Add lora loader node

invokeai/app/invocations/latent.py

@@ -173,7 +173,6 @@ class TextToLatentsInvocation(BaseInvocation):
     steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
     scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
-    model:       str = Field(default="", description="The model to use (currently ignored)")
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
 

invokeai/app/invocations/model.py

@@ -1,5 +1,6 @@
 from typing import Literal, Optional, Union, List
 from pydantic import BaseModel, Field
+import copy
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 
@@ -98,21 +99,6 @@ class ModelLoaderInvocation(BaseInvocation):
             )
         """
 
-        loras = [
-            LoraInfo(
-                model_name="sadcatmeme",
-                model_type=SDModelType.Lora,
-                submodel=None,
-                weight=0.75,
-            ),
-            LoraInfo(
-                model_name="gunAimingAtYouV1",
-                model_type=SDModelType.Lora,
-                submodel=None,
-                weight=0.75,
-            ),
-        ]
-
 
         return ModelLoaderOutput(
             unet=UNetField(
@@ -126,7 +112,7 @@ class ModelLoaderInvocation(BaseInvocation):
                     model_type=SDModelType.Diffusers,
                     submodel=SDModelType.Scheduler,
                 ),
-                loras=loras,
+                loras=[],
             ),
             clip=ClipField(
                 tokenizer=ModelInfo(
@@ -139,7 +125,7 @@ class ModelLoaderInvocation(BaseInvocation):
                     model_type=SDModelType.Diffusers,
                     submodel=SDModelType.TextEncoder,
                 ),
-                loras=loras,
+                loras=[],
             ),
             vae=VaeField(
                 vae=ModelInfo(
@@ -149,3 +135,65 @@ class ModelLoaderInvocation(BaseInvocation):
                 ),
             )
         )
+
+class LoraLoaderOutput(BaseInvocationOutput):
+    """Model loader output"""
+
+    #fmt: off
+    type: Literal["lora_loader_output"] = "lora_loader_output"
+
+    unet: Optional[UNetField] = Field(default=None, description="UNet submodel")
+    clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels")
+    #fmt: on
+
+class LoraLoaderInvocation(BaseInvocation):
+    """Apply selected lora to unet and text_encoder."""
+
+    type: Literal["lora_loader"] = "lora_loader"
+
+    lora_name: str = Field(description="Lora model name")
+    weight: float = Field(default=0.75, description="With what weight to apply lora")
+
+    unet: Optional[UNetField] = Field(description="UNet model for applying lora")
+    clip: Optional[ClipField] = Field(description="Clip model for applying lora")
+
+    def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.lora_name,
+            model_type=SDModelType.Lora,
+        ):
+            raise Exception(f"Unkown lora name: {self.lora_name}!")
+
+        if self.unet is not None and any(lora.model_name == self.lora_name for lora in self.unet.loras):
+            raise Exception(f"Lora \"{self.lora_name}\" already applied to unet")
+
+        if self.clip is not None and any(lora.model_name == self.lora_name for lora in self.clip.loras):
+            raise Exception(f"Lora \"{self.lora_name}\" already applied to clip")
+
+        output = LoraLoaderOutput()
+
+        if self.unet is not None:
+            output.unet = copy.deepcopy(self.unet)
+            output.unet.loras.append(
+                LoraInfo(
+                    model_name=self.lora_name,
+                    model_type=SDModelType.Lora,
+                    submodel=None,
+                    weight=self.weight,
+                )
+            )
+
+        if self.clip is not None:
+            output.clip = copy.deepcopy(self.clip)
+            output.clip.loras.append(
+                LoraInfo(
+                    model_name=self.lora_name,
+                    model_type=SDModelType.Lora,
+                    submodel=None,
+                    weight=self.weight,
+                )
+            )
+
+        return output
+