These files contain ComfyUI API prompt graphs: the object that belongs under the prompt key in a POST /prompt request. They are not frontend workflow exports and are not intended for drag-and-drop import into the canvas.

Before queueing:

Copy the named image/video into ComfyUI/input, or change the image/file widget value to an existing input filename.
Restart ComfyUI after installing or updating this node pack.
Confirm every class_type is present in /object_info.
Wrap the loaded JSON as {"prompt": graph} in the API request.

Examples

`grounding_dino_image_api.json`

Runs Grounding DINO Tiny over grounding_input.png. Node 2 outputs:

Index	Output
0	`VLM_DETECTIONS`
1	Structured detection JSON
2	Detection overlay
3	Box mask
4	Core nested per-frame `BOUNDING_BOX`
5	Flat metadata-rich `BOUNDING_BOXES`

PreviewImage displays output 2 and ViewText reports output 1.

`vlm_performance_preflight_api.json`

Loads vlm_api_people_birds.mp4 with Comfy core video nodes, applies the Fast video performance profile, runs the track-aware adaptive sampler, and then applies a 14-pixel-aligned image budget. The preview shows the exact batch that can be connected to any local or hosted VLM. Three ViewText nodes report the selected source indices/timestamps, pixel reduction, and active profile.

`moondream3_preview_svg_segment_api.json`

Runs the official Moondream 3 Preview SVG segmentation skill over moondream_segment_input.png. Read the linked model license and change license_accepted to true before queueing. The graph previews the black/white mask, isolated foreground cutout, and mask/box/polygon overlay; ViewText receives the exact native SVG path plus its normalized bbox.

Moondream's path coordinates are normalized within the returned bbox. The node preserves that path verbatim, safely flattens curves/arcs, applies an even-odd fill for subpath holes, and supersamples the raster edge. The canonical detection keeps both the primary polygon and the full in-process mask.

`moondream31_video_detect_api.json`

Loads moondream_video_input.mp4, passes the real frame batch and source FPS to Moondream, and analyzes every frame with four concurrent requests. Photon uses the Loader's max_batch_size=4 scheduler capacity to form dynamic batches. ViewText reports measured throughput and real-time factor. Increase frame_stride to 2, 3, or more when full-frame analysis cannot keep up with the source FPS; the canonical results preserve original frame indices and timestamps.

`sam2_video_tracking_api.json`

Runs this bounded pipeline:

LoadVideo → Video Slice → GetVideoComponents → ImageScale → ImageFromBatch → Grounding DINO first-frame detection → SAM2.1 propagation.

The example limits the source to two seconds, scales its largest dimension to 768 pixels while preserving aspect ratio, unloads Grounding DINO after seeding, and keeps SAM2.1 video state on CPU. The example requests only the union mask volume; change mask_output to union_and_objects only when every per-object mask is required. VLMTrackReport is an output node and the final PreviewImage displays SAM2.1 output index 4.

For a longer source, change start_time and keep a bounded duration. Independent slices create independent object-ID sessions.

`sam3_core_adapter_blueprint_api.json`

Uses ComfyUI core nodes to load and run SAM3.1, then passes core SAM3_TRACK_DATA through VLMSAM3TrackAdapter. The adapter's output 1 is the unchanged core payload consumed by SAM3_TrackPreview; output 0 is canonical VLM_TRACKS consumed by VLMTrackReport.

The graph intentionally names:

ComfyUI/models/checkpoints/sam3.1_multiplex_fp16.safetensors

The checkpoint is not bundled. Review the SAM License before downloading Comfy-Org/sam3.1. ComfyUI rejects the graph at prompt validation when the named checkpoint is absent. Use the SAM2.1 example when SAM3.1 access or compatible core support is unavailable.

Output history

ComfyUI returns image/video previews in the execution history and text reports in the output-node UI payload. Canonical JSON is also available on the linked string outputs. Dense masks intentionally stay as tensors rather than being embedded in the JSON report.

Creator mask outputs

VLM Detections to Masks preserves its original first three outputs and appends creator-ready derivatives:

Index	Output
0	Per-frame combined/union `MASK`
1	Flattened per-object `MASK` batch
2	JSON mapping each object mask to its frame/detection/track
3	Per-frame inverse/background `MASK`
4	Combined masks as black-and-white `IMAGE` batches
5	Individual masks as black-and-white `IMAGE` batches
6	Stable-color per-frame instance maps

All binary mask values are exactly zero or one. VLM Mask Processor can grow, shrink, and feather any of these masks and returns processed, binary, inverse, and black-and-white image outputs. VLM Mask Composite accepts the resulting mask plus still-image or video frames and returns a composite, isolated foreground, background-only plate, and mask image. Connect an optional background image/video batch to replace the solid background color.