diffusers/pr_13171/en/api/modular_diffusers/pipeline.md

Pipeline

ModularPipeline[[diffusers.ModularPipeline]]

diffusers.ModularPipeline[[diffusers.ModularPipeline]]

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Base class for all Modular pipelines.

> This is an experimental feature and is likely to change in the future.

from_pretraineddiffusers.ModularPipeline.from_pretrainedhttps://github.com/huggingface/diffusers/blob/vr_13171/src/diffusers/modular_pipelines/modular_pipeline.py#L1736[{"name": "pretrained_model_name_or_path", "val": ": str | os.PathLike | None"}, {"name": "trust_remote_code", "val": ": bool | None = None"}, {"name": "components_manager", "val": ": diffusers.modular_pipelines.components_manager.ComponentsManager | None = None"}, {"name": "collection", "val": ": str | None = None"}, {"name": "**kwargs", "val": ""}]- pretrained_model_name_or_path (str or os.PathLike, optional) -- Path to a pretrained pipeline configuration. It will first try to load config from modular_model_index.json, then fallback to model_index.json for compatibility with standard non-modular repositories. If the pretrained_model_name_or_path does not contain any pipeline config, it will be set to None during initialization.

• trust_remote_code (bool, optional) -- Whether to trust remote code when loading the pipeline, need to be set to True if you want to create pipeline blocks based on the custom code in pretrained_model_name_or_path
• components_manager (ComponentsManager, optional) -- ComponentsManager instance for managing multiple component cross different pipelines and apply offloading strategies.
• collection (str, optional) --` Collection name for organizing components in the ComponentsManager.0

Load a ModularPipeline from a huggingface hub repo.

Parameters:

blocks : ModularPipelineBlocks, the blocks to be used in the pipeline

get_component_spec[[diffusers.ModularPipeline.get_component_spec]]

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Returns:

• a copy of the ComponentSpec object for the given component name

load_components[[diffusers.ModularPipeline.load_components]]

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Load selected components from specs.

Parameters:

names : list of component names to load. If None, will load all components with default_creation_method == "from_pretrained". If provided as a list or string, will load only the specified components.

• **kwargs : additional kwargs to be passed to from_pretrained().Can be: - a single value to be applied to all components to be loaded, e.g. torch_dtype=torch.bfloat16 - a dict, e.g. torch_dtype={"unet": torch.bfloat16, "default": torch.float32} - if potentially override ComponentSpec if passed a different loading field in kwargs, e.g. pretrained_model_name_or_path, variant, revision, etc. - if potentially override ComponentSpec if passed a different loading field in kwargs, e.g. pretrained_model_name_or_path, variant, revision, etc.

register_components[[diffusers.ModularPipeline.register_components]]

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Register components with their corresponding specifications.

This method is responsible for:

1. Sets component objects as attributes on the loader (e.g., self.unet = unet)
2. Updates the config dict, which will be saved as modular_model_index.json during save_pretrained (only for from_pretrained components)
3. Adds components to the component manager if one is attached (only for from_pretrained components)

This method is called when:

• Components are first initialized in init:
  • from_pretrained components not loaded during init so they are registered as None;
  • non from_pretrained components are created during init and registered as the object itself
• Components are updated with the update_components() method: e.g. loader.update_components(unet=unet) or loader.update_components(guider=guider_spec)
• (from_pretrained) Components are loaded with the load_components() method: e.g. loader.load_components(names=["unet"]) or loader.load_components() to load all default components

Notes:

• When registering None for a component, it sets attribute to None but still syncs specs with the config dict, which will be saved as modular_model_index.json during save_pretrained
• component_specs are updated to match the new component outside of this method, e.g. in update_components() method

Parameters:

• **kwargs : Keyword arguments where keys are component names and values are component objects. E.g., register_components(unet=unet_model, text_encoder=encoder_model)

save_pretrained[[diffusers.ModularPipeline.save_pretrained]]

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Save the pipeline to a directory. It does not save components, you need to save them separately.

Parameters:

save_directory (str or os.PathLike) : Path to the directory where the pipeline will be saved.

push_to_hub (bool, optional) : Whether to push the pipeline to the huggingface hub.

• **kwargs : Additional arguments passed to save_config() method

to[[diffusers.ModularPipeline.to]]

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

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

Here are the ways to call to:

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

Parameters:

dtype (torch.dtype, optional) : Returns a pipeline with the specified dtype

device (torch.Device, optional) : Returns a pipeline with the specified device

silence_dtype_warnings (str, optional, defaults to False) : Whether to omit warnings if the target dtype is not compatible with the target device.

Returns:

[DiffusionPipeline](/docs/diffusers/pr_13171/en/api/pipelines/overview#diffusers.DiffusionPipeline)

The pipeline converted to specified dtype and/or dtype.

update_components[[diffusers.ModularPipeline.update_components]]

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Update components and configuration values and specs after the pipeline has been instantiated.

This method allows you to:

1. Replace existing components with new ones (e.g., updating self.unet or self.text_encoder)
2. Update configuration values (e.g., changing self.requires_safety_checker flag)

In addition to updating the components and configuration values as pipeline attributes, the method also updates:

• the corresponding specs in _component_specs and _config_specs
• the config dict, which will be saved as modular_model_index.json during save_pretrained

Examples:

# Update pre-trained model
pipeline.update_components(unet=new_unet_model, text_encoder=new_text_encoder)

# Update configuration values
pipeline.update_components(requires_safety_checker=False)

Notes:

• Components with trained weights should be loaded with AutoModel.from_pretrained() or ComponentSpec.load() so that loading specs are preserved for serialization.
• ConfigMixin objects without weights (e.g., schedulers, guiders) can be passed directly.

Parameters:

• **kwargs : Component objects or configuration values to update: - Component objects: Models loaded with AutoModel.from_pretrained() or ComponentSpec.load() are automatically tagged with loading information. ConfigMixin objects without weights (e.g., schedulers, guiders) can be passed directly. - Configuration values: Simple values to update configuration settings (e.g., requires_safety_checker=False)