feat(mm): probe for main model default settings

Currently, this is just the width and height, derived from the model base.

invokeai/backend/model_manager/probe.py

@@ -16,6 +16,7 @@
     BaseModelType,
     ControlAdapterDefaultSettings,
     InvalidModelConfigException,
+    MainModelDefaultSettings,
     ModelConfigFactory,
     ModelFormat,
     ModelRepoVariant,
@@ -162,11 +163,13 @@ def probe(
         fields["format"] = fields.get("format") or probe.get_format()
         fields["hash"] = fields.get("hash") or ModelHash().hash(model_path)
 
-        fields["default_settings"] = (
-            fields.get("default_settings") or probe.get_default_settings(fields["name"])
-            if isinstance(probe, ControlAdapterProbe)
-            else None
-        )
+        fields["default_settings"] = fields.get("default_settings")
+
+        if not fields["default_settings"]:
+            if fields["type"] in {ModelType.ControlNet, ModelType.T2IAdapter}:
+                fields["default_settings"] = get_default_settings_controlnet_t2i_adapter(fields["name"])
+            elif fields["type"] is ModelType.Main:
+                fields["default_settings"] = get_default_settings_main(fields["base"])
 
         if format_type == ModelFormat.Diffusers and isinstance(probe, FolderProbeBase):
             fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()
@@ -338,36 +341,41 @@ def _scan_model(cls, model_name: str, checkpoint: Path) -> None:
             raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
 
 
-class ControlAdapterProbe(ProbeBase):
-    """Adds `get_default_settings` for ControlNet and T2IAdapter probes"""
-
-    # TODO(psyche): It would be nice to get these from the invocations, but that creates circular dependencies.
-    # "canny": CannyImageProcessorInvocation.get_type()
-    MODEL_NAME_TO_PREPROCESSOR = {
-        "canny": "canny_image_processor",
-        "mlsd": "mlsd_image_processor",
-        "depth": "depth_anything_image_processor",
-        "bae": "normalbae_image_processor",
-        "normal": "normalbae_image_processor",
-        "sketch": "pidi_image_processor",
-        "scribble": "lineart_image_processor",
-        "lineart": "lineart_image_processor",
-        "lineart_anime": "lineart_anime_image_processor",
-        "softedge": "hed_image_processor",
-        "shuffle": "content_shuffle_image_processor",
-        "pose": "dw_openpose_image_processor",
-        "mediapipe": "mediapipe_face_processor",
-        "pidi": "pidi_image_processor",
-        "zoe": "zoe_depth_image_processor",
-        "color": "color_map_image_processor",
-    }
+# Probing utilities
+MODEL_NAME_TO_PREPROCESSOR = {
+    "canny": "canny_image_processor",
+    "mlsd": "mlsd_image_processor",
+    "depth": "depth_anything_image_processor",
+    "bae": "normalbae_image_processor",
+    "normal": "normalbae_image_processor",
+    "sketch": "pidi_image_processor",
+    "scribble": "lineart_image_processor",
+    "lineart": "lineart_image_processor",
+    "lineart_anime": "lineart_anime_image_processor",
+    "softedge": "hed_image_processor",
+    "shuffle": "content_shuffle_image_processor",
+    "pose": "dw_openpose_image_processor",
+    "mediapipe": "mediapipe_face_processor",
+    "pidi": "pidi_image_processor",
+    "zoe": "zoe_depth_image_processor",
+    "color": "color_map_image_processor",
+}
 
-    @classmethod
-    def get_default_settings(cls, model_name: str) -> Optional[ControlAdapterDefaultSettings]:
-        for k, v in cls.MODEL_NAME_TO_PREPROCESSOR.items():
-            if k in model_name:
-                return ControlAdapterDefaultSettings(preprocessor=v)
-        return None
+
+def get_default_settings_controlnet_t2i_adapter(model_name: str) -> Optional[ControlAdapterDefaultSettings]:
+    for k, v in MODEL_NAME_TO_PREPROCESSOR.items():
+        if k in model_name:
+            return ControlAdapterDefaultSettings(preprocessor=v)
+    return None
+
+
+def get_default_settings_main(model_base: BaseModelType) -> Optional[MainModelDefaultSettings]:
+    if model_base is BaseModelType.StableDiffusion1 or model_base is BaseModelType.StableDiffusion2:
+        return MainModelDefaultSettings(width=512, height=512)
+    elif model_base is BaseModelType.StableDiffusionXL:
+        return MainModelDefaultSettings(width=1024, height=1024)
+    # We don't provide defaults for BaseModelType.StableDiffusionXLRefiner, as they are not standalone models.
+    return None
 
 
 # ##################################################3
@@ -493,7 +501,7 @@ def get_base_type(self) -> BaseModelType:
             raise InvalidModelConfigException(f"{self.model_path}: Could not determine base type")
 
 
-class ControlNetCheckpointProbe(CheckpointProbeBase, ControlAdapterProbe):
+class ControlNetCheckpointProbe(CheckpointProbeBase):
     """Class for probing controlnets."""
 
     def get_base_type(self) -> BaseModelType:
@@ -521,7 +529,7 @@ def get_base_type(self) -> BaseModelType:
         raise NotImplementedError()
 
 
-class T2IAdapterCheckpointProbe(CheckpointProbeBase, ControlAdapterProbe):
+class T2IAdapterCheckpointProbe(CheckpointProbeBase):
     def get_base_type(self) -> BaseModelType:
         raise NotImplementedError()
 
@@ -659,7 +667,7 @@ def get_variant_type(self) -> ModelVariantType:
         return ModelVariantType.Normal
 
 
-class ControlNetFolderProbe(FolderProbeBase, ControlAdapterProbe):
+class ControlNetFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         config_file = self.model_path / "config.json"
         if not config_file.exists():
@@ -733,7 +741,7 @@ def get_base_type(self) -> BaseModelType:
         return BaseModelType.Any
 
 
-class T2IAdapterFolderProbe(FolderProbeBase, ControlAdapterProbe):
+class T2IAdapterFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         config_file = self.model_path / "config.json"
         if not config_file.exists():