import logging
import os
import re
import sys
import urllib.parse
from functools import lru_cache, wraps

import requests
import torch
from huggingface_hub import (
    HfFolder,
    hf_hub_download as _hf_hub_download,
    try_to_load_from_cache,
)
from omegaconf import OmegaConf
from refiners.foundationals.latent_diffusion import SD1ControlnetAdapter, SD1UNet
from safetensors.torch import load_file

from imaginairy import config as iconfig
from imaginairy.config import MODEL_SHORT_NAMES
from imaginairy.modules import attention
from imaginairy.paths import PKG_ROOT
from imaginairy.utils import get_device, instantiate_from_config
from imaginairy.utils.model_cache import memory_managed_model
from imaginairy.weight_management.conversion import cast_weights

logger = logging.getLogger(__name__)

MOST_RECENTLY_LOADED_MODEL = None


class HuggingFaceAuthorizationError(RuntimeError):
    pass


def load_tensors(tensorfile, map_location=None):
    if tensorfile == "empty":
        # used for testing
        return {}
    if tensorfile.endswith((".ckpt", ".pth")):
        return torch.load(tensorfile, map_location=map_location)
    if tensorfile.endswith(".safetensors"):
        return load_file(tensorfile, device=map_location)

    return load_file(tensorfile, device=map_location)

    # raise ValueError(f"Unknown tensorfile type: {tensorfile}")


def load_state_dict(weights_location, half_mode=False, device=None):
    if device is None:
        device = get_device()

    if weights_location.startswith("http"):
        ckpt_path = get_cached_url_path(weights_location, category="weights")
    else:
        ckpt_path = weights_location
    logger.info(f"Loading model {ckpt_path} onto {get_device()} backend...")
    state_dict = None
    # weights_cache_key = (ckpt_path, half_mode)
    # if weights_cache_key in GLOBAL_WEIGHTS_CACHE:
    #     return GLOBAL_WEIGHTS_CACHE.get(weights_cache_key)

    try:
        state_dict = load_tensors(ckpt_path, map_location="cpu")
    except FileNotFoundError as e:
        if e.errno == 2:
            logger.error(
                f'Error: "{ckpt_path}" not a valid path to model weights.\nPreconfigured models you can use: {MODEL_SHORT_NAMES}.'
            )
            sys.exit(1)
        raise
    except RuntimeError as e:
        err_str = str(e)
        if (
            "PytorchStreamReader failed reading zip archive" in err_str
            and weights_location.startswith("http")
        ):
            logger.warning("Corrupt checkpoint. deleting and re-downloading...")
            os.remove(ckpt_path)
            ckpt_path = get_cached_url_path(weights_location, category="weights")
            state_dict = load_tensors(ckpt_path, map_location="cpu")
        if state_dict is None:
            raise

    state_dict = state_dict.get("state_dict", state_dict)

    if half_mode:
        state_dict = {k: v.half() for k, v in state_dict.items()}

    # change device
    state_dict = {k: v.to(device) for k, v in state_dict.items()}

    # GLOBAL_WEIGHTS_CACHE.set(weights_cache_key, state_dict)

    return state_dict


def load_model_from_config(config, weights_location, half_mode=False):
    model = instantiate_from_config(config.model)
    base_model_dict = load_state_dict(weights_location, half_mode=half_mode)
    model.init_from_state_dict(base_model_dict)
    if half_mode:
        model = model.half()
    model.to(get_device())
    model.eval()
    return model


def load_model_from_config_old(
    config, weights_location, control_weights_locations=None, half_mode=False
):
    model = instantiate_from_config(config.model)
    print("instantiated")
    base_model_dict = load_state_dict(weights_location, half_mode=half_mode)
    model.init_from_state_dict(base_model_dict)

    control_weights_locations = control_weights_locations or []
    controlnets = []
    for control_weights_location in control_weights_locations:
        controlnet_state_dict = load_state_dict(
            control_weights_location, half_mode=half_mode
        )
        controlnet_state_dict = {
            k.replace("control_model.", ""): v for k, v in controlnet_state_dict.items()
        }
        controlnet = instantiate_from_config(model.control_stage_config)
        controlnet.load_state_dict(controlnet_state_dict)
        controlnet.to(get_device())
        controlnets.append(controlnet)
        model.set_control_models(controlnets)

    if half_mode:
        model = model.half()
    print("halved")

    model.to(get_device())
    print("moved to device")
    model.eval()
    print("set to eval mode")
    return model


def add_controlnet(base_state_dict, controlnet_state_dict):
    """Merges a base sd15 model with a controlnet model."""
    for key in controlnet_state_dict:
        base_state_dict[key] = controlnet_state_dict[key]
    return base_state_dict


def get_diffusion_model(
    weights_location=iconfig.DEFAULT_MODEL,
    config_path="configs/stable-diffusion-v1.yaml",
    control_weights_locations=None,
    half_mode=None,
    for_inpainting=False,
    for_training=False,
):
    """
    Load a diffusion model.

    Weights location may also be shortcut name, e.g. "SD-1.5"
    """
    try:
        return _get_diffusion_model(
            weights_location,
            config_path,
            half_mode,
            for_inpainting,
            control_weights_locations=control_weights_locations,
            for_training=for_training,
        )
    except HuggingFaceAuthorizationError as e:
        if for_inpainting:
            logger.warning(
                f"Failed to load inpainting model. Attempting to fall-back to standard model.   {e!s}"
            )
            return _get_diffusion_model(
                iconfig.DEFAULT_MODEL,
                config_path,
                half_mode,
                for_inpainting=False,
                for_training=for_training,
                control_weights_locations=control_weights_locations,
            )
        raise


def _get_diffusion_model(
    weights_location=iconfig.DEFAULT_MODEL,
    config_path="configs/stable-diffusion-v1.yaml",
    half_mode=None,
    for_inpainting=False,
    for_training=False,
    control_weights_locations=None,
):
    """
    Load a diffusion model.

    Weights location may also be shortcut name, e.g. "SD-1.5"
    """
    global MOST_RECENTLY_LOADED_MODEL

    (
        model_config,
        weights_location,
        config_path,
        control_weights_locations,
    ) = resolve_model_paths(
        weights_path=weights_location,
        config_path=config_path,
        control_weights_paths=control_weights_locations,
        for_inpainting=for_inpainting,
        for_training=for_training,
    )
    # some models need the attention calculated in float32
    if model_config is not None:
        attention.ATTENTION_PRECISION_OVERRIDE = model_config.forced_attn_precision
    else:
        attention.ATTENTION_PRECISION_OVERRIDE = "default"
    diffusion_model = _load_diffusion_model(
        config_path=config_path,
        weights_location=weights_location,
        half_mode=half_mode,
        for_training=for_training,
    )
    MOST_RECENTLY_LOADED_MODEL = diffusion_model
    if control_weights_locations:
        controlnets = []
        for control_weights_location in control_weights_locations:
            controlnets.append(load_controlnet(control_weights_location, half_mode))
        diffusion_model.set_control_models(controlnets)

    return diffusion_model


def get_diffusion_model_refiners(
    weights_location=iconfig.DEFAULT_MODEL,
    config_path="configs/stable-diffusion-v1.yaml",
    control_weights_locations=None,
    dtype=None,
    for_inpainting=False,
    for_training=False,
):
    """
    Load a diffusion model.

    Weights location may also be shortcut name, e.g. "SD-1.5"
    """
    try:
        return _get_diffusion_model_refiners(
            weights_location,
            config_path,
            for_inpainting,
            dtype=dtype,
            control_weights_locations=control_weights_locations,
            for_training=for_training,
        )
    except HuggingFaceAuthorizationError as e:
        if for_inpainting:
            logger.warning(
                f"Failed to load inpainting model. Attempting to fall-back to standard model.   {e!s}"
            )
            return _get_diffusion_model_refiners(
                iconfig.DEFAULT_MODEL,
                config_path,
                dtype=dtype,
                for_inpainting=False,
                for_training=for_training,
                control_weights_locations=control_weights_locations,
            )
        raise


def _get_diffusion_model_refiners(
    weights_location=iconfig.DEFAULT_MODEL,
    config_path="configs/stable-diffusion-v1.yaml",
    for_inpainting=False,
    for_training=False,
    control_weights_locations=None,
    device=None,
    dtype=torch.float16,
):
    """
    Load a diffusion model.

    Weights location may also be shortcut name, e.g. "SD-1.5"
    """

    sd = _get_diffusion_model_refiners_only(
        weights_location=weights_location,
        config_path=config_path,
        for_inpainting=for_inpainting,
        for_training=for_training,
        device=device,
        dtype=dtype,
    )

    return sd


@lru_cache(maxsize=1)
def _get_diffusion_model_refiners_only(
    weights_location=iconfig.DEFAULT_MODEL,
    config_path="configs/stable-diffusion-v1.yaml",
    for_inpainting=False,
    for_training=False,
    control_weights_locations=None,
    device=None,
    dtype=torch.float16,
):
    """
    Load a diffusion model.

    Weights location may also be shortcut name, e.g. "SD-1.5"
    """
    from imaginairy.modules.refiners_sd import (
        SD1AutoencoderSliced,
        StableDiffusion_1,
        StableDiffusion_1_Inpainting,
    )

    global MOST_RECENTLY_LOADED_MODEL

    device = device or get_device()

    (
        model_config,
        weights_location,
        config_path,
        control_weights_locations,
    ) = resolve_model_paths(
        weights_path=weights_location,
        config_path=config_path,
        control_weights_paths=control_weights_locations,
        for_inpainting=for_inpainting,
        for_training=for_training,
    )
    # some models need the attention calculated in float32
    if model_config is not None:
        attention.ATTENTION_PRECISION_OVERRIDE = model_config.forced_attn_precision
    else:
        attention.ATTENTION_PRECISION_OVERRIDE = "default"

    (
        vae_weights,
        unet_weights,
        text_encoder_weights,
    ) = load_stable_diffusion_compvis_weights(weights_location)

    if for_inpainting:
        unet = SD1UNet(in_channels=9)
        StableDiffusionCls = StableDiffusion_1_Inpainting
    else:
        unet = SD1UNet(in_channels=4)
        StableDiffusionCls = StableDiffusion_1
    logger.debug(f"Using class {StableDiffusionCls.__name__}")

    sd = StableDiffusionCls(
        device=device, dtype=dtype, lda=SD1AutoencoderSliced(), unet=unet
    )
    logger.debug("Loading VAE")
    sd.lda.load_state_dict(vae_weights)

    logger.debug("Loading text encoder")
    sd.clip_text_encoder.load_state_dict(text_encoder_weights)

    logger.debug("Loading UNet")
    sd.unet.load_state_dict(unet_weights, strict=False)

    logger.debug(f"'{weights_location}' Loaded")

    MOST_RECENTLY_LOADED_MODEL = sd

    sd.set_self_attention_guidance(enable=True)

    return sd


@memory_managed_model("stable-diffusion", memory_usage_mb=1951)
def _load_diffusion_model(config_path, weights_location, half_mode, for_training):
    model_config = OmegaConf.load(f"{PKG_ROOT}/{config_path}")
    if for_training:
        model_config.use_ema = True
        # model_config.use_scheduler = True

    # only run half-mode on cuda. run it by default
    half_mode = half_mode is None and get_device() == "cuda"

    model = load_model_from_config(
        config=model_config,
        weights_location=weights_location,
        half_mode=half_mode,
    )
    return model


def load_controlnet_adapter(
    name,
    control_weights_location,
    target_unet,
    scale=1.0,
):
    controlnet_state_dict = load_state_dict(control_weights_location, half_mode=False)
    controlnet_state_dict = cast_weights(
        source_weights=controlnet_state_dict,
        source_model_name="controlnet-1-1",
        source_component_name="all",
        source_format="diffusers",
        dest_format="refiners",
    )

    for key in controlnet_state_dict:
        controlnet_state_dict[key] = controlnet_state_dict[key].to(
            device=target_unet.device, dtype=target_unet.dtype
        )
    adapter = SD1ControlnetAdapter(
        target=target_unet, name=name, scale=scale, weights=controlnet_state_dict
    )

    return adapter


@memory_managed_model("controlnet")
def load_controlnet(control_weights_location, half_mode):
    controlnet_state_dict = load_state_dict(
        control_weights_location, half_mode=half_mode
    )
    controlnet_state_dict = {
        k.replace("control_model.", ""): v for k, v in controlnet_state_dict.items()
    }
    control_stage_config = OmegaConf.load(f"{PKG_ROOT}/configs/control-net-v15.yaml")[
        "model"
    ]["params"]["control_stage_config"]
    controlnet = instantiate_from_config(control_stage_config)
    controlnet.load_state_dict(controlnet_state_dict)
    controlnet.to(get_device())
    return controlnet


def resolve_model_paths(
    weights_path=iconfig.DEFAULT_MODEL,
    config_path=None,
    control_weights_paths=None,
    for_inpainting=False,
    for_training=False,
):
    """Resolve weight and config path if they happen to be shortcuts."""
    model_metadata_w = iconfig.MODEL_CONFIG_SHORTCUTS.get(weights_path, None)
    model_metadata_c = iconfig.MODEL_CONFIG_SHORTCUTS.get(config_path, None)

    control_weights_paths = control_weights_paths or []
    control_net_metadatas = [
        iconfig.CONTROLNET_CONFIG_SHORTCUTS.get(control_weights_path, None)
        for control_weights_path in control_weights_paths
    ]

    if not control_net_metadatas and for_inpainting:
        model_metadata_w = iconfig.MODEL_CONFIG_SHORTCUTS.get(
            f"{weights_path}-inpaint", model_metadata_w
        )
        model_metadata_c = iconfig.MODEL_CONFIG_SHORTCUTS.get(
            f"{config_path}-inpaint", model_metadata_c
        )

    if model_metadata_w:
        if config_path is None:
            config_path = model_metadata_w.config_path
        if for_training:
            weights_path = model_metadata_w.weights_url_full
            if weights_path is None:
                msg = "No full training weights configured for this model. Edit the code or subimt a github issue."
                raise ValueError(msg)
        else:
            weights_path = model_metadata_w.weights_url

    if model_metadata_c:
        config_path = model_metadata_c.config_path

    if config_path is None:
        config_path = iconfig.MODEL_CONFIG_SHORTCUTS[iconfig.DEFAULT_MODEL].config_path
    if control_net_metadatas:
        if "stable-diffusion-v1" not in config_path:
            msg = "Control net is only supported for stable diffusion v1. Please use a different model."
            raise ValueError(msg)
        control_weights_paths = [cnm.weights_url for cnm in control_net_metadatas]
        config_path = control_net_metadatas[0].config_path
    model_metadata = model_metadata_w or model_metadata_c
    logger.debug(f"Loading model weights from: {weights_path}")
    logger.debug(f"Loading model config from:  {config_path}")
    return model_metadata, weights_path, config_path, control_weights_paths


def get_model_default_image_size(weights_location):
    model_config = iconfig.MODEL_CONFIG_SHORTCUTS.get(weights_location, None)
    if model_config:
        return model_config.default_image_size
    return 512


def get_current_diffusion_model():
    return MOST_RECENTLY_LOADED_MODEL


def get_cache_dir():
    xdg_cache_home = os.getenv("XDG_CACHE_HOME", None)
    if xdg_cache_home is None:
        user_home = os.getenv("HOME", None)
        if user_home:
            xdg_cache_home = os.path.join(user_home, ".cache")

    if xdg_cache_home is not None:
        return os.path.join(xdg_cache_home, "imaginairy")

    return os.path.join(os.path.dirname(__file__), ".cached-aimg")


def get_cached_url_path(url, category=None):
    """
    Gets the contents of a url, but caches the response indefinitely.

    While we attempt to use the cached_path from huggingface transformers, we fall back
    to our own implementation if the url does not provide an etag header, which `cached_path`
    requires.  We also skip the `head` call that `cached_path` makes on every call if the file
    is already cached.
    """

    try:
        if url.startswith("https://huggingface.co"):
            return huggingface_cached_path(url)
    except (OSError, ValueError):
        pass
    filename = url.split("/")[-1]
    dest = get_cache_dir()
    if category:
        dest = os.path.join(dest, category)
    os.makedirs(dest, exist_ok=True)

    # Replace possibly illegal destination path characters
    safe_filename = re.sub('[*<>:"|?]', "_", filename)
    dest_path = os.path.join(dest, safe_filename)
    if os.path.exists(dest_path):
        return dest_path

    # check if it's saved at previous path and rename it
    old_dest_path = os.path.join(dest, filename)
    if os.path.exists(old_dest_path):
        os.rename(old_dest_path, dest_path)
        return dest_path

    r = requests.get(url)

    with open(dest_path, "wb") as f:
        f.write(r.content)
    return dest_path


def check_huggingface_url_authorized(url):
    if not url.startswith("https://huggingface.co/"):
        return None
    token = HfFolder.get_token()
    headers = {}
    if token is not None:
        headers["authorization"] = f"Bearer {token}"
    response = requests.head(url, allow_redirects=True, headers=headers, timeout=5)
    if response.status_code == 401:
        msg = "Unauthorized access to HuggingFace model. This model requires a huggingface token.  Please login to HuggingFace or set HUGGING_FACE_HUB_TOKEN to your User Access Token. See https://huggingface.co/docs/huggingface_hub/quick-start#login for more information"
        raise HuggingFaceAuthorizationError(msg)
    return None


@wraps(_hf_hub_download)
def hf_hub_download(*args, **kwargs):
    """
    backwards compatible wrapper for huggingface's hf_hub_download.

    they changed the argument name from `use_auth_token` to `token`
    """

    try:
        return _hf_hub_download(*args, **kwargs)
    except TypeError as e:
        if "unexpected keyword argument 'token'" in str(e):
            kwargs["use_auth_token"] = kwargs.pop("token")
            return _hf_hub_download(*args, **kwargs)
        raise


def huggingface_cached_path(url):
    # bypass all the HEAD calls done by the default `cached_path`
    repo, commit_hash, filepath = extract_huggingface_repo_commit_file_from_url(url)
    dest_path = try_to_load_from_cache(
        repo_id=repo, revision=commit_hash, filename=filepath
    )
    if not dest_path:
        check_huggingface_url_authorized(url)
        token = HfFolder.get_token()
        logger.info(f"Downloading {url} from huggingface")
        dest_path = hf_hub_download(
            repo_id=repo, revision=commit_hash, filename=filepath, token=token
        )
        # make a refs folder so caching works
        # work-around for
        # https://github.com/huggingface/huggingface_hub/pull/1306
        # https://github.com/brycedrennan/imaginAIry/issues/171
        refs_url = dest_path[: dest_path.index("/snapshots/")] + "/refs/"
        os.makedirs(refs_url, exist_ok=True)
    return dest_path


def extract_huggingface_repo_commit_file_from_url(url):
    parsed_url = urllib.parse.urlparse(url)
    path_components = parsed_url.path.strip("/").split("/")

    repo = "/".join(path_components[0:2])
    assert path_components[2] == "resolve"
    commit_hash = path_components[3]
    filepath = "/".join(path_components[4:])

    return repo, commit_hash, filepath


def download_diffusers_weights(repo, sub, filename):
    from imaginairy.model_manager import get_cached_url_path

    url = f"https://huggingface.co/{repo}/resolve/main/{sub}/{filename}"
    return get_cached_url_path(url, category="weights")


@lru_cache
def load_stable_diffusion_diffusers_weights(diffusers_repo, device=None):
    from imaginairy.utils import get_device
    from imaginairy.weight_management.conversion import cast_weights
    from imaginairy.weight_management.utils import (
        COMPONENT_NAMES,
        FORMAT_NAMES,
        MODEL_NAMES,
    )

    if device is None:
        device = get_device()
    vae_weights_path = download_diffusers_weights(
        repo=diffusers_repo, sub="vae", filename="diffusion_pytorch_model.safetensors"
    )
    vae_weights = open_weights(vae_weights_path, device=device)
    vae_weights = cast_weights(
        source_weights=vae_weights,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.VAE,
        source_format=FORMAT_NAMES.DIFFUSERS,
        dest_format=FORMAT_NAMES.REFINERS,
    )

    unet_weights_path = download_diffusers_weights(
        repo=diffusers_repo, sub="unet", filename="diffusion_pytorch_model.safetensors"
    )
    unet_weights = open_weights(unet_weights_path, device=device)
    unet_weights = cast_weights(
        source_weights=unet_weights,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.UNET,
        source_format=FORMAT_NAMES.DIFFUSERS,
        dest_format=FORMAT_NAMES.REFINERS,
    )

    text_encoder_weights_path = download_diffusers_weights(
        repo=diffusers_repo, sub="text_encoder", filename="model.safetensors"
    )
    text_encoder_weights = open_weights(text_encoder_weights_path, device=device)
    text_encoder_weights = cast_weights(
        source_weights=text_encoder_weights,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
        source_format=FORMAT_NAMES.DIFFUSERS,
        dest_format=FORMAT_NAMES.REFINERS,
    )

    return vae_weights, unet_weights, text_encoder_weights


def open_weights(filepath, device=None):
    from imaginairy.utils import get_device

    if device is None:
        device = get_device()

    if "safetensor" in filepath.lower():
        from refiners.fluxion.utils import safe_open

        with safe_open(path=filepath, framework="pytorch", device=device) as tensors:
            state_dict = {
                key: tensors.get_tensor(key) for key in tensors.keys()  # noqa
            }
    else:
        import torch

        state_dict = torch.load(filepath, map_location=device)

    while "state_dict" in state_dict:
        state_dict = state_dict["state_dict"]

    return state_dict


@lru_cache
def load_stable_diffusion_compvis_weights(weights_url):
    from imaginairy.model_manager import get_cached_url_path
    from imaginairy.utils import get_device
    from imaginairy.weight_management.conversion import cast_weights
    from imaginairy.weight_management.utils import (
        COMPONENT_NAMES,
        FORMAT_NAMES,
        MODEL_NAMES,
    )

    weights_path = get_cached_url_path(weights_url, category="weights")
    logger.info(f"Loading weights from {weights_path}")
    state_dict = open_weights(weights_path, device=get_device())

    text_encoder_prefix = "cond_stage_model."
    cut_start = len(text_encoder_prefix)
    text_encoder_state_dict = {
        k[cut_start:]: v
        for k, v in state_dict.items()
        if k.startswith(text_encoder_prefix)
    }
    text_encoder_state_dict = cast_weights(
        source_weights=text_encoder_state_dict,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
        source_format=FORMAT_NAMES.COMPVIS,
        dest_format=FORMAT_NAMES.DIFFUSERS,
    )
    text_encoder_state_dict = cast_weights(
        source_weights=text_encoder_state_dict,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
        source_format=FORMAT_NAMES.DIFFUSERS,
        dest_format=FORMAT_NAMES.REFINERS,
    )

    vae_prefix = "first_stage_model."
    cut_start = len(vae_prefix)
    vae_state_dict = {
        k[cut_start:]: v for k, v in state_dict.items() if k.startswith(vae_prefix)
    }
    vae_state_dict = cast_weights(
        source_weights=vae_state_dict,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.VAE,
        source_format=FORMAT_NAMES.COMPVIS,
        dest_format=FORMAT_NAMES.DIFFUSERS,
    )
    vae_state_dict = cast_weights(
        source_weights=vae_state_dict,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.VAE,
        source_format=FORMAT_NAMES.DIFFUSERS,
        dest_format=FORMAT_NAMES.REFINERS,
    )

    unet_prefix = "model."
    cut_start = len(unet_prefix)
    unet_state_dict = {
        k[cut_start:]: v for k, v in state_dict.items() if k.startswith(unet_prefix)
    }
    unet_state_dict = cast_weights(
        source_weights=unet_state_dict,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.UNET,
        source_format=FORMAT_NAMES.COMPVIS,
        dest_format=FORMAT_NAMES.DIFFUSERS,
    )

    unet_state_dict = cast_weights(
        source_weights=unet_state_dict,
        source_model_name=MODEL_NAMES.SD15,
        source_component_name=COMPONENT_NAMES.UNET,
        source_format=FORMAT_NAMES.DIFFUSERS,
        dest_format=FORMAT_NAMES.REFINERS,
    )

    return vae_state_dict, unet_state_dict, text_encoder_state_dict