def convert_ldm_unet_checkpoint(checkpoint, config, extract_ema=False, **kwargs):
    """
    Takes a state dict and a config, and returns a converted checkpoint.
    """
    # extract state_dict for UNet
    unet_state_dict = {}
    keys = list(checkpoint.keys())
    unet_key = LDM_UNET_KEY

    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
    if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
        logger.warning("Checkpoint has both EMA and non-EMA weights.")
        logger.warning(
            "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
            " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
        )
        for key in keys:
            if key.startswith("model.diffusion_model"):
                flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
                unet_state_dict[key.replace(unet_key, "")] = checkpoint.get(flat_ema_key)
    else:
        if sum(k.startswith("model_ema") for k in keys) > 100:
            logger.warning(
                "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
                " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
            )
        for key in keys:
            if key.startswith(unet_key):
                unet_state_dict[key.replace(unet_key, "")] = checkpoint.get(key)

    new_checkpoint = {}
    ldm_unet_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["layers"]
    for diffusers_key, ldm_key in ldm_unet_keys.items():
        if ldm_key not in unet_state_dict:
            continue
        new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]

    if ("class_embed_type" in config) and (config["class_embed_type"] in ["timestep", "projection"]):
        class_embed_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["class_embed_type"]
        for diffusers_key, ldm_key in class_embed_keys.items():
            new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]

    if ("addition_embed_type" in config) and (config["addition_embed_type"] == "text_time"):
        addition_embed_keys = DIFFUSERS_TO_LDM_MAPPING["unet"]["addition_embed_type"]
        for diffusers_key, ldm_key in addition_embed_keys.items():
            new_checkpoint[diffusers_key] = unet_state_dict[ldm_key]

    # Relevant to StableDiffusionUpscalePipeline
    if "num_class_embeds" in config:
        if (config["num_class_embeds"] is not None) and ("label_emb.weight" in unet_state_dict):
            new_checkpoint["class_embedding.weight"] = unet_state_dict["label_emb.weight"]

    # 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)
    }

    # Down blocks
    for i in range(1, num_input_blocks):
        block_id = (i - 1) // (config["layers_per_block"] + 1)
        layer_in_block_id = (i - 1) % (config["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
        ]
        update_unet_resnet_ldm_to_diffusers(
            resnets,
            new_checkpoint,
            unet_state_dict,
            {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"},
        )

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

        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
        if attentions:
            update_unet_attention_ldm_to_diffusers(
                attentions,
                new_checkpoint,
                unet_state_dict,
                {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"},
            )

    # Mid blocks
    for key in middle_blocks.keys():
        diffusers_key = max(key - 1, 0)
        if key % 2 == 0:
            update_unet_resnet_ldm_to_diffusers(
                middle_blocks[key],
                new_checkpoint,
                unet_state_dict,
                mapping={"old": f"middle_block.{key}", "new": f"mid_block.resnets.{diffusers_key}"},
            )
        else:
            update_unet_attention_ldm_to_diffusers(
                middle_blocks[key],
                new_checkpoint,
                unet_state_dict,
                mapping={"old": f"middle_block.{key}", "new": f"mid_block.attentions.{diffusers_key}"},
            )

    # Up Blocks
    for i in range(num_output_blocks):
        block_id = i // (config["layers_per_block"] + 1)
        layer_in_block_id = i % (config["layers_per_block"] + 1)

        resnets = [
            key for key in output_blocks[i] if f"output_blocks.{i}.0" in key and f"output_blocks.{i}.0.op" not in key
        ]
        update_unet_resnet_ldm_to_diffusers(
            resnets,
            new_checkpoint,
            unet_state_dict,
            {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"},
        )

        attentions = [
            key for key in output_blocks[i] if f"output_blocks.{i}.1" in key and f"output_blocks.{i}.1.conv" not in key
        ]
        if attentions:
            update_unet_attention_ldm_to_diffusers(
                attentions,
                new_checkpoint,
                unet_state_dict,
                {"old": f"output_blocks.{i}.1", "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}"},
            )

        if f"output_blocks.{i}.1.conv.weight" in unet_state_dict:
            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
                f"output_blocks.{i}.1.conv.weight"
            ]
            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
                f"output_blocks.{i}.1.conv.bias"
            ]
        if f"output_blocks.{i}.2.conv.weight" in unet_state_dict:
            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
                f"output_blocks.{i}.2.conv.weight"
            ]
            new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
                f"output_blocks.{i}.2.conv.bias"
            ]

    return new_checkpoint

    Takes a state dict and a config, and returns a converted checkpoint.
    
src/diffusers/loaders/single_file_utils.py