False
def _encode(self, x: torch.Tensor) -> torch.Tensor:
# TODO(aryan)... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
Args:
x (`torch.Tensor`):
Input batch of videos.
return_dict (`bool`, defaults to `True`):
Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
Returns:
The latent representations of the encoded vide... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
def _decode(self, z: torch.Tensor) -> torch.Tensor:
# TODO(aryan): refactor tiling implementation
# if self.use_tiling and (width > self.tile_latent_min_width or height > self.tile_latent_min_height):
if self.use_tiling:
return self.tiled_decode(z)
raise NotImplementedError(... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.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:
decoded_slices = [self._decode(z_slice) for z... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
output_num_frames = math.floor((num_frames - self.kernel[0]) / self.stride[0]) + 1
output_height = math.floor((height - self.kernel[1]) / self.stride[1]) + 1
output_width = math.floor((width - self.kernel[2]) / self.stride[2]) + 1
count = 0
output_latent = x.new_zeros(
(
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
for i in range(output_num_frames):
for j in range(output_height):
for k in range(output_width):
n_start, n_end = i * self.stride[0], i * self.stride[0] + self.kernel[0]
h_start, h_end = j * self.stride[1], j * self.stride[1] + self.kernel[1]
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
if (
count == output_num_frames * output_height * output_width - 1
and count % local_batch_size != local_batch_size - 1
):
output_latent[count - count % local_batch_size :] = latent[: count % local_batch_size + 1... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
latent = x.new_zeros(
(batch_size, 2 * self.config.latent_channels, num_frames // rt, height // rs, width // rs)
)
output_kernel = self.kernel[0] // rt, self.kernel[1] // rs, self.kernel[2] // rs
output_stride = self.stride[0] // rt, self.stride[1] // rs, self.stride[2] // rs
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
for i in range(output_num_frames):
n_start, n_end = i * output_stride[0], i * output_stride[0] + output_kernel[0]
for j in range(output_height):
h_start, h_end = j * output_stride[1], j * output_stride[1] + output_kernel[1]
for k in range(output_width):
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
latent = latent.permute(0, 2, 1, 3, 4).flatten(0, 1)
latent = self.quant_conv(latent)
latent = latent.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
return latent
def tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
local_batch_size = 1
rs = self.spatial_compressi... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
output_num_frames = math.floor((num_frames - latent_kernel[0]) / latent_stride[0]) + 1
output_height = math.floor((height - latent_kernel[1]) / latent_stride[1]) + 1
output_width = math.floor((width - latent_kernel[2]) / latent_stride[2]) + 1
count = 0
decoded_videos = z.new_zeros(
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
for i in range(output_num_frames):
for j in range(output_height):
for k in range(output_width):
n_start, n_end = i * latent_stride[0], i * latent_stride[0] + latent_kernel[0]
h_start, h_end = j * latent_stride[1], j * latent_stride[1] + latent_kernel[1... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
if (
count == output_num_frames * output_height * output_width - 1
and count % local_batch_size != local_batch_size - 1
):
decoded_videos[count - count % local_batch_size :] = current_video[
... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
video = z.new_zeros((batch_size, self.config.out_channels, num_frames * rt, height * rs, width * rs))
video_overlap = (
self.kernel[0] - self.stride[0],
self.kernel[1] - self.stride[1],
self.kernel[2] - self.stride[2],
) | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
for i in range(output_num_frames):
n_start, n_end = i * self.stride[0], i * self.stride[0] + self.kernel[0]
for j in range(output_height):
h_start, h_end = j * self.stride[1], j * self.stride[1] + self.kernel[1]
for k in range(output_width):
w_... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
) -> Union[DecoderOutput, torch.Tensor]:
r"""
Args:
sample (`torch.Tensor`): Input sample.
s... | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
return DecoderOutput(sample=dec) | 1,240 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py |
class HunyuanVideoCausalConv3d(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,
padding: Union[int, Tuple[int, int, int]] = 0,
dilation: Union[... | 1,241 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = F.pad(hidden_states, self.time_causal_padding, mode=self.pad_mode)
return self.conv(hidden_states) | 1,241 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class HunyuanVideoUpsampleCausal3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
kernel_size: int = 3,
stride: int = 1,
bias: bool = True,
upsample_factor: Tuple[float, float, float] = (2, 2, 2),
) -> None:
s... | 1,242 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
if num_frames > 1:
# See: https://github.com/pytorch/pytorch/issues/81665
# Unless you have a version of pytorch where non-contiguous implementation of F.interpolate
# is fixed, this will raise either a runtime error, or fail silently with bad outputs.
# If you are encoun... | 1,242 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class HunyuanVideoDownsampleCausal3D(nn.Module):
def __init__(
self,
channels: int,
out_channels: Optional[int] = None,
padding: int = 1,
kernel_size: int = 3,
bias: bool = True,
stride=2,
) -> None:
super().__init__()
out_channels = out_ch... | 1,243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class HunyuanVideoResnetBlockCausal3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
dropout: float = 0.0,
groups: int = 32,
eps: float = 1e-6,
non_linearity: str = "swish",
) -> None:
super().__init__()
... | 1,244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = hidden_states.contiguous()
residual = hidden_states
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv1(hidden_states)
hidde... | 1,244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class HunyuanVideoMidBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
add_attention: bool = True,
attention_hea... | 1,245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
for _ in range(num_layers):
if self.add_attention:
attentions.append(
Attention(
in_channels,
heads=in_channels // attention_head_dim,
dim_head=attention_head_dim,
eps=resnet_e... | 1,245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
resnets.append(
HunyuanVideoResnetBlockCausal3D(
in_channels=in_channels,
out_channels=in_channels,
eps=resnet_eps,
groups=resnet_groups,
dropout=dropout,
non_linearity=resnet_act_fn,
... | 1,245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.resnets[0]), hidden_states, **ckpt_kwargs
)
for attn, resnet in zip(self.attentions, self... | 1,245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, **ckpt_kwargs
)
else:
hidden_states = self.resnets[0](hidden_states)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
if attn ... | 1,245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
return hidden_states | 1,245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class HunyuanVideoDownBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
add_downsample: bool ... | 1,246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
if add_downsample:
self.downsamplers = nn.ModuleList(
[
HunyuanVideoDownsampleCausal3D(
out_channels,
out_channels=out_channels,
padding=downsample_padding,
stride=downsample_s... | 1,246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
for resnet in self.resnets:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, **ckpt_kwargs
)
else:
... | 1,246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class HunyuanVideoUpBlock3D(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
add_upsample: bool = Tr... | 1,247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
if add_upsample:
self.upsamplers = nn.ModuleList(
[
HunyuanVideoUpsampleCausal3D(
out_channels,
out_channels=out_channels,
upsample_factor=upsample_scale_factor,
)
... | 1,247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
for resnet in self.resnets:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, **ckpt_kwargs
)
else:
... | 1,247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class HunyuanVideoEncoder3D(nn.Module):
r"""
Causal encoder for 3D video-like data introduced in [Hunyuan Video](https://huggingface.co/papers/2412.03603).
"""
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str, ...] = (
... | 1,248 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
self.conv_in = HunyuanVideoCausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1)
self.mid_block = None
self.down_blocks = nn.ModuleList([])
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
if down_block_type !=... | 1,248 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
if temporal_compression_ratio == 4:
add_spatial_downsample = bool(i < num_spatial_downsample_layers)
add_time_downsample = bool(
i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block
)
elif temporal_compress... | 1,248 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
down_block = HunyuanVideoDownBlock3D(
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
add_downsample=bool(add_spatial_downsample or add_time_downsample),
resnet_eps=1e-6,
resnet_act_fn=ac... | 1,248 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
conv_out_channels = 2 * out_channels if double_z else out_channels
self.conv_out = HunyuanVideoCausalConv3d(block_out_channels[-1], conv_out_channels, kernel_size=3)
self.gradient_checkpointing = False
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.con... | 1,248 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
for down_block in self.down_blocks:
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(down_block), hidden_states, **ckpt_kwargs
)
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_bloc... | 1,248 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
class HunyuanVideoDecoder3D(nn.Module):
r"""
Causal decoder for 3D video-like data introduced in [Hunyuan Video](https://huggingface.co/papers/2412.03603).
"""
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
up_block_types: Tuple[str, ...] = (
... | 1,249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
# mid
self.mid_block = HunyuanVideoMidBlock3D(
in_channels=block_out_channels[-1],
resnet_eps=1e-6,
resnet_act_fn=act_fn,
attention_head_dim=block_out_channels[-1],
resnet_groups=norm_num_groups,
add_attention=mid_block_add_attention,
... | 1,249 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py |
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