text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
class AutoencoderKLMochi(ModelMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[Mochi 1 preview](https://github.com/genmoai/models).
This model inherits from [`ModelMixin`]. Check the superclass documentation fo... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of block output channels.
... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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.
""" | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["MochiResnetBlock3D"] | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
@register_to_config
def __init__(
self,
in_channels: int = 15,
out_channels: int = 3,
encoder_block_out_channels: Tuple[int] = (64, 128, 256, 384),
decoder_block_out_channels: Tuple[int] = (128, 256, 512, 768),
latent_channels: int = 12,
layers_per_block: Tupl... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
-0.0321993391887285,
),
latents_std: Tuple[float, ...] = (
0.9263795028493863,
0.9248894543193766,
0.9393059390890617,
0.959253732819592,
0.8244560132752793,
0.917259975397747,
0.9294154431013696,
1.372094235... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
self.encoder = MochiEncoder3D(
in_channels=in_channels,
out_channels=latent_channels,
block_out_channels=encoder_block_out_channels,
layers_per_block=layers_per_block,
temporal_expansions=temporal_expansions,
spatial_expansions=spatial_expansions,
... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
# When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
# to perform decoding of a single video latent at a time.
self.use_slicing = False
# When decoding spatially large video latents, the memory requirement is very high. By breaking the vi... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
# This can be used to determine how the number of output frames in the final decoded video. To maintain consistency with
# the original implementation, this defaults to `True`.
# - Original implementation (drop_last_temporal_frames=True):
# Output frames = (latent_frames - 1) * temporal_... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
# This can be configured based on the amount of GPU memory available.
# `12` 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 = 12
self.num_latent_frames_ba... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
def enable_tiling(
self,
tile_sample_min_height: Optional[int] = None,
tile_sample_min_width: Optional[int] = None,
tile_sample_stride_height: Optional[float] = None,
tile_sample_stride_width: Optional[float] = None,
) -> None:
r"""
Enable tiled VAE decoding. ... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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 ... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
def _enable_framewise_encoding(self):
r"""
Enables the framewise VAE encoding implementation with past latent padding. By default, Diffusers uses the
oneshot encoding implementation without current latent replicate padding.
Warning: Framewise encoding may not work as expected due to the... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
def _enable_framewise_decoding(self):
r"""
Enables the framewise VAE decoding implementation with past latent padding. By default, Diffusers uses the
oneshot decoding implementation without current latent replicate padding.
"""
self.use_framewise_decoding = True
for name,... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
if self.use_framewise_encoding:
raise NotImplementedError(
"Frame-wise encoding does not work with the Mochi VAE Encoder due to the presence of attention layers. "
"As intermediate frames are not independent from each other, they cannot be encoded frame-wise."
)
... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
if self.use_tiling and (width > tile_latent_min_width or height > tile_latent_min_height):
return self.tiled_decode(z, return_dict=return_dict)
if self.use_framewise_decoding:
conv_cache = None
dec = []
for i in range(0, num_frames, self.num_latent_frames_batch_... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
@apply_forward_hook
def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
"""
Decode a batch of images.
Args:
z (`torch.Tensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
Returns:
`torch.Tensor`:
The latent representation of the encoded videos.
"""
batch_size, num_channels, num_frames, height, width = x.shape
latent_height = height // self.spatial_compression_ratio
latent_width = width // self.spatial_compression_ratio
... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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, self.tile_sample_stride_height):
row = []
for j in range(0, width, self.tile_sample_stride_width):
if... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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`:
... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
for k in range(0, num_frames, self.num_latent_frames_batch_size):
tile = z[
:,
:,
k : k + self.num_latent_frames_batch_size,
i : i + tile_latent_min_height,
... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.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... | 1,188 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py |
class ConsistencyDecoderVAEOutput(BaseOutput):
"""
Output of encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for samp... | 1,189 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
r"""
The consistency decoder used with DALL-E 3.
Examples:
```py
>>> import torch
>>> from diffusers import StableDiffusionPipeline, ConsistencyDecoderVAE
>>> vae = ConsistencyDecoderVAE.from_pretrained("openai/consisten... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
@register_to_config
def __init__(
self,
scaling_factor: float = 0.18215,
latent_channels: int = 4,
sample_size: int = 32,
encoder_act_fn: str = "silu",
encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
encoder_double_z: bool = True,
e... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
decoder_downsample_padding: int = 1,
decoder_in_channels: int = 7,
decoder_layers_per_block: int = 3,
decoder_norm_eps: float = 1e-05,
decoder_norm_num_groups: int = 32,
decoder_num_train_timesteps: int = 1024,
decoder_out_channels: int = 6,
decoder_resnet_time_sc... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
norm_num_groups=encoder_norm_num_groups,
out_channels=encoder_out_channels,
) | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
self.decoder_unet = UNet2DModel(
add_attention=decoder_add_attention,
block_out_channels=decoder_block_out_channels,
down_block_types=decoder_down_block_types,
downsample_padding=decoder_downsample_padding,
in_channels=decoder_in_channels,
layers_p... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
torch.tensor([0.38862467, 0.02253063, 0.07381133, -0.0171294])[None, :, None, None],
persistent=False,
)
self.register_buffer(
"stds", torch.tensor([0.9654121, 1.0440036, 0.76147926, 0.77022034])[None, :, None, None], persistent=False
) | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
self.use_slicing = False
self.use_tiling = False
# only relevant if vae tiling is enabled
self.tile_sample_min_size = self.config.sample_size
sample_size = (
self.config.sample_size[0]
... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_tiling
def enable_tiling(self, use_tiling: bool = True):
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 ste... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_slicing
def enable_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memo... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor ... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESS... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
@apply_forward_hook
def encode(
self, x: torch.Tensor, return_dict: bool = True
) -> Union[ConsistencyDecoderVAEOutput, Tuple[DiagonalGaussianDistribution]]:
"""
Encode a batch of images into latents.
Args:
x (`torch.Tensor`): Input batch of images.
retur... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
if self.use_slicing and x.shape[0] > 1:
encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)]
h = torch.cat(encoded_slices)
else:
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
if not r... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
Args:
z (torch.Tensor): The input latent vector.
generator (Optional[torch.Generator]): The random number generator. Default is None.
return_dict (bool): Whether to return the output as a dictionary. Default is True.
num_inference_steps (int): The number of inference step... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
for t in self.decoder_scheduler.timesteps:
model_input = torch.concat([self.decoder_scheduler.scale_model_input(x_t, t), z], dim=1)
model_output = self.decoder_unet(model_input, t).sample[:, :3, :, :]
prev_sample = self.decoder_scheduler.step(model_output, t, x_t, generator).prev_sam... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
# Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.blend_h
def blend_h(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[:, :, :, -blen... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.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... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
Returns:
[`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`] or `tuple`:
If return_dict is True, a [`~models.autoencoders.consistency_decoder_vae.ConsistencyDecoderVAEOutput`]
is returned, otherwise a plain `tuple` is returned.
"""
ove... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
# Split the image into 512x512 tiles and encode them separately.
rows = []
for i in range(0, x.shape[2], overlap_size):
row = []
for j in range(0, x.shape[3], overlap_size):
tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
result_rows.append(torch.cat(result_row, dim=3)) | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
moments = torch.cat(result_rows, dim=2)
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return ConsistencyDecoderVAEOutput(latent_dist=posterior)
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool =... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
Returns:
[`DecoderOutput`] or `tuple`:
If return_dict is True, a [`DecoderOutput`] is returned, otherwise a plain `tuple` is returned.
"""
x = sample
posterior = self.encode(x).latent_dist
if sample_posterior:
z = posterior.sample(generator=generat... | 1,190 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/consistency_decoder_vae.py |
class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implem... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_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... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
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 are scaled with the formula `z = z * scaling_factor` before being pass... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
`force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
mid_block_add_attention (`bool`, *optional*, default to `True`):
If enabled, the mid_block of the Encoder and Decoder will have attention blocks. If set to false, the
mid_block will only have... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
_supports_gradient_checkpointing = True
_no_split_modules = ["BasicTransformerBlock", "ResnetBlock2D"]
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
up_block_types: Tuple[s... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_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=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) if use_quant_conv else None
self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1) if use_post_quant_conv else None
self.use_slicing = False
self.use_tiling = False
# only relevant if vae tiling is ... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
def enable_tiling(self, use_tiling: bool = True):
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 larg... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
def disable_slicing(self):
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
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DCondit... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copi... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) !=... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor) | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESS... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
if self.use_tiling and (width > self.tile_sample_min_size or height > self.tile_sample_min_size):
return self._tiled_encode(x)
enc = self.encoder(x)
if self.quant_conv is not None:
enc = self.quant_conv(enc)
return enc
@apply_forward_hook
def encode(
se... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
Returns:
The latent representations of the encoded images. 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... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
dec = self.decoder(z)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
@apply_forward_hook
def decode(
self, z: torch.FloatTensor, return_dict: bool = True, generator=None
) -> Union[DecoderOutput, torch.FloatTensor]:
"""
Decode a batc... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
"""
if self.use_slicing and z.shape[0] > 1:
decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
decoded = torch.cat(decoded_slices)
else:
decoded = self._decode(z).sample
if not return_dict:
return (decoded,)
return ... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
def blend_h(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] * (x / blend_extent)
r... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
Args:
x (`torch.Tensor`): Input batch of images.
Returns:
`torch.Tensor`:
The latent representation of the encoded videos.
"""
overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
blend_extent = int(self.tile_latent_min_... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
# Split the image into 512x512 tiles and encode them separately.
rows = []
for i in range(0, x.shape[2], overlap_size):
row = []
for j in range(0, x.shape[3], overlap_size):
tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
result_row.append(tile[:, :, :row_limit, :row_limit])
result_rows.append(torch.cat(result_row, dim=3)) | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
enc = torch.cat(result_rows, dim=2)
return enc
def tiled_encode(self, x: torch.Tensor, return_dict: bool = True) -> AutoencoderKLOutput:
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 se... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
Returns:
[`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`:
If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
`tuple` is returned.
"""
deprecation_message = (
"The tiled_encode implement... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
# Split the image into 512x512 tiles and encode them separately.
rows = []
for i in range(0, x.shape[2], overlap_size):
row = []
for j in range(0, x.shape[3], overlap_size):
tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
result_row.append(tile[:, :, :row_limit, :row_limit])
result_rows.append(torch.cat(result_row, dim=3)) | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
moments = torch.cat(result_rows, dim=2)
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.T... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
returned.
"""
overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
blend_exte... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
rows = []
for i in range(0, z.shape[2], overlap_size):
row = []
for j in range(0, z.shape[3], overlap_size):
tile = z[:, :, i : i +... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
tile = self.blend_h(row[j - 1], tile, blend_extent)
result_row.append(tile[:, :, :row_limit, :row_limit])
result_rows.append(torch.cat(result_row, dim=3)) | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
dec = torch.cat(result_rows, dim=2)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(
self,
sample: torch.Tensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection ... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
for module in self.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
self.set_attn_processor(FusedAttnProcessor2_0())
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
def unfuse_qkv_projections(... | 1,191 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl.py |
class Snake1d(nn.Module):
"""
A 1-dimensional Snake activation function module.
"""
def __init__(self, hidden_dim, logscale=True):
super().__init__()
self.alpha = nn.Parameter(torch.zeros(1, hidden_dim, 1))
self.beta = nn.Parameter(torch.zeros(1, hidden_dim, 1))
self.al... | 1,192 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class OobleckResidualUnit(nn.Module):
"""
A residual unit composed of Snake1d and weight-normalized Conv1d layers with dilations.
"""
def __init__(self, dimension: int = 16, dilation: int = 1):
super().__init__()
pad = ((7 - 1) * dilation) // 2
self.snake1 = Snake1d(dimension)
... | 1,193 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
Returns:
output_tensor (`torch.Tensor` of shape `(batch_size, channels, time_steps)`)
Input tensor after passing through the residual unit.
"""
output_tensor = hidden_state
output_tensor = self.conv1(self.snake1(output_tensor))
output_tensor = self.conv2(self.... | 1,193 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class OobleckEncoderBlock(nn.Module):
"""Encoder block used in Oobleck encoder."""
def __init__(self, input_dim, output_dim, stride: int = 1):
super().__init__()
self.res_unit1 = OobleckResidualUnit(input_dim, dilation=1)
self.res_unit2 = OobleckResidualUnit(input_dim, dilation=3)
... | 1,194 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class OobleckDecoderBlock(nn.Module):
"""Decoder block used in Oobleck decoder."""
def __init__(self, input_dim, output_dim, stride: int = 1):
super().__init__()
self.snake1 = Snake1d(input_dim)
self.conv_t1 = weight_norm(
nn.ConvTranspose1d(
input_dim,
... | 1,195 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class OobleckDiagonalGaussianDistribution(object):
def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
self.parameters = parameters
self.mean, self.scale = parameters.chunk(2, dim=1)
self.std = nn.functional.softplus(self.scale) + 1e-4
self.var = self.std * sel... | 1,196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
def kl(self, other: "OobleckDiagonalGaussianDistribution" = None) -> torch.Tensor:
if self.deterministic:
return torch.Tensor([0.0])
else:
if other is None:
return (self.mean * self.mean + self.var - self.logvar - 1.0).sum(1).mean()
else:
... | 1,196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class AutoencoderOobleckOutput(BaseOutput):
"""
Output of AutoencoderOobleck encoding method.
Args:
latent_dist (`OobleckDiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and standard deviation of
`OobleckDiagonalGaussianDistribution`. `Oob... | 1,197 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class OobleckDecoderOutput(BaseOutput):
r"""
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, audio_channels, sequence_length)`):
The decoded output sample from the last layer of the model.
"""
sample: torch.Tensor | 1,198 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class OobleckEncoder(nn.Module):
"""Oobleck Encoder"""
def __init__(self, encoder_hidden_size, audio_channels, downsampling_ratios, channel_multiples):
super().__init__()
strides = downsampling_ratios
channel_multiples = [1] + channel_multiples
# Create first convolution
... | 1,199 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
self.block = nn.ModuleList(self.block)
d_model = encoder_hidden_size * channel_multiples[-1]
self.snake1 = Snake1d(d_model)
self.conv2 = weight_norm(nn.Conv1d(d_model, encoder_hidden_size, kernel_size=3, padding=1))
def forward(self, hidden_state):
hidden_state = self.conv1(hidden_s... | 1,199 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class OobleckDecoder(nn.Module):
"""Oobleck Decoder"""
def __init__(self, channels, input_channels, audio_channels, upsampling_ratios, channel_multiples):
super().__init__()
strides = upsampling_ratios
channel_multiples = [1] + channel_multiples
# Add first conv layer
... | 1,200 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
self.block = nn.ModuleList(block)
output_dim = channels
self.snake1 = Snake1d(output_dim)
self.conv2 = weight_norm(nn.Conv1d(channels, audio_channels, kernel_size=7, padding=3, bias=False))
def forward(self, hidden_state):
hidden_state = self.conv1(hidden_state)
for layer i... | 1,200 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
class AutoencoderOobleck(ModelMixin, ConfigMixin):
r"""
An autoencoder for encoding waveforms into latents and decoding latent representations into waveforms. First
introduced in Stable Audio.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implement... | 1,201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.