r""" Function used to generate inverted latents given a prompt and image. 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*)

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

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

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

r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `list(int)`): prompt to b

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

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

r""" Args: Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without: af

Correct the predicted sample based on the output model_output of the network. This is often run repeatedly after making the predictio

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

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

Convert a PyTorch tensor to a NumPy image.

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

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

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

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

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

To run a test in a subprocess. In particular, this can avoid (GPU) memory issue. Args: test_case (`unittest.TestCase`):

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

r""" Monkey-patches the forward passes of attention modules of the text encoder.

Given a tile row and column numbers returns the range of latents affected by that tiles in the overall image Returns a tuple with: - Star

Encode an event to an index.