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Pipelines

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Pipelines

The DiffusionPipeline is the quickest way to load any pretrained diffusion pipeline from the Hub for inference.

You shouldn’t use the DiffusionPipeline class for training or finetuning a diffusion model. Individual components (for example, UNet2DModel and UNet2DConditionModel) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.

The pipeline type (for example StableDiffusionPipeline) of any diffusion pipeline loaded with from_pretrained() is automatically detected and pipeline components are loaded and passed to the __init__ function of the pipeline.

Any pipeline object can be saved locally with save_pretrained().

DiffusionPipeline

class diffusers.DiffusionPipeline

< >

( )

Base class for all pipelines.

DiffusionPipeline stores all components (models, schedulers, and processors) for diffusion pipelines and provides methods for loading, downloading and saving models. It also includes methods to:

  • move all PyTorch modules to the device of your choice
  • enabling/disabling the progress bar for the denoising iteration

Class attributes:

  • config_name (str) — The configuration filename that stores the class and module names of all the diffusion pipeline’s components.
  • _optional_components (Liststr) — List of all optional components that don’t have to be passed to the pipeline to function (should be overridden by subclasses).

__call__

( *args **kwargs )

Call self as a function.

device

< >

( ) → torch.device

Returns

torch.device

The torch device on which the pipeline is located.

to

< >

( torch_device: typing.Union[str, torch.device, NoneType] = None torch_dtype: typing.Optional[torch.dtype] = None silence_dtype_warnings: bool = False )

components

< >

( )

The self.components property can be useful to run different pipelines with the same weights and configurations without reallocating additional memory.

Returns (dict): A dictionary containing all the modules needed to initialize the pipeline.

Examples:

>>> from diffusers import (
...     StableDiffusionPipeline,
...     StableDiffusionImg2ImgPipeline,
...     StableDiffusionInpaintPipeline,
... )

>>> text2img = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
>>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)

disable_attention_slicing

< >

( )

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

disable_xformers_memory_efficient_attention

< >

( )

Disable memory efficient attention from xFormers.

download

< >

( pretrained_model_name **kwargs ) → os.PathLike

Parameters

  • pretrained_model_name (str or os.PathLike, optional) — A string, the repository id (for example CompVis/ldm-text2im-large-256) of a pretrained pipeline hosted on the Hub.
  • custom_pipeline (str, optional) — Can be either:

    • A string, the repository id (for example CompVis/ldm-text2im-large-256) of a pretrained pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines the custom pipeline.

    • A string, the file name of a community pipeline hosted on GitHub under Community. Valid file names must match the file name and not the pipeline script (clip_guided_stable_diffusion instead of clip_guided_stable_diffusion.py). Community pipelines are always loaded from the current main branch of GitHub.

    • A path to a directory (./my_pipeline_directory/) containing a custom pipeline. The directory must contain a file called pipeline.py that defines the custom pipeline.

    🧪 This is an experimental feature and may change in the future.

    For more information on how to load and create custom pipelines, take a look at How to contribute a community pipeline.

  • force_download (bool, optional, defaults to False) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
  • resume_download (bool, optional, defaults to False) — Whether or not to resume downloading the model weights and configuration files. If set to False, any incompletely downloaded files are deleted.
  • proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, for example, {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.
  • output_loading_info(bool, optional, defaults to False) — Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
  • local_files_only (bool, optional, defaults to False) — Whether to only load local model weights and configuration files or not. If set to True, the model won’t be downloaded from the Hub.
  • use_auth_token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, the token generated from diffusers-cli login (stored in ~/.huggingface) is used.
  • revision (str, optional, defaults to "main") — The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git.
  • custom_revision (str, optional, defaults to "main") — The specific model version to use. It can be a branch name, a tag name, or a commit id similar to revision when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a custom pipeline from GitHub, otherwise it defaults to "main" when loading from the Hub.
  • mirror (str, optional) — Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not guarantee the timeliness or safety of the source, and you should refer to the mirror site for more information.
  • variant (str, optional) — Load weights from a specified variant filename such as "fp16" or "ema". This is ignored when loading from_flax.

Returns

os.PathLike

A path to the downloaded pipeline.

Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights.

To use private or gated models, log-in with huggingface-cli login.

enable_attention_slicing

< >

( slice_size: typing.Union[str, int, NoneType] = 'auto' )

Parameters

  • slice_size (str or int, optional, defaults to "auto") — When "auto", halves the input to the attention heads, so attention will be computed in two steps. If "max", maximum amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as attention_head_dim // slice_size. In this case, attention_head_dim must be a multiple of slice_size.

Enable sliced attention computation.

When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease.

enable_xformers_memory_efficient_attention

< >

( attention_op: typing.Optional[typing.Callable] = None )

Parameters

  • attention_op (Callable, optional) — Override the default None operator for use as op argument to the memory_efficient_attention() function of xFormers.

Enable memory efficient attention from xFormers.

When this option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed up during training is not guaranteed.

⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes precedent.

Examples:

>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp

>>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
>>> # Workaround for not accepting attention shape using VAE for Flash Attention
>>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None)

from_pretrained

< >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] **kwargs )

Parameters

  • pretrained_model_name_or_path (str or os.PathLike, optional) — Can be either:

    • A string, the repo id (for example CompVis/ldm-text2im-large-256) of a pretrained pipeline hosted on the Hub.
    • A path to a directory (for example ./my_pipeline_directory/) containing pipeline weights saved using save_pretrained().
  • torch_dtype (str or torch.dtype, optional) — Override the default torch.dtype and load the model with another dtype. If “auto” is passed, the dtype is automatically derived from the model’s weights.
  • custom_pipeline (str, optional) —

    🧪 This is an experimental feature and may change in the future.

    Can be either:

    • A string, the repo id (for example hf-internal-testing/diffusers-dummy-pipeline) of a custom pipeline hosted on the Hub. The repository must contain a file called pipeline.py that defines the custom pipeline.
    • A string, the file name of a community pipeline hosted on GitHub under Community. Valid file names must match the file name and not the pipeline script (clip_guided_stable_diffusion instead of clip_guided_stable_diffusion.py). Community pipelines are always loaded from the current main branch of GitHub.
    • A path to a directory (./my_pipeline_directory/) containing a custom pipeline. The directory must contain a file called pipeline.py that defines the custom pipeline.

Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights.

The pipeline is set in evaluation mode (model.eval()) by default.

If you get the error message below, you need to finetune the weights for your downstream task:

Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

To use private or gated models, log-in with huggingface-cli login.

Examples:

>>> from diffusers import DiffusionPipeline

>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")

>>> # Download pipeline that requires an authorization token
>>> # For more information on access tokens, please refer to this section
>>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")

>>> # Use a different scheduler
>>> from diffusers import LMSDiscreteScheduler

>>> scheduler = LMSDiscreteScheduler.from_config(pipeline.scheduler.config)
>>> pipeline.scheduler = scheduler

numpy_to_pil

< >

( images )

Convert a NumPy image or a batch of images to a PIL image.

save_pretrained

< >

( save_directory: typing.Union[str, os.PathLike] safe_serialization: bool = False variant: typing.Optional[str] = None )

Parameters

  • save_directory (str or os.PathLike) — Directory to save a pipeline to. Will be created if it doesn’t exist.
  • safe_serialization (bool, optional, defaults to False) — Whether to save the model using safetensors or the traditional PyTorch way with pickle.
  • variant (str, optional) — If specified, weights are saved in the format pytorch_model.<variant>.bin.

Save all saveable variables of the pipeline to a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading method. The pipeline is easily reloaded using the from_pretrained() class method.

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