frigate vs InvokeAI
Two of the top projects in Image and video, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
frigate
Local NVR with realtime object detection for IP cameras
InvokeAI
Web UI for diffusion image generation with inpainting canvas and node workflows
Verdict
frigate ranks higher, 91 to 83. Of the 4 score parts we measure for both, frigate leads on 3, InvokeAI on none and 1 is even.
- Adoptionfrigate leads by 9 points, 86 to 77.
- FreshnessEven at 100.
- Maintenancefrigate leads by 5 points, 95 to 90.
- Agent-readyfrigate leads by 40 points, 70 to 30: a wide gap.
Pick frigate if you are
Home Assistant users running local AI detection on IP cameras
- Runs detection only where motion is found, which limits CPU use
- Retention rules based on detected objects, plus 24/7 recording
Pick InvokeAI if you are
Artists and self-hosters making images with local diffusion models
- Unified Canvas with in/out-painting and brush tools for iterative editing
- Node-based workflow editor for building and sharing generation pipelines
| What we compare | frigate | InvokeAI |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 86, widely used | 77, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 95, healthy | 90, healthy |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 37.1k | 29k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | TypeScript |
| Docker | Yes | Yes |
| GPU | Optional | Not stated |
| arm64 or Apple Silicon | Not stated | Not stated |
frigate
Frigate is a network video recorder that runs object detection locally on IP camera streams using OpenCV and TensorFlow. It uses low-overhead motion detection to decide where to run detection, records video with retention based on detected objects, and re-streams over RTSP. It integrates with Home Assistant through a custom component and publishes events over MQTT.
Who it is for: Home Assistant users running local AI detection on IP cameras
Strengths
- Runs detection only where motion is found, which limits CPU use
- Retention rules based on detected objects, plus 24/7 recording
- RTSP re-streaming, plus WebRTC and MSE for low-latency live view
- MQTT and a Home Assistant custom component for integration
Weaknesses
- README strongly recommends a GPU or AI accelerator for detection
- Tightly oriented to Home Assistant; other setups rely on MQTT
- Frigate name and logo are trademarks excluded from the MIT license
- GPU optional
- Docker + Compose
- Needs MQTT, Home Assistant (optional), IP cameras (RTSP)
- Models: TensorFlow
InvokeAI
Invoke runs a local web server with a React UI for generating and editing images with diffusion models. It includes a Unified Canvas for in/out-painting, a node-based workflow editor, a gallery with prompt metadata recall, and a model manager. It loads ckpt, diffusers and some GGUF models, covering SD 1.5, SDXL, SD 3.5, Flux.1/Flux.2, Qwen Image and Z-Image.
Who it is for: Artists and self-hosters making images with local diffusion models
Strengths
- Unified Canvas with in/out-painting and brush tools for iterative editing
- Node-based workflow editor for building and sharing generation pipelines
- Broad model list: SD 1.5 through Flux.2, Qwen Image, Z-Image, SAM/SAM2 segmentation
- Gallery stores prompt and settings metadata in images for recall
Weaknesses
- README gives no RAM, VRAM or GPU requirements
- Nano Banana, GPT Image and Wan are API-only, not local
- GGUF support is partial, described only as 'some gguf models'
- README points to a separate Launcher app for install, with no manual or Docker steps
- Docker + Compose
- Needs Invoke Launcher
- Models: SD 1.5, SD 2.0, SDXL, SD 3.5, CogView 4
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