Diffusers documentation
SequentialPipelineBlocks
SequentialPipelineBlocks
SequentialPipelineBlocks are a multi-block type that composes other ModularPipelineBlocks together in a sequence. Data flows linearly from one block to the next using intermediate_inputs and intermediate_outputs. Each block in SequentialPipelineBlocks usually represents a step in the pipeline, and by combining them, you gradually build a pipeline.
This guide shows you how to connect two blocks into a SequentialPipelineBlocks.
Create two ModularPipelineBlocks. The first block, InputBlock, outputs a batch_size value and the second block, ImageEncoderBlock uses batch_size as intermediate_inputs.
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, stateConnect the two blocks by defining an InsertableDict to map the block names to the block instances. Blocks are executed in the order they’re registered in blocks_dict.
Use from_blocks_dict() to create a SequentialPipelineBlocks.
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)Inspect the sub-blocks in SequentialPipelineBlocks by calling blocks, and for more details about the inputs and outputs, access the docs attribute.
print(blocks)
print(blocks.doc)