r""" Instantiate a pretrained MultiControlNet model from multiple pre-trained controlnet models. The model is set in evaluation mode

r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

r""" Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights. Parameters: pretrained_model_name

r""" Load Textual Inversion embeddings into the text encoder of [`StableDiffusionPipeline`] (both 🤗 Diffusers and Automatic1111 format

Parameters: pretrained_model_name_or_path_or_dict (`str` or `List[str]` or `os.PathLike` or `List[os.PathLike]` or `dict` or `Lis

Convert a PIL image or a list of PIL images to NumPy arrays.

r""" Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads` is the number of h

Check whether the module was compiled with torch.compile()

Set IP-Adapter scales per-transformer block. Input `scale` could be a single config or a list of configs for granular control over ea

Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_inde

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the linear multistep met

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the Runge-Kutta method.

r""" Fuses the LoRA parameters into the original parameters of the corresponding blocks. <Tip warning={true}> This is an exp

r""" Compute the attention scores. Args: query (`torch.Tensor`): The query tensor. key (`torch.Tensor`): The

r""" Get the attention processor in use. Args: return_deprecated_lora (`bool`, *optional*, defaults to `False`):

This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. :param timesteps: a 1-D

Args: Compares the current Transformers version to a given reference with an operation. operation (`str`): A string repre

r""" Enable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/). When this option is enabled, yo

Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa

r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits attention layers, and take

r""" Returns: `torch.device`: The torch device on which the pipeline is located.

r""" Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds to a multi-vector

Downsamples the provided mask tensor to match the expected dimensions for scaled dot-product attention. If the aspect ratio of the ma

r""" Converts a video tensor to a list of frames for export. Args: video (`torch.Tensor`): The video as a tensor.

r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to comp

r""" Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`]. Parameters: unet (`UNet2DConditionModel`):