text stringlengths 41 89.8k | type stringclasses 1
value | start int64 79 258k | end int64 342 260k | depth int64 0 0 | filepath stringlengths 81 164 | parent_class null | class_index int64 0 1.38k |
|---|---|---|---|---|---|---|---|
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
... | class_definition | 8,983 | 10,355 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,200 |
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... | class_definition | 10,358 | 17,045 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_oobleck.py | null | 1,201 |
class LTXVideoCausalConv3d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int, int]] = 3,
stride: Union[int, Tuple[int, int, int]] = 1,
dilation: Union[int, Tuple[int, int, int]] = 1,
groups: int = 1,
... | class_definition | 1,180 | 3,123 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,202 |
class LTXVideoResnetBlock3d(nn.Module):
r"""
A 3D ResNet block used in the LTXVideo model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
dropout (`float`, ... | class_definition | 3,126 | 7,840 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,203 |
class LTXVideoUpsampler3d(nn.Module):
def __init__(
self,
in_channels: int,
stride: Union[int, Tuple[int, int, int]] = 1,
is_causal: bool = True,
residual: bool = False,
upscale_factor: int = 1,
) -> None:
super().__init__()
self.stride = stride i... | class_definition | 7,843 | 9,764 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,204 |
class LTXVideoDownBlock3D(nn.Module):
r"""
Down block used in the LTXVideo model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
num_layers (`int`, defaults... | class_definition | 9,767 | 13,684 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,205 |
class LTXVideoMidBlock3d(nn.Module):
r"""
A middle block used in the LTXVideo model.
Args:
in_channels (`int`):
Number of input channels.
num_layers (`int`, defaults to `1`):
Number of resnet layers.
dropout (`float`, defaults to `0.0`):
Dropout r... | class_definition | 13,776 | 16,841 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,206 |
class LTXVideoUpBlock3d(nn.Module):
r"""
Up block used in the LTXVideo model.
Args:
in_channels (`int`):
Number of input channels.
out_channels (`int`, *optional*):
Number of output channels. If None, defaults to `in_channels`.
num_layers (`int`, defaults to ... | class_definition | 16,844 | 21,610 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,207 |
class LTXVideoEncoder3d(nn.Module):
r"""
The `LTXVideoEncoder3d` layer of a variational autoencoder that encodes input video samples to its latent
representation.
Args:
in_channels (`int`, defaults to 3):
Number of input channels.
out_channels (`int`, defaults to 128):
... | class_definition | 21,613 | 26,939 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,208 |
class LTXVideoDecoder3d(nn.Module):
r"""
The `LTXVideoDecoder3d` layer of a variational autoencoder that decodes its latent representation into an output
sample.
Args:
in_channels (`int`, defaults to 128):
Number of latent channels.
out_channels (`int`, defaults to 3):
... | class_definition | 26,942 | 33,729 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,209 |
class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
[LTX](https://huggingface.co/Lightricks/LTX-Video).
This model inherits from [`ModelMixin`]. Check the su... | class_definition | 33,732 | 57,730 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py | null | 1,210 |
class AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):
r"""
Designing a Better Asymmetric VQGAN for StableDiffusion https://arxiv.org/abs/2306.04632 . A VAE model with KL loss
for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Che... | class_definition | 994 | 7,719 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_asym_kl.py | null | 1,211 |
class VQEncoderOutput(BaseOutput):
"""
Output of VQModel encoding method.
Args:
latents (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The encoded output sample from the last layer of the model.
"""
latents: torch.Tensor | class_definition | 1,008 | 1,294 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py | null | 1,212 |
class VQModel(ModelMixin, ConfigMixin):
r"""
A VQ-VAE model for decoding latent representations.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
in_channels (int, *o... | class_definition | 1,297 | 7,811 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vq_model.py | null | 1,213 |
class ResBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
norm_type: str = "batch_norm",
act_fn: str = "relu6",
) -> None:
super().__init__()
self.norm_type = norm_type
self.nonlinearity = get_activation(act_fn) if act_f... | class_definition | 1,241 | 2,387 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py | null | 1,214 |
class EfficientViTBlock(nn.Module):
def __init__(
self,
in_channels: int,
mult: float = 1.0,
attention_head_dim: int = 32,
qkv_multiscales: Tuple[int, ...] = (5,),
norm_type: str = "batch_norm",
) -> None:
super().__init__()
self.attn = SanaMultis... | class_definition | 2,390 | 3,285 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py | null | 1,215 |
class DCDownBlock2d(nn.Module):
def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None:
super().__init__()
self.downsample = downsample
self.factor = 2
self.stride = 1 if downsample else 2
self.group_size = in_channel... | class_definition | 3,890 | 5,078 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py | null | 1,216 |
class DCUpBlock2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
interpolate: bool = False,
shortcut: bool = True,
interpolation_mode: str = "nearest",
) -> None:
super().__init__()
self.interpolate = interpolate
self... | class_definition | 5,081 | 6,333 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py | null | 1,217 |
class Encoder(nn.Module):
def __init__(
self,
in_channels: int,
latent_channels: int,
attention_head_dim: int = 32,
block_type: Union[str, Tuple[str]] = "ResBlock",
block_out_channels: Tuple[int] = (128, 256, 512, 512, 1024, 1024),
layers_per_block: Tuple[int]... | class_definition | 6,336 | 9,523 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py | null | 1,218 |
class Decoder(nn.Module):
def __init__(
self,
in_channels: int,
latent_channels: int,
attention_head_dim: int = 32,
block_type: Union[str, Tuple[str]] = "ResBlock",
block_out_channels: Tuple[int] = (128, 256, 512, 512, 1024, 1024),
layers_per_block: Tuple[int]... | class_definition | 9,526 | 12,949 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py | null | 1,219 |
class AutoencoderDC(ModelMixin, ConfigMixin, FromOriginalModelMixin):
r"""
An Autoencoder model introduced in [DCAE](https://arxiv.org/abs/2410.10733) and used in
[SANA](https://arxiv.org/abs/2410.10629).
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic metho... | class_definition | 12,952 | 30,342 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_dc.py | null | 1,220 |
class EncoderOutput(BaseOutput):
r"""
Output of encoding method.
Args:
latent (`torch.Tensor` of shape `(batch_size, num_channels, latent_height, latent_width)`):
The encoded latent.
"""
latent: torch.Tensor | class_definition | 1,050 | 1,299 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,221 |
class DecoderOutput(BaseOutput):
r"""
Output of decoding method.
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
The decoded output sample from the last layer of the model.
"""
sample: torch.Tensor
commit_loss: Optional[torch.FloatTensor]... | class_definition | 1,313 | 1,640 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,222 |
class Encoder(nn.Module):
r"""
The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, defaults to 3):
T... | class_definition | 1,643 | 6,818 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,223 |
class Decoder(nn.Module):
r"""
The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, defaults to 3):
... | class_definition | 6,821 | 13,058 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,224 |
class UpSample(nn.Module):
r"""
The `UpSample` layer of a variational autoencoder that upsamples its input.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, defaults to 3):
The number of output chann... | class_definition | 13,061 | 13,891 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,225 |
class MaskConditionEncoder(nn.Module):
"""
used in AsymmetricAutoencoderKL
"""
def __init__(
self,
in_ch: int,
out_ch: int = 192,
res_ch: int = 768,
stride: int = 16,
) -> None:
super().__init__()
channels = []
while stride > 1:
... | class_definition | 13,894 | 15,408 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,226 |
class MaskConditionDecoder(nn.Module):
r"""The `MaskConditionDecoder` should be used in combination with [`AsymmetricAutoencoderKL`] to enhance the model's
decoder with a conditioner on the mask and masked image.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input ... | class_definition | 15,411 | 24,285 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,227 |
class VectorQuantizer(nn.Module):
"""
Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
multiplications and allows for post-hoc remapping of indices.
"""
# NOTE: due to a bug the beta term was applied to the wrong term. for
# backwards comp... | class_definition | 24,288 | 29,190 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,228 |
class DiagonalGaussianDistribution(object):
def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = dete... | class_definition | 29,193 | 31,304 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,229 |
class EncoderTiny(nn.Module):
r"""
The `EncoderTiny` layer is a simpler version of the `Encoder` layer.
Args:
in_channels (`int`):
The number of input channels.
out_channels (`int`):
The number of output channels.
num_blocks (`Tuple[int, ...]`):
E... | class_definition | 31,307 | 33,914 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,230 |
class DecoderTiny(nn.Module):
r"""
The `DecoderTiny` layer is a simpler version of the `Decoder` layer.
Args:
in_channels (`int`):
The number of input channels.
out_channels (`int`):
The number of output channels.
num_blocks (`Tuple[int, ...]`):
E... | class_definition | 33,917 | 36,857 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/vae.py | null | 1,231 |
class AutoencoderTinyOutput(BaseOutput):
"""
Output of AutoencoderTiny encoding method.
Args:
latents (`torch.Tensor`): Encoded outputs of the `Encoder`.
"""
latents: torch.Tensor | class_definition | 986 | 1,196 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py | null | 1,232 |
class AutoencoderTiny(ModelMixin, ConfigMixin):
r"""
A tiny distilled VAE model for encoding images into latents and decoding latent representations into images.
[`AutoencoderTiny`] is a wrapper around the original implementation of `TAESD`.
This model inherits from [`ModelMixin`]. Check the superclas... | class_definition | 1,199 | 16,034 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_tiny.py | null | 1,233 |
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
"""... | class_definition | 1,177 | 4,997 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py | null | 1,234 |
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... | class_definition | 5,000 | 8,061 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py | null | 1,235 |
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",... | class_definition | 8,064 | 10,978 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py | null | 1,236 |
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... | class_definition | 10,981 | 15,021 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py | null | 1,237 |
class AllegroEncoder3D(nn.Module):
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str, ...] = (
"AllegroDownBlock3D",
"AllegroDownBlock3D",
"AllegroDownBlock3D",
"AllegroDownBlock3D",
),... | class_definition | 15,024 | 19,695 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py | null | 1,238 |
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... | class_definition | 19,698 | 24,779 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py | null | 1,239 |
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... | class_definition | 24,782 | 44,277 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py | null | 1,240 |
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[... | class_definition | 1,791 | 2,929 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,241 |
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... | class_definition | 2,932 | 4,730 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,242 |
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... | class_definition | 4,733 | 5,329 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,243 |
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__()
... | class_definition | 5,332 | 6,986 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,244 |
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... | class_definition | 6,989 | 11,561 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,245 |
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 ... | class_definition | 11,564 | 14,116 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,246 |
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... | class_definition | 14,119 | 16,612 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,247 |
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, ...] = (
... | class_definition | 16,615 | 21,724 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,248 |
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, ...] = (
... | class_definition | 21,727 | 26,659 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,249 |
class AutoencoderKLHunyuanVideo(ModelMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
Introduced in [HunyuanVideo](https://huggingface.co/papers/2412.03603).
This model inherits from [`ModelMixin`]. Check the superclass... | class_definition | 26,662 | 48,535 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py | null | 1,250 |
class EnvironmentCommand(BaseDiffusersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser) -> None:
download_parser = parser.add_parser("env")
download_parser.set_defaults(func=info_command_factory)
def run(self) -> dict:
hub_version = huggingface_hub.__version... | class_definition | 1,106 | 6,223 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/env.py | null | 1,251 |
class BaseDiffusersCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError() | class_definition | 681 | 919 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/__init__.py | null | 1,252 |
class FP16SafetensorsCommand(BaseDiffusersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
conversion_parser = parser.add_parser("fp16_safetensors")
conversion_parser.add_argument(
"--ckpt_id",
type=str,
help="Repo id of the checkpoi... | class_definition | 1,389 | 5,422 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/commands/fp16_safetensors.py | null | 1,253 |
class SD3Transformer2DLoadersMixin:
"""Load IP-Adapters and LoRA layers into a `[SD3Transformer2DModel]`."""
def _load_ip_adapter_weights(self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT) -> None:
"""Sets IP-Adapter attention processors, image projection, and loads state_dic... | class_definition | 859 | 4,592 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_sd3.py | null | 1,254 |
class FromSingleFileMixin:
"""
Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`].
"""
@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
r"""
Instantiate a [`DiffusionPipeline`] from pretrained pipelin... | class_definition | 9,715 | 24,685 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file.py | null | 1,255 |
class PeftAdapterMixin:
"""
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them in a base model, check out the PEFT
[documentation](https://huggingface.co/docs/peft/index).
Install the latest ... | class_definition | 4,417 | 33,865 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py | null | 1,256 |
class UNet2DConditionLoadersMixin:
"""
Load LoRA layers into a [`UNet2DCondtionModel`].
"""
text_encoder_name = TEXT_ENCODER_NAME
unet_name = UNET_NAME
@validate_hf_hub_args
def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
... | class_definition | 1,824 | 45,352 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py | null | 1,257 |
class TextualInversionLoaderMixin:
r"""
Load Textual Inversion tokens and embeddings to the tokenizer and text encoder.
"""
def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"): # noqa: F821
r"""
Processes prompts that include a special token ... | class_definition | 4,099 | 26,816 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py | null | 1,258 |
class FromOriginalModelMixin:
"""
Load pretrained weights saved in the `.ckpt` or `.safetensors` format into a model.
"""
@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] = None, **kwargs):
r"""
Instantiate a model... | class_definition | 5,000 | 17,847 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py | null | 1,259 |
class LoraBaseMixin:
"""Utility class for handling LoRAs."""
_lora_loadable_modules = []
num_fused_loras = 0
def load_lora_weights(self, **kwargs):
raise NotImplementedError("`load_lora_weights()` is not implemented.")
@classmethod
def save_lora_weights(cls, **kwargs):
raise N... | class_definition | 18,593 | 38,068 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py | null | 1,260 |
class SingleFileComponentError(Exception):
def __init__(self, message=None):
self.message = message
super().__init__(self.message) | class_definition | 16,023 | 16,173 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_utils.py | null | 1,261 |
class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into Stable Diffusion [`UNet2DConditionModel`] and
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
"""
_lora_loadable_modules = ["unet", "text_encoder"]
unet_name = ... | class_definition | 1,939 | 21,337 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,262 |
class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into Stable Diffusion XL [`UNet2DConditionModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), and
[`CLIPTextModelWithProjection`](https://huggingface.co/docs/transform... | class_definition | 21,340 | 42,760 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,263 |
class SD3LoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`SD3Transformer2DModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), and
[`CLIPTextModelWithProjection`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.C... | class_definition | 42,763 | 62,034 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,264 |
class FluxLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`FluxTransformer2DModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
Specific to [`StableDiffusion3Pipeline`].
"""
_lora_loadable_modules = ["transformer", "text_e... | class_definition | 62,037 | 101,713 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,265 |
class AmusedLoraLoaderMixin(StableDiffusionLoraLoaderMixin):
_lora_loadable_modules = ["transformer", "text_encoder"]
transformer_name = TRANSFORMER_NAME
text_encoder_name = TEXT_ENCODER_NAME
@classmethod
# Copied from diffusers.loaders.lora_pipeline.FluxLoraLoaderMixin.load_lora_into_transformer w... | class_definition | 101,883 | 109,698 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,266 |
class CogVideoXLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`CogVideoXTransformer3DModel`]. Specific to [`CogVideoXPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
# Copied from diffusers.load... | class_definition | 109,701 | 123,876 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,267 |
class Mochi1LoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`MochiTransformer3DModel`]. Specific to [`MochiPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
# Copied from diffusers.loaders.lora_pi... | class_definition | 123,879 | 138,088 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,268 |
class LTXVideoLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`LTXVideoTransformer3DModel`]. Specific to [`LTXPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
# Copied from diffusers.loaders.lora... | class_definition | 138,091 | 152,315 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,269 |
class SanaLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`SanaTransformer2DModel`]. Specific to [`SanaPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
# Copied from diffusers.loaders.lora_pipeli... | class_definition | 152,318 | 166,521 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,270 |
class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`HunyuanVideoTransformer3DModel`]. Specific to [`HunyuanVideoPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
def lora_state_dict... | class_definition | 166,524 | 180,871 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,271 |
class LoraLoaderMixin(StableDiffusionLoraLoaderMixin):
def __init__(self, *args, **kwargs):
deprecation_message = "LoraLoaderMixin is deprecated and this will be removed in a future version. Please use `StableDiffusionLoraLoaderMixin`, instead."
deprecate("LoraLoaderMixin", "1.0.0", deprecation_mess... | class_definition | 180,874 | 181,240 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py | null | 1,272 |
class IPAdapterMixin:
"""Mixin for handling IP Adapters."""
@validate_hf_hub_args
def load_ip_adapter(
self,
pretrained_model_name_or_path_or_dict: Union[str, List[str], Dict[str, torch.Tensor]],
subfolder: Union[str, List[str]],
weight_name: Union[str, List[str]],
i... | class_definition | 1,624 | 17,640 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py | null | 1,273 |
class FluxIPAdapterMixin:
"""Mixin for handling Flux IP Adapters."""
@validate_hf_hub_args
def load_ip_adapter(
self,
pretrained_model_name_or_path_or_dict: Union[str, List[str], Dict[str, torch.Tensor]],
weight_name: Union[str, List[str]],
subfolder: Optional[Union[str, Lis... | class_definition | 17,643 | 32,865 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py | null | 1,274 |
class SD3IPAdapterMixin:
"""Mixin for handling StableDiffusion 3 IP Adapters."""
@property
def is_ip_adapter_active(self) -> bool:
"""Checks if IP-Adapter is loaded and scale > 0.
IP-Adapter scale controls the influence of the image prompt versus text prompt. When this value is set to 0,
... | class_definition | 32,868 | 44,810 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py | null | 1,275 |
class AttnProcsLayers(torch.nn.Module):
def __init__(self, state_dict: Dict[str, torch.Tensor]):
super().__init__()
self.layers = torch.nn.ModuleList(state_dict.values())
self.mapping = dict(enumerate(state_dict.keys()))
self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys(... | class_definition | 647 | 2,422 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/utils.py | null | 1,276 |
class FluxTransformer2DLoadersMixin:
"""
Load layers into a [`FluxTransformer2DModel`].
"""
def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_usage=False):
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_emp... | class_definition | 966 | 7,814 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/transformer_flux.py | null | 1,277 |
class SASolverScheduler(SchedulerMixin, ConfigMixin):
"""
`SASolverScheduler` is a fast dedicated high-order solver for diffusion SDEs.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers s... | class_definition | 2,902 | 55,087 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_sasolver.py | null | 1,278 |
class KarrasVeSchedulerState:
# setable values
num_inference_steps: Optional[int] = None
timesteps: Optional[jnp.ndarray] = None
schedule: Optional[jnp.ndarray] = None # sigma(t_i)
@classmethod
def create(cls):
return cls() | class_definition | 943 | 1,200 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py | null | 1,279 |
class FlaxKarrasVeOutput(BaseOutput):
"""
Output class for the scheduler's step function output.
Args:
prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model ... | class_definition | 1,214 | 1,927 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py | null | 1,280 |
class FlaxKarrasVeScheduler(FlaxSchedulerMixin, ConfigMixin):
"""
Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
the VE column of Table 1 from [1] for reference.
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based... | class_definition | 1,930 | 9,605 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py | null | 1,281 |
class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` ... | class_definition | 1,076 | 1,509 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py | null | 1,282 |
class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Euler scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
n... | class_definition | 1,512 | 19,360 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py | null | 1,283 |
class EulerDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used... | class_definition | 1,230 | 2,000 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py | null | 1,284 |
class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Euler scheduler.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_... | class_definition | 4,806 | 34,931 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py | null | 1,285 |
class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
"""
Implements a variant of `DPMSolverMultistepScheduler` with cosine schedule, proposed by Nichol and Dhariwal (2021).
This scheduler was used in Stable Audio Open [1].
[1] Evans, Parker, et al. "Stable Audio Open" https://arxiv.org... | class_definition | 1,035 | 24,639 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py | null | 1,286 |
class LCMSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next m... | class_definition | 1,188 | 1,936 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lcm.py | null | 1,287 |
class LCMScheduler(SchedulerMixin, ConfigMixin):
"""
`LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. [`~ConfigMixin`] takes care of storing all con... | class_definition | 4,772 | 32,021 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_lcm.py | null | 1,288 |
class DDPMSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next ... | class_definition | 1,069 | 1,830 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm.py | null | 1,289 |
class DDPMScheduler(SchedulerMixin, ConfigMixin):
"""
`DDPMScheduler` explores the connections between denoising score matching and Langevin dynamics sampling.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library impleme... | class_definition | 4,565 | 25,987 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_ddpm.py | null | 1,290 |
class HeunDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used ... | class_definition | 1,110 | 1,879 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py | null | 1,291 |
class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Scheduler with Heun steps for discrete beta schedules.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and ... | class_definition | 3,499 | 27,699 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_heun_discrete.py | null | 1,292 |
class KDPM2DiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used... | class_definition | 1,111 | 1,881 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py | null | 1,293 |
class KDPM2DiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
KDPM2DiscreteScheduler is inspired by the DPMSolver2 and Algorithm 2 from the [Elucidating the Design Space of
Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper.
This model inherits from [`SchedulerMixin`] a... | class_definition | 3,501 | 26,131 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py | null | 1,294 |
class PNDMSchedulerState:
common: CommonSchedulerState
final_alpha_cumprod: jnp.ndarray
# setable values
init_noise_sigma: jnp.ndarray
timesteps: jnp.ndarray
num_inference_steps: Optional[int] = None
prk_timesteps: Optional[jnp.ndarray] = None
plms_timesteps: Optional[jnp.ndarray] = Non... | class_definition | 1,109 | 2,021 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py | null | 1,295 |
class FlaxPNDMSchedulerOutput(FlaxSchedulerOutput):
state: PNDMSchedulerState | class_definition | 2,035 | 2,116 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py | null | 1,296 |
class FlaxPNDMScheduler(FlaxSchedulerMixin, ConfigMixin):
"""
Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques,
namely Runge-Kutta method and a linear multi-step method.
[`~ConfigMixin`] takes care of storing all config attributes that are pas... | class_definition | 2,119 | 21,538 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_pndm_flax.py | null | 1,297 |
class DDIMSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next ... | class_definition | 1,248 | 2,009 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py | null | 1,298 |
class CogVideoXDPMScheduler(SchedulerMixin, ConfigMixin):
"""
`DDIMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation f... | class_definition | 4,502 | 23,326 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/schedulers/scheduling_dpm_cogvideox.py | null | 1,299 |
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