fix(metadata-overwrite): rename 'checkpoint' input to 'model'

Two bugs prevented type-signature-based fallback from working:

- metadata_hook.py used getattr(obj.__class__, 'RETURN_TYPES')
  which fails when _async_map_node_over_list is called with
  a class (not instance) — obj.__class__ is the metaclass
  'type', which has no RETURN_TYPES. Fixed: getattr(obj, ...).

- metadata_registry.py used type(extractor) is GenericNodeExtractor
  to dispatch return_types. NODE_EXTRACTORS stores class
  references, not instances; type(Class) is always 'type',
  never the class. Fixed: extractor is GenericNodeExtractor.

__init__.py

@@ -18,6 +18,7 @@ try:  # pragma: no cover - import fallback for pytest collection
     from .py.nodes.lora_info import LoraInfoLM
     from .py.nodes.lora_syntax_to_path import LoraSyntaxToPath
     from .py.nodes.create_hook_lora import CreateHookLoraLM
+    from .py.nodes.metadata_overwrite import MetadataOverwriteLM
     from .py.metadata_collector import init as init_metadata_collector
 except (
     ImportError
@@ -66,6 +67,9 @@ except (
     CreateHookLoraLM = importlib.import_module(
         "py.nodes.create_hook_lora"
     ).CreateHookLoraLM
+    MetadataOverwriteLM = importlib.import_module(
+        "py.nodes.metadata_overwrite"
+    ).MetadataOverwriteLM
     init_metadata_collector = importlib.import_module("py.metadata_collector").init
 
 NODE_CLASS_MAPPINGS = {
@@ -88,6 +92,7 @@ NODE_CLASS_MAPPINGS = {
     LoraInfoLM.NAME: LoraInfoLM,
     LoraSyntaxToPath.NAME: LoraSyntaxToPath,
     CreateHookLoraLM.NAME: CreateHookLoraLM,
+    MetadataOverwriteLM.NAME: MetadataOverwriteLM,
 }
 
 WEB_DIRECTORY = "./web/comfyui"

py/metadata_collector/constants.py

@@ -9,6 +9,14 @@ EMBEDDINGS = "embeddings"
 SIZE = "size"
 IMAGES = "images"
 IS_SAMPLER = "is_sampler"  # New constant to mark sampler nodes
+OVERWRITE = "overwrite"  # Manual metadata overwrite from MetadataOverwriteLM node
+
+# Field names that the MetadataOverwriteLM node and its extractor share
+METADATA_OVERWRITE_FIELDS = (
+    "prompt", "negative_prompt", "seed", "steps", "cfg_scale",
+    "sampler", "scheduler", "model", "loras", "size",
+    "clip_skip", "additional_data",
+)
 
 # Complete list of categories to track
-METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES]
+METADATA_CATEGORIES = [MODELS, PROMPTS, SAMPLING, LORAS, EMBEDDINGS, SIZE, IMAGES, OVERWRITE]

py/metadata_collector/metadata_hook.py

@@ -83,7 +83,8 @@ class MetadataHook:
                         
                         # Record inputs before execution
                         if node_id is not None:
-                            registry.record_node_execution(node_id, class_type, input_data_all, None)
+                            return_types = getattr(obj, 'RETURN_TYPES', None)
+                            registry.record_node_execution(node_id, class_type, input_data_all, None, return_types=return_types)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
             
@@ -114,7 +115,8 @@ class MetadataHook:
                         
                         # Record outputs after execution
                         if node_id is not None:
-                            registry.update_node_execution(node_id, class_type, results)
+                            return_types = getattr(obj, 'RETURN_TYPES', None)
+                            registry.update_node_execution(node_id, class_type, results, return_types=return_types)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (post-execution): {str(e)}")
             
@@ -135,10 +137,13 @@ 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
@@ -163,7 +168,8 @@ class MetadataHook:
                         class_type = obj.__class__.__name__
                         node_id = unique_id
                         if node_id is not None:
-                            registry.record_node_execution(node_id, class_type, input_data_all, None)
+                            return_types = getattr(obj, 'RETURN_TYPES', None)
+                            registry.record_node_execution(node_id, class_type, input_data_all, None, return_types=return_types)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
 
@@ -180,7 +186,8 @@ class MetadataHook:
                         class_type = obj.__class__.__name__
                         node_id = unique_id
                         if node_id is not None:
-                            registry.update_node_execution(node_id, class_type, results)
+                            return_types = getattr(obj, 'RETURN_TYPES', None)
+                            registry.update_node_execution(node_id, class_type, results, return_types=return_types)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (post-execution): {str(e)}")
 
@@ -202,6 +209,9 @@ 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

@@ -1,15 +1,68 @@
 import json
+import logging
 import os
 from .constants import IMAGES
 
 # Check if running in standalone mode
 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 .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IS_SAMPLER, OVERWRITE
+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):
         """
@@ -471,20 +524,57 @@ class MetadataProcessor:
             "checkpoint": None,
             "loras": "",
             "size": None,
-            "clip_skip": None
+            "clip_skip": None,
+            "additional_data": "",
         }
         
         # 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 +629,22 @@ 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, {}):
@@ -568,7 +673,21 @@ class MetadataProcessor:
                 break
         if params["clip_skip"] is None:
             params["clip_skip"] = "1"
-        
+
+        # ---- Apply manual metadata overwrites ----
+        for overwrite_info in metadata.get(OVERWRITE, {}).values():
+            overwrite_params = overwrite_info.get("parameters", {})
+            for key, value in overwrite_params.items():
+                if value:  # truthy check — only overwrite when user provided a real value
+                    params[key] = value
+
+        # Bridge: the overwrite node exposes the field as "model" (more accurate),
+        # but the internal pipeline key remains "checkpoint" for backward compatibility
+        # with A1111 metadata format and downstream consumers.
+        if params.get("model"):
+            params["checkpoint"] = params["model"]
+            del params["model"]
+
         return params
     
     @staticmethod

py/metadata_collector/metadata_registry.py

@@ -1,7 +1,7 @@
 import time
 from nodes import NODE_CLASS_MAPPINGS  # type: ignore
 from .node_extractors import NODE_EXTRACTORS, GenericNodeExtractor
-from .constants import METADATA_CATEGORIES, IMAGES
+from .constants import METADATA_CATEGORIES, IMAGES, OVERWRITE
 
 
 class MetadataRegistry:
@@ -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
@@ -122,20 +128,28 @@ class MetadataRegistry:
             cache_key = f"{node_id}:{class_type}"
 
             # Check if this node type is relevant for metadata collection
-            if class_type in NODE_EXTRACTORS:
+            if class_type in NODE_EXTRACTORS or cache_key in self.node_cache:
                 # Check if we have cached metadata for this node
                 if cache_key in self.node_cache:
                     cached_data = self.node_cache[cache_key]
 
+                    # Detect bypass (mode=4) / mute (mode=2) — these nodes
+                    # were intentionally disabled and should not contribute
+                    # overwrite values from a previous execution's cache.
+                    node_mode = node_data.get("mode", 0)
+                    node_is_disabled = node_mode in (2, 4)
+
                     # Apply cached metadata to the current metadata
                     for category in self.metadata_categories:
+                        if category == OVERWRITE and node_is_disabled:
+                            continue
                         if category in cached_data and node_id in cached_data[category]:
                             if node_id not in metadata[category]:
                                 metadata[category][node_id] = cached_data[category][
                                     node_id
                                 ]
 
-    def record_node_execution(self, node_id, class_type, inputs, outputs):
+    def record_node_execution(self, node_id, class_type, inputs, outputs, return_types=None):
         """Record information about a node's execution"""
         if not self.current_prompt_id:
             return
@@ -158,17 +172,18 @@ class MetadataRegistry:
 
         # Extract node-specific metadata
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
-        extractor.extract(
-            node_id,
-            processed_inputs,
-            outputs,
-            self.prompt_metadata[self.current_prompt_id],
-        )
+        if extractor is GenericNodeExtractor:
+            extractor.extract(node_id, processed_inputs, outputs,
+                              self.prompt_metadata[self.current_prompt_id],
+                              return_types=return_types)
+        else:
+            extractor.extract(node_id, processed_inputs, outputs,
+                              self.prompt_metadata[self.current_prompt_id])
 
         # Cache this node's metadata
         self._cache_node_metadata(node_id, class_type)
 
-    def update_node_execution(self, node_id, class_type, outputs):
+    def update_node_execution(self, node_id, class_type, outputs, return_types=None):
         """Update node metadata with output information"""
         if not self.current_prompt_id:
             return
@@ -179,9 +194,17 @@ class MetadataRegistry:
         # Use the same extractor to update with outputs
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
         if hasattr(extractor, "update"):
-            extractor.update(
-                node_id, processed_outputs, self.prompt_metadata[self.current_prompt_id]
-            )
+            if extractor is GenericNodeExtractor:
+                extractor.update(
+                    node_id, processed_outputs,
+                    self.prompt_metadata[self.current_prompt_id],
+                    return_types=return_types,
+                )
+            else:
+                extractor.update(
+                    node_id, processed_outputs,
+                    self.prompt_metadata[self.current_prompt_id],
+                )
 
         # Update the cached metadata for this node
         self._cache_node_metadata(node_id, class_type)

py/metadata_collector/node_extractors.py

@@ -2,7 +2,7 @@ import json
 import os
 import re
 
-from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
+from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER, OVERWRITE, METADATA_OVERWRITE_FIELDS
 
 
 def _store_checkpoint_metadata(metadata, node_id, model_name):
@@ -31,11 +31,78 @@ class NodeMetadataExtractor:
         pass
         
 class GenericNodeExtractor(NodeMetadataExtractor):
-    """Default extractor for nodes without specific handling"""
+    """Fallback extractor with type-signature-based detection.
+
+    When a node is not in the NODE_EXTRACTORS registry, the hook layer
+    passes ``return_types`` from ``obj.RETURN_TYPES``:
+
+    * ``MODEL`` output: common input fields (ckpt_name, unet_name, etc.)
+      are checked for a model file name and stored as checkpoint metadata.
+    * ``CONDITIONING`` output: common text input fields are checked for
+      prompt text and stored as prompt metadata.
+    """
+
+    # Input field names that carry a model path in loader-style nodes.
+    _MODEL_NAME_FIELDS = (
+        "ckpt_name", "unet_name", "model_path", "model_name", "gguf_name",
+    )
+
+    # Extensions used by checkpoint_scanner.py — only record values that look
+    # like real model filenames to avoid capturing unrelated string fields.
+    _MODEL_EXTENSIONS = {
+        ".ckpt", ".pt", ".pt2", ".bin", ".pth", ".safetensors", ".pkl", ".sft", ".gguf",
+    }
+
+    # Input field names that may carry prompt text in encoder-style nodes.
+    _TEXT_FIELDS = ("text", "clip_l", "t5xxl", "prompt", "positive", "negative")
+
     @staticmethod
-    def extract(node_id, inputs, outputs, metadata):
-        pass
-        
+    def extract(node_id, inputs, outputs, metadata, return_types=None):
+        if return_types is None:
+            return
+
+        # — MODEL loader detection (checkpoint / UNET / GGUF) —
+        if "MODEL" in return_types or any("MODEL" in str(t) for t in return_types):
+            for field in GenericNodeExtractor._MODEL_NAME_FIELDS:
+                val = inputs.get(field)
+                if val and isinstance(val, str) and val.strip():
+                    name = val.strip()
+                    if not any(name.lower().endswith(ext) for ext in GenericNodeExtractor._MODEL_EXTENSIONS):
+                        continue
+                    _store_checkpoint_metadata(metadata, node_id, name)
+                    return
+
+        # — CONDITIONING encoder detection (CLIPTextEncode, Flux, custom) —
+        if "CONDITIONING" in return_types or any("CONDITIONING" in str(t) for t in return_types):
+            text = None
+            for field in GenericNodeExtractor._TEXT_FIELDS:
+                val = inputs.get(field)
+                if val and isinstance(val, str) and val.strip():
+                    text = val.strip()
+                    break
+            if text:
+                prompt_data = metadata.setdefault(PROMPTS, {})
+                prompt_data[node_id] = {
+                    "text": text,
+                    "node_id": node_id,
+                }
+
+    @staticmethod
+    def update(node_id, outputs, metadata, return_types=None):
+        if return_types is None:
+            return
+        if "CONDITIONING" not in return_types and not any(
+            "CONDITIONING" in str(t) for t in return_types
+        ):
+            return
+        if node_id not in metadata.get(PROMPTS, {}):
+            return
+        if outputs and isinstance(outputs, list) and len(outputs) > 0:
+            if isinstance(outputs[0], tuple) and len(outputs[0]) > 0:
+                cond = outputs[0][0]
+                if cond is not None:
+                    metadata[PROMPTS][node_id]["conditioning"] = cond
+
 class CheckpointLoaderExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -1154,6 +1221,32 @@ class CR_ApplyControlNetStackExtractor(NodeMetadataExtractor):
                 metadata[PROMPTS][node_id]["positive_encoded"] = transformed_positive
                 metadata[PROMPTS][node_id]["negative_encoded"] = transformed_negative
 
+class MetadataOverwriteExtractor(NodeMetadataExtractor):
+    """Extract manually specified metadata from MetadataOverwriteLM node.
+
+    Stores truthy input values under the OVERWRITE category so that
+    extract_generation_params can merge them over the inferred params.
+    """
+
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        overwrite_params = {}
+        for key in METADATA_OVERWRITE_FIELDS:
+            value = inputs.get(key)
+            if value:  # truthy — only overwrite when user provided a real value
+                overwrite_params[key] = value
+
+        if overwrite_params:
+            metadata.setdefault(OVERWRITE, {})
+            metadata[OVERWRITE][node_id] = {
+                "parameters": overwrite_params,
+                "node_id": node_id,
+            }
+
+
 # Registry of node-specific extractors
 # Keys are node class names
 NODE_EXTRACTORS = {
@@ -1221,5 +1314,7 @@ NODE_EXTRACTORS = {
     "CFGGuider": CFGGuiderExtractor,            # Add CFGGuider
     # Image
     "VAEDecode": VAEDecodeExtractor,  # Added VAEDecode extractor
+    # Metadata overwrite
+    "MetadataOverwriteLM": MetadataOverwriteExtractor,
     # Add other nodes as needed
 }

py/nodes/metadata_overwrite.py

@@ -0,0 +1,157 @@
+"""Metadata Overwrite node — allows users to manually specify generation parameters
+that override the automatically collected/inferred metadata.
+
+All inputs have falsy defaults: only truthy (non-empty / non-zero) values
+will overwrite the corresponding field in the final metadata.
+"""
+
+from typing import Any
+
+from ..metadata_collector.constants import METADATA_OVERWRITE_FIELDS
+
+
+class MetadataOverwriteLM:
+    NAME = "Metadata Overwrite (LoraManager)"
+    CATEGORY = "Lora Manager/utils"
+    DESCRIPTION = (
+        "Manually specify generation parameters to override automatically collected "
+        "metadata. Only filled/connected inputs will take effect — empty defaults "
+        "are ignored."
+    )
+
+    @classmethod
+    def INPUT_TYPES(cls) -> dict[str, Any]:
+        return {
+            "optional": {
+                "prompt": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": "Positive prompt. Only overwrites when non-empty.",
+                    },
+                ),
+                "negative_prompt": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": "Negative prompt. Only overwrites when non-empty.",
+                    },
+                ),
+                "seed": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 0xFFFFFFFFFFFFFFFF,
+                        "control_after_generate": False,
+                        "tooltip": "Seed value. Only overwrites when > 0.",
+                    },
+                ),
+                "steps": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 10000,
+                        "tooltip": "Number of steps. Only overwrites when > 0.",
+                    },
+                ),
+                "cfg_scale": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": 0.0,
+                        "max": 100.0,
+                        "tooltip": "CFG scale. Only overwrites when > 0.",
+                    },
+                ),
+                "sampler": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": "Sampler name. Only overwrites when non-empty.",
+                    },
+                ),
+                "scheduler": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": "Scheduler name. Only overwrites when non-empty.",
+                    },
+                ),
+                "model": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": (
+                            "The checkpoint or diffusion model (UNet) used "
+                            "for generation. Only overwrites when non-empty."
+                        ),
+                    },
+                ),
+                "loras": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": (
+                            "LoRA syntax, e.g. <lora:name:strength> "
+                            "or <lora:name:model_strength:clip_strength>, "
+                            "separated by spaces. Only overwrites when non-empty."
+                        ),
+                    },
+                ),
+                "size": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": (
+                            "Image size in WIDTHxHEIGHT format (e.g. 512x768). "
+                            "Only overwrites when non-empty."
+                        ),
+                    },
+                ),
+                "clip_skip": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": -24,
+                        "max": 24,
+                        "tooltip": "Clip skip. Only overwrites when non-zero.",
+                    },
+                ),
+                "additional_data": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "multiline": True,
+                        "tooltip": (
+                            "Additional data to embed in the image metadata. "
+                            "Inserted between Clip skip and Model hash in the "
+                            "A1111-compatible parameters string. "
+                            'Example: "Copyright": "Some license info"'
+                        ),
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("METADATA",)
+    RETURN_NAMES = ("metadata",)
+    FUNCTION = "collect_metadata"
+    OUTPUT_NODE = True
+
+    def collect_metadata(self, **kwargs: Any) -> tuple[dict[str, Any]]:
+        """Collect non-falsy input values into a metadata dict.
+
+        Only values that are truthy (non-empty string, non-zero number)
+        are included — matching the overwrite logic in the metadata pipeline.
+        """
+        result: dict[str, Any] = {}
+        for key in METADATA_OVERWRITE_FIELDS:
+            value = kwargs.get(key)
+            if value:
+                result[key] = value
+        return (result,)

py/nodes/save_image.py

@@ -471,6 +471,9 @@ class SaveImageLM:
                     params.append(f"Clip skip: {abs(cs)}")
             except (ValueError, TypeError):
                 pass
+        additional_data = metadata_dict.get("additional_data", "")
+        if additional_data:
+            params.append(additional_data)
         if ckpt_hash:
             params.append(f"Model hash: {ckpt_hash[:10].upper()}")
         if ckpt_display_name:

tests/metadata_collector/test_metadata_collector.py

@@ -30,10 +30,10 @@ def test_metadata_hook_installs_and_traces_execution(monkeypatch, metadata_regis
 
     calls = []
 
-    def record_stub(self, node_id, class_type, inputs, outputs):
+    def record_stub(self, node_id, class_type, inputs, outputs, return_types=None):
         calls.append(("record", node_id, class_type, inputs))
 
-    def update_stub(self, node_id, class_type, outputs):
+    def update_stub(self, node_id, class_type, outputs, return_types=None):
         calls.append(("update", node_id, class_type, outputs))
 
     monkeypatch.setattr(MetadataRegistry, "record_node_execution", record_stub)
@@ -820,3 +820,220 @@ def test_lora_manager_checkpoint_and_unet_loaders_extract_models(metadata_regist
         "type": "checkpoint",
         "node_id": "unet_node",
     }
+
+
+# ---------------------------------------------------------------------------
+# MetadataOverwriteExtractor & overwrite merge tests
+# ---------------------------------------------------------------------------
+
+from py.metadata_collector.constants import OVERWRITE, METADATA_OVERWRITE_FIELDS
+from py.metadata_collector.node_extractors import MetadataOverwriteExtractor
+
+
+def test_metadata_overwrite_extractor_stores_truthy_values(metadata_registry):
+    """Extractor should store truthy inputs under the OVERWRITE category."""
+    metadata_registry.start_collection("prompt-ow")
+    metadata = metadata_registry.prompt_metadata["prompt-ow"]
+
+    inputs = {
+        "prompt": "a beautiful landscape",
+        "negative_prompt": "",
+        "seed": 42,
+        "steps": 0,
+        "cfg_scale": 7.5,
+        "sampler": "",
+        "scheduler": "",
+        "model": "myModel.safetensors",
+        "loras": "<lora:detail:0.8>",
+        "size": "1024x768",
+        "clip_skip": 0,
+        "additional_data": '{"Copyright": "CC0"}',
+    }
+
+    MetadataOverwriteExtractor.extract("ow-1", inputs, None, metadata)
+
+    assert OVERWRITE in metadata
+    assert "ow-1" in metadata[OVERWRITE]
+    params = metadata[OVERWRITE]["ow-1"]["parameters"]
+
+    # Truthy values stored
+    assert params["prompt"] == "a beautiful landscape"
+    assert params["seed"] == 42
+    assert params["cfg_scale"] == 7.5
+    assert params["model"] == "myModel.safetensors"
+    assert params["loras"] == "<lora:detail:0.8>"
+    assert params["size"] == "1024x768"
+    assert params["additional_data"] == '{"Copyright": "CC0"}'
+
+    # Falsy values NOT stored
+    assert "negative_prompt" not in params
+    assert "steps" not in params
+    assert "sampler" not in params
+    assert "scheduler" not in params
+    assert "clip_skip" not in params
+
+    metadata_registry.clear_metadata()
+
+
+def test_metadata_overwrite_extractor_empty_inputs(metadata_registry):
+    """Extractor with all-falsy inputs should NOT create OVERWRITE category."""
+    metadata_registry.start_collection("prompt-ow2")
+    metadata = metadata_registry.prompt_metadata["prompt-ow2"]
+
+    inputs = {key: "" for key in METADATA_OVERWRITE_FIELDS}
+    inputs.update({"seed": 0, "steps": 0, "cfg_scale": 0.0, "clip_skip": 0})
+
+    MetadataOverwriteExtractor.extract("ow-2", inputs, None, metadata)
+
+    # start_collection pre-creates empty dicts for all categories,
+    # but no node should have populated OVERWRITE with any data
+    assert not metadata[OVERWRITE]
+
+    metadata_registry.clear_metadata()
+
+
+def test_extract_generation_params_applies_overwrite(metadata_registry, populated_registry, monkeypatch):
+    """overwrite values should replace inferred params in extract_generation_params."""
+    import py.metadata_collector.metadata_processor as mp
+
+    monkeypatch.setattr(mp, "standalone_mode", False)
+
+    metadata = populated_registry["metadata"]
+    registry_obj = populated_registry["registry"]
+
+    # Simulate the MetadataOverwriteLM node having been executed with overwrite values
+    registry_obj.start_collection("promptA")
+    # Re-populate with the same data (start_collection resets)
+    registry_obj.set_current_prompt(populated_registry["prompt"])
+    metadata2 = registry_obj.prompt_metadata["promptA"]
+
+    # Inject overwrite data into metadata
+    metadata2[OVERWRITE] = {
+        "ow-1": {
+            "parameters": {
+                "seed": 777,
+                "additional_data": '{"AuthorURL": "https://civitai.com/user/foo"}',
+            },
+            "node_id": "ow-1",
+        }
+    }
+    # Copy other categories from original populated metadata
+    for cat in ("models", "prompts", "sampling", "loras", "size", "images"):
+        if cat in metadata:
+            metadata2[cat] = metadata[cat]
+    metadata2["execution_order"] = metadata["execution_order"]
+
+    params = MetadataProcessor.extract_generation_params(metadata2, id="vae")
+
+    # Overwritten values
+    assert params["seed"] == 777
+    assert params["additional_data"] == '{"AuthorURL": "https://civitai.com/user/foo"}'
+
+    # Inferred values still present (not overwritten)
+    assert params["prompt"] == "A castle on a hill"
+    assert params["cfg_scale"] == 7.5
+    assert params["checkpoint"] == "model.safetensors"
+
+    registry_obj.clear_metadata()
+
+
+def test_extract_generation_params_overwrite_falsy_skipped(metadata_registry, populated_registry, monkeypatch):
+    """Overwrite entries with falsy values should NOT replace inferred params."""
+    import py.metadata_collector.metadata_processor as mp
+
+    monkeypatch.setattr(mp, "standalone_mode", False)
+
+    metadata = populated_registry["metadata"]
+    registry_obj = populated_registry["registry"]
+
+    registry_obj.start_collection("promptA")
+    registry_obj.set_current_prompt(populated_registry["prompt"])
+    metadata2 = registry_obj.prompt_metadata["promptA"]
+
+    # Inject overwrite with falsy values
+    metadata2[OVERWRITE] = {
+        "ow-1": {
+            "parameters": {
+                "seed": 0,
+                "steps": 0,
+                "cfg_scale": 0.0,
+                "prompt": "",
+                "clip_skip": 0,
+            },
+            "node_id": "ow-1",
+        }
+    }
+    for cat in ("models", "prompts", "sampling", "loras", "size", "images"):
+        if cat in metadata:
+            metadata2[cat] = metadata[cat]
+    metadata2["execution_order"] = metadata["execution_order"]
+
+    params = MetadataProcessor.extract_generation_params(metadata2, id="vae")
+
+    # Falsy overwrites should NOT have replaced inferred values
+    assert params["prompt"] == "A castle on a hill"
+    assert params["cfg_scale"] == 7.5
+
+    registry_obj.clear_metadata()
+
+
+def test_fill_missing_metadata_skips_overwrite_for_bypassed_node(metadata_registry):
+    """Bypassed (mode=4) node should not have OVERWRITE filled from cache."""
+    metadata_registry.start_collection("prompt-bypass")
+
+    # Simulate a previous execution that cached overwrite data
+    metadata_registry.record_node_execution(
+        "ow-1",
+        "MetadataOverwriteLM",
+        {"seed": 99, "prompt": "test", "steps": 0, "cfg_scale": 0.0,
+         "negative_prompt": "", "sampler": "", "scheduler": "", "model": "",
+         "loras": "", "size": "", "clip_skip": 0, "additional_data": ""},
+        None,
+    )
+
+    # Now start a new prompt where the node is bypassed (mode=4)
+    metadata_registry.start_collection("prompt-bypass-2")
+    original_prompt = {
+        "ow-1": {"class_type": "MetadataOverwriteLM", "inputs": {}, "mode": 4},
+    }
+    metadata_registry.set_current_prompt(
+        SimpleNamespace(original_prompt=original_prompt)
+    )
+
+    metadata = metadata_registry.get_metadata("prompt-bypass-2")
+
+    # The overwrite data should NOT be present (node was bypassed, not
+    # a cache hit — it should not inherit previous execution's overwrite)
+    assert "ow-1" not in metadata.get(OVERWRITE, {})
+
+    metadata_registry.clear_metadata()
+
+
+def test_fill_missing_metadata_fills_overwrite_for_muted_node(metadata_registry):
+    """Muted (mode=2) node should also not have OVERWRITE filled from cache."""
+    metadata_registry.start_collection("prompt-mute")
+
+    # Simulate a previous execution that cached overwrite data
+    metadata_registry.record_node_execution(
+        "ow-1",
+        "MetadataOverwriteLM",
+        {"seed": 88, "prompt": "test2", "steps": 0, "cfg_scale": 0.0,
+         "negative_prompt": "", "sampler": "", "scheduler": "", "model": "",
+         "loras": "", "size": "", "clip_skip": 0, "additional_data": ""},
+        None,
+    )
+
+    # Start a new prompt where the node is muted (mode=2)
+    metadata_registry.start_collection("prompt-mute-2")
+    original_prompt = {
+        "ow-1": {"class_type": "MetadataOverwriteLM", "inputs": {}, "mode": 2},
+    }
+    metadata_registry.set_current_prompt(
+        SimpleNamespace(original_prompt=original_prompt)
+    )
+
+    metadata = metadata_registry.get_metadata("prompt-mute-2")
+
+    assert "ow-1" not in metadata.get(OVERWRITE, {})
+
+    metadata_registry.clear_metadata()

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),