refactor: unify model_type semantics by introducing sub_type field

This commit resolves the semantic confusion around the model_type field by
clearly distinguishing between:
- scanner_type: architecture-level (lora/checkpoint/embedding)
- sub_type: business-level subtype (lora/locon/dora/checkpoint/diffusion_model/embedding)

Backend Changes:
- Rename model_type to sub_type in CheckpointMetadata and EmbeddingMetadata
- Add resolve_sub_type() and normalize_sub_type() in model_query.py
- Update checkpoint_scanner to use _resolve_sub_type()
- Update service format_response to include both sub_type and model_type
- Add VALID_*_SUB_TYPES constants with backward compatible aliases

Frontend Changes:
- Add MODEL_SUBTYPE_DISPLAY_NAMES constants
- Keep MODEL_TYPE_DISPLAY_NAMES as backward compatible alias

Testing:
- Add 43 new tests covering sub_type resolution and API response

Documentation:
- Add refactoring todo document to docs/technical/

BREAKING CHANGE: None - full backward compatibility maintained

py/utils/constants.py

@@ -45,8 +45,13 @@ SUPPORTED_MEDIA_EXTENSIONS = {
     "videos": [".mp4", ".webm"],
 }
 
-# Valid Lora types
-VALID_LORA_TYPES = ["lora", "locon", "dora"]
+# Valid sub-types for each scanner type
+VALID_LORA_SUB_TYPES = ["lora", "locon", "dora"]
+VALID_CHECKPOINT_SUB_TYPES = ["checkpoint", "diffusion_model"]
+VALID_EMBEDDING_SUB_TYPES = ["embedding"]
+
+# Backward compatibility alias
+VALID_LORA_TYPES = VALID_LORA_SUB_TYPES
 
 # Supported Civitai model types for user model queries (case-insensitive)
 CIVITAI_USER_MODEL_TYPES = [

py/utils/models.py

@@ -173,14 +173,14 @@ class LoraMetadata(BaseModelMetadata):
 @dataclass
 class CheckpointMetadata(BaseModelMetadata):
     """Represents the metadata structure for a Checkpoint model"""
-    model_type: str = "checkpoint"  # Model type (checkpoint, diffusion_model, etc.)
+    sub_type: str = "checkpoint"  # Model sub-type (checkpoint, diffusion_model, etc.)
 
     @classmethod
     def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'CheckpointMetadata':
         """Create CheckpointMetadata instance from Civitai version info"""
         file_name = file_info['name']
         base_model = determine_base_model(version_info.get('baseModel', ''))
-        model_type = version_info.get('type', 'checkpoint')
+        sub_type = version_info.get('type', 'checkpoint')
         
         # Extract tags and description if available
         tags = []
@@ -203,7 +203,7 @@ class CheckpointMetadata(BaseModelMetadata):
             preview_nsfw_level=0,
             from_civitai=True,
             civitai=version_info,
-            model_type=model_type,
+            sub_type=sub_type,
             tags=tags,
             modelDescription=description
         )
@@ -211,14 +211,14 @@ class CheckpointMetadata(BaseModelMetadata):
 @dataclass
 class EmbeddingMetadata(BaseModelMetadata):
     """Represents the metadata structure for an Embedding model"""
-    model_type: str = "embedding"  # Model type (embedding, textual_inversion, etc.)
+    sub_type: str = "embedding"
 
     @classmethod
     def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'EmbeddingMetadata':
         """Create EmbeddingMetadata instance from Civitai version info"""
         file_name = file_info['name']
         base_model = determine_base_model(version_info.get('baseModel', ''))
-        model_type = version_info.get('type', 'embedding')
+        sub_type = version_info.get('type', 'embedding')
         
         # Extract tags and description if available
         tags = []
@@ -241,7 +241,7 @@ class EmbeddingMetadata(BaseModelMetadata):
             preview_nsfw_level=0,
             from_civitai=True,
             civitai=version_info,
-            model_type=model_type,
+            sub_type=sub_type,
             tags=tags,
             modelDescription=description
         )