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

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