Pipelines
Pipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different schedulers or even model components.
All pipelines are built from the base DiffusionPipeline class which provides basic functionality for loading, downloading, and saving all the components. Specific pipeline types (for example StableDiffusionPipeline) loaded with from_pretrained() are automatically detected and the pipeline components are loaded and passed to the __init__ function of the pipeline.
You shouldn’t use the DiffusionPipeline class for training. Individual components (for example, UNet2DModel and UNet2DConditionModel) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.
Pipelines do not offer any training functionality. You’ll notice PyTorch’s autograd is disabled by decorating the __call__() method with a torch.no_grad decorator because pipelines should not be used for training. If you’re interested in training, please take a look at the Training guides instead!
The table below lists all the pipelines currently available in 🤗 Diffusers and the tasks they support. Click on a pipeline to view its abstract and published paper.
| Pipeline | Tasks |
|---|---|
| AltDiffusion | image2image |
| AnimateDiff | text2video |
| Attend-and-Excite | text2image |
| Audio Diffusion | image2audio |
| AudioLDM | text2audio |
| AudioLDM2 | text2audio |
| BLIP Diffusion | text2image |
| Consistency Models | unconditional image generation |
| ControlNet | text2image, image2image, inpainting |
| ControlNet with Stable Diffusion XL | text2image |
| ControlNet-XS | text2image |
| ControlNet-XS with Stable Diffusion XL | text2image |
| Cycle Diffusion | image2image |
| Dance Diffusion | unconditional audio generation |
| DDIM | unconditional image generation |
| DDPM | unconditional image generation |
| DeepFloyd IF | text2image, image2image, inpainting, super-resolution |
| DiffEdit | inpainting |
| DiT | text2image |
| GLIGEN | text2image |
| InstructPix2Pix | image editing |
| Kandinsky 2.1 | text2image, image2image, inpainting, interpolation |
| Kandinsky 2.2 | text2image, image2image, inpainting |
| Kandinsky 3 | text2image, image2image |
| Latent Consistency Models | text2image |
| Latent Diffusion | text2image, super-resolution |
| LDM3D | text2image, text-to-3D, text-to-pano, upscaling |
| LEDITS++ | image editing |
| MultiDiffusion | text2image |
| MusicLDM | text2audio |
| Paint by Example | inpainting |
| ParaDiGMS | text2image |
| Pix2Pix Zero | image editing |
| PixArt-α | text2image |
| PNDM | unconditional image generation |
| RePaint | inpainting |
| Score SDE VE | unconditional image generation |
| Self-Attention Guidance | text2image |
| Semantic Guidance | text2image |
| Shap-E | text-to-3D, image-to-3D |
| Spectrogram Diffusion | |
| Stable Audio | text2audio |
| Stable Diffusion | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
| Stable Diffusion Model Editing | model editing |
| Stable Diffusion XL | text2image, image2image, inpainting |
| Stable Diffusion XL Turbo | text2image, image2image, inpainting |
| Stable unCLIP | text2image, image variation |
| Stochastic Karras VE | unconditional image generation |
| T2I-Adapter | text2image |
| Text2Video | text2video, video2video |
| Text2Video-Zero | text2video |
| unCLIP | text2image, image variation |
| Unconditional Latent Diffusion | unconditional image generation |
| UniDiffuser | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |
| Value-guided planning | value guided sampling |
| Versatile Diffusion | text2image, image variation |
| VQ Diffusion | text2image |
| Wuerstchen | text2image |
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 >( *args **kwargs ) → DiffusionPipeline
Parameters
- dtype (
torch.dtype, optional) — Returns a pipeline with the specifieddtype - device (
torch.Device, optional) — Returns a pipeline with the specifieddevice - silence_dtype_warnings (
str, optional, defaults toFalse) — Whether to omit warnings if the targetdtypeis not compatible with the targetdevice.
Returns
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) → DiffusionPipelineto return a pipeline with the specifieddtypeto(device, silence_dtype_warnings=False) → DiffusionPipelineto return a pipeline with the specifieddeviceto(device=None, dtype=None, silence_dtype_warnings=False) → DiffusionPipelineto return a pipeline with the specifieddeviceanddtype
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 (
stroros.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.pythat 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_diffusioninstead ofclip_guided_stable_diffusion.py). Community pipelines are always loaded from the currentmainbranch of GitHub. -
A path to a directory (
./my_pipeline_directory/) containing a custom pipeline. The directory must contain a file calledpipeline.pythat 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. - 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. - token (
stror 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 torevisionwhen 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_onnxdefaults to the_is_onnxclass attribute which isFalsefor non-ONNX pipelines andTruefor ONNX pipelines. ONNX weights include both files ending with.onnxand.pb. - trust_remote_code (
bool, optional, defaults toFalse) — Whether or not to allow for custom pipelines and components defined on the Hub in their own files. This option should only be set toTruefor repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
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: Union = 'auto' )
Parameters
- slice_size (
strorint, 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_dimmust 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_model_cpu_offload
< source >( gpu_id: Optional = None device: Union = '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.Deviceorstr, 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: Optional = None device: Union = '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.Deviceorstr, 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: Optional = None )
Parameters
- attention_op (
Callable, optional) — Override the defaultNoneoperator for use asopargument 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_pipe
< source >( pipeline **kwargs ) → DiffusionPipeline
Create a new pipeline from a given pipeline. This method is useful to create a new pipeline from the existing pipeline components without reallocating additional memory.
from_pretrained
< source >( pretrained_model_name_or_path: Union **kwargs )
Parameters
- pretrained_model_name_or_path (
stroros.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 (
strortorch.dtype, optional) — Override the defaulttorch.dtypeand 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_diffusioninstead 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.pythat 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. - 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. - token (
stror 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) — The specific model version to use. It can be a branch name, a tag name, or a commit id similar torevisionwhen loading a custom pipeline from the Hub. Defaults to the latest stable 🤗 Diffusers version. - 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 (
strorDict[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 (
stroros.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 toTruewhen there is some disk offload. - low_cpu_mem_usage (
bool, optional, defaults toTrueif 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 toTruewill 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_onnxdefaults to the_is_onnxclass attribute which isFalsefor non-ONNX pipelines andTruefor ONNX pipelines. ONNX weights include both files ending with.onnxand.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 = schedulerFunction 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.
Convert a NumPy image or a batch of images to a PIL image.
Removes all hooks that were added when using enable_sequential_cpu_offload or enable_model_cpu_offload.
Resets the device maps (if any) to None.
save_pretrained
< source >( save_directory: Union safe_serialization: bool = True variant: Optional = None push_to_hub: bool = False **kwargs )
Parameters
- save_directory (
stroros.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 usingsafetensorsor 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_directoryin 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.
diffusers.StableDiffusionMixin.enable_freeu
< source >( s1: float s2: float b1: float b2: float )
Parameters
- s1 (
float) — Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate “oversmoothing effect” in the enhanced denoising process. - s2 (
float) — Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate “oversmoothing effect” in the enhanced denoising process. - b1 (
float) — Scaling factor for stage 1 to amplify the contributions of backbone features. - b2 (
float) — Scaling factor for stage 2 to amplify the contributions of backbone features.
Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stages where they are being applied.
Please refer to the official repository for combinations of the values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
Disables the FreeU mechanism if enabled.
FlaxDiffusionPipeline
Base class for Flax-based pipelines.
FlaxDiffusionPipeline stores all components (models, schedulers, and processors) for diffusion pipelines and provides methods for loading, downloading and saving models. It also includes methods to:
- 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.
from_pretrained
< source >( pretrained_model_name_or_path: Union **kwargs )
Parameters
- pretrained_model_name_or_path (
stroros.PathLike, optional) — Can be either:- A string, the repo id (for example
runwayml/stable-diffusion-v1-5) of a pretrained pipeline hosted on the Hub. - A path to a directory (for example
./my_model_directory) containing the model weights saved using save_pretrained().
- A string, the repo id (for example
- dtype (
strorjnp.dtype, optional) — Override the defaultjnp.dtypeand load the model under this dtype. If"auto", the dtype is automatically derived from the model’s weights. - 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. - 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. - token (
stror 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. - 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. - 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.
Instantiate a Flax-based diffusion pipeline from pretrained pipeline weights.
The pipeline is set in evaluation mode (`model.eval()) by default and dropout modules are deactivated.
If you get the error message below, you need to finetune the weights for your downstream task:
Some weights of FlaxUNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:To use private or gated models, log-in with
huggingface-cli login.
Examples:
>>> from diffusers import FlaxDiffusionPipeline
>>> # Download pipeline from huggingface.co and cache.
>>> # Requires to be logged in to Hugging Face hub,
>>> # see more in [the documentation](https://huggingface.co/docs/hub/security-tokens)
>>> pipeline, params = FlaxDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5",
... variant="bf16",
... dtype=jnp.bfloat16,
... )
>>> # Download pipeline, but use a different scheduler
>>> from diffusers import FlaxDPMSolverMultistepScheduler
>>> model_id = "runwayml/stable-diffusion-v1-5"
>>> dpmpp, dpmpp_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
... model_id,
... subfolder="scheduler",
... )
>>> dpm_pipe, dpm_params = FlaxStableDiffusionPipeline.from_pretrained(
... model_id, variant="bf16", dtype=jnp.bfloat16, scheduler=dpmpp
... )
>>> dpm_params["scheduler"] = dpmpp_stateConvert a NumPy image or a batch of images to a PIL image.
save_pretrained
< source >( save_directory: Union params: Union push_to_hub: bool = False **kwargs )
Parameters
- save_directory (
stroros.PathLike) — Directory to which to save. Will be created if it doesn’t exist. - 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_directoryin 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.
PushToHubMixin
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub.
push_to_hub
< source >( repo_id: str commit_message: Optional = None private: Optional = None token: Optional = None create_pr: bool = False safe_serialization: bool = True variant: Optional = None )
Parameters
- repo_id (
str) — The name of the repository you want to push your model, scheduler, or pipeline files to. It should contain your organization name when pushing to an organization.repo_idcan also be a path to a local directory. - commit_message (
str, optional) — Message to commit while pushing. Default to"Upload {object}". - private (
bool, optional) — Whether or not the repository created should be private. - token (
str, optional) — The token to use as HTTP bearer authorization for remote files. The token generated when runninghuggingface-cli login(stored in~/.huggingface). - create_pr (
bool, optional, defaults toFalse) — Whether or not to create a PR with the uploaded files or directly commit. - safe_serialization (
bool, optional, defaults toTrue) — Whether or not to convert the model weights to thesafetensorsformat. - variant (
str, optional) — If specified, weights are saved in the formatpytorch_model.<variant>.bin.
Upload model, scheduler, or pipeline files to the 🤗 Hugging Face Hub.
Examples:
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="unet")
# Push the `unet` to your namespace with the name "my-finetuned-unet".
unet.push_to_hub("my-finetuned-unet")
# Push the `unet` to an organization with the name "my-finetuned-unet".
unet.push_to_hub("your-org/my-finetuned-unet")