feat: Introduce recipe management with data models, scanning, enrichment, and repair for generation configurations.

py/recipes/base.py

@@ -37,7 +37,8 @@ class RecipeMetadataParser(ABC):
         """
         pass
     
-    async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
+    @staticmethod
+    async def populate_lora_from_civitai(lora_entry: Dict[str, Any], civitai_info_tuple: Tuple[Dict[str, Any], Optional[str]], 
                                          recipe_scanner=None, base_model_counts=None, hash_value=None) -> Optional[Dict[str, Any]]:
         """
         Populate a lora entry with information from Civitai API response
@@ -148,8 +149,9 @@ class RecipeMetadataParser(ABC):
             logger.error(f"Error populating lora from Civitai info: {e}")
             
         return lora_entry
-        
-    async def populate_checkpoint_from_civitai(self, checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
+    
+    @staticmethod
+    async def populate_checkpoint_from_civitai(checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
         """
         Populate checkpoint information from Civitai API response
         
@@ -187,6 +189,7 @@ class RecipeMetadataParser(ABC):
             checkpoint['downloadUrl'] = civitai_data.get('downloadUrl', '')
 
             checkpoint['modelId'] = civitai_data.get('modelId', checkpoint.get('modelId', 0))
+            checkpoint['id'] = civitai_data.get('id', 0)
 
             if 'files' in civitai_data:
                 model_file = next(

py/recipes/enrichment.py

@@ -0,0 +1,224 @@
+import logging
+import json
+import re
+import os
+from typing import Any, Dict, Optional
+from .merger import GenParamsMerger
+from .base import RecipeMetadataParser
+from ..services.metadata_service import get_default_metadata_provider
+
+logger = logging.getLogger(__name__)
+
+class RecipeEnricher:
+    """Service to enrich recipe metadata from multiple sources (Civitai, Embedded, User)."""
+
+    @staticmethod
+    async def enrich_recipe(
+        recipe: Dict[str, Any],
+        civitai_client: Any,
+        request_params: Optional[Dict[str, Any]] = None
+    ) -> bool:
+        """
+        Enrich a recipe dictionary in-place with metadata from Civitai and embedded params.
+        
+        Args:
+            recipe: The recipe dictionary to enrich. Must have 'gen_params' initialized.
+            civitai_client: Authenticated Civitai client instance.
+            request_params: (Optional) Parameters from a user request (e.g. import).
+            
+        Returns:
+            bool: True if the recipe was modified, False otherwise.
+        """
+        updated = False
+        gen_params = recipe.get("gen_params", {})
+        
+        # 1. Fetch Civitai Info if available
+        civitai_meta = None
+        model_version_id = None
+        
+        source_url = recipe.get("source_url") or recipe.get("source_path", "")
+        
+        # Check if it's a Civitai image URL
+        image_id_match = re.search(r'civitai\.com/images/(\d+)', str(source_url))
+        if image_id_match:
+            image_id = image_id_match.group(1)
+            try:
+                image_info = await civitai_client.get_image_info(image_id)
+                if image_info:
+                    # Handle nested meta often found in Civitai API responses
+                    raw_meta = image_info.get("meta")
+                    if isinstance(raw_meta, dict):
+                        if "meta" in raw_meta and isinstance(raw_meta["meta"], dict):
+                            civitai_meta = raw_meta["meta"]
+                        else:
+                            civitai_meta = raw_meta
+                    
+                    model_version_id = image_info.get("modelVersionId")
+                    
+                    # If not at top level, check resources in meta
+                    if not model_version_id and civitai_meta:
+                        resources = civitai_meta.get("civitaiResources", [])
+                        for res in resources:
+                            if res.get("type") == "checkpoint":
+                                model_version_id = res.get("modelVersionId")
+                                break
+            except Exception as e:
+                logger.warning(f"Failed to fetch Civitai image info: {e}")
+
+        # 2. Merge Parameters
+        # Priority: request_params > civitai_meta > embedded (existing gen_params)
+        new_gen_params = GenParamsMerger.merge(
+            request_params=request_params,
+            civitai_meta=civitai_meta,
+            embedded_metadata=gen_params
+        )
+        
+        if new_gen_params != gen_params:
+            recipe["gen_params"] = new_gen_params
+            updated = True
+        
+        # 3. Checkpoint Enrichment
+        # If we have a checkpoint entry, or we can find one
+        # Use 'id' (from Civitai version) as a marker that it's been enriched
+        checkpoint_entry = recipe.get("checkpoint")
+        has_full_checkpoint = checkpoint_entry and checkpoint_entry.get("name") and checkpoint_entry.get("id")
+        
+        if not has_full_checkpoint:
+            # Helper to look up values in priority order
+            def start_lookup(keys):
+                for source in [request_params, civitai_meta, gen_params]:
+                    if source:
+                        if isinstance(keys, list):
+                            for k in keys:
+                                if k in source: return source[k]
+                        else:
+                            if keys in source: return source[keys]
+                return None
+
+            target_version_id = model_version_id or start_lookup("modelVersionId")
+            
+            # Also check existing checkpoint entry
+            if not target_version_id and checkpoint_entry:
+                target_version_id = checkpoint_entry.get("modelVersionId") or checkpoint_entry.get("id")
+            
+            # Check for version ID in resources (which might be a string in gen_params)
+            if not target_version_id:
+                # Look in all sources for "Civitai resources"
+                resources_val = start_lookup(["Civitai resources", "civitai_resources", "resources"])
+                if resources_val:
+                    target_version_id = RecipeEnricher._extract_version_id_from_resources({"Civitai resources": resources_val})
+            
+            target_hash = start_lookup(["Model hash", "checkpoint_hash", "hashes"])
+            if not target_hash and checkpoint_entry:
+                target_hash = checkpoint_entry.get("hash") or checkpoint_entry.get("model_hash")
+            
+            # Look for 'Model' which sometimes is the hash or name
+            model_val = start_lookup("Model")
+
+            # Look for Checkpoint name fallback
+            checkpoint_val = checkpoint_entry.get("name") if checkpoint_entry else None
+            if not checkpoint_val:
+                checkpoint_val = start_lookup(["Checkpoint", "checkpoint"])
+
+            checkpoint_updated = await RecipeEnricher._resolve_and_populate_checkpoint(
+                recipe, target_version_id, target_hash, model_val, checkpoint_val
+            )
+            if checkpoint_updated:
+                # Sync to gen_params for consistency with legacy usage
+                if "gen_params" not in recipe:
+                    recipe["gen_params"] = {}
+                recipe["gen_params"]["checkpoint"] = recipe["checkpoint"]
+                updated = True
+        else:
+            # Even if we have a checkpoint, ensure it is synced to gen_params if missing there
+            if "checkpoint" in recipe and recipe["checkpoint"]:
+                if "gen_params" not in recipe:
+                    recipe["gen_params"] = {}
+                if "checkpoint" not in recipe["gen_params"]:
+                    recipe["gen_params"]["checkpoint"] = recipe["checkpoint"]
+                    # We don't necessarily mark 'updated=True' just for this sync if the rest is the same,
+                    # but it's safer to ensure it's there.
+                    updated = True
+        # If base_model is empty or very generic, try to use what we found in checkpoint
+        current_base_model = recipe.get("base_model")
+        checkpoint_after = recipe.get("checkpoint")
+        if checkpoint_after and checkpoint_after.get("baseModel"):
+            resolved_base_model = checkpoint_after["baseModel"]
+            # Update if empty OR if it matches our generic prefix but is less specific
+            is_generic = not current_base_model or current_base_model.lower() in ["flux", "sdxl", "sd15"]
+            if is_generic and resolved_base_model != current_base_model:
+                recipe["base_model"] = resolved_base_model
+                updated = True
+
+        return updated
+        
+    @staticmethod
+    def _extract_version_id_from_resources(gen_params: Dict[str, Any]) -> Optional[Any]:
+        """Try to find modelVersionId in Civitai resources parameter."""
+        civitai_resources_raw = gen_params.get("Civitai resources")
+        if not civitai_resources_raw:
+            return None
+            
+        resources_list = None
+        if isinstance(civitai_resources_raw, str):
+            try:
+                resources_list = json.loads(civitai_resources_raw)
+            except Exception:
+                pass
+        elif isinstance(civitai_resources_raw, list):
+            resources_list = civitai_resources_raw
+            
+        if isinstance(resources_list, list):
+            for res in resources_list:
+                if res.get("type") == "checkpoint":
+                    return res.get("modelVersionId")
+        return None
+
+    @staticmethod
+    async def _resolve_and_populate_checkpoint(
+        recipe: Dict[str, Any], 
+        target_version_id: Optional[Any], 
+        target_hash: Optional[str],
+        model_val: Optional[str],
+        checkpoint_val: Optional[str]
+    ) -> bool:
+        """Find checkpoint metadata and populate it in the recipe."""
+        metadata_provider = await get_default_metadata_provider()
+        civitai_info = None
+        
+        if target_version_id:
+            civitai_info = await metadata_provider.get_model_version_info(str(target_version_id))
+        elif target_hash:
+            civitai_info = await metadata_provider.get_model_by_hash(target_hash)
+        else:
+            # Look for 'Model' which sometimes is the hash or name
+            if model_val and len(model_val) == 10: # Likely a short hash
+                civitai_info = await metadata_provider.get_model_by_hash(model_val)
+                
+        if civitai_info and not (isinstance(civitai_info, tuple) and civitai_info[1] == "Model not found"):
+            # If we already have a partial checkpoint, use it as base
+            existing_cp = recipe.get("checkpoint")
+            if existing_cp is None:
+                existing_cp = {}
+            checkpoint_data = await RecipeMetadataParser.populate_checkpoint_from_civitai(existing_cp, civitai_info)
+            recipe["checkpoint"] = checkpoint_data
+            
+            # Ensure the modelVersionId is stored if we found it
+            if target_version_id and "modelVersionId" not in recipe["checkpoint"]:
+                recipe["checkpoint"]["modelVersionId"] = int(target_version_id)
+            return True
+        else:
+            # Fallback to name extraction if we don't already have one
+            existing_cp = recipe.get("checkpoint")
+            if not existing_cp or not existing_cp.get("name"):
+                cp_name = checkpoint_val
+                if cp_name:
+                    recipe["checkpoint"] = {
+                        "type": "checkpoint",
+                        "name": cp_name,
+                        "modelName": cp_name,
+                        "file_name": os.path.splitext(cp_name)[0]
+                    }
+                    return True
+                
+        return False

py/recipes/merger.py

@@ -6,7 +6,31 @@ logger = logging.getLogger(__name__)
 class GenParamsMerger:
     """Utility to merge generation parameters from multiple sources with priority."""
 
-    BLACKLISTED_KEYS = {"id", "url", "userId", "username", "createdAt", "updatedAt", "hash"}
+    BLACKLISTED_KEYS = {
+        "id", "url", "userId", "username", "createdAt", "updatedAt", "hash", "meta",
+        "draft", "extra", "width", "height", "process", "quantity", "workflow",
+        "baseModel", "resources", "disablePoi", "aspectRatio", "Created Date",
+        "experimental", "civitaiResources", "civitai_resources", "Civitai resources",
+        "modelVersionId", "modelId", "hashes", "Model", "Model hash", "checkpoint_hash",
+        "checksum", "model_checksum"
+    }
+    
+    NORMALIZATION_MAPPING = {
+        # Civitai specific
+        "cfgScale": "cfg_scale",
+        "clipSkip": "clip_skip",
+        "negativePrompt": "negative_prompt",
+        # Case variations
+        "Sampler": "sampler",
+        "Steps": "steps",
+        "Seed": "seed",
+        "Size": "size",
+        "Prompt": "prompt",
+        "Negative prompt": "negative_prompt",
+        "Cfg scale": "cfg_scale",
+        "Clip skip": "clip_skip",
+        "Denoising strength": "denoising_strength",
+    }
 
     @staticmethod
     def merge(
@@ -33,18 +57,42 @@ class GenParamsMerger:
         if embedded_metadata:
             # If it's a full recipe metadata, we use its gen_params
             if "gen_params" in embedded_metadata and isinstance(embedded_metadata["gen_params"], dict):
-                result.update(embedded_metadata["gen_params"])
+                GenParamsMerger._update_normalized(result, embedded_metadata["gen_params"])
             else:
                 # Otherwise assume the dict itself contains gen_params
-                result.update(embedded_metadata)
+                GenParamsMerger._update_normalized(result, embedded_metadata)
 
         # 2. Layer Civitai meta (medium priority)
         if civitai_meta:
-            result.update(civitai_meta)
+            GenParamsMerger._update_normalized(result, civitai_meta)
 
         # 3. Layer request params (highest priority)
         if request_params:
-            result.update(request_params)
+            GenParamsMerger._update_normalized(result, request_params)
 
-        # Filter out blacklisted keys
-        return {k: v for k, v in result.items() if k not in GenParamsMerger.BLACKLISTED_KEYS}
+        # Filter out blacklisted keys and also the original camelCase keys if they were normalized
+        final_result = {}
+        for k, v in result.items():
+            if k in GenParamsMerger.BLACKLISTED_KEYS:
+                continue
+            if k in GenParamsMerger.NORMALIZATION_MAPPING:
+                continue
+            final_result[k] = v
+            
+        return final_result
+
+    @staticmethod
+    def _update_normalized(target: Dict[str, Any], source: Dict[str, Any]) -> None:
+        """Update target dict with normalized keys from source."""
+        for k, v in source.items():
+            normalized_key = GenParamsMerger.NORMALIZATION_MAPPING.get(k, k)
+            target[normalized_key] = v
+            # Also keep the original key for now if it's not the same, 
+            # so we can filter at the end or avoid losing it if it wasn't supposed to be renamed?
+            # Actually, if we rename it, we should probably NOT keep both in 'target' 
+            # because we want to filter them out at the end anyway.
+            if normalized_key != k:
+                # If we are overwriting an existing snake_case key with a camelCase one's value,
+                # that's fine because of the priority order of calls to _update_normalized.
+                pass
+            target[k] = v

py/routes/handlers/recipe_handlers.py

@@ -27,6 +27,7 @@ from ...services.metadata_service import get_default_metadata_provider
 from ...utils.civitai_utils import rewrite_preview_url
 from ...utils.exif_utils import ExifUtils
 from ...recipes.merger import GenParamsMerger
+from ...recipes.enrichment import RecipeEnricher
 from ...services.websocket_manager import ws_manager as default_ws_manager
 
 Logger = logging.Logger
@@ -585,13 +586,15 @@ class RecipeManagementHandler:
             self._logger.error("Error getting repair progress: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+
     async def import_remote_recipe(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
             recipe_scanner = self._recipe_scanner_getter()
             if recipe_scanner is None:
                 raise RuntimeError("Recipe scanner unavailable")
-
+            
+            # 1. Parse Parameters
             params = request.rel_url.query
             image_url = params.get("image_url")
             name = params.get("name")
@@ -605,30 +608,52 @@ class RecipeManagementHandler:
                 raise RecipeValidationError("Missing required field: resources")
 
             checkpoint_entry, lora_entries = self._parse_resources_payload(resources_raw)
-            gen_params = self._parse_gen_params(params.get("gen_params"))
+            gen_params_request = self._parse_gen_params(params.get("gen_params"))
+            
+            # 2. Initial Metadata Construction
             metadata: Dict[str, Any] = {
                 "base_model": params.get("base_model", "") or "",
                 "loras": lora_entries,
+                "gen_params": gen_params_request or {},
+                "source_url": image_url
             }
+            
             source_path = params.get("source_path")
             if source_path:
                 metadata["source_path"] = source_path
-            if gen_params is not None:
-                metadata["gen_params"] = gen_params
+                
+            # Checkpoint handling
             if checkpoint_entry:
                 metadata["checkpoint"] = checkpoint_entry
-                gen_params_ref = metadata.setdefault("gen_params", {})
-                if "checkpoint" not in gen_params_ref:
-                    gen_params_ref["checkpoint"] = checkpoint_entry
-                base_model_from_metadata = await self._resolve_base_model_from_checkpoint(checkpoint_entry)
-                if base_model_from_metadata:
-                    metadata["base_model"] = base_model_from_metadata
+                # Ensure checkpoint is also in gen_params for consistency if needed by enricher?
+                # Actually enricher looks at metadata['checkpoint'], so this is fine.
+                
+                # Try to resolve base model from checkpoint if not explicitly provided
+                if not metadata["base_model"]:
+                    base_model_from_metadata = await self._resolve_base_model_from_checkpoint(checkpoint_entry)
+                    if base_model_from_metadata:
+                        metadata["base_model"] = base_model_from_metadata
 
             tags = self._parse_tags(params.get("tags"))
-            image_bytes, extension, civitai_meta = await self._download_remote_media(image_url)
+            
+            # 3. Download Image
+            image_bytes, extension, civitai_meta_from_download = await self._download_remote_media(image_url)
 
-            # Extract embedded metadata from the downloaded image
-            embedded_metadata = None
+            # 4. Extract Embedded Metadata
+            # Note: We still extract this here because Enricher currently expects 'gen_params' to already be populated 
+            # with embedded data if we want it to merge it. 
+            # However, logic in Enricher merges: request > civitai > embedded.
+            # So we should gather embedded params and put them into the recipe's gen_params (as initial state) 
+            # OR pass them to enricher to handle?
+            # The interface of Enricher.enrich_recipe takes `recipe` (with gen_params) and `request_params`.
+            # So let's extract embedded and put it into recipe['gen_params'] but careful not to overwrite request params.
+            # Actually, `GenParamsMerger` which `Enricher` uses handles 3 layers.
+            # But `Enricher` interface is: recipe['gen_params'] (as embedded) + request_params + civitai (fetched internally).
+            # Wait, `Enricher` fetches Civitai info internally based on URL. 
+            # `civitai_meta_from_download` is returned by `_download_remote_media` which might be useful if URL didn't have ID.
+            
+            # Let's extract embedded metadata first
+            embedded_gen_params = {}
             try:
                 with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_img:
                     temp_img.write(image_bytes)
@@ -637,29 +662,39 @@ class RecipeManagementHandler:
                 try:
                     raw_embedded = ExifUtils.extract_image_metadata(temp_img_path)
                     if raw_embedded:
-                        # Try to parse it using standard parsers if it looks like a recipe
                         parser = self._analysis_service._recipe_parser_factory.create_parser(raw_embedded)
                         if parser:
                             parsed_embedded = await parser.parse_metadata(raw_embedded, recipe_scanner=recipe_scanner)
-                            embedded_metadata = parsed_embedded
+                            if parsed_embedded and "gen_params" in parsed_embedded:
+                                embedded_gen_params = parsed_embedded["gen_params"]
                         else:
-                            # Fallback to raw string if no parser matches (might be simple params)
-                            embedded_metadata = {"gen_params": {"raw_metadata": raw_embedded}}
+                            embedded_gen_params = {"raw_metadata": raw_embedded}
                 finally:
                     if os.path.exists(temp_img_path):
                         os.unlink(temp_img_path)
             except Exception as exc:
                 self._logger.warning("Failed to extract embedded metadata during import: %s", exc)
 
-            # Merge gen_params from all sources
-            merged_gen_params = GenParamsMerger.merge(
-                request_params=gen_params,
-                civitai_meta=civitai_meta,
-                embedded_metadata=embedded_metadata
+            # Pre-populate gen_params with embedded data so Enricher treats it as the "base" layer
+            if embedded_gen_params:
+                # Merge embedded into existing gen_params (which currently only has request params if any)
+                # But wait, we want request params to override everything.
+                # So we should set recipe['gen_params'] = embedded, and pass request params to enricher.
+                metadata["gen_params"] = embedded_gen_params
+            
+            # 5. Enrich with unified logic
+            # This will fetch Civitai info (if URL matches) and merge: request > civitai > embedded
+            civitai_client = self._civitai_client_getter()
+            await RecipeEnricher.enrich_recipe(
+                recipe=metadata, 
+                civitai_client=civitai_client,
+                request_params=gen_params_request # Pass explicit request params here to override
             )
-
-            if merged_gen_params:
-                metadata["gen_params"] = merged_gen_params
+            
+            # If we got civitai_meta from download but Enricher didn't fetch it (e.g. not a civitai URL or failed),
+            # we might want to manually merge it? 
+            # But usually `import_remote_recipe` is used with Civitai URLs.
+            # For now, relying on Enricher's internal fetch is consistent with repair.
 
             result = await self._persistence_service.save_recipe(
                 recipe_scanner=recipe_scanner,

py/services/recipe_scanner.py

@@ -18,6 +18,7 @@ from natsort import natsorted
 import sys
 import re
 from ..recipes.merger import GenParamsMerger
+from ..recipes.enrichment import RecipeEnricher
 
 logger = logging.getLogger(__name__)
 
@@ -184,18 +185,22 @@ class RecipeScanner:
             Dict summary of repair result
         """
         async with self._mutation_lock:
-            recipe = await self.get_recipe_by_id(recipe_id)
+            # Get raw recipe from cache directly to avoid formatted fields
+            cache = await self.get_cached_data()
+            recipe = next((r for r in cache.raw_data if str(r.get('id', '')) == recipe_id), None)
+            
             if not recipe:
                 raise RecipeNotFoundError(f"Recipe {recipe_id} not found")
             
             civitai_client = await self._get_civitai_client()
             success = await self._repair_single_recipe(recipe, civitai_client)
             
+            # If successfully repaired, we should return the formatted version for the UI
             return {
                 "success": True,
                 "repaired": 1 if success else 0,
                 "skipped": 0 if success else 1,
-                "recipe": recipe
+                "recipe": await self.get_recipe_by_id(recipe_id) if success else recipe
             }
 
     async def _repair_single_recipe(self, recipe: Dict[str, Any], civitai_client: Any) -> bool:
@@ -221,68 +226,28 @@ class RecipeScanner:
         
         if not needs_repair:
             # Even if no repair needed, we mark it with version if it was processed
-            if "repair_version" not in recipe:
-                recipe["repair_version"] = self.REPAIR_VERSION
-                await self._save_recipe_persistently(recipe)
-                return True
-            return False
+            # Always update and save because if we are here, the version is old (checked in step 1)
+            recipe["repair_version"] = self.REPAIR_VERSION
+            await self._save_recipe_persistently(recipe)
+            return True
         
-        # 3. Data Fetching & Merging
-        source_url = recipe.get("source_url", "")
-        civitai_meta = None
-        model_version_id = None
-        
-        # Check if it's a Civitai image URL
-        image_id_match = re.search(r'civitai\.com/images/(\d+)', source_url)
-        if image_id_match:
-            image_id = image_id_match.group(1)
-            image_info = await civitai_client.get_image_info(image_id)
-            if image_info:
-                if "meta" in image_info:
-                    civitai_meta = image_info["meta"]
-                model_version_id = image_info.get("modelVersionId")
-        
-        # Merge with existing data
-        new_gen_params = GenParamsMerger.merge(
-            civitai_meta=civitai_meta,
-            embedded_metadata=gen_params
-        )
-        
-        updated = False
-        if new_gen_params != gen_params:
-            recipe["gen_params"] = new_gen_params
-            updated = True
+        # 3. Use Enricher to repair/enrich
+        try:
+            updated = await RecipeEnricher.enrich_recipe(recipe, civitai_client)
+        except Exception as e:
+            logger.error(f"Error enriching recipe {recipe.get('id')}: {e}")
+            updated = False
+
+        # 4. Mark version and save if updated or just marking version
+        # If we updated it, OR if the version is old (which we know it is if we are here), save it.
+        # Actually, if we are here and updated is False, it means we tried to repair but couldn't/didn't need to.
+        # But we still want to mark it as processed so we don't try again until version bump.
+        if updated or recipe.get("repair_version", 0) < self.REPAIR_VERSION:
+            recipe["repair_version"] = self.REPAIR_VERSION
+            await self._save_recipe_persistently(recipe)
+            return True
             
-        # 4. Update checkpoint if missing or repairable
-        if not has_checkpoint:
-            metadata_provider = await get_default_metadata_provider()
-            
-            target_version_id = model_version_id or new_gen_params.get("modelVersionId")
-            target_hash = new_gen_params.get("Model hash")
-            
-            civitai_info = None
-            if target_version_id:
-                civitai_info = await metadata_provider.get_model_version_info(str(target_version_id))
-            elif target_hash:
-                civitai_info = await metadata_provider.get_model_by_hash(target_hash)
-                
-            if civitai_info and not (isinstance(civitai_info, tuple) and civitai_info[1] == "Model not found"):
-                recipe["checkpoint"] = await self._populate_checkpoint(civitai_info)
-                updated = True
-            else:
-                # Fallback to name extraction
-                cp_name = new_gen_params.get("Checkpoint") or new_gen_params.get("checkpoint")
-                if cp_name:
-                    recipe["checkpoint"] = {
-                        "name": cp_name,
-                        "file_name": os.path.splitext(cp_name)[0]
-                    }
-                    updated = True
-            
-        # 5. Mark version and save
-        recipe["repair_version"] = self.REPAIR_VERSION
-        await self._save_recipe_persistently(recipe)
-        return True
+        return False
 
     async def _save_recipe_persistently(self, recipe: Dict[str, Any]) -> bool:
         """Helper to save a recipe to both JSON and EXIF metadata."""
@@ -318,58 +283,16 @@ class RecipeScanner:
             logger.error(f"Error persisting recipe {recipe_id}: {e}")
             return False
 
-    async def _populate_checkpoint(self, civitai_info_tuple: Any) -> Dict[str, Any]:
-        """Helper to populate checkpoint info using common logic."""
-        civitai_data, error_msg = civitai_info_tuple if isinstance(civitai_info_tuple, tuple) else (civitai_info_tuple, None)
-        
-        checkpoint = {
-            "name": "",
-            "file_name": "",
-            "isDeleted": False,
-            "hash": ""
-        }
-        
-        if not civitai_data or error_msg == "Model not found":
-            checkpoint["isDeleted"] = True
-            return checkpoint
-            
-        try:
-            if "model" in civitai_data and "name" in civitai_data["model"]:
-                checkpoint["name"] = civitai_data["model"]["name"]
-                
-            if "name" in civitai_data:
-                checkpoint["version"] = civitai_data.get("name", "")
-                
-            if "images" in civitai_data and civitai_data["images"]:
-                from ..utils.civitai_utils import rewrite_preview_url
-                image_url = civitai_data["images"][0].get("url")
-                if image_url:
-                    rewritten_url, _ = rewrite_preview_url(image_url, media_type="image")
-                    checkpoint["thumbnailUrl"] = rewritten_url or image_url
-                    
-            checkpoint["baseModel"] = civitai_data.get("baseModel", "")
-            checkpoint["modelId"] = civitai_data.get("modelId", 0)
-            checkpoint["id"] = civitai_data.get("id", 0)
-            
-            if "files" in civitai_data:
-                model_file = next((f for f in civitai_data.get("files", []) if f.get("type") == "Model"), None)
-                if model_file:
-                    sha256 = model_file.get("hashes", {}).get("SHA256")
-                    if sha256:
-                        checkpoint["hash"] = sha256.lower()
-                    f_name = model_file.get("name", "")
-                    if f_name:
-                        checkpoint["file_name"] = os.path.splitext(f_name)[0]
-        except Exception as e:
-            logger.error(f"Error populating checkpoint: {e}")
-                    
-        return checkpoint
 
     def _sanitize_recipe_for_storage(self, recipe: Dict[str, Any]) -> Dict[str, Any]:
         """Create a clean copy of the recipe without runtime convenience fields."""
         import copy
         clean = copy.deepcopy(recipe)
         
+        # 0. Clean top-level runtime fields
+        for key in ("file_url", "created_date_formatted", "modified_formatted"):
+            clean.pop(key, None)
+
         # 1. Clean LORAs
         if "loras" in clean and isinstance(clean["loras"], list):
             for lora in clean["loras"]:

tests/routes/test_recipe_routes.py

@@ -338,7 +338,7 @@ async def test_move_recipe_invokes_persistence(monkeypatch, tmp_path: Path) -> N
 
 
 async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
-    provider_calls: list[int] = []
+    provider_calls: list[str | int] = []
 
     class Provider:
         async def get_model_version_info(self, model_version_id):
@@ -348,7 +348,7 @@ async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
     async def fake_get_default_metadata_provider():
         return Provider()
 
-    monkeypatch.setattr(recipe_handlers, "get_default_metadata_provider", fake_get_default_metadata_provider)
+    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
 
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         resources = [
@@ -390,7 +390,7 @@ async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
         assert call["tags"] == ["foo", "bar"]
         metadata = call["metadata"]
         assert metadata["base_model"] == "Flux Provider"
-        assert provider_calls == [33]
+        assert provider_calls == ["33"]
         assert metadata["checkpoint"]["modelVersionId"] == 33
         assert metadata["loras"][0]["weight"] == 0.25
         assert metadata["gen_params"]["prompt"] == "hello world"
@@ -399,7 +399,7 @@ async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
 
 
 async def test_import_remote_recipe_falls_back_to_request_base_model(monkeypatch, tmp_path: Path) -> None:
-    provider_calls: list[int] = []
+    provider_calls: list[str | int] = []
 
     class Provider:
         async def get_model_version_info(self, model_version_id):
@@ -409,7 +409,7 @@ async def test_import_remote_recipe_falls_back_to_request_base_model(monkeypatch
     async def fake_get_default_metadata_provider():
         return Provider()
 
-    monkeypatch.setattr(recipe_handlers, "get_default_metadata_provider", fake_get_default_metadata_provider)
+    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
 
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         resources = [
@@ -438,14 +438,14 @@ async def test_import_remote_recipe_falls_back_to_request_base_model(monkeypatch
 
         metadata = harness.persistence.save_calls[-1]["metadata"]
         assert metadata["base_model"] == "Flux"
-        assert provider_calls == [77]
+        assert provider_calls == ["77"]
 
 
 async def test_import_remote_video_recipe(monkeypatch, tmp_path: Path) -> None:
     async def fake_get_default_metadata_provider():
         return SimpleNamespace(get_model_version_info=lambda id: ({}, None))
 
-    monkeypatch.setattr(recipe_handlers, "get_default_metadata_provider", fake_get_default_metadata_provider)
+    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
 
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         harness.civitai.image_info["12345"] = {
@@ -537,7 +537,7 @@ async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path)
     async def fake_get_default_metadata_provider():
         return Provider()
 
-    monkeypatch.setattr(recipe_handlers, "get_default_metadata_provider", fake_get_default_metadata_provider)
+    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
 
     # 2. Mock ExifUtils to return some embedded metadata
     class MockExifUtils:

tests/services/test_gen_params_merger.py

@@ -57,3 +57,38 @@ def test_merge_filters_blacklisted_keys():
     assert "id" not in merged
     assert "url" not in merged
     assert "hash" not in merged
+
+def test_merge_filters_meta_and_normalizes_keys():
+    civitai_meta = {
+        "prompt": "masterpiece",
+        "cfgScale": 5,
+        "clipSkip": 2,
+        "negativePrompt": "low quality",
+        "meta": {"irrelevant": "data"},
+        "Size": "1024x1024",
+        "draft": False,
+        "workflow": "txt2img",
+        "civitaiResources": [{"type": "checkpoint"}]
+    }
+    request_params = {
+        "cfg_scale": 5.0,
+        "clip_skip": "2",
+        "Steps": 30
+    }
+    
+    merged = GenParamsMerger.merge(request_params, civitai_meta)
+    
+    assert "meta" not in merged
+    assert "cfgScale" not in merged
+    assert "clipSkip" not in merged
+    assert "negativePrompt" not in merged
+    assert "Size" not in merged
+    assert "draft" not in merged
+    assert "workflow" not in merged
+    assert "civitaiResources" not in merged
+    
+    assert merged["cfg_scale"] == 5.0  # From request_params
+    assert merged["clip_skip"] == "2"  # From request_params
+    assert merged["negative_prompt"] == "low quality" # Normalized from civitai_meta
+    assert merged["size"] == "1024x1024" # Normalized from civitai_meta
+    assert merged["steps"] == 30 # Normalized from request_params

tests/services/test_recipe_repair.py

@@ -43,7 +43,7 @@ def setup_scanner(recipe_scanner, mock_civitai_client, mock_metadata_provider, m
     mock_save = AsyncMock(side_effect=real_save)
     monkeypatch.setattr(recipe_scanner, "_save_recipe_persistently", mock_save)
     
-    monkeypatch.setattr("py.services.recipe_scanner.get_default_metadata_provider", AsyncMock(return_value=mock_metadata_provider))
+    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", AsyncMock(return_value=mock_metadata_provider))
     
     # Mock get_recipe_json_path to avoid file system issues in tests
     recipe_scanner.get_recipe_json_path = AsyncMock(return_value="/tmp/test_recipe.json")
@@ -259,11 +259,7 @@ async def test_repair_all_recipes_strips_runtime_fields(setup_scanner):
 
 @pytest.mark.asyncio
 async def test_sanitize_recipe_for_storage(recipe_scanner):
-    import sys
-    import py.services.recipe_scanner
-    print(f"\nDEBUG_ENV: sys.path: {sys.path}")
-    print(f"DEBUG_ENV: recipe_scanner file: {py.services.recipe_scanner.__file__}")
-    
+
     recipe = {
         "loras": [{"name": "L1", "inLibrary": True, "weight": 0.5}],
         "checkpoint": {"name": "CP", "localPath": "/tmp/cp"}