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

tests/utils/test_models_sub_type.py

@@ -0,0 +1,127 @@
+"""Tests for model sub_type field refactoring."""
+
+import pytest
+from py.utils.models import (
+    BaseModelMetadata,
+    LoraMetadata,
+    CheckpointMetadata,
+    EmbeddingMetadata,
+)
+
+
+class TestCheckpointMetadataSubType:
+    """Test CheckpointMetadata uses sub_type field."""
+
+    def test_checkpoint_has_sub_type_field(self):
+        """CheckpointMetadata should have sub_type field."""
+        metadata = CheckpointMetadata(
+            file_name="test",
+            model_name="Test Model",
+            file_path="/test/model.safetensors",
+            size=1000,
+            modified=1234567890.0,
+            sha256="abc123",
+            base_model="SDXL",
+            preview_url="",
+        )
+        assert hasattr(metadata, "sub_type")
+        assert metadata.sub_type == "checkpoint"
+
+    def test_checkpoint_sub_type_can_be_diffusion_model(self):
+        """CheckpointMetadata sub_type can be set to diffusion_model."""
+        metadata = CheckpointMetadata(
+            file_name="test",
+            model_name="Test Model",
+            file_path="/test/model.safetensors",
+            size=1000,
+            modified=1234567890.0,
+            sha256="abc123",
+            base_model="SDXL",
+            preview_url="",
+            sub_type="diffusion_model",
+        )
+        assert metadata.sub_type == "diffusion_model"
+
+    def test_checkpoint_from_civitai_info_uses_sub_type(self):
+        """from_civitai_info should use sub_type from version_info."""
+        version_info = {
+            "baseModel": "SDXL",
+            "model": {"name": "Test", "description": "", "tags": []},
+            "files": [{"name": "model.safetensors", "sizeKB": 1000, "hashes": {"SHA256": "abc123"}, "primary": True}],
+        }
+        file_info = version_info["files"][0]
+        save_path = "/test/model.safetensors"
+        
+        metadata = CheckpointMetadata.from_civitai_info(version_info, file_info, save_path)
+        
+        assert hasattr(metadata, "sub_type")
+        # When type is missing from version_info, defaults to "checkpoint"
+        assert metadata.sub_type == "checkpoint"
+
+
+class TestEmbeddingMetadataSubType:
+    """Test EmbeddingMetadata uses sub_type field."""
+
+    def test_embedding_has_sub_type_field(self):
+        """EmbeddingMetadata should have sub_type field."""
+        metadata = EmbeddingMetadata(
+            file_name="test",
+            model_name="Test Model",
+            file_path="/test/model.pt",
+            size=1000,
+            modified=1234567890.0,
+            sha256="abc123",
+            base_model="SD1.5",
+            preview_url="",
+        )
+        assert hasattr(metadata, "sub_type")
+        assert metadata.sub_type == "embedding"
+
+    def test_embedding_from_civitai_info_uses_sub_type(self):
+        """from_civitai_info should use sub_type from version_info."""
+        version_info = {
+            "baseModel": "SD1.5",
+            "model": {"name": "Test", "description": "", "tags": []},
+            "files": [{"name": "model.pt", "sizeKB": 1000, "hashes": {"SHA256": "abc123"}, "primary": True}],
+        }
+        file_info = version_info["files"][0]
+        save_path = "/test/model.pt"
+        
+        metadata = EmbeddingMetadata.from_civitai_info(version_info, file_info, save_path)
+        
+        assert hasattr(metadata, "sub_type")
+        assert metadata.sub_type == "embedding"
+
+
+class TestLoraMetadataConsistency:
+    """Test LoraMetadata consistency (no sub_type field, uses civitai data)."""
+
+    def test_lora_does_not_have_sub_type_field(self):
+        """LoraMetadata should not have sub_type field (uses civitai.model.type)."""
+        metadata = LoraMetadata(
+            file_name="test",
+            model_name="Test Model",
+            file_path="/test/model.safetensors",
+            size=1000,
+            modified=1234567890.0,
+            sha256="abc123",
+            base_model="SDXL",
+            preview_url="",
+        )
+        # Lora doesn't have sub_type field - it uses civitai data
+        assert not hasattr(metadata, "sub_type")
+
+    def test_lora_from_civitai_info_extracts_type(self):
+        """from_civitai_info should extract type from civitai data."""
+        version_info = {
+            "baseModel": "SDXL",
+            "model": {"name": "Test", "description": "", "tags": [], "type": "Lora"},
+            "files": [{"name": "model.safetensors", "sizeKB": 1000, "hashes": {"SHA256": "abc123"}, "primary": True}],
+        }
+        file_info = version_info["files"][0]
+        save_path = "/test/model.safetensors"
+        
+        metadata = LoraMetadata.from_civitai_info(version_info, file_info, save_path)
+        
+        # Type is stored in civitai dict
+        assert metadata.civitai.get("model", {}).get("type") == "Lora"