r""" Performs Pipeline dtype and/or device conversion. A torch.dtype and torch.device are inferred from the arguments of `self.to(*arg

r"""Move internal buffers of the ExponentialMovingAverage to `device`. Args: device: like `device` argument to `torch.Tensor.to`

A helper function to create random tensors on the desired `device` with the desired `dtype`. When passing a list of generators, you can seed each

r""" Args: Loads the ExponentialMovingAverage state. This method is used by accelerate during checkpointing to save the ema st

Resize image. Args: image (`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`): The image input, can be a

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`,

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. A

Args: t0: start time has shape [batch_size, *shape, 1] t1: finish time has shape [batch_size, *shape, 1]

r""" Processes prompts that include a special token corresponding to a multi-vector textual inversion embedding to be replaced with mu

Predict the sample at the previous timestep by reversing the ODE. Core function to propagate the diffusion process from the learned m

Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned m

r""" Returns the state of the ExponentialMovingAverage as a dict. This method is used by accelerate during checkpointing to save the e

Creates a random float32 tensor

r""" Function that offloads all components, removes all model hooks that were added when using `enable_model_cpu_offload` and then app

r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequenti

Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format.

Disables custom attention processors and sets the default attention implementation.

Helper function for reproducible behavior during distributed training. See - https://pytorch.org/docs/stable/notes/randomness.html for pytorc

r""" Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned to the adapter to

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

r"""Move internal buffers of the ExponentialMovingAverage to `device`. Args: device: like `device` argument to `torch.Tensor.to`

Convert a NumPy image or a batch of images to a PIL image.

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

Adjust the weightage given to the LoRA layers of the model. Args: model (`torch.nn.Module`): The model to scale.

r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`): The p

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

Removes the previously passed weight given to the LoRA layers of the model. Args: model (`torch.nn.Module`): The model t

Convert a numpy image or a batch of images to a PIL image.

Postprocess the image output from tensor to `output_type`. Args: image (`torch.FloatTensor`): The image

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. A

Checks if the LoRA layers are correctly set with peft

Function invoked when using the prior pipeline for interpolation. Args: images_and_prompts (`List[Union[str, PIL.Image.I

Runs `command` with `subprocess.check_output` and will potentially return the `stdout`. Will also properly capture if an error occurred while

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

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

Args: Returns a dict of object names and values in a module, while skipping private/internal objects module (ModuleType):

r""" Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state dicts of all `torch.nn

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

Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format.

Args: Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`. seed (`int`): The seed to set.

Return a logger with the specified name. This function is not supposed to be directly accessed unless you are writing a custom diffusers mod

`torch.device`: The device on which the module is (assuming that all the module parameters are on the same device).

Unified API to get any scheduler from its name. Args: name (`str` or `SchedulerType`): The name of the scheduler to use.

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

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

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

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