[feat] introduce `unload_lora()`. (#6451)

* introduce unload_lora.

* fix-copies

src/diffusers/models/unet_2d_condition.py

@@ -829,6 +829,17 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
         if self.original_attn_processors is not None:
             self.set_attn_processor(self.original_attn_processors)
 
+    def unload_lora(self):
+        """Unloads LoRA weights."""
+        deprecate(
+            "unload_lora",
+            "0.28.0",
+            "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().",
+        )
+        for module in self.modules():
+            if hasattr(module, "set_lora_layer"):
+                module.set_lora_layer(None)
+
     def forward(
         self,
         sample: torch.FloatTensor,

src/diffusers/models/unet_3d_condition.py

@@ -22,7 +22,7 @@ import torch.utils.checkpoint
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..loaders import UNet2DConditionLoadersMixin
-from ..utils import BaseOutput, logging
+from ..utils import BaseOutput, deprecate, logging
 from .activations import get_activation
 from .attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
@@ -503,6 +503,18 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
                 if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
                     setattr(upsample_block, k, None)
 
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.unload_lora
+    def unload_lora(self):
+        """Unloads LoRA weights."""
+        deprecate(
+            "unload_lora",
+            "0.28.0",
+            "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().",
+        )
+        for module in self.modules():
+            if hasattr(module, "set_lora_layer"):
+                module.set_lora_layer(None)
+
     def forward(
         self,
         sample: torch.FloatTensor,

src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -1034,6 +1034,17 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
         if self.original_attn_processors is not None:
             self.set_attn_processor(self.original_attn_processors)
 
+    def unload_lora(self):
+        """Unloads LoRA weights."""
+        deprecate(
+            "unload_lora",
+            "0.28.0",
+            "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().",
+        )
+        for module in self.modules():
+            if hasattr(module, "set_lora_layer"):
+                module.set_lora_layer(None)
+
     def forward(
         self,
         sample: torch.FloatTensor,

tests/lora/test_lora_layers_old_backend.py

@@ -151,9 +151,7 @@ def create_unet_lora_layers(unet: nn.Module, rank=4, mock_weights=True):
 
     unet_lora_sd = unet_lora_state_dict(unet)
     # Unload LoRA.
-    for module in unet.modules():
-        if hasattr(module, "set_lora_layer"):
-            module.set_lora_layer(None)
+    unet.unload_lora()
 
     return unet_lora_parameters, unet_lora_sd
 
@@ -230,9 +228,7 @@ def create_3d_unet_lora_layers(unet: nn.Module, rank=4, mock_weights=True):
     unet_lora_sd = unet_lora_state_dict(unet)
 
     # Unload LoRA.
-    for module in unet.modules():
-        if hasattr(module, "set_lora_layer"):
-            module.set_lora_layer(None)
+    unet.unload_lora()
 
     return unet_lora_sd
 
@@ -1545,9 +1541,7 @@ class UNet2DConditionLoRAModelTests(unittest.TestCase):
             sample = model(**inputs_dict, cross_attention_kwargs={"scale": 0.0}).sample
 
         # Unload LoRA.
-        for module in model.modules():
-            if hasattr(module, "set_lora_layer"):
-                module.set_lora_layer(None)
+        model.unload_lora()
 
         with torch.no_grad():
             new_sample = model(**inputs_dict).sample