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

r""" Load pretrained attention processor layers into [`UNet2DConditionModel`]. Attention processor layers have to be defined in

Updates paths inside resnets to the new naming scheme (local renaming)

r""" Save attention processor layers to a directory so that it can be reloaded with the [`~loaders.UNet2DConditionLoadersMixin.load_at

Removes segments. Positive values shave the first segments, negative shave the last segments.

Convert a NumPy image to a PyTorch tensor.

Applies the FreeU mechanism as introduced in https: //arxiv.org/abs/2309.11497. Adapted from the official code repository: https://github.com/Chen

r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*):

r""" Function used to generate inverted latents given a prompt and image. Args: prompt (`str` or `List[str]`, *optional*)

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

Construct a layernorm module in the T5 style. No bias and no subtraction of mean.

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

r""" Generate inverted latents given a prompt and image. Args: prompt (`str` or `List[str]`, *optional*):