feat: Introduce generation parameter merging from request, Civitai, and embedded image metadata, and enhance ComfyUI metadata parsing.

py/recipes/merger.py

@@ -0,0 +1,50 @@
+from typing import Any, Dict, Optional
+import logging
+
+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"}
+
+    @staticmethod
+    def merge(
+        request_params: Optional[Dict[str, Any]] = None,
+        civitai_meta: Optional[Dict[str, Any]] = None,
+        embedded_metadata: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """
+        Merge generation parameters from three sources.
+        
+        Priority: request_params > civitai_meta > embedded_metadata
+        
+        Args:
+            request_params: Params provided directly in the import request
+            civitai_meta: Params from Civitai Image API 'meta' field
+            embedded_metadata: Params extracted from image EXIF/embedded metadata
+            
+        Returns:
+            Merged parameters dictionary
+        """
+        result = {}
+
+        # 1. Start with embedded metadata (lowest priority)
+        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"])
+            else:
+                # Otherwise assume the dict itself contains gen_params
+                result.update(embedded_metadata)
+
+        # 2. Layer Civitai meta (medium priority)
+        if civitai_meta:
+            result.update(civitai_meta)
+
+        # 3. Layer request params (highest priority)
+        if request_params:
+            result.update(request_params)
+
+        # Filter out blacklisted keys
+        return {k: v for k, v in result.items() if k not in GenParamsMerger.BLACKLISTED_KEYS}

py/recipes/parsers/comfy.py

@@ -36,9 +36,6 @@ class ComfyMetadataParser(RecipeMetadataParser):
             # Find all LoraLoader nodes
             lora_nodes = {k: v for k, v in data.items() if isinstance(v, dict) and v.get('class_type') == 'LoraLoader'}
             
-            if not lora_nodes:
-                return {"error": "No LoRA information found in this ComfyUI workflow", "loras": []}
-            
             # Process each LoraLoader node
             for node_id, node in lora_nodes.items():
                 if 'inputs' not in node or 'lora_name' not in node['inputs']:

py/routes/handlers/recipe_handlers.py

@@ -24,6 +24,8 @@ from ...services.recipes import (
 )
 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
 
 Logger = logging.Logger
 EnsureDependenciesCallable = Callable[[], Awaitable[None]]
@@ -552,7 +554,41 @@ class RecipeManagementHandler:
                     metadata["base_model"] = base_model_from_metadata
 
             tags = self._parse_tags(params.get("tags"))
-            image_bytes, extension = await self._download_remote_media(image_url)
+            image_bytes, extension, civitai_meta = await self._download_remote_media(image_url)
+
+            # Extract embedded metadata from the downloaded image
+            embedded_metadata = None
+            try:
+                with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_img:
+                    temp_img.write(image_bytes)
+                    temp_img_path = temp_img.name
+                
+                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
+                        else:
+                            # Fallback to raw string if no parser matches (might be simple params)
+                            embedded_metadata = {"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
+            )
+
+            if merged_gen_params:
+                metadata["gen_params"] = merged_gen_params
 
             result = await self._persistence_service.save_recipe(
                 recipe_scanner=recipe_scanner,
@@ -900,7 +936,7 @@ class RecipeManagementHandler:
                 extension = ".webp" # Default to webp if unknown
 
             with open(temp_path, "rb") as file_obj:
-                return file_obj.read(), extension
+                return file_obj.read(), extension, image_info.get("meta") if civitai_match and image_info else None
         except RecipeDownloadError:
             raise
         except RecipeValidationError: