States¶

Blocks rely on the PipelineState and BlockState data structures for communicating and sharing data.

State	Description
`PipelineState`	Maintains the overall data required for a pipeline's execution and allows blocks to read and update its data.
`BlockState`	Allows each block to perform its computation with the necessary data from `inputs`

This guide explains how states work and how they connect blocks.

PipelineState¶

The PipelineState is a global state container for all blocks. It maintains the complete runtime state of the pipeline and provides a structured way for blocks to read from and write to shared data.

There are two dict's in PipelineState for structuring data.

The values dict is a mutable state containing a copy of user provided input values and intermediate output values generated by blocks. If a block modifies an input, it will be reflected in the values dict after calling set_block_state.

PipelineState(
  values={
    'prompt': 'a cat'
    'guidance_scale': 7.0
    'num_inference_steps': 25
    'prompt_embeds': tensor(dtype=ms.float32, shape=[1, 1, 1, 1])
    'negative_prompt_embeds': None
  },
)

BlockState¶

The BlockState is a local view of the relevant variables an individual block needs from PipelineState for performing it's computations.

Access these variables directly as attributes like block_state.image.

BlockState(
    image: <PIL.Image.Image image mode=RGB size=512x512 at 0x7F3ECC494640>
)

When a block's __call__ method is executed, it retrieves the BlockState with self.get_block_state(state), performs it's operations, and updates PipelineState with self.set_block_state(state, block_state).

def __call__(self, components, state):
    # retrieve BlockState
    block_state = self.get_block_state(state)

    # computation logic on inputs

    # update PipelineState
    self.set_block_state(state, block_state)
    return components, state

State interaction¶

PipelineState and BlockState interaction is defined by a block's inputs, and intermediate_outputs.

inputs, a block can modify an input - like block_state.image - and this change can be propagated globally to PipelineState by calling set_block_state.
intermediate_outputs, is a new variable that a block creates. It is added to the PipelineState's values dict and is available as for subsequent blocks or accessed by users as a final output from the pipeline.