#
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=torch.float32, shape=torch.Size([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.

Update on GitHub