# 顺序管道块 [`~modular_pipelines.SequentialPipelineBlocks`] 是一种多块类型，它将其他 [`~modular_pipelines.ModularPipelineBlocks`] 按顺序组合在一起。数据通过 `intermediate_inputs` 和 `intermediate_outputs` 线性地从一个块流向下一个块。[`~modular_pipelines.SequentialPipelineBlocks`] 中的每个块通常代表管道中的一个步骤，通过组合它们，您逐步构建一个管道。 本指南向您展示如何将两个块连接成一个 [`~modular_pipelines.SequentialPipelineBlocks`]。 创建两个 [`~modular_pipelines.ModularPipelineBlocks`]。第一个块 `InputBlock` 输出一个 `batch_size` 值，第二个块 `ImageEncoderBlock` 使用 `batch_size` 作为 `intermediate_inputs`。 ```py from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam class InputBlock(ModularPipelineBlocks): @property def inputs(self): return [ InputParam(name="prompt", type_hint=list, description="list of text prompts"), InputParam(name="num_images_per_prompt", type_hint=int, description="number of images per prompt"), ] @property def intermediate_outputs(self): return [ OutputParam(name="batch_size", description="calculated batch size"), ] @property def description(self): return "A block that determines batch_size based on the number of prompts and num_images_per_prompt argument." def __call__(self, components, state): block_state = self.get_block_state(state) batch_size = len(block_state.prompt) block_state.batch_size = batch_size * block_state.num_images_per_prompt self.set_block_state(state, block_state) return components, state ``` ```py import torch from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam class ImageEncoderBlock(ModularPipelineBlocks): @property def inputs(self): return [ InputParam(name="image", type_hint="PIL.Image", description="raw input image to process"), InputParam(name="batch_size", type_hint=int), ] @property def intermediate_outputs(self): return [ OutputParam(name="image_latents", description="latents representing the image" ] @property def description(self): return "Encode raw image into its latent presentation" def __call__(self, components, state): block_state = self.get_block_state(state) # 模拟处理图像 # 这将改变所有块的图像状态，从PIL图像变为张量 block_state.image = torch.randn(1, 3, 512, 512) block_state.batch_size = block_state.batch_size * 2 block_state.image_latents = torch.randn(1, 4, 64, 64) self.set_block_state(state, block_state) return components, state ``` 通过定义一个[`InsertableDict`]来连接两个块，将块名称映射到块实例。块按照它们在`blocks_dict`中注册的顺序执行。 使用[`~modular_pipelines.SequentialPipelineBlocks.from_blocks_dict`]来创建一个[`~modular_pipelines.SequentialPipelineBlocks`]。 ```py from diffusers.modular_pipelines import SequentialPipelineBlocks, InsertableDict blocks_dict = InsertableDict() blocks_dict["input"] = input_block blocks_dict["image_encoder"] = image_encoder_block blocks = SequentialPipelineBlocks.from_blocks_dict(blocks_dict) ``` 通过调用`blocks`来检查[`~modular_pipelines.SequentialPipelineBlocks`]中的子块，要获取更多关于输入和输出的详细信息，可以访问`docs`属性。 ```py print(blocks) print(blocks.doc) ```