diffusers/v0.43.0/zh/modular_diffusers/auto_pipeline_blocks.md

AutoPipelineBlocks

AutoPipelineBlocks 是一种多块类型，包含支持不同工作流的块。它会根据运行时提供的输入自动选择要运行的子块。这通常用于将多个工作流（如文生图、图生图、局部重绘）打包到单个流水线中，以方便使用。

本指南展示了如何创建 AutoPipelineBlocks。

创建三个 ModularPipelineBlocks，分别用于文本生成图像、图像生成图像和图像修复。这些代表了流水线中可用的不同工作流。

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

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

创建一个 AutoPipelineBlocks 类，其中包含子块类及其对应块名的列表。

您还需要包含 block_trigger_inputs，这是一个输入名称列表，用于触发对应的 block。如果在运行时提供了某个触发输入，则会选择该 block 来运行。使用 None 来指定在未检测到任何触发输入时要运行的默认 block。

最后，重要的是包含一个 description，清楚地说明哪些输入会触发哪些工作流。这有助于用户了解如何运行特定的工作流。

from diffusers.modular_pipelines import AutoPipelineBlocks

class AutoImageBlocks(AutoPipelineBlocks):
    # List of sub-block classes to choose from
    block_classes = [InpaintBlock, ImageToImageBlock, TextToImageBlock]
    # 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]

    @property
    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"
        )

包含 description 以明确如何运行该 block 以及需要哪些输入，这一点非常重要。虽然 AutoPipelineBlocks 用起来很方便，但如果缺乏清晰的说明，其条件逻辑可能难以理解。

创建一个 AutoImageBlocks 实例。

auto_blocks = AutoImageBlocks()

对于更复杂的组合，例如在大型流水线中作为子块使用的嵌套 AutoPipelineBlocks 块，请使用 get_execution_blocks() 方法提取根据输入实际执行的块。

auto_blocks.get_execution_blocks(mask=True)

ConditionalPipelineBlocks

AutoPipelineBlocks 是 ConditionalPipelineBlocks 的一个特例。AutoPipelineBlocks 根据是否提供了触发输入来选择块，而 ConditionalPipelineBlocks 则能够基于在 select_block 方法中提供的自定义选择逻辑来选择块。

以下是直接使用 ConditionalPipelineBlocks 编写的相同示例：

from diffusers.modular_pipelines import ConditionalPipelineBlocks

class AutoImageBlocks(ConditionalPipelineBlocks):
    block_classes = [InpaintBlock, ImageToImageBlock, TextToImageBlock]
    block_names = ["inpaint", "img2img", "text2img"]
    block_trigger_inputs = ["mask", "image"]
    default_block_name = "text2img"

    @property
    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"
        )

    def select_block(self, mask=None, image=None) -> str | None:
        if mask is not None:
            return "inpaint"
        if image is not None:
            return "img2img"
        return None  # falls back to default_block_name ("text2img")

block_trigger_inputs 中列出的输入会作为关键字参数传递给 select_block()。当 select_block 返回 None 时，将回退到 default_block_name。如果 default_block_name 也是 None，则整个条件块会被跳过——这对于仅在提供了特定输入时才应执行的可选处理步骤非常有用。

工作流程

包含条件块（AutoPipelineBlocks 或 ConditionalPipelineBlocks）的 Pipeline 可以支持多种工作流——例如，我们的 SDXL 模块化 Pipeline 在单个 Pipeline 中支持十余种工作流。但这也意味着，用户可能难以了解支持哪些工作流以及如何运行它们。对于 Pipeline 构建者而言，能够仅提取与特定工作流相关的块会非常有用。

我们建议定义一个 _workflow_map，为每个工作流指定名称，并明确列出其所需的输入。

from diffusers.modular_pipelines import SequentialPipelineBlocks

class MyPipelineBlocks(SequentialPipelineBlocks):
    block_classes = [TextEncoderBlock, AutoImageBlocks, DecodeBlock]
    block_names = ["text_encoder", "auto_image", "decode"]

    _workflow_map = {
        "text2image": {"prompt": True},
        "image2image": {"image": True, "prompt": True},
        "inpaint": {"mask": True, "image": True, "prompt": True},
    }

我们所有内置的模块化流水线都带有预定义的工作流。available_workflows 属性列出了所有支持的工作流：

pipeline_blocks = MyPipelineBlocks()
pipeline_blocks.available_workflows
# ['text2image', 'image2image', 'inpaint']

使用 get_workflow 获取特定工作流，以检查并调试执行该工作流的特定代码块。

pipeline_blocks.get_workflow("inpaint")