ComfyUI-Grounding

Grounding toolbox

🎯 8 Nodes Total - 2 Loaders + 2 Detectors + 2 SAM2 + 2 Utilities

🚀 6 Model Families - GroundingDINO, MM-GroundingDINO, OWLv2, Florence-2, YOLO-World, SA2VA

🤖 33 Models - 19 bbox detection + 6 mask generation + 8 SAM2 variants

💾 Smart Caching - Instant reload

📦 Batch Processing - Multiple images at once

🎭 Built-in Masks - No separate BboxToMask node needed

Visual Demos

Model Switching

Switch between 19+ detection models with a single dropdown. One node for everything.

SA2VA Vision-Language Segmentation

When Florence2 isn't enough. Sa2va has VERY advanced semantic understanding and reasoning capabilities.

SAM2 Support

Batch Processing

Process multiple images simultaneously with all nodes supporting batch operations.

Label Splitting Logic

Control label separation: use periods for multiple labels, commas for single compound labels.

Installation

cd ComfyUI/custom_nodes/
git clone https://github.com/PozzettiAndrea/ComfyUI-Grounding
cd ComfyUI-Grounding
pip install -r requirements.txt

On first startup, example assets and workflows are auto-installed.

The Nodes

Detection Nodes

1. Grounding Model Loader - 19+ models in dropdown (see footnotes)

2. Grounding Detector - Universal detector, splits labels on "." only

Mask Generation Nodes

3. Grounding Mask Loader - Florence-2 (2) + SA2VA (4) models

4. Grounding Mask Detector - Direct masks from text, outputs masks + overlays + descriptions

SAM2 Segmentation Nodes

5. SAM2 Model Loader - SAM2/2.1 (8 variants), auto-downloads, fp16/bf16/fp32

6. SAM2 Segment - Segment from bboxes or points

Utility Nodes

7. Bounding Box Visualizer - Custom line width

8. Batch Crop and Pad - Uniform sizing for batches

Example Workflows

• normal_grounding.json - Detection + SAM2 segmentation
• batch_normal_grounding.json - Multi-image processing
• mask_grounding.json - Direct SA2VA masking

Advanced Features

Detection modes: single_box_mode (top result only) • single_box_per_prompt_mode (best per label) Output formats: list_only (SAM2-compatible) • dict_with_data (with labels/scores) Prompt format: Use periods for multiple labels "dog. cat." • Use commas for single label "small, brown dog"

Credits

• GroundingDINO - IDEA-Research
• OWLv2 - Google Research
• Florence-2 - Microsoft Research
• YOLO-World - Ultralytics

License

MIT License

Footnotes

Full list of models:

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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
15. Florence-2: Large (0.77B params) - microsoft/Florence-2-large
16. YOLO-World: v8s (Small) - yolov8s-worldv2.pt
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

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