#
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

< source >

( )

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
- enable/disable 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 (`List[str]`) — 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

< source >

( ) →
`torch.device`

Returns

`torch.device`

The torch device on which the pipeline is located.

####
to

< source >

( *args **kwargs ) →
DiffusionPipeline

Parameters

- dtype (`torch.dtype`, optional) — Returns a pipeline with the specified `dtype`
- device (`torch.Device`, optional) — Returns a pipeline with the specified `device`
- silence_dtype_warnings (`str`, optional, defaults to `False`) — Whether to omit warnings if the target `dtype` is not compatible with the target `device`.

Returns

DiffusionPipeline

The pipeline converted to specified `dtype` and/or `dtype`.

Performs Pipeline dtype and/or device conversion. A torch.dtype and torch.device are inferred from the arguments of `self.to(*args, **kwargs).`

If the pipeline already has the correct torch.dtype and torch.device, then it is returned as is. Otherwise, the returned pipeline is a copy of self with the desired torch.dtype and torch.device.

Here are the ways to call `to`:

- `to(dtype, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the specified `dtype`
- `to(device, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the specified `device`
- `to(device=None, dtype=None, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the specified `device` and `dtype`

####
components

< source >

( )

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

< source >

( )

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

####
disable_xformers_memory_efficient_attention

< source >

( )

Disable memory efficient attention from xFormers.

####
download

< source >

( 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`.
- use_safetensors (`bool`, optional, defaults to `None`) — If set to `None`, the safetensors weights are downloaded if they’re available and if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded.
- use_onnx (`bool`, optional, defaults to `False`) — If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending with `.onnx` and `.pb`.

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

< source >

( 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. For more than one attention head, the computation is performed sequentially over each head. This is useful to save some memory in exchange for a small speed decrease.

⚠️ Don’t enable attention slicing if you’re already using `scaled_dot_product_attention` (SDPA) from PyTorch 2.0 or xFormers. These attention computations are already very memory efficient so you won’t need to enable this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs!

Examples:

    >>> import torch
    >>> from diffusers import StableDiffusionPipeline

    >>> pipe = StableDiffusionPipeline.from_pretrained(
    ...     "runwayml/stable-diffusion-v1-5",
    ...     torch_dtype=torch.float16,
    ...     use_safetensors=True,
    ... )

    >>> prompt = "a photo of an astronaut riding a horse on mars"
    >>> pipe.enable_attention_slicing()
    >>> image = pipe(prompt).images[0]

####
enable_model_cpu_offload

< source >

( gpu_id: typing.Optional[int] = None device: typing.Union[torch.device, str] = 'cuda' )

Parameters

- gpu_id (`int`, optional) — The ID of the accelerator that shall be used in inference. If not specified, it will default to 0.
- device (`torch.Device` or `str`, optional, defaults to “cuda”) — The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will default to “cuda”.

Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.

####
enable_sequential_cpu_offload

< source >

( gpu_id: typing.Optional[int] = None device: typing.Union[torch.device, str] = 'cuda' )

Parameters

- gpu_id (`int`, optional) — The ID of the accelerator that shall be used in inference. If not specified, it will default to 0.
- device (`torch.Device` or `str`, optional, defaults to “cuda”) — The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will default to “cuda”.

Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state dicts of all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are saved to CPU and then moved to `torch.device('meta')` and loaded to GPU only when their specific submodule has its `forward` method called. Offloading happens on a submodule basis. Memory savings are higher than with `enable_model_cpu_offload`, but performance is lower.

####
enable_xformers_memory_efficient_attention

< source >

( 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

< source >

( 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.

  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 where a downloaded pretrained model configuration is cached if the standard cache is not used.
- 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.
- 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 defined for each parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the same device.

  Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For more information about each option see designing a device map.

- max_memory (`Dict`, optional) — A dictionary device identifier for the 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) — The path to offload weights if device_map contains the value `"disk"`.
- offload_state_dict (`bool`, optional) — If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running 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 only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to `True` will raise an error.
- use_safetensors (`bool`, optional, defaults to `None`) — If set to `None`, the safetensors weights are downloaded if they’re available and if the safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors weights. If set to `False`, safetensors weights are not loaded.
- use_onnx (`bool`, optional, defaults to `None`) — If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending with `.onnx` and `.pb`.
- kwargs (remaining dictionary of keyword arguments, optional) — Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline class). The overwritten components are passed directly to the pipelines `__init__` method. See example below 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`.

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

####
maybe_free_model_hooks

< source >

( )

Function that offloads all components, removes all model hooks that were added when using `enable_model_cpu_offload` and then applies them again. In case the model has not been offloaded this function is a no-op. Make sure to add this function to the end of the `__call__` function of your pipeline so that it functions correctly when applying enable_model_cpu_offload.

####
numpy_to_pil

< source >

( images )

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

####
save_pretrained

< source >

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

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 `True`) — 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`.
- push_to_hub (`bool`, optional, defaults to `False`) — Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace).
- kwargs (`Dict[str, Any]`, optional) — Additional keyword arguments passed along to the push_to_hub() method.

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.