fix(widgets): isolate autocomplete text cleanup

py/metadata_collector/metadata_processor.py

@@ -352,50 +352,101 @@ class MetadataProcessor:
         
         # Check if we have stored conditioning objects for this sampler
         if sampler_id in metadata.get(PROMPTS, {}) and (
-            "pos_conditioning" in metadata[PROMPTS][sampler_id] or 
-            "neg_conditioning" in metadata[PROMPTS][sampler_id]):
-            
+            "pos_conditioning" in metadata[PROMPTS][sampler_id] or
+            "neg_conditioning" in metadata[PROMPTS][sampler_id]
+        ):
             pos_conditioning = metadata[PROMPTS][sampler_id].get("pos_conditioning")
             neg_conditioning = metadata[PROMPTS][sampler_id].get("neg_conditioning")
-            
-            # Helper function to recursively find prompt text for a conditioning object
-            def find_prompt_text_for_conditioning(conditioning_obj, is_positive=True):
+
+            def extend_unique(target, values):
+                for value in values:
+                    if value and value not in target:
+                        target.append(value)
+
+            # Helper function to recursively find prompt texts for a conditioning object.
+            # Transform nodes can map one output conditioning to multiple source conditionings.
+            def find_prompt_texts_for_conditioning(
+                conditioning_obj, is_positive=True, visited=None
+            ):
                 if conditioning_obj is None:
-                    return ""
-                    
+                    return []
+
+                if visited is None:
+                    visited = set()
+
+                conditioning_id = id(conditioning_obj)
+                if conditioning_id in visited:
+                    return []
+                visited.add(conditioning_id)
+
+                prompt_texts = []
+
                 # Try to match conditioning objects with those stored by extractors
                 for prompt_node_id, prompt_data in metadata[PROMPTS].items():
-                    # For nodes with single conditioning output
-                    if "conditioning" in prompt_data:
-                        if id(prompt_data["conditioning"]) == id(conditioning_obj):
-                            return prompt_data.get("text", "")
-                    
-                    # For nodes with separate pos_conditioning and neg_conditioning outputs (like TSC_EfficientLoader)
-                    if is_positive and "positive_encoded" in prompt_data:
-                        if id(prompt_data["positive_encoded"]) == id(conditioning_obj):
-                            if "positive_text" in prompt_data:
-                                return prompt_data["positive_text"]
-                            else:
-                                orig_conditioning = prompt_data.get("orig_pos_cond", None)
-                                if orig_conditioning is not None:
-                                    # Recursively find the prompt text for the original conditioning
-                                    return find_prompt_text_for_conditioning(orig_conditioning, is_positive=True)
-                    
-                    if not is_positive and "negative_encoded" in prompt_data:
-                        if id(prompt_data["negative_encoded"]) == id(conditioning_obj):
-                            if "negative_text" in prompt_data:
-                                return prompt_data["negative_text"]
-                            else:
-                                orig_conditioning = prompt_data.get("orig_neg_cond", None)
-                                if orig_conditioning is not None:
-                                    # Recursively find the prompt text for the original conditioning
-                                    return find_prompt_text_for_conditioning(orig_conditioning, is_positive=False)
-                
-                return ""
-            
+                    if not isinstance(prompt_data, dict):
+                        continue
+
+                    # For CLIP text nodes with a single conditioning output.
+                    if id(prompt_data.get("conditioning")) == conditioning_id:
+                        text = prompt_data.get("text", "")
+                        if text:
+                            extend_unique(prompt_texts, [text])
+
+                    # Generic provenance for passthrough/transform/combine nodes.
+                    for source in prompt_data.get("conditioning_sources", []):
+                        if id(source.get("output")) != conditioning_id:
+                            continue
+                        for input_conditioning in source.get("inputs", []):
+                            extend_unique(
+                                prompt_texts,
+                                find_prompt_texts_for_conditioning(
+                                    input_conditioning, is_positive, visited
+                                ),
+                            )
+
+                    # For nodes with separate pos_conditioning and neg_conditioning outputs
+                    # like TSC_EfficientLoader and existing ControlNet-style metadata.
+                    if (
+                        is_positive
+                        and id(prompt_data.get("positive_encoded")) == conditioning_id
+                    ):
+                        if prompt_data.get("positive_text"):
+                            extend_unique(prompt_texts, [prompt_data["positive_text"]])
+                        else:
+                            extend_unique(
+                                prompt_texts,
+                                find_prompt_texts_for_conditioning(
+                                    prompt_data.get("orig_pos_cond"),
+                                    is_positive=True,
+                                    visited=visited,
+                                ),
+                            )
+
+                    if (
+                        not is_positive
+                        and id(prompt_data.get("negative_encoded")) == conditioning_id
+                    ):
+                        if prompt_data.get("negative_text"):
+                            extend_unique(prompt_texts, [prompt_data["negative_text"]])
+                        else:
+                            extend_unique(
+                                prompt_texts,
+                                find_prompt_texts_for_conditioning(
+                                    prompt_data.get("orig_neg_cond"),
+                                    is_positive=False,
+                                    visited=visited,
+                                ),
+                            )
+
+                return prompt_texts
+
             # Find prompt texts using the helper function
-            result["prompt"] = find_prompt_text_for_conditioning(pos_conditioning, is_positive=True)
-            result["negative_prompt"] = find_prompt_text_for_conditioning(neg_conditioning, is_positive=False)
+            result["prompt"] = ", ".join(
+                find_prompt_texts_for_conditioning(pos_conditioning, is_positive=True)
+            )
+            result["negative_prompt"] = ", ".join(
+                find_prompt_texts_for_conditioning(neg_conditioning, is_positive=False)
+            )
             
         return result
     

py/metadata_collector/node_extractors.py

@@ -163,6 +163,251 @@ class CLIPTextEncodeExtractor(NodeMetadataExtractor):
                 conditioning = outputs[0][0]
                 metadata[PROMPTS][node_id]["conditioning"] = conditioning
 
+
+class MyOriginalWaifuTextExtractor(NodeMetadataExtractor):
+    """Extractor for ComfyUI-MyOriginalWaifu TextProvider nodes."""
+
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        positive_text = inputs.get("positive", "")
+        negative_text = inputs.get("negative", "")
+
+        if positive_text or negative_text:
+            metadata[PROMPTS][node_id] = {
+                "positive_text": positive_text,
+                "negative_text": negative_text,
+                "node_id": node_id,
+            }
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        output_tuple = _first_output_tuple(outputs)
+        if not output_tuple or len(output_tuple) < 2:
+            return
+
+        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+        prompt_metadata["positive_text"] = output_tuple[0]
+        prompt_metadata["negative_text"] = output_tuple[1]
+
+
+class MyOriginalWaifuClipExtractor(NodeMetadataExtractor):
+    """Extractor for ComfyUI-MyOriginalWaifu ClipProvider nodes."""
+
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        positive_text = inputs.get("positive", "")
+        negative_text = inputs.get("negative", "")
+
+        if positive_text or negative_text:
+            metadata[PROMPTS][node_id] = {
+                "positive_text": positive_text,
+                "negative_text": negative_text,
+                "node_id": node_id,
+            }
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        output_tuple = _first_output_tuple(outputs)
+        if not output_tuple or len(output_tuple) < 2:
+            return
+
+        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+        prompt_metadata["positive_encoded"] = output_tuple[0]
+        prompt_metadata["negative_encoded"] = output_tuple[1]
+
+
+def _ensure_prompt_metadata(metadata, node_id):
+    if node_id not in metadata[PROMPTS]:
+        metadata[PROMPTS][node_id] = {"node_id": node_id}
+    return metadata[PROMPTS][node_id]
+
+
+def _first_output_tuple(outputs):
+    if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
+        return None
+    first_output = outputs[0]
+    if isinstance(first_output, tuple):
+        return first_output
+    return None
+
+
+def _record_conditioning_source(
+    metadata, node_id, output_conditioning, input_conditionings
+):
+    if output_conditioning is None:
+        return
+
+    sources = [
+        conditioning for conditioning in input_conditionings if conditioning is not None
+    ]
+    if not sources:
+        return
+
+    prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+    prompt_metadata.setdefault("conditioning_sources", []).append(
+        {
+            "output": output_conditioning,
+            "inputs": sources,
+        }
+    )
+
+
+def _get_variable_name(inputs):
+    for key in ("key", "name", "variable_name", "tag", "text"):
+        value = inputs.get(key)
+        if isinstance(value, str) and value:
+            return value
+    return None
+
+
+def _get_node_variable_name(metadata, node_id, inputs):
+    variable_name = _get_variable_name(inputs)
+    if variable_name:
+        return variable_name
+
+    prompt = metadata.get("current_prompt")
+    original_prompt = getattr(prompt, "original_prompt", None)
+    if not original_prompt or node_id not in original_prompt:
+        return None
+
+    node_data = original_prompt[node_id]
+    variable_name = _get_variable_name(node_data.get("inputs", {}))
+    if variable_name:
+        return variable_name
+
+    widgets_values = node_data.get("widgets_values", [])
+    if widgets_values and isinstance(widgets_values[0], str):
+        return widgets_values[0]
+
+    return None
+
+
+class ControlNetApplyAdvancedExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+        if inputs.get("positive") is not None:
+            prompt_metadata["orig_pos_cond"] = inputs["positive"]
+        if inputs.get("negative") is not None:
+            prompt_metadata["orig_neg_cond"] = inputs["negative"]
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        output_tuple = _first_output_tuple(outputs)
+        if not output_tuple:
+            return
+
+        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+        positive_input = prompt_metadata.get("orig_pos_cond")
+        negative_input = prompt_metadata.get("orig_neg_cond")
+
+        if len(output_tuple) >= 1:
+            prompt_metadata["positive_encoded"] = output_tuple[0]
+            _record_conditioning_source(
+                metadata, node_id, output_tuple[0], [positive_input]
+            )
+        if len(output_tuple) >= 2:
+            prompt_metadata["negative_encoded"] = output_tuple[1]
+            _record_conditioning_source(
+                metadata, node_id, output_tuple[1], [negative_input]
+            )
+
+
+class ConditioningCombineExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        input_conditionings = []
+        for input_name in inputs:
+            if (
+                input_name.startswith("conditioning")
+                and inputs[input_name] is not None
+            ):
+                input_conditionings.append(inputs[input_name])
+
+        if input_conditionings:
+            prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+            prompt_metadata["orig_conditionings"] = input_conditionings
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        output_tuple = _first_output_tuple(outputs)
+        if not output_tuple or len(output_tuple) < 1:
+            return
+
+        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+        output_conditioning = output_tuple[0]
+        prompt_metadata["conditioning"] = output_conditioning
+        _record_conditioning_source(
+            metadata,
+            node_id,
+            output_conditioning,
+            prompt_metadata.get("orig_conditionings", []),
+        )
+
+
+class SetNodeExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        variable_name = _get_node_variable_name(metadata, node_id, inputs)
+        conditioning = inputs.get("CONDITIONING")
+        if conditioning is None:
+            conditioning = inputs.get("conditioning")
+        if conditioning is None:
+            return
+
+        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+        prompt_metadata["conditioning"] = conditioning
+        if variable_name:
+            prompt_metadata["variable_name"] = variable_name
+            metadata[PROMPTS].setdefault("__conditioning_variables__", {})[
+                variable_name
+            ] = conditioning
+
+
+class GetNodeExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        variable_name = _get_node_variable_name(metadata, node_id, inputs or {})
+        if variable_name:
+            prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+            prompt_metadata["variable_name"] = variable_name
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        output_tuple = _first_output_tuple(outputs)
+        if not output_tuple or len(output_tuple) < 1:
+            return
+
+        prompt_metadata = _ensure_prompt_metadata(metadata, node_id)
+        output_conditioning = output_tuple[0]
+        prompt_metadata["conditioning"] = output_conditioning
+
+        variable_name = prompt_metadata.get("variable_name")
+        if not variable_name:
+            return
+
+        input_conditioning = metadata[PROMPTS].get("__conditioning_variables__", {}).get(
+            variable_name
+        )
+        _record_conditioning_source(
+            metadata, node_id, output_conditioning, [input_conditioning]
+        )
+
 # Base Sampler Extractor to reduce code redundancy
 class BaseSamplerExtractor(NodeMetadataExtractor):
     """Base extractor for sampler nodes with common functionality"""
@@ -798,6 +1043,12 @@ NODE_EXTRACTORS = {
     "smZ_CLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/shiimizu/ComfyUI_smZNodes
     "CR_ApplyControlNetStack": CR_ApplyControlNetStackExtractor,  # Add CR_ApplyControlNetStack
     "PCTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/asagi4/comfyui-prompt-control
+    "TextProvider": MyOriginalWaifuTextExtractor,  # ComfyUI-MyOriginalWaifu
+    "ClipProvider": MyOriginalWaifuClipExtractor,  # ComfyUI-MyOriginalWaifu
+    "ControlNetApplyAdvanced": ControlNetApplyAdvancedExtractor,
+    "ConditioningCombine": ConditioningCombineExtractor,
+    "SetNode": SetNodeExtractor,
+    "GetNode": GetNodeExtractor,
     # Latent
     "EmptyLatentImage": ImageSizeExtractor,
     # Flux

py/nodes/save_image.py

@@ -1,12 +1,17 @@
 import json
 import os
 import re
+import time
+import uuid
 from typing import Any, Dict, Optional
 import numpy as np
 import folder_paths  # type: ignore
 from ..services.service_registry import ServiceRegistry
 from ..metadata_collector.metadata_processor import MetadataProcessor
 from ..metadata_collector import get_metadata
+from ..utils.constants import CARD_PREVIEW_WIDTH
+from ..utils.exif_utils import ExifUtils
+from ..utils.utils import calculate_recipe_fingerprint
 from PIL import Image, PngImagePlugin
 import piexif
 import logging
@@ -86,6 +91,13 @@ class SaveImageLM:
                         "tooltip": "Adds an incremental counter to filenames to prevent overwriting previous images.",
                     },
                 ),
+                "save_as_recipe": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Also saves each generated image as a LoRA Manager recipe.",
+                    },
+                ),
             },
             "hidden": {
                 "id": "UNIQUE_ID",
@@ -346,6 +358,203 @@ class SaveImageLM:
 
         return filename
 
+    @staticmethod
+    def _get_cached_model_by_name(scanner, name):
+        cache = getattr(scanner, "_cache", None)
+        if cache is None or not name:
+            return None
+
+        candidates = [
+            name,
+            os.path.basename(name),
+            os.path.splitext(os.path.basename(name))[0],
+        ]
+        for model in getattr(cache, "raw_data", []):
+            file_name = model.get("file_name")
+            if file_name in candidates:
+                return model
+        return None
+
+    def _build_recipe_loras(self, recipe_scanner, lora_stack):
+        lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", lora_stack or "")
+        lora_scanner = getattr(recipe_scanner, "_lora_scanner", None)
+        loras_data = []
+        base_model_counts = {}
+
+        for name, strength in lora_matches:
+            lora_info = self._get_cached_model_by_name(lora_scanner, name)
+            civitai = (lora_info or {}).get("civitai") or {}
+            civitai_model = civitai.get("model") or {}
+            try:
+                parsed_strength = float(strength)
+            except (TypeError, ValueError):
+                parsed_strength = 1.0
+
+            loras_data.append(
+                {
+                    "file_name": name,
+                    "strength": parsed_strength,
+                    "hash": ((lora_info or {}).get("sha256") or "").lower(),
+                    "modelVersionId": civitai.get("id", 0),
+                    "modelName": civitai_model.get("name", name) if lora_info else "",
+                    "modelVersionName": civitai.get("name", "") if lora_info else "",
+                    "isDeleted": False,
+                    "exclude": False,
+                }
+            )
+
+            base_model = (lora_info or {}).get("base_model")
+            if base_model:
+                base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+
+        return lora_matches, loras_data, base_model_counts
+
+    def _build_recipe_checkpoint(self, recipe_scanner, checkpoint_raw):
+        if not isinstance(checkpoint_raw, str) or not checkpoint_raw.strip():
+            return None
+
+        checkpoint_name = checkpoint_raw.strip()
+        file_name = os.path.splitext(os.path.basename(checkpoint_name))[0]
+        checkpoint_scanner = getattr(recipe_scanner, "_checkpoint_scanner", None)
+        checkpoint_info = self._get_cached_model_by_name(
+            checkpoint_scanner, checkpoint_name
+        )
+
+        if not checkpoint_info:
+            return {
+                "type": "checkpoint",
+                "name": checkpoint_name,
+                "file_name": file_name,
+                "hash": self.get_checkpoint_hash(checkpoint_name) or "",
+            }
+
+        civitai = checkpoint_info.get("civitai") or {}
+        civitai_model = civitai.get("model") or {}
+        file_path = checkpoint_info.get("file_path") or checkpoint_info.get("path") or ""
+        cached_file_name = (
+            checkpoint_info.get("file_name")
+            or (os.path.splitext(os.path.basename(file_path))[0] if file_path else "")
+            or file_name
+        )
+
+        return {
+            "type": "checkpoint",
+            "modelId": civitai_model.get("id", 0),
+            "modelVersionId": civitai.get("id", 0),
+            "name": civitai_model.get("name")
+            or checkpoint_info.get("model_name")
+            or checkpoint_name,
+            "version": civitai.get("name", ""),
+            "hash": (
+                checkpoint_info.get("sha256") or checkpoint_info.get("hash") or ""
+            ).lower(),
+            "file_name": cached_file_name,
+            "modelName": civitai_model.get("name", ""),
+            "modelVersionName": civitai.get("name", ""),
+            "baseModel": checkpoint_info.get("base_model")
+            or civitai.get("baseModel", ""),
+        }
+
+    @staticmethod
+    def _derive_recipe_name(lora_matches):
+        recipe_name_parts = [
+            f"{name.strip()}-{float(strength):.2f}" for name, strength in lora_matches[:3]
+        ]
+        return "_".join(recipe_name_parts) or "recipe"
+
+    @staticmethod
+    def _sync_recipe_cache(recipe_scanner, recipe_data, json_path):
+        cache = getattr(recipe_scanner, "_cache", None)
+        if cache is not None:
+            cache.raw_data.append(recipe_data)
+            cache.sorted_by_name = sorted(
+                cache.raw_data, key=lambda item: item.get("title", "").lower()
+            )
+            cache.sorted_by_date = sorted(
+                cache.raw_data,
+                key=lambda item: (
+                    item.get("modified", item.get("created_date", 0)),
+                    item.get("file_path", ""),
+                ),
+                reverse=True,
+            )
+            recipe_scanner._update_folder_metadata(cache)
+            recipe_scanner._update_fts_index_for_recipe(recipe_data, "add")
+
+        recipe_id = str(recipe_data.get("id", ""))
+        if recipe_id:
+            recipe_scanner._json_path_map[recipe_id] = json_path
+        persistent_cache = getattr(recipe_scanner, "_persistent_cache", None)
+        if persistent_cache:
+            persistent_cache.update_recipe(recipe_data, json_path)
+
+    def _save_image_as_recipe(self, file_path, metadata_dict):
+        if not metadata_dict:
+            raise ValueError("No generation metadata found")
+
+        recipe_scanner = ServiceRegistry.get_service_sync("recipe_scanner")
+        if recipe_scanner is None:
+            raise RuntimeError("Recipe scanner unavailable")
+
+        recipes_dir = recipe_scanner.recipes_dir
+        if not recipes_dir:
+            raise RuntimeError("Recipes directory unavailable")
+        os.makedirs(recipes_dir, exist_ok=True)
+
+        recipe_id = str(uuid.uuid4())
+        optimized_image, extension = ExifUtils.optimize_image(
+            image_data=file_path,
+            target_width=CARD_PREVIEW_WIDTH,
+            format="webp",
+            quality=85,
+            preserve_metadata=True,
+        )
+        image_path = os.path.normpath(os.path.join(recipes_dir, f"{recipe_id}{extension}"))
+        with open(image_path, "wb") as file_obj:
+            file_obj.write(optimized_image)
+
+        lora_stack = metadata_dict.get("loras", "")
+        lora_matches, loras_data, base_model_counts = self._build_recipe_loras(
+            recipe_scanner, lora_stack
+        )
+        checkpoint_entry = self._build_recipe_checkpoint(
+            recipe_scanner, metadata_dict.get("checkpoint")
+        )
+        most_common_base_model = (
+            max(base_model_counts.items(), key=lambda item: item[1])[0]
+            if base_model_counts
+            else ""
+        )
+        current_time = time.time()
+        recipe_data = {
+            "id": recipe_id,
+            "file_path": image_path,
+            "title": self._derive_recipe_name(lora_matches),
+            "modified": current_time,
+            "created_date": current_time,
+            "base_model": most_common_base_model
+            or (checkpoint_entry or {}).get("baseModel", ""),
+            "loras": loras_data,
+            "gen_params": {
+                key: value
+                for key, value in metadata_dict.items()
+                if key not in ["checkpoint", "loras"]
+            },
+            "loras_stack": lora_stack,
+            "fingerprint": calculate_recipe_fingerprint(loras_data),
+        }
+        if checkpoint_entry:
+            recipe_data["checkpoint"] = checkpoint_entry
+
+        json_path = os.path.normpath(
+            os.path.join(recipes_dir, f"{recipe_id}.recipe.json")
+        )
+        with open(json_path, "w", encoding="utf-8") as file_obj:
+            json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
+
+        ExifUtils.append_recipe_metadata(image_path, recipe_data)
+        self._sync_recipe_cache(recipe_scanner, recipe_data, json_path)
+
     def save_images(
         self,
         images,
@@ -359,6 +568,7 @@ class SaveImageLM:
         embed_workflow=False,
         save_with_metadata=True,
         add_counter_to_filename=True,
+        save_as_recipe=False,
     ):
         """Save images with metadata"""
         results = []
@@ -477,6 +687,14 @@ class SaveImageLM:
 
                     img.save(file_path, format="WEBP", **save_kwargs)
 
+                if save_as_recipe:
+                    try:
+                        self._save_image_as_recipe(file_path, metadata_dict)
+                    except Exception as e:
+                        logger.warning(
+                            "Failed to save image as recipe: %s", e, exc_info=True
+                        )
+
                 results.append(
                     {"filename": file, "subfolder": subfolder, "type": self.type}
                 )
@@ -499,6 +717,7 @@ class SaveImageLM:
         embed_workflow=False,
         save_with_metadata=True,
         add_counter_to_filename=True,
+        save_as_recipe=False,
     ):
         """Process and save image with metadata"""
         # Make sure the output directory exists
@@ -527,6 +746,7 @@ class SaveImageLM:
             embed_workflow,
             save_with_metadata,
             add_counter_to_filename,
+            save_as_recipe,
         )
 
         return {

tests/metadata_collector/test_metadata_collector.py

@@ -177,6 +177,383 @@ def test_attention_bias_clip_text_encode_prompts_are_collected(metadata_registry
     assert prompt_results["negative_prompt"] == "low quality"
 
 
+def test_myoriginalwaifu_text_provider_uses_processed_prompt_outputs(
+    metadata_registry, monkeypatch
+):
+    prompt_graph = {
+        "text_provider": {
+            "class_type": "TextProvider",
+            "inputs": {
+                "positive": "raw positive",
+                "negative": "raw negative",
+            },
+        },
+        "encode_pos": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"text": ["text_provider", 0], "clip": ["clip", 0]},
+        },
+        "encode_neg": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"text": ["text_provider", 1], "clip": ["clip", 0]},
+        },
+        "sampler": {
+            "class_type": "KSampler",
+            "inputs": {
+                "seed": 123,
+                "steps": 20,
+                "cfg": 7.0,
+                "sampler_name": "Euler",
+                "scheduler": "karras",
+                "denoise": 1.0,
+                "positive": ["encode_pos", 0],
+                "negative": ["encode_neg", 0],
+                "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
+            },
+        },
+    }
+    prompt = SimpleNamespace(original_prompt=prompt_graph)
+
+    pos_conditioning = object()
+    neg_conditioning = object()
+
+    monkeypatch.setattr(metadata_processor, "standalone_mode", False)
+
+    metadata_registry.start_collection("prompt-myoriginalwaifu-text")
+    metadata_registry.set_current_prompt(prompt)
+
+    metadata_registry.record_node_execution(
+        "text_provider",
+        "TextProvider",
+        {"positive": "raw positive", "negative": "raw negative"},
+        None,
+    )
+    metadata_registry.update_node_execution(
+        "text_provider",
+        "TextProvider",
+        [("processed positive", "processed negative")],
+    )
+    metadata_registry.record_node_execution(
+        "encode_pos", "CLIPTextEncode", {"text": "processed positive"}, None
+    )
+    metadata_registry.update_node_execution(
+        "encode_pos", "CLIPTextEncode", [(pos_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "encode_neg", "CLIPTextEncode", {"text": "processed negative"}, None
+    )
+    metadata_registry.update_node_execution(
+        "encode_neg", "CLIPTextEncode", [(neg_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "sampler",
+        "KSampler",
+        {
+            "seed": 123,
+            "steps": 20,
+            "cfg": 7.0,
+            "sampler_name": "Euler",
+            "scheduler": "karras",
+            "denoise": 1.0,
+            "positive": pos_conditioning,
+            "negative": neg_conditioning,
+            "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
+        },
+        None,
+    )
+
+    metadata = metadata_registry.get_metadata("prompt-myoriginalwaifu-text")
+    params = MetadataProcessor.extract_generation_params(metadata)
+
+    assert metadata[PROMPTS]["text_provider"]["positive_text"] == "processed positive"
+    assert metadata[PROMPTS]["text_provider"]["negative_text"] == "processed negative"
+    assert params["prompt"] == "processed positive"
+    assert params["negative_prompt"] == "processed negative"
+
+
+def test_myoriginalwaifu_clip_provider_prompts_are_collected_without_clip_text_encode(
+    metadata_registry, monkeypatch
+):
+    prompt_graph = {
+        "clip_provider": {
+            "class_type": "ClipProvider",
+            "inputs": {
+                "positive": "direct positive",
+                "negative": "direct negative",
+                "clip": ["clip", 0],
+            },
+        },
+        "sampler": {
+            "class_type": "KSampler",
+            "inputs": {
+                "seed": 123,
+                "steps": 20,
+                "cfg": 7.0,
+                "sampler_name": "Euler",
+                "scheduler": "karras",
+                "denoise": 1.0,
+                "positive": ["clip_provider", 0],
+                "negative": ["clip_provider", 1],
+                "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
+            },
+        },
+    }
+    prompt = SimpleNamespace(original_prompt=prompt_graph)
+
+    pos_conditioning = object()
+    neg_conditioning = object()
+
+    monkeypatch.setattr(metadata_processor, "standalone_mode", False)
+
+    metadata_registry.start_collection("prompt-myoriginalwaifu-clip")
+    metadata_registry.set_current_prompt(prompt)
+
+    metadata_registry.record_node_execution(
+        "clip_provider",
+        "ClipProvider",
+        {"positive": "direct positive", "negative": "direct negative"},
+        None,
+    )
+    metadata_registry.update_node_execution(
+        "clip_provider", "ClipProvider", [(pos_conditioning, neg_conditioning)]
+    )
+    metadata_registry.record_node_execution(
+        "sampler",
+        "KSampler",
+        {
+            "seed": 123,
+            "steps": 20,
+            "cfg": 7.0,
+            "sampler_name": "Euler",
+            "scheduler": "karras",
+            "denoise": 1.0,
+            "positive": pos_conditioning,
+            "negative": neg_conditioning,
+            "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
+        },
+        None,
+    )
+
+    metadata = metadata_registry.get_metadata("prompt-myoriginalwaifu-clip")
+    params = MetadataProcessor.extract_generation_params(metadata)
+
+    assert params["prompt"] == "direct positive"
+    assert params["negative_prompt"] == "direct negative"
+
+
+def test_conditioning_provenance_recovers_combined_controlnet_prompts(
+    metadata_registry, monkeypatch
+):
+    import types
+
+    prompt_graph = {
+        "encode_wd": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"text": "wd14 tags", "clip": ["clip", 0]},
+        },
+        "encode_manual": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"text": "manual tags", "clip": ["clip", 0]},
+        },
+        "encode_neg": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"text": "low quality", "clip": ["clip", 0]},
+        },
+        "combine": {
+            "class_type": "ConditioningCombine",
+            "inputs": {
+                "conditioning_1": ["encode_wd", 0],
+                "conditioning_2": ["encode_manual", 0],
+            },
+        },
+        "controlnet": {
+            "class_type": "ControlNetApplyAdvanced",
+            "inputs": {
+                "positive": ["combine", 0],
+                "negative": ["encode_neg", 0],
+            },
+        },
+        "sampler": {
+            "class_type": "KSampler",
+            "inputs": {
+                "seed": 123,
+                "steps": 20,
+                "cfg": 7.0,
+                "sampler_name": "Euler",
+                "scheduler": "karras",
+                "denoise": 1.0,
+                "positive": ["controlnet", 0],
+                "negative": ["controlnet", 1],
+                "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
+            },
+        },
+    }
+    prompt = SimpleNamespace(original_prompt=prompt_graph)
+
+    wd_conditioning = object()
+    manual_conditioning = object()
+    negative_conditioning = object()
+    combined_conditioning = object()
+    controlnet_positive = object()
+    controlnet_negative = object()
+
+    monkeypatch.setattr(metadata_processor, "standalone_mode", False)
+
+    metadata_registry.start_collection("prompt-provenance")
+    metadata_registry.set_current_prompt(prompt)
+
+    metadata_registry.record_node_execution(
+        "encode_wd", "CLIPTextEncode", {"text": "wd14 tags"}, None
+    )
+    metadata_registry.update_node_execution(
+        "encode_wd", "CLIPTextEncode", [(wd_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "encode_manual", "CLIPTextEncode", {"text": "manual tags"}, None
+    )
+    metadata_registry.update_node_execution(
+        "encode_manual", "CLIPTextEncode", [(manual_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "encode_neg", "CLIPTextEncode", {"text": "low quality"}, None
+    )
+    metadata_registry.update_node_execution(
+        "encode_neg", "CLIPTextEncode", [(negative_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "combine",
+        "ConditioningCombine",
+        {
+            "conditioning_1": wd_conditioning,
+            "conditioning_2": manual_conditioning,
+        },
+        None,
+    )
+    metadata_registry.update_node_execution(
+        "combine", "ConditioningCombine", [(combined_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "controlnet",
+        "ControlNetApplyAdvanced",
+        {
+            "positive": combined_conditioning,
+            "negative": negative_conditioning,
+        },
+        None,
+    )
+    metadata_registry.update_node_execution(
+        "controlnet",
+        "ControlNetApplyAdvanced",
+        [(controlnet_positive, controlnet_negative)],
+    )
+    metadata_registry.record_node_execution(
+        "sampler",
+        "KSampler",
+        {
+            "seed": 123,
+            "steps": 20,
+            "cfg": 7.0,
+            "sampler_name": "Euler",
+            "scheduler": "karras",
+            "denoise": 1.0,
+            "positive": controlnet_positive,
+            "negative": controlnet_negative,
+            "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
+        },
+        None,
+    )
+
+    metadata = metadata_registry.get_metadata("prompt-provenance")
+    params = MetadataProcessor.extract_generation_params(metadata)
+
+    assert params["prompt"] == "wd14 tags, manual tags"
+    assert params["negative_prompt"] == "low quality"
+
+
+def test_conditioning_provenance_recovers_kj_set_get_prompts(
+    metadata_registry, monkeypatch
+):
+    import types
+
+    prompt_graph = {
+        "encode_pos": {
+            "class_type": "CLIPTextEncode",
+            "inputs": {"text": "from set node", "clip": ["clip", 0]},
+        },
+        "set_positive": {
+            "class_type": "SetNode",
+            "inputs": {"CONDITIONING": ["encode_pos", 0], "name": "positive"},
+        },
+        "get_positive": {
+            "class_type": "GetNode",
+            "inputs": {"name": "positive"},
+        },
+        "sampler": {
+            "class_type": "KSampler",
+            "inputs": {
+                "seed": 123,
+                "steps": 20,
+                "cfg": 7.0,
+                "sampler_name": "Euler",
+                "scheduler": "karras",
+                "denoise": 1.0,
+                "positive": ["get_positive", 0],
+                "negative": ["encode_pos", 0],
+                "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
+            },
+        },
+    }
+    prompt = SimpleNamespace(original_prompt=prompt_graph)
+
+    original_conditioning = object()
+    get_conditioning = object()
+
+    monkeypatch.setattr(metadata_processor, "standalone_mode", False)
+
+    metadata_registry.start_collection("prompt-kj-get")
+    metadata_registry.set_current_prompt(prompt)
+
+    metadata_registry.record_node_execution(
+        "encode_pos", "CLIPTextEncode", {"text": "from set node"}, None
+    )
+    metadata_registry.update_node_execution(
+        "encode_pos", "CLIPTextEncode", [(original_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "set_positive",
+        "SetNode",
+        {"CONDITIONING": original_conditioning, "name": "positive"},
+        None,
+    )
+    metadata_registry.record_node_execution(
+        "get_positive", "GetNode", {"name": "positive"}, None
+    )
+    metadata_registry.update_node_execution(
+        "get_positive", "GetNode", [(get_conditioning,)]
+    )
+    metadata_registry.record_node_execution(
+        "sampler",
+        "KSampler",
+        {
+            "seed": 123,
+            "steps": 20,
+            "cfg": 7.0,
+            "sampler_name": "Euler",
+            "scheduler": "karras",
+            "denoise": 1.0,
+            "positive": get_conditioning,
+            "negative": original_conditioning,
+            "latent_image": {"samples": types.SimpleNamespace(shape=(1, 4, 16, 16))},
+        },
+        None,
+    )
+
+    metadata = metadata_registry.get_metadata("prompt-kj-get")
+    params = MetadataProcessor.extract_generation_params(metadata)
+
+    assert params["prompt"] == "from set node"
+    assert params["negative_prompt"] == "from set node"
+
+
 def test_sampler_custom_advanced_recovers_prompt_text_through_guidance_nodes(metadata_registry, monkeypatch):
     import types
 

tests/nodes/test_save_image.py

@@ -1,9 +1,11 @@
 import json
+import os
 
 import numpy as np
 import piexif
 from PIL import Image
 
+from py.services.service_registry import ServiceRegistry
 from py.nodes.save_image import SaveImageLM
 
 
@@ -151,3 +153,213 @@ def test_process_image_returns_empty_ui_images_when_save_fails(monkeypatch, tmp_
 
     assert result["result"] == (images,)
     assert result["ui"] == {"images": []}
+
+
+def test_save_image_does_not_save_recipe_by_default(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    calls = []
+    monkeypatch.setattr(
+        SaveImageLM,
+        "_save_image_as_recipe",
+        lambda self, file_path, metadata_dict: calls.append((file_path, metadata_dict)),
+    )
+
+    node = SaveImageLM()
+    node.save_images([_make_image()], "ComfyUI", "png", id="node-1")
+
+    assert calls == []
+
+
+def test_save_image_saves_recipe_when_enabled(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    metadata_dict = {"prompt": "prompt text", "seed": 123}
+    _configure_metadata(monkeypatch, metadata_dict)
+
+    calls = []
+    monkeypatch.setattr(
+        SaveImageLM,
+        "_save_image_as_recipe",
+        lambda self, file_path, metadata_dict: calls.append((file_path, metadata_dict)),
+    )
+
+    node = SaveImageLM()
+    node.save_images(
+        [_make_image()],
+        "ComfyUI",
+        "png",
+        id="node-1",
+        save_as_recipe=True,
+    )
+
+    assert calls == [(str(tmp_path / "sample_00001_.png"), metadata_dict)]
+
+
+def test_save_image_saves_recipe_for_each_successful_batch_image(monkeypatch, tmp_path):
+    monkeypatch.setattr("folder_paths.get_output_directory", lambda: str(tmp_path), raising=False)
+    monkeypatch.setattr(
+        "folder_paths.get_save_image_path",
+        lambda *_args, **_kwargs: (str(tmp_path), "sample", 7, "", "sample"),
+        raising=False,
+    )
+    metadata_dict = {"prompt": "prompt text", "seed": 123}
+    _configure_metadata(monkeypatch, metadata_dict)
+
+    calls = []
+    monkeypatch.setattr(
+        SaveImageLM,
+        "_save_image_as_recipe",
+        lambda self, file_path, metadata_dict: calls.append((file_path, metadata_dict)),
+    )
+
+    node = SaveImageLM()
+    node.save_images(
+        [_make_image(), _make_image()],
+        "ComfyUI",
+        "png",
+        id="node-1",
+        save_as_recipe=True,
+    )
+
+    assert calls == [
+        (str(tmp_path / "sample_00007_.png"), metadata_dict),
+        (str(tmp_path / "sample_00008_.png"), metadata_dict),
+    ]
+
+
+def test_save_image_does_not_save_recipe_when_image_save_fails(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    def _raise_save_error(*args, **kwargs):
+        raise OSError("disk full")
+
+    calls = []
+    monkeypatch.setattr(Image.Image, "save", _raise_save_error)
+    monkeypatch.setattr(
+        SaveImageLM,
+        "_save_image_as_recipe",
+        lambda self, file_path, metadata_dict: calls.append((file_path, metadata_dict)),
+    )
+
+    node = SaveImageLM()
+    node.save_images(
+        [_make_image()],
+        "ComfyUI",
+        "png",
+        id="node-1",
+        save_as_recipe=True,
+    )
+
+    assert calls == []
+
+
+def test_process_image_keeps_image_result_when_recipe_save_fails(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
+
+    def _raise_recipe_error(*args, **kwargs):
+        raise RuntimeError("recipe unavailable")
+
+    monkeypatch.setattr(SaveImageLM, "_save_image_as_recipe", _raise_recipe_error)
+
+    images = [_make_image()]
+    node = SaveImageLM()
+
+    result = node.process_image(images, id="node-1", save_as_recipe=True)
+
+    assert result["result"] == (images,)
+    assert result["ui"] == {
+        "images": [{"filename": "sample_00001_.png", "subfolder": "", "type": "output"}]
+    }
+
+
+def test_save_image_as_recipe_writes_recipe_without_async_scanner_calls(
+    monkeypatch, tmp_path
+):
+    _configure_save_paths(monkeypatch, tmp_path)
+    source_image = tmp_path / "source.png"
+    Image.new("RGB", (16, 16), color=(10, 20, 30)).save(source_image)
+    recipes_dir = tmp_path / "recipes"
+
+    class _Cache:
+        def __init__(self, raw_data=None):
+            self.raw_data = raw_data or []
+            self.sorted_by_name = []
+            self.sorted_by_date = []
+            self.folders = []
+            self.folder_tree = {}
+
+    class _ModelScanner:
+        def __init__(self, raw_data):
+            self._cache = _Cache(raw_data)
+
+    class _PersistentCache:
+        def __init__(self):
+            self.updates = []
+
+        def update_recipe(self, recipe_data, json_path):
+            self.updates.append((recipe_data, json_path))
+
+    class _RecipeScanner:
+        def __init__(self):
+            self.recipes_dir = str(recipes_dir)
+            self._cache = _Cache([])
+            self._json_path_map = {}
+            self._persistent_cache = _PersistentCache()
+            self._lora_scanner = _ModelScanner(
+                [
+                    {
+                        "file_name": "foo",
+                        "sha256": "ABC123",
+                        "base_model": "SDXL",
+                        "civitai": {
+                            "id": 456,
+                            "name": "Foo v1",
+                            "model": {"name": "Foo"},
+                        },
+                    }
+                ]
+            )
+            self._checkpoint_scanner = _ModelScanner([])
+            self.fts_updates = []
+
+        def _update_folder_metadata(self, cache):
+            cache.folders = [""]
+            cache.folder_tree = {}
+
+        def _update_fts_index_for_recipe(self, recipe_data, operation):
+            self.fts_updates.append((recipe_data["id"], operation))
+
+    scanner = _RecipeScanner()
+    monkeypatch.setitem(ServiceRegistry._services, "recipe_scanner", scanner)
+
+    node = SaveImageLM()
+    node._save_image_as_recipe(
+        str(source_image),
+        {
+            "prompt": "prompt text",
+            "seed": 123,
+            "checkpoint": "model.safetensors",
+            "loras": "<lora:foo:0.7>",
+        },
+    )
+
+    recipe_files = list(recipes_dir.glob("*.recipe.json"))
+    preview_files = list(recipes_dir.glob("*.webp"))
+
+    assert len(recipe_files) == 1
+    assert len(preview_files) == 1
+    assert len(scanner._cache.raw_data) == 1
+    assert len(scanner._persistent_cache.updates) == 1
+
+    recipe = json.loads(recipe_files[0].read_text(encoding="utf-8"))
+    assert recipe["file_path"] == os.path.normpath(str(preview_files[0]))
+    assert recipe["title"] == "foo-0.70"
+    assert recipe["base_model"] == "SDXL"
+    assert recipe["loras"][0]["hash"] == "abc123"
+    assert recipe["loras"][0]["modelVersionId"] == 456
+    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")]

vue-widgets/src/main.ts

@@ -5,6 +5,7 @@ import LoraRandomizerWidget from '@/components/LoraRandomizerWidget.vue'
 import LoraCyclerWidget from '@/components/LoraCyclerWidget.vue'
 import JsonDisplayWidget from '@/components/JsonDisplayWidget.vue'
 import AutocompleteTextWidget from '@/components/AutocompleteTextWidget.vue'
+import { createVueWidgetCleanup } from './vue-widget-cleanup'
 import type { LoraPoolConfig, RandomizerConfig, CyclerConfig } from './composables/types'
 import {
   setupModeChangeHandler,
@@ -66,6 +67,12 @@ function forwardMiddleMouseToCanvas(container: HTMLElement) {
 }
 
 const vueApps = new Map<number, VueApp>()
+let autocompleteTextWidgetInstanceId = 0
+
+export function createAutocompleteTextWidgetInstanceId() {
+  autocompleteTextWidgetInstanceId += 1
+  return autocompleteTextWidgetInstanceId
+}
 
 // Cache for dynamically loaded addLorasWidget module
 let addLorasWidgetCache: any = null
@@ -562,8 +569,9 @@ function createAutocompleteTextWidgetFactory(
   inputOptions: { placeholder?: string } = {}
 ) {
   const metadataWidgetName = `__lm_autocomplete_meta_${widgetName}`
+  const instanceId = createAutocompleteTextWidgetInstanceId()
   const container = document.createElement('div')
-  container.id = `autocomplete-text-widget-${node.id}-${widgetName}`
+  container.id = `autocomplete-text-widget-${instanceId}`
   container.style.width = '100%'
   container.style.height = '100%'
   container.style.display = 'flex'
@@ -644,17 +652,12 @@ function createAutocompleteTextWidgetFactory(
   })
 
   vueApp.mount(container)
-  // Use a unique key combining node.id and widget name to avoid collisions
-  const appKey = node.id * 100000 + widgetName.charCodeAt(0)
+  const appKey = instanceId
   vueApps.set(appKey, vueApp)
 
-  widget.onRemove = () => {
-    const vueApp = vueApps.get(appKey)
-    if (vueApp) {
-      vueApp.unmount()
-      vueApps.delete(appKey)
-    }
-  }
+  widget.onRemove = createVueWidgetCleanup(vueApp, () => {
+    vueApps.delete(appKey)
+  })
 
   return { widget }
 }

vue-widgets/src/vue-widget-cleanup.ts

@@ -0,0 +1,15 @@
+import type { App as VueApp } from 'vue'
+
+export function createVueWidgetCleanup(vueApp: VueApp, onCleanup?: () => void) {
+  let didUnmount = false
+
+  return () => {
+    if (didUnmount) {
+      return
+    }
+
+    vueApp.unmount()
+    didUnmount = true
+    onCleanup?.()
+  }
+}

vue-widgets/tests/unit/vueWidgetCleanup.test.ts

@@ -0,0 +1,38 @@
+import { describe, expect, it, vi } from 'vitest'
+import { createVueWidgetCleanup } from '@/vue-widget-cleanup'
+
+describe('createVueWidgetCleanup', () => {
+  it('cleans up only the Vue app bound to the widget remove handler', () => {
+    const firstCleanup = vi.fn()
+    const secondCleanup = vi.fn()
+    const firstApp = { unmount: vi.fn() }
+    const secondApp = { unmount: vi.fn() }
+
+    const removeFirst = createVueWidgetCleanup(firstApp as any, firstCleanup)
+    const removeSecond = createVueWidgetCleanup(secondApp as any, secondCleanup)
+
+    removeFirst()
+
+    expect(firstApp.unmount).toHaveBeenCalledTimes(1)
+    expect(firstCleanup).toHaveBeenCalledTimes(1)
+    expect(secondApp.unmount).not.toHaveBeenCalled()
+    expect(secondCleanup).not.toHaveBeenCalled()
+
+    removeSecond()
+
+    expect(secondApp.unmount).toHaveBeenCalledTimes(1)
+    expect(secondCleanup).toHaveBeenCalledTimes(1)
+  })
+
+  it('is idempotent when ComfyUI calls the remove handler more than once', () => {
+    const cleanup = vi.fn()
+    const app = { unmount: vi.fn() }
+    const remove = createVueWidgetCleanup(app as any, cleanup)
+
+    remove()
+    remove()
+
+    expect(app.unmount).toHaveBeenCalledTimes(1)
+    expect(cleanup).toHaveBeenCalledTimes(1)
+  })
+})

web/comfyui/vue-widgets/lora-manager-widgets.js

@@ -14933,6 +14933,17 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
   }
 });
 const AutocompleteTextWidget = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-76ce0f19"]]);
+function createVueWidgetCleanup(vueApp, onCleanup) {
+  let didUnmount = false;
+  return () => {
+    if (didUnmount) {
+      return;
+    }
+    vueApp.unmount();
+    didUnmount = true;
+    onCleanup == null ? void 0 : onCleanup();
+  };
+}
 const LORA_PROVIDER_NODE_TYPES$1 = [
   "Lora Stacker (LoraManager)",
   "Lora Randomizer (LoraManager)",
@@ -15274,6 +15285,11 @@ function forwardMiddleMouseToCanvas(container) {
   });
 }
 const vueApps = /* @__PURE__ */ new Map();
+let autocompleteTextWidgetInstanceId = 0;
+function createAutocompleteTextWidgetInstanceId() {
+  autocompleteTextWidgetInstanceId += 1;
+  return autocompleteTextWidgetInstanceId;
+}
 let addLorasWidgetCache = null;
 function createLoraPoolWidget(node) {
   const container = document.createElement("div");
@@ -15653,8 +15669,9 @@ if ((_a = app$1.ui) == null ? void 0 : _a.settings) {
 function createAutocompleteTextWidgetFactory(node, widgetName, modelType, inputOptions = {}) {
   var _a2, _b, _c;
   const metadataWidgetName = `__lm_autocomplete_meta_${widgetName}`;
+  const instanceId = createAutocompleteTextWidgetInstanceId();
   const container = document.createElement("div");
-  container.id = `autocomplete-text-widget-${node.id}-${widgetName}`;
+  container.id = `autocomplete-text-widget-${instanceId}`;
   container.style.width = "100%";
   container.style.height = "100%";
   container.style.display = "flex";
@@ -15721,15 +15738,11 @@ function createAutocompleteTextWidgetFactory(node, widgetName, modelType, inputO
     ripple: false
   });
   vueApp.mount(container);
-  const appKey = node.id * 1e5 + widgetName.charCodeAt(0);
+  const appKey = instanceId;
   vueApps.set(appKey, vueApp);
-  widget.onRemove = () => {
-    const vueApp2 = vueApps.get(appKey);
-    if (vueApp2) {
-      vueApp2.unmount();
-      vueApps.delete(appKey);
-    }
-  };
+  widget.onRemove = createVueWidgetCleanup(vueApp, () => {
+    vueApps.delete(appKey);
+  });
   return { widget };
 }
 app$1.registerExtension({
@@ -15834,4 +15847,7 @@ app$1.registerExtension({
     }
   }
 });
+export {
+  createAutocompleteTextWidgetInstanceId
+};
 //# sourceMappingURL=lora-manager-widgets.js.map