An error indicating that an optional dependency of Diffusers was not found in the environment.

Image processor for VAE. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to downscale the image's (hei

Output class for Stable Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL

r""" A Resnet block. Parameters: in_channels (`int`): The number of channels in the input. out_channels (`int`, *optional*, d

Module class that surfaces all objects but only performs associated imports when the objects are requested.

Output class for image pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of

r""" A cross attention layer. Parameters: query_dim (`int`): The number of channels in the query. cross_attention

r""" A linear layer that is used with LoRA. Parameters: in_features (`int`): Number of input features. out_featur

r""" Default processor for performing attention-related computations.

r""" Processor for performing attention-related computations with extra learnable key and value matrices for the text encoder.

Args: Context manager to capture `logging` streams logger: 'logging` logger object Returns: The captured output is availa

Output class for Stable Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL

A ControlNet model. Args: in_channels (`int`, defaults to 4): The number of channels in the input sample. flip_s

A 2D upsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and output

r""" A VQ-VAE model for decoding latent representations. This model inherits from [`ModelMixin`]. Check the superclass documentation for it's

A 2D downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outp

r""" Multiple `ControlNetModel` wrapper class for Multi-ControlNet This module is a wrapper for multiple instances of the `ControlNetModel`.

r""" Output of decoding method. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):

r""" A simple ResNet-like model that accepts images containing control signals such as keyposes and depth. The model generates multiple featur

Exponential Moving Average of models weights

Base class for the output of a scheduler's `step` function. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channe

r""" A Resnet block that use normalization layer that incorporate conditioning information. Parameters: in_channels (`int`): The numb

A Transformer model for video-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to

r""" A feed-forward layer. Parameters: dim (`int`): The number of channels in the input. dim_out (`int`, *optional*): The num

Flax implementation of 2D Resnet Block. Args: in_channels (`int`): Input channels out_channels (`int`):

A 2D Transformer model for image-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads

r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).

r""" Pipeline for text-to-audio generation using AudioLDM2. This model inherits from [`DiffusionPipeline`]. Check the superclass documentatio

r""" Pipeline for text-to-audio generation using AudioLDM. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation

Output class for audio pipelines. Args: audios (`np.ndarray`) List of denoised audio samples of a NumPy array of shape `

r""" Pipeline for text-to-audio generation using MusicLDM. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation

Residual 1D block with temporal convolutions. Parameters: inp_channels (`int`): Number of input channels. out_channels (`int

r""" A SpatioTemporal Resnet block. Parameters: in_channels (`int`): The number of channels in the input. out_channels (`int`

r""" Norm layer modified to incorporate timestep embeddings. Parameters: embedding_dim (`int`): The size of each embedding vector.

r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads

Dual transformer wrapper that combines two `Transformer2DModel`s for mixed inference. Parameters: num_attention_heads (`int`, *optio

Args: Output class for Stable Diffusion pipelines. images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL

Output class for KandinskyPriorPipeline. Args: image_embeds (`torch.FloatTensor`) clip image embeddings for text prompt

r""" Pipeline for text-to-image generation using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check the superclass docum

Temporal convolutional layer that can be used for video (sequence of images) input Code mostly copied from: https://github.com/modelscope/mod

r""" An AdapterBlock is a helper model that contains multiple ResNet-like blocks. It is used in the `FullAdapter` and `FullAdapterXL` models.

r""" Output class for AnimateDiff pipelines. Args: frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):

A 1D downsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and outp

r""" The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation. Args: in_channels (`int`,

A convolutional layer that can be used with LoRA.

r""" MultiAdapter is a wrapper model that contains multiple adapter models and merges their outputs according to user-assigned weighting.

r"""VQ-VAE model from Paella model. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the libra

Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002. Args: f_channels (`int`): The number

A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks. Args: in_channels (`int`): The n

r""" GroupNorm layer modified to incorporate timestep embeddings. Parameters: embedding_dim (`int`): The size of each embedding vecto

r""" Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra learnable key and valu