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Pipelines

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Pipelines

The DiffusionPipeline is the easiest way to load any pretrained diffusion pipeline from the Hub and use it for inference.

You shouldn't use the [DiffusionPipeline](/docs/diffusers/v0.17.1/en/api/diffusion_pipeline#diffusers.DiffusionPipeline) class for training or finetuning a diffusion model. Individual components (for example, `UNetModel` and `UNetConditionModel`) of diffusion pipelines are usually trained individually, so we suggest directly working with 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 models.

DiffusionPipeline takes care of storing all components (models, schedulers, processors) for diffusion pipelines and handles methods for loading, downloading and saving models as well as a few methods common to all pipelines 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) — name of the config file that will store the class and module names of all components of the diffusion pipeline.
  • _optional_components (Liststr) — list of all components that are optional so they don’t have to be passed for 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 to not have to re-allocate memory.

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 invoked, this method will go back to computing attention in one step.

disable_xformers_memory_efficient_attention

< >

( )

Disable memory efficient attention as implemented in 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" when loading from the Hub and to local version of —
  • diffusers when loading from GitHub) — 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.
  • 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 will split 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 as implemented in xformers.

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

Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention is used.

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 of a pretrained pipeline hosted inside a model repo on https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like CompVis/ldm-text2im-large-256.
    • A path to a directory containing pipeline weights saved using save_pretrained(), e.g., ./my_pipeline_directory/.
  • torch_dtype (str or torch.dtype, optional) — Override the default torch.dtype and load the model under this dtype. If "auto" is passed the dtype will be automatically derived from the model’s weights.
  • custom_pipeline (str, optional) —

    This is an experimental feature and is likely to change in the future.

    Can be either:

    • A string, the repo id of a custom pipeline hosted inside a model repo on https://huggingface.co/. Valid repo ids have to be located under a user or organization name, like hf-internal-testing/diffusers-dummy-pipeline.

      It is required that the model repo has a file, called pipeline.py that defines the custom pipeline.

    • A string, the file name of a community pipeline hosted on GitHub under https://github.com/huggingface/diffusers/tree/main/examples/community. Valid file names have to match exactly the file name without .py located under the above link, e.g. clip_guided_stable_diffusion.

      Community pipelines are always loaded from the current main branch of GitHub.

    • A path to a directory containing a custom pipeline, e.g., ./my_pipeline_directory/.

      It is required that the directory has a file, called pipeline.py that defines the custom pipeline.

    For more information on how to load and create custom pipelines, please have a look at Loading and Adding Custom Pipelines

  • 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.
  • cache_dir (Union[str, os.PathLike], optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
  • resume_download (bool, optional, defaults to False) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
  • proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'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 or not to only look at local files (i.e., do not try to download the model).
  • use_auth_token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running huggingface-cli login (stored in ~/.huggingface).
  • 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, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
  • custom_revision (str, optional, defaults to "main" when loading from the Hub and to local version of diffusers when loading from GitHub) — 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.
  • mirror (str, optional) — Mirror source to accelerate downloads in China. If you are from China and have an accessibility problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. Please refer to the mirror site for more information. specify the folder name here.
  • device_map (str or Dict[str, Union[int, str, torch.device]], optional) — A map that specifies where each submodule should go. It doesn’t need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device.

    To have Accelerate compute the most optimized device_map automatically, set device_map="auto". For more information about each option see designing a device map.

  • max_memory (Dict, optional) — A dictionary device identifier to maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset.
  • offload_folder (str or os.PathLike, optional) — If the device_map contains any value "disk", the folder where we will offload weights.
  • offload_state_dict (bool, optional) — If True, will temporarily offload the CPU state dict to the hard drive to avoid getting out of CPU RAM if the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to True when there is some disk offload.
  • low_cpu_mem_usage (bool, optional, defaults to True if torch version >= 1.9.0 else False) — Speed up model loading by not initializing the weights and only loading the pre-trained weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch, setting this argument to True will raise an error.
  • use_safetensors (bool, optional, defaults to None) — If set to None, the pipeline will load the safetensors weights if they’re available and if the safetensors library is installed. If set to True, the pipeline will forcibly load the models from safetensors weights. If set to False the pipeline will not use safetensors.
  • kwargs (remaining dictionary of keyword arguments, optional) — Can be used to overwrite load - and saveable variables - i.e. the pipeline components - of the specific pipeline class. The overwritten components are then directly passed to the pipelines __init__ method. See example below for more information.
  • variant (str, optional) — If specified load weights from variant filename, e.g. pytorch_model..bin. variant is ignored when using from_flax.

Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights.

The pipeline is set in evaluation mode by default using model.eval() (Dropout modules are deactivated).

The warning Weights from XXX not initialized from pretrained model means that the weights of XXX do not come pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning task.

The warning Weights from XXX not used in YYY means that the layer XXX is not used by YYY, therefore those weights are discarded.

It is required to be logged in (huggingface-cli login) when you want to use private or gated models, e.g. "runwayml/stable-diffusion-v1-5"

Activate the special “offline-mode” to use this method in a firewalled environment.

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 which to save. 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 (that uses pickle).
  • variant (str, optional) — If specified, weights are saved in the format pytorch_model..bin.

Save all variables of the pipeline that can be saved and loaded as well as the pipelines configuration file to a directory. A pipeline variable can be saved and loaded if its class implements both a save and loading method. The pipeline can easily be re-loaded using the from_pretrained() class method.

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