[feat] add `load_lora_adapter()` for compatible models (#9712)

* add first draft.

* fix

* updates.

* updates.

* updates

* updates

* updates.

* fix-copies

* lora constants.

* add tests

* Apply suggestions from code review
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* docstrings.

---------
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

src/diffusers/loaders/lora_base.py

@@ -51,6 +51,9 @@ if is_accelerate_available():
 
 logger = logging.get_logger(__name__)
 
+LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"
+LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"
+
 
 def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, adapter_names=None):
     """
@@ -181,6 +184,119 @@ def _remove_text_encoder_monkey_patch(text_encoder):
         text_encoder._hf_peft_config_loaded = None
 
 
+def _fetch_state_dict(
+    pretrained_model_name_or_path_or_dict,
+    weight_name,
+    use_safetensors,
+    local_files_only,
+    cache_dir,
+    force_download,
+    proxies,
+    token,
+    revision,
+    subfolder,
+    user_agent,
+    allow_pickle,
+):
+    model_file = None
+    if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+        # Let's first try to load .safetensors weights
+        if (use_safetensors and weight_name is None) or (
+            weight_name is not None and weight_name.endswith(".safetensors")
+        ):
+            try:
+                # Here we're relaxing the loading check to enable more Inference API
+                # friendliness where sometimes, it's not at all possible to automatically
+                # determine `weight_name`.
+                if weight_name is None:
+                    weight_name = _best_guess_weight_name(
+                        pretrained_model_name_or_path_or_dict,
+                        file_extension=".safetensors",
+                        local_files_only=local_files_only,
+                    )
+                model_file = _get_model_file(
+                    pretrained_model_name_or_path_or_dict,
+                    weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    token=token,
+                    revision=revision,
+                    subfolder=subfolder,
+                    user_agent=user_agent,
+                )
+                state_dict = safetensors.torch.load_file(model_file, device="cpu")
+            except (IOError, safetensors.SafetensorError) as e:
+                if not allow_pickle:
+                    raise e
+                # try loading non-safetensors weights
+                model_file = None
+                pass
+
+        if model_file is None:
+            if weight_name is None:
+                weight_name = _best_guess_weight_name(
+                    pretrained_model_name_or_path_or_dict, file_extension=".bin", local_files_only=local_files_only
+                )
+            model_file = _get_model_file(
+                pretrained_model_name_or_path_or_dict,
+                weights_name=weight_name or LORA_WEIGHT_NAME,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                subfolder=subfolder,
+                user_agent=user_agent,
+            )
+            state_dict = load_state_dict(model_file)
+    else:
+        state_dict = pretrained_model_name_or_path_or_dict
+
+    return state_dict
+
+
+def _best_guess_weight_name(
+    pretrained_model_name_or_path_or_dict, file_extension=".safetensors", local_files_only=False
+):
+    if local_files_only or HF_HUB_OFFLINE:
+        raise ValueError("When using the offline mode, you must specify a `weight_name`.")
+
+    targeted_files = []
+
+    if os.path.isfile(pretrained_model_name_or_path_or_dict):
+        return
+    elif os.path.isdir(pretrained_model_name_or_path_or_dict):
+        targeted_files = [f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)]
+    else:
+        files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings
+        targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]
+    if len(targeted_files) == 0:
+        return
+
+    # "scheduler" does not correspond to a LoRA checkpoint.
+    # "optimizer" does not correspond to a LoRA checkpoint
+    # only top-level checkpoints are considered and not the other ones, hence "checkpoint".
+    unallowed_substrings = {"scheduler", "optimizer", "checkpoint"}
+    targeted_files = list(
+        filter(lambda x: all(substring not in x for substring in unallowed_substrings), targeted_files)
+    )
+
+    if any(f.endswith(LORA_WEIGHT_NAME) for f in targeted_files):
+        targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME), targeted_files))
+    elif any(f.endswith(LORA_WEIGHT_NAME_SAFE) for f in targeted_files):
+        targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME_SAFE), targeted_files))
+
+    if len(targeted_files) > 1:
+        raise ValueError(
+            f"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one  `.safetensors` or `.bin` file in  {pretrained_model_name_or_path_or_dict}."
+        )
+    weight_name = targeted_files[0]
+    return weight_name
+
+
 class LoraBaseMixin:
     """Utility class for handling LoRAs."""
 
@@ -234,124 +350,16 @@ class LoraBaseMixin:
         return (is_model_cpu_offload, is_sequential_cpu_offload)
 
     @classmethod
-    def _fetch_state_dict(
-        cls,
-        pretrained_model_name_or_path_or_dict,
-        weight_name,
-        use_safetensors,
-        local_files_only,
-        cache_dir,
-        force_download,
-        proxies,
-        token,
-        revision,
-        subfolder,
-        user_agent,
-        allow_pickle,
-    ):
-        from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
-
-        model_file = None
-        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
-            # Let's first try to load .safetensors weights
-            if (use_safetensors and weight_name is None) or (
-                weight_name is not None and weight_name.endswith(".safetensors")
-            ):
-                try:
-                    # Here we're relaxing the loading check to enable more Inference API
-                    # friendliness where sometimes, it's not at all possible to automatically
-                    # determine `weight_name`.
-                    if weight_name is None:
-                        weight_name = cls._best_guess_weight_name(
-                            pretrained_model_name_or_path_or_dict,
-                            file_extension=".safetensors",
-                            local_files_only=local_files_only,
-                        )
-                    model_file = _get_model_file(
-                        pretrained_model_name_or_path_or_dict,
-                        weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
-                        cache_dir=cache_dir,
-                        force_download=force_download,
-                        proxies=proxies,
-                        local_files_only=local_files_only,
-                        token=token,
-                        revision=revision,
-                        subfolder=subfolder,
-                        user_agent=user_agent,
-                    )
-                    state_dict = safetensors.torch.load_file(model_file, device="cpu")
-                except (IOError, safetensors.SafetensorError) as e:
-                    if not allow_pickle:
-                        raise e
-                    # try loading non-safetensors weights
-                    model_file = None
-                    pass
-
-            if model_file is None:
-                if weight_name is None:
-                    weight_name = cls._best_guess_weight_name(
-                        pretrained_model_name_or_path_or_dict, file_extension=".bin", local_files_only=local_files_only
-                    )
-                model_file = _get_model_file(
-                    pretrained_model_name_or_path_or_dict,
-                    weights_name=weight_name or LORA_WEIGHT_NAME,
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    token=token,
-                    revision=revision,
-                    subfolder=subfolder,
-                    user_agent=user_agent,
-                )
-                state_dict = load_state_dict(model_file)
-        else:
-            state_dict = pretrained_model_name_or_path_or_dict
-
-        return state_dict
+    def _fetch_state_dict(cls, *args, **kwargs):
+        deprecation_message = f"Using the `_fetch_state_dict()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _fetch_state_dict`."
+        deprecate("_fetch_state_dict", "0.35.0", deprecation_message)
+        return _fetch_state_dict(*args, **kwargs)
 
     @classmethod
-    def _best_guess_weight_name(
-        cls, pretrained_model_name_or_path_or_dict, file_extension=".safetensors", local_files_only=False
-    ):
-        from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
-
-        if local_files_only or HF_HUB_OFFLINE:
-            raise ValueError("When using the offline mode, you must specify a `weight_name`.")
-
-        targeted_files = []
-
-        if os.path.isfile(pretrained_model_name_or_path_or_dict):
-            return
-        elif os.path.isdir(pretrained_model_name_or_path_or_dict):
-            targeted_files = [
-                f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)
-            ]
-        else:
-            files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings
-            targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]
-        if len(targeted_files) == 0:
-            return
-
-        # "scheduler" does not correspond to a LoRA checkpoint.
-        # "optimizer" does not correspond to a LoRA checkpoint
-        # only top-level checkpoints are considered and not the other ones, hence "checkpoint".
-        unallowed_substrings = {"scheduler", "optimizer", "checkpoint"}
-        targeted_files = list(
-            filter(lambda x: all(substring not in x for substring in unallowed_substrings), targeted_files)
-        )
-
-        if any(f.endswith(LORA_WEIGHT_NAME) for f in targeted_files):
-            targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME), targeted_files))
-        elif any(f.endswith(LORA_WEIGHT_NAME_SAFE) for f in targeted_files):
-            targeted_files = list(filter(lambda x: x.endswith(LORA_WEIGHT_NAME_SAFE), targeted_files))
-
-        if len(targeted_files) > 1:
-            raise ValueError(
-                f"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one  `.safetensors` or `.bin` file in  {pretrained_model_name_or_path_or_dict}."
-            )
-        weight_name = targeted_files[0]
-        return weight_name
+    def _best_guess_weight_name(cls, *args, **kwargs):
+        deprecation_message = f"Using the `_best_guess_weight_name()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _best_guess_weight_name`."
+        deprecate("_best_guess_weight_name", "0.35.0", deprecation_message)
+        return _best_guess_weight_name(*args, **kwargs)
 
     def unload_lora_weights(self):
         """
@@ -725,8 +733,6 @@ class LoraBaseMixin:
         save_function: Callable,
         safe_serialization: bool,
     ):
-        from .lora_pipeline import LORA_WEIGHT_NAME, LORA_WEIGHT_NAME_SAFE
-
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return

src/diffusers/loaders/peft.py

@@ -16,18 +16,32 @@ import inspect
 from functools import partial
 from typing import Dict, List, Optional, Union
 
+import torch.nn as nn
+
 from ..utils import (
     MIN_PEFT_VERSION,
     USE_PEFT_BACKEND,
     check_peft_version,
+    convert_unet_state_dict_to_peft,
     delete_adapter_layers,
+    get_adapter_name,
+    get_peft_kwargs,
+    is_accelerate_available,
     is_peft_available,
+    is_peft_version,
+    logging,
     set_adapter_layers,
     set_weights_and_activate_adapters,
 )
+from .lora_base import _fetch_state_dict
 from .unet_loader_utils import _maybe_expand_lora_scales
 
 
+if is_accelerate_available():
+    from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
+
+logger = logging.get_logger(__name__)
+
 _SET_ADAPTER_SCALE_FN_MAPPING = {
     "UNet2DConditionModel": _maybe_expand_lora_scales,
     "UNetMotionModel": _maybe_expand_lora_scales,
@@ -53,6 +67,215 @@ class PeftAdapterMixin:
 
     _hf_peft_config_loaded = False
 
+    @classmethod
+    # Copied from diffusers.loaders.lora_base.LoraBaseMixin._optionally_disable_offloading
+    def _optionally_disable_offloading(cls, _pipeline):
+        """
+        Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.
+
+        Args:
+            _pipeline (`DiffusionPipeline`):
+                The pipeline to disable offloading for.
+
+        Returns:
+            tuple:
+                A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
+        """
+        is_model_cpu_offload = False
+        is_sequential_cpu_offload = False
+
+        if _pipeline is not None and _pipeline.hf_device_map is None:
+            for _, component in _pipeline.components.items():
+                if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
+                    if not is_model_cpu_offload:
+                        is_model_cpu_offload = isinstance(component._hf_hook, CpuOffload)
+                    if not is_sequential_cpu_offload:
+                        is_sequential_cpu_offload = (
+                            isinstance(component._hf_hook, AlignDevicesHook)
+                            or hasattr(component._hf_hook, "hooks")
+                            and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)
+                        )
+
+                    logger.info(
+                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
+                    )
+                    remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
+
+        return (is_model_cpu_offload, is_sequential_cpu_offload)
+
+    def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="transformer", **kwargs):
+        r"""
+        Loads a LoRA adapter into the underlying model.
+
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                Can be either:
+
+                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
+                      the Hub.
+                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
+                      with [`ModelMixin.save_pretrained`].
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+
+            prefix (`str`, *optional*): Prefix to filter the state dict.
+
+            cache_dir (`Union[str, os.PathLike]`, *optional*):
+                Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
+                is not used.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+                cached versions if they exist.
+            proxies (`Dict[str, str]`, *optional*):
+                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+            local_files_only (`bool`, *optional*, defaults to `False`):
+                Whether to only load local model weights and configuration files or not. If set to `True`, the model
+                won't be downloaded from the Hub.
+            token (`str` or *bool*, *optional*):
+                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
+                `diffusers-cli login` (stored in `~/.huggingface`) is used.
+            revision (`str`, *optional*, defaults to `"main"`):
+                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
+                allowed by Git.
+            subfolder (`str`, *optional*, defaults to `""`):
+                The subfolder location of a model file within a larger model repository on the Hub or locally.
+            network_alphas (`Dict[str, float]`):
+                The value of the network alpha used for stable learning and preventing underflow. This value has the
+                same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
+                link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
+            low_cpu_mem_usage (`bool`, *optional*):
+                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
+                weights.
+        """
+        from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
+
+        cache_dir = kwargs.pop("cache_dir", None)
+        force_download = kwargs.pop("force_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", None)
+        token = kwargs.pop("token", None)
+        revision = kwargs.pop("revision", None)
+        subfolder = kwargs.pop("subfolder", None)
+        weight_name = kwargs.pop("weight_name", None)
+        use_safetensors = kwargs.pop("use_safetensors", None)
+        adapter_name = kwargs.pop("adapter_name", None)
+        network_alphas = kwargs.pop("network_alphas", None)
+        _pipeline = kwargs.pop("_pipeline", None)
+        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", False)
+        allow_pickle = False
+
+        if low_cpu_mem_usage and is_peft_version("<=", "0.13.0"):
+            raise ValueError(
+                "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
+            )
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+
+        state_dict = _fetch_state_dict(
+            pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
+            weight_name=weight_name,
+            use_safetensors=use_safetensors,
+            local_files_only=local_files_only,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            token=token,
+            revision=revision,
+            subfolder=subfolder,
+            user_agent=user_agent,
+            allow_pickle=allow_pickle,
+        )
+
+        keys = list(state_dict.keys())
+        transformer_keys = [k for k in keys if k.startswith(prefix)]
+        if len(transformer_keys) > 0:
+            state_dict = {k.replace(f"{prefix}.", ""): v for k, v in state_dict.items() if k in transformer_keys}
+
+        if len(state_dict.keys()) > 0:
+            # check with first key if is not in peft format
+            first_key = next(iter(state_dict.keys()))
+            if "lora_A" not in first_key:
+                state_dict = convert_unet_state_dict_to_peft(state_dict)
+
+            if adapter_name in getattr(self, "peft_config", {}):
+                raise ValueError(
+                    f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
+                )
+
+            rank = {}
+            for key, val in state_dict.items():
+                if "lora_B" in key:
+                    rank[key] = val.shape[1]
+
+            if network_alphas is not None and len(network_alphas) >= 1:
+                alpha_keys = [k for k in network_alphas.keys() if k.startswith(prefix) and k.split(".")[0] == prefix]
+                network_alphas = {k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys}
+
+            lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict)
+            if "use_dora" in lora_config_kwargs:
+                if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
+                    raise ValueError(
+                        "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                    )
+                else:
+                    lora_config_kwargs.pop("use_dora")
+            lora_config = LoraConfig(**lora_config_kwargs)
+
+            # adapter_name
+            if adapter_name is None:
+                adapter_name = get_adapter_name(self)
+
+            # <Unsafe code
+            # We can be sure that the following works as it just sets attention processors, lora layers and puts all in the same dtype
+            # Now we remove any existing hooks to `_pipeline`.
+
+            # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
+            # otherwise loading LoRA weights will lead to an error
+            is_model_cpu_offload, is_sequential_cpu_offload = self._optionally_disable_offloading(_pipeline)
+
+            peft_kwargs = {}
+            if is_peft_version(">=", "0.13.1"):
+                peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
+
+            inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
+            incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs)
+
+            warn_msg = ""
+            if incompatible_keys is not None:
+                # Check only for unexpected keys.
+                unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+                if unexpected_keys:
+                    lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k and adapter_name in k]
+                    if lora_unexpected_keys:
+                        warn_msg = (
+                            f"Loading adapter weights from state_dict led to unexpected keys found in the model:"
+                            f" {', '.join(lora_unexpected_keys)}. "
+                        )
+
+                # Filter missing keys specific to the current adapter.
+                missing_keys = getattr(incompatible_keys, "missing_keys", None)
+                if missing_keys:
+                    lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
+                    if lora_missing_keys:
+                        warn_msg += (
+                            f"Loading adapter weights from state_dict led to missing keys in the model:"
+                            f" {', '.join(lora_missing_keys)}."
+                        )
+
+            if warn_msg:
+                logger.warning(warn_msg)
+
+            # Offload back.
+            if is_model_cpu_offload:
+                _pipeline.enable_model_cpu_offload()
+            elif is_sequential_cpu_offload:
+                _pipeline.enable_sequential_cpu_offload()
+            # Unsafe code />
+
     def set_adapters(
         self,
         adapter_names: Union[List[str], str],

tests/lora/test_deprecated_utilities.py

+import os
+import tempfile
+import unittest
+
+import torch
+
+from diffusers.loaders.lora_base import LoraBaseMixin
+
+
+class UtilityMethodDeprecationTests(unittest.TestCase):
+    def test_fetch_state_dict_cls_method_raises_warning(self):
+        state_dict = torch.nn.Linear(3, 3).state_dict()
+        with self.assertWarns(FutureWarning) as warning:
+            _ = LoraBaseMixin._fetch_state_dict(
+                state_dict,
+                weight_name=None,
+                use_safetensors=False,
+                local_files_only=True,
+                cache_dir=None,
+                force_download=False,
+                proxies=None,
+                token=None,
+                revision=None,
+                subfolder=None,
+                user_agent=None,
+                allow_pickle=None,
+            )
+        warning_message = str(warning.warnings[0].message)
+        assert "Using the `_fetch_state_dict()` method from" in warning_message
+
+    def test_best_guess_weight_name_cls_method_raises_warning(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            state_dict = torch.nn.Linear(3, 3).state_dict()
+            torch.save(state_dict, os.path.join(tmpdir, "pytorch_lora_weights.bin"))
+
+            with self.assertWarns(FutureWarning) as warning:
+                _ = LoraBaseMixin._best_guess_weight_name(pretrained_model_name_or_path_or_dict=tmpdir)
+            warning_message = str(warning.warnings[0].message)
+            assert "Using the `_best_guess_weight_name()` method from" in warning_message

tests/lora/utils.py

@@ -1787,7 +1787,7 @@ class PeftLoraLoaderMixinTests:
         logger = (
             logging.get_logger("diffusers.loaders.unet")
             if self.unet_kwargs is not None
-            else logging.get_logger("diffusers.loaders.lora_pipeline")
+            else logging.get_logger("diffusers.loaders.peft")
         )
         logger.setLevel(30)
         with CaptureLogger(logger) as cap_logger:
@@ -1826,7 +1826,7 @@ class PeftLoraLoaderMixinTests:
         logger = (
             logging.get_logger("diffusers.loaders.unet")
             if self.unet_kwargs is not None
-            else logging.get_logger("diffusers.loaders.lora_pipeline")
+            else logging.get_logger("diffusers.loaders.peft")
         )
         logger.setLevel(30)
         with CaptureLogger(logger) as cap_logger: