| Pros | - Fine-tunes 100+ LLMs and vision-language models from one framework, with fast day-one support for new releases
- Covers many methods: SFT, pretraining, reward modeling, PPO, DPO, KTO, ORPO, and full/freeze/LoRA/QLoRA tuning
- Zero-code option via the LLaMA Board Gradio web UI, plus a config-driven CLI
- Built-in efficiency tricks (FlashAttention-2, Unsloth, Liger Kernel) and experiment tracking (TensorBoard, W&B, MLflow, SwanLab)
- vLLM/SGLang inference with an OpenAI-style API, model export, and Apache-2.0 licensing
| - Commercially friendly Apache-2.0 license for the application code
- Unified Canvas with inpainting, outpainting, and brush tools for iterative editing
- Node-based Workflows for building and sharing repeatable generation pipelines
- Broad model support including Stable Diffusion, SDXL, SD 3.5, and Flux variants
- Runs entirely locally with a built-in model manager, gallery, and rich image metadata
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| Cons | - Requires an NVIDIA (or compatible) GPU with enough VRAM; it is not a CPU-friendly tool
- You still need to understand fine-tuning concepts (datasets, methods, hyperparameters) to get good results
- Rapidly adding new models and features can bring transformers/torch/CUDA version-compatibility friction
- Windows users must manually install a CUDA build of PyTorch before it works
| - Needs a reasonably powerful GPU with enough VRAM for good speed and larger models
- Model weights carry their own licenses separate from the app, so check usage terms
- The node/workflow system has a real learning curve for newcomers
- Local install, updates, and model management take more setup than a hosted service
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