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

Activates gradient checkpointing for the current model (may be referred to as *activation checkpointing* or *checkpoint activations*

Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize.

Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and `self.text_encoder`. All kwargs

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

Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a sing

Groups elements of an iterable based on a provided function. Parameters: - arr (Iterable): The iterable to be grouped.

Save a configuration object to the directory specified in `save_directory` so that it can be reloaded using the [`~ConfigMixin.from_c

r""" Prepare the attention mask for the attention computation. Args: attention_mask (`torch.Tensor`): The

r""" Create a new pipeline from a given pipeline. This method is useful to create a new pipeline from the existing pipeline components

r""" Set the attention processor to use. Args: processor (`AttnProcessor`): The attention processor to us

r""" Load a model or scheduler configuration. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optiona

Convert self to a tuple containing all the attributes/keys that are not `None`.

r""" Args: Save the current parameters for restoring later. parameters: Iterable of `torch.nn.Parameter`; the parameters t

Unloads the LoRA parameters. Examples: ```python >>> # Assuming `pipeline` is already loaded with the LoRA paramete

Preprocess the image input. Args: image (`pipeline_image_input`): The image input, accepted formats are

r""" Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the `Attention` class.

Copy current averaged parameters into given collection of parameters. Args: parameters: Iterable of `torch.nn.Parameter`;

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

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

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

Helper function to get activation function from string. Args: act_fn (str): Name of activation function. Returns: nn.Module:

Loads or creates a model card. Args: repo_id_or_path (`str`): The repo id (e.g., "runwayml/stable-diffusion-v1-5") or lo

Populates the `model_card` with library name and optional tags.

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

Normalize an image array to [-1,1].

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

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

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

Walks through a series of prompts and seeds, interpolating between them and saving the results to disk. Args: prompts (`

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

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

r""" Return state dict for lora weights and the network alphas. <Tip warning={true}> We support loading A1111 formatted LoRA

Args: original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). Returns: predictions (dict):

Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/