AutoPipelineBlocks

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.

Create three ModularPipelineBlocks for text-to-image, image-to-image, and inpainting. These represent the different workflows available in the pipeline.

import mindspore as ms
from mindone.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

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

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

Create an 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.

from mindone.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 are convenient, it's conditional logic may be difficult to figure out if it isn't properly explained.

Create an instance of AutoImageBlocks.

auto_blocks = AutoImageBlocks()

For more complex compositions, such as nested 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.

auto_blocks.get_execution_blocks("mask")