feat(civitai): expand image metadata detection criteria, see #700

Add additional CivitAI image metadata fields to detection logic including generation parameters (prompt, steps, sampler, etc.) and model information. Also improve LoRA hash detection by checking both main metadata and nested meta objects. This ensures more comprehensive identification of CivitAI image metadata across different response formats.

py/recipes/parsers/civitai_image.py

@@ -25,27 +25,41 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             return False
 
         def has_markers(payload: Dict[str, Any]) -> bool:
-            return any(
-                key in payload
-                for key in (
-                    "resources",
-                    "civitaiResources",
-                    "additionalResources",
-                )
+            # Check for common CivitAI image metadata fields
+            civitai_image_fields = (
+                "resources",
+                "civitaiResources", 
+                "additionalResources",
+                "hashes",
+                "prompt",
+                "negativePrompt",
+                "steps",
+                "sampler",
+                "cfgScale",
+                "seed",
+                "width",
+                "height",
+                "Model",
+                "Model hash"
             )
+            return any(key in payload for key in civitai_image_fields)
 
+        # Check the main metadata object
         if has_markers(metadata):
             return True
 
+        # Check for LoRA hash patterns
         hashes = metadata.get("hashes")
         if isinstance(hashes, dict) and any(str(key).lower().startswith("lora:") for key in hashes):
             return True
 
+        # Check nested meta object (common in CivitAI image responses)
         nested_meta = metadata.get("meta")
         if isinstance(nested_meta, dict):
             if has_markers(nested_meta):
                 return True
 
+            # Also check for LoRA hash patterns in nested meta
             hashes = nested_meta.get("hashes")
             if isinstance(hashes, dict) and any(str(key).lower().startswith("lora:") for key in hashes):
                 return True