InvokeAI vs AI Toolkit
Two of the top image and video, side by side: score, setup, license, activity and what each review found.
InvokeAI
Canvas-first web UI for Stable Diffusion and Flux image generation
AI Toolkit
Fine-tuning suite for image, video and audio diffusion models, with GUI and CLI
| What we compare | InvokeAI | AI Toolkit |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 58, popular | 34, known |
| Freshness | 100, active | 100, active |
| Maintenance | 86, healthy | 52, fair |
| Easy to run | 33, some setup | 50, easy |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 28.6k | 12.3k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | None published |
| Language | Not stated | Python |
| Docker | Yes | Yes |
| GPU | Not stated | Required |
| arm64 or Apple Silicon | Not stated | Mentioned |
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
AI Toolkit
AI Toolkit trains LoRA and LoKr adapters for diffusion models, covering FLUX.1, FLUX.2, Qwen-Image, Wan 2.1/2.2, LTX-2 and SDXL, plus audio models like ACE-Step 1.5. Jobs are defined in YAML and run with `python run.py`, or started and monitored from a web UI on port 8675. The UI can be protected with an AI_TOOLKIT_AUTH token, and training can also run on Modal or RunPod.
Who it is for: Self-hosters fine-tuning LoRAs for image and video diffusion models
Strengths
- Supports a wide range of image, edit, video and audio models in one tool
- Same YAML configs work from the CLI or the web UI
- Training resumes from the last checkpoint after interruption
- Layer targeting via only_if_contains and ignore_if_contains, plus LoKr support
Weaknesses
- Manual install requires an Nvidia GPU; Mac support is experimental
- Install manager is labeled experimental by the author
- Dataset images must be jpg, jpeg or png; webp has known issues
- Ctrl+C during a checkpoint save can corrupt that checkpoint
- GPU required
- Docker + Compose
- Needs PyTorch, Node.js, Hugging Face
- Models: FLUX.1, FLUX.2, Qwen-Image, Wan 2.1/2.2, LTX-2
- port 8675