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

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

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

r"""Manually set the generation mode to image-conditioned text generation.

r"""Manually set the generation mode to text-conditioned image generation.

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

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

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

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

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

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

r""" Combines a latent iamge img_vae of shape (B, C, H, W) and a CLIP-embedded image img_clip of shape (B, 1, clip_img_dim) into a sin

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

Pretty print a list of elements with one line per element and a - starting each line.

Args: Compares a library version to some requirement using a given operation. library_or_version (`str` or `packaging.version.Version

Create the code for the dummy object corresponding to `name`.

r""" Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, text_encoder, vae and safety

r""" Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗 Accelerate, significantly re

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

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

Return [min_id, max_id] for an event type.

Takes a state dict and returns the state dict for a particular sub-model.

Find one (or multiple) backend in a code line of the init.

Find one (or multiple) backend in a code line of the init.

Generates a sequence of hidden-states from the language model, conditioned on the embedding inputs. Parameters: inputs_

Get dummy components with x8 VAE downscaling and 4 UNet down blocks. These dummy components are intended to fully-exercise the T2I-Adapter

Returns the indent in given line (as string).

Gets the current list of all available adapters in the pipeline.

Refines the result of `extract_imports` to remove subfolders and get a proper list of module filenames: if a file as an import `from utils im