fix(recipes): fall back to Civitai API meta when EXIF is empty, enrich checkpoint in analyze_remote_image

- When downloaded Civitai image has no embedded EXIF, parse the
  already-fetched Civitai API meta (resources, hashes) directly
  instead of skipping parser altogether.
- Extract loras and model from parser output to fill metadata gaps
  when the primary import path doesn't provide them.
- Read modelVersionIds[0] as fallback when modelVersionId is None
  (Civitai API returns both but the singular form can be absent).
- Run RecipeEnricher in analyze_remote_image before returning, so
  the LM UI receives complete metadata including checkpoint with
  zero additional API calls (reuses the image_info already fetched).

py/routes/handlers/recipe_handlers.py

@@ -834,6 +834,7 @@ class RecipeManagementHandler:
 
         # Extract embedded EXIF metadata (offloaded to thread pool in this call)
         embedded_gen_params = {}
+        parsed_embedded = None
         try:
             with tempfile.NamedTemporaryFile(
                 suffix=extension, delete=False
@@ -867,10 +868,35 @@ class RecipeManagementHandler:
                 "Failed to extract embedded metadata during import: %s", exc
             )
 
+        # Fallback: if EXIF extraction yielded nothing, parse Civitai API meta directly
+        # (same approach as analyze_remote_image — downloaded Civitai images often
+        # have no embedded EXIF but the API meta contains resources/hashes)
+        if parsed_embedded is None and civitai_meta_raw:
+            civitai_inner_meta = civitai_meta_raw
+            if isinstance(civitai_meta_raw, dict) and "meta" in civitai_meta_raw:
+                civitai_inner_meta = civitai_meta_raw["meta"]
+            if isinstance(civitai_inner_meta, dict):
+                parser = self._analysis_service._recipe_parser_factory.create_parser(
+                    civitai_inner_meta
+                )
+                if parser:
+                    parsed_embedded = await parser.parse_metadata(
+                        civitai_inner_meta, recipe_scanner=recipe_scanner
+                    )
+                    if parsed_embedded and "gen_params" in parsed_embedded:
+                        embedded_gen_params = parsed_embedded["gen_params"]
+
         if embedded_gen_params:
             metadata["gen_params"] = embedded_gen_params
 
-        # Enrich with Civitai API and merge gen_params
+        if parsed_embedded:
+            parsed_loras = parsed_embedded.get("loras")
+            if parsed_loras and not metadata.get("loras"):
+                metadata["loras"] = parsed_loras
+            parsed_model = parsed_embedded.get("model")
+            if parsed_model and not metadata.get("checkpoint"):
+                metadata["checkpoint"] = parsed_model
+
         civitai_client = self._civitai_client_getter()
         await RecipeEnricher.enrich_recipe(
             recipe=metadata,
@@ -1192,7 +1218,7 @@ class RecipeManagementHandler:
             "exclude": False,
         }
 
-    async def _download_remote_media(self, image_url: str) -> tuple[bytes, str, Any]:
+    async def _download_remote_media(self, image_url: str) -> tuple[bytes, str, Any, Any]:
         civitai_client = self._civitai_client_getter()
         downloader = await self._downloader_factory()
         temp_path = None
@@ -1240,11 +1266,18 @@ class RecipeManagementHandler:
                 extension = ".webp"  # Default to webp if unknown
 
             with open(temp_path, "rb") as file_obj:
+                model_ver_id = None
+                if civitai_image_id and image_info:
+                    model_ver_id = image_info.get("modelVersionId")
+                    if not model_ver_id:
+                        ids = image_info.get("modelVersionIds")
+                        if isinstance(ids, list) and ids:
+                            model_ver_id = ids[0]
                 return (
                     file_obj.read(),
                     extension,
                     image_info.get("meta") if civitai_image_id and image_info else None,
-                    image_info.get("modelVersionId") if civitai_image_id and image_info else None,
+                    model_ver_id,
                 )
         except RecipeDownloadError:
             raise
@@ -1399,6 +1432,7 @@ class RecipeManagementHandler:
 
         # Extract embedded EXIF metadata
         embedded_gen_params = {}
+        parsed_embedded = None
         try:
             with tempfile.NamedTemporaryFile(
                 suffix=extension, delete=False
@@ -1430,6 +1464,21 @@ class RecipeManagementHandler:
                 "Failed to extract embedded metadata: %s", exc
             )
 
+        if parsed_embedded is None and civitai_meta_raw:
+            civitai_inner_meta = civitai_meta_raw
+            if isinstance(civitai_meta_raw, dict) and "meta" in civitai_meta_raw:
+                civitai_inner_meta = civitai_meta_raw["meta"]
+            if isinstance(civitai_inner_meta, dict):
+                parser = self._analysis_service._recipe_parser_factory.create_parser(
+                    civitai_inner_meta
+                )
+                if parser:
+                    parsed_embedded = await parser.parse_metadata(
+                        civitai_inner_meta, recipe_scanner=recipe_scanner
+                    )
+                    if parsed_embedded and "gen_params" in parsed_embedded:
+                        embedded_gen_params = parsed_embedded["gen_params"]
+
         metadata: Dict[str, Any] = {
             "base_model": "",
             "loras": [],
@@ -1437,6 +1486,14 @@ class RecipeManagementHandler:
             "source_path": image_url,
         }
 
+        if parsed_embedded:
+            parsed_loras = parsed_embedded.get("loras")
+            if parsed_loras and not metadata.get("loras"):
+                metadata["loras"] = parsed_loras
+            parsed_model = parsed_embedded.get("model")
+            if parsed_model and not metadata.get("checkpoint"):
+                metadata["checkpoint"] = parsed_model
+
         civitai_client = self._civitai_client_getter()
         await RecipeEnricher.enrich_recipe(
             recipe=metadata,

py/services/recipes/analysis_service.py

@@ -15,6 +15,7 @@ from PIL import Image
 
 from ...utils.utils import calculate_recipe_fingerprint
 from ...utils.civitai_utils import extract_civitai_image_id, rewrite_preview_url
+from ...recipes.enrichment import RecipeEnricher
 from .errors import (
     RecipeDownloadError,
     RecipeNotFoundError,
@@ -175,7 +176,7 @@ class RecipeAnalysisService:
                     self._exif_utils.extract_image_metadata, temp_path
                 )
 
-            return await self._parse_metadata(
+            result = await self._parse_metadata(
                 metadata or {},
                 recipe_scanner=recipe_scanner,
                 image_path=temp_path,
@@ -183,6 +184,37 @@ class RecipeAnalysisService:
                 is_video=is_video,
                 extension=extension,
             )
+
+            if civitai_image_id and image_info and not result.payload.get("error"):
+                mvid = image_info.get("modelVersionId")
+                if not mvid:
+                    mvids = image_info.get("modelVersionIds")
+                    if isinstance(mvids, list) and mvids:
+                        mvid = mvids[0]
+
+                recipe_for_enrich = {
+                    "gen_params": result.payload.get("gen_params", {}),
+                    "loras": result.payload.get("loras", []),
+                    "base_model": result.payload.get("base_model", "") or "",
+                    "checkpoint": result.payload.get("checkpoint") or result.payload.get("model"),
+                    "source_path": url,
+                }
+
+                await RecipeEnricher.enrich_recipe(
+                    recipe=recipe_for_enrich,
+                    civitai_client=civitai_client,
+                    request_params=None,
+                    prefetched_civitai_meta_raw=image_info.get("meta"),
+                    prefetched_model_version_id=mvid,
+                )
+
+                result.payload["gen_params"] = recipe_for_enrich["gen_params"]
+                if recipe_for_enrich.get("checkpoint"):
+                    result.payload["checkpoint"] = recipe_for_enrich["checkpoint"]
+                if recipe_for_enrich.get("base_model"):
+                    result.payload["base_model"] = recipe_for_enrich["base_model"]
+
+            return result
         finally:
             if temp_path:
                 self._safe_cleanup(temp_path)