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
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).
device
< source >(
)
→
torch.device
Returns
torch.device
The torch device on which the pipeline is located.
to
< source >( torch_device: typing.Union[str, torch.device, NoneType] = None torch_dtype: typing.Optional[torch.dtype] = None silence_dtype_warnings: bool = False )
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 sliced attention computation. If enable_attention_slicing
was previously called, attention is
computed in one step.
Disable memory efficient attention from xFormers.
download
< source >(
pretrained_model_name
**kwargs
)
→
os.PathLike
Parameters
-
pretrained_model_name (
str
oros.PathLike
, optional) — A string, the repository id (for exampleCompVis/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 calledpipeline.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 ofclip_guided_stable_diffusion.py
). Community pipelines are always loaded from the currentmain
branch of GitHub. -
A path to a directory (
./my_pipeline_directory/
) containing a custom pipeline. The directory must contain a file calledpipeline.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 toFalse
) — 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 toFalse
) — Whether or not to resume downloading the model weights and configuration files. If set toFalse
, 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 toFalse
) — Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only (
bool
, optional, defaults toFalse
) — Whether to only load local model weights and configuration files or not. If set toTrue
, 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. IfTrue
, the token generated fromdiffusers-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 torevision
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 loadingfrom_flax
. -
use_safetensors (
bool
, optional, defaults toNone
) — If set toNone
, the safetensors weights are downloaded if they’re available and if the safetensors library is installed. If set toTrue
, the model is forcibly loaded from safetensors weights. If set toFalse
, safetensors weights are not loaded. -
use_onnx (
bool
, optional, defaults toFalse
) — If set toTrue
, ONNX weights will always be downloaded if present. If set toFalse
, ONNX weights will never be downloaded. By defaultuse_onnx
defaults to the_is_onnx
class attribute which isFalse
for non-ONNX pipelines andTrue
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
orint
, 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 asattention_head_dim // slice_size
. In this case,attention_head_dim
must be a multiple ofslice_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_sequential_cpu_offload
< source >( gpu_id: int = 0 device: typing.Union[torch.device, str] = 'cuda' )
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
torch.device('meta') and loaded to GPU only when their specific submodule has its
forwardmethod called. Note that 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 defaultNone
operator for use asop
argument to thememory_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
oros.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().
- A string, the repo id (for example
-
torch_dtype (
str
ortorch.dtype
, optional) — Override the defaulttorch.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 ofclip_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 calledpipeline.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
- A string, the repo id (for example
-
force_download (
bool
, optional, defaults toFalse
) — 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 toFalse
) — Whether or not to resume downloading the model weights and configuration files. If set toFalse
, 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 toFalse
) — Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. -
local_files_only (
bool
, optional, defaults toFalse
) — Whether to only load local model weights and configuration files or not. If set toTrue
, 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. IfTrue
, the token generated fromdiffusers-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 torevision
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
orDict[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 optimizeddevice_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
oros.PathLike
, optional) — The path to offload weights if device_map contains the value"disk"
. -
offload_state_dict (
bool
, optional) — IfTrue
, 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 toTrue
when there is some disk offload. -
low_cpu_mem_usage (
bool
, optional, defaults toTrue
if torch version >= 1.9.0 elseFalse
) — 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 toTrue
will raise an error. -
use_safetensors (
bool
, optional, defaults toNone
) — If set toNone
, the safetensors weights are downloaded if they’re available and if the safetensors library is installed. If set toTrue
, the model is forcibly loaded from safetensors weights. If set toFalse
, safetensors weights are not loaded. -
use_onnx (
bool
, optional, defaults toNone
) — If set toTrue
, ONNX weights will always be downloaded if present. If set toFalse
, ONNX weights will never be downloaded. By defaultuse_onnx
defaults to the_is_onnx
class attribute which isFalse
for non-ONNX pipelines andTrue
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 loadingfrom_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
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
oros.PathLike
) — Directory to save a pipeline to. Will be created if it doesn’t exist. -
safe_serialization (
bool
, optional, defaults toTrue
) — Whether to save the model usingsafetensors
or the traditional PyTorch way withpickle
. -
variant (
str
, optional) — If specified, weights are saved in the formatpytorch_model.<variant>.bin
. -
push_to_hub (
bool
, optional, defaults toFalse
) — 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 withrepo_id
(will default to the name ofsave_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.