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

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

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

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

    # Retrieves the keys for the input blocks only
    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "input_blocks" in layer})
    input_blocks = {
        layer_id: [key for key in checkpoint 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 checkpoint if "middle_block" in layer})
    middle_blocks = {
        layer_id: [key for key in checkpoint 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 checkpoint if "output_blocks" in layer})
    output_blocks = {
        layer_id: [key for key in checkpoint 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) // (config["num_res_blocks"] + 1)
        layer_in_block_id = (i - 1) % (config["num_res_blocks"] + 1)

        resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" 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 checkpoint:
            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = checkpoint[
                f"input_blocks.{i}.0.op.weight"
            ]
            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = checkpoint[
                f"input_blocks.{i}.0.op.bias"
            ]
            continue

        paths = renew_resnet_paths(resnets)
        meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
        resnet_op = {"old": "resnets.2.op", "new": "downsamplers.0.op"}
        assign_to_checkpoint(
            paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config
        )

        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}",
            }
            to_split = {
                f"input_blocks.{i}.1.qkv.bias": {
                    "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                    "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                    "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                },
                f"input_blocks.{i}.1.qkv.weight": {
                    "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                    "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                    "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                },
            }
            assign_to_checkpoint(
                paths,
                new_checkpoint,
                checkpoint,
                additional_replacements=[meta_path],
                attention_paths_to_split=to_split,
                config=config,
            )

    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, checkpoint, config=config)

    resnet_1_paths = renew_resnet_paths(resnet_1)
    assign_to_checkpoint(resnet_1_paths, new_checkpoint, checkpoint, config=config)

    attentions_paths = renew_attention_paths(attentions)
    to_split = {
        "middle_block.1.qkv.bias": {
            "key": "mid_block.attentions.0.key.bias",
            "query": "mid_block.attentions.0.query.bias",
            "value": "mid_block.attentions.0.value.bias",
        },
        "middle_block.1.qkv.weight": {
            "key": "mid_block.attentions.0.key.weight",
            "query": "mid_block.attentions.0.query.weight",
            "value": "mid_block.attentions.0.value.weight",
        },
    }
    assign_to_checkpoint(
        attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config
    )

    for i in range(num_output_blocks):
        block_id = i // (config["num_res_blocks"] + 1)
        layer_in_block_id = i % (config["num_res_blocks"] + 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, checkpoint, additional_replacements=[meta_path], config=config)

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[
                    f"output_blocks.{i}.{index}.conv.weight"
                ]
                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[
                    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}",
                }
                to_split = {
                    f"output_blocks.{i}.1.qkv.bias": {
                        "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                        "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                        "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                    },
                    f"output_blocks.{i}.1.qkv.weight": {
                        "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                        "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                        "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                    },
                }
                assign_to_checkpoint(
                    paths,
                    new_checkpoint,
                    checkpoint,
                    additional_replacements=[meta_path],
                    attention_paths_to_split=to_split if any("qkv" in key for key in attentions) else None,
                    config=config,
                )
        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] = checkpoint[old_path]

    return new_checkpoint

    Takes a state dict and a config, and returns a converted checkpoint.
    
promptist/diffusers/scripts/convert_ldm_original_checkpoint_to_diffusers.py