r""" Instantiates a text-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class. The from_pipe() meth

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

r""" Converts a state dict to new diffusers format. The state dict can be from previous diffusers format (`OLD_DIFFUSERS`), or PEFT format (`P

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

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

r""" Instantiate a [`DiffusionPipeline`] from pretrained pipeline weights saved in the `.ckpt` or `.safetensors` format. The pipeline

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

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

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

Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be converted to ``torch.Tens

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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.

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

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

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

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

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

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

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