def METHOD_NAME(positive, negative, dtype):
    """loads additional models in positive and negative conditioning"""
    control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))
    inference_memory = 0
    control_models = []
    for m in control_nets:
        control_models += m.get_models()
        inference_memory += m.inference_memory_requirements(dtype)
    gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen")
    gligen = [x[1] for x in gligen]
    models = control_models + gligen
    return models, inference_memory