[`core` / `PEFT` / `LoRA`] Integrate PEFT into Unet (#5151)

* v1

* add tests and fix previous failing tests

* fix CI

* add tests + v1 `PeftLayerScaler`

* style

* add scale retrieving mechanism system

* fix CI

* up

* up

* simple approach --> not same results for some reason

* fix issues

* fix copies

* remove unneeded method

* active adapters!

* fix merge conflicts

* up

* up

* kohya - test-1

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix scale

* fix copies

* add comment

* multi adapters

* fix tests

* oops

* v1 faster loading - in progress

* Revert "v1 faster loading - in progress"

This reverts commit ac925f81321e95fc8168184c3346bf3d75404d5a.

* kohya same generation

* fix some slow tests

* peft integration features for unet lora

1. Support for Multiple ranks/alphas
2. Support for Multiple active adapters
3. Support for enabling/disabling LoRAs

* fix `get_peft_kwargs`

* Update loaders.py

* add some tests

* add unfuse tests

* fix tests

* up

* add set adapter from sourab and tests

* fix multi adapter tests

* style & quality

* style

* remove comment

* fix `adapter_name` issues

* fix unet adapter name for sdxl

* fix enabling/disabling adapters

* fix fuse / unfuse unet

* nit

* fix

* up

* fix cpu offloading

* fix another slow test

* fix another offload test

* add more tests

* all slow tests pass

* style

* fix alpha pattern for unet and text encoder

* Update src/diffusers/loaders.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* Update src/diffusers/models/attention.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* up

* up

* clarify comment

* comments

* change comment order

* change comment order

* stylr & quality

* Update tests/lora/test_lora_layers_peft.py
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix bugs and add tests

* Update src/diffusers/models/modeling_utils.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* Update src/diffusers/models/modeling_utils.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* refactor

* suggestion

* add break statemebt

* add compile tests

* move slow tests to peft tests as I modified them

* quality

* refactor a bit

* style

* change import

* style

* fix CI

* refactor slow tests one last time

* style

* oops

* oops

* oops

* final tweak tests

* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/loaders.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* comments

* Apply suggestions from code review
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* remove comments

* more comments

* try

* revert

* add `safe_merge` tests

* add comment

* style, comments and run tests in fp16

* add warnings

* fix doc test

* replace with `adapter_weights`

* add `get_active_adapters()`

* expose `get_list_adapters` method

* better error message

* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* style

* trigger slow lora tests

* fix tests

* maybe fix last test

* revert

* Update src/diffusers/loaders.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* Update src/diffusers/loaders.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* Update src/diffusers/loaders.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* Update src/diffusers/loaders.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* move `MIN_PEFT_VERSION`

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* let's not use class variable

* fix few nits

* change a bit offloading logic

* check earlier

* rm unneeded block

* break long line

* return empty list

* change logic a bit and address comments

* add typehint

* remove parenthesis

* fix

* revert to fp16 in tests

* add to gpu

* revert to old test

* style

* Update src/diffusers/loaders.py
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>

* change indent

* Apply suggestions from code review

* Apply suggestions from code review

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

src/diffusers/loaders.py

@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import importlib
 import os
 import re
 from collections import defaultdict
@@ -32,15 +31,16 @@ from .models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_i
 from .utils import (
     DIFFUSERS_CACHE,
     HF_HUB_OFFLINE,
+    USE_PEFT_BACKEND,
     _get_model_file,
     convert_state_dict_to_diffusers,
     convert_state_dict_to_peft,
+    convert_unet_state_dict_to_peft,
     deprecate,
     get_adapter_name,
     get_peft_kwargs,
     is_accelerate_available,
     is_omegaconf_available,
-    is_peft_available,
     is_transformers_available,
     logging,
     recurse_remove_peft_layers,
@@ -72,19 +72,6 @@ TEXT_INVERSION_NAME_SAFE = "learned_embeds.safetensors"
 CUSTOM_DIFFUSION_WEIGHT_NAME = "pytorch_custom_diffusion_weights.bin"
 CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = "pytorch_custom_diffusion_weights.safetensors"
 
-
-# Below should be `True` if the current version of `peft` and `transformers` are compatible with
-# PEFT backend. Will automatically fall back to PEFT backend if the correct versions of the libraries are
-# available.
-# For PEFT it is has to be greater than 0.6.0 and for transformers it has to be greater than 4.33.1.
-_required_peft_version = is_peft_available() and version.parse(
-    version.parse(importlib.metadata.version("peft")).base_version
-) > version.parse("0.5")
-_required_transformers_version = version.parse(
-    version.parse(importlib.metadata.version("transformers")).base_version
-) > version.parse("4.33")
-
-USE_PEFT_BACKEND = _required_peft_version and _required_transformers_version
 LORA_DEPRECATION_MESSAGE = "You are using an old version of LoRA backend. This will be deprecated in the next releases in favor of PEFT make sure to install the latest PEFT and transformers packages in the future."
 
 
@@ -413,7 +400,7 @@ class UNet2DConditionLoadersMixin:
         # fill attn processors
         lora_layers_list = []
 
-        is_lora = all(("lora" in k or k.endswith(".alpha")) for k in state_dict.keys())
+        is_lora = all(("lora" in k or k.endswith(".alpha")) for k in state_dict.keys()) and not USE_PEFT_BACKEND
         is_custom_diffusion = any("custom_diffusion" in k for k in state_dict.keys())
 
         if is_lora:
@@ -527,6 +514,10 @@ class UNet2DConditionLoadersMixin:
                         cross_attention_dim=cross_attention_dim,
                     )
                     attn_processors[key].load_state_dict(value_dict)
+        elif USE_PEFT_BACKEND:
+            # In that case we have nothing to do as loading the adapter weights is already handled above by `set_peft_model_state_dict`
+            # on the Unet
+            pass
         else:
             raise ValueError(
                 f"{model_file} does not seem to be in the correct format expected by LoRA or Custom Diffusion training."
@@ -537,33 +528,36 @@ class UNet2DConditionLoadersMixin:
         # Now we remove any existing hooks to
         is_model_cpu_offload = False
         is_sequential_cpu_offload = False
-        if _pipeline is not None:
-            for _, component in _pipeline.components.items():
-                if isinstance(component, nn.Module):
-                    if hasattr(component, "_hf_hook"):
+
+        # For PEFT backend the Unet is already offloaded at this stage as it is handled inside `lora_lora_weights_into_unet`
+        if not USE_PEFT_BACKEND:
+            if _pipeline is not None:
+                for _, component in _pipeline.components.items():
+                    if isinstance(component, nn.Module) and hasattr(component, "_hf_hook"):
                         is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
                         is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), 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)
 
-        # only custom diffusion needs to set attn processors
-        if is_custom_diffusion:
-            self.set_attn_processor(attn_processors)
+            # only custom diffusion needs to set attn processors
+            if is_custom_diffusion:
+                self.set_attn_processor(attn_processors)
 
-        # set lora layers
-        for target_module, lora_layer in lora_layers_list:
-            target_module.set_lora_layer(lora_layer)
+            # set lora layers
+            for target_module, lora_layer in lora_layers_list:
+                target_module.set_lora_layer(lora_layer)
 
-        self.to(dtype=self.dtype, device=self.device)
+            self.to(dtype=self.dtype, device=self.device)
 
-        # Offload back.
-        if is_model_cpu_offload:
-            _pipeline.enable_model_cpu_offload()
-        elif is_sequential_cpu_offload:
-            _pipeline.enable_sequential_cpu_offload()
-        # Unsafe code />
+            # 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 convert_state_dict_legacy_attn_format(self, state_dict, network_alphas):
         is_new_lora_format = all(
@@ -686,15 +680,77 @@ class UNet2DConditionLoadersMixin:
         self.apply(self._fuse_lora_apply)
 
     def _fuse_lora_apply(self, module):
-        if hasattr(module, "_fuse_lora"):
-            module._fuse_lora(self.lora_scale, self._safe_fusing)
+        if not USE_PEFT_BACKEND:
+            if hasattr(module, "_fuse_lora"):
+                module._fuse_lora(self.lora_scale, self._safe_fusing)
+        else:
+            from peft.tuners.tuners_utils import BaseTunerLayer
+
+            if isinstance(module, BaseTunerLayer):
+                if self.lora_scale != 1.0:
+                    module.scale_layer(self.lora_scale)
+                module.merge(safe_merge=self._safe_fusing)
 
     def unfuse_lora(self):
         self.apply(self._unfuse_lora_apply)
 
     def _unfuse_lora_apply(self, module):
-        if hasattr(module, "_unfuse_lora"):
-            module._unfuse_lora()
+        if not USE_PEFT_BACKEND:
+            if hasattr(module, "_unfuse_lora"):
+                module._unfuse_lora()
+        else:
+            from peft.tuners.tuners_utils import BaseTunerLayer
+
+            if isinstance(module, BaseTunerLayer):
+                module.unmerge()
+
+    def set_adapters(
+        self,
+        adapter_names: Union[List[str], str],
+        weights: Optional[Union[List[float], float]] = None,
+    ):
+        """
+        Sets the adapter layers for the unet.
+
+        Args:
+            adapter_names (`List[str]` or `str`):
+                The names of the adapters to use.
+            weights (`Union[List[float], float]`, *optional*):
+                The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the
+                adapters.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for `set_adapters()`.")
+
+        adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names
+
+        if weights is None:
+            weights = [1.0] * len(adapter_names)
+        elif isinstance(weights, float):
+            weights = [weights] * len(adapter_names)
+
+        if len(adapter_names) != len(weights):
+            raise ValueError(
+                f"Length of adapter names {len(adapter_names)} is not equal to the length of their weights {len(weights)}."
+            )
+
+        set_weights_and_activate_adapters(self, adapter_names, weights)
+
+    def disable_lora(self):
+        """
+        Disables the active LoRA layers for the unet.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+        set_adapter_layers(self, enabled=False)
+
+    def enable_lora(self):
+        """
+        Enables the active LoRA layers for the unet.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+        set_adapter_layers(self, enabled=True)
 
 
 def load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs):
@@ -1113,7 +1169,6 @@ class LoraLoaderMixin:
     text_encoder_name = TEXT_ENCODER_NAME
     unet_name = UNET_NAME
     num_fused_loras = 0
-    use_peft_backend = USE_PEFT_BACKEND
 
     def load_lora_weights(
         self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
@@ -1155,6 +1210,7 @@ class LoraLoaderMixin:
             network_alphas=network_alphas,
             unet=self.unet,
             low_cpu_mem_usage=low_cpu_mem_usage,
+            adapter_name=adapter_name,
             _pipeline=self,
         )
         self.load_lora_into_text_encoder(
@@ -1464,7 +1520,40 @@ class LoraLoaderMixin:
         return new_state_dict
 
     @classmethod
-    def load_lora_into_unet(cls, state_dict, network_alphas, unet, low_cpu_mem_usage=None, _pipeline=None):
+    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:
+            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)
+
+                    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)
+
+    @classmethod
+    def load_lora_into_unet(
+        cls, state_dict, network_alphas, unet, low_cpu_mem_usage=None, adapter_name=None, _pipeline=None
+    ):
         """
         This will load the LoRA layers specified in `state_dict` into `unet`.
 
@@ -1482,6 +1571,9 @@ class LoraLoaderMixin:
                 tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
                 Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
                 argument to `True` will raise an error.
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
         """
         low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
         # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
@@ -1508,6 +1600,56 @@ class LoraLoaderMixin:
             warn_message = "You have saved the LoRA weights using the old format. To convert the old LoRA weights to the new format, you can first load them in a dictionary and then create a new dictionary like the following: `new_state_dict = {f'unet.{module_name}': params for module_name, params in old_state_dict.items()}`."
             logger.warn(warn_message)
 
+        if USE_PEFT_BACKEND and len(state_dict.keys()) > 0:
+            from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
+
+            if adapter_name in getattr(unet, "peft_config", {}):
+                raise ValueError(
+                    f"Adapter name {adapter_name} already in use in the Unet - please select a new adapter name."
+                )
+
+            state_dict = convert_unet_state_dict_to_peft(state_dict)
+
+            if network_alphas is not None:
+                # The alphas state dict have the same structure as Unet, thus we convert it to peft format using
+                # `convert_unet_state_dict_to_peft` method.
+                network_alphas = convert_unet_state_dict_to_peft(network_alphas)
+
+            rank = {}
+            for key, val in state_dict.items():
+                if "lora_B" in key:
+                    rank[key] = val.shape[1]
+
+            lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict, is_unet=True)
+            lora_config = LoraConfig(**lora_config_kwargs)
+
+            # adapter_name
+            if adapter_name is None:
+                adapter_name = get_adapter_name(unet)
+
+            # 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 = cls._optionally_disable_offloading(_pipeline)
+
+            inject_adapter_in_model(lora_config, unet, adapter_name=adapter_name)
+            incompatible_keys = set_peft_model_state_dict(unet, state_dict, adapter_name)
+
+            if incompatible_keys is not None:
+                # check only for unexpected keys
+                unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+                if unexpected_keys:
+                    logger.warning(
+                        f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                        f" {unexpected_keys}. "
+                    )
+
+            # Offload back.
+            if is_model_cpu_offload:
+                _pipeline.enable_model_cpu_offload()
+            elif is_sequential_cpu_offload:
+                _pipeline.enable_sequential_cpu_offload()
+            # Unsafe code />
+
         unet.load_attn_procs(
             state_dict, network_alphas=network_alphas, low_cpu_mem_usage=low_cpu_mem_usage, _pipeline=_pipeline
         )
@@ -1570,7 +1712,7 @@ class LoraLoaderMixin:
                 rank = {}
                 text_encoder_lora_state_dict = convert_state_dict_to_diffusers(text_encoder_lora_state_dict)
 
-                if cls.use_peft_backend:
+                if USE_PEFT_BACKEND:
                     # convert state dict
                     text_encoder_lora_state_dict = convert_state_dict_to_peft(text_encoder_lora_state_dict)
 
@@ -1583,6 +1725,7 @@ class LoraLoaderMixin:
                         for name, _ in text_encoder_mlp_modules(text_encoder):
                             rank_key_fc1 = f"{name}.fc1.lora_B.weight"
                             rank_key_fc2 = f"{name}.fc2.lora_B.weight"
+
                             rank[rank_key_fc1] = text_encoder_lora_state_dict[rank_key_fc1].shape[1]
                             rank[rank_key_fc2] = text_encoder_lora_state_dict[rank_key_fc2].shape[1]
                 else:
@@ -1606,10 +1749,12 @@ class LoraLoaderMixin:
                         k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
                     }
 
-                if cls.use_peft_backend:
+                if USE_PEFT_BACKEND:
                     from peft import LoraConfig
 
-                    lora_config_kwargs = get_peft_kwargs(rank, network_alphas, text_encoder_lora_state_dict)
+                    lora_config_kwargs = get_peft_kwargs(
+                        rank, network_alphas, text_encoder_lora_state_dict, is_unet=False
+                    )
 
                     lora_config = LoraConfig(**lora_config_kwargs)
 
@@ -1617,17 +1762,18 @@ class LoraLoaderMixin:
                     if adapter_name is None:
                         adapter_name = get_adapter_name(text_encoder)
 
+                    is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)
+
                     # inject LoRA layers and load the state dict
+                    # in transformers we automatically check whether the adapter name is already in use or not
                     text_encoder.load_adapter(
                         adapter_name=adapter_name,
                         adapter_state_dict=text_encoder_lora_state_dict,
                         peft_config=lora_config,
                     )
+
                     # scale LoRA layers with `lora_scale`
                     scale_lora_layers(text_encoder, weight=lora_scale)
-
-                    is_model_cpu_offload = False
-                    is_sequential_cpu_offload = False
                 else:
                     cls._modify_text_encoder(
                         text_encoder,
@@ -1699,7 +1845,7 @@ class LoraLoaderMixin:
         return self._lora_scale if hasattr(self, "_lora_scale") else 1.0
 
     def _remove_text_encoder_monkey_patch(self):
-        if self.use_peft_backend:
+        if USE_PEFT_BACKEND:
             remove_method = recurse_remove_peft_layers
         else:
             remove_method = self._remove_text_encoder_monkey_patch_classmethod
@@ -1707,12 +1853,13 @@ class LoraLoaderMixin:
         if hasattr(self, "text_encoder"):
             remove_method(self.text_encoder)
 
-            if self.use_peft_backend:
+            # In case text encoder have no Lora attached
+            if USE_PEFT_BACKEND and getattr(self.text_encoder, "peft_config", None) is not None:
                 del self.text_encoder.peft_config
                 self.text_encoder._hf_peft_config_loaded = None
         if hasattr(self, "text_encoder_2"):
             remove_method(self.text_encoder_2)
-            if self.use_peft_backend:
+            if USE_PEFT_BACKEND:
                 del self.text_encoder_2.peft_config
                 self.text_encoder_2._hf_peft_config_loaded = None
 
@@ -2088,9 +2235,20 @@ class LoraLoaderMixin:
         >>> ...
         ```
         """
-        for _, module in self.unet.named_modules():
-            if hasattr(module, "set_lora_layer"):
-                module.set_lora_layer(None)
+        if not USE_PEFT_BACKEND:
+            if version.parse(__version__) > version.parse("0.23"):
+                logger.warn(
+                    "You are using `unload_lora_weights` to disable and unload lora weights. If you want to iteratively enable and disable adapter weights,"
+                    "you can use `pipe.enable_lora()` or `pipe.disable_lora()`. After installing the latest version of PEFT."
+                )
+
+            for _, module in self.unet.named_modules():
+                if hasattr(module, "set_lora_layer"):
+                    module.set_lora_layer(None)
+        else:
+            recurse_remove_peft_layers(self.unet)
+            if hasattr(self.unet, "peft_config"):
+                del self.unet.peft_config
 
         # Safe to call the following regardless of LoRA.
         self._remove_text_encoder_monkey_patch()
@@ -2131,7 +2289,7 @@ class LoraLoaderMixin:
         if fuse_unet:
             self.unet.fuse_lora(lora_scale, safe_fusing=safe_fusing)
 
-        if self.use_peft_backend:
+        if USE_PEFT_BACKEND:
             from peft.tuners.tuners_utils import BaseTunerLayer
 
             def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False):
@@ -2184,9 +2342,16 @@ class LoraLoaderMixin:
                 LoRA parameters then it won't have any effect.
         """
         if unfuse_unet:
-            self.unet.unfuse_lora()
+            if not USE_PEFT_BACKEND:
+                self.unet.unfuse_lora()
+            else:
+                from peft.tuners.tuners_utils import BaseTunerLayer
 
-        if self.use_peft_backend:
+                for module in self.unet.modules():
+                    if isinstance(module, BaseTunerLayer):
+                        module.unmerge()
+
+        if USE_PEFT_BACKEND:
             from peft.tuners.tuners_utils import BaseTunerLayer
 
             def unfuse_text_encoder_lora(text_encoder):
@@ -2219,7 +2384,7 @@ class LoraLoaderMixin:
 
         self.num_fused_loras -= 1
 
-    def set_adapter_for_text_encoder(
+    def set_adapters_for_text_encoder(
         self,
         adapter_names: Union[List[str], str],
         text_encoder: Optional[PreTrainedModel] = None,
@@ -2237,7 +2402,7 @@ class LoraLoaderMixin:
             text_encoder_weights (`List[float]`, *optional*):
                 The weights to use for the text encoder. If `None`, the weights are set to `1.0` for all the adapters.
         """
-        if not self.use_peft_backend:
+        if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
 
         def process_weights(adapter_names, weights):
@@ -2270,7 +2435,7 @@ class LoraLoaderMixin:
                 The text encoder module to disable the LoRA layers for. If `None`, it will try to get the
                 `text_encoder` attribute.
         """
-        if not self.use_peft_backend:
+        if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
 
         text_encoder = text_encoder or getattr(self, "text_encoder", None)
@@ -2287,13 +2452,146 @@ class LoraLoaderMixin:
                 The text encoder module to enable the LoRA layers for. If `None`, it will try to get the `text_encoder`
                 attribute.
         """
-        if not self.use_peft_backend:
+        if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
         text_encoder = text_encoder or getattr(self, "text_encoder", None)
         if text_encoder is None:
             raise ValueError("Text Encoder not found.")
         set_adapter_layers(self.text_encoder, enabled=True)
 
+    def set_adapters(
+        self,
+        adapter_names: Union[List[str], str],
+        adapter_weights: Optional[List[float]] = None,
+    ):
+        # Handle the UNET
+        self.unet.set_adapters(adapter_names, adapter_weights)
+
+        # Handle the Text Encoder
+        if hasattr(self, "text_encoder"):
+            self.set_adapters_for_text_encoder(adapter_names, self.text_encoder, adapter_weights)
+        if hasattr(self, "text_encoder_2"):
+            self.set_adapters_for_text_encoder(adapter_names, self.text_encoder_2, adapter_weights)
+
+    def disable_lora(self):
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        # Disable unet adapters
+        self.unet.disable_lora()
+
+        # Disable text encoder adapters
+        if hasattr(self, "text_encoder"):
+            self.disable_lora_for_text_encoder(self.text_encoder)
+        if hasattr(self, "text_encoder_2"):
+            self.disable_lora_for_text_encoder(self.text_encoder_2)
+
+    def enable_lora(self):
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        # Enable unet adapters
+        self.unet.enable_lora()
+
+        # Enable text encoder adapters
+        if hasattr(self, "text_encoder"):
+            self.enable_lora_for_text_encoder(self.text_encoder)
+        if hasattr(self, "text_encoder_2"):
+            self.enable_lora_for_text_encoder(self.text_encoder_2)
+
+    def get_active_adapters(self) -> List[str]:
+        """
+        Gets the list of the current active adapters.
+
+        Example:
+
+        ```python
+        from diffusers import DiffusionPipeline
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0",
+        ).to("cuda")
+        pipeline.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
+        pipeline.get_active_adapters()
+        ```
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError(
+                "PEFT backend is required for this method. Please install the latest version of PEFT `pip install -U peft`"
+            )
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        active_adapters = []
+
+        for module in self.unet.modules():
+            if isinstance(module, BaseTunerLayer):
+                active_adapters = module.active_adapters
+                break
+
+        return active_adapters
+
+    def get_list_adapters(self) -> Dict[str, List[str]]:
+        """
+        Gets the current list of all available adapters in the pipeline.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError(
+                "PEFT backend is required for this method. Please install the latest version of PEFT `pip install -U peft`"
+            )
+
+        set_adapters = {}
+
+        if hasattr(self, "text_encoder") and hasattr(self.text_encoder, "peft_config"):
+            set_adapters["text_encoder"] = list(self.text_encoder.peft_config.keys())
+
+        if hasattr(self, "text_encoder_2") and hasattr(self.text_encoder_2, "peft_config"):
+            set_adapters["text_encoder_2"] = list(self.text_encoder_2.peft_config.keys())
+
+        if hasattr(self, "unet") and hasattr(self.unet, "peft_config"):
+            set_adapters["unet"] = list(self.unet.peft_config.keys())
+
+        return set_adapters
+
+    def set_lora_device(self, adapter_names: List[str], device: Union[torch.device, str, int]) -> None:
+        """
+        Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case
+        you want to load multiple adapters and free some GPU memory.
+
+        Args:
+            adapter_names (`List[str]`):
+                List of adapters to send device to.
+            device (`Union[torch.device, str, int]`):
+                Device to send the adapters to. Can be either a torch device, a str or an integer.
+        """
+        if not USE_PEFT_BACKEND:
+            raise ValueError("PEFT backend is required for this method.")
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        # Handle the UNET
+        for unet_module in self.unet.modules():
+            if isinstance(unet_module, BaseTunerLayer):
+                for adapter_name in adapter_names:
+                    unet_module.lora_A[adapter_name].to(device)
+                    unet_module.lora_B[adapter_name].to(device)
+
+        # Handle the text encoder
+        modules_to_process = []
+        if hasattr(self, "text_encoder"):
+            modules_to_process.append(self.text_encoder)
+
+        if hasattr(self, "text_encoder_2"):
+            modules_to_process.append(self.text_encoder_2)
+
+        for text_encoder in modules_to_process:
+            # loop over submodules
+            for text_encoder_module in text_encoder.modules():
+                if isinstance(text_encoder_module, BaseTunerLayer):
+                    for adapter_name in adapter_names:
+                        text_encoder_module.lora_A[adapter_name].to(device)
+                        text_encoder_module.lora_B[adapter_name].to(device)
+
 
 class FromSingleFileMixin:
     """
@@ -2878,7 +3176,12 @@ class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
     """This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL"""
 
     # Overrride to properly handle the loading and unloading of the additional text encoder.
-    def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
+    def load_lora_weights(
+        self,
+        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
+        adapter_name: Optional[str] = None,
+        **kwargs,
+    ):
         """
         Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.unet` and
         `self.text_encoder`.
@@ -2896,6 +3199,9 @@ class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
         Parameters:
             pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                 See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+            adapter_name (`str`, *optional*):
+                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
+                `default_{i}` where i is the total number of adapters being loaded.
             kwargs (`dict`, *optional*):
                 See [`~loaders.LoraLoaderMixin.lora_state_dict`].
         """
@@ -2913,7 +3219,9 @@ class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
-        self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet, _pipeline=self)
+        self.load_lora_into_unet(
+            state_dict, network_alphas=network_alphas, unet=self.unet, adapter_name=adapter_name, _pipeline=self
+        )
         text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
         if len(text_encoder_state_dict) > 0:
             self.load_lora_into_text_encoder(
@@ -2922,6 +3230,7 @@ class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
                 text_encoder=self.text_encoder,
                 prefix="text_encoder",
                 lora_scale=self.lora_scale,
+                adapter_name=adapter_name,
                 _pipeline=self,
             )
 
@@ -2933,6 +3242,7 @@ class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
                 text_encoder=self.text_encoder_2,
                 prefix="text_encoder_2",
                 lora_scale=self.lora_scale,
+                adapter_name=adapter_name,
                 _pipeline=self,
             )
 
@@ -2999,16 +3309,17 @@ class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
         )
 
     def _remove_text_encoder_monkey_patch(self):
-        if self.use_peft_backend:
+        if USE_PEFT_BACKEND:
             recurse_remove_peft_layers(self.text_encoder)
             # TODO: @younesbelkada handle this in transformers side
-            del self.text_encoder.peft_config
-            self.text_encoder._hf_peft_config_loaded = None
+            if getattr(self.text_encoder, "peft_config", None) is not None:
+                del self.text_encoder.peft_config
+                self.text_encoder._hf_peft_config_loaded = None
 
             recurse_remove_peft_layers(self.text_encoder_2)
-
-            del self.text_encoder_2.peft_config
-            self.text_encoder_2._hf_peft_config_loaded = None
+            if getattr(self.text_encoder_2, "peft_config", None) is not None:
+                del self.text_encoder_2.peft_config
+                self.text_encoder_2._hf_peft_config_loaded = None
         else:
             self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
             self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)

src/diffusers/models/attention.py

@@ -17,6 +17,7 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 
+from ..utils import USE_PEFT_BACKEND
 from ..utils.torch_utils import maybe_allow_in_graph
 from .activations import get_activation
 from .attention_processor import Attention
@@ -300,6 +301,7 @@ class FeedForward(nn.Module):
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = dim_out if dim_out is not None else dim
+        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
 
         if activation_fn == "gelu":
             act_fn = GELU(dim, inner_dim)
@@ -316,14 +318,15 @@ class FeedForward(nn.Module):
         # project dropout
         self.net.append(nn.Dropout(dropout))
         # project out
-        self.net.append(LoRACompatibleLinear(inner_dim, dim_out))
+        self.net.append(linear_cls(inner_dim, dim_out))
         # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
         if final_dropout:
             self.net.append(nn.Dropout(dropout))
 
     def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
+        compatible_cls = (GEGLU,) if USE_PEFT_BACKEND else (GEGLU, LoRACompatibleLinear)
         for module in self.net:
-            if isinstance(module, (LoRACompatibleLinear, GEGLU)):
+            if isinstance(module, compatible_cls):
                 hidden_states = module(hidden_states, scale)
             else:
                 hidden_states = module(hidden_states)
@@ -368,7 +371,9 @@ class GEGLU(nn.Module):
 
     def __init__(self, dim_in: int, dim_out: int):
         super().__init__()
-        self.proj = LoRACompatibleLinear(dim_in, dim_out * 2)
+        linear_cls = LoRACompatibleLinear if not USE_PEFT_BACKEND else nn.Linear
+
+        self.proj = linear_cls(dim_in, dim_out * 2)
 
     def gelu(self, gate: torch.Tensor) -> torch.Tensor:
         if gate.device.type != "mps":
@@ -377,7 +382,8 @@ class GEGLU(nn.Module):
         return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
 
     def forward(self, hidden_states, scale: float = 1.0):
-        hidden_states, gate = self.proj(hidden_states, scale).chunk(2, dim=-1)
+        args = () if USE_PEFT_BACKEND else (scale,)
+        hidden_states, gate = self.proj(hidden_states, *args).chunk(2, dim=-1)
         return hidden_states * self.gelu(gate)
 
 

src/diffusers/models/attention_processor.py

@@ -18,7 +18,7 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 
-from ..utils import deprecate, logging
+from ..utils import USE_PEFT_BACKEND, deprecate, logging
 from ..utils.import_utils import is_xformers_available
 from ..utils.torch_utils import maybe_allow_in_graph
 from .lora import LoRACompatibleLinear, LoRALinearLayer
@@ -137,22 +137,27 @@ class Attention(nn.Module):
                 f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
             )
 
-        self.to_q = LoRACompatibleLinear(query_dim, self.inner_dim, bias=bias)
+        if USE_PEFT_BACKEND:
+            linear_cls = nn.Linear
+        else:
+            linear_cls = LoRACompatibleLinear
+
+        self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)
 
         if not self.only_cross_attention:
             # only relevant for the `AddedKVProcessor` classes
-            self.to_k = LoRACompatibleLinear(self.cross_attention_dim, self.inner_dim, bias=bias)
-            self.to_v = LoRACompatibleLinear(self.cross_attention_dim, self.inner_dim, bias=bias)
+            self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
+            self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
         else:
             self.to_k = None
             self.to_v = None
 
         if self.added_kv_proj_dim is not None:
-            self.add_k_proj = LoRACompatibleLinear(added_kv_proj_dim, self.inner_dim)
-            self.add_v_proj = LoRACompatibleLinear(added_kv_proj_dim, self.inner_dim)
+            self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
+            self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
 
         self.to_out = nn.ModuleList([])
-        self.to_out.append(LoRACompatibleLinear(self.inner_dim, query_dim, bias=out_bias))
+        self.to_out.append(linear_cls(self.inner_dim, query_dim, bias=out_bias))
         self.to_out.append(nn.Dropout(dropout))
 
         # set attention processor
@@ -545,6 +550,8 @@ class AttnProcessor:
     ):
         residual = hidden_states
 
+        args = () if USE_PEFT_BACKEND else (scale,)
+
         if attn.spatial_norm is not None:
             hidden_states = attn.spatial_norm(hidden_states, temb)
 
@@ -562,15 +569,15 @@ class AttnProcessor:
         if attn.group_norm is not None:
             hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
 
-        query = attn.to_q(hidden_states, scale=scale)
+        query = attn.to_q(hidden_states, *args)
 
         if encoder_hidden_states is None:
             encoder_hidden_states = hidden_states
         elif attn.norm_cross:
             encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
 
-        key = attn.to_k(encoder_hidden_states, scale=scale)
-        value = attn.to_v(encoder_hidden_states, scale=scale)
+        key = attn.to_k(encoder_hidden_states, *args)
+        value = attn.to_v(encoder_hidden_states, *args)
 
         query = attn.head_to_batch_dim(query)
         key = attn.head_to_batch_dim(key)
@@ -581,7 +588,7 @@ class AttnProcessor:
         hidden_states = attn.batch_to_head_dim(hidden_states)
 
         # linear proj
-        hidden_states = attn.to_out[0](hidden_states, scale=scale)
+        hidden_states = attn.to_out[0](hidden_states, *args)
         # dropout
         hidden_states = attn.to_out[1](hidden_states)
 
@@ -1007,15 +1014,20 @@ class AttnProcessor2_0:
         if attn.group_norm is not None:
             hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
 
-        query = attn.to_q(hidden_states, scale=scale)
+        args = () if USE_PEFT_BACKEND else (scale,)
+        query = attn.to_q(hidden_states, *args)
 
         if encoder_hidden_states is None:
             encoder_hidden_states = hidden_states
         elif attn.norm_cross:
             encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
 
-        key = attn.to_k(encoder_hidden_states, scale=scale)
-        value = attn.to_v(encoder_hidden_states, scale=scale)
+        key = (
+            attn.to_k(encoder_hidden_states, scale=scale) if not USE_PEFT_BACKEND else attn.to_k(encoder_hidden_states)
+        )
+        value = (
+            attn.to_v(encoder_hidden_states, scale=scale) if not USE_PEFT_BACKEND else attn.to_v(encoder_hidden_states)
+        )
 
         inner_dim = key.shape[-1]
         head_dim = inner_dim // attn.heads
@@ -1035,7 +1047,9 @@ class AttnProcessor2_0:
         hidden_states = hidden_states.to(query.dtype)
 
         # linear proj
-        hidden_states = attn.to_out[0](hidden_states, scale=scale)
+        hidden_states = (
+            attn.to_out[0](hidden_states, scale=scale) if not USE_PEFT_BACKEND else attn.to_out[0](hidden_states)
+        )
         # dropout
         hidden_states = attn.to_out[1](hidden_states)
 

src/diffusers/models/embeddings.py

@@ -18,6 +18,7 @@ import numpy as np
 import torch
 from torch import nn
 
+from ..utils import USE_PEFT_BACKEND
 from .activations import get_activation
 from .lora import LoRACompatibleLinear
 
@@ -166,8 +167,9 @@ class TimestepEmbedding(nn.Module):
         cond_proj_dim=None,
     ):
         super().__init__()
+        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
 
-        self.linear_1 = LoRACompatibleLinear(in_channels, time_embed_dim)
+        self.linear_1 = linear_cls(in_channels, time_embed_dim)
 
         if cond_proj_dim is not None:
             self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
@@ -180,7 +182,7 @@ class TimestepEmbedding(nn.Module):
             time_embed_dim_out = out_dim
         else:
             time_embed_dim_out = time_embed_dim
-        self.linear_2 = LoRACompatibleLinear(time_embed_dim, time_embed_dim_out)
+        self.linear_2 = linear_cls(time_embed_dim, time_embed_dim_out)
 
         if post_act_fn is None:
             self.post_act = None

src/diffusers/models/modeling_utils.py

@@ -32,10 +32,12 @@ from ..utils import (
     DIFFUSERS_CACHE,
     FLAX_WEIGHTS_NAME,
     HF_HUB_OFFLINE,
+    MIN_PEFT_VERSION,
     SAFETENSORS_WEIGHTS_NAME,
     WEIGHTS_NAME,
     _add_variant,
     _get_model_file,
+    check_peft_version,
     deprecate,
     is_accelerate_available,
     is_torch_version,
@@ -187,6 +189,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
     _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
     _supports_gradient_checkpointing = False
     _keys_to_ignore_on_load_unexpected = None
+    _hf_peft_config_loaded = False
 
     def __init__(self):
         super().__init__()
@@ -292,6 +295,153 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         """
         self.set_use_memory_efficient_attention_xformers(False)
 
+    def add_adapter(self, adapter_config, adapter_name: str = "default") -> None:
+        r"""
+        Adds a new adapter to the current model for training. If no adapter name is passed, a default name is assigned
+        to the adapter to follow the convention of the PEFT library.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them in the PEFT
+        [documentation](https://huggingface.co/docs/peft).
+
+        Args:
+            adapter_config (`[~peft.PeftConfig]`):
+                The configuration of the adapter to add; supported adapters are non-prefix tuning and adaption prompt
+                methods.
+            adapter_name (`str`, *optional*, defaults to `"default"`):
+                The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        from peft import PeftConfig, inject_adapter_in_model
+
+        if not self._hf_peft_config_loaded:
+            self._hf_peft_config_loaded = True
+        elif adapter_name in self.peft_config:
+            raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
+
+        if not isinstance(adapter_config, PeftConfig):
+            raise ValueError(
+                f"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead."
+            )
+
+        # Unlike transformers, here we don't need to retrieve the name_or_path of the unet as the loading logic is
+        # handled by the `load_lora_layers` or `LoraLoaderMixin`. Therefore we set it to `None` here.
+        adapter_config.base_model_name_or_path = None
+        inject_adapter_in_model(adapter_config, self, adapter_name)
+        self.set_adapter(adapter_name)
+
+    def set_adapter(self, adapter_name: Union[str, List[str]]) -> None:
+        """
+        Sets a specific adapter by forcing the model to only use that adapter and disables the other adapters.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
+        official documentation: https://huggingface.co/docs/peft
+
+        Args:
+            adapter_name (Union[str, List[str]])):
+                The list of adapters to set or the adapter name in case of single adapter.
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not self._hf_peft_config_loaded:
+            raise ValueError("No adapter loaded. Please load an adapter first.")
+
+        if isinstance(adapter_name, str):
+            adapter_name = [adapter_name]
+
+        missing = set(adapter_name) - set(self.peft_config)
+        if len(missing) > 0:
+            raise ValueError(
+                f"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s)."
+                f" current loaded adapters are: {list(self.peft_config.keys())}"
+            )
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        _adapters_has_been_set = False
+
+        for _, module in self.named_modules():
+            if isinstance(module, BaseTunerLayer):
+                if hasattr(module, "set_adapter"):
+                    module.set_adapter(adapter_name)
+                # Previous versions of PEFT does not support multi-adapter inference
+                elif not hasattr(module, "set_adapter") and len(adapter_name) != 1:
+                    raise ValueError(
+                        "You are trying to set multiple adapters and you have a PEFT version that does not support multi-adapter inference. Please upgrade to the latest version of PEFT."
+                        " `pip install -U peft` or `pip install -U git+https://github.com/huggingface/peft.git`"
+                    )
+                else:
+                    module.active_adapter = adapter_name
+                _adapters_has_been_set = True
+
+        if not _adapters_has_been_set:
+            raise ValueError(
+                "Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters."
+            )
+
+    def disable_adapters(self) -> None:
+        r"""
+        Disable all adapters attached to the model and fallback to inference with the base model only.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
+        official documentation: https://huggingface.co/docs/peft
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not self._hf_peft_config_loaded:
+            raise ValueError("No adapter loaded. Please load an adapter first.")
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        for _, module in self.named_modules():
+            if isinstance(module, BaseTunerLayer):
+                if hasattr(module, "enable_adapters"):
+                    module.enable_adapters(enabled=False)
+                else:
+                    # support for older PEFT versions
+                    module.disable_adapters = True
+
+    def enable_adapters(self) -> None:
+        """
+        Enable adapters that are attached to the model. The model will use `self.active_adapters()` to retrieve the
+        list of adapters to enable.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
+        official documentation: https://huggingface.co/docs/peft
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not self._hf_peft_config_loaded:
+            raise ValueError("No adapter loaded. Please load an adapter first.")
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        for _, module in self.named_modules():
+            if isinstance(module, BaseTunerLayer):
+                if hasattr(module, "enable_adapters"):
+                    module.enable_adapters(enabled=True)
+                else:
+                    # support for older PEFT versions
+                    module.disable_adapters = False
+
+    def active_adapters(self) -> List[str]:
+        """
+        Gets the current list of active adapters of the model.
+
+        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
+        official documentation: https://huggingface.co/docs/peft
+        """
+        check_peft_version(min_version=MIN_PEFT_VERSION)
+
+        if not self._hf_peft_config_loaded:
+            raise ValueError("No adapter loaded. Please load an adapter first.")
+
+        from peft.tuners.tuners_utils import BaseTunerLayer
+
+        for _, module in self.named_modules():
+            if isinstance(module, BaseTunerLayer):
+                return module.active_adapter
+
     def save_pretrained(
         self,
         save_directory: Union[str, os.PathLike],

src/diffusers/models/resnet.py

@@ -20,6 +20,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from ..utils import USE_PEFT_BACKEND
 from .activations import get_activation
 from .attention import AdaGroupNorm
 from .attention_processor import SpatialNorm
@@ -149,12 +150,13 @@ class Upsample2D(nn.Module):
         self.use_conv = use_conv
         self.use_conv_transpose = use_conv_transpose
         self.name = name
+        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
 
         conv = None
         if use_conv_transpose:
             conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
         elif use_conv:
-            conv = LoRACompatibleConv(self.channels, self.out_channels, 3, padding=1)
+            conv = conv_cls(self.channels, self.out_channels, 3, padding=1)
 
         # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
         if name == "conv":
@@ -193,12 +195,12 @@ class Upsample2D(nn.Module):
         # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
         if self.use_conv:
             if self.name == "conv":
-                if isinstance(self.conv, LoRACompatibleConv):
+                if isinstance(self.conv, LoRACompatibleConv) and not USE_PEFT_BACKEND:
                     hidden_states = self.conv(hidden_states, scale)
                 else:
                     hidden_states = self.conv(hidden_states)
             else:
-                if isinstance(self.Conv2d_0, LoRACompatibleConv):
+                if isinstance(self.Conv2d_0, LoRACompatibleConv) and not USE_PEFT_BACKEND:
                     hidden_states = self.Conv2d_0(hidden_states, scale)
                 else:
                     hidden_states = self.Conv2d_0(hidden_states)
@@ -237,9 +239,10 @@ class Downsample2D(nn.Module):
         self.padding = padding
         stride = 2
         self.name = name
+        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
 
         if use_conv:
-            conv = LoRACompatibleConv(self.channels, self.out_channels, 3, stride=stride, padding=padding)
+            conv = conv_cls(self.channels, self.out_channels, 3, stride=stride, padding=padding)
         else:
             assert self.channels == self.out_channels
             conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
@@ -255,13 +258,18 @@ class Downsample2D(nn.Module):
 
     def forward(self, hidden_states, scale: float = 1.0):
         assert hidden_states.shape[1] == self.channels
+
         if self.use_conv and self.padding == 0:
             pad = (0, 1, 0, 1)
             hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
 
         assert hidden_states.shape[1] == self.channels
-        if isinstance(self.conv, LoRACompatibleConv):
-            hidden_states = self.conv(hidden_states, scale)
+
+        if not USE_PEFT_BACKEND:
+            if isinstance(self.conv, LoRACompatibleConv):
+                hidden_states = self.conv(hidden_states, scale)
+            else:
+                hidden_states = self.conv(hidden_states)
         else:
             hidden_states = self.conv(hidden_states)
 
@@ -608,6 +616,9 @@ class ResnetBlock2D(nn.Module):
         self.time_embedding_norm = time_embedding_norm
         self.skip_time_act = skip_time_act
 
+        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
+
         if groups_out is None:
             groups_out = groups
 
@@ -618,13 +629,13 @@ class ResnetBlock2D(nn.Module):
         else:
             self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
 
-        self.conv1 = LoRACompatibleConv(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.conv1 = conv_cls(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
 
         if temb_channels is not None:
             if self.time_embedding_norm == "default":
-                self.time_emb_proj = LoRACompatibleLinear(temb_channels, out_channels)
+                self.time_emb_proj = linear_cls(temb_channels, out_channels)
             elif self.time_embedding_norm == "scale_shift":
-                self.time_emb_proj = LoRACompatibleLinear(temb_channels, 2 * out_channels)
+                self.time_emb_proj = linear_cls(temb_channels, 2 * out_channels)
             elif self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
                 self.time_emb_proj = None
             else:
@@ -641,7 +652,7 @@ class ResnetBlock2D(nn.Module):
 
         self.dropout = torch.nn.Dropout(dropout)
         conv_2d_out_channels = conv_2d_out_channels or out_channels
-        self.conv2 = LoRACompatibleConv(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
+        self.conv2 = conv_cls(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
 
         self.nonlinearity = get_activation(non_linearity)
 
@@ -667,7 +678,7 @@ class ResnetBlock2D(nn.Module):
 
         self.conv_shortcut = None
         if self.use_in_shortcut:
-            self.conv_shortcut = LoRACompatibleConv(
+            self.conv_shortcut = conv_cls(
                 in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias
             )
 
@@ -708,12 +719,16 @@ class ResnetBlock2D(nn.Module):
                 else self.downsample(hidden_states)
             )
 
-        hidden_states = self.conv1(hidden_states, scale)
+        hidden_states = self.conv1(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv1(hidden_states)
 
         if self.time_emb_proj is not None:
             if not self.skip_time_act:
                 temb = self.nonlinearity(temb)
-            temb = self.time_emb_proj(temb, scale)[:, :, None, None]
+            temb = (
+                self.time_emb_proj(temb, scale)[:, :, None, None]
+                if not USE_PEFT_BACKEND
+                else self.time_emb_proj(temb)[:, :, None, None]
+            )
 
         if temb is not None and self.time_embedding_norm == "default":
             hidden_states = hidden_states + temb
@@ -730,10 +745,12 @@ class ResnetBlock2D(nn.Module):
         hidden_states = self.nonlinearity(hidden_states)
 
         hidden_states = self.dropout(hidden_states)
-        hidden_states = self.conv2(hidden_states, scale)
+        hidden_states = self.conv2(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv2(hidden_states)
 
         if self.conv_shortcut is not None:
-            input_tensor = self.conv_shortcut(input_tensor, scale)
+            input_tensor = (
+                self.conv_shortcut(input_tensor, scale) if not USE_PEFT_BACKEND else self.conv_shortcut(input_tensor)
+            )
 
         output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
 

src/diffusers/models/transformer_2d.py

@@ -20,7 +20,7 @@ from torch import nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..models.embeddings import ImagePositionalEmbeddings
-from ..utils import BaseOutput, deprecate
+from ..utils import USE_PEFT_BACKEND, BaseOutput, deprecate
 from .attention import BasicTransformerBlock
 from .embeddings import PatchEmbed
 from .lora import LoRACompatibleConv, LoRACompatibleLinear
@@ -100,6 +100,9 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
         self.attention_head_dim = attention_head_dim
         inner_dim = num_attention_heads * attention_head_dim
 
+        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
+        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+
         # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
         # Define whether input is continuous or discrete depending on configuration
         self.is_input_continuous = (in_channels is not None) and (patch_size is None)
@@ -139,9 +142,9 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
 
             self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
             if use_linear_projection:
-                self.proj_in = LoRACompatibleLinear(in_channels, inner_dim)
+                self.proj_in = linear_cls(in_channels, inner_dim)
             else:
-                self.proj_in = LoRACompatibleConv(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+                self.proj_in = conv_cls(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
         elif self.is_input_vectorized:
             assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
             assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
@@ -197,9 +200,9 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
         if self.is_input_continuous:
             # TODO: should use out_channels for continuous projections
             if use_linear_projection:
-                self.proj_out = LoRACompatibleLinear(inner_dim, in_channels)
+                self.proj_out = linear_cls(inner_dim, in_channels)
             else:
-                self.proj_out = LoRACompatibleConv(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+                self.proj_out = conv_cls(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
         elif self.is_input_vectorized:
             self.norm_out = nn.LayerNorm(inner_dim)
             self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
@@ -292,13 +295,21 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
 
             hidden_states = self.norm(hidden_states)
             if not self.use_linear_projection:
-                hidden_states = self.proj_in(hidden_states, scale=lora_scale)
+                hidden_states = (
+                    self.proj_in(hidden_states, scale=lora_scale)
+                    if not USE_PEFT_BACKEND
+                    else self.proj_in(hidden_states)
+                )
                 inner_dim = hidden_states.shape[1]
                 hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
             else:
                 inner_dim = hidden_states.shape[1]
                 hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
-                hidden_states = self.proj_in(hidden_states, scale=lora_scale)
+                hidden_states = (
+                    self.proj_in(hidden_states, scale=lora_scale)
+                    if not USE_PEFT_BACKEND
+                    else self.proj_in(hidden_states)
+                )
 
         elif self.is_input_vectorized:
             hidden_states = self.latent_image_embedding(hidden_states)
@@ -334,9 +345,17 @@ class Transformer2DModel(ModelMixin, ConfigMixin):
         if self.is_input_continuous:
             if not self.use_linear_projection:
                 hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
-                hidden_states = self.proj_out(hidden_states, scale=lora_scale)
+                hidden_states = (
+                    self.proj_out(hidden_states, scale=lora_scale)
+                    if not USE_PEFT_BACKEND
+                    else self.proj_out(hidden_states)
+                )
             else:
-                hidden_states = self.proj_out(hidden_states, scale=lora_scale)
+                hidden_states = (
+                    self.proj_out(hidden_states, scale=lora_scale)
+                    if not USE_PEFT_BACKEND
+                    else self.proj_out(hidden_states)
+                )
                 hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
 
             output = hidden_states + residual

src/diffusers/models/unet_2d_condition.py

@@ -20,7 +20,7 @@ import torch.utils.checkpoint
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..loaders import UNet2DConditionLoadersMixin
-from ..utils import BaseOutput, logging
+from ..utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
 from .activations import get_activation
 from .attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
@@ -995,6 +995,9 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
 
         # 3. down
         lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
 
         is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
         is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
@@ -1094,6 +1097,10 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
             sample = self.conv_act(sample)
         sample = self.conv_out(sample)
 
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self)
+
         if not return_dict:
             return (sample,)
 

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -25,7 +25,14 @@ from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
+from ...utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline
 from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
@@ -304,7 +311,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -432,7 +439,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
 
@@ -668,6 +675,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
         # 0. Default height and width to unet
         height = height or self.unet.config.sample_size * self.vae_scale_factor
         width = width or self.unet.config.sample_size * self.vae_scale_factor
+        # to deal with lora scaling and other possible forward hooks
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
@@ -689,9 +697,8 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
         do_classifier_free_guidance = guidance_scale > 1.0
 
         # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
+        lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+
         prompt_embeds, negative_prompt_embeds = self.encode_prompt(
             prompt,
             device,
@@ -700,7 +707,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
             negative_prompt,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
+            lora_scale=lora_scale,
             clip_skip=clip_skip,
         )
         # For classifier free guidance, we need to do two forward passes.

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -29,6 +29,7 @@ from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     PIL_INTERPOLATION,
+    USE_PEFT_BACKEND,
     deprecate,
     logging,
     replace_example_docstring,
@@ -309,7 +310,7 @@ class AltDiffusionImg2ImgPipeline(
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -437,7 +438,7 @@ class AltDiffusionImg2ImgPipeline(
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
 

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -27,7 +27,14 @@ from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoa
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
+from ...utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
 from ...utils.torch_utils import is_compiled_module, randn_tensor
 from ..pipeline_utils import DiffusionPipeline
 from ..stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
@@ -287,7 +294,7 @@ class StableDiffusionControlNetPipeline(
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -415,7 +422,7 @@ class StableDiffusionControlNetPipeline(
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -27,6 +27,7 @@ from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
+    USE_PEFT_BACKEND,
     deprecate,
     logging,
     replace_example_docstring,
@@ -317,7 +318,7 @@ class StableDiffusionControlNetImg2ImgPipeline(
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -445,7 +446,7 @@ class StableDiffusionControlNetImg2ImgPipeline(
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -28,7 +28,14 @@ from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoa
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
+from ...utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
 from ...utils.torch_utils import is_compiled_module, randn_tensor
 from ..pipeline_utils import DiffusionPipeline
 from ..stable_diffusion import StableDiffusionPipelineOutput
@@ -438,7 +445,7 @@ class StableDiffusionControlNetInpaintPipeline(
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -566,7 +573,7 @@ class StableDiffusionControlNetInpaintPipeline(
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -33,6 +33,7 @@ from ...models.attention_processor import (
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
+    USE_PEFT_BACKEND,
     is_invisible_watermark_available,
     logging,
     replace_example_docstring,
@@ -316,7 +317,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
                 adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
             else:
@@ -458,7 +459,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
                 bs_embed * num_images_per_prompt, -1
             )
 
-        if isinstance(self, StableDiffusionXLLoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
             unscale_lora_layers(self.text_encoder_2)

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -35,7 +35,7 @@ from ...models.attention_processor import (
 )
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import is_compiled_module, randn_tensor
 from ..pipeline_utils import DiffusionPipeline
 from ..stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -285,7 +285,7 @@ class StableDiffusionXLControlNetPipeline(
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
                 adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
             else:
@@ -427,7 +427,7 @@ class StableDiffusionXLControlNetPipeline(
                 bs_embed * num_images_per_prompt, -1
             )
 
-        if isinstance(self, StableDiffusionXLLoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
             unscale_lora_layers(self.text_encoder_2)

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -36,6 +36,7 @@ from ...models.attention_processor import (
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
+    USE_PEFT_BACKEND,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -328,7 +329,7 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
                 adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
             else:
@@ -470,7 +471,7 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
                 bs_embed * num_images_per_prompt, -1
             )
 
-        if isinstance(self, StableDiffusionXLLoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
             unscale_lora_layers(self.text_encoder_2)

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -27,7 +27,7 @@ from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import DDIMScheduler
-from ...utils import PIL_INTERPOLATION, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline
 from .pipeline_output import StableDiffusionPipelineOutput
@@ -308,7 +308,7 @@ class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lor
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -436,7 +436,7 @@ class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lor
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -25,7 +25,14 @@ from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoa
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
+from ...utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline
 from .pipeline_output import StableDiffusionPipelineOutput
@@ -297,7 +304,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -425,7 +432,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
 
@@ -658,6 +665,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
         # 0. Default height and width to unet
         height = height or self.unet.config.sample_size * self.vae_scale_factor
         width = width or self.unet.config.sample_size * self.vae_scale_factor
+        # to deal with lora scaling and other possible forward hooks
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
@@ -679,9 +687,8 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
         do_classifier_free_guidance = guidance_scale > 1.0
 
         # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
+        lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+
         prompt_embeds, negative_prompt_embeds = self.encode_prompt(
             prompt,
             device,
@@ -690,7 +697,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
             negative_prompt,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
+            lora_scale=lora_scale,
             clip_skip=clip_skip,
         )
         # For classifier free guidance, we need to do two forward passes.

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py

@@ -27,7 +27,14 @@ from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.attention_processor import Attention
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
+from ...utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline
 from . import StableDiffusionPipelineOutput
@@ -332,7 +339,7 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, TextualInversion
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -460,7 +467,7 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, TextualInversion
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py

@@ -28,7 +28,7 @@ from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import PIL_INTERPOLATION, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
@@ -213,7 +213,7 @@ class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoader
             self._lora_scale = lora_scale
 
             # dynamically adjust the LoRA scale
-            if not self.use_peft_backend:
+            if not USE_PEFT_BACKEND:
                 adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
             else:
                 scale_lora_layers(self.text_encoder, lora_scale)
@@ -341,7 +341,7 @@ class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoader
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
 
-        if isinstance(self, LoraLoaderMixin) and self.use_peft_backend:
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(self.text_encoder)