partial implementation of SDXL model loader

invokeai/app/api/routers/models.py

@@ -32,11 +32,16 @@ class ModelsList(BaseModel):
     responses={200: {"model": ModelsList }},
 )
 async def list_models(
-    base_model: Optional[BaseModelType] = Query(default=None, description="Base model"),
+    base_models: Optional[List[Union[BaseModelType,None]]] = Query(default=None, description="Base models to include"),
     model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
 ) -> ModelsList:
     """Gets a list of models"""
-    models_raw = ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type)
+    if base_models and len(base_models)>0:
+        models_raw = list()
+        for base_model in base_models:
+            models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
+    else:
+        models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
     models = parse_obj_as(ModelsList, { "models": models_raw })
     return models
 

invokeai/app/invocations/model.py

@@ -33,7 +33,6 @@ class ClipField(BaseModel):
     skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
 
-
 class VaeField(BaseModel):
     # TODO: better naming?
     vae: ModelInfo = Field(description="Info to load vae submodel")
@@ -50,6 +49,18 @@ class ModelLoaderOutput(BaseInvocationOutput):
     vae: VaeField = Field(default=None, description="Vae submodel")
     # fmt: on
 
+class SDXLModelLoaderOutput(BaseInvocationOutput):
+    """SDXL model loader output"""
+
+    # fmt: off
+    type: Literal["sdxl_model_loader_output"] = "sdxl_model_loader_output"
+
+    unet: UNetField = Field(default=None, description="UNet submodel")
+    clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
+    clip2: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels (2d set)")
+    vae: VaeField = Field(default=None, description="Vae submodel")
+    # fmt: on
+
 
 class MainModelField(BaseModel):
     """Main model field"""
@@ -64,7 +75,6 @@ class LoRAModelField(BaseModel):
     model_name: str = Field(description="Name of the LoRA model")
     base_model: BaseModelType = Field(description="Base model")
 
-
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
 
@@ -167,7 +177,97 @@ class MainModelLoaderInvocation(BaseInvocation):
             ),
         )
 
+class SDXLMainModelLoaderInvocation(BaseInvocation):
+    """Loads an SDXL main model, outputting its submodels."""
 
+    type: Literal["sdxl_main_model_loader"] = "sdxl_main_model_loader"
+
+    model: MainModelField = Field(description="The SDXL model to load")
+    # TODO: precision?
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "SDXL Model Loader",
+                "tags": ["model", "loader", "sdxl"],
+                "type_hints": {"model": "model"},
+            },
+        }
+
+    def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.Main
+
+        # TODO: not found exceptions
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
+
+        return SDXLModelLoaderOutput(
+            unet=UNetField(
+                unet=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
+                ),
+                scheduler=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
+                ),
+                loras=[],
+            ),
+            clip=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder,
+                ),
+                loras=[],
+                skipped_layers=0,
+            ),
+            clip2=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer2,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder2,
+                ),
+                loras=[],
+                skipped_layers=0,
+            ),
+            vae=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Vae,
+                ),
+            ),
+        )
+
+
+    
 class LoraLoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 

invokeai/frontend/web/src/features/system/components/ModelSelect.tsx

@@ -13,6 +13,8 @@ import { useGetMainModelsQuery } from 'services/api/endpoints/models';
 export const MODEL_TYPE_MAP = {
   'sd-1': 'Stable Diffusion 1.x',
   'sd-2': 'Stable Diffusion 2.x',
+  sdxl: 'Stable Diffusion XL',
+  'sdxl-refiner': 'Stable Diffusion XL Refiner',
 };
 
 const ModelSelect = () => {