[`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 ...

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py

@@ -31,7 +31,14 @@ from ...models.attention_processor import (
 )
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
+from ...utils import (
+    PIL_INTERPOLATION,
+    USE_PEFT_BACKEND,
+    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_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -283,7 +290,7 @@ class StableDiffusionXLAdapterPipeline(
             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:
@@ -425,7 +432,7 @@ class StableDiffusionXLAdapterPipeline(
                 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/text_to_video_synthesis/pipeline_text_to_video_synth.py

@@ -23,7 +23,14 @@ from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet3DConditionModel
 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 TextToVideoSDPipelineOutput
@@ -224,7 +231,7 @@ class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lora
             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)
@@ -352,7 +359,7 @@ class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lora
             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/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py

@@ -24,7 +24,14 @@ from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet3DConditionModel
 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 TextToVideoSDPipelineOutput
@@ -286,7 +293,7 @@ class VideoToVideoSDPipeline(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)
@@ -414,7 +421,7 @@ class VideoToVideoSDPipeline(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/unidiffuser/pipeline_unidiffuser.py

@@ -18,7 +18,7 @@ from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.outputs import BaseOutput
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline
@@ -426,7 +426,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
             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)
@@ -554,7 +554,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
             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/versatile_diffusion/modeling_text_unet.py

@@ -31,7 +31,7 @@ from ...models.embeddings import (
 )
 from ...models.transformer_2d import Transformer2DModel
 from ...models.unet_2d_condition import UNet2DConditionOutput
-from ...utils import is_torch_version, logging
+from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import apply_freeu
 
 
@@ -1211,6 +1211,9 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
 
         # 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
@@ -1310,6 +1313,10 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
             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/utils/__init__.py

@@ -26,9 +26,11 @@ from .constants import (
     FLAX_WEIGHTS_NAME,
     HF_MODULES_CACHE,
     HUGGINGFACE_CO_RESOLVE_ENDPOINT,
+    MIN_PEFT_VERSION,
     ONNX_EXTERNAL_WEIGHTS_NAME,
     ONNX_WEIGHTS_NAME,
     SAFETENSORS_WEIGHTS_NAME,
+    USE_PEFT_BACKEND,
     WEIGHTS_NAME,
 )
 from .deprecation_utils import deprecate
@@ -86,6 +88,7 @@ from .loading_utils import load_image
 from .logging import get_logger
 from .outputs import BaseOutput
 from .peft_utils import (
+    check_peft_version,
     get_adapter_name,
     get_peft_kwargs,
     recurse_remove_peft_layers,
@@ -95,7 +98,11 @@ from .peft_utils import (
     unscale_lora_layers,
 )
 from .pil_utils import PIL_INTERPOLATION, make_image_grid, numpy_to_pil, pt_to_pil
-from .state_dict_utils import convert_state_dict_to_diffusers, convert_state_dict_to_peft
+from .state_dict_utils import (
+    convert_state_dict_to_diffusers,
+    convert_state_dict_to_peft,
+    convert_unet_state_dict_to_peft,
+)
 
 
 logger = get_logger(__name__)

src/diffusers/utils/constants.py

@@ -11,13 +11,19 @@
 # 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
 
 from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
+from packaging import version
+
+from .import_utils import is_peft_available, is_transformers_available
 
 
 default_cache_path = HUGGINGFACE_HUB_CACHE
 
+MIN_PEFT_VERSION = "0.5.0"
+
 
 CONFIG_NAME = "config.json"
 WEIGHTS_NAME = "diffusion_pytorch_model.bin"
@@ -30,3 +36,16 @@ DIFFUSERS_CACHE = default_cache_path
 DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
 HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
 DEPRECATED_REVISION_ARGS = ["fp16", "non-ema"]
+
+# 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(MIN_PEFT_VERSION)
+_required_transformers_version = is_transformers_available() and version.parse(
+    version.parse(importlib.metadata.version("transformers")).base_version
+) > version.parse("4.33")
+
+USE_PEFT_BACKEND = _required_peft_version and _required_transformers_version

src/diffusers/utils/peft_utils.py

@@ -15,8 +15,11 @@
 PEFT utilities: Utilities related to peft library
 """
 import collections
+import importlib
 
-from .import_utils import is_torch_available
+from packaging import version
+
+from .import_utils import is_peft_available, is_torch_available
 
 
 def recurse_remove_peft_layers(model):
@@ -53,7 +56,6 @@ def recurse_remove_peft_layers(model):
                 module.padding,
                 module.dilation,
                 module.groups,
-                module.bias,
             ).to(module.weight.device)
 
             new_module.weight = module.weight
@@ -106,10 +108,11 @@ def unscale_lora_layers(model):
             module.unscale_layer()
 
 
-def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict):
+def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True):
     rank_pattern = {}
     alpha_pattern = {}
     r = lora_alpha = list(rank_dict.values())[0]
+
     if len(set(rank_dict.values())) > 1:
         # get the rank occuring the most number of times
         r = collections.Counter(rank_dict.values()).most_common()[0][0]
@@ -118,13 +121,22 @@ def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict):
         rank_pattern = dict(filter(lambda x: x[1] != r, rank_dict.items()))
         rank_pattern = {k.split(".lora_B.")[0]: v for k, v in rank_pattern.items()}
 
-    if network_alpha_dict is not None and len(set(network_alpha_dict.values())) > 1:
-        # get the alpha occuring the most number of times
-        lora_alpha = collections.Counter(network_alpha_dict.values()).most_common()[0][0]
-
-        # for modules with alpha different from the most occuring alpha, add it to the `alpha_pattern`
-        alpha_pattern = dict(filter(lambda x: x[1] != lora_alpha, network_alpha_dict.items()))
-        alpha_pattern = {".".join(k.split(".down.")[0].split(".")[:-1]): v for k, v in alpha_pattern.items()}
+    if network_alpha_dict is not None:
+        if len(set(network_alpha_dict.values())) > 1:
+            # get the alpha occuring the most number of times
+            lora_alpha = collections.Counter(network_alpha_dict.values()).most_common()[0][0]
+
+            # for modules with alpha different from the most occuring alpha, add it to the `alpha_pattern`
+            alpha_pattern = dict(filter(lambda x: x[1] != lora_alpha, network_alpha_dict.items()))
+            if is_unet:
+                alpha_pattern = {
+                    ".".join(k.split(".lora_A.")[0].split(".")).replace(".alpha", ""): v
+                    for k, v in alpha_pattern.items()
+                }
+            else:
+                alpha_pattern = {".".join(k.split(".down.")[0].split(".")[:-1]): v for k, v in alpha_pattern.items()}
+        else:
+            lora_alpha = set(network_alpha_dict.values()).pop()
 
     # layer names without the Diffusers specific
     target_modules = list({name.split(".lora")[0] for name in peft_state_dict.keys()})
@@ -155,9 +167,9 @@ def set_adapter_layers(model, enabled=True):
         if isinstance(module, BaseTunerLayer):
             # The recent version of PEFT needs to call `enable_adapters` instead
             if hasattr(module, "enable_adapters"):
-                module.enable_adapters(enabled=False)
+                module.enable_adapters(enabled=enabled)
             else:
-                module.disable_adapters = True
+                module.disable_adapters = not enabled
 
 
 def set_weights_and_activate_adapters(model, adapter_names, weights):
@@ -182,3 +194,23 @@ def set_weights_and_activate_adapters(model, adapter_names, weights):
                 module.set_adapter(adapter_names)
             else:
                 module.active_adapter = adapter_names
+
+
+def check_peft_version(min_version: str) -> None:
+    r"""
+    Checks if the version of PEFT is compatible.
+
+    Args:
+        version (`str`):
+            The version of PEFT to check against.
+    """
+    if not is_peft_available():
+        raise ValueError("PEFT is not installed. Please install it with `pip install peft`")
+
+    is_peft_version_compatible = version.parse(importlib.metadata.version("peft")) > version.parse(min_version)
+
+    if not is_peft_version_compatible:
+        raise ValueError(
+            f"The version of PEFT you are using is not compatible, please use a version that is greater"
+            f" than {min_version}"
+        )

src/diffusers/utils/state_dict_utils.py

@@ -28,6 +28,22 @@ class StateDictType(enum.Enum):
     DIFFUSERS = "diffusers"
 
 
+# We need to define a proper mapping for Unet since it uses different output keys than text encoder
+# e.g. to_q_lora -> q_proj / to_q
+UNET_TO_DIFFUSERS = {
+    ".to_out_lora.up": ".to_out.0.lora_B",
+    ".to_out_lora.down": ".to_out.0.lora_A",
+    ".to_q_lora.down": ".to_q.lora_A",
+    ".to_q_lora.up": ".to_q.lora_B",
+    ".to_k_lora.down": ".to_k.lora_A",
+    ".to_k_lora.up": ".to_k.lora_B",
+    ".to_v_lora.down": ".to_v.lora_A",
+    ".to_v_lora.up": ".to_v.lora_B",
+    ".lora.up": ".lora_B",
+    ".lora.down": ".lora_A",
+}
+
+
 DIFFUSERS_TO_PEFT = {
     ".q_proj.lora_linear_layer.up": ".q_proj.lora_B",
     ".q_proj.lora_linear_layer.down": ".q_proj.lora_A",
@@ -50,6 +66,8 @@ DIFFUSERS_OLD_TO_PEFT = {
     ".to_v_lora.down": ".v_proj.lora_A",
     ".to_out_lora.up": ".out_proj.lora_B",
     ".to_out_lora.down": ".out_proj.lora_A",
+    ".lora_linear_layer.up": ".lora_B",
+    ".lora_linear_layer.down": ".lora_A",
 }
 
 PEFT_TO_DIFFUSERS = {
@@ -84,6 +102,10 @@ DIFFUSERS_STATE_DICT_MAPPINGS = {
     StateDictType.PEFT: PEFT_TO_DIFFUSERS,
 }
 
+KEYS_TO_ALWAYS_REPLACE = {
+    ".processor.": ".",
+}
+
 
 def convert_state_dict(state_dict, mapping):
     r"""
@@ -103,6 +125,12 @@ def convert_state_dict(state_dict, mapping):
     """
     converted_state_dict = {}
     for k, v in state_dict.items():
+        # First, filter out the keys that we always want to replace
+        for pattern in KEYS_TO_ALWAYS_REPLACE.keys():
+            if pattern in k:
+                new_pattern = KEYS_TO_ALWAYS_REPLACE[pattern]
+                k = k.replace(pattern, new_pattern)
+
         for pattern in mapping.keys():
             if pattern in k:
                 new_pattern = mapping[pattern]
@@ -184,3 +212,11 @@ def convert_state_dict_to_diffusers(state_dict, original_type=None, **kwargs):
 
     mapping = DIFFUSERS_STATE_DICT_MAPPINGS[original_type]
     return convert_state_dict(state_dict, mapping)
+
+
+def convert_unet_state_dict_to_peft(state_dict):
+    r"""
+    Converts a state dict from UNet format to diffusers format - i.e. by removing some keys
+    """
+    mapping = UNET_TO_DIFFUSERS
+    return convert_state_dict(state_dict, mapping)

tests/lora/test_lora_layers_old_backend.py

@@ -673,6 +673,7 @@ class LoraLoaderMixinTests(unittest.TestCase):
         self.assertFalse(torch.allclose(torch.from_numpy(orig_image_slice), torch.from_numpy(lora_image_slice)))
 
 
+@deprecate_after_peft_backend
 class SDXInpaintLoraMixinTests(unittest.TestCase):
     def get_dummy_inputs(self, device, seed=0, img_res=64, output_pil=True):
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
@@ -1387,6 +1388,7 @@ class SDXLLoraLoaderMixinTests(unittest.TestCase):
         ), "The pipeline was serialized with LoRA parameters fused inside of the respected modules. The loaded pipeline should yield proper outputs, henceforth."
 
 
+@deprecate_after_peft_backend
 class UNet2DConditionLoRAModelTests(unittest.TestCase):
     model_class = UNet2DConditionModel
     main_input_name = "sample"
@@ -1635,6 +1637,7 @@ class UNet2DConditionLoRAModelTests(unittest.TestCase):
         assert max_diff_off_sample < expected_max_diff
 
 
+@deprecate_after_peft_backend
 class UNet3DConditionModelTests(unittest.TestCase):
     model_class = UNet3DConditionModel
     main_input_name = "sample"
@@ -1877,6 +1880,7 @@ class UNet3DConditionModelTests(unittest.TestCase):
 
 
 @slow
+@deprecate_after_peft_backend
 @require_torch_gpu
 class LoraIntegrationTests(unittest.TestCase):
     def test_dreambooth_old_format(self):

tests/lora/test_lora_layers_peft.py

@@ -12,21 +12,27 @@
 # 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 copy
 import os
 import tempfile
+import time
 import unittest
 
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from huggingface_hub.repocard import RepoCard
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
+    ControlNetModel,
     DDIMScheduler,
+    DiffusionPipeline,
     EulerDiscreteScheduler,
     StableDiffusionPipeline,
+    StableDiffusionXLControlNetPipeline,
     StableDiffusionXLPipeline,
     UNet2DConditionModel,
 )
@@ -35,9 +41,20 @@ from diffusers.models.attention_processor import (
     LoRAAttnProcessor,
     LoRAAttnProcessor2_0,
 )
-from diffusers.utils.import_utils import is_peft_available
-from diffusers.utils.testing_utils import floats_tensor, require_peft_backend, require_torch_gpu, slow
+from diffusers.utils.import_utils import is_accelerate_available, is_peft_available
+from diffusers.utils.testing_utils import (
+    floats_tensor,
+    load_image,
+    nightly,
+    require_peft_backend,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
+
 
+if is_accelerate_available():
+    from accelerate.utils import release_memory
 
 if is_peft_available():
     from peft import LoraConfig
@@ -45,6 +62,18 @@ if is_peft_available():
     from peft.utils import get_peft_model_state_dict
 
 
+def state_dicts_almost_equal(sd1, sd2):
+    sd1 = dict(sorted(sd1.items()))
+    sd2 = dict(sorted(sd2.items()))
+
+    models_are_equal = True
+    for ten1, ten2 in zip(sd1.values(), sd2.values()):
+        if (ten1 - ten2).abs().max() > 1e-3:
+            models_are_equal = False
+
+    return models_are_equal
+
+
 def create_unet_lora_layers(unet: nn.Module):
     lora_attn_procs = {}
     for name in unet.attn_processors.keys():
@@ -94,6 +123,10 @@ class PeftLoraLoaderMixinTests:
             r=4, lora_alpha=4, target_modules=["q_proj", "k_proj", "v_proj", "out_proj"], init_lora_weights=False
         )
 
+        unet_lora_config = LoraConfig(
+            r=4, lora_alpha=4, target_modules=["to_q", "to_k", "to_v", "to_out.0"], init_lora_weights=False
+        )
+
         unet_lora_attn_procs, unet_lora_layers = create_unet_lora_layers(unet)
 
         if self.has_two_text_encoders:
@@ -120,7 +153,7 @@ class PeftLoraLoaderMixinTests:
             "unet_lora_layers": unet_lora_layers,
             "unet_lora_attn_procs": unet_lora_attn_procs,
         }
-        return pipeline_components, lora_components, text_lora_config
+        return pipeline_components, lora_components, text_lora_config, unet_lora_config
 
     def get_dummy_inputs(self, with_generator=True):
         batch_size = 1
@@ -166,7 +199,7 @@ class PeftLoraLoaderMixinTests:
         """
         Tests a simple inference and makes sure it works as expected
         """
-        components, _, _ = self.get_dummy_components()
+        components, _, _, _ = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(self.torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -180,7 +213,7 @@ class PeftLoraLoaderMixinTests:
         Tests a simple inference with lora attached on the text encoder
         and makes sure it works as expected
         """
-        components, _, text_lora_config = self.get_dummy_components()
+        components, _, text_lora_config, _ = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(self.torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -208,7 +241,7 @@ class PeftLoraLoaderMixinTests:
         Tests a simple inference with lora attached on the text encoder + scale argument
         and makes sure it works as expected
         """
-        components, _, text_lora_config = self.get_dummy_components()
+        components, _, text_lora_config, _ = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(self.torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -252,7 +285,7 @@ class PeftLoraLoaderMixinTests:
         Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
         and makes sure it works as expected
         """
-        components, _, text_lora_config = self.get_dummy_components()
+        components, _, text_lora_config, _ = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(self.torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -289,7 +322,7 @@ class PeftLoraLoaderMixinTests:
         Tests a simple inference with lora attached to text encoder, then unloads the lora weights
         and makes sure it works as expected
         """
-        components, _, text_lora_config = self.get_dummy_components()
+        components, _, text_lora_config, _ = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(self.torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -327,7 +360,7 @@ class PeftLoraLoaderMixinTests:
         """
         Tests a simple usecase where users could use saving utilities for LoRA.
         """
-        components, _, text_lora_config = self.get_dummy_components()
+        components, _, text_lora_config, _ = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(self.torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -387,7 +420,7 @@ class PeftLoraLoaderMixinTests:
         """
         Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
         """
-        components, _, text_lora_config = self.get_dummy_components()
+        components, _, text_lora_config, _ = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(self.torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -431,108 +464,680 @@ class PeftLoraLoaderMixinTests:
             "Loading from saved checkpoints should give same results.",
         )
 
+    def test_simple_inference_with_text_unet_lora_save_load(self):
+        """
+        Tests a simple usecase where users could use saving utilities for LoRA for Unet + text encoder
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
-class StableDiffusionLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):
-    pipeline_class = StableDiffusionPipeline
-    scheduler_cls = DDIMScheduler
-    scheduler_kwargs = {
-        "beta_start": 0.00085,
-        "beta_end": 0.012,
-        "beta_schedule": "scaled_linear",
-        "clip_sample": False,
-        "set_alpha_to_one": False,
-        "steps_offset": 1,
-    }
-    unet_kwargs = {
-        "block_out_channels": (32, 64),
-        "layers_per_block": 2,
-        "sample_size": 32,
-        "in_channels": 4,
-        "out_channels": 4,
-        "down_block_types": ("DownBlock2D", "CrossAttnDownBlock2D"),
-        "up_block_types": ("CrossAttnUpBlock2D", "UpBlock2D"),
-        "cross_attention_dim": 32,
-    }
-    vae_kwargs = {
-        "block_out_channels": [32, 64],
-        "in_channels": 3,
-        "out_channels": 3,
-        "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
-        "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
-        "latent_channels": 4,
-    }
+        output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+        self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
-    @slow
-    @require_torch_gpu
-    def test_integration_logits_with_scale(self):
-        path = "runwayml/stable-diffusion-v1-5"
-        lora_id = "takuma104/lora-test-text-encoder-lora-target"
+        pipe.text_encoder.add_adapter(text_lora_config)
+        pipe.unet.add_adapter(unet_lora_config)
 
-        pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)
-        pipe.load_lora_weights(lora_id)
-        pipe = pipe.to("cuda")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
+
+        if self.has_two_text_encoders:
+            pipe.text_encoder_2.add_adapter(text_lora_config)
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
+
+        images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)
+            unet_state_dict = get_peft_model_state_dict(pipe.unet)
+            if self.has_two_text_encoders:
+                text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)
+
+                self.pipeline_class.save_lora_weights(
+                    save_directory=tmpdirname,
+                    text_encoder_lora_layers=text_encoder_state_dict,
+                    text_encoder_2_lora_layers=text_encoder_2_state_dict,
+                    unet_lora_layers=unet_state_dict,
+                    safe_serialization=False,
+                )
+            else:
+                self.pipeline_class.save_lora_weights(
+                    save_directory=tmpdirname,
+                    text_encoder_lora_layers=text_encoder_state_dict,
+                    unet_lora_layers=unet_state_dict,
+                    safe_serialization=False,
+                )
+
+            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
+            pipe.unload_lora_weights()
+
+            pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.bin"))
+
+        images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
+
+        if self.has_two_text_encoders:
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
 
         self.assertTrue(
-            self.check_if_lora_correctly_set(pipe.text_encoder),
-            "Lora not correctly set in text encoder 2",
+            np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
+            "Loading from saved checkpoints should give same results.",
         )
 
-        prompt = "a red sks dog"
+    def test_simple_inference_with_text_unet_lora_and_scale(self):
+        """
+        Tests a simple inference with lora attached on the text encoder + Unet + scale argument
+        and makes sure it works as expected
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
-        images = pipe(
-            prompt=prompt,
-            num_inference_steps=15,
-            cross_attention_kwargs={"scale": 0.5},
-            generator=torch.manual_seed(0),
-            output_type="np",
+        output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+        self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
+
+        pipe.text_encoder.add_adapter(text_lora_config)
+        pipe.unet.add_adapter(unet_lora_config)
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
+
+        if self.has_two_text_encoders:
+            pipe.text_encoder_2.add_adapter(text_lora_config)
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
+
+        output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+        self.assertTrue(
+            not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
+        )
+
+        output_lora_scale = pipe(
+            **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5}
         ).images
+        self.assertTrue(
+            not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
+            "Lora + scale should change the output",
+        )
 
-        expected_slice_scale = np.array([0.307, 0.283, 0.310, 0.310, 0.300, 0.314, 0.336, 0.314, 0.321])
+        output_lora_0_scale = pipe(
+            **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0}
+        ).images
+        self.assertTrue(
+            np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),
+            "Lora + 0 scale should lead to same result as no LoRA",
+        )
 
-        predicted_slice = images[0, -3:, -3:, -1].flatten()
+        self.assertTrue(
+            pipe.text_encoder.text_model.encoder.layers[0].self_attn.q_proj.scaling["default"] == 1.0,
+            "The scaling parameter has not been correctly restored!",
+        )
 
-        self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3))
+    def test_simple_inference_with_text_lora_unet_fused(self):
+        """
+        Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
+        and makes sure it works as expected - with unet
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
-    @slow
-    @require_torch_gpu
-    def test_integration_logits_no_scale(self):
-        path = "runwayml/stable-diffusion-v1-5"
-        lora_id = "takuma104/lora-test-text-encoder-lora-target"
+        output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+        self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
 
-        pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)
-        pipe.load_lora_weights(lora_id)
-        pipe = pipe.to("cuda")
+        pipe.text_encoder.add_adapter(text_lora_config)
+        pipe.unet.add_adapter(unet_lora_config)
+
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
+
+        if self.has_two_text_encoders:
+            pipe.text_encoder_2.add_adapter(text_lora_config)
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
+
+        pipe.fuse_lora()
+        # Fusing should still keep the LoRA layers
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in unet")
+
+        if self.has_two_text_encoders:
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
+
+        ouput_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
+        self.assertFalse(
+            np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
+        )
+
+    def test_simple_inference_with_text_unet_lora_unloaded(self):
+        """
+        Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
+        and makes sure it works as expected
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+        output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+        self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))
+
+        pipe.text_encoder.add_adapter(text_lora_config)
+        pipe.unet.add_adapter(unet_lora_config)
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
+
+        if self.has_two_text_encoders:
+            pipe.text_encoder_2.add_adapter(text_lora_config)
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
+
+        pipe.unload_lora_weights()
+        # unloading should remove the LoRA layers
+        self.assertFalse(
+            self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder"
+        )
+        self.assertFalse(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly unloaded in Unet")
+
+        if self.has_two_text_encoders:
+            self.assertFalse(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly unloaded in text encoder 2"
+            )
 
+        ouput_unloaded = pipe(**inputs, generator=torch.manual_seed(0)).images
         self.assertTrue(
-            self.check_if_lora_correctly_set(pipe.text_encoder),
-            "Lora not correctly set in text encoder",
+            np.allclose(ouput_unloaded, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
         )
 
-        prompt = "a red sks dog"
+    def test_simple_inference_with_text_unet_lora_unfused(self):
+        """
+        Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
+        and makes sure it works as expected
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
 
-        images = pipe(prompt=prompt, num_inference_steps=30, generator=torch.manual_seed(0), output_type="np").images
+        pipe.text_encoder.add_adapter(text_lora_config)
+        pipe.unet.add_adapter(unet_lora_config)
 
-        expected_slice_scale = np.array([0.074, 0.064, 0.073, 0.0842, 0.069, 0.0641, 0.0794, 0.076, 0.084])
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
 
-        predicted_slice = images[0, -3:, -3:, -1].flatten()
+        if self.has_two_text_encoders:
+            pipe.text_encoder_2.add_adapter(text_lora_config)
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
 
-        self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3))
+        pipe.fuse_lora()
 
+        output_fused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
 
-class StableDiffusionXLLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):
-    has_two_text_encoders = True
-    pipeline_class = StableDiffusionXLPipeline
-    scheduler_cls = EulerDiscreteScheduler
-    scheduler_kwargs = {
-        "beta_start": 0.00085,
-        "beta_end": 0.012,
-        "beta_schedule": "scaled_linear",
-        "timestep_spacing": "leading",
-        "steps_offset": 1,
-    }
-    unet_kwargs = {
-        "block_out_channels": (32, 64),
-        "layers_per_block": 2,
+        pipe.unfuse_lora()
+
+        output_unfused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+        # unloading should remove the LoRA layers
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Unfuse should still keep LoRA layers")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Unfuse should still keep LoRA layers")
+
+        if self.has_two_text_encoders:
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Unfuse should still keep LoRA layers"
+            )
+
+        # Fuse and unfuse should lead to the same results
+        self.assertTrue(
+            np.allclose(output_fused_lora, output_unfused_lora, atol=1e-3, rtol=1e-3),
+            "Fused lora should change the output",
+        )
+
+    def test_simple_inference_with_text_unet_multi_adapter(self):
+        """
+        Tests a simple inference with lora attached to text encoder and unet, attaches
+        multiple adapters and set them
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+        output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+        pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
+        pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
+
+        pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+        pipe.unet.add_adapter(unet_lora_config, "adapter-2")
+
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
+
+        if self.has_two_text_encoders:
+            pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
+            pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
+
+        pipe.set_adapters("adapter-1")
+
+        output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+        pipe.set_adapters("adapter-2")
+        output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+        pipe.set_adapters(["adapter-1", "adapter-2"])
+
+        output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+        # Fuse and unfuse should lead to the same results
+        self.assertFalse(
+            np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),
+            "Adapter 1 and 2 should give different results",
+        )
+
+        self.assertFalse(
+            np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3),
+            "Adapter 1 and mixed adapters should give different results",
+        )
+
+        self.assertFalse(
+            np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3),
+            "Adapter 2 and mixed adapters should give different results",
+        )
+
+        pipe.disable_lora()
+
+        output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+        self.assertTrue(
+            np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3),
+            "output with no lora and output with lora disabled should give same results",
+        )
+
+    def test_lora_fuse_nan(self):
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+        pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
+
+        pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
+
+        # corrupt one LoRA weight with `inf` values
+        with torch.no_grad():
+            pipe.unet.mid_block.attentions[0].transformer_blocks[0].attn1.to_q.lora_A["adapter-1"].weight += float(
+                "inf"
+            )
+
+        # with `safe_fusing=True` we should see an Error
+        with self.assertRaises(ValueError):
+            pipe.fuse_lora(safe_fusing=True)
+
+        # without we should not see an error, but every image will be black
+        pipe.fuse_lora(safe_fusing=False)
+
+        out = pipe("test", num_inference_steps=2, output_type="np").images
+
+        self.assertTrue(np.isnan(out).all())
+
+    def test_get_adapters(self):
+        """
+        Tests a simple usecase where we attach multiple adapters and check if the results
+        are the expected results
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+        pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
+        pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+
+        adapter_names = pipe.get_active_adapters()
+        self.assertListEqual(adapter_names, ["adapter-1"])
+
+        pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
+        pipe.unet.add_adapter(unet_lora_config, "adapter-2")
+
+        adapter_names = pipe.get_active_adapters()
+        self.assertListEqual(adapter_names, ["adapter-2"])
+
+        pipe.set_adapters(["adapter-1", "adapter-2"])
+        self.assertListEqual(pipe.get_active_adapters(), ["adapter-1", "adapter-2"])
+
+    def test_get_list_adapters(self):
+        """
+        Tests a simple usecase where we attach multiple adapters and check if the results
+        are the expected results
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
+        pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+
+        adapter_names = pipe.get_list_adapters()
+        self.assertDictEqual(adapter_names, {"text_encoder": ["adapter-1"], "unet": ["adapter-1"]})
+
+        pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
+        pipe.unet.add_adapter(unet_lora_config, "adapter-2")
+
+        adapter_names = pipe.get_list_adapters()
+        self.assertDictEqual(
+            adapter_names, {"text_encoder": ["adapter-1", "adapter-2"], "unet": ["adapter-1", "adapter-2"]}
+        )
+
+        pipe.set_adapters(["adapter-1", "adapter-2"])
+        self.assertDictEqual(
+            pipe.get_list_adapters(), {"unet": ["adapter-1", "adapter-2"], "text_encoder": ["adapter-1", "adapter-2"]}
+        )
+
+        pipe.unet.add_adapter(unet_lora_config, "adapter-3")
+        self.assertDictEqual(
+            pipe.get_list_adapters(),
+            {"unet": ["adapter-1", "adapter-2", "adapter-3"], "text_encoder": ["adapter-1", "adapter-2"]},
+        )
+
+    @unittest.skip("This is failing for now - need to investigate")
+    def test_simple_inference_with_text_unet_lora_unfused_torch_compile(self):
+        """
+        Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
+        and makes sure it works as expected
+        """
+        components, _, text_lora_config, unet_lora_config = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(self.torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+        pipe.text_encoder.add_adapter(text_lora_config)
+        pipe.unet.add_adapter(unet_lora_config)
+
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+        self.assertTrue(self.check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")
+
+        if self.has_two_text_encoders:
+            pipe.text_encoder_2.add_adapter(text_lora_config)
+            self.assertTrue(
+                self.check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+            )
+
+        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+        pipe.text_encoder = torch.compile(pipe.text_encoder, mode="reduce-overhead", fullgraph=True)
+
+        if self.has_two_text_encoders:
+            pipe.text_encoder_2 = torch.compile(pipe.text_encoder_2, mode="reduce-overhead", fullgraph=True)
+
+        # Just makes sure it works..
+        _ = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+
+class StableDiffusionLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):
+    pipeline_class = StableDiffusionPipeline
+    scheduler_cls = DDIMScheduler
+    scheduler_kwargs = {
+        "beta_start": 0.00085,
+        "beta_end": 0.012,
+        "beta_schedule": "scaled_linear",
+        "clip_sample": False,
+        "set_alpha_to_one": False,
+        "steps_offset": 1,
+    }
+    unet_kwargs = {
+        "block_out_channels": (32, 64),
+        "layers_per_block": 2,
+        "sample_size": 32,
+        "in_channels": 4,
+        "out_channels": 4,
+        "down_block_types": ("DownBlock2D", "CrossAttnDownBlock2D"),
+        "up_block_types": ("CrossAttnUpBlock2D", "UpBlock2D"),
+        "cross_attention_dim": 32,
+    }
+    vae_kwargs = {
+        "block_out_channels": [32, 64],
+        "in_channels": 3,
+        "out_channels": 3,
+        "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
+        "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
+        "latent_channels": 4,
+    }
+
+    @slow
+    @require_torch_gpu
+    def test_integration_move_lora_cpu(self):
+        path = "runwayml/stable-diffusion-v1-5"
+        lora_id = "takuma104/lora-test-text-encoder-lora-target"
+
+        pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        pipe.load_lora_weights(lora_id, adapter_name="adapter-1")
+        pipe.load_lora_weights(lora_id, adapter_name="adapter-2")
+        pipe = pipe.to("cuda")
+
+        self.assertTrue(
+            self.check_if_lora_correctly_set(pipe.text_encoder),
+            "Lora not correctly set in text encoder",
+        )
+
+        self.assertTrue(
+            self.check_if_lora_correctly_set(pipe.unet),
+            "Lora not correctly set in text encoder",
+        )
+
+        # We will offload the first adapter in CPU and check if the offloading
+        # has been performed correctly
+        pipe.set_lora_device(["adapter-1"], "cpu")
+
+        for name, module in pipe.unet.named_modules():
+            if "adapter-1" in name and not isinstance(module, (nn.Dropout, nn.Identity)):
+                self.assertTrue(module.weight.device == torch.device("cpu"))
+            elif "adapter-2" in name and not isinstance(module, (nn.Dropout, nn.Identity)):
+                self.assertTrue(module.weight.device != torch.device("cpu"))
+
+        for name, module in pipe.text_encoder.named_modules():
+            if "adapter-1" in name and not isinstance(module, (nn.Dropout, nn.Identity)):
+                self.assertTrue(module.weight.device == torch.device("cpu"))
+            elif "adapter-2" in name and not isinstance(module, (nn.Dropout, nn.Identity)):
+                self.assertTrue(module.weight.device != torch.device("cpu"))
+
+        pipe.set_lora_device(["adapter-1"], 0)
+
+        for n, m in pipe.unet.named_modules():
+            if "adapter-1" in n and not isinstance(m, (nn.Dropout, nn.Identity)):
+                self.assertTrue(m.weight.device != torch.device("cpu"))
+
+        for n, m in pipe.text_encoder.named_modules():
+            if "adapter-1" in n and not isinstance(m, (nn.Dropout, nn.Identity)):
+                self.assertTrue(m.weight.device != torch.device("cpu"))
+
+        pipe.set_lora_device(["adapter-1", "adapter-2"], "cuda")
+
+        for n, m in pipe.unet.named_modules():
+            if ("adapter-1" in n or "adapter-2" in n) and not isinstance(m, (nn.Dropout, nn.Identity)):
+                self.assertTrue(m.weight.device != torch.device("cpu"))
+
+        for n, m in pipe.text_encoder.named_modules():
+            if ("adapter-1" in n or "adapter-2" in n) and not isinstance(m, (nn.Dropout, nn.Identity)):
+                self.assertTrue(m.weight.device != torch.device("cpu"))
+
+    @slow
+    @require_torch_gpu
+    def test_integration_logits_with_scale(self):
+        path = "runwayml/stable-diffusion-v1-5"
+        lora_id = "takuma104/lora-test-text-encoder-lora-target"
+
+        pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)
+        pipe.load_lora_weights(lora_id)
+        pipe = pipe.to("cuda")
+
+        self.assertTrue(
+            self.check_if_lora_correctly_set(pipe.text_encoder),
+            "Lora not correctly set in text encoder 2",
+        )
+
+        prompt = "a red sks dog"
+
+        images = pipe(
+            prompt=prompt,
+            num_inference_steps=15,
+            cross_attention_kwargs={"scale": 0.5},
+            generator=torch.manual_seed(0),
+            output_type="np",
+        ).images
+
+        expected_slice_scale = np.array([0.307, 0.283, 0.310, 0.310, 0.300, 0.314, 0.336, 0.314, 0.321])
+
+        predicted_slice = images[0, -3:, -3:, -1].flatten()
+
+        self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3))
+
+    @slow
+    @require_torch_gpu
+    def test_integration_logits_no_scale(self):
+        path = "runwayml/stable-diffusion-v1-5"
+        lora_id = "takuma104/lora-test-text-encoder-lora-target"
+
+        pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)
+        pipe.load_lora_weights(lora_id)
+        pipe = pipe.to("cuda")
+
+        self.assertTrue(
+            self.check_if_lora_correctly_set(pipe.text_encoder),
+            "Lora not correctly set in text encoder",
+        )
+
+        prompt = "a red sks dog"
+
+        images = pipe(prompt=prompt, num_inference_steps=30, generator=torch.manual_seed(0), output_type="np").images
+
+        expected_slice_scale = np.array([0.074, 0.064, 0.073, 0.0842, 0.069, 0.0641, 0.0794, 0.076, 0.084])
+
+        predicted_slice = images[0, -3:, -3:, -1].flatten()
+
+        self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3))
+
+    @nightly
+    @require_torch_gpu
+    def test_integration_logits_multi_adapter(self):
+        path = "stabilityai/stable-diffusion-xl-base-1.0"
+        lora_id = "CiroN2022/toy-face"
+
+        pipe = StableDiffusionXLPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        pipe.load_lora_weights(lora_id, weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
+        pipe = pipe.to("cuda")
+
+        self.assertTrue(
+            self.check_if_lora_correctly_set(pipe.unet),
+            "Lora not correctly set in Unet",
+        )
+
+        prompt = "toy_face of a hacker with a hoodie"
+
+        lora_scale = 0.9
+
+        images = pipe(
+            prompt=prompt,
+            num_inference_steps=30,
+            generator=torch.manual_seed(0),
+            cross_attention_kwargs={"scale": lora_scale},
+            output_type="np",
+        ).images
+        expected_slice_scale = np.array([0.538, 0.539, 0.540, 0.540, 0.542, 0.539, 0.538, 0.541, 0.539])
+
+        predicted_slice = images[0, -3:, -3:, -1].flatten()
+        # import pdb; pdb.set_trace()
+        self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3))
+
+        pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipe.set_adapters("pixel")
+
+        prompt = "pixel art, a hacker with a hoodie, simple, flat colors"
+        images = pipe(
+            prompt,
+            num_inference_steps=30,
+            guidance_scale=7.5,
+            cross_attention_kwargs={"scale": lora_scale},
+            generator=torch.manual_seed(0),
+            output_type="np",
+        ).images
+
+        predicted_slice = images[0, -3:, -3:, -1].flatten()
+        expected_slice_scale = np.array(
+            [0.61973065, 0.62018543, 0.62181497, 0.61933696, 0.6208608, 0.620576, 0.6200281, 0.62258327, 0.6259889]
+        )
+        self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3))
+
+        # multi-adapter inference
+        pipe.set_adapters(["pixel", "toy"], adapter_weights=[0.5, 1.0])
+        images = pipe(
+            prompt,
+            num_inference_steps=30,
+            guidance_scale=7.5,
+            cross_attention_kwargs={"scale": 1.0},
+            generator=torch.manual_seed(0),
+            output_type="np",
+        ).images
+        predicted_slice = images[0, -3:, -3:, -1].flatten()
+        expected_slice_scale = np.array([0.5977, 0.5985, 0.6039, 0.5976, 0.6025, 0.6036, 0.5946, 0.5979, 0.5998])
+        self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3))
+
+        # Lora disabled
+        pipe.disable_lora()
+        images = pipe(
+            prompt,
+            num_inference_steps=30,
+            guidance_scale=7.5,
+            cross_attention_kwargs={"scale": lora_scale},
+            generator=torch.manual_seed(0),
+            output_type="np",
+        ).images
+        predicted_slice = images[0, -3:, -3:, -1].flatten()
+        expected_slice_scale = np.array([0.54625, 0.5473, 0.5495, 0.5465, 0.5476, 0.5461, 0.5452, 0.5485, 0.5493])
+        self.assertTrue(np.allclose(expected_slice_scale, predicted_slice, atol=1e-3, rtol=1e-3))
+
+
+class StableDiffusionXLLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):
+    has_two_text_encoders = True
+    pipeline_class = StableDiffusionXLPipeline
+    scheduler_cls = EulerDiscreteScheduler
+    scheduler_kwargs = {
+        "beta_start": 0.00085,
+        "beta_end": 0.012,
+        "beta_schedule": "scaled_linear",
+        "timestep_spacing": "leading",
+        "steps_offset": 1,
+    }
+    unet_kwargs = {
+        "block_out_channels": (32, 64),
+        "layers_per_block": 2,
         "sample_size": 32,
         "in_channels": 4,
         "out_channels": 4,
@@ -555,3 +1160,606 @@ class StableDiffusionXLLoRATests(PeftLoraLoaderMixinTests, unittest.TestCase):
         "latent_channels": 4,
         "sample_size": 128,
     }
+
+
+@slow
+@require_torch_gpu
+class LoraIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        import gc
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        gc.collect()
+
+    def test_dreambooth_old_format(self):
+        generator = torch.Generator("cpu").manual_seed(0)
+
+        lora_model_id = "hf-internal-testing/lora_dreambooth_dog_example"
+        card = RepoCard.load(lora_model_id)
+        base_model_id = card.data.to_dict()["base_model"]
+
+        pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
+        pipe = pipe.to(torch_device)
+        pipe.load_lora_weights(lora_model_id)
+
+        images = pipe(
+            "A photo of a sks dog floating in the river", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+
+        expected = np.array([0.7207, 0.6787, 0.6010, 0.7478, 0.6838, 0.6064, 0.6984, 0.6443, 0.5785])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-4))
+        release_memory(pipe)
+
+    def test_dreambooth_text_encoder_new_format(self):
+        generator = torch.Generator().manual_seed(0)
+
+        lora_model_id = "hf-internal-testing/lora-trained"
+        card = RepoCard.load(lora_model_id)
+        base_model_id = card.data.to_dict()["base_model"]
+
+        pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
+        pipe = pipe.to(torch_device)
+        pipe.load_lora_weights(lora_model_id)
+
+        images = pipe("A photo of a sks dog", output_type="np", generator=generator, num_inference_steps=2).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+
+        expected = np.array([0.6628, 0.6138, 0.5390, 0.6625, 0.6130, 0.5463, 0.6166, 0.5788, 0.5359])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-4))
+        release_memory(pipe)
+
+    def test_a1111(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None).to(
+            torch_device
+        )
+        lora_model_id = "hf-internal-testing/civitai-light-shadow-lora"
+        lora_filename = "light_and_shadow.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_lycoris(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "hf-internal-testing/Amixx", safety_checker=None, use_safetensors=True, variant="fp16"
+        ).to(torch_device)
+        lora_model_id = "hf-internal-testing/edgLycorisMugler-light"
+        lora_filename = "edgLycorisMugler-light.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.6463, 0.658, 0.599, 0.6542, 0.6512, 0.6213, 0.658, 0.6485, 0.6017])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_a1111_with_model_cpu_offload(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None)
+        pipe.enable_model_cpu_offload()
+        lora_model_id = "hf-internal-testing/civitai-light-shadow-lora"
+        lora_filename = "light_and_shadow.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_a1111_with_sequential_cpu_offload(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None)
+        pipe.enable_sequential_cpu_offload()
+        lora_model_id = "hf-internal-testing/civitai-light-shadow-lora"
+        lora_filename = "light_and_shadow.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_kohya_sd_v15_with_higher_dimensions(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
+            torch_device
+        )
+        lora_model_id = "hf-internal-testing/urushisato-lora"
+        lora_filename = "urushisato_v15.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.7165, 0.6616, 0.5833, 0.7504, 0.6718, 0.587, 0.6871, 0.6361, 0.5694])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_vanilla_funetuning(self):
+        generator = torch.Generator().manual_seed(0)
+
+        lora_model_id = "hf-internal-testing/sd-model-finetuned-lora-t4"
+        card = RepoCard.load(lora_model_id)
+        base_model_id = card.data.to_dict()["base_model"]
+
+        pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
+        pipe = pipe.to(torch_device)
+        pipe.load_lora_weights(lora_model_id)
+
+        images = pipe("A pokemon with blue eyes.", output_type="np", generator=generator, num_inference_steps=2).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+
+        expected = np.array([0.7406, 0.699, 0.5963, 0.7493, 0.7045, 0.6096, 0.6886, 0.6388, 0.583])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-4))
+        release_memory(pipe)
+
+    def test_unload_kohya_lora(self):
+        generator = torch.manual_seed(0)
+        prompt = "masterpiece, best quality, mountain"
+        num_inference_steps = 2
+
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
+            torch_device
+        )
+        initial_images = pipe(
+            prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps
+        ).images
+        initial_images = initial_images[0, -3:, -3:, -1].flatten()
+
+        lora_model_id = "hf-internal-testing/civitai-colored-icons-lora"
+        lora_filename = "Colored_Icons_by_vizsumit.safetensors"
+
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        generator = torch.manual_seed(0)
+        lora_images = pipe(
+            prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps
+        ).images
+        lora_images = lora_images[0, -3:, -3:, -1].flatten()
+
+        pipe.unload_lora_weights()
+        generator = torch.manual_seed(0)
+        unloaded_lora_images = pipe(
+            prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps
+        ).images
+        unloaded_lora_images = unloaded_lora_images[0, -3:, -3:, -1].flatten()
+
+        self.assertFalse(np.allclose(initial_images, lora_images))
+        self.assertTrue(np.allclose(initial_images, unloaded_lora_images, atol=1e-3))
+        release_memory(pipe)
+
+    def test_load_unload_load_kohya_lora(self):
+        # This test ensures that a Kohya-style LoRA can be safely unloaded and then loaded
+        # without introducing any side-effects. Even though the test uses a Kohya-style
+        # LoRA, the underlying adapter handling mechanism is format-agnostic.
+        generator = torch.manual_seed(0)
+        prompt = "masterpiece, best quality, mountain"
+        num_inference_steps = 2
+
+        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
+            torch_device
+        )
+        initial_images = pipe(
+            prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps
+        ).images
+        initial_images = initial_images[0, -3:, -3:, -1].flatten()
+
+        lora_model_id = "hf-internal-testing/civitai-colored-icons-lora"
+        lora_filename = "Colored_Icons_by_vizsumit.safetensors"
+
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        generator = torch.manual_seed(0)
+        lora_images = pipe(
+            prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps
+        ).images
+        lora_images = lora_images[0, -3:, -3:, -1].flatten()
+
+        pipe.unload_lora_weights()
+        generator = torch.manual_seed(0)
+        unloaded_lora_images = pipe(
+            prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps
+        ).images
+        unloaded_lora_images = unloaded_lora_images[0, -3:, -3:, -1].flatten()
+
+        self.assertFalse(np.allclose(initial_images, lora_images))
+        self.assertTrue(np.allclose(initial_images, unloaded_lora_images, atol=1e-3))
+
+        # make sure we can load a LoRA again after unloading and they don't have
+        # any undesired effects.
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        generator = torch.manual_seed(0)
+        lora_images_again = pipe(
+            prompt, output_type="np", generator=generator, num_inference_steps=num_inference_steps
+        ).images
+        lora_images_again = lora_images_again[0, -3:, -3:, -1].flatten()
+
+        self.assertTrue(np.allclose(lora_images, lora_images_again, atol=1e-3))
+        release_memory(pipe)
+
+
+@slow
+@require_torch_gpu
+class LoraSDXLIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        import gc
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        gc.collect()
+
+    def test_sdxl_0_9_lora_one(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9")
+        lora_model_id = "hf-internal-testing/sdxl-0.9-daiton-lora"
+        lora_filename = "daiton-xl-lora-test.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe.enable_model_cpu_offload()
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.3838, 0.3482, 0.3588, 0.3162, 0.319, 0.3369, 0.338, 0.3366, 0.3213])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_sdxl_0_9_lora_two(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9")
+        lora_model_id = "hf-internal-testing/sdxl-0.9-costumes-lora"
+        lora_filename = "saijo.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe.enable_model_cpu_offload()
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.3137, 0.3269, 0.3355, 0.255, 0.2577, 0.2563, 0.2679, 0.2758, 0.2626])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_sdxl_0_9_lora_three(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9")
+        lora_model_id = "hf-internal-testing/sdxl-0.9-kamepan-lora"
+        lora_filename = "kame_sdxl_v2-000020-16rank.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe.enable_model_cpu_offload()
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.4015, 0.3761, 0.3616, 0.3745, 0.3462, 0.3337, 0.3564, 0.3649, 0.3468])
+
+        self.assertTrue(np.allclose(images, expected, atol=5e-3))
+        release_memory(pipe)
+
+    def test_sdxl_1_0_lora(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+        pipe.enable_model_cpu_offload()
+        lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
+        lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-4))
+        release_memory(pipe)
+
+    def test_sdxl_1_0_lora_fusion(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+        lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
+        lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        pipe.fuse_lora()
+        # We need to unload the lora weights since in the previous API `fuse_lora` led to lora weights being
+        # silently deleted - otherwise this will CPU OOM
+        pipe.unload_lora_weights()
+
+        pipe.enable_model_cpu_offload()
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        # This way we also test equivalence between LoRA fusion and the non-fusion behaviour.
+        expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-4))
+        release_memory(pipe)
+
+    def test_sdxl_1_0_lora_unfusion(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+        lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
+        lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe.fuse_lora()
+
+        pipe.enable_model_cpu_offload()
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+        images_with_fusion = images[0, -3:, -3:, -1].flatten()
+
+        pipe.unfuse_lora()
+        generator = torch.Generator().manual_seed(0)
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+        images_without_fusion = images[0, -3:, -3:, -1].flatten()
+
+        self.assertTrue(np.allclose(images_with_fusion, images_without_fusion, atol=1e-3))
+        release_memory(pipe)
+
+    def test_sdxl_1_0_lora_unfusion_effectivity(self):
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+        pipe.enable_model_cpu_offload()
+
+        generator = torch.Generator().manual_seed(0)
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+        original_image_slice = images[0, -3:, -3:, -1].flatten()
+
+        lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
+        lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe.fuse_lora()
+
+        generator = torch.Generator().manual_seed(0)
+        _ = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        pipe.unfuse_lora()
+
+        # We need to unload the lora weights - in the old API unfuse led to unloading the adapter weights
+        pipe.unload_lora_weights()
+
+        generator = torch.Generator().manual_seed(0)
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+        images_without_fusion_slice = images[0, -3:, -3:, -1].flatten()
+
+        self.assertTrue(np.allclose(original_image_slice, images_without_fusion_slice, atol=1e-3))
+        release_memory(pipe)
+
+    def test_sdxl_1_0_lora_fusion_efficiency(self):
+        generator = torch.Generator().manual_seed(0)
+        lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
+        lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
+
+        pipe = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16
+        )
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.bfloat16)
+        pipe.enable_model_cpu_offload()
+
+        start_time = time.time()
+        for _ in range(3):
+            pipe(
+                "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+            ).images
+        end_time = time.time()
+        elapsed_time_non_fusion = end_time - start_time
+
+        del pipe
+
+        pipe = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16
+        )
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.bfloat16)
+        pipe.fuse_lora()
+        # We need to unload the lora weights since in the previous API `fuse_lora` led to lora weights being
+        # silently deleted - otherwise this will CPU OOM
+        pipe.unload_lora_weights()
+
+        pipe.enable_model_cpu_offload()
+
+        start_time = time.time()
+        generator = torch.Generator().manual_seed(0)
+        for _ in range(3):
+            pipe(
+                "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+            ).images
+        end_time = time.time()
+        elapsed_time_fusion = end_time - start_time
+
+        self.assertTrue(elapsed_time_fusion < elapsed_time_non_fusion)
+        release_memory(pipe)
+
+    def test_sdxl_1_0_last_ben(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+        pipe.enable_model_cpu_offload()
+        lora_model_id = "TheLastBen/Papercut_SDXL"
+        lora_filename = "papercut.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe("papercut.safetensors", output_type="np", generator=generator, num_inference_steps=2).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.5244, 0.4347, 0.4312, 0.4246, 0.4398, 0.4409, 0.4884, 0.4938, 0.4094])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_sdxl_1_0_fuse_unfuse_all(self):
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+        text_encoder_1_sd = copy.deepcopy(pipe.text_encoder.state_dict())
+        text_encoder_2_sd = copy.deepcopy(pipe.text_encoder_2.state_dict())
+        unet_sd = copy.deepcopy(pipe.unet.state_dict())
+
+        pipe.load_lora_weights(
+            "davizca87/sun-flower", weight_name="snfw3rXL-000004.safetensors", torch_dtype=torch.float16
+        )
+
+        fused_te_state_dict = pipe.text_encoder.state_dict()
+        fused_te_2_state_dict = pipe.text_encoder_2.state_dict()
+        unet_state_dict = pipe.unet.state_dict()
+
+        for key, value in text_encoder_1_sd.items():
+            self.assertTrue(torch.allclose(fused_te_state_dict[key], value))
+
+        for key, value in text_encoder_2_sd.items():
+            self.assertTrue(torch.allclose(fused_te_2_state_dict[key], value))
+
+        for key, value in unet_state_dict.items():
+            self.assertTrue(torch.allclose(unet_state_dict[key], value))
+
+        pipe.fuse_lora()
+        pipe.unload_lora_weights()
+
+        assert not state_dicts_almost_equal(text_encoder_1_sd, pipe.text_encoder.state_dict())
+        assert not state_dicts_almost_equal(text_encoder_2_sd, pipe.text_encoder_2.state_dict())
+        assert not state_dicts_almost_equal(unet_sd, pipe.unet.state_dict())
+        release_memory(pipe)
+        del unet_sd, text_encoder_1_sd, text_encoder_2_sd
+
+    def test_sdxl_1_0_lora_with_sequential_cpu_offloading(self):
+        generator = torch.Generator().manual_seed(0)
+
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+        pipe.enable_sequential_cpu_offload()
+        lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
+        lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
+
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+
+        images = images[0, -3:, -3:, -1].flatten()
+        expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535])
+
+        self.assertTrue(np.allclose(images, expected, atol=1e-3))
+        release_memory(pipe)
+
+    def test_canny_lora(self):
+        controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0")
+
+        pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet
+        )
+        pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors")
+        pipe.enable_sequential_cpu_offload()
+
+        generator = torch.Generator(device="cpu").manual_seed(0)
+        prompt = "corgi"
+        image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
+        )
+
+        images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
+
+        assert images[0].shape == (768, 512, 3)
+
+        original_image = images[0, -3:, -3:, -1].flatten()
+        expected_image = np.array([0.4574, 0.4461, 0.4435, 0.4462, 0.4396, 0.439, 0.4474, 0.4486, 0.4333])
+        assert np.allclose(original_image, expected_image, atol=1e-04)
+        release_memory(pipe)
+
+    @nightly
+    def test_sequential_fuse_unfuse(self):
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+
+        # 1. round
+        pipe.load_lora_weights("Pclanglais/TintinIA", torch_dtype=torch.float16)
+        pipe.to("cuda")
+        pipe.fuse_lora()
+
+        generator = torch.Generator().manual_seed(0)
+        images = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+        image_slice = images[0, -3:, -3:, -1].flatten()
+
+        pipe.unfuse_lora()
+
+        # 2. round
+        pipe.load_lora_weights("ProomptEngineer/pe-balloon-diffusion-style", torch_dtype=torch.float16)
+        pipe.fuse_lora()
+        pipe.unfuse_lora()
+
+        # 3. round
+        pipe.load_lora_weights("ostris/crayon_style_lora_sdxl", torch_dtype=torch.float16)
+        pipe.fuse_lora()
+        pipe.unfuse_lora()
+
+        # 4. back to 1st round
+        pipe.load_lora_weights("Pclanglais/TintinIA", torch_dtype=torch.float16)
+        pipe.fuse_lora()
+
+        generator = torch.Generator().manual_seed(0)
+        images_2 = pipe(
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+        ).images
+        image_slice_2 = images_2[0, -3:, -3:, -1].flatten()
+
+        self.assertTrue(np.allclose(image_slice, image_slice_2, atol=1e-3))
+        release_memory(pipe)