Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_inde

Set IP-Adapter scales per-transformer block. Input `scale` could be a single config or a list of configs for granular control over ea

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

r""" Get the attention processor in use. Args: return_deprecated_lora (`bool`, *optional*, defaults to `False`):

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

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the linear multistep met

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the Runge-Kutta method.

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

This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. :param timesteps: a 1-D

Args: Compares the current Transformers version to a given reference with an operation. operation (`str`): A string repre

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

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

r""" Converts a state dict from UNet format to diffusers format - i.e. by removing some keys

Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa

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

dumps the current config as a dictionary object, as a printable format. null fields will not be printed. Used for dumping results alon

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

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

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

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

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

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

r""" Returns: `torch.device`: The torch device on which the pipeline is located.

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

r""" Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds to a multi-vector

Sets the `lora_state_dict` into `text_encoder` coming from `transformers`. Args: lora_state_dict: The state dictionary to be set.

Downsamples the provided mask tensor to match the expected dimensions for scaled dot-product attention. If the aspect ratio of the ma

Upload model, scheduler, or pipeline files to the 🤗 Hugging Face Hub. Parameters: repo_id (`str`): The n

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)

Args: Compares the current Accelerate version to a given reference with an operation. operation (`str`): A string represe

r""" Converts a video tensor to a list of frames for export. Args: video (`torch.Tensor`): The video as a tensor.

r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to comp