AutoPipeline
The AutoPipeline
is designed to make it easy to load a checkpoint for a task without needing to know the specific pipeline class. Based on the task, the AutoPipeline
automatically retrieves the correct pipeline class from the checkpoint model_index.json
file.
Check out the AutoPipeline tutorial to learn how to use this API!
AutoPipelineForText2Image
AutoPipelineForText2Image is a generic pipeline class that instantiates a text-to-image pipeline class. The specific underlying pipeline class is automatically selected from either the from_pretrained() or from_pipe() methods.
This class cannot be instantiated using __init__()
(throws an error).
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_or_path **kwargs )
Parameters
- pretrained_model_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. - 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 (
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. - 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
.
Instantiates a text-to-image Pytorch diffusion pipeline from pretrained pipeline weight.
The from_pretrained() method takes care of returning the correct pipeline class instance by:
- Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object
- Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.
If a controlnet
argument is passed, it will instantiate a StableDiffusionControlNetPipeline object.
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
.
from_pipe
< source >( pipeline **kwargs )
Instantiates a text-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.
All the modules the pipeline contains will be used to initialize the new pipeline without reallocating additional memory.
The pipeline is set in evaluation mode (model.eval()
) by default.
>>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
>>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
... "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
... )
>>> pipe_t2i = AutoPipelineForText2Image.from_pipe(pipe_i2i)
>>> image = pipe_t2i(prompt).images[0]
AutoPipelineForImage2Image
AutoPipelineForImage2Image is a generic pipeline class that instantiates an image-to-image pipeline class. The specific underlying pipeline class is automatically selected from either the from_pretrained() or from_pipe() methods.
This class cannot be instantiated using __init__()
(throws an error).
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_or_path **kwargs )
Parameters
- pretrained_model_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. - 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 (
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. - 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
.
Instantiates a image-to-image Pytorch diffusion pipeline from pretrained pipeline weight.
The from_pretrained() method takes care of returning the correct pipeline class instance by:
- Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object
- Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.
If a controlnet
argument is passed, it will instantiate a StableDiffusionControlNetImg2ImgPipeline
object.
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
.
from_pipe
< source >( pipeline **kwargs )
Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
The from_pipe() method takes care of returning the correct pipeline class instance by finding the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.
All the modules the pipeline contains will be used to initialize the new pipeline without reallocating additional memory.
The pipeline is set in evaluation mode (model.eval()
) by default.
Examples:
>>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
... "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
... )
>>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i)
>>> image = pipe_i2i(prompt, image).images[0]
AutoPipelineForInpainting
AutoPipelineForInpainting is a generic pipeline class that instantiates an inpainting pipeline class. The specific underlying pipeline class is automatically selected from either the from_pretrained() or from_pipe() methods.
This class cannot be instantiated using __init__()
(throws an error).
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_or_path **kwargs )
Parameters
- pretrained_model_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. - 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 (
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. - 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
.
Instantiates a inpainting Pytorch diffusion pipeline from pretrained pipeline weight.
The from_pretrained() method takes care of returning the correct pipeline class instance by:
- Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its config object
- Find the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.
If a controlnet
argument is passed, it will instantiate a StableDiffusionControlNetInpaintPipeline
object.
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
.
from_pipe
< source >( pipeline **kwargs )
Instantiates a inpainting Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.
All the modules the pipeline class contain will be used to initialize the new pipeline without reallocating additional memory.
The pipeline is set in evaluation mode (model.eval()
) by default.
Examples:
>>> from diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting
>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0", requires_safety_checker=False
... )
>>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)
>>> image = pipe_inpaint(prompt, image=init_image, mask_image=mask_image).images[0]