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

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

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

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

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

The output of [`TransformerTemporalModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, heig

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

Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN [11] to convert the entire da

The output of [`ControlNetModel`]. Args: down_block_res_samples (`tuple[torch.Tensor]`): A tuple of downsample activatio

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

r""" A gated self-attention dense layer that combines visual features and object features. Parameters: query_dim (`int`): The number

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

r""" Output class for zero-shot text-to-video pipeline. Args: frames (`[List[PIL.Image.Image]`, `np.ndarray`]): List of d

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

The output of [`UNetSpatioTemporalConditionModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channel

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

r""" A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample shaped o

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""" Pipeline to generate video from an input image using Stable Video Diffusion. This model inherits from [`DiffusionPipeline`]. Check the s

r""" A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample shaped o