feat: Add CR_ApplyControlNetStack extractor and enhance prompt conditioning handling in metadata processing. Fixes #277

py/metadata_collector/metadata_processor.py

@@ -239,24 +239,44 @@ class MetadataProcessor:
             pos_conditioning = metadata[PROMPTS][sampler_id].get("pos_conditioning")
             neg_conditioning = metadata[PROMPTS][sampler_id].get("neg_conditioning")
             
-            # Try to match conditioning objects with those stored by CLIPTextEncodeExtractor
+            # Helper function to recursively find prompt text for a conditioning object
-            for prompt_node_id, prompt_data in metadata[PROMPTS].items():
+            def find_prompt_text_for_conditioning(conditioning_obj, is_positive=True):
-                # For nodes with single conditioning output
+                if conditioning_obj is None:
-                if "conditioning" in prompt_data:
+                    return ""
-                    if pos_conditioning is not None and id(prompt_data["conditioning"]) == id(pos_conditioning):
-                        result["prompt"] = prompt_data.get("text", "")
                     
-                    if neg_conditioning is not None and id(prompt_data["conditioning"]) == id(neg_conditioning):
+                # Try to match conditioning objects with those stored by extractors
-                        result["negative_prompt"] = prompt_data.get("text", "")
+                for prompt_node_id, prompt_data in metadata[PROMPTS].items():
+                    # For nodes with single conditioning output
+                    if "conditioning" in prompt_data:
+                        if id(prompt_data["conditioning"]) == id(conditioning_obj):
+                            return prompt_data.get("text", "")
                     
-                # For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
+                    # For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
-                if "positive_encoded" in prompt_data:
+                    if is_positive and "positive_encoded" in prompt_data:
-                    if pos_conditioning is not None and id(prompt_data["positive_encoded"]) == id(pos_conditioning):
+                        if id(prompt_data["positive_encoded"]) == id(conditioning_obj):
-                        result["prompt"] = prompt_data.get("positive_text", "")
+                            if "positive_text" in prompt_data:
+                                return prompt_data["positive_text"]
+                            else:
+                                orig_conditioning = prompt_data.get("orig_pos_cond", None)
+                                if orig_conditioning is not None:
+                                    # Recursively find the prompt text for the original conditioning
+                                    return find_prompt_text_for_conditioning(orig_conditioning, is_positive=True)
                     
-                if "negative_encoded" in prompt_data:
+                    if not is_positive and "negative_encoded" in prompt_data:
-                    if neg_conditioning is not None and id(prompt_data["negative_encoded"]) == id(neg_conditioning):
+                        if id(prompt_data["negative_encoded"]) == id(conditioning_obj):
-                        result["negative_prompt"] = prompt_data.get("negative_text", "")
+                            if "negative_text" in prompt_data:
+                                return prompt_data["negative_text"]
+                            else:
+                                orig_conditioning = prompt_data.get("orig_neg_cond", None)
+                                if orig_conditioning is not None:
+                                    # Recursively find the prompt text for the original conditioning
+                                    return find_prompt_text_for_conditioning(orig_conditioning, is_positive=False)
+                
+                return ""
+            
+            # Find prompt texts using the helper function
+            result["prompt"] = find_prompt_text_for_conditioning(pos_conditioning, is_positive=True)
+            result["negative_prompt"] = find_prompt_text_for_conditioning(neg_conditioning, is_positive=False)
             
         return result
     

py/metadata_collector/node_extractors.py

@@ -569,6 +569,40 @@ class CFGGuiderExtractor(NodeMetadataExtractor):
             
         metadata[SAMPLING][node_id]["parameters"]["cfg"] = cfg_value
 
+class CR_ApplyControlNetStackExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+            
+        # Save the original conditioning inputs
+        base_positive = inputs.get("base_positive")
+        base_negative = inputs.get("base_negative")
+        
+        if base_positive is not None or base_negative is not None:
+            if node_id not in metadata[PROMPTS]:
+                metadata[PROMPTS][node_id] = {"node_id": node_id}
+            
+            metadata[PROMPTS][node_id]["orig_pos_cond"] = base_positive
+            metadata[PROMPTS][node_id]["orig_neg_cond"] = base_negative
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        # Extract transformed conditionings from outputs
+        # outputs structure: [(base_positive, base_negative, show_help, )]
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            first_output = outputs[0]
+            if isinstance(first_output, tuple) and len(first_output) >= 2:
+                transformed_positive = first_output[0]
+                transformed_negative = first_output[1]
+                
+                # Save transformed conditioning objects in metadata
+                if node_id not in metadata[PROMPTS]:
+                    metadata[PROMPTS][node_id] = {"node_id": node_id}
+                    
+                metadata[PROMPTS][node_id]["positive_encoded"] = transformed_positive
+                metadata[PROMPTS][node_id]["negative_encoded"] = transformed_negative
+
 # Registry of node-specific extractors
 # Keys are node class names
 NODE_EXTRACTORS = {
@@ -595,6 +629,7 @@ NODE_EXTRACTORS = {
     "WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,
     "AdvancedCLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb
     "smZ_CLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/shiimizu/ComfyUI_smZNodes
+    "CR_ApplyControlNetStack": CR_ApplyControlNetStackExtractor,  # Add CR_ApplyControlNetStack
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
     # Flux