OpenShot-ComfyUI provides production-focused ComfyUI nodes built for OpenShot integration, with a strong focus on reliable SAM2 workflows for longer videos.
The goal is simple: make advanced segmentation and video analysis features feel native inside OpenShot's UI, while keeping the underlying Comfy graphs stable, predictable, and memory-safe.
OpenShot needs SAM2 pipelines that can handle real-world clips, not just short demos.
Many SAM2 custom-node workflows process or retain full-video state in ways that become fragile or memory-heavy as clip length grows. In practice, that can lead to slowdowns, failures, or OOM behavior on longer timelines.
This project addresses that gap with chunk-oriented processing designed specifically for OpenShot's planned UI integration path.
- Keep node interfaces close to standard ComfyUI types and patterns.
- Process video segmentation in bounded chunks instead of retaining full-video mask history.
- Return outputs that are easier for OpenShot to consume and orchestrate in larger editing workflows.
- Include practical companion nodes (GroundingDINO + TransNetV2) that support automated, timeline-aware tooling.
OpenShotDownloadAndLoadSAM2ModelOpenShotSam2Segmentation(single-image)OpenShotSam2VideoSegmentationAddPointsOpenShotSam2VideoSegmentationChunked(meta-batch/chunk friendly)OpenShotGroundingDinoDetect(text-prompted object detection -> mask + JSON)OpenShotTransNetSceneDetect(direct TransNetV2 inference -> IN/OUT JSON ranges)
This project is inspired by and partially based on ideas and APIs from:
kijai/ComfyUI-segment-anything-2- Meta SAM2 research/code
Please see upstream projects for full original implementations and credits.
- ComfyUI
- PyTorch (as used by your Comfy install)
decord(required by many SAM2 video predictor builds)sam2Python package/module available in your Comfy runtime- GroundingDINO runtime via Hugging Face Transformers (installed from
requirements.txt) transnetv2-pytorch(installed fromrequirements.txt)
Install this node pack into ComfyUI/custom_nodes/OpenShot-ComfyUI and restart ComfyUI.
Install this node's Python dependencies:
python -m pip install -r requirements.txtInstall SAM2 manually (required, not in requirements.txt):
python -m pip install git+https://github.com/facebookresearch/sam2.gitOptional: verify the SAM2 import works in your Comfy environment:
python -c "import sam2; print('sam2 import OK')"Restart ComfyUI after install.
requirements.txt intentionally does not install SAM2 from Git. This avoids repeated large temporary downloads and long install times during routine updates.
OpenShotGroundingDinoDetect will download selected model weights from Hugging Face on first use and cache them in your HF cache directory.
OpenShotTransNetSceneDetect runs TransNetV2 inference directly and returns scene range JSON (start_seconds, end_seconds), so no external TransNet Comfy node pack is required.
OpenShotDownloadAndLoadSAM2Model auto-downloads supported SAM2 checkpoints into ComfyUI/models/sam2 if missing.
OpenShotSam2VideoSegmentationChunkedreturns only the requested chunk range (bounded memory) instead of collecting whole-video masks.- For very long videos, pair chunked outputs with batch-safe downstream nodes (VHS meta-batch, staged processing, or on-disk intermediates).
Copyright (C) 2026 OpenShot Studios, LLC
Licensed under the GNU General Public License v3.0 (GPLv3). See LICENSE.md for the full license text.