feat: add LoraDemoNode and LoraRandomizerNode with documentation

- Import and register two new nodes: LoraDemoNode and LoraRandomizerNode - Update import exception handling for better readability with multi-line formatting - Add comprehensive documentation file `docs/custom-node-ui-output.md` for UI output usage in custom nodes - Ensure proper node registration in NODE_CLASS_MAPPINGS for ComfyUI integration - Maintain backward compatibility with existing node structure and import fallbacks
2026-03-21 21:22:11 -03:00 · 2026-01-12 15:06:38 +08:00
parent 65cede7335
commit 177b20263d
18 changed files with 2404 additions and 242 deletions
--- a/init.py
+++ b/init.py
@@ -9,8 +9,12 @@ try:  # pragma: no cover - import fallback for pytest collection
    from .py.nodes.wanvideo_lora_select import WanVideoLoraSelectLM
    from .py.nodes.wanvideo_lora_select_from_text import WanVideoLoraSelectFromText
    from .py.nodes.lora_pool import LoraPoolNode
+    from .py.nodes.lora_demo import LoraDemoNode
+    from .py.nodes.lora_randomizer import LoraRandomizerNode
    from .py.metadata_collector import init as init_metadata_collector
-except ImportError:  # pragma: no cover - allows running under pytest without package install
+except (
+    ImportError
+):  # pragma: no cover - allows running under pytest without package install
    import importlib
    import pathlib
    import sys
@@ -21,15 +25,27 @@ except ImportError:  # pragma: no cover - allows running under pytest without pa

    PromptLoraManager = importlib.import_module("py.nodes.prompt").PromptLoraManager
    LoraManager = importlib.import_module("py.lora_manager").LoraManager
-    LoraManagerLoader = importlib.import_module("py.nodes.lora_loader").LoraManagerLoader
-    LoraManagerTextLoader = importlib.import_module("py.nodes.lora_loader").LoraManagerTextLoader
-    TriggerWordToggle = importlib.import_module("py.nodes.trigger_word_toggle").TriggerWordToggle
+    LoraManagerLoader = importlib.import_module(
+        "py.nodes.lora_loader"
+    ).LoraManagerLoader
+    LoraManagerTextLoader = importlib.import_module(
+        "py.nodes.lora_loader"
+    ).LoraManagerTextLoader
+    TriggerWordToggle = importlib.import_module(
+        "py.nodes.trigger_word_toggle"
+    ).TriggerWordToggle
    LoraStacker = importlib.import_module("py.nodes.lora_stacker").LoraStacker
    SaveImageLM = importlib.import_module("py.nodes.save_image").SaveImageLM
    DebugMetadata = importlib.import_module("py.nodes.debug_metadata").DebugMetadata
-    WanVideoLoraSelectLM = importlib.import_module("py.nodes.wanvideo_lora_select").WanVideoLoraSelectLM
-    WanVideoLoraSelectFromText = importlib.import_module("py.nodes.wanvideo_lora_select_from_text").WanVideoLoraSelectFromText
+    WanVideoLoraSelectLM = importlib.import_module(
+        "py.nodes.wanvideo_lora_select"
+    ).WanVideoLoraSelectLM
+    WanVideoLoraSelectFromText = importlib.import_module(
+        "py.nodes.wanvideo_lora_select_from_text"
+    ).WanVideoLoraSelectFromText
    LoraPoolNode = importlib.import_module("py.nodes.lora_pool").LoraPoolNode
+    LoraDemoNode = importlib.import_module("py.nodes.lora_demo").LoraDemoNode
+    LoraRandomizerNode = importlib.import_module("py.nodes.lora_randomizer").LoraRandomizerNode
    init_metadata_collector = importlib.import_module("py.metadata_collector").init

 NODE_CLASS_MAPPINGS = {
@@ -42,7 +58,9 @@ NODE_CLASS_MAPPINGS = {
    DebugMetadata.NAME: DebugMetadata,
    WanVideoLoraSelectLM.NAME: WanVideoLoraSelectLM,
    WanVideoLoraSelectFromText.NAME: WanVideoLoraSelectFromText,
-    LoraPoolNode.NAME: LoraPoolNode
+    LoraPoolNode.NAME: LoraPoolNode,
+    LoraDemoNode.NAME: LoraDemoNode,
+    LoraRandomizerNode.NAME: LoraRandomizerNode,
 }

 WEB_DIRECTORY = "./web/comfyui"
@@ -50,15 +68,20 @@ WEB_DIRECTORY = "./web/comfyui"
 # Check and build Vue widgets if needed (development mode)
 try:
    from .py.vue_widget_builder import check_and_build_vue_widgets
+
    # Auto-build in development, warn only if fails
    check_and_build_vue_widgets(auto_build=True, warn_only=True)
 except ImportError:
    # Fallback for pytest
    import importlib
-    check_and_build_vue_widgets = importlib.import_module("py.vue_widget_builder").check_and_build_vue_widgets
+
+    check_and_build_vue_widgets = importlib.import_module(
+        "py.vue_widget_builder"
+    ).check_and_build_vue_widgets
    check_and_build_vue_widgets(auto_build=True, warn_only=True)
 except Exception as e:
    import logging
+
    logging.warning(f"[LoRA Manager] Vue widget build check skipped: {e}")

 # Initialize metadata collector
@@ -66,4 +89,4 @@ init_metadata_collector()

 # Register routes on import
 LoraManager.add_routes()
-__all__ = ['NODE_CLASS_MAPPINGS', 'WEB_DIRECTORY']
+__all__ = ["NODE_CLASS_MAPPINGS", "WEB_DIRECTORY"]
--- a/py/nodes/lora_demo.py
+++ b/py/nodes/lora_demo.py
@@ -0,0 +1,95 @@
+"""
+Lora Demo Node - Demonstrates LORAS custom widget type usage.
+
+This node accepts LORAS widget input and outputs a summary string.
+"""
+
+import logging
+import random
+
+logger = logging.getLogger(__name__)
+
+
+class LoraDemoNode:
+    """Demo node that uses LORAS custom widget type."""
+
+    NAME = "Lora Demo (LoraManager)"
+    CATEGORY = "Lora Manager/demo"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "loras": ("LORAS", {}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("summary",)
+
+    FUNCTION = "process"
+    OUTPUT_NODE = False
+
+    async def process(self, loras):
+        """
+        Process LoRAs input and return summary + UI data for widget.
+
+        Args:
+            loras: List of LoRA dictionaries with structure:
+                [{'name': str, 'strength': float, 'clipStrength': float, 'active': bool, ...}]
+
+        Returns:
+            Dictionary with 'result' (for workflow) and 'ui' (for frontend display)
+        """
+        from ..services.service_registry import ServiceRegistry
+
+        # Get lora scanner to access available loras
+        scanner = await ServiceRegistry.get_lora_scanner()
+
+        # Get available loras from cache
+        available_loras = []
+        try:
+            cache_data = await scanner.get_cached_data(force_refresh=False)
+            if cache_data and hasattr(cache_data, "raw_data"):
+                available_loras = cache_data.raw_data
+        except Exception as e:
+            logger.warning(f"[LoraDemoNode] Failed to get lora cache: {e}")
+
+        # Randomly select 3-5 loras
+        num_to_select = random.randint(3, 5)
+        if len(available_loras) < num_to_select:
+            num_to_select = len(available_loras)
+
+        selected_loras = (
+            random.sample(available_loras, num_to_select) if num_to_select > 0 else []
+        )
+
+        # Generate random loras data for widget
+        widget_loras = []
+        for lora in selected_loras:
+            strength = round(random.uniform(0.1, 1.0), 2)
+            widget_loras.append(
+                {
+                    "name": lora.get("file_name", "Unknown"),
+                    "strength": strength,
+                    "clipStrength": strength,
+                    "active": True,
+                    "expanded": False,
+                }
+            )
+
+        # Create summary string
+        active_names = [l["name"] for l in widget_loras]
+        summary = f"Randomized {len(active_names)} LoRAs: {', '.join(active_names)}"
+
+        logger.info(f"[LoraDemoNode] {summary}")
+
+        # Return format: result for workflow + ui for frontend
+        return {"result": (summary,), "ui": {"loras": widget_loras}}
+
+
+# Node class mappings for ComfyUI
+NODE_CLASS_MAPPINGS = {"LoraDemoNode": LoraDemoNode}
+
+# Display name mappings
+NODE_DISPLAY_NAME_MAPPINGS = {"LoraDemoNode": "LoRA Demo"}
--- a/py/nodes/lora_pool.py
+++ b/py/nodes/lora_pool.py
@@ -31,28 +31,28 @@ class LoraPoolNode:
            "hidden": {
                # Hidden input to pass through unique node ID for frontend
                "unique_id": "UNIQUE_ID",
-            }
+            },
        }

-    RETURN_TYPES = ("LORA_POOL_CONFIG",)
-    RETURN_NAMES = ("pool_config",)
+    RETURN_TYPES = ("POOL_CONFIG",)
+    RETURN_NAMES = ("POOL_CONFIG",)

    FUNCTION = "process"
    OUTPUT_NODE = False

    def process(self, pool_config, unique_id=None):
        """
-        Pass through the pool configuration.
+        Pass through the pool configuration filters.

        The config is generated entirely by the frontend widget.
-        This function validates and passes through the configuration.
+        This function validates and returns only the filters field.

        Args:
            pool_config: Dict containing filter criteria from widget
            unique_id: Node's unique ID (hidden)

        Returns:
-            Tuple containing the validated pool_config
+            Tuple containing the filters dict from pool_config
        """
        # Validate required structure
        if not isinstance(pool_config, dict):
@@ -63,10 +63,13 @@ class LoraPoolNode:
        if "version" not in pool_config:
            pool_config["version"] = 1

-        # Log for debugging
-        logger.debug(f"[LoraPoolNode] Processing config: {pool_config}")
+        # Extract filters field
+        filters = pool_config.get("filters", self._default_config()["filters"])

-        return (pool_config,)
+        # Log for debugging
+        logger.debug(f"[LoraPoolNode] Processing filters: {filters}")
+
+        return (filters,)

    @staticmethod
    def _default_config():
@@ -76,23 +79,16 @@ class LoraPoolNode:
            "filters": {
                "baseModels": [],
                "tags": {"include": [], "exclude": []},
-                "folder": {"path": None, "recursive": True},
+                "folders": {"include": [], "exclude": []},
                "favoritesOnly": False,
-                "license": {
-                    "noCreditRequired": None,
-                    "allowSellingGeneratedContent": None
-                }
+                "license": {"noCreditRequired": False, "allowSelling": False},
            },
-            "preview": {"matchCount": 0, "lastUpdated": 0}
+            "preview": {"matchCount": 0, "lastUpdated": 0},
        }


 # Node class mappings for ComfyUI
-NODE_CLASS_MAPPINGS = {
-    "LoraPoolNode": LoraPoolNode
-}
+NODE_CLASS_MAPPINGS = {"LoraPoolNode": LoraPoolNode}

 # Display name mappings
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "LoraPoolNode": "LoRA Pool (Filter)"
-}
+NODE_DISPLAY_NAME_MAPPINGS = {"LoraPoolNode": "LoRA Pool (Filter)"}
--- a/py/nodes/lora_randomizer.py
+++ b/py/nodes/lora_randomizer.py
@@ -0,0 +1,297 @@
+"""
+Lora Randomizer Node - Randomly selects LoRAs from a pool with configurable settings.
+
+This node accepts optional pool_config input to filter available LoRAs, and outputs
+a LORA_STACK with randomly selected LoRAs. Supports both frontend roll (fixed selection)
+and backend roll (randomizes each execution).
+"""
+
+import logging
+import random
+import os
+from ..utils.utils import get_lora_info
+from .utils import extract_lora_name
+
+logger = logging.getLogger(__name__)
+
+
+class LoraRandomizerNode:
+    """Node that randomly selects LoRAs from a pool"""
+
+    NAME = "Lora Randomizer (LoraManager)"
+    CATEGORY = "Lora Manager/randomizer"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "randomizer_config": ("RANDOMIZER_CONFIG", {}),
+                "loras": ("LORAS", {}),
+            },
+            "optional": {
+                "pool_config": ("POOL_CONFIG", {}),
+            },
+        }
+
+    RETURN_TYPES = ("LORA_STACK",)
+    RETURN_NAMES = ("lora_stack",)
+
+    FUNCTION = "randomize"
+    OUTPUT_NODE = False
+
+    async def randomize(self, randomizer_config, loras, pool_config=None):
+        """
+        Randomize LoRAs based on configuration and pool filters.
+
+        Args:
+            randomizer_config: Dict with randomizer settings (count, strength ranges, roll mode)
+            loras: List of LoRA dicts from LORAS widget (includes locked state)
+            pool_config: Optional config from LoRA Pool node for filtering
+
+        Returns:
+            Dictionary with 'result' (LORA_STACK tuple) and 'ui' (for widget display)
+        """
+        from ..services.service_registry import ServiceRegistry
+
+        # Get lora scanner to access available loras
+        scanner = await ServiceRegistry.get_lora_scanner()
+
+        # Parse randomizer settings
+        count_mode = randomizer_config.get("count_mode", "range")
+        count_fixed = randomizer_config.get("count_fixed", 5)
+        count_min = randomizer_config.get("count_min", 3)
+        count_max = randomizer_config.get("count_max", 7)
+        model_strength_min = randomizer_config.get("model_strength_min", 0.0)
+        model_strength_max = randomizer_config.get("model_strength_max", 1.0)
+        use_same_clip_strength = randomizer_config.get("use_same_clip_strength", True)
+        clip_strength_min = randomizer_config.get("clip_strength_min", 0.0)
+        clip_strength_max = randomizer_config.get("clip_strength_max", 1.0)
+        roll_mode = randomizer_config.get("roll_mode", "frontend")
+
+        # Determine target count
+        if count_mode == "fixed":
+            target_count = count_fixed
+        else:
+            target_count = random.randint(count_min, count_max)
+
+        logger.info(
+            f"[LoraRandomizerNode] Target count: {target_count}, Roll mode: {roll_mode}"
+        )
+
+        # Extract locked LoRAs from input
+        locked_loras = [lora for lora in loras if lora.get("locked", False)]
+        locked_count = len(locked_loras)
+
+        logger.info(f"[LoraRandomizerNode] Locked LoRAs: {locked_count}")
+
+        # Get available loras from cache
+        try:
+            cache_data = await scanner.get_cached_data(force_refresh=False)
+            if cache_data and hasattr(cache_data, "raw_data"):
+                available_loras = cache_data.raw_data
+            else:
+                available_loras = []
+        except Exception as e:
+            logger.warning(f"[LoraRandomizerNode] Failed to get lora cache: {e}")
+            available_loras = []
+
+        # Apply pool filters if provided
+        if pool_config:
+            available_loras = await self._apply_pool_filters(
+                available_loras, pool_config, scanner
+            )
+
+        logger.info(
+            f"[LoraRandomizerNode] Available LoRAs after filtering: {len(available_loras)}"
+        )
+
+        # Calculate how many new LoRAs to select
+        # In frontend mode, if loras already has data, preserve unlocked ones if roll_mode requires
+        # For simplicity in backend mode, we regenerate all unlocked slots
+        slots_needed = target_count - locked_count
+
+        if slots_needed < 0:
+            slots_needed = 0
+            # Too many locked, trim to target
+            locked_loras = locked_loras[:target_count]
+            locked_count = len(locked_loras)
+
+        # Filter out locked LoRAs from available pool
+        locked_names = {lora["name"] for lora in locked_loras}
+        available_pool = [
+            l for l in available_loras if l["file_name"] not in locked_names
+        ]
+
+        # Ensure we don't try to select more than available
+        if slots_needed > len(available_pool):
+            slots_needed = len(available_pool)
+
+        logger.info(
+            f"[LoraRandomizerNode] Selecting {slots_needed} new LoRAs from {len(available_pool)} available"
+        )
+
+        # Random sample
+        selected = []
+        if slots_needed > 0:
+            selected = random.sample(available_pool, slots_needed)
+
+        # Generate random strengths for selected LoRAs
+        result_loras = []
+        for lora in selected:
+            model_str = round(random.uniform(model_strength_min, model_strength_max), 2)
+
+            if use_same_clip_strength:
+                clip_str = model_str
+            else:
+                clip_str = round(
+                    random.uniform(clip_strength_min, clip_strength_max), 2
+                )
+
+            result_loras.append(
+                {
+                    "name": lora["file_name"],
+                    "strength": model_str,
+                    "clipStrength": clip_str,
+                    "active": True,
+                    "expanded": abs(model_str - clip_str) > 0.001,
+                    "locked": False,
+                }
+            )
+
+        # Merge with locked LoRAs
+        result_loras.extend(locked_loras)
+
+        logger.info(f"[LoraRandomizerNode] Final LoRA count: {len(result_loras)}")
+
+        # Build LORA_STACK output
+        lora_stack = []
+        for lora in result_loras:
+            if not lora.get("active", False):
+                continue
+
+            # Get file path
+            lora_path, trigger_words = get_lora_info(lora["name"])
+            if not lora_path:
+                logger.warning(
+                    f"[LoraRandomizerNode] Could not find path for LoRA: {lora['name']}"
+                )
+                continue
+
+            # Normalize path separators
+            lora_path = lora_path.replace("/", os.sep)
+
+            # Extract strengths
+            model_strength = lora.get("strength", 1.0)
+            clip_strength = lora.get("clipStrength", model_strength)
+
+            lora_stack.append((lora_path, model_strength, clip_strength))
+
+        # Return format: result for workflow + ui for frontend display
+        return {"result": (lora_stack,), "ui": {"loras": result_loras}}
+
+    async def _apply_pool_filters(self, available_loras, pool_config, scanner):
+        """
+        Apply pool_config filters to available LoRAs.
+
+        Args:
+            available_loras: List of all LoRA dicts
+            pool_config: Dict with filter settings from LoRA Pool node
+            scanner: Scanner instance for accessing filter utilities
+
+        Returns:
+            Filtered list of LoRA dicts
+        """
+        from ..services.lora_service import LoraService
+        from ..services.model_query import FilterCriteria
+
+        # Create lora service instance for filtering
+        lora_service = LoraService(scanner)
+
+        # Extract filter parameters from pool_config
+        selected_base_models = pool_config.get("baseModels", [])
+        tags_dict = pool_config.get("tags", {})
+        include_tags = tags_dict.get("include", [])
+        exclude_tags = tags_dict.get("exclude", [])
+        folders_dict = pool_config.get("folders", {})
+        include_folders = folders_dict.get("include", [])
+        exclude_folders = folders_dict.get("exclude", [])
+        license_dict = pool_config.get("license", {})
+        no_credit_required = license_dict.get("noCreditRequired", False)
+        allow_selling = license_dict.get("allowSelling", False)
+
+        # Build tag filters dict
+        tag_filters = {}
+        for tag in include_tags:
+            tag_filters[tag] = "include"
+        for tag in exclude_tags:
+            tag_filters[tag] = "exclude"
+
+        # Build folder filter
+        # LoRA Pool uses include/exclude folders, we need to apply this logic
+        # For now, we'll filter based on folder path matching
+        if include_folders or exclude_folders:
+            filtered = []
+            for lora in available_loras:
+                folder = lora.get("folder", "")
+
+                # Check exclude folders first
+                excluded = False
+                for exclude_folder in exclude_folders:
+                    if folder.startswith(exclude_folder):
+                        excluded = True
+                        break
+
+                if excluded:
+                    continue
+
+                # Check include folders
+                if include_folders:
+                    included = False
+                    for include_folder in include_folders:
+                        if folder.startswith(include_folder):
+                            included = True
+                            break
+                    if not included:
+                        continue
+
+                filtered.append(lora)
+
+            available_loras = filtered
+
+        # Apply base model filter
+        if selected_base_models:
+            available_loras = [
+                lora
+                for lora in available_loras
+                if lora.get("base_model") in selected_base_models
+            ]
+
+        # Apply tag filters
+        if tag_filters:
+            criteria = FilterCriteria(tags=tag_filters)
+            available_loras = lora_service.filter_set.apply(available_loras, criteria)
+
+        # Apply license filters
+        if no_credit_required:
+            available_loras = [
+                lora
+                for lora in available_loras
+                if not lora.get("civitai", {}).get("allowNoCredit", True)
+            ]
+
+        if allow_selling:
+            available_loras = [
+                lora
+                for lora in available_loras
+                if lora.get("civitai", {}).get("allowCommercialUse", ["None"])[0]
+                != "None"
+            ]
+
+        return available_loras
+
+
+# Node class mappings for ComfyUI
+NODE_CLASS_MAPPINGS = {"LoraRandomizerNode": LoraRandomizerNode}
+
+# Display name mappings
+NODE_DISPLAY_NAME_MAPPINGS = {"LoraRandomizerNode": "LoRA Randomizer"}
--- a/py/routes/lora_routes.py
+++ b/py/routes/lora_routes.py
@@ -45,6 +45,9 @@ class LoraRoutes(BaseModelRoutes):
        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/get-trigger-words', prefix, self.get_lora_trigger_words)
        registrar.add_prefixed_route('GET', '/api/lm/{prefix}/usage-tips-by-path', prefix, self.get_lora_usage_tips_by_path)

+        # Randomizer routes
+        registrar.add_prefixed_route('POST', '/api/lm/{prefix}/random-sample', prefix, self.get_random_loras)
+
        # ComfyUI integration
        registrar.add_prefixed_route('POST', '/api/lm/{prefix}/get_trigger_words', prefix, self.get_trigger_words)
    
@@ -215,6 +218,74 @@ class LoraRoutes(BaseModelRoutes):
                'error': str(e)
            }, status=500)
    
+    async def get_random_loras(self, request: web.Request) -> web.Response:
+        """Get random LoRAs based on filters and strength ranges"""
+        try:
+            json_data = await request.json()
+
+            # Parse parameters
+            count = json_data.get('count', 5)
+            count_min = json_data.get('count_min')
+            count_max = json_data.get('count_max')
+            model_strength_min = float(json_data.get('model_strength_min', 0.0))
+            model_strength_max = float(json_data.get('model_strength_max', 1.0))
+            use_same_clip_strength = json_data.get('use_same_clip_strength', True)
+            clip_strength_min = float(json_data.get('clip_strength_min', 0.0))
+            clip_strength_max = float(json_data.get('clip_strength_max', 1.0))
+            locked_loras = json_data.get('locked_loras', [])
+            pool_config = json_data.get('pool_config')
+
+            # Determine target count
+            if count_min is not None and count_max is not None:
+                import random
+                target_count = random.randint(count_min, count_max)
+            else:
+                target_count = count
+
+            # Validate parameters
+            if target_count < 1 or target_count > 100:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Count must be between 1 and 100'
+                }, status=400)
+
+            if model_strength_min < 0 or model_strength_max > 10:
+                return web.json_response({
+                    'success': False,
+                    'error': 'Model strength must be between 0 and 10'
+                }, status=400)
+
+            # Get random LoRAs from service
+            result_loras = await self.service.get_random_loras(
+                count=target_count,
+                model_strength_min=model_strength_min,
+                model_strength_max=model_strength_max,
+                use_same_clip_strength=use_same_clip_strength,
+                clip_strength_min=clip_strength_min,
+                clip_strength_max=clip_strength_max,
+                locked_loras=locked_loras,
+                pool_config=pool_config
+            )
+
+            return web.json_response({
+                'success': True,
+                'loras': result_loras,
+                'count': len(result_loras)
+            })
+
+        except ValueError as e:
+            logger.error(f"Invalid parameter for random LoRAs: {e}")
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=400)
+        except Exception as e:
+            logger.error(f"Error getting random LoRAs: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
    async def get_trigger_words(self, request: web.Request) -> web.Response:
        """Get trigger words for specified LoRA models"""
        try:
--- a/py/services/lora_service.py
+++ b/py/services/lora_service.py
@@ -178,7 +178,188 @@ class LoraService(BaseModelService):
    def find_duplicate_hashes(self) -> Dict:
        """Find LoRAs with duplicate SHA256 hashes"""
        return self.scanner._hash_index.get_duplicate_hashes()
-    
+
    def find_duplicate_filenames(self) -> Dict:
        """Find LoRAs with conflicting filenames"""
        return self.scanner._hash_index.get_duplicate_filenames()
+
+    async def get_random_loras(
+        self,
+        count: int,
+        model_strength_min: float = 0.0,
+        model_strength_max: float = 1.0,
+        use_same_clip_strength: bool = True,
+        clip_strength_min: float = 0.0,
+        clip_strength_max: float = 1.0,
+        locked_loras: Optional[List[Dict]] = None,
+        pool_config: Optional[Dict] = None
+    ) -> List[Dict]:
+        """
+        Get random LoRAs with specified strength ranges.
+
+        Args:
+            count: Number of LoRAs to select
+            model_strength_min: Minimum model strength
+            model_strength_max: Maximum model strength
+            use_same_clip_strength: Whether to use same strength for clip
+            clip_strength_min: Minimum clip strength
+            clip_strength_max: Maximum clip strength
+            locked_loras: List of locked LoRA dicts to preserve
+            pool_config: Optional pool config for filtering
+
+        Returns:
+            List of LoRA dicts with randomized strengths
+        """
+        import random
+
+        if locked_loras is None:
+            locked_loras = []
+
+        # Get available loras from cache
+        cache = await self.scanner.get_cached_data(force_refresh=False)
+        available_loras = cache.raw_data if cache else []
+
+        # Apply pool filters if provided
+        if pool_config:
+            available_loras = await self._apply_pool_filters(
+                available_loras, pool_config
+            )
+
+        # Calculate slots needed (total - locked)
+        locked_count = len(locked_loras)
+        slots_needed = count - locked_count
+
+        if slots_needed < 0:
+            slots_needed = 0
+            # Too many locked, trim to target
+            locked_loras = locked_loras[:count]
+
+        # Filter out locked LoRAs from available pool
+        locked_names = {lora['name'] for lora in locked_loras}
+        available_pool = [
+            l for l in available_loras
+            if l['model_name'] not in locked_names
+        ]
+
+        # Ensure we don't try to select more than available
+        if slots_needed > len(available_pool):
+            slots_needed = len(available_pool)
+
+        # Random sample
+        selected = []
+        if slots_needed > 0:
+            selected = random.sample(available_pool, slots_needed)
+
+        # Generate random strengths for selected LoRAs
+        result_loras = []
+        for lora in selected:
+            model_str = round(
+                random.uniform(model_strength_min, model_strength_max), 2
+            )
+
+            if use_same_clip_strength:
+                clip_str = model_str
+            else:
+                clip_str = round(
+                    random.uniform(clip_strength_min, clip_strength_max), 2
+                )
+
+            result_loras.append({
+                'name': lora['model_name'],
+                'strength': model_str,
+                'clipStrength': clip_str,
+                'active': True,
+                'expanded': abs(model_str - clip_str) > 0.001,
+                'locked': False
+            })
+
+        # Merge with locked LoRAs
+        result_loras.extend(locked_loras)
+
+        return result_loras
+
+    async def _apply_pool_filters(self, available_loras: List[Dict], pool_config: Dict) -> List[Dict]:
+        """
+        Apply pool_config filters to available LoRAs.
+
+        Args:
+            available_loras: List of all LoRA dicts
+            pool_config: Dict with filter settings from LoRA Pool node
+
+        Returns:
+            Filtered list of LoRA dicts
+        """
+        from .model_query import FilterCriteria
+
+        # Extract filter parameters from pool_config
+        selected_base_models = pool_config.get('selected_base_models', [])
+        include_tags = pool_config.get('include_tags', [])
+        exclude_tags = pool_config.get('exclude_tags', [])
+        include_folders = pool_config.get('include_folders', [])
+        exclude_folders = pool_config.get('exclude_folders', [])
+        no_credit_required = pool_config.get('no_credit_required', False)
+        allow_selling = pool_config.get('allow_selling', False)
+
+        # Build tag filters dict
+        tag_filters = {}
+        for tag in include_tags:
+            tag_filters[tag] = 'include'
+        for tag in exclude_tags:
+            tag_filters[tag] = 'exclude'
+
+        # Build folder filter
+        if include_folders or exclude_folders:
+            filtered = []
+            for lora in available_loras:
+                folder = lora.get('folder', '')
+
+                # Check exclude folders first
+                excluded = False
+                for exclude_folder in exclude_folders:
+                    if folder.startswith(exclude_folder):
+                        excluded = True
+                        break
+
+                if excluded:
+                    continue
+
+                # Check include folders
+                if include_folders:
+                    included = False
+                    for include_folder in include_folders:
+                        if folder.startswith(include_folder):
+                            included = True
+                            break
+                    if not included:
+                        continue
+
+                filtered.append(lora)
+
+            available_loras = filtered
+
+        # Apply base model filter
+        if selected_base_models:
+            available_loras = [
+                lora for lora in available_loras
+                if lora.get('base_model') in selected_base_models
+            ]
+
+        # Apply tag filters
+        if tag_filters:
+            criteria = FilterCriteria(tags=tag_filters)
+            available_loras = self.filter_set.apply(available_loras, criteria)
+
+        # Apply license filters
+        if no_credit_required:
+            available_loras = [
+                lora for lora in available_loras
+                if not lora.get('civitai', {}).get('allowNoCredit', True)
+            ]
+
+        if allow_selling:
+            available_loras = [
+                lora for lora in available_loras
+                if lora.get('civitai', {}).get('allowCommercialUse', ['None'])[0] != 'None'
+            ]
+
+        return available_loras
--- a/tests/routes/test_randomizer_endpoints.py
+++ b/tests/routes/test_randomizer_endpoints.py
@@ -0,0 +1,191 @@
+import json
+from unittest.mock import MagicMock
+
+import pytest
+
+from py.routes.lora_routes import LoraRoutes
+
+
+class DummyRequest:
+    def __init__(self, *, query=None, match_info=None, json_data=None):
+        self.query = query or {}
+        self.match_info = match_info or {}
+        self._json_data = json_data or {}
+
+    async def json(self):
+        return self._json_data
+
+
+class StubLoraService:
+    """Stub service for testing randomizer endpoints"""
+
+    def __init__(self):
+        self.random_loras = []
+
+    async def get_random_loras(self, **kwargs):
+        return self.random_loras
+
+
+@pytest.fixture
+def routes():
+    handler = LoraRoutes()
+    handler.service = StubLoraService()
+    return handler
+
+
+async def test_get_random_loras_success(routes):
+    """Test successful random LoRA generation"""
+    routes.service.random_loras = [
+        {
+            'name': 'test_lora_1',
+            'strength': 0.8,
+            'clipStrength': 0.8,
+            'active': True,
+            'expanded': False,
+            'locked': False
+        },
+        {
+            'name': 'test_lora_2',
+            'strength': 0.6,
+            'clipStrength': 0.6,
+            'active': True,
+            'expanded': False,
+            'locked': False
+        }
+    ]
+
+    request = DummyRequest(json_data={
+        'count': 5,
+        'model_strength_min': 0.5,
+        'model_strength_max': 1.0,
+        'use_same_clip_strength': True,
+        'locked_loras': []
+    })
+
+    response = await routes.get_random_loras(request)
+    payload = json.loads(response.text)
+
+    assert response.status == 200
+    assert payload['success'] is True
+    assert 'loras' in payload
+    assert payload['count'] == 2
+
+
+async def test_get_random_loras_with_range(routes):
+    """Test random LoRAs with count range"""
+    routes.service.random_loras = [
+        {
+            'name': 'test_lora_1',
+            'strength': 0.8,
+            'clipStrength': 0.8,
+            'active': True,
+            'expanded': False,
+            'locked': False
+        }
+    ]
+
+    request = DummyRequest(json_data={
+        'count_min': 3,
+        'count_max': 7,
+        'model_strength_min': 0.0,
+        'model_strength_max': 1.0,
+        'use_same_clip_strength': True
+    })
+
+    response = await routes.get_random_loras(request)
+    payload = json.loads(response.text)
+
+    assert response.status == 200
+    assert payload['success'] is True
+
+
+async def test_get_random_loras_invalid_count(routes):
+    """Test invalid count parameter"""
+    request = DummyRequest(json_data={
+        'count': 150,  # Over limit
+        'model_strength_min': 0.0,
+        'model_strength_max': 1.0
+    })
+
+    response = await routes.get_random_loras(request)
+    payload = json.loads(response.text)
+
+    assert response.status == 400
+    assert payload['success'] is False
+    assert 'Count must be between 1 and 100' in payload['error']
+
+
+async def test_get_random_loras_invalid_strength(routes):
+    """Test invalid strength range"""
+    request = DummyRequest(json_data={
+        'count': 5,
+        'model_strength_min': -0.5,  # Invalid
+        'model_strength_max': 1.0
+    })
+
+    response = await routes.get_random_loras(request)
+    payload = json.loads(response.text)
+
+    assert response.status == 400
+    assert payload['success'] is False
+
+
+async def test_get_random_loras_with_locked(routes):
+    """Test random LoRAs with locked items"""
+    routes.service.random_loras = [
+        {
+            'name': 'new_lora',
+            'strength': 0.7,
+            'clipStrength': 0.7,
+            'active': True,
+            'expanded': False,
+            'locked': False
+        },
+        {
+            'name': 'locked_lora',
+            'strength': 0.9,
+            'clipStrength': 0.9,
+            'active': True,
+            'expanded': False,
+            'locked': True
+        }
+    ]
+
+    request = DummyRequest(json_data={
+        'count': 5,
+        'model_strength_min': 0.5,
+        'model_strength_max': 1.0,
+        'use_same_clip_strength': True,
+        'locked_loras': [
+            {
+                'name': 'locked_lora',
+                'strength': 0.9,
+                'clipStrength': 0.9,
+                'active': True,
+                'expanded': False,
+                'locked': True
+            }
+        ]
+    })
+
+    response = await routes.get_random_loras(request)
+    payload = json.loads(response.text)
+
+    assert response.status == 200
+    assert payload['success'] is True
+
+
+async def test_get_random_loras_error(routes, monkeypatch):
+    """Test error handling"""
+    async def failing(*_args, **_kwargs):
+        raise RuntimeError("Service error")
+
+    routes.service.get_random_loras = failing
+    request = DummyRequest(json_data={'count': 5})
+
+    response = await routes.get_random_loras(request)
+    payload = json.loads(response.text)
+
+    assert response.status == 500
+    assert payload['success'] is False
+    assert 'error' in payload
--- a/vue-widgets/src/components/LoraRandomizerWidget.vue
+++ b/vue-widgets/src/components/LoraRandomizerWidget.vue
@@ -0,0 +1,110 @@
+<template>
+  <div class="lora-randomizer-widget">
+    <LoraRandomizerSettingsView
+      :count-mode="state.countMode.value"
+      :count-fixed="state.countFixed.value"
+      :count-min="state.countMin.value"
+      :count-max="state.countMax.value"
+      :model-strength-min="state.modelStrengthMin.value"
+      :model-strength-max="state.modelStrengthMax.value"
+      :use-same-clip-strength="state.useSameClipStrength.value"
+      :clip-strength-min="state.clipStrengthMin.value"
+      :clip-strength-max="state.clipStrengthMax.value"
+      :roll-mode="state.rollMode.value"
+      :is-rolling="state.isRolling.value"
+      :is-clip-strength-disabled="state.isClipStrengthDisabled.value"
+      @update:count-mode="state.countMode.value = $event"
+      @update:count-fixed="state.countFixed.value = $event"
+      @update:count-min="state.countMin.value = $event"
+      @update:count-max="state.countMax.value = $event"
+      @update:model-strength-min="state.modelStrengthMin.value = $event"
+      @update:model-strength-max="state.modelStrengthMax.value = $event"
+      @update:use-same-clip-strength="state.useSameClipStrength.value = $event"
+      @update:clip-strength-min="state.clipStrengthMin.value = $event"
+      @update:clip-strength-max="state.clipStrengthMax.value = $event"
+      @update:roll-mode="state.rollMode.value = $event"
+      @roll="handleRoll"
+    />
+  </div>
+</template>
+
+<script setup lang="ts">
+import { onMounted } from 'vue'
+import LoraRandomizerSettingsView from './lora-randomizer/LoraRandomizerSettingsView.vue'
+import { useLoraRandomizerState } from '../composables/useLoraRandomizerState'
+import type { ComponentWidget, RandomizerConfig } from '../composables/types'
+
+// Props
+const props = defineProps<{
+  widget: ComponentWidget
+  node: { id: number }
+}>()
+
+// State management
+const state = useLoraRandomizerState(props.widget)
+
+// Handle roll button click
+const handleRoll = async () => {
+  try {
+    console.log('[LoraRandomizerWidget] Roll button clicked')
+
+    // Get pool config from connected input (if any)
+    // This would need to be passed from the node's pool_config input
+    const poolConfig = null // TODO: Get from node input if connected
+
+    // Get locked loras from the loras widget
+    // This would need to be retrieved from the loras widget on the node
+    const lockedLoras: any[] = [] // TODO: Get from loras widget
+
+    // Call API to get random loras
+    const randomLoras = await state.rollLoras(poolConfig, lockedLoras)
+
+    console.log('[LoraRandomizerWidget] Got random LoRAs:', randomLoras)
+
+    // Update the loras widget with the new selection
+    // This will be handled by emitting an event or directly updating the loras widget
+    // For now, we'll emit a custom event that the parent widget handler can catch
+    if (typeof (props.widget as any).onRoll === 'function') {
+      (props.widget as any).onRoll(randomLoras)
+    }
+  } catch (error) {
+    console.error('[LoraRandomizerWidget] Error rolling LoRAs:', error)
+    alert('Failed to roll LoRAs: ' + (error as Error).message)
+  }
+}
+
+// Lifecycle
+onMounted(async () => {
+  // Setup serialization
+  props.widget.serializeValue = async () => {
+    const config = state.buildConfig()
+    console.log('[LoraRandomizerWidget] Serializing config:', config)
+    return config
+  }
+
+  // Handle external value updates (e.g., loading workflow, paste)
+  props.widget.onSetValue = (v) => {
+    console.log('[LoraRandomizerWidget] Restoring from config:', v)
+    state.restoreFromConfig(v as RandomizerConfig)
+  }
+
+  // Restore from saved value
+  if (props.widget.value) {
+    console.log('[LoraRandomizerWidget] Restoring from saved value:', props.widget.value)
+    state.restoreFromConfig(props.widget.value as RandomizerConfig)
+  }
+})
+</script>
+
+<style scoped>
+.lora-randomizer-widget {
+  padding: 12px;
+  background: rgba(40, 44, 52, 0.6);
+  border-radius: 4px;
+  height: 100%;
+  display: flex;
+  flex-direction: column;
+  overflow: hidden;
+  box-sizing: border-box;
+}
+</style>
--- a/vue-widgets/src/components/lora-randomizer/LoraRandomizerSettingsView.vue
+++ b/vue-widgets/src/components/lora-randomizer/LoraRandomizerSettingsView.vue
@@ -0,0 +1,359 @@
+<template>
+  <div class="randomizer-settings">
+    <div class="settings-header">
+      <h3 class="settings-title">RANDOMIZER SETTINGS</h3>
+    </div>
+
+    <!-- LoRA Count -->
+    <div class="setting-section">
+      <label class="setting-label">LoRA Count</label>
+      <div class="count-mode-selector">
+        <label class="radio-label">
+          <input
+            type="radio"
+            name="count-mode"
+            value="fixed"
+            :checked="countMode === 'fixed'"
+            @change="$emit('update:countMode', 'fixed')"
+          />
+          <span>Fixed:</span>
+          <input
+            type="number"
+            class="number-input"
+            :value="countFixed"
+            :disabled="countMode !== 'fixed'"
+            min="1"
+            max="100"
+            @input="$emit('update:countFixed', parseInt(($event.target as HTMLInputElement).value))"
+          />
+        </label>
+      </div>
+      <div class="count-mode-selector">
+        <label class="radio-label">
+          <input
+            type="radio"
+            name="count-mode"
+            value="range"
+            :checked="countMode === 'range'"
+            @change="$emit('update:countMode', 'range')"
+          />
+          <span>Range:</span>
+          <input
+            type="number"
+            class="number-input"
+            :value="countMin"
+            :disabled="countMode !== 'range'"
+            min="1"
+            max="100"
+            @input="$emit('update:countMin', parseInt(($event.target as HTMLInputElement).value))"
+          />
+          <span>to</span>
+          <input
+            type="number"
+            class="number-input"
+            :value="countMax"
+            :disabled="countMode !== 'range'"
+            min="1"
+            max="100"
+            @input="$emit('update:countMax', parseInt(($event.target as HTMLInputElement).value))"
+          />
+        </label>
+      </div>
+    </div>
+
+    <!-- Model Strength Range -->
+    <div class="setting-section">
+      <label class="setting-label">Model Strength Range</label>
+      <div class="strength-inputs">
+        <div class="strength-input-group">
+          <label>Min:</label>
+          <input
+            type="number"
+            class="number-input"
+            :value="modelStrengthMin"
+            min="0"
+            max="10"
+            step="0.1"
+            @input="$emit('update:modelStrengthMin', parseFloat(($event.target as HTMLInputElement).value))"
+          />
+        </div>
+        <div class="strength-input-group">
+          <label>Max:</label>
+          <input
+            type="number"
+            class="number-input"
+            :value="modelStrengthMax"
+            min="0"
+            max="10"
+            step="0.1"
+            @input="$emit('update:modelStrengthMax', parseFloat(($event.target as HTMLInputElement).value))"
+          />
+        </div>
+      </div>
+    </div>
+
+    <!-- Clip Strength Range -->
+    <div class="setting-section">
+      <label class="setting-label">Clip Strength Range</label>
+      <div class="checkbox-group">
+        <label class="checkbox-label">
+          <input
+            type="checkbox"
+            :checked="useSameClipStrength"
+            @change="$emit('update:useSameClipStrength', ($event.target as HTMLInputElement).checked)"
+          />
+          <span>Same as model</span>
+        </label>
+      </div>
+      <div class="strength-inputs" :class="{ disabled: isClipStrengthDisabled }">
+        <div class="strength-input-group">
+          <label>Min:</label>
+          <input
+            type="number"
+            class="number-input"
+            :value="clipStrengthMin"
+            :disabled="isClipStrengthDisabled"
+            min="0"
+            max="10"
+            step="0.1"
+            @input="$emit('update:clipStrengthMin', parseFloat(($event.target as HTMLInputElement).value))"
+          />
+        </div>
+        <div class="strength-input-group">
+          <label>Max:</label>
+          <input
+            type="number"
+            class="number-input"
+            :value="clipStrengthMax"
+            :disabled="isClipStrengthDisabled"
+            min="0"
+            max="10"
+            step="0.1"
+            @input="$emit('update:clipStrengthMax', parseFloat(($event.target as HTMLInputElement).value))"
+          />
+        </div>
+      </div>
+    </div>
+
+    <!-- Roll Mode -->
+    <div class="setting-section">
+      <label class="setting-label">Roll Mode</label>
+      <div class="roll-mode-selector">
+        <label class="radio-label">
+          <input
+            type="radio"
+            name="roll-mode"
+            value="frontend"
+            :checked="rollMode === 'frontend'"
+            @change="$emit('update:rollMode', 'frontend')"
+          />
+          <span>Frontend Roll (fixed until re-rolled)</span>
+        </label>
+        <button
+          class="roll-button"
+          :disabled="rollMode !== 'frontend' || isRolling"
+          @click="$emit('roll')"
+        >
+          <span v-if="!isRolling">🎲 Roll</span>
+          <span v-else>Rolling...</span>
+        </button>
+      </div>
+      <div class="roll-mode-selector">
+        <label class="radio-label">
+          <input
+            type="radio"
+            name="roll-mode"
+            value="backend"
+            :checked="rollMode === 'backend'"
+            @change="$emit('update:rollMode', 'backend')"
+          />
+          <span>Backend Roll (randomizes each execution)</span>
+        </label>
+      </div>
+    </div>
+  </div>
+</template>
+
+<script setup lang="ts">
+defineProps<{
+  countMode: 'fixed' | 'range'
+  countFixed: number
+  countMin: number
+  countMax: number
+  modelStrengthMin: number
+  modelStrengthMax: number
+  useSameClipStrength: boolean
+  clipStrengthMin: number
+  clipStrengthMax: number
+  rollMode: 'frontend' | 'backend'
+  isRolling: boolean
+  isClipStrengthDisabled: boolean
+}>()
+
+defineEmits<{
+  'update:countMode': [value: 'fixed' | 'range']
+  'update:countFixed': [value: number]
+  'update:countMin': [value: number]
+  'update:countMax': [value: number]
+  'update:modelStrengthMin': [value: number]
+  'update:modelStrengthMax': [value: number]
+  'update:useSameClipStrength': [value: boolean]
+  'update:clipStrengthMin': [value: number]
+  'update:clipStrengthMax': [value: number]
+  'update:rollMode': [value: 'frontend' | 'backend']
+  roll: []
+}>()
+</script>
+
+<style scoped>
+.randomizer-settings {
+  display: flex;
+  flex-direction: column;
+  gap: 16px;
+  font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+  color: #e4e4e7;
+}
+
+.settings-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 8px;
+}
+
+.settings-title {
+  font-size: 11px;
+  font-weight: 600;
+  letter-spacing: 0.05em;
+  color: #a1a1aa;
+  margin: 0;
+  text-transform: uppercase;
+}
+
+.setting-section {
+  display: flex;
+  flex-direction: column;
+  gap: 8px;
+}
+
+.setting-label {
+  font-size: 12px;
+  font-weight: 500;
+  color: #d4d4d8;
+}
+
+.count-mode-selector,
+.roll-mode-selector {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 6px 8px;
+  background: rgba(30, 30, 36, 0.5);
+  border-radius: 4px;
+}
+
+.radio-label {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  font-size: 13px;
+  color: #e4e4e7;
+  cursor: pointer;
+  flex: 1;
+}
+
+.radio-label input[type='radio'] {
+  cursor: pointer;
+}
+
+.radio-label input[type='radio']:disabled {
+  cursor: not-allowed;
+}
+
+.number-input {
+  width: 60px;
+  padding: 4px 8px;
+  background: rgba(20, 20, 24, 0.6);
+  border: 1px solid rgba(255, 255, 255, 0.1);
+  border-radius: 3px;
+  color: #e4e4e7;
+  font-size: 13px;
+}
+
+.number-input:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+.strength-inputs {
+  display: flex;
+  gap: 12px;
+  padding: 6px 8px;
+  background: rgba(30, 30, 36, 0.5);
+  border-radius: 4px;
+}
+
+.strength-inputs.disabled {
+  opacity: 0.5;
+}
+
+.strength-input-group {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  flex: 1;
+}
+
+.strength-input-group label {
+  font-size: 12px;
+  color: #d4d4d8;
+}
+
+.checkbox-group {
+  padding: 6px 8px;
+  background: rgba(30, 30, 36, 0.5);
+  border-radius: 4px;
+}
+
+.checkbox-label {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  font-size: 13px;
+  color: #e4e4e7;
+  cursor: pointer;
+}
+
+.checkbox-label input[type='checkbox'] {
+  cursor: pointer;
+}
+
+.roll-button {
+  padding: 6px 16px;
+  background: linear-gradient(135deg, #3b82f6 0%, #2563eb 100%);
+  border: none;
+  border-radius: 4px;
+  color: white;
+  font-size: 13px;
+  font-weight: 500;
+  cursor: pointer;
+  transition: all 0.2s;
+  white-space: nowrap;
+}
+
+.roll-button:hover:not(:disabled) {
+  background: linear-gradient(135deg, #2563eb 0%, #1d4ed8 100%);
+  transform: translateY(-1px);
+  box-shadow: 0 2px 8px rgba(59, 130, 246, 0.3);
+}
+
+.roll-button:active:not(:disabled) {
+  transform: translateY(0);
+}
+
+.roll-button:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+  background: linear-gradient(135deg, #52525b 0%, #3f3f46 100%);
+}
+</style>
--- a/vue-widgets/src/composables/types.ts
+++ b/vue-widgets/src/composables/types.ts
@@ -37,13 +37,6 @@ export interface FolderTreeNode {
  children?: FolderTreeNode[]
 }

-export interface ComponentWidget {
-  serializeValue?: () => Promise<LoraPoolConfig>
-  value?: LoraPoolConfig | LegacyLoraPoolConfig
-  onSetValue?: (v: LoraPoolConfig | LegacyLoraPoolConfig) => void
-  updateConfig?: (v: LoraPoolConfig) => void
-}
-
 // Legacy config for migration (v1)
 export interface LegacyLoraPoolConfig {
  version: 1
@@ -59,3 +52,33 @@ export interface LegacyLoraPoolConfig {
  }
  preview: { matchCount: number; lastUpdated: number }
 }
+
+// Randomizer config
+export interface RandomizerConfig {
+  count_mode: 'fixed' | 'range'
+  count_fixed: number
+  count_min: number
+  count_max: number
+  model_strength_min: number
+  model_strength_max: number
+  use_same_clip_strength: boolean
+  clip_strength_min: number
+  clip_strength_max: number
+  roll_mode: 'frontend' | 'backend'
+}
+
+export interface LoraEntry {
+  name: string
+  strength: number
+  clipStrength: number
+  active: boolean
+  expanded: boolean
+  locked: boolean
+}
+
+export interface ComponentWidget {
+  serializeValue?: () => Promise<LoraPoolConfig | RandomizerConfig>
+  value?: LoraPoolConfig | LegacyLoraPoolConfig | RandomizerConfig
+  onSetValue?: (v: LoraPoolConfig | LegacyLoraPoolConfig | RandomizerConfig) => void
+  updateConfig?: (v: LoraPoolConfig | RandomizerConfig) => void
+}
--- a/vue-widgets/src/composables/useLoraRandomizerState.ts
+++ b/vue-widgets/src/composables/useLoraRandomizerState.ts
@@ -0,0 +1,142 @@
+import { ref, computed } from 'vue'
+import type { ComponentWidget, RandomizerConfig, LoraEntry } from './types'
+
+export function useLoraRandomizerState(widget: ComponentWidget) {
+  // State refs
+  const countMode = ref<'fixed' | 'range'>('range')
+  const countFixed = ref(5)
+  const countMin = ref(3)
+  const countMax = ref(7)
+  const modelStrengthMin = ref(0.0)
+  const modelStrengthMax = ref(1.0)
+  const useSameClipStrength = ref(true)
+  const clipStrengthMin = ref(0.0)
+  const clipStrengthMax = ref(1.0)
+  const rollMode = ref<'frontend' | 'backend'>('frontend')
+  const isRolling = ref(false)
+
+  // Build config object from current state
+  const buildConfig = (): RandomizerConfig => ({
+    count_mode: countMode.value,
+    count_fixed: countFixed.value,
+    count_min: countMin.value,
+    count_max: countMax.value,
+    model_strength_min: modelStrengthMin.value,
+    model_strength_max: modelStrengthMax.value,
+    use_same_clip_strength: useSameClipStrength.value,
+    clip_strength_min: clipStrengthMin.value,
+    clip_strength_max: clipStrengthMax.value,
+    roll_mode: rollMode.value,
+  })
+
+  // Restore state from config object
+  const restoreFromConfig = (config: RandomizerConfig) => {
+    countMode.value = config.count_mode || 'range'
+    countFixed.value = config.count_fixed || 5
+    countMin.value = config.count_min || 3
+    countMax.value = config.count_max || 7
+    modelStrengthMin.value = config.model_strength_min ?? 0.0
+    modelStrengthMax.value = config.model_strength_max ?? 1.0
+    useSameClipStrength.value = config.use_same_clip_strength ?? true
+    clipStrengthMin.value = config.clip_strength_min ?? 0.0
+    clipStrengthMax.value = config.clip_strength_max ?? 1.0
+    rollMode.value = config.roll_mode || 'frontend'
+  }
+
+  // Roll loras - call API to get random selection
+  const rollLoras = async (
+    poolConfig: any | null,
+    lockedLoras: LoraEntry[]
+  ): Promise<LoraEntry[]> => {
+    try {
+      isRolling.value = true
+
+      const config = buildConfig()
+
+      // Build request body
+      const requestBody: any = {
+        model_strength_min: config.model_strength_min,
+        model_strength_max: config.model_strength_max,
+        use_same_clip_strength: config.use_same_clip_strength,
+        clip_strength_min: config.clip_strength_min,
+        clip_strength_max: config.clip_strength_max,
+        locked_loras: lockedLoras,
+      }
+
+      // Add count parameters
+      if (config.count_mode === 'fixed') {
+        requestBody.count = config.count_fixed
+      } else {
+        requestBody.count_min = config.count_min
+        requestBody.count_max = config.count_max
+      }
+
+      // Add pool config if provided
+      if (poolConfig) {
+        // Convert pool config to backend format
+        requestBody.pool_config = {
+          selected_base_models: poolConfig.filters?.baseModels || [],
+          include_tags: poolConfig.filters?.tags?.include || [],
+          exclude_tags: poolConfig.filters?.tags?.exclude || [],
+          include_folders: poolConfig.filters?.folders?.include || [],
+          exclude_folders: poolConfig.filters?.folders?.exclude || [],
+          no_credit_required: poolConfig.filters?.license?.noCreditRequired || false,
+          allow_selling: poolConfig.filters?.license?.allowSelling || false,
+        }
+      }
+
+      // Call API endpoint
+      const response = await fetch('/api/lm/loras/random-sample', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify(requestBody),
+      })
+
+      if (!response.ok) {
+        const errorData = await response.json()
+        throw new Error(errorData.error || 'Failed to fetch random LoRAs')
+      }
+
+      const data = await response.json()
+
+      if (!data.success) {
+        throw new Error(data.error || 'Failed to get random LoRAs')
+      }
+
+      return data.loras || []
+    } catch (error) {
+      console.error('[LoraRandomizerState] Error rolling LoRAs:', error)
+      throw error
+    } finally {
+      isRolling.value = false
+    }
+  }
+
+  // Computed properties
+  const isClipStrengthDisabled = computed(() => useSameClipStrength.value)
+
+  return {
+    // State refs
+    countMode,
+    countFixed,
+    countMin,
+    countMax,
+    modelStrengthMin,
+    modelStrengthMax,
+    useSameClipStrength,
+    clipStrengthMin,
+    clipStrengthMax,
+    rollMode,
+    isRolling,
+
+    // Computed
+    isClipStrengthDisabled,
+
+    // Methods
+    buildConfig,
+    restoreFromConfig,
+    rollLoras,
+  }
+}
--- a/vue-widgets/src/main.ts
+++ b/vue-widgets/src/main.ts
@@ -1,13 +1,17 @@
 import { createApp, type App as VueApp } from 'vue'
 import PrimeVue from 'primevue/config'
 import LoraPoolWidget from '@/components/LoraPoolWidget.vue'
-import type { LoraPoolConfig, LegacyLoraPoolConfig } from './composables/types'
+import LoraRandomizerWidget from '@/components/LoraRandomizerWidget.vue'
+import type { LoraPoolConfig, LegacyLoraPoolConfig, RandomizerConfig } from './composables/types'

 // @ts-ignore - ComfyUI external module
 import { app } from '../../../scripts/app.js'

 const vueApps = new Map<number, VueApp>()

+// Cache for dynamically loaded addLorasWidget module
+let addLorasWidgetCache: any = null
+
 // @ts-ignore
 function createLoraPoolWidget(node) {
  const container = document.createElement('div')
@@ -78,14 +82,109 @@ function createLoraPoolWidget(node) {
  return { widget }
 }

+// @ts-ignore
+function createLoraRandomizerWidget(node) {
+  const container = document.createElement('div')
+  container.id = `lora-randomizer-widget-${node.id}`
+  container.style.width = '100%'
+  container.style.height = '100%'
+  container.style.display = 'flex'
+  container.style.flexDirection = 'column'
+  container.style.overflow = 'hidden'
+
+  let internalValue: RandomizerConfig | undefined
+
+  const widget = node.addDOMWidget(
+    'randomizer_config',
+    'RANDOMIZER_CONFIG',
+    container,
+    {
+      getValue() {
+        return internalValue
+      },
+      setValue(v: RandomizerConfig) {
+        internalValue = v
+        if (typeof widget.onSetValue === 'function') {
+          widget.onSetValue(v)
+        }
+      },
+      serialize: true,
+      getMinHeight() {
+        return 500
+      }
+    }
+  )
+
+  widget.updateConfig = (v: RandomizerConfig) => {
+    internalValue = v
+  }
+
+  // Handle roll event from Vue component
+  widget.onRoll = (randomLoras: any[]) => {
+    console.log('[createLoraRandomizerWidget] Roll event received:', randomLoras)
+
+    // Find the loras widget on this node and update it
+    const lorasWidget = node.widgets.find((w: any) => w.name === 'loras')
+    if (lorasWidget) {
+      lorasWidget.value = randomLoras
+      console.log('[createLoraRandomizerWidget] Updated loras widget with rolled LoRAs')
+    } else {
+      console.warn('[createLoraRandomizerWidget] loras widget not found on node')
+    }
+  }
+
+  const vueApp = createApp(LoraRandomizerWidget, {
+    widget,
+    node
+  })
+
+  vueApp.use(PrimeVue, {
+    unstyled: true,
+    ripple: false
+  })
+
+  vueApp.mount(container)
+  vueApps.set(node.id + 10000, vueApp) // Offset to avoid collision with pool widget
+
+  widget.computeLayoutSize = () => {
+    const minWidth = 500
+    const minHeight = 500
+    const maxHeight = 500
+
+    return { minHeight, minWidth, maxHeight }
+  }
+
+  widget.onRemove = () => {
+    const vueApp = vueApps.get(node.id + 10000)
+    if (vueApp) {
+      vueApp.unmount()
+      vueApps.delete(node.id + 10000)
+    }
+  }
+
+  return { widget }
+}
+
 app.registerExtension({
  name: 'LoraManager.VueWidgets',

-  getCustomWidgets() {
+    getCustomWidgets() {
    return {
      // @ts-ignore
      LORA_POOL_CONFIG(node) {
        return createLoraPoolWidget(node)
+      },
+      // @ts-ignore
+      RANDOMIZER_CONFIG(node) {
+        return createLoraRandomizerWidget(node)
+      },
+      // @ts-ignore
+      async LORAS(node: any) {
+        if (!addLorasWidgetCache) {
+          const module = await import(/* @vite-ignore */ '../loras_widget.js')
+          addLorasWidgetCache = module.addLorasWidget
+        }
+        return addLorasWidgetCache(node, 'loras', {}, null)
      }
    }
  }
--- a/vue-widgets/vite.config.mts
+++ b/vue-widgets/vite.config.mts
@@ -21,7 +21,8 @@ export default defineConfig({
        },
        rollupOptions: {
            external: [
-                '../../../scripts/app.js'
+                '../../../scripts/app.js',
+                '../loras_widget.js'
            ],
            output: {
                dir: '../web/comfyui/vue-widgets',
--- a/web/comfyui/lora_demo_widget.js
+++ b/web/comfyui/lora_demo_widget.js
@@ -0,0 +1,40 @@
+import { app } from "../../../scripts/app.js";
+
+app.registerExtension({
+  name: "LoraManager.LoraDemo",
+
+  // Hook into node creation
+  async nodeCreated(node) {
+    if (node.comfyClass !== "Lora Demo (LoraManager)") {
+      return;
+    }
+
+    // Store original onExecuted
+    const originalOnExecuted = node.onExecuted?.bind(node);
+
+    // Override onExecuted to handle UI updates
+    node.onExecuted = function(output) {
+      // Check if output has loras data
+      if (output?.loras && Array.isArray(output.loras)) {
+        console.log("[LoraDemoNode] Received loras data from backend:", output.loras);
+
+        // Find the loras widget on this node
+        const lorasWidget = node.widgets.find(w => w.name === 'loras');
+
+        if (lorasWidget) {
+          // Update widget value with backend data
+          lorasWidget.value = output.loras;
+
+          console.log(`[LoraDemoNode] Updated widget with ${output.loras.length} loras`);
+        } else {
+          console.warn("[LoraDemoNode] loras widget not found on node");
+        }
+      }
+
+      // Call original onExecuted if it exists
+      if (originalOnExecuted) {
+        return originalOnExecuted(output);
+      }
+    };
+  }
+});
--- a/web/comfyui/lora_randomizer_widget.js
+++ b/web/comfyui/lora_randomizer_widget.js
@@ -0,0 +1,44 @@
+import { app } from "../../../scripts/app.js";
+
+app.registerExtension({
+  name: "LoraManager.LoraRandomizer",
+
+  // Hook into node creation
+  async nodeCreated(node) {
+    if (node.comfyClass !== "Lora Randomizer (LoraManager)") {
+      return;
+    }
+
+    console.log("[LoraRandomizerWidget] Node created:", node.id);
+
+    // Store original onExecuted
+    const originalOnExecuted = node.onExecuted?.bind(node);
+
+    // Override onExecuted to handle UI updates
+    node.onExecuted = function(output) {
+      console.log("[LoraRandomizerWidget] Node executed with output:", output);
+
+      // Check if output has loras data
+      if (output?.loras && Array.isArray(output.loras)) {
+        console.log("[LoraRandomizerWidget] Received loras data from backend:", output.loras);
+
+        // Find the loras widget on this node
+        const lorasWidget = node.widgets.find(w => w.name === 'loras');
+
+        if (lorasWidget) {
+          // Update widget value with backend data
+          lorasWidget.value = output.loras;
+
+          console.log(`[LoraRandomizerWidget] Updated widget with ${output.loras.length} loras`);
+        } else {
+          console.warn("[LoraRandomizerWidget] loras widget not found on node");
+        }
+      }
+
+      // Call original onExecuted if it exists
+      if (originalOnExecuted) {
+        return originalOnExecuted(output);
+      }
+    };
+  }
+});
--- a/web/comfyui/lora_stacker.js
+++ b/web/comfyui/lora_stacker.js
@@ -97,27 +97,27 @@ app.registerExtension({
            if (isUpdating) return;
            isUpdating = true;

-          try {
-            // Update this stacker's direct trigger toggles with its own active loras
-            // Only if the stacker node itself is active (mode 0 for Always, mode 3 for On Trigger)
-            const isNodeActive = this.mode === undefined || this.mode === 0 || this.mode === 3;
-            const activeLoraNames = new Set();
-            if (isNodeActive) {
-              value.forEach((lora) => {
-                if (lora.active) {
-                  activeLoraNames.add(lora.name);
-                }
-              });
+            try {
+              // Update this stacker's direct trigger toggles with its own active loras
+              // Only if the stacker node itself is active (mode 0 for Always, mode 3 for On Trigger)
+              const isNodeActive = this.mode === undefined || this.mode === 0 || this.mode === 3;
+              const activeLoraNames = new Set();
+              if (isNodeActive) {
+                value.forEach((lora) => {
+                  if (lora.active) {
+                    activeLoraNames.add(lora.name);
+                  }
+                });
+              }
+              updateConnectedTriggerWords(this, activeLoraNames);
+
+              // Find all Lora Loader nodes in the chain that might need updates
+              updateDownstreamLoaders(this);
+            } finally {
+              isUpdating = false;
            }
-            updateConnectedTriggerWords(this, activeLoraNames);

-            // Find all Lora Loader nodes in the chain that might need updates
-            updateDownstreamLoaders(this);
-          } finally {
-            isUpdating = false;
-          }
-
-          scheduleInputSync(value);
+            scheduleInputSync(value);
        });

        this.lorasWidget = result.widget;
--- a/web/comfyui/vue-widgets/lora-manager-widgets.js
+++ b/web/comfyui/vue-widgets/lora-manager-widgets.js
--- a/web/comfyui/vue-widgets/lora-manager-widgets.js.map
+++ b/web/comfyui/vue-widgets/lora-manager-widgets.js.map