docs/source/en/modular_diffusers/modular_pipeline.md

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# ModularPipeline

[`ModularPipeline`] converts [`~modular_pipelines.ModularPipelineBlocks`]'s into an executable pipeline that loads models and performs the computation steps defined in the block. It is the main interface for running a pipeline and it is very similar to the [`DiffusionPipeline`] API.

The main difference is to include an expected `output` argument in the pipeline.

<hfoptions id="example">
<hfoption id="text-to-image">

```py
import torch
from diffusers.modular_pipelines import SequentialPipelineBlocks
from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS

blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS)

modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
pipeline = blocks.init_pipeline(modular_repo_id)

pipeline.load_components(torch_dtype=torch.float16)
pipeline.to("cuda")

image = pipeline(prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", output="images")[0]
image.save("modular_t2i_out.png")
```

</hfoption>
<hfoption id="image-to-image">

```py
import torch
from diffusers.modular_pipelines import SequentialPipelineBlocks
from diffusers.modular_pipelines.stable_diffusion_xl import IMAGE2IMAGE_BLOCKS

blocks = SequentialPipelineBlocks.from_blocks_dict(IMAGE2IMAGE_BLOCKS)

modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
pipeline = blocks.init_pipeline(modular_repo_id)

pipeline.load_components(torch_dtype=torch.float16)
pipeline.to("cuda")

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
init_image = load_image(url)
prompt = "a dog catching a frisbee in the jungle"
image = pipeline(prompt=prompt, image=init_image, strength=0.8, output="images")[0]
image.save("modular_i2i_out.png")
```

</hfoption>
<hfoption id="inpainting">

```py
import torch
from diffusers.modular_pipelines import SequentialPipelineBlocks
from diffusers.modular_pipelines.stable_diffusion_xl import INPAINT_BLOCKS
from diffusers.utils import load_image

blocks = SequentialPipelineBlocks.from_blocks_dict(INPAINT_BLOCKS)

modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
pipeline = blocks.init_pipeline(modular_repo_id)

pipeline.load_components(torch_dtype=torch.float16)
pipeline.to("cuda")

img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
mask_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png"

init_image = load_image(img_url)
mask_image = load_image(mask_url)

prompt = "A deep sea diver floating"
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, output="images")[0]
image.save("moduar_inpaint_out.png")
```

</hfoption>
</hfoptions>

This guide will show you how to create a [`ModularPipeline`] and manage the components in it.

## Adding blocks

Blocks are [`InsertableDict`] objects that can be inserted at specific positions, providing a flexible way to mix-and-match blocks.

Use [`~modular_pipelines.modular_pipeline_utils.InsertableDict.insert`] on either the block class or `sub_blocks` attribute to add a block.

```py
# BLOCKS is dict of block classes, you need to add class to it
BLOCKS.insert("block_name", BlockClass, index)
# sub_blocks attribute contains instance, add a block instance to the  attribute
t2i_blocks.sub_blocks.insert("block_name", block_instance, index)
```

Use [`~modular_pipelines.modular_pipeline_utils.InsertableDict.pop`] on either the block class or `sub_blocks` attribute to remove a block.

```py
# remove a block class from preset
BLOCKS.pop("text_encoder")
# split out a block instance on its own
text_encoder_block = t2i_blocks.sub_blocks.pop("text_encoder")
```

Swap blocks by setting the existing block to the new block.

```py
# Replace block class in preset
BLOCKS["prepare_latents"] = CustomPrepareLatents
# Replace in sub_blocks attribute using an block instance
t2i_blocks.sub_blocks["prepare_latents"] = CustomPrepareLatents()
```

## Creating a pipeline

There are two ways to create a [`ModularPipeline`]. Assemble and create a pipeline from [`ModularPipelineBlocks`] or load an existing pipeline with [`~ModularPipeline.from_pretrained`].

You should also initialize a [`ComponentsManager`] to handle device placement and memory and component management.

> [!TIP]
> Refer to the [ComponentsManager](./components_manager) doc for more details about how it can help manage components across different workflows.

<hfoptions id="create">
<hfoption id="ModularPipelineBlocks">

Use the [`~ModularPipelineBlocks.init_pipeline`] method to create a [`ModularPipeline`] from the component and configuration specifications. This method loads the *specifications* from a `modular_model_index.json` file, but it doesn't load the *models* yet.

```py
from diffusers import ComponentsManager
from diffusers.modular_pipelines import SequentialPipelineBlocks
from diffusers.modular_pipelines.stable_diffusion_xl import TEXT2IMAGE_BLOCKS

t2i_blocks = SequentialPipelineBlocks.from_blocks_dict(TEXT2IMAGE_BLOCKS)

modular_repo_id = "YiYiXu/modular-loader-t2i-0704"
components = ComponentsManager()
t2i_pipeline = t2i_blocks.init_pipeline(modular_repo_id, components_manager=components)
```

</hfoption>
<hfoption id="from_pretrained">

The [`~ModularPipeline.from_pretrained`] method creates a [`ModularPipeline`] from a modular repository on the Hub.

```py
from diffusers import ModularPipeline, ComponentsManager

components = ComponentsManager()
pipeline = ModularPipeline.from_pretrained("YiYiXu/modular-loader-t2i-0704", components_manager=components)
```

Add the `trust_remote_code` argument to load a custom [`ModularPipeline`].

```py
from diffusers import ModularPipeline, ComponentsManager

components = ComponentsManager()
modular_repo_id = "YiYiXu/modular-diffdiff-0704"
diffdiff_pipeline = ModularPipeline.from_pretrained(modular_repo_id, trust_remote_code=True, components_manager=components)
```

</hfoption>
</hfoptions>

## Loading components

A [`ModularPipeline`] doesn't automatically instantiate with components. It only loads the configuration and component specifications. You can load all components with [`~ModularPipeline.load_components`] or only load specific components with [`~ModularPipeline.load_components`].

<hfoptions id="load">
<hfoption id="load_components">

```py
import torch

t2i_pipeline.load_components(torch_dtype=torch.float16)
t2i_pipeline.to("cuda")
```

</hfoption>
<hfoption id="load_components">

The example below only loads the UNet and VAE.

```py
import torch

t2i_pipeline.load_components(names=["unet", "vae"], torch_dtype=torch.float16)
```

</hfoption>
</hfoptions>

Print the pipeline to inspect the loaded pretrained components.

```py
t2i_pipeline
```

This should match the `modular_model_index.json` file from the modular repository a pipeline is initialized from. If a pipeline doesn't need a component, it won't be included even if it exists in the modular repository.

To modify where components are loaded from, edit the `modular_model_index.json` file in the repository and change it to your desired loading path. The example below loads a UNet from a different repository.

```json
# original
"unet": [
  null, null,
  {
    "repo": "stabilityai/stable-diffusion-xl-base-1.0",
    "subfolder": "unet",
    "variant": "fp16"
  }
]

# modified
"unet": [
  null, null,
  {
    "repo": "RunDiffusion/Juggernaut-XL-v9",
    "subfolder": "unet",
    "variant": "fp16"
  }
]
```

### Component loading status

The pipeline properties below provide more information about which components are loaded.

Use `component_names` to return all expected components.

```py
t2i_pipeline.component_names
['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'guider', 'scheduler', 'unet', 'vae', 'image_processor']
```

Use `null_component_names` to return components that aren't loaded yet. Load these components with [`~ModularPipeline.from_pretrained`].

```py
t2i_pipeline.null_component_names
['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'scheduler']
```

Use `pretrained_component_names` to return components that will be loaded from pretrained models.

```py
t2i_pipeline.pretrained_component_names
['text_encoder', 'text_encoder_2', 'tokenizer', 'tokenizer_2', 'scheduler', 'unet', 'vae']
```

Use `config_component_names` to return components that are created with the default config (not loaded from a modular repository). Components from a config aren't included because they are already initialized during pipeline creation. This is why they aren't listed in `null_component_names`.

```py
t2i_pipeline.config_component_names
['guider', 'image_processor']
```

## Updating components

Components may be updated depending on whether it is a *pretrained component* or a *config component*.

> [!WARNING]
> A component may change from pretrained to config when updating a component. The component type is initially defined in a block's `expected_components` field.

A pretrained component is updated with [`ComponentSpec`] whereas a config component is updated by eihter passing the object directly or with [`ComponentSpec`].

The [`ComponentSpec`] shows `default_creation_method="from_pretrained"` for a pretrained component shows `default_creation_method="from_config` for a config component.

To update a pretrained component, create a [`ComponentSpec`] with the name of the component and where to load it from. Use the [`~ComponentSpec.load`] method to load the component.

```py
from diffusers import ComponentSpec, UNet2DConditionModel

unet_spec = ComponentSpec(name="unet",type_hint=UNet2DConditionModel, repo="stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", variant="fp16")
unet = unet_spec.load(torch_dtype=torch.float16)
```

The [`~ModularPipeline.update_components`] method replaces the component with a new one.

```py
t2i_pipeline.update_components(unet=unet2)
```

When a component is updated, the loading specifications are also updated in the pipeline config.

### Component extraction and modification

When you use [`~ComponentSpec.load`], the new component maintains its loading specifications. This makes it possible to extract the specification and recreate the component.

```py
spec = ComponentSpec.from_component("unet", unet2)
spec
ComponentSpec(name='unet', type_hint=<class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>, description=None, config=None, repo='stabilityai/stable-diffusion-xl-base-1.0', subfolder='unet', variant='fp16', revision=None, default_creation_method='from_pretrained')
unet2_recreated = spec.load(torch_dtype=torch.float16)
```

The [`~ModularPipeline.get_component_spec`] method gets a copy of the current component specification to modify or update.

```py
unet_spec = t2i_pipeline.get_component_spec("unet")
unet_spec
ComponentSpec(
    name='unet',
    type_hint=<class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>,
    pretrained_model_name_or_path='RunDiffusion/Juggernaut-XL-v9',
    subfolder='unet',
    variant='fp16',
    default_creation_method='from_pretrained'
)

# modify to load from a different repository
unet_spec.pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"

# load component with modified spec
unet = unet_spec.load(torch_dtype=torch.float16)
```

## Modular repository

A repository is required if the pipeline blocks use *pretrained components*. The repository supplies loading specifications and metadata.

[`ModularPipeline`] specifically requires *modular repositories* (see [example repository](https://huggingface.co/YiYiXu/modular-diffdiff)) which are more flexible than a typical repository. It contains a `modular_model_index.json` file containing the following 3 elements.

- `library` and `class` shows which library the component was loaded from and it's class. If `null`, the component hasn't been loaded yet.
- `loading_specs_dict` contains the information required to load the component such as the repository and subfolder it is loaded from.

Unlike standard repositories, a modular repository can fetch components from different repositories based on the `loading_specs_dict`. Components don't need to exist in the same repository.

A modular repository may contain custom code for loading a [`ModularPipeline`]. This allows you to use specialized blocks that aren't native to Diffusers.

```
modular-diffdiff-0704/
├── block.py                    # Custom pipeline blocks implementation
├── config.json                 # Pipeline configuration and auto_map
└── modular_model_index.json    # Component loading specifications
```

The [config.json](https://huggingface.co/YiYiXu/modular-diffdiff-0704/blob/main/config.json) file contains an `auto_map` key that points to where a custom block is defined in `block.py`.

```json
{
  "_class_name": "DiffDiffBlocks",
  "auto_map": {
    "ModularPipelineBlocks": "block.DiffDiffBlocks"
  }
}
```