# AutoPipelineBlocks

[AutoPipelineBlocks](/docs/diffusers/main/en/api/modular_diffusers/pipeline_blocks#diffusers.modular_pipelines.AutoPipelineBlocks) are a multi-block type containing blocks that support different workflows. It automatically selects which sub-blocks to run based on the input provided at runtime. This is typically used to package multiple workflows - text-to-image, image-to-image, inpaint - into a single pipeline for convenience.

This guide shows how to create [AutoPipelineBlocks](/docs/diffusers/main/en/api/modular_diffusers/pipeline_blocks#diffusers.modular_pipelines.AutoPipelineBlocks).

Create three [ModularPipelineBlocks](/docs/diffusers/main/en/api/modular_diffusers/pipeline_blocks#diffusers.ModularPipelineBlocks) for text-to-image, image-to-image, and inpainting. These represent the different workflows available in the pipeline.

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

```py
import torch
from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam

class TextToImageBlock(ModularPipelineBlocks):
    model_name = "text2img"

    @property
    def inputs(self):
        return [InputParam(name="prompt")]

    @property
    def intermediate_outputs(self):
        return []

    @property
    def description(self):
        return "I'm a text-to-image workflow!"

    def __call__(self, components, state):
        block_state = self.get_block_state(state)
        print("running the text-to-image workflow")
        # Add your text-to-image logic here
        # For example: generate image from prompt
        self.set_block_state(state, block_state)
        return components, state
```


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

```py
class ImageToImageBlock(ModularPipelineBlocks):
    model_name = "img2img"

    @property
    def inputs(self):
        return [InputParam(name="prompt"), InputParam(name="image")]

    @property
    def intermediate_outputs(self):
        return []

    @property
    def description(self):
        return "I'm an image-to-image workflow!"

    def __call__(self, components, state):
        block_state = self.get_block_state(state)
        print("running the image-to-image workflow")
        # Add your image-to-image logic here
        # For example: transform input image based on prompt
        self.set_block_state(state, block_state)
        return components, state
```


</hfoption>
<hfoption id="inpaint">

```py
class InpaintBlock(ModularPipelineBlocks):
    model_name = "inpaint"

    @property
    def inputs(self):
        return [InputParam(name="prompt"), InputParam(name="image"), InputParam(name="mask")]

    @property
    def intermediate_outputs(self):
        return []

    @property
    def description(self):
        return "I'm an inpaint workflow!"

    def __call__(self, components, state):
        block_state = self.get_block_state(state)
        print("running the inpaint workflow")
        # Add your inpainting logic here
        # For example: fill masked areas based on prompt
        self.set_block_state(state, block_state)
        return components, state
```

</hfoption>
</hfoptions>

Create an [AutoPipelineBlocks](/docs/diffusers/main/en/api/modular_diffusers/pipeline_blocks#diffusers.modular_pipelines.AutoPipelineBlocks) class that includes a list of the sub-block classes and their corresponding block names.

You also need to include `block_trigger_inputs`, a list of input names that trigger the corresponding block. If a trigger input is provided at runtime, then that block is selected to run. Use `None` to specify the default block to run if no trigger inputs are detected.

Lastly, it is important to include a `description` that clearly explains which inputs trigger which workflow. This helps users understand how to run specific workflows.

```py
from diffusers.modular_pipelines import AutoPipelineBlocks

class AutoImageBlocks(AutoPipelineBlocks):
    # List of sub-block classes to choose from
    block_classes = [block_inpaint_cls, block_i2i_cls, block_t2i_cls]
    # Names for each block in the same order
    block_names = ["inpaint", "img2img", "text2img"]
    # Trigger inputs that determine which block to run
    # - "mask" triggers inpaint workflow
    # - "image" triggers img2img workflow (but only if mask is not provided)
    # - if none of above, runs the text2img workflow (default)
    block_trigger_inputs = ["mask", "image", None]
    # Description is extremely important for AutoPipelineBlocks

    def description(self):
        return (
            "Pipeline generates images given different types of conditions!\n"
            + "This is an auto pipeline block that works for text2img, img2img and inpainting tasks.\n"
            + " - inpaint workflow is run when `mask` is provided.\n"
            + " - img2img workflow is run when `image` is provided (but only when `mask` is not provided).\n"
            + " - text2img workflow is run when neither `image` nor `mask` is provided.\n"
        )
```

It is **very** important to include a `description` to avoid any confusion over how to run a block and what inputs are required. While [AutoPipelineBlocks](/docs/diffusers/main/en/api/modular_diffusers/pipeline_blocks#diffusers.modular_pipelines.AutoPipelineBlocks) are convenient, it's conditional logic may be difficult to figure out if it isn't properly explained.

Create an instance of `AutoImageBlocks`.

```py
auto_blocks = AutoImageBlocks()
```

For more complex compositions, such as nested [AutoPipelineBlocks](/docs/diffusers/main/en/api/modular_diffusers/pipeline_blocks#diffusers.modular_pipelines.AutoPipelineBlocks) blocks when they're used as sub-blocks in larger pipelines, use the `get_execution_blocks()` method to extract the a block that is actually run based on your input.

```py
auto_blocks.get_execution_blocks("mask")
```

<EditOnGithub source="https://github.com/huggingface/diffusers/blob/main/docs/source/en/modular_diffusers/auto_pipeline_blocks.md" />