Convert a NumPy image to a PyTorch tensor.

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

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)

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

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

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.

helper function to translate prompt to English

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

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

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.

Constructs the noise schedule of Karras et al. (2022).

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

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

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

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

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

helper function to detect language(s) of prompt

r""" Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing

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

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

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

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

Reverse step process: recover noisy image from generated image. Args: images (`List[PIL Image]`): list of images to encode

r""" Function used to generate a latent mask given a mask prompt, a target prompt, and an image. Args: image (`PIL.Image.

Get the model modules inside the transformers library.

Get the objects in module that are models.