def METHOD_NAME(self):
    pipeline_prior = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-prior")
    pipeline_prior_connected = DiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-random-kandinsky-v22-prior", load_connected_pipeline=True
    )
    # Passing `load_connected_pipeline` to prior is a no-op as the pipeline has no connected pipeline
    assert pipeline_prior.__class__ == pipeline_prior_connected.__class__
    pipeline = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-decoder")
    pipeline_connected = DiffusionPipeline.from_pretrained(
        "hf-internal-testing/tiny-random-kandinsky-v22-decoder", load_connected_pipeline=True
    )
    # Passing `load_connected_pipeline` to decoder loads the combined pipeline
    assert pipeline.__class__ != pipeline_connected.__class__
    assert pipeline.__class__ == KandinskyV22Pipeline
    assert pipeline_connected.__class__ == KandinskyV22CombinedPipeline
    # check that loaded components match prior and decoder components
    assert set(pipeline_connected.components.keys()) == set(
        ["prior_" + k for k in pipeline_prior.components.keys()] + list(pipeline.components.keys())
    )