feat(model): add model type filtering support

- Add model_types parameter to ModelListingHandler to support filtering by model type
- Implement get_model_types endpoint in ModelQueryHandler to retrieve available model types
- Register new /api/lm/{prefix}/model-types route for model type queries
- Extend BaseModelService to handle model type filtering in queries
- Support both model_type and civitai_model_type query parameters for backward compatibility

This enables users to filter models by specific types, improving model discovery and organization capabilities.

py/routes/handlers/model_handlers.py

@@ -152,6 +152,8 @@ class ModelListingHandler:
         fuzzy_search = request.query.get("fuzzy_search", "false").lower() == "true"
 
         base_models = request.query.getall("base_model", [])
+        model_types = list(request.query.getall("model_type", []))
+        model_types.extend(request.query.getall("civitai_model_type", []))
         # Support legacy ?tag=foo plus new ?tag_include/foo & ?tag_exclude parameters
         legacy_tags = request.query.getall("tag", [])
         if not legacy_tags:
@@ -225,6 +227,7 @@ class ModelListingHandler:
             "update_available_only": update_available_only,
             "credit_required": credit_required,
             "allow_selling_generated_content": allow_selling_generated_content,
+            "model_types": model_types,
             **self._parse_specific_params(request),
         }
 
@@ -557,6 +560,17 @@ class ModelQueryHandler:
             self._logger.error("Error retrieving base models: %s", exc)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def get_model_types(self, request: web.Request) -> web.Response:
+        try:
+            limit = int(request.query.get("limit", "20"))
+            if limit < 1 or limit > 100:
+                limit = 20
+            model_types = await self._service.get_model_types(limit)
+            return web.json_response({"success": True, "model_types": model_types})
+        except Exception as exc:
+            self._logger.error("Error retrieving model types: %s", exc)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
     async def scan_models(self, request: web.Request) -> web.Response:
         try:
             full_rebuild = request.query.get("full_rebuild", "false").lower() == "true"
@@ -1579,6 +1593,7 @@ class ModelHandlerSet:
             "verify_duplicates": self.management.verify_duplicates,
             "get_top_tags": self.query.get_top_tags,
             "get_base_models": self.query.get_base_models,
+            "get_model_types": self.query.get_model_types,
             "scan_models": self.query.scan_models,
             "get_model_roots": self.query.get_model_roots,
             "get_folders": self.query.get_folders,

py/routes/model_route_registrar.py

@@ -39,6 +39,7 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/{prefix}/auto-organize-progress", "get_auto_organize_progress"),
     RouteDefinition("GET", "/api/lm/{prefix}/top-tags", "get_top_tags"),
     RouteDefinition("GET", "/api/lm/{prefix}/base-models", "get_base_models"),
+    RouteDefinition("GET", "/api/lm/{prefix}/model-types", "get_model_types"),
     RouteDefinition("GET", "/api/lm/{prefix}/scan", "scan_models"),
     RouteDefinition("GET", "/api/lm/{prefix}/roots", "get_model_roots"),
     RouteDefinition("GET", "/api/lm/{prefix}/folders", "get_folders"),

py/services/base_model_service.py

@@ -1,12 +1,19 @@
 from abc import ABC, abstractmethod
 import asyncio
-from typing import Dict, List, Optional, Type, TYPE_CHECKING
+from typing import Any, Dict, List, Optional, Type, TYPE_CHECKING
 import logging
 import os
 
 from ..utils.models import BaseModelMetadata
 from ..utils.metadata_manager import MetadataManager
-from .model_query import FilterCriteria, ModelCacheRepository, ModelFilterSet, SearchStrategy, SettingsProvider
+from .model_query import (
+    FilterCriteria,
+    ModelCacheRepository,
+    ModelFilterSet,
+    SearchStrategy,
+    SettingsProvider,
+    resolve_civitai_model_type,
+)
 from .settings_manager import get_settings_manager
 
 logger = logging.getLogger(__name__)
@@ -59,6 +66,7 @@ class BaseModelService(ABC):
         search: str = None,
         fuzzy_search: bool = False,
         base_models: list = None,
+        model_types: list = None,
         tags: Optional[Dict[str, str]] = None,
         search_options: dict = None,
         hash_filters: dict = None,
@@ -80,6 +88,7 @@ class BaseModelService(ABC):
                 sorted_data,
                 folder=folder,
                 base_models=base_models,
+                model_types=model_types,
                 tags=tags,
                 favorites_only=favorites_only,
                 search_options=search_options,
@@ -149,6 +158,7 @@ class BaseModelService(ABC):
         data: List[Dict],
         folder: str = None,
         base_models: list = None,
+        model_types: list = None,
         tags: Optional[Dict[str, str]] = None,
         favorites_only: bool = False,
         search_options: dict = None,
@@ -158,6 +168,7 @@ class BaseModelService(ABC):
         criteria = FilterCriteria(
             folder=folder,
             base_models=base_models,
+            model_types=model_types,
             tags=tags,
             favorites_only=favorites_only,
             search_options=normalized_options,
@@ -456,6 +467,22 @@ class BaseModelService(ABC):
     async def get_base_models(self, limit: int = 20) -> List[Dict]:
         """Get base models sorted by frequency"""
         return await self.scanner.get_base_models(limit)
+
+    async def get_model_types(self, limit: int = 20) -> List[Dict[str, Any]]:
+        """Get counts of CivitAI model types present in the cache."""
+        cache = await self.scanner.get_cached_data()
+        type_counts: Dict[str, int] = {}
+        for entry in cache.raw_data:
+            model_type = resolve_civitai_model_type(entry)
+            type_counts[model_type] = type_counts.get(model_type, 0) + 1
+
+        sorted_types = sorted(
+            [{"type": model_type, "count": count} for model_type, count in type_counts.items()],
+            key=lambda value: value["count"],
+            reverse=True,
+        )
+
+        return sorted_types[:limit]
     
     def has_hash(self, sha256: str) -> bool:
         """Check if a model with given hash exists"""

py/services/model_query.py

@@ -1,12 +1,49 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Protocol, Callable
+from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Protocol, Callable
 
 from ..utils.constants import NSFW_LEVELS
 from ..utils.utils import fuzzy_match as default_fuzzy_match
 
 
+DEFAULT_CIVITAI_MODEL_TYPE = "LORA"
+
+
+def _coerce_to_str(value: Any) -> Optional[str]:
+    if value is None:
+        return None
+
+    candidate = str(value).strip()
+    return candidate if candidate else None
+
+
+def normalize_civitai_model_type(value: Any) -> Optional[str]:
+    """Return a lowercase string suitable for comparisons."""
+    candidate = _coerce_to_str(value)
+    return candidate.lower() if candidate else None
+
+
+def resolve_civitai_model_type(entry: Mapping[str, Any]) -> str:
+    """Extract the model type from CivitAI metadata, defaulting to LORA."""
+    if not isinstance(entry, Mapping):
+        return DEFAULT_CIVITAI_MODEL_TYPE
+
+    civitai = entry.get("civitai")
+    if isinstance(civitai, Mapping):
+        civitai_model = civitai.get("model")
+        if isinstance(civitai_model, Mapping):
+            model_type = _coerce_to_str(civitai_model.get("type"))
+            if model_type:
+                return model_type
+
+    model_type = _coerce_to_str(entry.get("model_type"))
+    if model_type:
+        return model_type
+
+    return DEFAULT_CIVITAI_MODEL_TYPE
+
+
 class SettingsProvider(Protocol):
     """Protocol describing the SettingsManager contract used by query helpers."""
 
@@ -31,6 +68,7 @@ class FilterCriteria:
     tags: Optional[Dict[str, str]] = None
     favorites_only: bool = False
     search_options: Optional[Dict[str, Any]] = None
+    model_types: Optional[Sequence[str]] = None
 
 
 class ModelCacheRepository:
@@ -134,6 +172,19 @@ class ModelFilterSet:
                 if not any(tag in exclude_tags for tag in (item.get("tags", []) or []))
             ]
 
+        model_types = criteria.model_types or []
+        normalized_model_types = {
+            model_type for model_type in (
+                normalize_civitai_model_type(value) for value in model_types
+            )
+            if model_type
+        }
+        if normalized_model_types:
+            items = [
+                item for item in items
+                if normalize_civitai_model_type(resolve_civitai_model_type(item)) in normalized_model_types
+            ]
+
         return items
 
 

py/services/model_scanner.py

@@ -161,6 +161,12 @@ class ModelScanner:
         if trained_words:
             slim['trainedWords'] = list(trained_words) if isinstance(trained_words, list) else trained_words
 
+        civitai_model = civitai.get('model')
+        if isinstance(civitai_model, Mapping):
+            model_type_value = civitai_model.get('type')
+            if model_type_value not in (None, '', []):
+                slim['model'] = {'type': model_type_value}
+
         return slim or None
 
     def _build_cache_entry(

py/services/persistent_model_cache.py

@@ -5,7 +5,7 @@ import re
 import sqlite3
 import threading
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Sequence, Tuple
+from typing import Dict, List, Mapping, Optional, Sequence, Tuple
 
 from ..utils.settings_paths import get_project_root, get_settings_dir
 
@@ -47,6 +47,7 @@ class PersistentModelCache:
         "metadata_source",
         "civitai_id",
         "civitai_model_id",
+        "civitai_model_type",
         "civitai_name",
         "civitai_creator_username",
         "trained_words",
@@ -138,7 +139,8 @@ class PersistentModelCache:
             creator_username = row["civitai_creator_username"]
             civitai: Optional[Dict] = None
             civitai_has_data = any(
-                row[col] is not None for col in ("civitai_id", "civitai_model_id", "civitai_name")
+                row[col] is not None
+                for col in ("civitai_id", "civitai_model_id", "civitai_model_type", "civitai_name")
             ) or trained_words or creator_username
             if civitai_has_data:
                 civitai = {}
@@ -152,6 +154,9 @@ class PersistentModelCache:
                     civitai["trainedWords"] = trained_words
                 if creator_username:
                     civitai.setdefault("creator", {})["username"] = creator_username
+                model_type_value = row["civitai_model_type"]
+                if model_type_value:
+                    civitai.setdefault("model", {})["type"] = model_type_value
 
             license_value = row["license_flags"]
             if license_value is None:
@@ -443,6 +448,7 @@ class PersistentModelCache:
                             metadata_source TEXT,
                             civitai_id INTEGER,
                             civitai_model_id INTEGER,
+                            civitai_model_type TEXT,
                             civitai_name TEXT,
                             civitai_creator_username TEXT,
                             trained_words TEXT,
@@ -492,6 +498,7 @@ class PersistentModelCache:
         required_columns = {
             "metadata_source": "TEXT",
             "civitai_creator_username": "TEXT",
+            "civitai_model_type": "TEXT",
             "civitai_deleted": "INTEGER DEFAULT 0",
             # Persisting without explicit flags should assume CivitAI's documented defaults (0b111001 == 57).
             "license_flags": f"INTEGER DEFAULT {DEFAULT_LICENSE_FLAGS}",
@@ -528,6 +535,13 @@ class PersistentModelCache:
         creator_data = civitai.get("creator") if isinstance(civitai, dict) else None
         if isinstance(creator_data, dict):
             creator_username = creator_data.get("username") or None
+        model_type_value = None
+        if isinstance(civitai, Mapping):
+            civitai_model_info = civitai.get("model")
+            if isinstance(civitai_model_info, Mapping):
+                candidate_type = civitai_model_info.get("type")
+                if candidate_type not in (None, "", []):
+                    model_type_value = candidate_type
 
         license_flags = item.get("license_flags")
         if license_flags is None:
@@ -552,6 +566,7 @@ class PersistentModelCache:
             metadata_source,
             civitai.get("id"),
             civitai.get("modelId"),
+            model_type_value,
             civitai.get("name"),
             creator_username,
             trained_words_json,

tests/conftest.py

@@ -212,6 +212,7 @@ class MockModelService:
         self.model_type = "test-model"
         self.paginated_items: List[Dict[str, Any]] = []
         self.formatted: List[Dict[str, Any]] = []
+        self.model_types: List[Dict[str, Any]] = []
 
     async def get_paginated_data(self, **params: Any) -> Dict[str, Any]:
         items = [dict(item) for item in self.paginated_items]
@@ -257,6 +258,9 @@ class MockModelService:
     async def get_relative_paths(self, *_args, **_kwargs):  # pragma: no cover
         return []
 
+    async def get_model_types(self, limit: int = 20):
+        return list(self.model_types)[:limit]
+
     def has_hash(self, *_args, **_kwargs):  # pragma: no cover
         return False
 
@@ -283,4 +287,3 @@ def mock_scanner(mock_cache: MockCache, mock_hash_index: MockHashIndex) -> MockS
 def mock_service(mock_scanner: MockScanner) -> MockModelService:
     return MockModelService(scanner=mock_scanner)
 
-

tests/routes/test_base_model_routes_smoke.py

@@ -185,6 +185,26 @@ def test_list_models_returns_formatted_items(mock_service, mock_scanner):
     asyncio.run(scenario())
 
 
+def test_model_types_endpoint_returns_counts(mock_service, mock_scanner):
+    mock_service.model_types = [
+        {"type": "LoRa", "count": 3},
+        {"type": "Checkpoint", "count": 1},
+    ]
+
+    async def scenario():
+        client = await create_test_client(mock_service)
+        try:
+            response = await client.get("/api/lm/test-models/model-types?limit=1")
+            payload = await response.json()
+
+            assert response.status == 200
+            assert payload["model_types"] == mock_service.model_types[:1]
+        finally:
+            await client.close()
+
+    asyncio.run(scenario())
+
+
 def test_routes_return_service_not_ready_when_unattached():
     async def scenario():
         client = await create_test_client(None)

tests/services/test_base_model_service.py

@@ -776,6 +776,67 @@ def test_model_filter_set_supports_legacy_tag_arrays():
     assert [item["model_name"] for item in result] == ["StyleOnly", "StyleAnime"]
 
 
+def test_model_filter_set_filters_by_model_types():
+    settings = StubSettings({})
+    filter_set = ModelFilterSet(settings)
+    data = [
+        {"model_name": "LoConModel", "civitai": {"model": {"type": "LoCon"}}},
+        {"model_name": "LoRaModel", "civitai": {"model": {"type": "LoRa"}}},
+    ]
+
+    criteria = FilterCriteria(model_types=["locon"])
+    result = filter_set.apply(data, criteria)
+
+    assert [item["model_name"] for item in result] == ["LoConModel"]
+
+
+def test_model_filter_set_defaults_missing_model_type_to_lora():
+    settings = StubSettings({})
+    filter_set = ModelFilterSet(settings)
+    data = [
+        {"model_name": "DefaultModel"},
+        {"model_name": "CheckpointModel", "civitai": {"model": {"type": "checkpoint"}}},
+    ]
+
+    criteria = FilterCriteria(model_types=["lora"])
+    result = filter_set.apply(data, criteria)
+
+    assert [item["model_name"] for item in result] == ["DefaultModel"]
+
+
+@pytest.mark.asyncio
+async def test_get_model_types_counts_and_limits():
+    raw_data = [
+        {"civitai": {"model": {"type": "LoRa"}}},
+        {"model_type": "LoRa"},
+        {"civitai": {"model": {"type": "LoCon"}}},
+        {},
+    ]
+
+    class CacheStub:
+        def __init__(self, raw_data):
+            self.raw_data = raw_data
+
+    class ScannerStub:
+        def __init__(self, cache):
+            self._cache = cache
+
+        async def get_cached_data(self, *_, **__):
+            return self._cache
+
+    cache = CacheStub(raw_data)
+    scanner = ScannerStub(cache)
+    service = DummyService(
+        model_type="stub",
+        scanner=scanner,
+        metadata_class=BaseModelMetadata,
+    )
+
+    types = await service.get_model_types(limit=1)
+
+    assert types == [{"type": "LoRa", "count": 2}]
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "service_cls, extra_fields",