first version finished

README.md

@@ -1,354 +1,85 @@
 # ComfyUI-Grounding
 
-ComfyUI custom nodes for open-vocabulary object detection using state-of-the-art grounding models.
+**Grounding for dummies, simplest workflow**
 
-## Features
+![Simple Workflow](docs/simple.png)
 
-- **GroundingDINO**: Open-vocabulary object detection with natural language prompts
-- **YOLO-World**: Real-time open-vocabulary detection
-- **Batch Processing**: Process multiple images at once for improved efficiency
-- **Referring Expression Comprehension (REC)**: Use full text as label when no separators present
-- **Flexible Architecture**: Support for both local and HuggingFace implementations
-- **Utility Nodes**: Bounding box visualization and mask conversion
+🎯 **3 Nodes Total** - Loader → Detector → Visualizer
+🚀 **5 Model Families** - GroundingDINO, MM-GroundingDINO, OWLv2, Florence-2, YOLO-World
+💾 **Smart Caching** - Instant reload
+📦 **Batch Processing** - Multiple images at once
+🎭 **Built-in Masks** - No separate BboxToMask node needed
 
-## Supported Models
+## Quick Start
 
-### GroundingDINO (Original)
-- GroundingDINO-T (SwinT backbone, 694MB)
-- GroundingDINO-B (SwinB backbone, 938MB)
-
-### MM-GroundingDINO (Multimodal)
-**Tiny Models (Swin-T backbone):**
-- MM-GroundingDINO-Tiny (O365+GoldG, 50.4 mAP)
-- MM-GroundingDINO-Tiny (O365+GoldG+GRIT, 50.5 mAP)
-- MM-GroundingDINO-Tiny (O365+GoldG+V3Det, 50.6 mAP)
-- MM-GroundingDINO-Tiny (O365+GoldG+GRIT+V3Det, 50.4 mAP)
-
-**Base Models (Swin-B backbone):**
-- MM-GroundingDINO-Base (O365+GoldG+V3Det, 52.5 mAP)
-- MM-GroundingDINO-Base (All datasets, 59.5 mAP) ⭐ Recommended
-
-**Large Models (Swin-L backbone):**
-- MM-GroundingDINO-Large (O365v2+OIv6+GoldG, 53.0 mAP)
-- MM-GroundingDINO-Large (All datasets, 60.3 mAP) ⭐ Best accuracy
-
-### YOLO-World
-- YOLOv8s-World (small)
-- YOLOv8m-World (medium)
-- YOLOv8l-World (large)
-- YOLOv8x-World (extra large)
-
-## Installation
-
-1. Navigate to your ComfyUI custom nodes directory:
 ```bash
 cd ComfyUI/custom_nodes/
-```
-
-2. Clone or copy this repository:
-```bash
 git clone <repository-url> ComfyUI-Grounding
-```
-
-3. Install dependencies:
-```bash
 cd ComfyUI-Grounding
 pip install -r requirements.txt
 ```
 
-## Nodes
+## The Nodes
 
-### 1. GroundingDINOModelLoader
-Loads a GroundingDINO or MM-GroundingDINO model for object detection.
+### 1. Grounding Model Loader
+Load any of 10+ models from a single dropdown.
 
-**Inputs:**
-- `model_name`: Select from available GroundingDINO/MM-GroundingDINO models
-- `keep_in_memory`: Cache model in memory for faster reuse (default: True)
+  1. GroundingDINO: SwinT OGC (694MB) - IDEA-Research/grounding-dino-tiny
+  2. GroundingDINO: SwinB (938MB) - IDEA-Research/grounding-dino-base
+  3. MM-GroundingDINO: Tiny O365+GoldG (50.4 mAP) - openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg
+  4. MM-GroundingDINO: Tiny O365+GoldG+GRIT (50.5 mAP) - openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_grit
+  5. MM-GroundingDINO: Tiny O365+GoldG+V3Det (50.6 mAP) - openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det
+  6. MM-GroundingDINO: Base O365+GoldG+V3Det (52.5 mAP) - openmmlab-community/mm_grounding_dino_base_o365v1_goldg_v3det
+  7. MM-GroundingDINO: Base All Datasets (59.5 mAP) - openmmlab-community/mm_grounding_dino_base_all
+  8. MM-GroundingDINO: Large O365v2+OIv6+GoldG (53.0 mAP) - openmmlab-community/mm_grounding_dino_large_o365v2_oiv6_goldg
+  9. MM-GroundingDINO: Large All Datasets (60.3 mAP) - openmmlab-community/mm_grounding_dino_large_all
+  10. OWLv2: Base Patch16 - google/owlv2-base-patch16
+  11. OWLv2: Large Patch14 - google/owlv2-large-patch14
+  12. OWLv2: Base Patch16 Ensemble - google/owlv2-base-patch16-ensemble
+  13. OWLv2: Large Patch14 Ensemble - google/owlv2-large-patch14-ensemble ⭐
+  14. Florence-2: Base (0.23B params) - microsoft/Florence-2-base ⚡ Best Balance
+  15. Florence-2: Large (0.77B params) - microsoft/Florence-2-large ⭐
+  16. YOLO-World: v8s (Small) - yolov8s-worldv2.pt ⚡ Fastest
+  17. YOLO-World: v8m (Medium) - yolov8m-worldv2.pt
+  18. YOLO-World: v8l (Large) - yolov8l-worldv2.pt
+  19. YOLO-World: v8x (Extra Large) - yolov8x-worldv2.pt
 
-**Outputs:**
-- `model`: Loaded GroundingDINO model
-
-**Features:**
-- ✅ Model caching - loads instantly on second use
-- ✅ Local model storage in `ComfyUI-Grounding/Models/`
-- ✅ Supports both original and MM-GroundingDINO variants
-
-### 2. GroundingDINODetector
-Detects objects in images using text prompts.
-
-**Inputs:**
-- `model`: GroundingDINO model (from loader)
-- `image`: Input image (supports batches)
-- `prompt`: Text description of objects to detect (e.g., "person . car . dog .")
-- `box_threshold`: Confidence threshold for bounding boxes (0.0-1.0)
-- `text_threshold`: Confidence threshold for text matching (0.0-1.0)
-- `single_box_mode`: (Optional) Return only highest-scoring detection for REC
-- `output_format`: (Optional) "list_only" (default, SAM2-compatible) or "dict_with_data"
-
-**Outputs:**
-- `bboxes`: Detected bounding boxes (format depends on output_format setting)
-  - `list_only`: List of numpy arrays `[[boxes_img0], [boxes_img1], ...]` - SAM2-compatible
-  - `dict_with_data`: Dict `{"boxes": [...], "labels": [...], "scores": [...]}`
-- `annotated_image`: Image with drawn bounding boxes (batched)
-- `labels`: String with detected object labels
-
-**Features:**
-- ✅ Batch processing - process multiple images at once
-- ✅ Automatic REC mode - when prompt has no separators (. or ,), uses full text as label
-- ✅ Single box mode - returns only highest-scoring detection for referring expressions
-- ✅ Flexible output format - choose between SAM2-compatible list or full dict with metadata
-
-### 3. YOLOWorldModelLoader
-Loads a YOLO-World model for real-time detection.
-
-**Inputs:**
-- `model_name`: Select from available YOLO-World models
-- `keep_in_memory`: Cache model in memory for faster reuse (default: True)
-
-**Outputs:**
-- `model`: Loaded YOLO-World model
-
-**Features:**
-- ✅ Model caching - loads instantly on second use
-- ✅ Local model storage in `ComfyUI-Grounding/Models/`
-- ✅ Auto-downloads models on first use
-
-### 4. YOLOWorldDetector
-Detects objects using YOLO-World with custom classes.
-
-**Inputs:**
-- `model`: YOLO-World model (from loader)
-- `image`: Input image (supports batches)
-- `classes`: Comma-separated list of classes (e.g., "person, car, dog")
-- `confidence_threshold`: Minimum confidence for detections (0.0-1.0)
-- `single_box_mode`: (Optional) Return only highest-scoring detection for REC
-- `output_format`: (Optional) "list_only" (default, SAM2-compatible) or "dict_with_data"
-
-**Outputs:**
-- `bboxes`: Detected bounding boxes (format depends on output_format setting)
-  - `list_only`: List of numpy arrays `[[boxes_img0], [boxes_img1], ...]` - SAM2-compatible
-  - `dict_with_data`: Dict `{"boxes": [...], "labels": [...], "scores": [...]}`
-- `annotated_image`: Image with drawn bounding boxes (batched)
-- `labels`: String with detected object labels
-
-**Features:**
-- ✅ Batch processing - process multiple images at once
-- ✅ Automatic REC mode - when classes has no comma, uses full text as label
-- ✅ Single box mode - returns only highest-scoring detection for referring expressions
-- ✅ Flexible output format - choose between SAM2-compatible list or full dict with metadata
-
-### 5. BboxVisualizer
-Draws bounding boxes on images.
-
-**Inputs:**
-- `image`: Input image
-- `bboxes`: Bounding box data
-- `line_width`: Thickness of bounding box lines (optional)
-
-**Outputs:**
-- `annotated_image`: Image with drawn bounding boxes
-
-### 5. UnifiedDetector ⭐ NEW
-Universal detector that works with BOTH GroundingDINO and YOLO-World models.
-
-**Inputs:**
-- `model`: Any model (GroundingDINO or YOLO-World)
-- `image`: Input image
-- `prompt`: Text prompt (supports both formats)
-  - For GroundingDINO: "cat . dog . car ."
-  - For YOLO-World: "cat, dog, car"
-- `threshold`: Confidence threshold (0.0-1.0)
-- `text_threshold`: Text matching threshold (optional, GroundingDINO only)
-
-**Outputs:**
-- `bboxes`: Detected bounding boxes with labels and scores
-- `annotated_image`: Image with drawn bounding boxes
-- `labels`: String with detected object labels
-
-**Features:**
-- ✅ Auto-detects model type
-- ✅ Single node for all detection models
-- ✅ Simplified workflow
-
-### 6. BboxToMask
-Converts bounding boxes to binary masks.
-
-**Inputs:**
-- `image`: Input image (for dimensions)
-- `bboxes`: Bounding box data
-- `combine_masks`: Whether to combine all boxes into one mask (optional)
-
-**Outputs:**
-- `masks`: Binary mask(s) for detected regions
+### 2. Grounding Detector
+Universal detector for all models.
 
 ## Key Features
 
-### 🚀 Model Caching
-Models are cached in memory by default for instant reuse:
-- First load: Downloads and initializes model
-- Second load: Instant loading from cache
-- Toggle `keep_in_memory=False` to clear cache
+- Overrides standard text label splitting. It splits only at ".", otherwise label is what you write
+- Enable `single_box_mode` for single detection
+- First load: Downloads model
+- Second load: Instant from cache
+- Models stored in `ComfyUI-Grounding/models/`
+- Process multiple images in one pass
+- All nodes support batches
 
-### 📁 Local Model Storage
-All models are downloaded to the plugin's local Models directory:
-- Location: `ComfyUI/custom_nodes/ComfyUI-Grounding/Models/`
-- Auto-created on first use with a `models_go_here.txt` placeholder
-- GroundingDINO/MM-GroundingDINO: Stored in HuggingFace transformers cache format
-- YOLO-World: Stored as `.pt` files (yolov8s-world.pt, etc.)
+## Florence-2 Attention Modes
 
-### 🎯 UnifiedDetector
-One detector for all models - automatically detects whether you're using GroundingDINO or YOLO-World and routes to the appropriate inference method.
+- `eager` - Most compatible (default)
+- `sdpa` - Faster on PyTorch 2.0+
+- `flash_attention_2` - Fastest on A100/H100
 
-### 📦 Batch Processing
-All detector nodes support batch processing:
-- Process multiple images in a single forward pass
-- Input images as a batch tensor (B, H, W, C)
-- Returns batched bounding boxes with lists of detections for each image
-- Annotated images are returned as a single batch tensor
+### 2. Bounding Box Visualizer
 
-### 🎯 Referring Expression Comprehension (REC)
-Automatic REC mode for natural language queries:
-- **GroundingDINO**: If prompt has no periods (`.`) or commas (`,`), uses full text as label
-  - Example: `"main object in the center of the picture"` → all detections labeled with full text
-  - With separators: `"person . car . dog ."` → uses standard noun phrase extraction
-- **YOLO-World**: If classes has no comma (`,`), uses full text as label
-  - Example: `"red car in the parking lot"` → all detections labeled with full text
-  - With separators: `"person, car, dog"` → uses standard class mapping
-- **Single Box Mode**: Enable `single_box_mode` to return only the highest-scoring detection (perfect for REC tasks)
-
-### 📋 Output Formats
-Detector nodes support two output formats for bounding boxes:
-
-**1. `list_only` (Default - SAM2 Compatible)**
-- Returns: List of numpy arrays, one per image in batch
-- Format: `[[bbox1, bbox2], [bbox3], ...]` where each bbox is `[x1, y1, x2, y2]`
-- Use when: Connecting to SAM2 segmentation nodes
-- Example:
-  ```python
-  # For 2 images with 2 and 1 detections respectively
-  [
-      array([[10, 20, 100, 200], [50, 60, 150, 250]]),  # Image 0: 2 boxes
-      array([[30, 40, 120, 180]])                       # Image 1: 1 box
-  ]
-  ```
-
-**2. `dict_with_data` (Full Metadata)**
-- Returns: Dictionary with boxes, labels, and scores
-- Format: `{"boxes": [...], "labels": [...], "scores": [...]}`
-- Use when: Need label and confidence information for downstream processing
-- NOT compatible with SAM2 (dict iteration gives keys, not values)
-- Example:
-  ```python
-  {
-      "boxes": [array([...]), array([...])],      # Boxes per image
-      "labels": [["cat", "dog"], ["bird"]],       # Labels per image
-      "scores": [array([0.9, 0.8]), array([0.7])] # Scores per image
-  }
-  ```
-
-**Recommendation:** Use `list_only` (default) unless you specifically need label/score metadata for your workflow.
-
-## Usage Examples
-
-### Example 1: Basic Object Detection with GroundingDINO
-
-1. Add `GroundingDINOModelLoader` node
-2. Select model (e.g., "GroundingDINO_SwinT_OGC (694MB)")
-3. Add `GroundingDINODetector` node
-4. Connect model output to detector
-5. Load an image and connect to detector
-6. Set prompt: "person . car . bicycle ."
-7. Adjust thresholds as needed
-8. View annotated image output
-
-### Example 2: YOLO-World Detection
-
-1. Add `YOLOWorldModelLoader` node
-2. Select model (e.g., "yolov8s-world")
-3. Add `YOLOWorldDetector` node
-4. Connect model and image
-5. Set classes: "cat, dog, bird, horse"
-6. Adjust confidence threshold
-7. View results
-
-### Example 3: Using UnifiedDetector (Recommended)
-
-1. Add any model loader (GroundingDINO or YOLO-World)
-2. Load your model
-3. Add `UnifiedDetector` node
-4. Connect model and image
-5. Set prompt (works with both formats!)
-6. The node automatically detects model type and uses the right inference
-
-### Example 4: MM-GroundingDINO for High Accuracy
-
-1. Add `GroundingDINOModelLoader` node
-2. Select "MM-GroundingDINO-Base (All datasets, 59.5 mAP)"
-3. Enable `keep_in_memory=True` (default)
-4. Add `GroundingDINODetector` or `UnifiedDetector`
-5. Set prompt: "specific object . another object ."
-6. Enjoy state-of-the-art detection accuracy!
-
-### Example 5: Convert Detections to Masks
-
-1. Use any detector node (GroundingDINO or YOLO-World)
-2. Add `BboxToMask` node
-3. Connect image and bboxes outputs
-4. Choose whether to combine masks
-5. Use masks for segmentation, inpainting, etc.
-
-## Model Downloads
-
-Models will be automatically downloaded on first use:
-- **All models**: Stored in `ComfyUI/custom_nodes/ComfyUI-Grounding/Models/`
-- **GroundingDINO/MM-GroundingDINO**: HuggingFace transformers cache format
-- **YOLO-World**: Direct `.pt` files (e.g., yolov8s-world.pt)
+Re-draw bboxes on images with custom line width. Optional since detector already returns annotated images.
 
 ## Tips
 
-- **GroundingDINO prompts**: End each object with a period (e.g., "cat . dog . car .")
-- **YOLO-World classes**: Use comma-separated values (e.g., "cat, dog, car")
-- **Threshold tuning**: Start with 0.3 and adjust based on results
-  - Lower threshold = more detections (including false positives)
-  - Higher threshold = fewer, more confident detections
-- **Performance**: YOLO-World is faster but GroundingDINO may be more accurate
-
-## Integration with SAM2
-
-These nodes work seamlessly with SAM2 for instance segmentation:
-
-1. Use GroundingDINO or YOLO-World to detect objects
-2. Convert bboxes to masks using `BboxToMask`
-3. Use masks with SAM2 for precise segmentation
-
-## Troubleshooting
-
-### ImportError: transformers not found
-```bash
-pip install transformers timm
-```
-
-### ImportError: ultralytics not found
-```bash
-pip install ultralytics
-```
-
-### Models not downloading
-Check your internet connection and ensure you have write permissions to the ComfyUI models directory.
-
-### CUDA out of memory
-Try using a smaller model variant (e.g., GroundingDINO-T instead of -B, or yolov8s instead of yolov8x).
-
-## License
-
-MIT License - See LICENSE file for details
+**Prompt Format:**
+- Use periods for multiple labels → `"cat . dog ."`
+- Use commas to keep a single label → `"cat, dog"`
 
 ## Credits
 
-- GroundingDINO: [IDEA-Research](https://github.com/IDEA-Research/GroundingDINO)
-- YOLO-World: [Ultralytics](https://github.com/ultralytics/ultralytics)
-- Transformers: [HuggingFace](https://huggingface.co/transformers)
+- [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO) - IDEA-Research
+- [OWLv2](https://huggingface.co/google/owlv2-base-patch16) - Google Research
+- [Florence-2](https://huggingface.co/microsoft/Florence-2-base) - Microsoft Research
+- [YOLO-World](https://github.com/ultralytics/ultralytics) - Ultralytics
 
-## Support
+## License
 
-For issues and feature requests, please visit the GitHub repository.
+MIT License

__init__.py

@@ -1,36 +1,24 @@
 """
-ComfyUI-Grounding: Object detection nodes for ComfyUI
-Supports GroundingDINO, MM-GroundingDINO, and YOLO-World models
+ComfyUI-Grounding: Simplified object detection nodes for ComfyUI
+Supports GroundingDINO, MM-GroundingDINO, OWLv2, Florence-2, and YOLO-World
 """
 
 from .nodes import (
-    GroundingDINOModelLoader,
-    GroundingDINODetector,
-    YOLOWorldModelLoader,
-    YOLOWorldDetector,
-    UnifiedDetector,
+    GroundingModelLoader,
+    GroundingDetector,
     BboxVisualizer,
-    BboxToMask
 )
 
 NODE_CLASS_MAPPINGS = {
-    "GroundingDINOModelLoader": GroundingDINOModelLoader,
-    "GroundingDINODetector": GroundingDINODetector,
-    "YOLOWorldModelLoader": YOLOWorldModelLoader,
-    "YOLOWorldDetector": YOLOWorldDetector,
-    "UnifiedDetector": UnifiedDetector,
+    "GroundingModelLoader": GroundingModelLoader,
+    "GroundingDetector": GroundingDetector,
     "BboxVisualizer": BboxVisualizer,
-    "BboxToMask": BboxToMask,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "GroundingDINOModelLoader": "Grounding DINO Model Loader",
-    "GroundingDINODetector": "Grounding DINO Detector",
-    "YOLOWorldModelLoader": "YOLO-World Model Loader",
-    "YOLOWorldDetector": "YOLO-World Detector",
-    "UnifiedDetector": "Unified Detector (Auto)",
+    "GroundingModelLoader": "Grounding Model Loader",
+    "GroundingDetector": "Grounding Detector",
     "BboxVisualizer": "Bounding Box Visualizer",
-    "BboxToMask": "Bounding Box to Mask",
 }
 
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

models/.gitignore

@@ -0,0 +1,8 @@
+# Ignore all files in this directory (model downloads)
+*
+
+# But keep this .gitignore file
+!.gitignore
+
+# And keep the placeholder file
+!models_go_here.txt

requirements.txt

@@ -1,15 +1,14 @@
 # Core dependencies for ComfyUI-Grounding
 
-# For GroundingDINO (HuggingFace transformers implementation)
+# For GroundingDINO, MM-GroundingDINO, and OWLv2 (HuggingFace transformers)
 transformers>=4.30.0
 timm>=0.9.0
 
 # For YOLO-World
 ultralytics>=8.2.0
 
-# Image processing
-opencv-python>=4.8.0
-pillow>=10.0.0
+# Image processing (usually already installed with ComfyUI)
+# pillow>=10.0.0
 
 # Optional: supervision for advanced visualization
 # supervision>=0.16.0

workflows/Example.json

@@ -0,0 +1,353 @@
+{
+  "id": "b29971c7-6074-4707-895e-9f2360e11e2d",
+  "revision": 0,
+  "last_node_id": 30,
+  "last_link_id": 40,
+  "nodes": [
+    {
+      "id": 29,
+      "type": "LoadImage",
+      "pos": [
+        691.3480657963805,
+        244.71705551823334
+      ],
+      "size": [
+        274.080078125,
+        314
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            38,
+            40
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.65",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "example.png",
+        "image"
+      ]
+    },
+    {
+      "id": 28,
+      "type": "MaskPreview",
+      "pos": [
+        1252.4602357504743,
+        329.44736978995775
+      ],
+      "size": [
+        140,
+        246.00000000000003
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 37
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.65",
+        "Node name for S&R": "MaskPreview"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 22,
+      "type": "PreviewImage",
+      "pos": [
+        1937.3573937178255,
+        228.10370816997101
+      ],
+      "size": [
+        140,
+        246.00000000000003
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 32
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.65",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 30,
+      "type": "PreviewImage",
+      "pos": [
+        1664.5457193908678,
+        -114.32244507189236
+      ],
+      "size": [
+        140,
+        246
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 39
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.65",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 21,
+      "type": "BboxVisualizer",
+      "pos": [
+        1608.8053963516986,
+        258.9104840919287
+      ],
+      "size": [
+        210,
+        78
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 40
+        },
+        {
+          "name": "bboxes",
+          "type": "BBOX",
+          "link": 31
+        }
+      ],
+      "outputs": [
+        {
+          "name": "annotated_image",
+          "type": "IMAGE",
+          "links": [
+            32
+          ]
+        }
+      ],
+      "properties": {
+        "aux_id": "PozzettiAndrea/ComfyUI-Grounding",
+        "ver": "c9c72642cd7affb18d5aa54f21ee7d3acf7412eb",
+        "Node name for S&R": "BboxVisualizer"
+      },
+      "widgets_values": [
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