Output can be used to condition the original UNet’s middle block activation.    The output of ControlNetModel.  FlaxControlNetModel  class diffusers.FlaxControlNetModel  < source > ( sample_size: int = 32 in_channels: int = 4 down_block_types: Tuple = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D') only_cross_attention: Union = False block_out_channels: Tuple = (320, 640, 1280, 1280) layers_per_block: int = 2 attention_head_dim: Union = 8 num_attention_heads: Union = None cross_attention_dim: int = 1280 dropout: float = 0.0 use_linear_projection: bool = False dtype: dtype = <class 'jax.numpy.float32'> flip_sin_to_cos: bool = True freq_shift: int = 0 controlnet_conditioning_channel_order: str = 'rgb' conditioning_embedding_out_channels: Tuple = (16, 32, 96, 256) parent: Union = <flax.linen.module._Sentinel object at 0x7fbece3b2b00> name: Optional = None  )   Parameters   sample_size (int, optional) —