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

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

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

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

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

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

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

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

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

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

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

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

r""" A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output. This model inherits from [`ModelMixin`]. Ch

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

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

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

r"""Simple video processor.

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

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

Exponential Moving Average of models weights

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

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

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

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

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

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

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

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

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

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

r"""A 3D causal convolution layer that pads the input tensor to ensure causality in CogVideoX Model. Args: in_channels (`int`): Number of

2D Image to Patch Embedding with support for SD3 cropping.

r""" Norm layer adaptive layer norm zero (adaLN-Zero). Parameters: embedding_dim (`int`): The size of each embedding vector.

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

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

Projects caption embeddings. Also handles dropout for classifier-free guidance. Adapted from https://github.com/PixArt-alpha/PixArt-alpha/bl

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.

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

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

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

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

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

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