text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
resnets = []
for i in range(num_layers):
in_channel = in_channels if i == 0 else out_channels
resnets.append(
CogVideoXResnetBlock3D(
in_channels=in_channel,
out_channels=out_channels,
dropout=dropout,
... | 1,174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
zq: Optional[torch.Tensor] = None,
conv_cache: Optional[Dict[str, torch.Tensor]] = None,
) -> torch.Tensor:
r"""Forward method of the `CogVideoXDownBlock3D` class."""
new_con... | 1,174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
zq,
conv_cache.get(conv_cache_key),
)
else:
... | 1,174 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class CogVideoXMidBlock3D(nn.Module):
r"""
A middle block used in the CogVideoX model.
Args:
in_channels (`int`):
Number of input channels.
temb_channels (`int`, defaults to `512`):
Number of time embedding channels.
dropout (`float`, defaults to `0.0`):
... | 1,175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def __init__(
self,
in_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
spatial_norm_dim: Optional[int] = None,
pad_mode: str = "f... | 1,175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
zq: Optional[torch.Tensor] = None,
conv_cache: Optional[Dict[str, torch.Tensor]] = None,
) -> torch.Tensor:
r"""Forward method of the `CogVideoXMidBlock3D` class."""
new_conv... | 1,175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet), hidden_states, temb, zq, conv_cache.get(conv_cache_key)
)
else:
hidden_states, new_conv_cache[conv_cache_key] = resnet(
hi... | 1,175 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class CogVideoXUpBlock3D(nn.Module):
r"""
An upsampling block used in the CogVideoX model. | 1,176 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
temb_channels (`int`, defaults to `512`):
Number of time embedding channels.
dropout (`float`, default... | 1,176 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
Whether or not to use a upsampling layer. If not used, output dimension would be same as input dimension.
compress_time (`bool`, defaults to `False`):
Whether or not to downsample across temporal dimension.
pad_mode (str, defaults to `"first"`):
Padding mode.
""" | 1,176 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def __init__(
self,
in_channels: int,
out_channels: int,
temb_channels: int,
dropout: float = 0.0,
num_layers: int = 1,
resnet_eps: float = 1e-6,
resnet_act_fn: str = "swish",
resnet_groups: int = 32,
spatial_norm_dim: int = 16,
add... | 1,176 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
resnets = []
for i in range(num_layers):
in_channel = in_channels if i == 0 else out_channels
resnets.append(
CogVideoXResnetBlock3D(
in_channels=in_channel,
out_channels=out_channels,
dropout=dropout,
... | 1,176 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
temb: Optional[torch.Tensor] = None,
zq: Optional[torch.Tensor] = None,
conv_cache: Optional[Dict[str, torch.Tensor]] = None,
) -> torch.Tensor:
r"""Forward method of the `CogVide... | 1,176 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
temb,
zq,
conv_cache.get(conv_cache_key),
)
else:
... | 1,176 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class CogVideoXEncoder3D(nn.Module):
r"""
The `CogVideoXEncoder3D` layer of a variational autoencoder that encodes its input into a latent representation. | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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... | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
_supports_gradient_checkpointing = True
def __init__(
self,
in_channels: int = 3,
out_channels: int = 16,
down_block_types: Tuple[str, ...] = (
"CogVideoXDownBlock3D",
"CogVideoXDownBlock3D",
"CogVideoXDownBlock3D",
"CogVideoXDownBlock... | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
# down blocks
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
compress_time = i < temporal... | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
if down_block_type == "CogVideoXDownBlock3D":
down_block = CogVideoXDownBlock3D(
in_channels=input_channel,
out_channels=output_channel,
temb_channels=0,
dropout=dropout,
num_layers=layers_per_block,
... | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
# mid block
self.mid_block = CogVideoXMidBlock3D(
in_channels=block_out_channels[-1],
temb_channels=0,
dropout=dropout,
num_layers=2,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
pad_mode... | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
hidden_states, new_conv_cache["conv_in"] = self.conv_in(sample, conv_cache=conv_cache.get("conv_in"))
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
... | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
# 2. Mid
hidden_states, new_conv_cache["mid_block"] = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block),
hidden_states,
temb,
None,
conv_cache.get("mid_block"),
)
else:
# 1.... | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
hidden_states, new_conv_cache["conv_out"] = self.conv_out(hidden_states, conv_cache=conv_cache.get("conv_out"))
return hidden_states, new_conv_cache | 1,177 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class CogVideoXDecoder3D(nn.Module):
r"""
The `CogVideoXDecoder3D` layer of a variational autoencoder that decodes its latent representation into an output
sample. | 1,178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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... | 1,178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def __init__(
self,
in_channels: int = 16,
out_channels: int = 3,
up_block_types: Tuple[str, ...] = (
"CogVideoXUpBlock3D",
"CogVideoXUpBlock3D",
"CogVideoXUpBlock3D",
"CogVideoXUpBlock3D",
),
block_out_channels: Tuple[int, ... | 1,178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
# mid block
self.mid_block = CogVideoXMidBlock3D(
in_channels=reversed_block_out_channels[0],
temb_channels=0,
num_layers=2,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
spatial_norm_dim=in_channels,... | 1,178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
if up_block_type == "CogVideoXUpBlock3D":
up_block = CogVideoXUpBlock3D(
in_channels=prev_output_channel,
out_channels=output_channel,
temb_channels=0,
dropout=dropout,
num_layers=layers_per_block + 1,
... | 1,178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
self.norm_out = CogVideoXSpatialNorm3D(reversed_block_out_channels[-1], in_channels, groups=norm_num_groups)
self.conv_act = nn.SiLU()
self.conv_out = CogVideoXCausalConv3d(
reversed_block_out_channels[-1], out_channels, kernel_size=3, pad_mode=pad_mode
)
self.gradient_check... | 1,178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
return custom_forward
# 1. Mid
hidden_states, new_conv_cache["mid_block"] = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block),
hidden_states,
temb,
sample,
conv_cache.get("mid_block"),
... | 1,178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
# 2. Up
for i, up_block in enumerate(self.up_blocks):
conv_cache_key = f"up_block_{i}"
hidden_states, new_conv_cache[conv_cache_key] = up_block(
hidden_states, temb, sample, conv_cache=conv_cache.get(conv_cache_key)
)
# 3. Post-pro... | 1,178 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[CogVideoX](https://github.com/THUDM/CogVideo).
This model inherits from [`ModelMixin`]. Check the super... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
training set. This is used to scale the latent space to have unit variance when training the diffusion
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 for... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["CogVideoXResnetBlock3D"] | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = (
"CogVideoXDownBlock3D",
"CogVideoXDownBlock3D",
"CogVideoXDownBlock3D",
"CogVideoXDownBlock3D",
),
up_... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
latents_std: Optional[Tuple[float]] = None,
force_upcast: float = True,
use_quant_conv: bool = False,
use_post_quant_conv: bool = False,
invert_scale_latents: bool = False,
):
super().__init__() | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
self.encoder = CogVideoXEncoder3D(
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,
norm_eps=norm_eps,
... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
self.post_quant_conv = CogVideoXSafeConv3d(out_channels, out_channels, 1) if use_post_quant_conv else None | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
self.use_slicing = False
self.use_tiling = False | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
# Can be increased to decode more latent frames at once, but comes at a reasonable memory cost and it is not
# recommended because the temporal parts of the VAE, here, are tricky to understand.
# If you decode X latent frames together, the number of output frames is:
# (X + (2 conv cache) + ... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
# => (1 frame slice) * ((3 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale)) +
# ((13 - 3) // 2) * ((2 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale))
... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
# We make the minimum height and width of sample for tiling half that of the generally supported
self.tile_sample_min_height = sample_height // 2
self.tile_sample_min_width = sample_width // 2
self.tile_latent_min_height = int(
self.tile_sample_min_height / (2 ** (len(self.config.blo... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (CogVideoXEncoder3D, CogVideoXDecoder3D)):
module.gradient_checkpointing = value
def enable_tiling(
self,
tile_sample_min_height: Optional[int] = None,
tile_sample_min_width: Optional[int] ... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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 ... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
are no tiling artifacts produced across the width dimension. Must be between 0 and 1. Setting a higher
value might cause more tiles to be processed leading to slow down of the decoding process.
"""
self.use_tiling = True
self.tile_sample_min_height = tile_sample_min_height or sel... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
return self.tiled_encode(x)
frame_batch_size = self.num_sample_frames_batch_size
# Note: We expect the number of frames to be either `1` or `frame_batch_size * k` or `frame_batch_size * k + ... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
for i in range(num_batches):
remaining_frames = num_frames % frame_batch_size
start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
end_frame = frame_batch_size * (i + 1) + remaining_frames
x_intermediate = x[:, :, start_frame:end_frame]
x_i... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
Args:
x (`torch.Tensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
Returns:
The latent representations of the encoded videos. ... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
batch_size, num_channels, num_frames, height, width = z.shape
if self.use_tiling and (width > self.tile_latent_min_width or height > self.tile_latent_min_height):
return self.tiled_decode(z, ... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
for i in range(num_batches):
remaining_frames = num_frames % frame_batch_size
start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
end_frame = frame_batch_size * (i + 1) + remaining_frames
z_intermediate = z[:, :, start_frame:end_frame]
if ... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
If re... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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. The end result of tiled encoding is
different from non-tiled encoding because each tile uses a different encoder. To avoi... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
overlap_height = int(self.tile_sample_min_height * (1 - self.tile_overlap_factor_height))
overlap_width = int(self.tile_sample_min_width * (1 - self.tile_overlap_factor_width))
blend_extent_height = int(self.tile_latent_min_height * self.tile_overlap_factor_height)
blend_extent_width = int(self.... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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, height, overlap_height):
row = []
for j in range(0, width, overlap_width):
# Note: We expect the number of fr... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
for k in range(num_batches):
remaining_frames = num_frames % frame_batch_size
start_frame = frame_batch_size * k + (0 if k == 0 else remaining_frames)
end_frame = frame_batch_size * (k + 1) + remaining_frames
tile = x[
... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
batch_size, num_channels, num_frames, height, width = z.shape
overlap_height = int(self.tile_latent_min_height * (1 - self.tile_overlap_factor_height))
overlap_width = int(self.tile_latent_min_width * (1 - self.tile_overlap_factor_width))
blend_extent_height = int(self.tile_sample_min_height * ... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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, overlap_height):
row = []
for j in range(0, width, overlap_width):
num_batches = max(num_frames // fr... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
for k in range(num_batches):
remaining_frames = num_frames % frame_batch_size
start_frame = frame_batch_size * k + (0 if k == 0 else remaining_frames)
end_frame = frame_batch_size * (k + 1) + remaining_frames
tile = z[
... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.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... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
) -> Union[torch.Tensor, torch.Tensor]:
x = sample
posterior = self.encode(x).latent_dist
if sample_posterio... | 1,179 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py |
class MochiChunkedGroupNorm3D(nn.Module):
r"""
Applies per-frame group normalization for 5D video inputs. It also supports memory-efficient chunked group
normalization.
Args:
num_channels (int): Number of channels expected in input
num_groups (int, optional): Number of groups to separat... | 1,180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
x = x.permute(0, 2, 1, 3, 4).flatten(0, 1)
output = torch.cat([self.norm_layer(chunk) for chunk in x.split(self.chunk_size, dim=0)], dim=0)
output = output.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
return output | 1,180 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
class MochiResnetBlock3D(nn.Module):
r"""
A 3D ResNet block used in the Mochi model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
non_linearity (`str`, de... | 1,181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
self.norm1 = MochiChunkedGroupNorm3D(num_channels=in_channels)
self.conv1 = CogVideoXCausalConv3d(
in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, pad_mode="replicate"
)
self.norm2 = MochiChunkedGroupNorm3D(num_channels=out_channels)
self.conv2 = ... | 1,181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states, new_conv_cache["conv2"] = self.conv2(hidden_states, conv_cache=conv_cache.get("conv2"))
hidden_states = hidden_states + inputs
return hidden_states, new_conv_cache | 1,181 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
class MochiDownBlock3D(nn.Module):
r"""
An downsampling block used in the Mochi model.
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`, def... | 1,182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
self.conv_in = CogVideoXCausalConv3d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(temporal_expansion, spatial_expansion, spatial_expansion),
stride=(temporal_expansion, spatial_expansion, spatial_expansion),
pad_mode="replicate",
) | 1,182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
resnets = []
norms = []
attentions = []
for _ in range(num_layers):
resnets.append(MochiResnetBlock3D(in_channels=out_channels))
if add_attention:
norms.append(MochiChunkedGroupNorm3D(num_channels=out_channels))
attentions.append(
... | 1,182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
def forward(
self,
hidden_states: torch.Tensor,
conv_cache: Optional[Dict[str, torch.Tensor]] = None,
chunk_size: int = 2**15,
) -> torch.Tensor:
r"""Forward method of the `MochiUpBlock3D` class."""
new_conv_cache = {}
conv_cache = conv_cache or {}
h... | 1,182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
conv_cache=conv_cache.get(conv_cache_key),
)
else:
hidden_states, new_conv_cache[conv_c... | 1,182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
# Perform attention in chunks to avoid following error:
# RuntimeError: CUDA error: invalid configuration argument
if hidden_states.size(0) <= chunk_size:
hidden_states = attn(hidden_states)
else:
hidden_states_chunks = []
... | 1,182 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
class MochiMidBlock3D(nn.Module):
r"""
A middle block used in the Mochi model.
Args:
in_channels (`int`):
Number of input channels.
num_layers (`int`, defaults to `3`):
Number of resnet blocks in the block.
"""
def __init__(
self,
in_channels... | 1,183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
if add_attention:
norms.append(MochiChunkedGroupNorm3D(num_channels=in_channels))
attentions.append(
Attention(
query_dim=in_channels,
heads=in_channels // 32,
dim_head=32,
... | 1,183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
new_conv_cache = {}
conv_cache = conv_cache or {}
for i, (resnet, norm, attn) in enumerate(zip(self.resnets, self.norms, self.attentions)):
conv_cache_key = f"resnet_{i}"
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(modu... | 1,183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
batch_size, num_channels, num_frames, height, width = hidden_states.shape
hidden_states = hidden_states.permute(0, 3, 4, 2, 1).flatten(0, 2).contiguous()
hidden_states = attn(hidden_states)
hidden_states = hidden_states.unflatten(0, (batch_size, height, width)).permute(0,... | 1,183 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
class MochiUpBlock3D(nn.Module):
r"""
An upsampling block used in the Mochi model.
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`, default... | 1,184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
resnets = []
for _ in range(num_layers):
resnets.append(MochiResnetBlock3D(in_channels=in_channels))
self.resnets = nn.ModuleList(resnets)
self.proj = nn.Linear(in_channels, out_channels * temporal_expansion * spatial_expansion**2)
self.gradient_checkpointing = False
d... | 1,184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
hidden_states,
conv_cache=conv_cache.get(conv_cache_key),
)
else:
hidden_states, new_conv_cache[conv_c... | 1,184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
# Reshape and unpatchify
hidden_states = hidden_states.view(batch_size, -1, st, sh, sw, num_frames, height, width)
hidden_states = hidden_states.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
hidden_states = hidden_states.view(batch_size, -1, num_frames * st, height * sh, width * sw)
retu... | 1,184 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
class FourierFeatures(nn.Module):
def __init__(self, start: int = 6, stop: int = 8, step: int = 1):
super().__init__()
self.start = start
self.stop = stop
self.step = step
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
r"""Forward method of the `FourierFeature... | 1,185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
return torch.cat([inputs, torch.sin(h), torch.cos(h)], dim=1).to(original_dtype) | 1,185 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
class MochiEncoder3D(nn.Module):
r"""
The `MochiEncoder3D` layer of a variational autoencoder that encodes input video samples to its latent
representation. | 1,186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
Args:
in_channels (`int`, *optional*):
The number of input channels.
out_channels (`int`, *optional*):
The number of output channels.
block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(128, 256, 512, 768)`):
The number of output channels for each... | 1,186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
def __init__(
self,
in_channels: int,
out_channels: int,
block_out_channels: Tuple[int, ...] = (128, 256, 512, 768),
layers_per_block: Tuple[int, ...] = (3, 3, 4, 6, 3),
temporal_expansions: Tuple[int, ...] = (1, 2, 3),
spatial_expansions: Tuple[int, ...] = (2, 2,... | 1,186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
down_blocks = []
for i in range(len(block_out_channels) - 1):
down_block = MochiDownBlock3D(
in_channels=block_out_channels[i],
out_channels=block_out_channels[i + 1],
num_layers=layers_per_block[i + 1],
temporal_expansion=temporal_expa... | 1,186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
def forward(
self, hidden_states: torch.Tensor, conv_cache: Optional[Dict[str, torch.Tensor]] = None
) -> torch.Tensor:
r"""Forward method of the `MochiEncoder3D` class."""
new_conv_cache = {}
conv_cache = conv_cache or {}
hidden_states = self.fourier_features(hidden_states... | 1,186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
for i, down_block in enumerate(self.down_blocks):
conv_cache_key = f"down_block_{i}"
hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
create_custom_forward(down_block), hidden_states, conv_cache=conv_cache.get(conv_cache_key)
... | 1,186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
hidden_states = self.norm_out(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = hidden_states.permute(0, 2, 3, 4, 1)
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.permute(0, 4, 1, 2, 3)
return hidden_states, new_conv_ca... | 1,186 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
class MochiDecoder3D(nn.Module):
r"""
The `MochiDecoder3D` layer of a variational autoencoder that decodes its latent representation into an output
sample. | 1,187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
Args:
in_channels (`int`, *optional*):
The number of input channels.
out_channels (`int`, *optional*):
The number of output channels.
block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(128, 256, 512, 768)`):
The number of output channels for each... | 1,187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
def __init__(
self,
in_channels: int, # 12
out_channels: int, # 3
block_out_channels: Tuple[int, ...] = (128, 256, 512, 768),
layers_per_block: Tuple[int, ...] = (3, 3, 4, 6, 3),
temporal_expansions: Tuple[int, ...] = (1, 2, 3),
spatial_expansions: Tuple[int, ..... | 1,187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
up_blocks = []
for i in range(len(block_out_channels) - 1):
up_block = MochiUpBlock3D(
in_channels=block_out_channels[-i - 1],
out_channels=block_out_channels[-i - 2],
num_layers=layers_per_block[-i - 2],
temporal_expansion=temporal_exp... | 1,187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
new_conv_cache = {}
conv_cache = conv_cache or {}
hidden_states = self.conv_in(hidden_states)
# 1. Mid
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module):
def create_forward(*inputs):
return mod... | 1,187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
for i, up_block in enumerate(self.up_blocks):
conv_cache_key = f"up_block_{i}"
hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
create_custom_forward(up_block), hidden_states, conv_cache=conv_cache.get(conv_cache_key)
... | 1,187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
hidden_states = hidden_states.permute(0, 2, 3, 4, 1)
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.permute(0, 4, 1, 2, 3)
return hidden_states, new_conv_cache | 1,187 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
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