feat(recipes): add Unknown base-model filter bucket for undetermined recipes

Normalize undetermined recipe base_model to None in RecipeFormatParser
(previously ''). get_base_models now reports an "Unknown" bucket backed
by a dedicated __unknown__ marker, and the listing filter matches it
against recipes whose base model is falsy. Frontend renders the bucket
label as "Unknown" while filtering via the marker.

Tests: handler, scanner, parser, and frontend filtering.

py/recipes/parsers/recipe_format.py

@@ -196,7 +196,7 @@ class RecipeFormatParser(RecipeMetadataParser):
                         filtered_gen_params[key] = value
             
             return {
-                'base_model': checkpoint['baseModel'] if checkpoint and checkpoint.get('baseModel') else recipe_metadata.get('base_model', ''),
+                'base_model': checkpoint['baseModel'] if checkpoint and checkpoint.get('baseModel') else (recipe_metadata.get('base_model') or None),
                 'loras': loras,
                 'gen_params': filtered_gen_params,
                 'tags': recipe_metadata.get('tags', []),

py/routes/handlers/recipe_handlers.py

@@ -26,6 +26,7 @@ from ...services.recipes import (
     RecipeValidationError,
 )
 from ...services.metadata_service import get_default_metadata_provider
+from ...services.recipe_scanner import UNKNOWN_BASE_MODEL_FILTER
 from ...utils.civitai_utils import (
     build_civitai_image_page_url,
     extract_civitai_image_id,
@@ -473,17 +474,32 @@ class RecipeQueryHandler:
             cache = await recipe_scanner.get_cached_data()
 
             base_model_counts: Dict[str, int] = {}
+            unknown_count = 0
             for recipe in getattr(cache, "raw_data", []):
                 base_model = recipe.get("base_model")
                 if base_model:
                     base_model_counts[base_model] = (
                         base_model_counts.get(base_model, 0) + 1
                     )
+                else:
+                    unknown_count += 1
 
             sorted_models = [
                 {"name": model, "count": count}
                 for model, count in base_model_counts.items()
             ]
+            if unknown_count:
+                # Synthetic "Unknown" bucket for recipes whose base model could
+                # not be determined. `value` carries the filter marker so the
+                # UI can display "Unknown" without colliding with real base
+                # model strings.
+                sorted_models.append(
+                    {
+                        "name": "Unknown",
+                        "value": UNKNOWN_BASE_MODEL_FILTER,
+                        "count": unknown_count,
+                    }
+                )
             sorted_models.sort(key=lambda entry: entry["count"], reverse=True)
             if limit > 0:
                 sorted_models = sorted_models[:limit]

py/services/recipe_scanner.py

@@ -48,6 +48,12 @@ _CHECKPOINT_MODEL_TYPE_ALIASES = {"diffusionmodel": "diffusion_model"}
 # Valid LoRA availability statuses for the recipe listing filter.
 _VALID_LORA_AVAILABILITY_STATUSES = frozenset({"ready", "missing", "deleted"})
 
+# Filter marker for recipes whose base model could not be determined
+# (base_model is None or empty). The UI displays "Unknown" for this bucket;
+# the marker keeps the semantics explicit and disjoint from any real base
+# model string.
+UNKNOWN_BASE_MODEL_FILTER = "__unknown__"
+
 
 class RecipeScanner:
     """Service for scanning and managing recipe images"""
@@ -3530,11 +3536,23 @@ class RecipeScanner:
             if filters:
                 # Filter by base model
                 if "base_model" in filters and filters["base_model"]:
-                    filtered_data = [
-                        item
-                        for item in filtered_data
-                        if item.get("base_model", "") in filters["base_model"]
-                    ]
+                    base_model_filter = filters["base_model"]
+                    if UNKNOWN_BASE_MODEL_FILTER in base_model_filter:
+                        # The unknown bucket matches recipes whose base model
+                        # could not be determined (None/empty); real base
+                        # models in the list still match by exact name.
+                        filtered_data = [
+                            item
+                            for item in filtered_data
+                            if not item.get("base_model")
+                            or item.get("base_model") in base_model_filter
+                        ]
+                    else:
+                        filtered_data = [
+                            item
+                            for item in filtered_data
+                            if item.get("base_model", "") in base_model_filter
+                        ]
 
                 # Filter by favorite
                 if "favorite" in filters and filters["favorite"]: