pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)  ControlNetModel  class diffusers.ControlNetModel  < source > ( in_channels: int = 4 conditioning_channels: int = 3 flip_sin_to_cos: bool = True freq_shift: int = 0 down_block_types: Tuple = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D') mid_block_type: Optional = 'UNetMidBlock2DCrossAttn' only_cross_attention: Union = False block_out_channels: Tuple = (320, 640, 1280, 1280) layers_per_block: int = 2 downsample_padding: int = 1 mid_block_scale_factor: float = 1 act_fn: str = 'silu' norm_num_groups: Optional = 32 norm_eps: float = 1e-05 cross_attention_dim: int = 1280 transformer_layers_per_block: Union = 1 encoder_hid_dim: Optional = None encoder_hid_dim_type: Optional = None attention_head_dim: Union = 8 num_attention_heads: Union = None use_linear_projection: bool = False class_embed_type: Optional = None addition_embed_type: Optional = None addition_time_embed_dim: Optional = None num_class_embeds: Optional = None upcast_attention: bool = False resnet_time_scale_shift: str = 'default' projection_class_embeddings_input_dim: Optional = None controlnet_conditioning_channel_order: str = 'rgb' conditioning_embedding_out_channels: Optional = (16, 32, 96, 256) global_pool_conditions: bool = False addition_embed_type_num_heads: int = 64  )   Parameters   in_channels (int, defaults to 4) —