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

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

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

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

`LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance.

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 memory efficient attention using xFormers. Args: attention_op (`Callable`, *optional*, defaults to `N

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

r""" Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method. Args: train_kv (`bool`, defaul

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

r""" Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers. Args: hidden_size (`int`

r""" Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder. Args: slice_size (

r""" Processor for implementing sliced attention. Args: slice_size (`int`, *optional*): The number of steps to compute at

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

The output of [`Transformer2DModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(ba

The output of [`UNet2DConditionModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):

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

r""" Processor for implementing memory efficient attention using xFormers. Args: attention_op (`Callable`, *optional*, defaults to `N

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

Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU blocks. Args: i

r""" Processor for implementing attention for the Custom Diffusion method. Args: train_kv (`bool`, defaults to `True`): W

r""" Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled dot-product attention.

r""" Attention processor for IP-Adapater. Args: hidden_size (`int`): The hidden size of the attention layer. cros

r""" Attention processor for IP-Adapater for PyTorch 2.0. Args: hidden_size (`int`): The hidden size of the attention lay

r""" Default kandinsky3 proccesor for performing attention-related computations.

r""" Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text encoder. Args:

r""" Processor for implementing the LoRA attention mechanism. Args: hidden_size (`int`, *optional*): The hidden size of t

r""" Processor for implementing the LoRA attention mechanism using PyTorch 2.0's memory-efficient scaled dot-product attention. Args:

r""" A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample shaped output. This mo