decreased verbosity
This commit is contained in:
Andrea Pozzetti
@@ -44,119 +44,44 @@ Control label separation: use periods for multiple labels, commas for single com
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## Installation
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**On first ComfyUI startup:**
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- Example assets auto-copied to `ComfyUI/input/`
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- Example workflows auto-copied to user workflows with "ComfyUI-Grounding_" prefix
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```bash
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cd ComfyUI/custom_nodes/
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git clone https://github.com/PozzettiAndrea/ComfyUI-Grounding
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cd ComfyUI-Grounding
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pip install -r requirements.txt
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```
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On first startup, example assets and workflows are auto-installed.
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## The Nodes
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### Detection Nodes
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#### 1. Grounding Model (down)Loader
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Load any of 15+ models from a single dropdown. See footnotes for full list
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#### 2. Grounding Detector
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Universal detector for all models with bbox/mask output.
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#### Key Features
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<div style="font-size: 0.9em; line-height: 1.4;">
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- Overrides standard text label splitting. It splits only at ".", otherwise label is what you write
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- Enable `single_box_mode` for single detection
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- First load: Downloads model
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- Second load: Instant from cache
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- Models stored in ComfyUI standard folders:
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- GroundingDINO → `models/grounding-dino/`
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- Florence-2 & OWLv2 → `models/LLM/`
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- YOLO-World → `models/yolo_world/`
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- SAM2 → `models/sam2/`
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- Process multiple images in one pass
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- All nodes support batches
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</div>
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**1. Grounding Model Loader** - 19+ models in dropdown (see footnotes)
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**2. Grounding Detector** - Universal detector, splits labels on "." only
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### Mask Generation Nodes
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#### 3. Grounding Mask (down)Loader
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<div style="font-size: 0.9em; line-height: 1.4;">
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Load mask generation models:
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**Florence-2 Segmentation (2 models)**
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- Direct segmentation masks from text prompts
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**SA2VA Vision-Language (4 models)**
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- Visual grounding with advanced semantic understanding
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- Requires `trust_remote_code=True`
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- Supports fp16/bf16/fp32 precision options
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</div>
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#### 4. Grounding Mask Detector
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<div style="font-size: 0.9em; line-height: 1.4;">
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Direct mask generation from text prompts. Outputs masks, overlaid images, and generated text descriptions.
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</div>
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**3. Grounding Mask Loader** - Florence-2 (2) + SA2VA (4) models
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**4. Grounding Mask Detector** - Direct masks from text, outputs masks + overlays + descriptions
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### SAM2 Segmentation Nodes
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#### 5. SAM2 Model (down)Loader
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<div style="font-size: 0.9em; line-height: 1.4;">
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Load SAM2 models for high-quality segmentation:
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**SAM2/2.1 (4 variants)**
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Auto-downloads from HuggingFace (Kijai/sam2-safetensors) if not found locally.
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Supports fp16/bf16/fp32 precision and 3 segmentor modes: single_image, video, automaskgenerator.
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</div>
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#### 6. SAM2 Segment
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<div style="font-size: 0.9em; line-height: 1.4;">
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Segment using bboxes from grounding models or point coordinates.
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- Supports batch processing
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- Models cached in memory for instant reload
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- Compatible with all grounding model outputs
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</div>
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**5. SAM2 Model Loader** - SAM2/2.1 (8 variants), auto-downloads, fp16/bf16/fp32
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**6. SAM2 Segment** - Segment from bboxes or points
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### Utility Nodes
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#### 7. Bounding Box Visualizer
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<div style="font-size: 0.9em; line-height: 1.4;">
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Re-draw bboxes on images with custom line width. Optional since detector already returns annotated images.
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</div>
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#### 8. Batch Crop and Pad From Mask
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<div style="font-size: 0.9em; line-height: 1.4;">
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Crops images to mask bounding boxes and pads them to uniform size for batch processing.
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</div>
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**7. Bounding Box Visualizer** - Custom line width
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**8. Batch Crop and Pad** - Uniform sizing for batches
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## Example Workflows
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Three ready-to-use workflows in `/workflows/`:
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- **normal_grounding.json** - Object detection + SAM2 segmentation pipeline
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- **batch_normal_grounding.json** - Batch version for processing multiple images
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- **mask_grounding.json** - Direct mask generation using SA2VA (faster for segmentation)
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Load these in ComfyUI to see the nodes in action.
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- **normal_grounding.json** - Detection + SAM2 segmentation
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- **batch_normal_grounding.json** - Multi-image processing
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- **mask_grounding.json** - Direct SA2VA masking
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## Advanced Features
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**Detection Modes:**
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- `single_box_mode` - Returns only highest-scoring detection (useful for referring expressions)
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- `single_box_per_prompt_mode` - Returns best detection per label
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**Output Formats:**
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- `list_only` - Simple bbox list (SAM2-compatible)
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- `dict_with_data` - Includes labels and confidence scores
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**Smart Caching:**
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- Models cached in memory after first load
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- Instant reload on subsequent uses
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- Cache key includes model type and configuration
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**Detection modes:** `single_box_mode` (top result only) • `single_box_per_prompt_mode` (best per label)
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**Output formats:** `list_only` (SAM2-compatible) • `dict_with_data` (with labels/scores)
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**Prompt format:** Use periods for multiple labels `"dog. cat."` • Use commas for single label `"small, brown dog"`
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## Credits
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