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)

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)

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

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

Set the verbosity level for the 🤗 Diffusers' root logger. Args: verbosity (`int`): Logging level which can be one of:

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

Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion process

Aggregates the attention for each token and computes the max activation value for each token to alter.

Helper method to cast floating-point values of given parameter `PyTree` to given `dtype`.

r""" Combines a latent image img_vae of shape (B, C, H, W), a CLIP-embedded image img_clip of shape (B, L_img, clip_img_dim), and a te

Computes the attend-and-excite loss using the maximum attention value for each token.

r""" Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim) into two tensors of shape (B, C, H, W) and (B, 1, clip_img_

r""" Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim + text_seq_len * text_dim] into (img_vae, img_clip, text) wh

Unwraps a model.

Perform backward process given list of time steps Args: latents: Latents at time timesteps[0].

Perform backward process given list of time steps. Args: latents: Latents at time timesteps[0].

Batched version of the `step` function, to be able to reverse the SDE for multiple samples/timesteps at once. Also, does not add any

Create a mask. Args: image (`PIL.Image.Image`): The image input, should be a PIL image. Returns

Context manager that checks out a given commit when entered, but gets back to the reference it was at on exit. Args: repo (`git.Repo

Cleans the table of content of the model documentation by removing duplicates and sorting models alphabetically.

r""" Simply iterates over the state dict and replaces the patterns in `mapping` with the corresponding values. Args: state_dict (`dic

Maps the keys in a UniDiffuser transformer block (`Block`) to the keys in a diffusers transformer block (`UTransformerBlock`/`UniDiffuserBloc

Creates a config for the diffusers based on the config of the LDM model.

Args: Deletes the LoRA layers of `adapter_name` for the unet and text-encoder(s). adapter_names (`Union[List[str], str]`)

r""" Disable sliced attention computation. If `enable_attention_slicing` was previously called, attention is computed in one step.

r""" Disable memory efficient attention from [xFormers](https://facebookresearch.github.io/xformers/).

r""" The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`): The prompt or

r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in sev

Reverse the denoising step process to recover a noisy image from the generated image. Args: images (`List[PIL Image]`):

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*):

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*):

r""" Generate a latent mask given a mask prompt, a target prompt, and an image. Args: image (`PIL.Image.Image`):

grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or

Gets the list of the current active adapters. Example: ```python from diffusers import DiffusionPipeline p