add peft, set_adapter

model.py

@@ -44,7 +44,7 @@ class Model:
             and self.pipe is not None
         ):
             unet: UNet2DConditionModelEx = self.pipe.unet
-            unet.activate_adapters([task_name])
+            unet.set_adapter(task_name)
             return self.pipe
         unet: UNet2DConditionModelEx = UNet2DConditionModelEx.from_pretrained(
             base_model_id, subfolder="unet", torch_dtype=torch.float16
@@ -82,7 +82,7 @@ class Model:
         if task_name == self.task_name:
             return
         unet: UNet2DConditionModelEx = self.pipe.unet
-        unet.activate_adapters([task_name])
+        unet.set_adapter(task_name)
         self.task_name = task_name
 
     def get_prompt(self, prompt: str, additional_prompt: str) -> str:

pipeline.py

@@ -949,7 +949,7 @@ class StableDiffusionControlLoraV3Pipeline(
             if adapter_name_ori is not None:
                 break
         
-        unet.activate_adapters()
+        unet.activate_extra_condition_adapters()
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)

requirements.txt

@@ -6,6 +6,7 @@ gradio>=4.26.0
 huggingface-hub>=0.16.4
 mediapipe>=0.10.1
 opencv-python-headless>=4.8.0.74
+peft>=0.11.1
 safetensors>=0.3.1
 torch>=2.0.1
 torchvision>=0.15.2

unet.py

@@ -145,11 +145,13 @@ class UNet2DConditionModelEx(UNet2DConditionModel):
         
         return self
     
-    def activate_adapters(self, adapter_names: Union[List[str], None] = None):
+    def activate_extra_condition_adapters(self):
         lora_layers = [layer for layer in self.modules() if isinstance(layer, LoraLayer)]
         for lora_layer in lora_layers:
-            _adapter_names = adapter_names or list(lora_layer.scaling.keys())
-            lora_layer.set_adapter(_adapter_names)
+            adapter_names = [k for k in lora_layer.scaling.keys() if k in self.config.extra_condition_names] 
+            adapter_names += lora_layer.active_adapters
+            adapter_names = list(set(adapter_names))
+            lora_layer.set_adapter(adapter_names)
     
     def set_extra_condition_scale(self, scale: Union[float, List[float]] = 1.0):
         if isinstance(scale, float):