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`

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

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

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

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

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

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

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

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

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

See `AutoTokenizer.from_pretrained` docstring.

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

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

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

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""" 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

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

Disables custom attention processors and sets the default attention implementation.

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

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

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

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

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

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.

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

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

Checks if the LoRA layers are correctly set with peft

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

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

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

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

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

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

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

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

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

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

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

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

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