[`Peft` / `Lora`] Add `adapter_names` in `fuse_lora` (#5823)

* add adapter_name in fuse

* add tesrt

* up

* fix CI

* adapt from suggestion

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

* change to `require_peft_version_greater`

* change variable names in test

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

* break into 2 lines

* final comments

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

docs/source/en/tutorials/using_peft_for_inference.md

@@ -183,3 +183,26 @@ image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).ima
 # Gets the Unet back to the original state
 pipe.unfuse_lora()
 ```
+
+You can also fuse some adapters using `adapter_names` for faster generation:
+
+```py
+pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
+
+pipe.set_adapters(["pixel"], adapter_weights=[0.5, 1.0])
+# Fuses the LoRAs into the Unet
+pipe.fuse_lora(adapter_names=["pixel"])
+
+prompt = "a hacker with a hoodie, pixel art"
+image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]
+
+# Gets the Unet back to the original state
+pipe.unfuse_lora()
+
+# Fuse all adapters
+pipe.fuse_lora(adapter_names=["pixel", "toy"])
+
+prompt = "toy_face of a hacker with a hoodie, pixel art"
+image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]
+```

src/diffusers/loaders/lora.py

@@ -11,6 +11,7 @@
 # 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 inspect
 import os
 from contextlib import nullcontext
 from typing import Callable, Dict, List, Optional, Union
@@ -1001,6 +1002,7 @@ class LoraLoaderMixin:
         fuse_text_encoder: bool = True,
         lora_scale: float = 1.0,
         safe_fusing: bool = False,
+        adapter_names: Optional[List[str]] = None,
     ):
         r"""
         Fuses the LoRA parameters into the original parameters of the corresponding blocks.
@@ -1020,6 +1022,21 @@ class LoraLoaderMixin:
                 Controls how much to influence the outputs with the LoRA parameters.
             safe_fusing (`bool`, defaults to `False`):
                 Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
+            adapter_names (`List[str]`, *optional*):
+                Adapter names to be used for fusing. If nothing is passed, all active adapters will be fused.
+
+        Example:
+
+        ```py
+        from diffusers import DiffusionPipeline
+        import torch
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.fuse_lora(lora_scale=0.7)
+        ```
         """
         if fuse_unet or fuse_text_encoder:
             self.num_fused_loras += 1
@@ -1030,24 +1047,43 @@ class LoraLoaderMixin:
 
         if fuse_unet:
             unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
-            unet.fuse_lora(lora_scale, safe_fusing=safe_fusing)
+            unet.fuse_lora(lora_scale, safe_fusing=safe_fusing, adapter_names=adapter_names)
 
         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):
-                # TODO(Patrick, Younes): enable "safe" fusing
+            def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, adapter_names=None):
+                merge_kwargs = {"safe_merge": safe_fusing}
+
                 for module in text_encoder.modules():
                     if isinstance(module, BaseTunerLayer):
                         if lora_scale != 1.0:
                             module.scale_layer(lora_scale)
 
-                        module.merge()
+                        # For BC with previous PEFT versions, we need to check the signature
+                        # of the `merge` method to see if it supports the `adapter_names` argument.
+                        supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
+                        if "adapter_names" in supported_merge_kwargs:
+                            merge_kwargs["adapter_names"] = adapter_names
+                        elif "adapter_names" not in supported_merge_kwargs and adapter_names is not None:
+                            raise ValueError(
+                                "The `adapter_names` argument is not supported with your PEFT version. "
+                                "Please upgrade to the latest version of PEFT. `pip install -U peft`"
+                            )
+
+                        module.merge(**merge_kwargs)
 
         else:
             deprecate("fuse_text_encoder_lora", "0.27", LORA_DEPRECATION_MESSAGE)
 
-            def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False):
+            def fuse_text_encoder_lora(text_encoder, lora_scale=1.0, safe_fusing=False, **kwargs):
+                if "adapter_names" in kwargs and kwargs["adapter_names"] is not None:
+                    raise ValueError(
+                        "The `adapter_names` argument is not supported in your environment. Please switch to PEFT "
+                        "backend to use this argument by installing latest PEFT and transformers."
+                        " `pip install -U peft transformers`"
+                    )
+
                 for _, attn_module in text_encoder_attn_modules(text_encoder):
                     if isinstance(attn_module.q_proj, PatchedLoraProjection):
                         attn_module.q_proj._fuse_lora(lora_scale, safe_fusing)
@@ -1062,9 +1098,9 @@ class LoraLoaderMixin:
 
         if fuse_text_encoder:
             if hasattr(self, "text_encoder"):
-                fuse_text_encoder_lora(self.text_encoder, lora_scale, safe_fusing)
+                fuse_text_encoder_lora(self.text_encoder, lora_scale, safe_fusing, adapter_names=adapter_names)
             if hasattr(self, "text_encoder_2"):
-                fuse_text_encoder_lora(self.text_encoder_2, lora_scale, safe_fusing)
+                fuse_text_encoder_lora(self.text_encoder_2, lora_scale, safe_fusing, adapter_names=adapter_names)
 
     def unfuse_lora(self, unfuse_unet: bool = True, unfuse_text_encoder: bool = True):
         r"""

src/diffusers/loaders/unet.py

@@ -11,9 +11,11 @@
 # 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 inspect
 import os
 from collections import defaultdict
 from contextlib import nullcontext
+from functools import partial
 from typing import Callable, Dict, List, Optional, Union
 
 import safetensors
@@ -504,22 +506,43 @@ class UNet2DConditionLoadersMixin:
         save_function(state_dict, os.path.join(save_directory, weight_name))
         logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}")
 
-    def fuse_lora(self, lora_scale=1.0, safe_fusing=False):
+    def fuse_lora(self, lora_scale=1.0, safe_fusing=False, adapter_names=None):
         self.lora_scale = lora_scale
         self._safe_fusing = safe_fusing
-        self.apply(self._fuse_lora_apply)
+        self.apply(partial(self._fuse_lora_apply, adapter_names=adapter_names))
 
-    def _fuse_lora_apply(self, module):
+    def _fuse_lora_apply(self, module, adapter_names=None):
         if not USE_PEFT_BACKEND:
             if hasattr(module, "_fuse_lora"):
                 module._fuse_lora(self.lora_scale, self._safe_fusing)
+
+            if adapter_names is not None:
+                raise ValueError(
+                    "The `adapter_names` argument is not supported in your environment. Please switch"
+                    " to PEFT backend to use this argument by installing latest PEFT and transformers."
+                    " `pip install -U peft transformers`"
+                )
         else:
             from peft.tuners.tuners_utils import BaseTunerLayer
 
+            merge_kwargs = {"safe_merge": self._safe_fusing}
+
             if isinstance(module, BaseTunerLayer):
                 if self.lora_scale != 1.0:
                     module.scale_layer(self.lora_scale)
-                module.merge(safe_merge=self._safe_fusing)
+
+                # For BC with prevous PEFT versions, we need to check the signature
+                # of the `merge` method to see if it supports the `adapter_names` argument.
+                supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
+                if "adapter_names" in supported_merge_kwargs:
+                    merge_kwargs["adapter_names"] = adapter_names
+                elif "adapter_names" not in supported_merge_kwargs and adapter_names is not None:
+                    raise ValueError(
+                        "The `adapter_names` argument is not supported with your PEFT version. Please upgrade"
+                        " to the latest version of PEFT. `pip install -U peft`"
+                    )
+
+                module.merge(**merge_kwargs)
 
     def unfuse_lora(self):
         self.apply(self._unfuse_lora_apply)

src/diffusers/utils/testing_utils.py

@@ -300,6 +300,23 @@ def require_peft_backend(test_case):
     return unittest.skipUnless(USE_PEFT_BACKEND, "test requires PEFT backend")(test_case)
 
 
+def require_peft_version_greater(peft_version):
+    """
+    Decorator marking a test that requires PEFT backend with a specific version, this would require some specific
+    versions of PEFT and transformers.
+    """
+
+    def decorator(test_case):
+        correct_peft_version = is_peft_available() and version.parse(
+            version.parse(importlib.metadata.version("peft")).base_version
+        ) > version.parse(peft_version)
+        return unittest.skipUnless(
+            correct_peft_version, f"test requires PEFT backend with the version greater than {peft_version}"
+        )(test_case)
+
+    return decorator
+
+
 def deprecate_after_peft_backend(test_case):
     """
     Decorator marking a test that will be skipped after PEFT backend

tests/lora/test_lora_layers_peft.py

@@ -50,6 +50,7 @@ from diffusers.utils.testing_utils import (
     nightly,
     numpy_cosine_similarity_distance,
     require_peft_backend,
+    require_peft_version_greater,
     require_torch_gpu,
     slow,
     torch_device,
@@ -1105,6 +1106,68 @@ class PeftLoraLoaderMixinTests:
                 {"unet": ["adapter-1", "adapter-2", "adapter-3"], "text_encoder": ["adapter-1", "adapter-2"]},
             )
 
+    @require_peft_version_greater(peft_version="0.6.2")
+    def test_simple_inference_with_text_lora_unet_fused_multi(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 and multi-adapter case
+        """
+        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
+            components, _, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
+            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, "adapter-1")
+            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
+
+            # Attach a second adapter
+            pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
+            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"
+                )
+
+            # set them to multi-adapter inference mode
+            pipe.set_adapters(["adapter-1", "adapter-2"])
+            ouputs_all_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+            pipe.set_adapters(["adapter-1"])
+            ouputs_lora_1 = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+            pipe.fuse_lora(adapter_names=["adapter-1"])
+
+            # Fusing should still keep the LoRA layers so outpout should remain the same
+            outputs_lora_1_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
+
+            self.assertTrue(
+                np.allclose(ouputs_lora_1, outputs_lora_1_fused, atol=1e-3, rtol=1e-3),
+                "Fused lora should not change the output",
+            )
+
+            pipe.unfuse_lora()
+            pipe.fuse_lora(adapter_names=["adapter-2", "adapter-1"])
+
+            # Fusing should still keep the LoRA layers
+            output_all_lora_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
+            self.assertTrue(
+                np.allclose(output_all_lora_fused, ouputs_all_lora, atol=1e-3, rtol=1e-3),
+                "Fused lora should not change the output",
+            )
+
     @unittest.skip("This is failing for now - need to investigate")
     def test_simple_inference_with_text_unet_lora_unfused_torch_compile(self):
         """