def create_unet_diffusers_config(original_config, image_size: int, controlnet=False):
    """
    Creates a config for the diffusers based on the config of the LDM model.
    """
    if controlnet:
        unet_params = original_config.model.params.control_stage_config.params
    else:
        if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None:
            unet_params = original_config.model.params.unet_config.params
        else:
            unet_params = original_config.model.params.network_config.params

    vae_params = original_config.model.params.first_stage_config.params.encoder_config.params

    block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]

    down_block_types = []
    resolution = 1
    for i in range(len(block_out_channels)):
        block_type = (
            "CrossAttnDownBlockSpatioTemporal"
            if resolution in unet_params.attention_resolutions
            else "DownBlockSpatioTemporal"
        )
        down_block_types.append(block_type)
        if i != len(block_out_channels) - 1:
            resolution *= 2

    up_block_types = []
    for i in range(len(block_out_channels)):
        block_type = (
            "CrossAttnUpBlockSpatioTemporal"
            if resolution in unet_params.attention_resolutions
            else "UpBlockSpatioTemporal"
        )
        up_block_types.append(block_type)
        resolution //= 2

    if unet_params.transformer_depth is not None:
        transformer_layers_per_block = (
            unet_params.transformer_depth
            if isinstance(unet_params.transformer_depth, int)
            else list(unet_params.transformer_depth)
        )
    else:
        transformer_layers_per_block = 1

    vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)

    head_dim = unet_params.num_heads if "num_heads" in unet_params else None
    use_linear_projection = (
        unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
    )
    if use_linear_projection:
        # stable diffusion 2-base-512 and 2-768
        if head_dim is None:
            head_dim_mult = unet_params.model_channels // unet_params.num_head_channels
            head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)]

    class_embed_type = None
    addition_embed_type = None
    addition_time_embed_dim = None
    projection_class_embeddings_input_dim = None
    context_dim = None

    if unet_params.context_dim is not None:
        context_dim = (
            unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0]
        )

    if "num_classes" in unet_params:
        if unet_params.num_classes == "sequential":
            addition_time_embed_dim = 256
            assert "adm_in_channels" in unet_params
            projection_class_embeddings_input_dim = unet_params.adm_in_channels

    config = {
        "sample_size": image_size // vae_scale_factor,
        "in_channels": unet_params.in_channels,
        "down_block_types": tuple(down_block_types),
        "block_out_channels": tuple(block_out_channels),
        "layers_per_block": unet_params.num_res_blocks,
        "cross_attention_dim": context_dim,
        "attention_head_dim": head_dim,
        "use_linear_projection": use_linear_projection,
        "class_embed_type": class_embed_type,
        "addition_embed_type": addition_embed_type,
        "addition_time_embed_dim": addition_time_embed_dim,
        "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
        "transformer_layers_per_block": transformer_layers_per_block,
    }

    if "disable_self_attentions" in unet_params:
        config["only_cross_attention"] = unet_params.disable_self_attentions

    if "num_classes" in unet_params and isinstance(unet_params.num_classes, int):
        config["num_class_embeds"] = unet_params.num_classes

    if controlnet:
        config["conditioning_channels"] = unet_params.hint_channels
    else:
        config["out_channels"] = unet_params.out_channels
        config["up_block_types"] = tuple(up_block_types)

    return config

    Creates a config for the diffusers based on the config of the LDM model.
    
scripts/convert_svd_to_diffusers.py