def convert_ldm_unet_checkpoint(unet_state_dict, layers_per_block=2):
    """
    Takes a state dict and a config, and returns a converted checkpoint.
    """
    new_checkpoint = {}

    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict[
        "time_embed.0.weight"
    ]
    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict[
        "time_embed.0.bias"
    ]
    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict[
        "time_embed.2.weight"
    ]
    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict[
        "time_embed.2.bias"
    ]

    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]

    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]

    # Retrieves the keys for the input blocks only
    num_input_blocks = len(
        {
            ".".join(layer.split(".")[:2])
            for layer in unet_state_dict
            if "input_blocks" in layer
        }
    )
    input_blocks = {
        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
        for layer_id in range(num_input_blocks)
    }

    # Retrieves the keys for the middle blocks only
    num_middle_blocks = len(
        {
            ".".join(layer.split(".")[:2])
            for layer in unet_state_dict
            if "middle_block" in layer
        }
    )
    middle_blocks = {
        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
        for layer_id in range(num_middle_blocks)
    }

    # Retrieves the keys for the output blocks only
    num_output_blocks = len(
        {
            ".".join(layer.split(".")[:2])
            for layer in unet_state_dict
            if "output_blocks" in layer
        }
    )
    output_blocks = {
        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
        for layer_id in range(num_output_blocks)
    }

    for i in range(1, num_input_blocks):
        block_id = (i - 1) // (layers_per_block + 1)
        layer_in_block_id = (i - 1) % (layers_per_block + 1)

        resnets = [
            key
            for key in input_blocks[i]
            if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
        ]
        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = (
                unet_state_dict.pop(f"input_blocks.{i}.0.op.weight")
            )
            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = (
                unet_state_dict.pop(f"input_blocks.{i}.0.op.bias")
            )

        paths = renew_resnet_paths(resnets)
        meta_path = {
            "old": f"input_blocks.{i}.0",
            "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}",
        }
        assign_to_checkpoint(
            paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path]
        )

        if len(attentions):
            paths = renew_attention_paths(attentions)
            meta_path = {
                "old": f"input_blocks.{i}.1",
                "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
            }
            assign_to_checkpoint(
                paths,
                new_checkpoint,
                unet_state_dict,
                additional_replacements=[meta_path],
            )

    resnet_0 = middle_blocks[0]
    attentions = middle_blocks[1]
    resnet_1 = middle_blocks[2]

    resnet_0_paths = renew_resnet_paths(resnet_0)
    assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict)

    resnet_1_paths = renew_resnet_paths(resnet_1)
    assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict)

    attentions_paths = renew_attention_paths(attentions)
    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(
        attentions_paths,
        new_checkpoint,
        unet_state_dict,
        additional_replacements=[meta_path],
    )

    for i in range(num_output_blocks):
        block_id = i // (layers_per_block + 1)
        layer_in_block_id = i % (layers_per_block + 1)
        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
        output_block_list = {}

        for layer in output_block_layers:
            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(layer_name)
            else:
                output_block_list[layer_id] = [layer_name]

        if len(output_block_list) > 1:
            resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
            attentions = [
                key for key in output_blocks[i] if f"output_blocks.{i}.1" in key
            ]

            resnet_0_paths = renew_resnet_paths(resnets)
            paths = renew_resnet_paths(resnets)

            meta_path = {
                "old": f"output_blocks.{i}.0",
                "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}",
            }
            assign_to_checkpoint(
                paths,
                new_checkpoint,
                unet_state_dict,
                additional_replacements=[meta_path],
            )

            output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
            if ["conv.bias", "conv.weight"] in output_block_list.values():
                index = list(output_block_list.values()).index(
                    ["conv.bias", "conv.weight"]
                )
                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = (
                    unet_state_dict[f"output_blocks.{i}.{index}.conv.weight"]
                )
                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = (
                    unet_state_dict[f"output_blocks.{i}.{index}.conv.bias"]
                )

                # Clear attentions as they have been attributed above.
                if len(attentions) == 2:
                    attentions = []

            if len(attentions):
                paths = renew_attention_paths(attentions)
                meta_path = {
                    "old": f"output_blocks.{i}.1",
                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                }
                assign_to_checkpoint(
                    paths,
                    new_checkpoint,
                    unet_state_dict,
                    additional_replacements=[meta_path],
                )
        else:
            resnet_0_paths = renew_resnet_paths(
                output_block_layers, n_shave_prefix_segments=1
            )
            for path in resnet_0_paths:
                old_path = ".".join(["output_blocks", str(i), path["old"]])
                new_path = ".".join(
                    [
                        "up_blocks",
                        str(block_id),
                        "resnets",
                        str(layer_in_block_id),
                        path["new"],
                    ]
                )

                new_checkpoint[new_path] = unet_state_dict[old_path]

    return new_checkpoint
Takes a state dict and a config, and returns a converted checkpoint.
examples/05_stable_diffusion/src/pipeline_stable_diffusion_controlnet_ait.py