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if "enable_pag" in kwargs:
enable_pag = kwargs.pop("enable_pag")
if enable_pag:
text_2_image_cls = _get_task_class(
AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
text_2_image_cls.__name__.replace("PAG", "").replace("Pipeline", "PAGPipeline"),
)
else:
text_2_image_cls = _get_task_class(
AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
text_2_image_cls.__name__.replace("PAG", ""),
)
# define expected module and optional kwargs given the pipeline signature
expected_modules, optional_kwargs = text_2_image_cls._get_signature_keys(text_2_image_cls)
pretrained_model_name_or_path = original_config.pop("_name_or_path", None) | 32 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
# allow users pass modules in `kwargs` to override the original pipeline's components
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
original_class_obj = {
k: pipeline.components[k]
for k, v in pipeline.components.items()
if k in expected_modules and k not in passed_class_obj
}
# allow users pass optional kwargs to override the original pipelines config attribute
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
original_pipe_kwargs = {
k: original_config[k]
for k, v in original_config.items()
if k in optional_kwargs and k not in passed_pipe_kwargs
} | 32 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
# config that were not expected by original pipeline is stored as private attribute
# we will pass them as optional arguments if they can be accepted by the pipeline
additional_pipe_kwargs = [
k[1:]
for k in original_config.keys()
if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
]
for k in additional_pipe_kwargs:
original_pipe_kwargs[k] = original_config.pop(f"_{k}")
text_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
# store unused config as private attribute
unused_original_config = {
f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
for k, v in original_config.items()
if k not in text_2_image_kwargs
} | 32 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
missing_modules = (
set(expected_modules) - set(text_2_image_cls._optional_components) - set(text_2_image_kwargs.keys())
)
if len(missing_modules) > 0:
raise ValueError(
f"Pipeline {text_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"
)
model = text_2_image_cls(**text_2_image_kwargs)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
model.register_to_config(**unused_original_config)
return model | 32 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
class AutoPipelineForImage2Image(ConfigMixin):
r"""
[`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
[`~AutoPipelineForImage2Image.from_pretrained`] or [`~AutoPipelineForImage2Image.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.
"""
config_name = "model_index.json"
def __init__(self, *args, **kwargs):
raise EnvironmentError(
f"{self.__class__.__name__} is designed to be instantiated "
f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
) | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
r"""
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:
1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
config object
2. 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: | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/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.
```
Parameters:
pretrained_model_or_path (`str` or `os.PathLike`, *optional*):
Can be either: | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
- 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
[`~DiffusionPipeline.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.
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*): | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used. | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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.
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"`): | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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*): | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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. | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#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` | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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.
kwargs (remaining dictionary of keyword arguments, *optional*): | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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`. | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
<Tip>
To use private or [gated](https://huggingface.co./docs/hub/models-gated#gated-models) models, log-in with
`huggingface-cli login`.
</Tip>
Examples:
```py
>>> from diffusers import AutoPipelineForImage2Image
>>> pipeline = AutoPipelineForImage2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> image = pipeline(prompt, image).images[0]
```
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None) | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
load_config_kwargs = {
"cache_dir": cache_dir,
"force_download": force_download,
"proxies": proxies,
"token": token,
"local_files_only": local_files_only,
"revision": revision,
}
config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
orig_class_name = config["_class_name"]
# the `orig_class_name` can be:
# `- *Pipeline` (for regular text-to-image checkpoint)
# - `*ControlPipeline` (for Flux tools specific checkpoint)
# `- *Img2ImgPipeline` (for refiner checkpoint)
if "Img2Img" in orig_class_name:
to_replace = "Img2ImgPipeline"
elif "ControlPipeline" in orig_class_name:
to_replace = "ControlPipeline"
else:
to_replace = "Pipeline" | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
if "controlnet" in kwargs:
if isinstance(kwargs["controlnet"], ControlNetUnionModel):
orig_class_name = orig_class_name.replace(to_replace, "ControlNetUnion" + to_replace)
else:
orig_class_name = orig_class_name.replace(to_replace, "ControlNet" + to_replace)
if "enable_pag" in kwargs:
enable_pag = kwargs.pop("enable_pag")
if enable_pag:
orig_class_name = orig_class_name.replace(to_replace, "PAG" + to_replace)
if to_replace == "ControlPipeline":
orig_class_name = orig_class_name.replace(to_replace, "ControlImg2ImgPipeline")
image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, orig_class_name)
kwargs = {**load_config_kwargs, **kwargs}
return image_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs) | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
@classmethod
def from_pipe(cls, pipeline, **kwargs):
r"""
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.
Parameters:
pipeline (`DiffusionPipeline`):
an instantiated `DiffusionPipeline` object
Examples:
```py
>>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
... "stable-diffusion-v1-5/stable-diffusion-v1-5", requires_safety_checker=False
... )
>>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i)
>>> image = pipe_i2i(prompt, image).images[0]
```
"""
original_config = dict(pipeline.config)
original_cls_name = pipeline.__class__.__name__
# derive the pipeline class to instantiate
image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, original_cls_name) | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
if "controlnet" in kwargs:
if kwargs["controlnet"] is not None:
to_replace = "Img2ImgPipeline"
if "PAG" in image_2_image_cls.__name__:
to_replace = "PAG" + to_replace
image_2_image_cls = _get_task_class(
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
image_2_image_cls.__name__.replace("ControlNet", "").replace(
to_replace, "ControlNet" + to_replace
),
)
else:
image_2_image_cls = _get_task_class(
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
image_2_image_cls.__name__.replace("ControlNet", ""),
) | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
if "enable_pag" in kwargs:
enable_pag = kwargs.pop("enable_pag")
if enable_pag:
image_2_image_cls = _get_task_class(
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
image_2_image_cls.__name__.replace("PAG", "").replace("Img2ImgPipeline", "PAGImg2ImgPipeline"),
)
else:
image_2_image_cls = _get_task_class(
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
image_2_image_cls.__name__.replace("PAG", ""),
)
# define expected module and optional kwargs given the pipeline signature
expected_modules, optional_kwargs = image_2_image_cls._get_signature_keys(image_2_image_cls)
pretrained_model_name_or_path = original_config.pop("_name_or_path", None) | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
# allow users pass modules in `kwargs` to override the original pipeline's components
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
original_class_obj = {
k: pipeline.components[k]
for k, v in pipeline.components.items()
if k in expected_modules and k not in passed_class_obj
}
# allow users pass optional kwargs to override the original pipelines config attribute
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
original_pipe_kwargs = {
k: original_config[k]
for k, v in original_config.items()
if k in optional_kwargs and k not in passed_pipe_kwargs
} | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
# config attribute that were not expected by original pipeline is stored as its private attribute
# we will pass them as optional arguments if they can be accepted by the pipeline
additional_pipe_kwargs = [
k[1:]
for k in original_config.keys()
if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
]
for k in additional_pipe_kwargs:
original_pipe_kwargs[k] = original_config.pop(f"_{k}")
image_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
# store unused config as private attribute
unused_original_config = {
f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
for k, v in original_config.items()
if k not in image_2_image_kwargs
} | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
missing_modules = (
set(expected_modules) - set(image_2_image_cls._optional_components) - set(image_2_image_kwargs.keys())
)
if len(missing_modules) > 0:
raise ValueError(
f"Pipeline {image_2_image_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"
)
model = image_2_image_cls(**image_2_image_kwargs)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
model.register_to_config(**unused_original_config)
return model | 33 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
class AutoPipelineForInpainting(ConfigMixin):
r"""
[`AutoPipelineForInpainting`] is a generic pipeline class that instantiates an inpainting pipeline class. The
specific underlying pipeline class is automatically selected from either the
[`~AutoPipelineForInpainting.from_pretrained`] or [`~AutoPipelineForInpainting.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.
"""
config_name = "model_index.json"
def __init__(self, *args, **kwargs):
raise EnvironmentError(
f"{self.__class__.__name__} is designed to be instantiated "
f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
) | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
r"""
Instantiates a inpainting Pytorch diffusion pipeline from pretrained pipeline weight.
The from_pretrained() method takes care of returning the correct pipeline class instance by:
1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
config object
2. 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: | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/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.
```
Parameters:
pretrained_model_or_path (`str` or `os.PathLike`, *optional*):
Can be either: | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
- 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
[`~DiffusionPipeline.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.
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*): | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used. | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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.
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"`): | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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*): | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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. | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#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` | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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.
kwargs (remaining dictionary of keyword arguments, *optional*): | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
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`. | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
<Tip>
To use private or [gated](https://huggingface.co./docs/hub/models-gated#gated-models) models, log-in with
`huggingface-cli login`.
</Tip>
Examples:
```py
>>> from diffusers import AutoPipelineForInpainting
>>> pipeline = AutoPipelineForInpainting.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> image = pipeline(prompt, image=init_image, mask_image=mask_image).images[0]
```
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
local_files_only = kwargs.pop("local_files_only", False)
revision = kwargs.pop("revision", None) | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
load_config_kwargs = {
"cache_dir": cache_dir,
"force_download": force_download,
"proxies": proxies,
"token": token,
"local_files_only": local_files_only,
"revision": revision,
}
config = cls.load_config(pretrained_model_or_path, **load_config_kwargs)
orig_class_name = config["_class_name"]
# The `orig_class_name`` can be:
# `- *InpaintPipeline` (for inpaint-specific checkpoint)
# - `*ControlPipeline` (for Flux tools specific checkpoint)
# - or *Pipeline (for regular text-to-image checkpoint)
if "Inpaint" in orig_class_name:
to_replace = "InpaintPipeline"
elif "ControlPipeline" in orig_class_name:
to_replace = "ControlPipeline"
else:
to_replace = "Pipeline" | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
if "controlnet" in kwargs:
if isinstance(kwargs["controlnet"], ControlNetUnionModel):
orig_class_name = orig_class_name.replace(to_replace, "ControlNetUnion" + to_replace)
else:
orig_class_name = orig_class_name.replace(to_replace, "ControlNet" + to_replace)
if "enable_pag" in kwargs:
enable_pag = kwargs.pop("enable_pag")
if enable_pag:
orig_class_name = orig_class_name.replace(to_replace, "PAG" + to_replace)
if to_replace == "ControlPipeline":
orig_class_name = orig_class_name.replace(to_replace, "ControlInpaintPipeline")
inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, orig_class_name)
kwargs = {**load_config_kwargs, **kwargs}
return inpainting_cls.from_pretrained(pretrained_model_or_path, **kwargs) | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
@classmethod
def from_pipe(cls, pipeline, **kwargs):
r"""
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.
Parameters:
pipeline (`DiffusionPipeline`):
an instantiated `DiffusionPipeline` object
Examples:
```py
>>> from diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting
>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0", requires_safety_checker=False
... ) | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
>>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)
>>> image = pipe_inpaint(prompt, image=init_image, mask_image=mask_image).images[0]
```
"""
original_config = dict(pipeline.config)
original_cls_name = pipeline.__class__.__name__
# derive the pipeline class to instantiate
inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, original_cls_name) | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
if "controlnet" in kwargs:
if kwargs["controlnet"] is not None:
inpainting_cls = _get_task_class(
AUTO_INPAINT_PIPELINES_MAPPING,
inpainting_cls.__name__.replace("ControlNet", "").replace(
"InpaintPipeline", "ControlNetInpaintPipeline"
),
)
else:
inpainting_cls = _get_task_class(
AUTO_INPAINT_PIPELINES_MAPPING,
inpainting_cls.__name__.replace("ControlNetInpaintPipeline", "InpaintPipeline"),
) | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
if "enable_pag" in kwargs:
enable_pag = kwargs.pop("enable_pag")
if enable_pag:
inpainting_cls = _get_task_class(
AUTO_INPAINT_PIPELINES_MAPPING,
inpainting_cls.__name__.replace("PAG", "").replace("InpaintPipeline", "PAGInpaintPipeline"),
)
else:
inpainting_cls = _get_task_class(
AUTO_INPAINT_PIPELINES_MAPPING,
inpainting_cls.__name__.replace("PAGInpaintPipeline", "InpaintPipeline"),
)
# define expected module and optional kwargs given the pipeline signature
expected_modules, optional_kwargs = inpainting_cls._get_signature_keys(inpainting_cls)
pretrained_model_name_or_path = original_config.pop("_name_or_path", None) | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
# allow users pass modules in `kwargs` to override the original pipeline's components
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
original_class_obj = {
k: pipeline.components[k]
for k, v in pipeline.components.items()
if k in expected_modules and k not in passed_class_obj
}
# allow users pass optional kwargs to override the original pipelines config attribute
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
original_pipe_kwargs = {
k: original_config[k]
for k, v in original_config.items()
if k in optional_kwargs and k not in passed_pipe_kwargs
} | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
# config that were not expected by original pipeline is stored as private attribute
# we will pass them as optional arguments if they can be accepted by the pipeline
additional_pipe_kwargs = [
k[1:]
for k in original_config.keys()
if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
]
for k in additional_pipe_kwargs:
original_pipe_kwargs[k] = original_config.pop(f"_{k}")
inpainting_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
# store unused config as private attribute
unused_original_config = {
f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
for k, v in original_config.items()
if k not in inpainting_kwargs
}
missing_modules = (
set(expected_modules) - set(inpainting_cls._optional_components) - set(inpainting_kwargs.keys())
) | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
if len(missing_modules) > 0:
raise ValueError(
f"Pipeline {inpainting_cls} expected {expected_modules}, but only {set(list(passed_class_obj.keys()) + list(original_class_obj.keys()))} were passed"
)
model = inpainting_cls(**inpainting_kwargs)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
model.register_to_config(**unused_original_config)
return model | 34 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/auto_pipeline.py |
class FreeInitMixin:
r"""Mixin class for FreeInit."""
def enable_free_init(
self,
num_iters: int = 3,
use_fast_sampling: bool = False,
method: str = "butterworth",
order: int = 4,
spatial_stop_frequency: float = 0.25,
temporal_stop_frequency: float = 0.25,
):
"""Enables the FreeInit mechanism as in https://arxiv.org/abs/2312.07537.
This implementation has been adapted from the [official repository](https://github.com/TianxingWu/FreeInit). | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
Args:
num_iters (`int`, *optional*, defaults to `3`):
Number of FreeInit noise re-initialization iterations.
use_fast_sampling (`bool`, *optional*, defaults to `False`):
Whether or not to speedup sampling procedure at the cost of probably lower quality results. Enables the
"Coarse-to-Fine Sampling" strategy, as mentioned in the paper, if set to `True`.
method (`str`, *optional*, defaults to `butterworth`):
Must be one of `butterworth`, `ideal` or `gaussian` to use as the filtering method for the FreeInit low
pass filter.
order (`int`, *optional*, defaults to `4`):
Order of the filter used in `butterworth` method. Larger values lead to `ideal` method behaviour
whereas lower values lead to `gaussian` method behaviour.
spatial_stop_frequency (`float`, *optional*, defaults to `0.25`): | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
Normalized stop frequency for spatial dimensions. Must be between 0 to 1. Referred to as `d_s` in the
original implementation.
temporal_stop_frequency (`float`, *optional*, defaults to `0.25`):
Normalized stop frequency for temporal dimensions. Must be between 0 to 1. Referred to as `d_t` in the
original implementation.
"""
self._free_init_num_iters = num_iters
self._free_init_use_fast_sampling = use_fast_sampling
self._free_init_method = method
self._free_init_order = order
self._free_init_spatial_stop_frequency = spatial_stop_frequency
self._free_init_temporal_stop_frequency = temporal_stop_frequency | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
def disable_free_init(self):
"""Disables the FreeInit mechanism if enabled."""
self._free_init_num_iters = None
@property
def free_init_enabled(self):
return hasattr(self, "_free_init_num_iters") and self._free_init_num_iters is not None
def _get_free_init_freq_filter(
self,
shape: Tuple[int, ...],
device: Union[str, torch.dtype],
filter_type: str,
order: float,
spatial_stop_frequency: float,
temporal_stop_frequency: float,
) -> torch.Tensor:
r"""Returns the FreeInit filter based on filter type and other input conditions."""
time, height, width = shape[-3], shape[-2], shape[-1]
mask = torch.zeros(shape)
if spatial_stop_frequency == 0 or temporal_stop_frequency == 0:
return mask
if filter_type == "butterworth": | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
def retrieve_mask(x):
return 1 / (1 + (x / spatial_stop_frequency**2) ** order)
elif filter_type == "gaussian":
def retrieve_mask(x):
return math.exp(-1 / (2 * spatial_stop_frequency**2) * x)
elif filter_type == "ideal":
def retrieve_mask(x):
return 1 if x <= spatial_stop_frequency * 2 else 0
else:
raise NotImplementedError("`filter_type` must be one of gaussian, butterworth or ideal")
for t in range(time):
for h in range(height):
for w in range(width):
d_square = (
((spatial_stop_frequency / temporal_stop_frequency) * (2 * t / time - 1)) ** 2
+ (2 * h / height - 1) ** 2
+ (2 * w / width - 1) ** 2
)
mask[..., t, h, w] = retrieve_mask(d_square)
return mask.to(device) | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
def _apply_freq_filter(self, x: torch.Tensor, noise: torch.Tensor, low_pass_filter: torch.Tensor) -> torch.Tensor:
r"""Noise reinitialization."""
# FFT
x_freq = fft.fftn(x, dim=(-3, -2, -1))
x_freq = fft.fftshift(x_freq, dim=(-3, -2, -1))
noise_freq = fft.fftn(noise, dim=(-3, -2, -1))
noise_freq = fft.fftshift(noise_freq, dim=(-3, -2, -1))
# frequency mix
high_pass_filter = 1 - low_pass_filter
x_freq_low = x_freq * low_pass_filter
noise_freq_high = noise_freq * high_pass_filter
x_freq_mixed = x_freq_low + noise_freq_high # mix in freq domain
# IFFT
x_freq_mixed = fft.ifftshift(x_freq_mixed, dim=(-3, -2, -1))
x_mixed = fft.ifftn(x_freq_mixed, dim=(-3, -2, -1)).real
return x_mixed | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
def _apply_free_init(
self,
latents: torch.Tensor,
free_init_iteration: int,
num_inference_steps: int,
device: torch.device,
dtype: torch.dtype,
generator: torch.Generator,
):
if free_init_iteration == 0:
self._free_init_initial_noise = latents.detach().clone()
else:
latent_shape = latents.shape
free_init_filter_shape = (1, *latent_shape[1:])
free_init_freq_filter = self._get_free_init_freq_filter(
shape=free_init_filter_shape,
device=device,
filter_type=self._free_init_method,
order=self._free_init_order,
spatial_stop_frequency=self._free_init_spatial_stop_frequency,
temporal_stop_frequency=self._free_init_temporal_stop_frequency,
) | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
current_diffuse_timestep = self.scheduler.config.num_train_timesteps - 1
diffuse_timesteps = torch.full((latent_shape[0],), current_diffuse_timestep).long()
z_t = self.scheduler.add_noise(
original_samples=latents, noise=self._free_init_initial_noise, timesteps=diffuse_timesteps.to(device)
).to(dtype=torch.float32)
z_rand = randn_tensor(
shape=latent_shape,
generator=generator,
device=device,
dtype=torch.float32,
)
latents = self._apply_freq_filter(z_t, z_rand, low_pass_filter=free_init_freq_filter)
latents = latents.to(dtype)
# Coarse-to-Fine Sampling for faster inference (can lead to lower quality)
if self._free_init_use_fast_sampling:
num_inference_steps = max(
1, int(num_inference_steps / self._free_init_num_iters * (free_init_iteration + 1))
) | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
if num_inference_steps > 0:
self.scheduler.set_timesteps(num_inference_steps, device=device)
return latents, self.scheduler.timesteps | 35 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/free_init_utils.py |
class OnnxRuntimeModel:
def __init__(self, model=None, **kwargs):
logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")
self.model = model
self.model_save_dir = kwargs.get("model_save_dir", None)
self.latest_model_name = kwargs.get("latest_model_name", ONNX_WEIGHTS_NAME)
def __call__(self, **kwargs):
inputs = {k: np.array(v) for k, v in kwargs.items()}
return self.model.run(None, inputs)
@staticmethod
def load_model(path: Union[str, Path], provider=None, sess_options=None):
"""
Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider` | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
Arguments:
path (`str` or `Path`):
Directory from which to load
provider(`str`, *optional*):
Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider`
"""
if provider is None:
logger.info("No onnxruntime provider specified, using CPUExecutionProvider")
provider = "CPUExecutionProvider"
return ort.InferenceSession(path, providers=[provider], sess_options=sess_options)
def _save_pretrained(self, save_directory: Union[str, Path], file_name: Optional[str] = None, **kwargs):
"""
Save a model and its configuration file to a directory, so that it can be re-loaded using the
[`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the
latest_model_name. | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
Arguments:
save_directory (`str` or `Path`):
Directory where to save the model file.
file_name(`str`, *optional*):
Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to save the
model with a different name.
"""
model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME
src_path = self.model_save_dir.joinpath(self.latest_model_name)
dst_path = Path(save_directory).joinpath(model_file_name)
try:
shutil.copyfile(src_path, dst_path)
except shutil.SameFileError:
pass | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
# copy external weights (for models >2GB)
src_path = self.model_save_dir.joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
if src_path.exists():
dst_path = Path(save_directory).joinpath(ONNX_EXTERNAL_WEIGHTS_NAME)
try:
shutil.copyfile(src_path, dst_path)
except shutil.SameFileError:
pass
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
**kwargs,
):
"""
Save a model to a directory, so that it can be re-loaded using the [`~OnnxModel.from_pretrained`] class
method.:
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(save_directory, exist_ok=True) | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
# saving model weights/files
self._save_pretrained(save_directory, **kwargs)
@classmethod
@validate_hf_hub_args
def _from_pretrained(
cls,
model_id: Union[str, Path],
token: Optional[Union[bool, str, None]] = None,
revision: Optional[Union[str, None]] = None,
force_download: bool = False,
cache_dir: Optional[str] = None,
file_name: Optional[str] = None,
provider: Optional[str] = None,
sess_options: Optional["ort.SessionOptions"] = None,
**kwargs,
):
"""
Load a model from a directory or the HF Hub. | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
Arguments:
model_id (`str` or `Path`):
Directory from which to load
token (`str` or `bool`):
Is needed to load models from a private or gated repository
revision (`str`):
Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id
cache_dir (`Union[str, Path]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
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.
file_name(`str`):
Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to load | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
different model files from the same repository or directory.
provider(`str`):
The ONNX runtime provider, e.g. `CPUExecutionProvider` or `CUDAExecutionProvider`.
kwargs (`Dict`, *optional*):
kwargs will be passed to the model during initialization
"""
model_file_name = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(model_id):
model = OnnxRuntimeModel.load_model(
Path(model_id, model_file_name).as_posix(), provider=provider, sess_options=sess_options
)
kwargs["model_save_dir"] = Path(model_id)
# load model from hub
else:
# download model
model_cache_path = hf_hub_download(
repo_id=model_id,
filename=model_file_name,
token=token,
revision=revision,
cache_dir=cache_dir, | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
force_download=force_download,
)
kwargs["model_save_dir"] = Path(model_cache_path).parent
kwargs["latest_model_name"] = Path(model_cache_path).name
model = OnnxRuntimeModel.load_model(model_cache_path, provider=provider, sess_options=sess_options)
return cls(model=model, **kwargs) | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
@classmethod
@validate_hf_hub_args
def from_pretrained(
cls,
model_id: Union[str, Path],
force_download: bool = True,
token: Optional[str] = None,
cache_dir: Optional[str] = None,
**model_kwargs,
):
revision = None
if len(str(model_id).split("@")) == 2:
model_id, revision = model_id.split("@")
return cls._from_pretrained(
model_id=model_id,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
token=token,
**model_kwargs,
) | 36 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/onnx_utils.py |
class ImagePipelineOutput(BaseOutput):
"""
Output class for image pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
"""
images: Union[List[PIL.Image.Image], np.ndarray] | 37 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
class AudioPipelineOutput(BaseOutput):
"""
Output class for audio pipelines.
Args:
audios (`np.ndarray`)
List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`.
"""
audios: np.ndarray | 38 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
class DiffusionPipeline(ConfigMixin, PushToHubMixin):
r"""
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).
""" | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
config_name = "model_index.json"
model_cpu_offload_seq = None
hf_device_map = None
_optional_components = []
_exclude_from_cpu_offload = []
_load_connected_pipes = False
_is_onnx = False
def register_modules(self, **kwargs):
for name, module in kwargs.items():
# retrieve library
if module is None or isinstance(module, (tuple, list)) and module[0] is None:
register_dict = {name: (None, None)}
else:
library, class_name = _fetch_class_library_tuple(module)
register_dict = {name: (library, class_name)}
# save model index config
self.register_to_config(**register_dict)
# set models
setattr(self, name, module) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def __setattr__(self, name: str, value: Any):
if name in self.__dict__ and hasattr(self.config, name):
# We need to overwrite the config if name exists in config
if isinstance(getattr(self.config, name), (tuple, list)):
if value is not None and self.config[name][0] is not None:
class_library_tuple = _fetch_class_library_tuple(value)
else:
class_library_tuple = (None, None)
self.register_to_config(**{name: class_library_tuple})
else:
self.register_to_config(**{name: value})
super().__setattr__(name, value) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
safe_serialization: bool = True,
variant: Optional[str] = None,
max_shard_size: Optional[Union[int, str]] = None,
push_to_hub: bool = False,
**kwargs,
):
"""
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
[`~DiffusionPipeline.from_pretrained`] class method. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Arguments:
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`.
max_shard_size (`int` or `str`, defaults to `None`):
The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5GB"`).
If expressed as an integer, the unit is bytes. Note that this limit will be decreased after a certain
period of time (starting from Oct 2024) to allow users to upgrade to the latest version of `diffusers`. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
This is to establish a common default size for this argument across different libraries in the Hugging
Face ecosystem (`transformers`, and `accelerate`, for example).
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). | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
kwargs (`Dict[str, Any]`, *optional*):
Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
model_index_dict = dict(self.config)
model_index_dict.pop("_class_name", None)
model_index_dict.pop("_diffusers_version", None)
model_index_dict.pop("_module", None)
model_index_dict.pop("_name_or_path", None)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
private = kwargs.pop("private", None)
create_pr = kwargs.pop("create_pr", False)
token = kwargs.pop("token", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
expected_modules, optional_kwargs = self._get_signature_keys(self) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def is_saveable_module(name, value):
if name not in expected_modules:
return False
if name in self._optional_components and value[0] is None:
return False
return True
model_index_dict = {k: v for k, v in model_index_dict.items() if is_saveable_module(k, v)}
for pipeline_component_name in model_index_dict.keys():
sub_model = getattr(self, pipeline_component_name)
model_cls = sub_model.__class__
# Dynamo wraps the original model in a private class.
# I didn't find a public API to get the original class.
if is_compiled_module(sub_model):
sub_model = _unwrap_model(sub_model)
model_cls = sub_model.__class__ | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
save_method_name = None
# search for the model's base class in LOADABLE_CLASSES
for library_name, library_classes in LOADABLE_CLASSES.items():
if library_name in sys.modules:
library = importlib.import_module(library_name)
else:
logger.info(
f"{library_name} is not installed. Cannot save {pipeline_component_name} as {library_classes} from {library_name}"
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
for base_class, save_load_methods in library_classes.items():
class_candidate = getattr(library, base_class, None)
if class_candidate is not None and issubclass(model_cls, class_candidate):
# if we found a suitable base class in LOADABLE_CLASSES then grab its save method
save_method_name = save_load_methods[0]
break
if save_method_name is not None:
break
if save_method_name is None:
logger.warning(
f"self.{pipeline_component_name}={sub_model} of type {type(sub_model)} cannot be saved."
)
# make sure that unsaveable components are not tried to be loaded afterward
self.register_to_config(**{pipeline_component_name: (None, None)})
continue
save_method = getattr(sub_model, save_method_name) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# Call the save method with the argument safe_serialization only if it's supported
save_method_signature = inspect.signature(save_method)
save_method_accept_safe = "safe_serialization" in save_method_signature.parameters
save_method_accept_variant = "variant" in save_method_signature.parameters
save_method_accept_max_shard_size = "max_shard_size" in save_method_signature.parameters
save_kwargs = {}
if save_method_accept_safe:
save_kwargs["safe_serialization"] = safe_serialization
if save_method_accept_variant:
save_kwargs["variant"] = variant
if save_method_accept_max_shard_size and max_shard_size is not None:
# max_shard_size is expected to not be None in ModelMixin
save_kwargs["max_shard_size"] = max_shard_size
save_method(os.path.join(save_directory, pipeline_component_name), **save_kwargs) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# finally save the config
self.save_config(save_directory)
if push_to_hub:
# Create a new empty model card and eventually tag it
model_card = load_or_create_model_card(repo_id, token=token, is_pipeline=True)
model_card = populate_model_card(model_card)
model_card.save(os.path.join(save_directory, "README.md"))
self._upload_folder(
save_directory,
repo_id,
token=token,
commit_message=commit_message,
create_pr=create_pr,
)
def to(self, *args, **kwargs):
r"""
Performs Pipeline dtype and/or device conversion. A torch.dtype and torch.device are inferred from the
arguments of `self.to(*args, **kwargs).`
<Tip> | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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.
</Tip>
Here are the ways to call `to`:
- `to(dtype, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the specified
[`dtype`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)
- `to(device, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the specified
[`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.device)
- `to(device=None, dtype=None, silence_dtype_warnings=False) → DiffusionPipeline` to return a pipeline with the
specified [`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.device) and
[`dtype`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
Arguments:
dtype (`torch.dtype`, *optional*):
Returns a pipeline with the specified
[`dtype`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)
device (`torch.Device`, *optional*):
Returns a pipeline with the specified
[`device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.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`.
"""
dtype = kwargs.pop("dtype", None)
device = kwargs.pop("device", None)
silence_dtype_warnings = kwargs.pop("silence_dtype_warnings", False) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
dtype_arg = None
device_arg = None
if len(args) == 1:
if isinstance(args[0], torch.dtype):
dtype_arg = args[0]
else:
device_arg = torch.device(args[0]) if args[0] is not None else None
elif len(args) == 2:
if isinstance(args[0], torch.dtype):
raise ValueError(
"When passing two arguments, make sure the first corresponds to `device` and the second to `dtype`."
)
device_arg = torch.device(args[0]) if args[0] is not None else None
dtype_arg = args[1]
elif len(args) > 2:
raise ValueError("Please make sure to pass at most two arguments (`device` and `dtype`) `.to(...)`")
if dtype is not None and dtype_arg is not None:
raise ValueError(
"You have passed `dtype` both as an argument and as a keyword argument. Please only pass one of the two."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
dtype = dtype or dtype_arg
if device is not None and device_arg is not None:
raise ValueError(
"You have passed `device` both as an argument and as a keyword argument. Please only pass one of the two."
)
device = device or device_arg
pipeline_has_bnb = any(any((_check_bnb_status(module))) for _, module in self.components.items())
# throw warning if pipeline is in "offloaded"-mode but user tries to manually set to GPU.
def module_is_sequentially_offloaded(module):
if not is_accelerate_available() or is_accelerate_version("<", "0.14.0"):
return False
return hasattr(module, "_hf_hook") and (
isinstance(module._hf_hook, accelerate.hooks.AlignDevicesHook)
or hasattr(module._hf_hook, "hooks")
and isinstance(module._hf_hook.hooks[0], accelerate.hooks.AlignDevicesHook)
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
def module_is_offloaded(module):
if not is_accelerate_available() or is_accelerate_version("<", "0.17.0.dev0"):
return False
return hasattr(module, "_hf_hook") and isinstance(module._hf_hook, accelerate.hooks.CpuOffload)
# .to("cuda") would raise an error if the pipeline is sequentially offloaded, so we raise our own to make it clearer
pipeline_is_sequentially_offloaded = any(
module_is_sequentially_offloaded(module) for _, module in self.components.items()
)
is_pipeline_device_mapped = self.hf_device_map is not None and len(self.hf_device_map) > 1
if is_pipeline_device_mapped:
raise ValueError(
"It seems like you have activated a device mapping strategy on the pipeline which doesn't allow explicit device placement using `to()`. You can call `reset_device_map()` to remove the existing device map from the pipeline."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if device and torch.device(device).type == "cuda":
if pipeline_is_sequentially_offloaded and not pipeline_has_bnb:
raise ValueError(
"It seems like you have activated sequential model offloading by calling `enable_sequential_cpu_offload`, but are now attempting to move the pipeline to GPU. This is not compatible with offloading. Please, move your pipeline `.to('cpu')` or consider removing the move altogether if you use sequential offloading."
)
# PR: https://github.com/huggingface/accelerate/pull/3223/
elif pipeline_has_bnb and is_accelerate_version("<", "1.1.0.dev0"):
raise ValueError(
"You are trying to call `.to('cuda')` on a pipeline that has models quantized with `bitsandbytes`. Your current `accelerate` installation does not support it. Please upgrade the installation."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
# Display a warning in this case (the operation succeeds but the benefits are lost)
pipeline_is_offloaded = any(module_is_offloaded(module) for _, module in self.components.items())
if pipeline_is_offloaded and device and torch.device(device).type == "cuda":
logger.warning(
f"It seems like you have activated model offloading by calling `enable_model_cpu_offload`, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components {', '.join(self.components.keys())} to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: `pipeline.to('cpu')` or removing the move altogether if you use offloading."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
module_names, _ = self._get_signature_keys(self)
modules = [getattr(self, n, None) for n in module_names]
modules = [m for m in modules if isinstance(m, torch.nn.Module)]
is_offloaded = pipeline_is_offloaded or pipeline_is_sequentially_offloaded
for module in modules:
_, is_loaded_in_4bit_bnb, is_loaded_in_8bit_bnb = _check_bnb_status(module)
if (is_loaded_in_4bit_bnb or is_loaded_in_8bit_bnb) and dtype is not None:
logger.warning(
f"The module '{module.__class__.__name__}' has been loaded in `bitsandbytes` {'4bit' if is_loaded_in_4bit_bnb else '8bit'} and conversion to {dtype} is not supported. Module is still in {'4bit' if is_loaded_in_4bit_bnb else '8bit'} precision."
) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if is_loaded_in_8bit_bnb and device is not None:
logger.warning(
f"The module '{module.__class__.__name__}' has been loaded in `bitsandbytes` 8bit and moving it to {device} via `.to()` is not supported. Module is still on {module.device}."
)
# This can happen for `transformer` models. CPU placement was added in
# https://github.com/huggingface/transformers/pull/33122. So, we guard this accordingly.
if is_loaded_in_4bit_bnb and device is not None and is_transformers_version(">", "4.44.0"):
module.to(device=device)
elif not is_loaded_in_4bit_bnb and not is_loaded_in_8bit_bnb:
module.to(device, dtype) | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
if (
module.dtype == torch.float16
and str(device) in ["cpu"]
and not silence_dtype_warnings
and not is_offloaded
):
logger.warning(
"Pipelines loaded with `dtype=torch.float16` cannot run with `cpu` device. It"
" is not recommended to move them to `cpu` as running them will fail. Please make"
" sure to use an accelerator to run the pipeline in inference, due to the lack of"
" support for`float16` operations on this device in PyTorch. Please, remove the"
" `torch_dtype=torch.float16` argument, or use another device for inference."
)
return self | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
@property
def device(self) -> torch.device:
r"""
Returns:
`torch.device`: The torch device on which the pipeline is located.
"""
module_names, _ = self._get_signature_keys(self)
modules = [getattr(self, n, None) for n in module_names]
modules = [m for m in modules if isinstance(m, torch.nn.Module)]
for module in modules:
return module.device
return torch.device("cpu")
@property
def dtype(self) -> torch.dtype:
r"""
Returns:
`torch.dtype`: The torch dtype on which the pipeline is located.
"""
module_names, _ = self._get_signature_keys(self)
modules = [getattr(self, n, None) for n in module_names]
modules = [m for m in modules if isinstance(m, torch.nn.Module)]
for module in modules:
return module.dtype
return torch.float32 | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
@classmethod
@validate_hf_hub_args
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
r"""
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 stable-diffusion-v1-5/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.
``` | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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
[`~DiffusionPipeline.save_pretrained`].
- A path to a *directory* (for example `./my_pipeline_directory/`) containing a dduf file
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*):
<Tip warning={true}> | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
🧪 This is an experimental feature and may change in the future.
</Tip>
Can be either: | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
- 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](https://github.com/huggingface/diffusers/tree/main/examples/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. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
For more information on how to load and create custom pipelines, please have a look at [Loading and
Adding Custom
Pipelines](https://huggingface.co./docs/diffusers/using-diffusers/custom_pipeline_overview)
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. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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.
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"`): | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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 to
`revision` when 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 (`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 | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device. | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#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` | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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`): | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |
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`.
dduf_file(`str`, *optional*): | 39 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pipeline_utils.py |