ComfyUI vs AI Toolkit
Two of the top image and video, side by side: score, setup, license, activity and what each review found.
ComfyUI
Node-graph engine for diffusion image, video, audio and 3D models
AI Toolkit
Fine-tuning suite for image, video and audio diffusion models, with GUI and CLI
| What we compare | ComfyUI | AI Toolkit |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 97, widely used | 34, known |
| Freshness | 100, active | 100, active |
| Maintenance | 77, fair | 52, fair |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 136.9k | 12.3k |
| License | GPL-3.0 (copyleft) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Not stated | Python |
| Docker | No | Yes |
| GPU | Optional | Required |
| arm64 or Apple Silicon | Mentioned | Mentioned |
ComfyUI
Builds generation pipelines as a visual node graph and runs them locally for image (SD 1.5, SDXL, SD3.5, Flux.1 and Flux.2, Qwen Image), video (Wan 2.x, LTX-Video, HunyuanVideo), audio (ACE-Step, Stable Audio) and 3D (Hunyuan3D) models, with a local API and an App Mode that exposes a workflow as a simple UI. Runs on NVIDIA, AMD, Intel, Apple Silicon and Ascend. For professionals who want control over every parameter.
Who it is for: Visual professionals running diffusion models locally
Strengths
- Asynchronous weight streaming runs large models on 4 GB VRAM plus 8 GB RAM
- Workflows saved as JSON and recoverable from generated media metadata
- Runs fully offline; --offline disables the paid API nodes
- Loads checkpoints, separate diffusion models, VAEs, text encoders, LoRAs, ControlNets
Weaknesses
- Commits outside stable tags can break many custom nodes; stable releases roughly biweekly
- GPL-3.0 license constrains embedding in proprietary products
- NVIDIA 20-series and newer require PyTorch built with CUDA 13.0 or above
- Paid partner and API nodes stay on unless --offline or --disable-partner-nodes is set
- RAM ≥ 8 GB
- GPU optional
- Models: Stable Diffusion 1.5, SDXL, SD3.5, Flux.1 and Flux.2, Qwen Image and Qwen Image Edit, Wan 2.1/2.2, LTX-Video 2
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