feat(metadata_processor): enhance primary sampler selection logic

- Add pre-processing step to populate missing parameters for candidate samplers, especially for SamplerCustomAdvanced requiring tracing
- Change sampler selection from most recent (closest to downstream) to first in execution order to prioritize base samplers over refine samplers
- Improve parameter handling by updating sampler parameters with traced values before ranking
- Maintain backward compatibility with fallback to first sampler if no criteria match

py/metadata_collector/metadata_processor.py

@@ -40,7 +40,38 @@ class MetadataProcessor:
                         candidate_samplers[node_id] = metadata[SAMPLING][node_id]
                 
                     # If we found candidate samplers, apply primary sampler logic to these candidates only
-                if candidate_samplers:
+                    
+                    # PRE-PROCESS: Ensure all candidate samplers have their parameters populated
+                    # This is especially important for SamplerCustomAdvanced which needs tracing
+                    prompt = metadata.get("current_prompt")
+                    for node_id in candidate_samplers:
+                        # If a sampler is missing common parameters like steps or denoise, 
+                        # try to populate them using tracing before ranking
+                        sampler_info = candidate_samplers[node_id]
+                        params = sampler_info.get("parameters", {})
+                        
+                        if prompt and (params.get("steps") is None or params.get("denoise") is None):
+                            # Create a temporary params dict to use the handler
+                            temp_params = {
+                                "steps": params.get("steps"),
+                                "denoise": params.get("denoise"),
+                                "sampler": params.get("sampler_name"),
+                                "scheduler": params.get("scheduler")
+                            }
+                            
+                            # Check if it's SamplerCustomAdvanced
+                            if prompt.original_prompt and node_id in prompt.original_prompt:
+                                if prompt.original_prompt[node_id].get("class_type") == "SamplerCustomAdvanced":
+                                    MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, node_id, temp_params)
+                                    
+                                    # Update the actual parameters with found values
+                                    params["steps"] = temp_params.get("steps")
+                                    params["denoise"] = temp_params.get("denoise")
+                                    if temp_params.get("sampler"):
+                                        params["sampler_name"] = temp_params.get("sampler")
+                                    if temp_params.get("scheduler"):
+                                        params["scheduler"] = temp_params.get("scheduler")
+
                     # Collect potential primary samplers based on different criteria
                     custom_advanced_samplers = []
                     advanced_add_noise_samplers = []
@@ -49,7 +80,6 @@ class MetadataProcessor:
                     high_denoise_id = None
                     
                     # First, check for SamplerCustomAdvanced among candidates
-                    prompt = metadata.get("current_prompt")
                     if prompt and prompt.original_prompt:
                         for node_id in candidate_samplers:
                             node_info = prompt.original_prompt.get(node_id, {})
@@ -77,15 +107,16 @@ class MetadataProcessor:
                     # Combine all potential primary samplers
                     potential_samplers = custom_advanced_samplers + advanced_add_noise_samplers + high_denoise_samplers
                     
-                    # Find the most recent potential primary sampler (closest to downstream node)
+                    # Find the first potential primary sampler (prefer base sampler over refine)
-                    for i in range(downstream_index - 1, -1, -1):
+                    # Use forward search to prioritize the first one in execution order
+                    for i in range(downstream_index):
                         node_id = execution_order[i]
                         if node_id in potential_samplers:
                             return node_id, candidate_samplers[node_id]
                     
-                    # If no potential sampler found from our criteria, return the most recent sampler
+                    # If no potential sampler found from our criteria, return the first sampler
                     if candidate_samplers:
-                        for i in range(downstream_index - 1, -1, -1):
+                        for i in range(downstream_index):
                             node_id = execution_order[i]
                             if node_id in candidate_samplers:
                                 return node_id, candidate_samplers[node_id]
@@ -176,7 +207,10 @@ class MetadataProcessor:
                 found_node_id = input_value[0]  # Connected node_id
                 
                 # If we're looking for a specific node class
-                if target_class and prompt.original_prompt[found_node_id].get("class_type") == target_class:
+                if target_class:
+                    if found_node_id not in prompt.original_prompt:
+                        return None
+                    if prompt.original_prompt[found_node_id].get("class_type") == target_class:
                         return found_node_id
                 
                 # If we're not looking for a specific class, update the last valid node
@@ -185,11 +219,19 @@ class MetadataProcessor:
                 
                 # Continue tracing through intermediate nodes
                 current_node_id = found_node_id
-                # For most conditioning nodes, the input we want to follow is named "conditioning"
+                
-                if "conditioning" in prompt.original_prompt[current_node_id].get("inputs", {}):
+                # Check if current source node exists
+                if current_node_id not in prompt.original_prompt:
+                    return found_node_id if not target_class else None
+                    
+                # Determine which input to follow next on the source node
+                source_node_inputs = prompt.original_prompt[current_node_id].get("inputs", {})
+                if input_name in source_node_inputs:
+                    current_input = input_name
+                elif "conditioning" in source_node_inputs:
                     current_input = "conditioning"
                 else:
-                    # If there's no "conditioning" input, return the current node
+                    # If there's no suitable input to follow, return the current node
                     # if we're not looking for a specific target_class
                     return found_node_id if not target_class else None
             else:
@@ -523,6 +565,7 @@ class MetadataProcessor:
                 scheduler_params = metadata[SAMPLING][scheduler_node_id].get("parameters", {})
                 params["steps"] = scheduler_params.get("steps")
                 params["scheduler"] = scheduler_params.get("scheduler")
+                params["denoise"] = scheduler_params.get("denoise")
         
         # 2. Trace sampler input to find KSamplerSelect (only if sampler input exists)
         if "sampler" in sampler_inputs: