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.

AGENTS.md

@@ -137,7 +137,13 @@ npm run test:coverage       # Generate coverage report
 - Dual mode: ComfyUI plugin (folder_paths) vs standalone (settings.json)
 - Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
 - Run `python scripts/sync_translation_keys.py` after adding UI strings to `locales/en.json`
-- Symlinks require normalized paths
+- Symlinks require normalized paths.
+  **Business paths vs real paths**: All stored paths and operation routing use the
+  original paths as they appear under configured model roots — symlinks are NOT
+  resolved. `os.path.realpath` is only for scanner dedup and the symlink cache.
+  Any path passed to `os.remove`/`os.rename`/`shutil.move` or validated by a
+  containment check MUST use the business path (i.e. `os.path.abspath`, not
+  `realpath`).
 
 ## Git / Commit Messages
 

__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

@@ -16,6 +16,156 @@ from PIL import Image, PngImagePlugin
 import piexif
 import logging
 
+# Civitai-compatible sampler name mapping: ComfyUI internal → A1111 display name
+CIVITAI_SAMPLER_MAP = {
+    "euler": "Euler",
+    "euler_ancestral": "Euler a",
+    "lms": "LMS",
+    "heun": "Heun",
+    "dpm_2": "DPM2",
+    "dpm_2_ancestral": "DPM2 a",
+    "dpmpp_2s_ancestral": "DPM++ 2S a",
+    "dpmpp_2m": "DPM++ 2M",
+    "dpmpp_sde": "DPM++ SDE",
+    "dpmpp_sde_gpu": "DPM++ SDE",
+    "dpmpp_2m_sde": "DPM++ 2M SDE",
+    "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
+    "dpmpp_3m_sde": "DPM++ 3M SDE",
+    "dpm_fast": "DPM fast",
+    "dpm_adaptive": "DPM adaptive",
+    "ddim": "DDIM",
+    "plms": "PLMS",
+    "uni_pc_bh2": "UniPC",
+    "uni_pc": "UniPC",
+    "lcm": "LCM",
+}
+
+# Base model display name → AIR URN slug
+# Sourced from civitai source: src/shared/constants/basemodel.constants.ts
+BASE_MODEL_AIR_SLUG = {
+    # Stable Diffusion family
+    "SD 1.4": "sd1",
+    "SD 1.5": "sd1",
+    "SD 1.5 LCM": "sd1",
+    "SD 1.5 Hyper": "sd1",
+    "SD 2.0": "sd2",
+    "SD 2.0 768": "sd2",
+    "SD 2.1": "sd2",
+    "SD 2.1 768": "sd2",
+    "SD 2.1 Unclip": "sd2",
+    "SD 3.0": "sd3",
+    "SD 3.5": "sd35",
+    "SD 3.5 Large": "sd35",
+    "SD 3.5 Large Turbo": "sd35",
+    "SD 3.5 Medium": "sd35",
+    "SDXL 0.9": "sdxl",
+    "SDXL 1.0": "sdxl",
+    "SDXL 1.0 LCM": "sdxl",
+    "SDXL Lightning": "sdxl",
+    "SDXL Hyper": "sdxl",
+    "SDXL Turbo": "sdxl",
+    "SDXL Distilled": "sdxldistilled",
+    "Stable Cascade": "scascade",
+    "Stable Video Diffusion": "svd",
+    "SVD": "svd",
+    "SVD XT": "svdxt",
+
+    # SDXL community fine-tunes
+    "Pony": "pony",
+    "Pony Diffusion": "pony",
+    "Illustrious": "illustrious",
+    "NoobAI": "noobai",
+    "Animagine": "illustrious",
+
+    # Flux family
+    "Flux.1": "flux1",
+    "Flux.1 D": "flux1",
+    "Flux.1 S": "flux1",
+    "Flux.1 Krea": "fluxkrea",
+    "Flux.1 Kontext": "flux1kontext",
+    "Flux.2": "flux2",
+    "Flux.2 D": "flux2",
+    "Flux.2 Klein 9B": "flux2klein_9b",
+    "Flux.2 Klein 9B Base": "flux2klein_9b_base",
+    "Flux.2 Klein 4B": "flux2klein_4b",
+    "Flux.2 Klein 4B Base": "flux2klein_4b_base",
+
+    # Other image models (sorted alphabetically)
+    "AuraFlow": "auraflow",
+    "Chroma": "chroma",
+    "HiDream": "hidream",
+    "HiDream-O1": "hidream-o1",
+    "Hunyuan DiT": "hydit1",
+    "Hunyuan Video": "hyv1",
+    "Kolors": "kolors",
+    "Lumina": "lumina",
+    "Mochi": "mochi",
+    "ODOR": "odor",
+    "PixArt Alpha": "pixarta",
+    "PixArt Sigma": "pixarte",
+    "Playground v2": "playgroundv2",
+    "Playground v2.5": "playgroundv2",
+    "Pony Diffusion V7": "ponyv7",
+
+    # Video models
+    "CogVideoX": "cogvideox",
+    "LTX Video": "ltxv",
+    "LTX Video 2": "ltxv2",
+    "LTX Video 2.3": "ltxv23",
+    "Wan Video": "wanvideo",
+    "Wan Video 1.3B T2V": "wanvideo_13b_t2v",
+    "Wan Video 14B T2V": "wanvideo_14b_t2v",
+    "Wan Video 14B I2V 480p": "wanvideo_14b_i2v_480p",
+    "Wan Video 14B I2V 720p": "wanvideo_14b_i2v_720p",
+
+    # Third-party / proprietary image models
+    "Boogu": "boogu",
+    "Ernie": "ernie",
+    "Grok": "grok",
+    "HappyHorse": "happyhorse",
+    "Ideogram": "ideogram",
+    "Ideogram 4.0": "ideogram",
+    "Imagen": "imagen4",
+    "Imagen 4": "imagen4",
+    "Krea": "krea2",
+    "Krea 2": "krea2",
+    "Lens": "lens",
+    "MAI": "mai",
+    "Nano Banana": "nanobanana",
+    "OpenAI": "openai",
+    "Reve": "reve",
+    "Reve 2": "reve",
+    "Reve 2.1": "reve",
+    "Seedream": "seedream",
+    "Sora": "sora2",
+    "Sora 2": "sora2",
+    "Veo": "veo3",
+    "Veo 2": "veo3",
+    "Veo 3": "veo3",
+    "ZImageTurbo": "zimageturbo",
+    "ZImageBase": "zimagebase",
+    "ZImage": "zimagebase",
+
+    # Third-party video models
+    "Hailuo by MiniMax": "minimax",
+    "Haiper": "haiper",
+    "Kling": "kling",
+    "Lightricks": "lightricks",
+    "Seedance": "seedance",
+    "Vidu": "vidu",
+
+    # Qwen family
+    "Qwen": "qwen",
+    "Qwen 2": "qwen2",
+
+    # Anima
+    "Anima": "anima",
+
+    # Special
+    "Upscaler": "upscaler",
+    "Other": "other",
+}
+
 logger = logging.getLogger(__name__)
 
 
@@ -70,11 +220,29 @@ class SaveImageLM:
                         "tooltip": "Compression quality for JPEG and lossy WebP formats (1-100). Higher values mean better quality but larger files.",
                     },
                 ),
+                "webp_method": (
+                    "INT",
+                    {
+                        "default": 6,
+                        "min": 0,
+                        "max": 6,
+                        "tooltip": "WebP compression method (0-6). 0=fastest/largest, 6=slowest/smallest. Only applies when file_format is 'webp'.",
+                    },
+                ),
+                "jpeg_subsampling": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 2,
+                        "tooltip": "JPEG chroma subsampling level. 0=4:4:4 (best quality), 1=4:2:2, 2=4:2:0 (smallest files). Only applies when file_format is 'jpeg'.",
+                    },
+                ),
                 "embed_workflow": (
                     "BOOLEAN",
                     {
                         "default": False,
-                        "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats.",
+                        "tooltip": "When enabled, saved images store the complete workflow. Drag the image back into ComfyUI to restore the original node graph. PNG and WebP only.",
                     },
                 ),
                 "save_with_metadata": (
@@ -142,148 +310,184 @@ class SaveImageLM:
 
         return None
 
-    def format_metadata(self, metadata_dict):
-        """Format metadata in the requested format similar to userComment example"""
-        if not metadata_dict:
-            return ""
+    def _resolve_model_cache_entry(self, scanner_type: str, name: str):
+        """Resolve model hash, civitai metadata, and base_model from scanner cache.
+        Returns (hash_str, civitai_dict, base_model_str). All values are empty defaults when not found."""
+        scanner = ServiceRegistry.get_service_sync(scanner_type)
+        if scanner is None or not name:
+            return "", {}, ""
 
-        # Helper function to only add parameter if value is not None
-        def add_param_if_not_none(param_list, label, value):
-            if value is not None:
-                param_list.append(f"{label}: {value}")
+        entry = self._get_cached_model_by_name(scanner, name)
+        if entry is None:
+            basename = os.path.splitext(os.path.basename(name))[0]
+            hash_val = scanner.get_hash_by_filename(basename)
+            return (hash_val or "").lower(), {}, ""
+
+        hash_val = (entry.get("sha256") or "").lower()
+        civitai = entry.get("civitai") or {}
+        base_model = entry.get("base_model") or ""
+        return hash_val, civitai, base_model
+
+    @staticmethod
+    def _get_civitai_sampler_name(sampler_name: str, scheduler: str) -> str:
+        if sampler_name in CIVITAI_SAMPLER_MAP:
+            civitai_name = CIVITAI_SAMPLER_MAP[sampler_name]
+            if scheduler == "karras":
+                civitai_name += " Karras"
+            elif scheduler == "exponential":
+                civitai_name += " Exponential"
+            return civitai_name
+        else:
+            if scheduler and scheduler != "normal":
+                return f"{sampler_name}_{scheduler}"
+            return sampler_name
+
+    @staticmethod
+    def _build_air_string(base_model: str, model_type: str, model_id: int, version_id: int) -> str:
+        slug = BASE_MODEL_AIR_SLUG.get(base_model, "other")
+        type_lower = model_type.lower() if model_type else "other"
+        return f"urn:air:{slug}:{type_lower}:civitai:{model_id}@{version_id}"
+
+    def format_metadata(self, metadata_dict: dict) -> str:
+        """Format metadata as A1111-compatible parameters string with Hashes JSON and Civitai resources."""
+        if not metadata_dict: return ""
 
-        # Extract the prompt and negative prompt
         prompt = metadata_dict.get("prompt", "")
         negative_prompt = metadata_dict.get("negative_prompt", "")
-
-        # Extract loras from the prompt if present
+        steps = metadata_dict.get("steps")
+        cfg = metadata_dict.get("guidance")
+        if cfg is None:
+            cfg = metadata_dict.get("cfg_scale")
+        if cfg is None:
+            cfg = metadata_dict.get("cfg")
+        seed = metadata_dict.get("seed")
+        size = metadata_dict.get("size")
+        sampler = metadata_dict.get("sampler") or ""
+        scheduler = metadata_dict.get("scheduler") or "normal"
+        checkpoint = metadata_dict.get("checkpoint") or ""
         loras_text = metadata_dict.get("loras", "")
-        lora_hashes = {}
+        clip_skip = metadata_dict.get("clip_skip")
 
-        # If loras are found, add them on a new line after the prompt
+        # Parse LoRA entries from <lora:name:strength> format
+        lora_entries: list[tuple[str, float]] = []
         if loras_text:
-            prompt_with_loras = f"{prompt}\n{loras_text}"
+            for match in re.findall(r"<lora:([^:]+):([^>]+)>", loras_text):
+                lora_name, strength_str = match
+                try:
+                    strength = float(strength_str)
+                except (ValueError, TypeError):
+                    strength = 1.0
+                lora_entries.append((lora_name, strength))
 
-            # Extract lora names from the format <lora:name:strength>
-            lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", loras_text)
+        # Resolve checkpoint hash and Civitai data from local cache
+        ckpt_hash, ckpt_civitai, ckpt_base_model = "", {}, ""
+        ckpt_display_name = ""
+        if checkpoint:
+            ckpt_hash, ckpt_civitai, ckpt_base_model = self._resolve_model_cache_entry(
+                "checkpoint_scanner", checkpoint
+            )
+            ckpt_display_name = os.path.splitext(os.path.basename(checkpoint))[0]
 
-            # Get hash for each lora
-            for lora_name, strength in lora_matches:
-                hash_value = self.get_lora_hash(lora_name)
-                if hash_value:
-                    lora_hashes[lora_name] = hash_value
-        else:
-            prompt_with_loras = prompt
+        # Resolve LoRA hash and Civitai data from local cache
+        loras_data: list[dict] = []
+        for lora_name, strength in lora_entries:
+            lora_hash, lora_civitai, lora_base_model = self._resolve_model_cache_entry(
+                "lora_scanner", lora_name
+            )
+            loras_data.append({
+                "name": lora_name,
+                "strength": strength,
+                "hash": lora_hash,
+                "civitai": lora_civitai,
+                "base_model": lora_base_model,
+            })
 
-        # Format the first part (prompt and loras)
-        metadata_parts = [prompt_with_loras]
+        # Build Hashes JSON (A1111 / Civitai standard format)
+        hashes: dict[str, str] = {}
+        if ckpt_hash:
+            hashes["model"] = ckpt_hash[:10].upper()
+        for lora in loras_data:
+            if lora["hash"]:
+                hashes[f"LORA:{lora['name']}"] = lora["hash"][:10].upper()
 
-        # Add negative prompt
+        # Build Civitai resources JSON array
+        civitai_resources: list[dict] = []
+        if ckpt_civitai.get("id", 0) > 0:
+            ckpt_resource: dict = {}
+            ckpt_type = (ckpt_civitai.get("model") or {}).get("type", "Checkpoint")
+            model_id = ckpt_civitai.get("modelId", 0)
+            version_id = ckpt_civitai.get("id", 0)
+            if model_id and version_id:
+                ckpt_resource["air"] = self._build_air_string(
+                    ckpt_base_model, ckpt_type, int(model_id), int(version_id)
+                )
+            elif version_id:
+                ckpt_resource["modelVersionId"] = int(version_id)
+            if ckpt_civitai.get("name"):
+                ckpt_resource["versionName"] = ckpt_civitai["name"]
+            if ckpt_resource:
+                civitai_resources.append(ckpt_resource)
+
+        for lora in loras_data:
+            lora_civitai = lora["civitai"]
+            if not lora_civitai or lora_civitai.get("id", 0) <= 0:
+                continue
+            lora_resource: dict = {"weight": lora["strength"]}
+            lora_type = (lora_civitai.get("model") or {}).get("type", "LORA")
+            model_id = lora_civitai.get("modelId", 0)
+            version_id = lora_civitai.get("id", 0)
+            if model_id and version_id:
+                lora_resource["air"] = self._build_air_string(
+                    lora["base_model"], lora_type, int(model_id), int(version_id)
+                )
+            elif version_id:
+                lora_resource["modelVersionId"] = int(version_id)
+            if lora_civitai.get("name"):
+                lora_resource["versionName"] = lora_civitai["name"]
+            civitai_resources.append(lora_resource)
+
+        sampler_display = self._get_civitai_sampler_name(sampler, scheduler)
+
+        # Build output lines
+        lines = [prompt] if prompt else [""]
         if negative_prompt:
-            metadata_parts.append(f"Negative prompt: {negative_prompt}")
+            lines.append(f"Negative prompt: {negative_prompt}")
 
-        # Format the second part (generation parameters)
-        params = []
+        params: list[str] = []
+        if steps is not None:
+            params.append(f"Steps: {steps}")
+        if sampler_display:
+            params.append(f"Sampler: {sampler_display}")
+        if cfg is not None:
+            params.append(f"CFG scale: {cfg}")
+        if seed is not None:
+            params.append(f"Seed: {seed}")
+        if size:
+            params.append(f"Size: {size}")
+        if clip_skip:
+            try:
+                cs = int(clip_skip)
+                if cs != 0:
+                    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:
+            params.append(f"Model: {ckpt_display_name}")
+        if hashes:
+            params.append(f"Hashes: {json.dumps(hashes, separators=(',', ':'))}")
+        params.append("Version: ComfyUI")
+        if civitai_resources:
+            params.append(
+                f"Civitai resources: {json.dumps(civitai_resources, separators=(',', ':'))}"
+            )
 
-        # Add standard parameters in the correct order
-        if "steps" in metadata_dict:
-            add_param_if_not_none(params, "Steps", metadata_dict.get("steps"))
-
-        # Combine sampler and scheduler information
-        sampler_name = None
-        scheduler_name = None
-
-        if "sampler" in metadata_dict:
-            sampler = metadata_dict.get("sampler")
-            # Convert ComfyUI sampler names to user-friendly names
-            sampler_mapping = {
-                "euler": "Euler",
-                "euler_ancestral": "Euler a",
-                "dpm_2": "DPM2",
-                "dpm_2_ancestral": "DPM2 a",
-                "heun": "Heun",
-                "dpm_fast": "DPM fast",
-                "dpm_adaptive": "DPM adaptive",
-                "lms": "LMS",
-                "dpmpp_2s_ancestral": "DPM++ 2S a",
-                "dpmpp_sde": "DPM++ SDE",
-                "dpmpp_sde_gpu": "DPM++ SDE",
-                "dpmpp_2m": "DPM++ 2M",
-                "dpmpp_2m_sde": "DPM++ 2M SDE",
-                "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
-                "ddim": "DDIM",
-            }
-            sampler_name = sampler_mapping.get(sampler, sampler)
-
-        if "scheduler" in metadata_dict:
-            scheduler = metadata_dict.get("scheduler")
-            scheduler_mapping = {
-                "normal": "Simple",
-                "karras": "Karras",
-                "exponential": "Exponential",
-                "sgm_uniform": "SGM Uniform",
-                "sgm_quadratic": "SGM Quadratic",
-            }
-            scheduler_name = scheduler_mapping.get(scheduler, scheduler)
-
-        # Add combined sampler and scheduler information
-        if sampler_name:
-            if scheduler_name:
-                params.append(f"Sampler: {sampler_name} {scheduler_name}")
-            else:
-                params.append(f"Sampler: {sampler_name}")
-
-        # CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
-        if "guidance" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("guidance"))
-        elif "cfg_scale" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg_scale"))
-        elif "cfg" in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg"))
-
-        # Seed
-        if "seed" in metadata_dict:
-            add_param_if_not_none(params, "Seed", metadata_dict.get("seed"))
-
-        # Size
-        if "size" in metadata_dict:
-            add_param_if_not_none(params, "Size", metadata_dict.get("size"))
-
-        # Model info
-        if "checkpoint" in metadata_dict:
-            # Ensure checkpoint is a string before processing
-            checkpoint = metadata_dict.get("checkpoint")
-            if checkpoint is not None:
-                # Get model hash
-                model_hash = self.get_checkpoint_hash(checkpoint)
-
-                # Extract basename without path
-                checkpoint_name = os.path.basename(checkpoint)
-                # Remove extension if present
-                checkpoint_name = os.path.splitext(checkpoint_name)[0]
-
-                # Add model hash if available
-                if model_hash:
-                    params.append(
-                        f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}"
-                    )
-                else:
-                    params.append(f"Model: {checkpoint_name}")
-
-        # Add LoRA hashes if available
-        if lora_hashes:
-            lora_hash_parts = []
-            for lora_name, hash_value in lora_hashes.items():
-                lora_hash_parts.append(f"{lora_name}: {hash_value[:10]}")
-
-            if lora_hash_parts:
-                params.append(f'Lora hashes: "{", ".join(lora_hash_parts)}"')
-
-        # Combine all parameters with commas
-        metadata_parts.append(", ".join(params))
-
-        # Join all parts with a new line
-        return "\n".join(metadata_parts)
+        lines.append(", ".join(params))
+        return "\n".join(lines)
 
     # credit to nkchocoai
     # Add format_filename method to handle pattern substitution
@@ -573,6 +777,8 @@ class SaveImageLM:
         extra_pnginfo=None,
         lossless_webp=True,
         quality=100,
+        webp_method=6,
+        jpeg_subsampling=0,
         embed_workflow=False,
         save_with_metadata=True,
         add_counter_to_filename=True,
@@ -627,15 +833,14 @@ class SaveImageLM:
             elif file_format == "jpeg":
                 file = base_filename + ".jpg"
                 file_extension = ".jpg"
-                save_kwargs = {"quality": quality, "optimize": True}
+                save_kwargs = {"quality": quality, "optimize": True, "subsampling": jpeg_subsampling}
             elif file_format == "webp":
                 file = base_filename + ".webp"
                 file_extension = ".webp"
-                # Add optimization param to control performance
                 save_kwargs = {
                     "quality": quality,
                     "lossless": lossless_webp,
-                    "method": 0,
+                    "method": webp_method,
                 }
             else:
                 raise ValueError(f"Unsupported file format: {file_format}")
@@ -722,6 +927,8 @@ class SaveImageLM:
         extra_pnginfo=None,
         lossless_webp=True,
         quality=100,
+        webp_method=6,
+        jpeg_subsampling=0,
         embed_workflow=False,
         save_with_metadata=True,
         add_counter_to_filename=True,
@@ -751,6 +958,8 @@ class SaveImageLM:
             extra_pnginfo,
             lossless_webp,
             quality,
+            webp_method,
+            jpeg_subsampling,
             embed_workflow,
             save_with_metadata,
             add_counter_to_filename,

py/routes/handlers/model_handlers.py

@@ -537,6 +537,7 @@ class ModelManagementHandler:
                     # Update model_data with new hash
                     model_data["sha256"] = sha256
                     model_data["hash_status"] = "completed"
+                    hash_status = "completed"
                 else:
                     return web.json_response(
                         {"success": False, "error": "No SHA256 hash found"}, status=400
@@ -544,6 +545,32 @@ class ModelManagementHandler:
 
             await MetadataManager.hydrate_model_data(model_data)
 
+            # hydrate_model_data replaces model_data with .metadata.json content,
+            # which may lack sha256. Restore from cache and persist the fix.
+            if not model_data.get("sha256"):
+                if sha256:
+                    model_data["sha256"] = sha256
+                    model_data["hash_status"] = model_data.get("hash_status", hash_status)
+                    data_to_save = model_data.copy()
+                    data_to_save.pop("folder", None)
+                    await MetadataManager.save_metadata(file_path, data_to_save)
+                else:
+                    sha256 = await calculate_sha256(file_path)
+                    if sha256:
+                        model_data["sha256"] = sha256.lower()
+                        model_data["hash_status"] = "completed"
+                        data_to_save = model_data.copy()
+                        data_to_save.pop("folder", None)
+                        await MetadataManager.save_metadata(file_path, data_to_save)
+                    else:
+                        return web.json_response(
+                            {
+                                "success": False,
+                                "error": "Failed to compute SHA256 hash for model",
+                            },
+                            status=500,
+                        )
+
             success, error = await self._metadata_sync.fetch_and_update_model(
                 sha256=model_data["sha256"],
                 file_path=file_path,
@@ -566,7 +593,12 @@ class ModelManagementHandler:
                     {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
                     status=503,
                 )
-            self._logger.error("Error fetching from CivitAI: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error fetching from CivitAI for %s: %s",
+                locals().get("file_path", "unknown"),
+                exc,
+                exc_info=True,
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def relink_civitai(self, request: web.Request) -> web.Response:

py/services/download_manager.py

@@ -1392,8 +1392,8 @@ class DownloadManager:
                 base_save_dir = save_dir
                 save_dir = os.path.join(save_dir, relative_path)
                 # Security: validate path containment after joining
-                resolved_dir = os.path.realpath(os.path.normpath(save_dir))
-                base_dir = os.path.realpath(os.path.normpath(base_save_dir))
+                resolved_dir = os.path.abspath(os.path.normpath(save_dir))
+                base_dir = os.path.abspath(os.path.normpath(base_save_dir))
                 if not resolved_dir.startswith(base_dir + os.sep) and resolved_dir != base_dir:
                     logger.warning(
                         "Path traversal detected: %s escapes %s",

py/services/llm_service.py

@@ -566,18 +566,52 @@ class LLMService:
         if effective_max is None:
             effective_max = 4096
 
-        result = await self.chat_completion(
-            messages=messages,
-            model=model,
-            temperature=temperature,
-            response_format={"type": "json_object"},
-            max_tokens=effective_max,
-        )
+        # Use json_schema (not json_object) for broader provider compatibility:
+        # LM Studio and some other OpenAI-compatible servers reject
+        # json_object but accept json_schema.  {"type": "object"} is
+        # functionally equivalent — it accepts any JSON object without
+        # constraining specific fields.
+        response_format = {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "metadata",
+                "schema": {"type": "object"},
+            },
+        }
+
+        try:
+            result = await self.chat_completion(
+                messages=messages,
+                model=model,
+                temperature=temperature,
+                response_format=response_format,
+                max_tokens=effective_max,
+            )
+        except LLMResponseError as e:
+            # Only fall back when the provider rejects the response_format
+            # type value (e.g. "'response_format.type' must be...").  Avoid
+            # catching unrelated 400 errors whose body happens to mention
+            # "response_format" (e.g. "model does not support
+            # response_format restrictions on this endpoint").
+            if "'response_format.type'" not in str(e).lower():
+                raise
+            logger.info(
+                "Provider rejected response_format, retrying without it. "
+                "Falling back to prompt-only JSON mode. Error: %s",
+                e,
+            )
+            result = await self.chat_completion(
+                messages=messages,
+                model=model,
+                temperature=temperature,
+                response_format=None,
+                max_tokens=effective_max,
+            )
 
         content = result.get("content", "") or ""
         if not content:
             raise LLMResponseError(
-                "LLM returned empty content in json_object mode. "
+                "LLM returned empty content. "
                 f"Raw response: {json.dumps(result)[:500]}"
             )
 

py/services/model_lifecycle_service.py

@@ -51,9 +51,10 @@ async def delete_model_artifacts(
 def _require_path_in_library_roots(file_path: str, scanner, *, label: str = "path") -> None:
     """Raise ``ValueError`` if *file_path* is not inside a configured model root.
 
-    Uses ``os.path.realpath()`` to resolve symlinks before comparing,
-    so symlink-based escapes are also caught.  Skips when the scanner
-    does not expose ``get_model_roots`` or the list is empty.
+    Uses ``os.path.abspath()`` (NOT ``realpath``) to resolve ``..`` and ``.``
+    while preserving symlinks — this keeps the check in business-path space.
+    Skips when the scanner does not expose ``get_model_roots`` or the list
+    is empty.
     """
 
     roots = None
@@ -65,10 +66,10 @@ def _require_path_in_library_roots(file_path: str, scanner, *, label: str = "pat
     if not roots:
         return
 
-    resolved = os.path.realpath(os.path.normpath(file_path))
+    resolved = os.path.abspath(os.path.normpath(file_path))
 
     for root in roots:
-        root_resolved = os.path.realpath(os.path.normpath(root))
+        root_resolved = os.path.abspath(os.path.normpath(root))
         if resolved == root_resolved or resolved.startswith(root_resolved + os.sep):
             return
 

py/services/recipe_scanner.py

@@ -21,7 +21,7 @@ from .checkpoint_scanner import CheckpointScanner
 from .settings_manager import get_settings_manager
 from .recipes.errors import RecipeNotFoundError
 from ..utils.civitai_utils import extract_civitai_image_id
-from ..utils.utils import calculate_recipe_fingerprint, fuzzy_match
+from ..utils.utils import calculate_recipe_fingerprint
 from natsort import natsorted
 import sys
 import re
@@ -1020,13 +1020,16 @@ class RecipeScanner:
 
         try:
             result = self._fts_index.search(search, fields)
-            # Return None if empty to trigger fuzzy fallback
-            # Empty FTS results may indicate query syntax issues or need for fuzzy matching
+            # Return empty set for empty FTS results — do NOT fall back to
+            # Python fuzzy matching, which freezes the server with 10k+ recipes.
+            # FTS5 prefix matching with unicode61 tokenizer correctly handles
+            # compound tokens (e.g. "illustrious" matches "path/illustrious/model").
+            # If FTS returns nothing, there are genuinely no matching recipes.
             if not result:
-                return None
+                return set()
             return result
         except Exception as exc:
-            logger.debug("FTS search failed, falling back to fuzzy search: %s", exc)
+            logger.debug("FTS search failed, falling back to title-only search: %s", exc)
             return None
 
     def _update_fts_index_for_recipe(
@@ -2079,49 +2082,14 @@ class RecipeScanner:
                         if str(item.get("id", "")) in fts_matching_ids
                     ]
                 else:
-                    # Fallback to fuzzy_match (slower but always available)
-                    # Build the search predicate based on search options
-                    def matches_search(item):
-                        # Search in title if enabled
-                        if search_options.get("title", True):
-                            if fuzzy_match(str(item.get("title", "")), search):
-                                return True
-
-                        # Search in tags if enabled
-                        if search_options.get("tags", True) and "tags" in item:
-                            for tag in item["tags"]:
-                                if fuzzy_match(tag, search):
-                                    return True
-
-                        # Search in lora file names if enabled
-                        if search_options.get("lora_name", True) and "loras" in item:
-                            for lora in item["loras"]:
-                                if fuzzy_match(str(lora.get("file_name", "")), search):
-                                    return True
-
-                        # Search in lora model names if enabled
-                        if search_options.get("lora_model", True) and "loras" in item:
-                            for lora in item["loras"]:
-                                if fuzzy_match(str(lora.get("modelName", "")), search):
-                                    return True
-
-                        # Search in prompt and negative_prompt if enabled
-                        if search_options.get("prompt", True) and "gen_params" in item:
-                            gen_params = item["gen_params"]
-                            if fuzzy_match(str(gen_params.get("prompt", "")), search):
-                                return True
-                            if fuzzy_match(
-                                str(gen_params.get("negative_prompt", "")), search
-                            ):
-                                return True
-
-                        # No match found
-                        return False
-
-                    # Filter the data using the search predicate
-                    filtered_data = [
-                        item for item in filtered_data if matches_search(item)
-                    ]
+                    # FTS index not yet built — return empty rather than
+                    # scanning 42k+ items in Python. The FTS background build
+                    # finishes in seconds; by the time a user navigates here
+                    # and types a search, it is already available.
+                    logger.debug(
+                        "FTS index not ready — search '%s' returning empty", search
+                    )
+                    filtered_data = []
 
             # Apply additional filters
             if filters:

py/services/use_cases/bulk_metadata_refresh_use_case.py

@@ -126,6 +126,7 @@ class BulkMetadataRefreshUseCase:
                         if sha256:
                             model["sha256"] = sha256
                             model["hash_status"] = "completed"
+                            hash_status = "completed"
                         else:
                             self._logger.error(f"Failed to calculate hash for {file_path}")
                             failures.append({"name": model.get("model_name", file_path or "Unknown"), "error": "Failed to calculate hash"})
@@ -148,6 +149,16 @@ class BulkMetadataRefreshUseCase:
                     continue
 
                 await MetadataManager.hydrate_model_data(model)
+
+                # hydrate_model_data replaces model with .metadata.json content,
+                # which may lack sha256. Restore from cache and persist the fix.
+                if not model.get("sha256"):
+                    model["sha256"] = sha256
+                    model["hash_status"] = model.get("hash_status", hash_status)
+                    data_to_save = model.copy()
+                    data_to_save.pop("folder", None)
+                    await MetadataManager.save_metadata(file_path, data_to_save)
+
                 result, error_msg = await self._metadata_sync.fetch_and_update_model(
                     sha256=model["sha256"],
                     file_path=model["file_path"],

static/js/components/shared/ModelModal.js

@@ -885,7 +885,8 @@ function setupEventHandlers(filePath, modelType) {
             case 'view-creator':
                 const username = target.dataset.username;
                 if (username) {
-                    window.open(`https://civitai.com/user/${username}`, '_blank');
+                    const host = state.global.settings.civitai_host || 'civitai.com';
+                    window.open(`https://${host}/user/${username}`, '_blank');
                 }
                 break;
             case 'open-file-location':

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()

tests/nodes/test_save_image.py

@@ -59,7 +59,7 @@ def test_save_image_defaults_to_writing_png_metadata(monkeypatch, tmp_path):
 
     image_path = tmp_path / "sample_00001_.png"
     with Image.open(image_path) as img:
-        assert img.info["parameters"] == "prompt text\nSeed: 123"
+        assert img.info["parameters"] == "prompt text\nSeed: 123, Version: ComfyUI"
 
 
 def test_save_image_skips_png_parameters_when_metadata_disabled_and_keeps_workflow(
@@ -363,3 +363,102 @@ def test_save_image_as_recipe_writes_recipe_without_async_scanner_calls(
     assert recipe["gen_params"] == {"prompt": "prompt text", "seed": 123}
     assert scanner._json_path_map[recipe["id"]] == os.path.normpath(str(recipe_files[0]))
     assert scanner.fts_updates == [(recipe["id"], "add")]
+
+
+# ---------------------------------------------------------------------------
+# Tests for webp_method and jpeg_subsampling parameters
+# ---------------------------------------------------------------------------
+
+def _capture_save_kwargs(monkeypatch):
+    """Monkeypatch Image.Image.save to capture kwargs while still saving to disk."""
+    real_save = Image.Image.save
+    captured_kwargs = {}
+
+    def _fake_save(self, fp, *args, **kwargs):
+        captured_kwargs.update(kwargs)
+        return real_save(self, fp, *args, **kwargs)
+
+    monkeypatch.setattr(Image.Image, "save", _fake_save)
+    return captured_kwargs
+
+
+def test_webp_method_default_passed_to_pillow_save(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "test", "seed": 1})
+    captured = _capture_save_kwargs(monkeypatch)
+
+    node = SaveImageLM()
+    node.save_images([_make_image()], "ComfyUI", "webp", id="node-1")
+
+    assert "method" in captured
+    assert captured["method"] == 6
+
+
+def test_webp_method_custom_value_passed_to_pillow_save(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "test", "seed": 1})
+    captured = _capture_save_kwargs(monkeypatch)
+
+    node = SaveImageLM()
+    node.save_images(
+        [_make_image()], "ComfyUI", "webp", id="node-1", webp_method=3
+    )
+
+    assert captured["method"] == 3
+
+
+def test_jpeg_subsampling_default_passed_to_pillow_save(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "test", "seed": 1})
+    captured = _capture_save_kwargs(monkeypatch)
+
+    node = SaveImageLM()
+    node.save_images([_make_image()], "ComfyUI", "jpeg", id="node-1")
+
+    assert "subsampling" in captured
+    assert captured["subsampling"] == 0
+
+
+def test_jpeg_subsampling_custom_value_passed_to_pillow_save(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "test", "seed": 1})
+    captured = _capture_save_kwargs(monkeypatch)
+
+    node = SaveImageLM()
+    node.save_images(
+        [_make_image()], "ComfyUI", "jpeg", id="node-1", jpeg_subsampling=1
+    )
+
+    assert captured["subsampling"] == 1
+
+
+class TestParameterDefaultConsistency:
+    """Verify defaults match across INPUT_TYPES, save_images(), and process_image()."""
+
+    def test_webp_method_defaults_are_consistent(self):
+        input_types = SaveImageLM.INPUT_TYPES()
+        optional = input_types["optional"]
+
+        assert optional["webp_method"][1]["default"] == 6
+        assert SaveImageLM.save_images.__defaults__[4] == 6  # positional: webp_method=6 is at index 4
+        assert SaveImageLM.process_image.__defaults__[6] == 6
+
+    def test_jpeg_subsampling_defaults_are_consistent(self):
+        input_types = SaveImageLM.INPUT_TYPES()
+        optional = input_types["optional"]
+
+        assert optional["jpeg_subsampling"][1]["default"] == 0
+        assert SaveImageLM.save_images.__defaults__[5] == 0
+        assert SaveImageLM.process_image.__defaults__[7] == 0
+
+
+def test_png_does_not_pass_webp_method_or_jpeg_subsampling(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "test", "seed": 1})
+    captured = _capture_save_kwargs(monkeypatch)
+
+    node = SaveImageLM()
+    node.save_images([_make_image()], "ComfyUI", "png", id="node-1")
+
+    assert "method" not in captured
+    assert "subsampling" not in captured

tests/services/test_download_manager_basic.py

@@ -1248,6 +1248,50 @@ def test_relative_path_sanitizes_double_slashes():
     assert relative_path == "SDXL/no tags/Author"
 
 
+def test_download_containment_accepts_symlink_save_dir(tmp_path):
+    """Verify the download path containment check (download_manager.py:1395-1397)
+    accepts save directories reached through user-created symlinks inside the
+    library root — reproducing the symlink scenario from issue #1028."""
+    # Library root with a symlink subdirectory pointing to an external drive
+    lora_root = tmp_path / "loras"
+    lora_root.mkdir()
+
+    external_drive = tmp_path / "external" / "models"
+    external_drive.mkdir(parents=True)
+
+    symlink = lora_root / "Krea 2"
+    symlink.symlink_to(str(external_drive))
+
+    # Simulate a download: base_save_dir = library root,
+    # relative_path = "Krea 2/concept/NewModel"
+    base_save_dir = str(lora_root)
+    save_dir = os.path.join(base_save_dir, "Krea 2", "concept", "NewModel")
+
+    # Replicate the exact containment check from download_manager.py
+    resolved_dir = os.path.abspath(os.path.normpath(save_dir))
+    base_dir = os.path.abspath(os.path.normpath(base_save_dir))
+
+    # Must NOT be rejected — symlinks are legitimate business paths
+    assert resolved_dir.startswith(base_dir + os.sep)
+
+
+def test_download_containment_rejects_dot_dot_traversal(tmp_path):
+    """Verify the download path containment check still blocks ``..`` traversal
+    after the realpath → abspath change."""
+    lora_root = tmp_path / "loras"
+    lora_root.mkdir()
+
+    base_save_dir = str(lora_root)
+    save_dir = os.path.join(base_save_dir, "..", "..", "etc", "passwd")
+
+    resolved_dir = os.path.abspath(os.path.normpath(save_dir))
+    base_dir = os.path.abspath(os.path.normpath(base_save_dir))
+
+    # Must be rejected — dot-dot escapes the library root
+    assert not resolved_dir.startswith(base_dir + os.sep)
+    assert resolved_dir != base_dir
+
+
 def test_distribute_preview_to_entries_moves_and_copies(tmp_path):
     """Test that preview distribution moves file to first entry and copies to others."""
     manager = DownloadManager()

tests/services/test_llm_service.py

@@ -243,6 +243,56 @@ class TestLLMServiceChatCompletionJson:
 
         assert result == {"key": "value"}
 
+    @pytest.mark.asyncio
+    async def test_chat_completion_json_falls_back_on_response_format_rejection(
+        self, llm_service,
+    ):
+        """Retry without response_format when provider rejects it (HTTP 400)."""
+        error_response = MockResponse(
+            400,
+            text_data=(
+                '{"error":"\'response_format.type\' must be '
+                '\'json_schema\' or \'text\'"}'
+            ),
+        )
+        success_response = MockResponse(
+            200,
+            json_data={
+                "choices": [{"message": {"content": '{"key": "value"}'}}],
+                "usage": {},
+                "model": "local-model",
+            },
+        )
+
+        call_index = 0
+
+        class FallbackMockSession:
+            def __init__(self):
+                self.last_url = None
+                self.last_json = None
+
+            def post(self, url, json=None, headers=None):
+                nonlocal call_index
+                self.last_url = url
+                self.last_json = json
+                call_index += 1
+                return error_response if call_index == 1 else success_response
+
+            async def __aenter__(self):
+                return self
+
+            async def __aexit__(self, *args):
+                pass
+
+        with mock.patch("aiohttp.ClientSession", return_value=FallbackMockSession()):
+            result = await llm_service.chat_completion_json(
+                system_prompt="You are helpful.",
+                user_prompt="Return JSON.",
+            )
+
+        assert result == {"key": "value"}
+        assert call_index == 2
+
     @pytest.mark.asyncio
     async def test_chat_completion_json_raises_on_non_json(self, llm_service):
         # Non-JSON content raises LLMResponseError (salvage also fails)

tests/services/test_model_lifecycle_service.py

@@ -1,4 +1,5 @@
 import json
+import os
 from pathlib import Path
 
 import pytest
@@ -51,11 +52,12 @@ class TestRequirePathInLibraryRoots:
         scanner = ScannerWithRoots([str(root)])
         _require_path_in_library_roots(str(root), scanner)
 
-    def test_rejects_symlink_escape(self, tmp_path):
+    def test_accepts_symlink_within_root(self, tmp_path):
+        """Symlinks under a configured root are legitimate business paths
+        and should be accepted — containment works on business-path space,
+        not resolved physical paths."""
         root = tmp_path / "loras"
         root.mkdir()
-        model = root / "model.safetensors"
-        model.write_text("")
 
         outside_dir = tmp_path / "outside"
         outside_dir.mkdir()
@@ -65,9 +67,22 @@ class TestRequirePathInLibraryRoots:
         symlink = root / "link.safetensors"
         symlink.symlink_to(outside_file)
 
+        scanner = ScannerWithRoots([str(root)])
+        # Symlink path is under root in business-path space → accepted
+        _require_path_in_library_roots(str(symlink), scanner)
+
+    def test_rejects_dot_dot_traversal(self, tmp_path):
+        """Verify that ``..`` components are still resolved and blocked —
+        ``abspath`` normalises dot-dot but does not resolve symlinks."""
+        root = tmp_path / "loras"
+        root.mkdir()
+
+        # A path that traverses up out of the root via ..
+        escaped = os.path.join(str(root), "..", "..", "etc", "passwd")
+
         scanner = ScannerWithRoots([str(root)])
         with pytest.raises(ValueError, match="outside configured library"):
-            _require_path_in_library_roots(str(symlink), scanner)
+            _require_path_in_library_roots(escaped, scanner)
 
 
 class ScannerForDelete:

tests/services/test_recipe_scanner.py

@@ -77,7 +77,15 @@ def recipe_scanner(tmp_path: Path, monkeypatch):
     monkeypatch.setattr(config, "loras_roots", [str(tmp_path)])
     stub = StubLoraScanner()
     scanner = RecipeScanner(lora_scanner=stub)
-    asyncio.run(scanner.refresh_cache(force=True))
+
+    async def _init():
+        await scanner.refresh_cache(force=True)
+        # Wait for FTS index build to finish — asyncio.run()
+        # cancels background tasks on return, so we must await it here.
+        if scanner._fts_index_task:
+            await scanner._fts_index_task
+
+    asyncio.run(_init())
     yield scanner, stub
     RecipeScanner._instance = None
     settings_manager_module.reset_settings_manager()

web/comfyui/lora_loader.js

@@ -37,22 +37,28 @@ app.registerExtension({
 
     // Handle broadcast mode (for Desktop/non-browser support)
     if (numericNodeId === -1) {
-      // Find all Lora Loader nodes in the current graph
-      const loraLoaderNodes = getAllGraphNodes(app.graph)
+      // Find all compatible nodes in the current graph
+      const compatibleClasses = new Set([
+        "Lora Loader (LoraManager)",
+        "Lora Stacker (LoraManager)",
+        "WanVideo Lora Select (LoraManager)",
+        "Create Hook LoRA (LoraManager)",
+      ]);
+      const targetNodes = getAllGraphNodes(app.graph)
         .map(({ node }) => node)
-        .filter((node) => node?.comfyClass === "Lora Loader (LoraManager)");
+        .filter((node) => compatibleClasses.has(node?.comfyClass));
 
-      // Update each Lora Loader node found
-      if (loraLoaderNodes.length > 0) {
-        loraLoaderNodes.forEach((node) => {
+      // Update each node found
+      if (targetNodes.length > 0) {
+        targetNodes.forEach((node) => {
           this.updateNodeLoraCode(node, loraCode, mode);
         });
         console.log(
-          `Updated ${loraLoaderNodes.length} Lora Loader nodes in broadcast mode`
+          `Updated ${targetNodes.length} nodes in broadcast mode`
         );
       } else {
         console.warn(
-          "No Lora Loader nodes found in the workflow for broadcast update"
+          "No compatible LoRA nodes found in the workflow for broadcast update"
         );
       }
 
@@ -65,10 +71,11 @@ app.registerExtension({
       !node ||
       (node.comfyClass !== "Lora Loader (LoraManager)" &&
         node.comfyClass !== "Lora Stacker (LoraManager)" &&
-        node.comfyClass !== "WanVideo Lora Select (LoraManager)")
+        node.comfyClass !== "WanVideo Lora Select (LoraManager)" &&
+        node.comfyClass !== "Create Hook LoRA (LoraManager)")
     ) {
       console.warn(
-        "Node not found or not a LoraLoader:",
+        "Node not found or not a compatible LoRA node:",
         graphId ?? "root",
         nodeId
       );

web/comfyui/loras_widget.js

@@ -751,7 +751,11 @@ export function addLorasWidget(node, name, opts, callback) {
         }
       }
 
-      renderLoras(widgetValue, widget);
+      // Skip DOM re-render during drag to preserve pointer capture and event listeners.
+      // The strength inputs are updated directly via the pointermove handler instead.
+      if (!widget.__dragActive) {
+        renderLoras(widgetValue, widget);
+      }
     },
     hideOnZoom: true,
     selectOn: ['click', 'focus']

web/comfyui/loras_widget_events.js

@@ -37,17 +37,18 @@ export function handleStrengthDrag(name, initialStrength, initialX, event, widge
       syncClipStrengthIfCollapsed(lorasData[loraIndex]);
     }
     
-    // Update the widget value only if updateWidget flag is true
-    // This allows us to update inputs directly during drag without triggering re-render
-    if (updateWidget) {
-      widget.value = formatLoraValue(lorasData);
-    }
+    // Always write back to widget.value to persist the mutation.
+    // During drag (updateWidget=false), setValue skips renderLoras via __dragActive flag,
+    // so the DOM survives and pointer capture is preserved.
+    widget.value = formatLoraValue(lorasData);
     
-    // Force re-render via callback only if updateWidget is true
+    // Only fire callback on the final commit, not during drag
     if (updateWidget && widget.callback) {
       widget.callback(widget.value);
     }
   }
+  
+  return newStrength;
 }
 
 // Function to handle proportional strength adjustment for all LoRAs via header dragging
@@ -90,12 +91,11 @@ export function handleAllStrengthsDrag(initialStrengths, initialX, event, widget
     lorasData[index].clipStrength = Number(newClipStrength);
   });
   
-  // Update widget value only if updateWidget flag is true
-  if (updateWidget) {
-    widget.value = formatLoraValue(lorasData);
-  }
+  // Always write back to widget.value to persist mutations.
+  // During drag (updateWidget=false), setValue skips renderLoras via __dragActive flag.
+  widget.value = formatLoraValue(lorasData);
   
-  // Force re-render via callback only if updateWidget is true
+  // Only fire callback on the final commit, not during drag
   if (updateWidget && widget.callback) {
     widget.callback(widget.value);
   }
@@ -149,6 +149,13 @@ export function initDrag(
     activePointerId = e.pointerId;
     currentDragElement = e.currentTarget;
 
+    // Suppress renderLoras in setValue during drag so the DOM survives.
+    // The getter creates a new array on every read, so mutations to a
+    // parsed copy are lost unless we write back through widget.value.
+    // Writing back would normally trigger a full DOM re-render via setValue,
+    // destroying pointer capture. __dragActive tells setValue to skip the render.
+    widget.__dragActive = true;
+
     // Capture pointer to receive all subsequent events regardless of stopPropagation
     const target = e.currentTarget;
     target.setPointerCapture(e.pointerId);
@@ -181,17 +188,12 @@ export function initDrag(
     }
 
     // Call the strength adjustment function without updating widget.value during drag
-    handleStrengthDrag(name, initialStrength, initialX, e, widget, isClipStrength, false);
+    const newStrength = handleStrengthDrag(name, initialStrength, initialX, e, widget, isClipStrength, false);
     
     // Update strength input directly instead of re-rendering to avoid losing event listeners
     const strengthInput = currentDragElement.querySelector('.lm-lora-strength-input');
-    if (strengthInput) {
-      const lorasData = parseLoraValue(widget.value);
-      const loraData = lorasData.find(l => l.name === name);
-      if (loraData) {
-        const strengthValue = isClipStrength ? loraData.clipStrength : loraData.strength;
-        strengthInput.value = Number(strengthValue).toFixed(2);
-      }
+    if (strengthInput && typeof newStrength === 'number') {
+      strengthInput.value = newStrength.toFixed(2);
     }
     
     // Prevent showing the preview tooltip during drag
@@ -226,23 +228,30 @@ export function initDrag(
     // Remove the class to restore normal cursor behavior
     document.body.classList.remove('lm-lora-strength-dragging');
 
-    // Only call onDragEnd and re-render if we actually dragged
-    if (wasDragging) {
-      if (typeof onDragEnd === 'function') {
-        onDragEnd();
-      }
+    // Only call onDragEnd and re-render if we actually dragged.
+    // try-finally guarantees __dragActive is always cleared, preventing a
+    // permanent UI freeze if onDragEnd or setValue throws during cleanup.
+    try {
+      if (wasDragging) {
+        if (typeof onDragEnd === 'function') {
+          onDragEnd();
+        }
 
-      // Commit final value through options.setValue so external observers are notified.
-      // During drag, handleStrengthDrag mutates widgetValue in-place (updateWidget=false),
-      // bypassing widget.value setter and options.setValue entirely. This assignment
-      // flushes the in-place mutation through the setter so any setValue wrappers fire.
-      widget.value = widget.value;
-      if (typeof widget.callback === 'function') {
-        widget.callback(widget.value);
+        // Re-enable renderLoras in setValue and flush final value through setter.
+        // The last handleStrengthDrag call already wrote the final strength to
+        // widgetValue via setValue (with render suppressed). widget.value = widget.value
+        // triggers setValue again, which now calls renderLoras since __dragActive is false.
+        widget.__dragActive = false;
+        widget.value = widget.value;
+        if (typeof widget.callback === 'function') {
+          widget.callback(widget.value);
+        }
       }
+    } finally {
+      widget.__dragActive = false;
     }
   };
-  
+
   dragEl.addEventListener('pointerup', endDrag);
   dragEl.addEventListener('pointercancel', endDrag);
 }
@@ -285,6 +294,9 @@ export function initHeaderDrag(headerEl, widget, renderFunction) {
     activePointerId = e.pointerId;
     currentHeaderElement = e.currentTarget;
 
+    // Suppress renderLoras in setValue during drag (see initDrag for rationale)
+    widget.__dragActive = true;
+
     // Capture pointer to receive all subsequent events regardless of stopPropagation
     const target = e.currentTarget;
     target.setPointerCapture(e.pointerId);
@@ -352,13 +364,20 @@ export function initHeaderDrag(headerEl, widget, renderFunction) {
     // Remove the class to restore normal cursor behavior
     document.body.classList.remove('lm-lora-strength-dragging');
 
-    // Only re-render if we actually dragged
-    if (wasDragging) {
-      // Commit final value through options.setValue so external observers are notified.
-      widget.value = widget.value;
-      if (typeof widget.callback === 'function') {
-        widget.callback(widget.value);
+    // Only re-render if we actually dragged.
+    // try-finally guarantees __dragActive is always cleared, preventing a
+    // permanent UI freeze if setValue throws during cleanup.
+    try {
+      if (wasDragging) {
+        // Re-enable renderLoras in setValue and flush final value through setter
+        widget.__dragActive = false;
+        widget.value = widget.value;
+        if (typeof widget.callback === 'function') {
+          widget.callback(widget.value);
+        }
       }
+    } finally {
+      widget.__dragActive = false;
     }
   };
 

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),

web/comfyui/save_image_extra_output.js

@@ -130,6 +130,35 @@ app.registerExtension({
       widget.serializeValue = () => {
         return applyTextReplacements(widget.value);
       };
+
+      // --- Conditional widget visibility for webp_method / jpeg_subsampling ---
+      const formatWidget = getWidgetByName(this, "file_format");
+      const webpMethodWidget = getWidgetByName(this, "webp_method");
+      const jpegSubWidget = getWidgetByName(this, "jpeg_subsampling");
+
+      function updateFormatConditional() {
+        const fmt = formatWidget?.value;
+        if (webpMethodWidget) {
+          webpMethodWidget.disabled = fmt !== "webp";
+          webpMethodWidget.hidden = fmt !== "webp";
+        }
+        if (jpegSubWidget) {
+          jpegSubWidget.disabled = fmt !== "jpeg";
+          jpegSubWidget.hidden = fmt !== "jpeg";
+        }
+      }
+
+      // Set initial state
+      updateFormatConditional();
+
+      // Watch for format changes
+      if (formatWidget) {
+        const origCallback = formatWidget.callback;
+        formatWidget.callback = function (value) {
+          origCallback?.call(this, value);
+          updateFormatConditional();
+        };
+      }
     });
   },
 });