feat: add meta hints user marks for metadata heuristic override

Users can now right-click nodes and assign meta hints
(primary_model, primary_sampler, positive_prompt,
negative_prompt) to override the metadata processor's
heuristic inference.

- Store extra_data from the API request so workflow node
  properties (including lm_marker_role) are accessible
  during metadata processing.
- _get_user_marks scans extra_data.extra_pnginfo.workflow
  for meta_* marks, falling back to prompt.original_prompt.
- extract_generation_params checks user marks before
  heuristic inference for sampler, model, and prompts.
- Warn on duplicate marks or invalid marked nodes.

py/metadata_collector/metadata_hook.py

@@ -135,10 +135,13 @@ class MetadataHook:
                 # Store the dynprompt reference for node lookups
                 if hasattr(prompt, 'original_prompt'):
                     registry.set_current_prompt(prompt)
-            
+
+                # Store extra_data for accessing full workflow node properties
+                registry.set_extra_data(extra_data)
+
             # Execute the original function
             return original_execute(*args, **kwargs)
-            
+
         # Replace the functions
         execution._map_node_over_list = map_node_over_list_with_metadata
         execution.execute = execute_with_prompt_tracking
@@ -202,6 +205,9 @@ class MetadataHook:
                 if hasattr(prompt, 'original_prompt'):
                     registry.set_current_prompt(prompt)
 
+                # Store extra_data for accessing full workflow node properties
+                registry.set_extra_data(extra_data)
+
             # Execute the original function
             return await original_execute(*args, **kwargs)
 

py/metadata_collector/metadata_processor.py

@@ -1,4 +1,5 @@
 import json
+import logging
 import os
 from .constants import IMAGES
 
@@ -6,10 +7,62 @@ from .constants import IMAGES
 standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
 
 from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER
+from .node_extractors import NODE_EXTRACTORS
+
+logger = logging.getLogger(__name__)
+
+# Keys that identify metadata hint marks stored in node.properties.lm_marker_role
+_META_MARK_PREFIX = "meta_"
+_MARK_PRIMARY_MODEL = "primary_model"
+_MARK_PRIMARY_SAMPLER = "primary_sampler"
+_MARK_POSITIVE_PROMPT = "positive_prompt"
+_MARK_NEGATIVE_PROMPT = "negative_prompt"
 
 class MetadataProcessor:
     """Process and format collected metadata"""
-    
+
+    @staticmethod
+    def _get_user_marks(metadata):
+        """Scan workflow nodes (from extra_data.extra_pnginfo.workflow) for user-assigned
+        metadata hint marks stored in node.properties.lm_marker_role.
+
+        Returns a dict mapping mark type keys to node IDs.
+        Example: {'primary_model': '42', 'primary_sampler': '17'}
+        """
+        marks: dict[str, str] = {}
+
+        # Primary source: extra_data.extra_pnginfo.workflow.nodes (has full properties)
+        extra_data = metadata.get("extra_data")
+        if extra_data and isinstance(extra_data, dict):
+            extra_pnginfo = extra_data.get("extra_pnginfo", {})
+            if isinstance(extra_pnginfo, dict):
+                workflow = extra_pnginfo.get("workflow", {})
+                nodes = workflow.get("nodes", [])
+                for node in nodes:
+                    node_id = str(node.get("id", ""))
+                    role = node.get("properties", {}).get("lm_marker_role", "")
+                    if role.startswith(_META_MARK_PREFIX):
+                        mark_type = role[len(_META_MARK_PREFIX):]
+                        if mark_type in marks:
+                            logger.warning(
+                                "Duplicate meta hint '%s': node %s (previous: %s), "
+                                "last match wins",
+                                mark_type, node_id, marks[mark_type],
+                            )
+                        marks[mark_type] = node_id
+
+        # Fallback: try prompt.original_prompt (API-only submissions may not have workflow)
+        if not marks:
+            prompt = metadata.get("current_prompt")
+            if prompt and getattr(prompt, "original_prompt", None):
+                for node_id, node_data in prompt.original_prompt.items():
+                    role = node_data.get("properties", {}).get("lm_marker_role", "")
+                    if role.startswith(_META_MARK_PREFIX):
+                        mark_type = role[len(_META_MARK_PREFIX):]
+                        marks[mark_type] = node_id
+
+        return marks
+
     @staticmethod
     def find_primary_sampler(metadata, downstream_id=None):
         """
@@ -476,15 +529,51 @@ class MetadataProcessor:
         
         # Get the prompt object for node relationship tracing
         prompt = metadata.get("current_prompt")
-        
-        # Find the primary KSampler node
-        primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
-        
-        # Directly get checkpoint from metadata instead of tracing
-        # Pass primary_sampler_id to avoid redundant calculation
-        checkpoint = MetadataProcessor.find_primary_checkpoint(metadata, id, primary_sampler_id)
-        if checkpoint:
-            params["checkpoint"] = checkpoint
+
+        # ---- User marks: override heuristic inference with user-assigned hints ----
+        user_marks = MetadataProcessor._get_user_marks(metadata)
+
+        # Find the primary KSampler node (user mark takes priority)
+        primary_sampler_id = None
+        primary_sampler = None
+        if _MARK_PRIMARY_SAMPLER in user_marks:
+            marked_id = user_marks[_MARK_PRIMARY_SAMPLER]
+            sampler_data = metadata.get(SAMPLING, {}).get(marked_id)
+            if sampler_data and sampler_data.get(IS_SAMPLER):
+                primary_sampler_id = marked_id
+                primary_sampler = sampler_data
+            else:
+                logger.warning(
+                    "User-marked primary sampler %s has no runtime metadata, "
+                    "falling back to heuristic",
+                    marked_id,
+                )
+        if primary_sampler is None:
+            primary_sampler_id, primary_sampler = MetadataProcessor.find_primary_sampler(metadata, id)
+
+        # Resolve checkpoint / model (user mark takes priority)
+        if _MARK_PRIMARY_MODEL in user_marks:
+            marked_id = user_marks[_MARK_PRIMARY_MODEL]
+            if marked_id in metadata.get(MODELS, {}):
+                params["checkpoint"] = metadata[MODELS][marked_id].get("name")
+            else:
+                extra_data = metadata.get("extra_data")
+                extra_pnginfo = extra_data.get("extra_pnginfo", {}) if extra_data and isinstance(extra_data, dict) else {}
+                workflow = extra_pnginfo.get("workflow", {}) if isinstance(extra_pnginfo, dict) else {}
+                node_type = "unknown"
+                for n in workflow.get("nodes", []):
+                    if str(n.get("id", "")) == marked_id:
+                        node_type = n.get("type", "unknown")
+                        break
+                logger.warning(
+                    "User-marked primary model %s (type=%s, registered=%s) has no runtime metadata, "
+                    "falling back to heuristic",
+                    marked_id, node_type, node_type in NODE_EXTRACTORS,
+                )
+        if params["checkpoint"] is None:
+            checkpoint = MetadataProcessor.find_primary_checkpoint(metadata, id, primary_sampler_id)
+            if checkpoint:
+                params["checkpoint"] = checkpoint
         
         # Check if guidance parameter exists in any sampling node
         for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
@@ -539,7 +628,22 @@ class MetadataProcessor:
                     
                     # For SamplerCustom, handle any additional parameters
                     MetadataProcessor.handle_custom_advanced_sampler(metadata, prompt, primary_sampler_id, params)
-                
+
+            # ---- User marks: override prompts with explicitly tagged nodes ----
+            prompts_data = metadata.get(PROMPTS, {})
+            if _MARK_POSITIVE_PROMPT in user_marks:
+                pos_id = user_marks[_MARK_POSITIVE_PROMPT]
+                if pos_id in prompts_data:
+                    prompt_text = prompts_data[pos_id].get("text") or prompts_data[pos_id].get("positive_text")
+                    if prompt_text:
+                        params["prompt"] = prompt_text
+            if _MARK_NEGATIVE_PROMPT in user_marks:
+                neg_id = user_marks[_MARK_NEGATIVE_PROMPT]
+                if neg_id in prompts_data:
+                    prompt_text = prompts_data[neg_id].get("text") or prompts_data[neg_id].get("negative_text")
+                    if prompt_text:
+                        params["negative_prompt"] = prompt_text
+
             # Size extraction is same for all sampler types
             # Check if the sampler itself has size information (from latent_image)
             if primary_sampler_id in metadata.get(SIZE, {}):

py/metadata_collector/metadata_registry.py

@@ -61,6 +61,7 @@ class MetadataRegistry:
             {
                 "execution_order": [],
                 "current_prompt": None,  # Will store the prompt object
+                "extra_data": None,  # Will store the API extra_data for workflow metadata
                 "timestamp": time.time(),
             }
         )
@@ -75,6 +76,11 @@ class MetadataRegistry:
             # Store the prompt in the metadata for later relationship tracing
             self.prompt_metadata[self.current_prompt_id]["current_prompt"] = prompt
 
+    def set_extra_data(self, extra_data):
+        """Store the API extra_data (contains extra_pnginfo.workflow with node properties)"""
+        if self.current_prompt_id and self.current_prompt_id in self.prompt_metadata:
+            self.prompt_metadata[self.current_prompt_id]["extra_data"] = extra_data
+
     def get_metadata(self, prompt_id=None):
         """Get collected metadata for a prompt"""
         key = prompt_id if prompt_id is not None else self.current_prompt_id

web/comfyui/node_marker.js

@@ -7,12 +7,16 @@ import { app } from "../../scripts/app.js";
 // Roles are stored in ``node.properties.lm_marker_role`` and automatically
 // persist with the workflow JSON.
 //
+// Two categories:
+//   send_* – consumed by the standalone UI's "Send to Workflow" feature
+//   meta_* – consumed by the metadata processor to override heuristic inference
+//
 // The workflow registry reads these markers and makes them available to the
 // standalone UI (e.g. ``sendEmbeddingToWorkflow`` also considers nodes marked
 // as ``send_prompt_target``).
 // =============================================================================
 
-const ROLES = {
+const SEND_ROLES = {
   send_prompt_target: {
     label: "Send Prompt Target",
     emoji: "\uD83D\uDCDD",
@@ -23,6 +27,28 @@ const ROLES = {
   },
 };
 
+const META_ROLES = {
+  meta_primary_model: {
+    label: "Meta hints: Primary Model",
+    emoji: "\uD83D\uDCA1",
+  },
+  meta_primary_sampler: {
+    label: "Meta hints: Primary Sampler",
+    emoji: "\uD83D\uDCA1",
+  },
+  meta_positive_prompt: {
+    label: "Meta hints: Positive Prompt",
+    emoji: "\uD83D\uDCA1",
+  },
+  meta_negative_prompt: {
+    label: "Meta hints: Negative Prompt",
+    emoji: "\uD83D\uDCA1",
+  },
+};
+
+// Flat lookup for setMarker / getMarker / clearMarker
+const ROLES = { ...SEND_ROLES, ...META_ROLES };
+
 // ---- Helpers ----------------------------------------------------------------
 
 function getMarker(node) {
@@ -54,7 +80,7 @@ function clearMarker(node) {
   // Restore original title: prefer stripping emoji from current title
   // (captures user renames after marking), fall back to saved original.
   const cleaned = node.title?.replace(
-    /^(\u2709\uFE0F?|\u2699\uFE0F?|\uD83D\uDCDD|\uD83C\uDF9B\uFE0F?|\uD83D\uDD27)\s*/,
+    /^(\u2709\uFE0F?|\u2699\uFE0F?|\uD83D\uDCDD|\uD83C\uDF9B\uFE0F?|\uD83D\uDD27|\uD83D\uDCA1)\s*/,
     ''
   );
   if (cleaned && cleaned !== node.title) {
@@ -84,16 +110,23 @@ function buildSubmenuOptions(node) {
   const currentRole = getMarker(node);
   const options = [];
 
-  for (const [key, def] of Object.entries(ROLES)) {
-    const isActive = currentRole === key;
-    options.push({
-      content: `${isActive ? "\u2713 " : ""}${def.label}`,
-      disabled: isActive,
-      callback: () => setMarker(node, key),
-    });
-  }
+  const buildGroup = (roles) => {
+    for (const [key, def] of Object.entries(roles)) {
+      const isActive = currentRole === key;
+      options.push({
+        content: `${isActive ? "\u2713 " : ""}${def.label}`,
+        disabled: isActive,
+        callback: () => setMarker(node, key),
+      });
+    }
+  };
+
+  buildGroup(SEND_ROLES);
+  options.push(null);  // separator
+  buildGroup(META_ROLES);
 
   if (currentRole) {
+    options.push(null);  // separator
     options.push({
       content: "Clear marker",
       callback: () => clearMarker(node),