open-generative-ai 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.
open-generative-ai
Image, video and lip-sync studio UI that calls MuAPI, with optional local engines
InvokeAI
Canvas-first web UI for Stable Diffusion and Flux image generation
Verdict
Both score 60; open-generative-ai ranks higher on stars, 30,318 to 28,636. Of the 5 score parts we measure for both, open-generative-ai leads on 2, InvokeAI on 2 and 1 is even.
- Adoptionopen-generative-ai leads by 14 points, 54 to 40.
- FreshnessEven at 100.
- MaintenanceInvokeAI leads by 28 points, 86 to 58: a wide gap.
- Easy to runopen-generative-ai leads by 17 points, 50 to 33: a wide gap.
- Agent-readyInvokeAI leads by 30 points, 30 to 0: a wide gap.
Pick open-generative-ai if you are
Creators wanting one UI for hosted and local image and video generation
- Desktop app can run sd.cpp locally with Metal, CUDA, Vulkan or ROCm, no API key
- Wan2GP integration runs video models locally or on a remote GPU box via MCP
Pick InvokeAI if you are
Artists iterating on images with a canvas workflow
- Unified Canvas with in/outpainting, brush tools and SAM/SAM2 segmentation
- Broad model list including Flux.2 Dev and Klein, SD 3.5 Large, Qwen Image Edit
| What we compare | open-generative-ai | InvokeAI |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 54, popular | 40, known |
| Freshness | 100, active | 100, active |
| Maintenance | 58, fair | 86, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 0, none | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 30.3k | 28.6k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | May 2026 | Sep 2026 |
| Language | JavaScript | TypeScript |
| Docker | Yes | Yes |
| GPU | Optional | Not stated |
| arm64 or Apple Silicon | Mentioned | Not stated |
open-generative-ai
A JavaScript app, available as a web build and an Electron desktop app, that front-ends hosted image, video, audio and lip-sync models through the MuAPI service. The desktop app can also run local inference: a bundled stable-diffusion.cpp engine for SD 1.5, SDXL and Z-Image, or a user-installed Wan2GP for video models and Flux. Most generation depends on a MuAPI account, and the README promotes a paid white-label offering.
Who it is for: Creators wanting one UI for hosted and local image and video generation
Strengths
- Desktop app can run sd.cpp locally with Metal, CUDA, Vulkan or ROCm, no API key
- Wan2GP integration runs video models locally or on a remote GPU box via MCP
- Multi-image input accepts up to 14 reference images on compatible edit models
- Installers provided for macOS (arm64, x64), Windows and Linux (AppImage, .deb)
Weaknesses
- Cloud generation requires MuAPI; the 600+/200+ model claims run through that paid service
- Local inference is desktop-only; the hosted web version always uses cloud APIs
- Installers are unsigned, so macOS Gatekeeper and Windows SmartScreen need manual bypass
- Z-Image needs about 16 GB RAM and is reported to hang 8 GB Macs
- RAM ≥ 16 GB
- GPU optional
- Docker + Compose
- Needs MuAPI account/API key, Wan2GP (optional, for local video), stable-diffusion.cpp (bundled), Node.js (for source builds)
- Models: Flux, Nano Banana, Kling, Sora, Veo
InvokeAI
Local web server and React UI for image generation with a Unified Canvas (inpainting, outpainting, brushes), a node-based workflow editor and a boards gallery with per-image metadata. Loads SD 1.5 to SD 3.5, SDXL, Flux.1 and Flux.2 variants, Qwen Image, Z-Image, Krea 2 and CogView 4 in ckpt, diffusers and some GGUF formats; Nano Banana, GPT Image and Wan are API-only. For artists iterating on images.
Who it is for: Artists iterating on images with a canvas workflow
Strengths
- Unified Canvas with in/outpainting, brush tools and SAM/SAM2 segmentation
- Broad model list including Flux.2 Dev and Klein, SD 3.5 Large, Qwen Image Edit
- Apache-2.0 license; serves as the base for commercial products
- Dedicated launcher application handles install and updates
Weaknesses
- No Dockerfile or compose file at the repo root; install goes through the Launcher
- README lists features only; ports, hardware needs and env vars are in external docs
- Video generation (Wan) is API-only, not local
- Nano Banana and GPT Image require third-party API access
- Docker + Compose
- Models: SD 1.5, SD 2.0, SDXL, SD 3.5 Medium/Large, CogView 4
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