--- title: "SimpleTuner vs lightly-train" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bghira-simpletuner-vs-lightly-ai-lightly-train" tools: ["bghira-simpletuner", "lightly-ai-lightly-train"] --- # SimpleTuner vs lightly-train *GraphCanon updated Aug 23, 2026* ## Verdict Pick SimpleTuner if simpleTuner is a Python-based tool for fine-tuning diffusion models used in machine learning tasks such as image, video, and audio processing. It offers utilities and scripts to streamline the process; pick lightly-train if lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. [SimpleTuner](https://github.com/bghira/SimpleTuner) reports 2.9k GitHub stars, 289 forks, and 5 open issues, last pushed Aug 23, 2026. [lightly-train](https://docs.lightly.ai/train) has 1.6k stars, 107 forks, and 68 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [SimpleTuner's repository](https://github.com/bghira/SimpleTuner) and [lightly-train's repository](https://github.com/lightly-ai/lightly-train). | | [SimpleTuner](/tools/bghira-simpletuner.md) | [lightly-train](/tools/lightly-ai-lightly-train.md) | | --- | --- | --- | | Tagline | A Python-based general fine-tuning kit for image/video/audio diffusion models | All-in-one training for vision models: pretraining, fine-tuning, distillation. | | Stars | 2,906 | 1,650 | | Forks | 289 | 107 | | Open issues | 5 | 68 | | Language | Python | Python | | Adopt for | SimpleTuner is a Python-based tool for fine-tuning diffusion models used in machine learning tasks such as image, video, and audio processing. It offers utilities and scripts to streamline the process. | Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. | | Persona | - | - | | Runtime | - | - | | License | The AGPL-3.0 license ensures the source code is available and permits free alteration of the software but may require derivative works to also be distributed under this license. | AGPL-3.0 | | Categories | Computer Vision, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [SimpleTuner](/tools/bghira-simpletuner.md) | [lightly-train](/tools/lightly-ai-lightly-train.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 7d | | Open issues (now) | 5 | 68 | | Stars delta | +21 (30d) | +28 (30d) | | Open issues delta | -8 (30d) | +5 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/bghira-simpletuner/trust.md) | [trust report](/tools/lightly-ai-lightly-train/trust.md) | ## Shared compatibility - **Python**: [SimpleTuner](/tools/bghira-simpletuner.md) - Python runtime; [lightly-train](/tools/lightly-ai-lightly-train.md) - Python runtime ## Decision facts: SimpleTuner - **Requirements:** SimpleTuner does not have a stated requirement for Docker, making deployment more flexible. - **Adopt for:** SimpleTuner is a Python-based tool for fine-tuning diffusion models used in machine learning tasks such as image, video, and audio processing. It offers utilities and scripts to streamline the process. - **License detail:** The AGPL-3.0 license ensures the source code is available and permits free alteration of the software but may require derivative works to also be distributed under this license. ## Decision facts: lightly-train - **Requirements:** Min 8 GB RAM - **Adopt for:** Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. ## Choose when ### Choose SimpleTuner if… - Requirements: SimpleTuner does not have a stated requirement for Docker, making deployment more flexible.. - Tags unique to SimpleTuner: diffusers, diffusion-models, fine-tuning, flux-dev. - SimpleTuner ships Docker support for self-hosted deployment. - Use SimpleTuner when you need specialized fine-tuning capabilities for diffusion models involving image, video, or audio data. ### Choose lightly-train if… - Requirements: Min 8 GB RAM. - Tags unique to lightly-train: computer-vision, contrastive-learning, deep-learning, depth-estimation. - Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. ## When NOT to use SimpleTuner - Do not use SimpleTuner if your project requires proprietary licensing, since it is released under AGPL-3.0 which may impose conditions that could be incompatible with commercial projects. - Avoid SimpleTuner for tasks unrelated to diffusion models such as natural language processing, as it was designed specifically for image, video, and audio data. ## When NOT to use lightly-train - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between SimpleTuner and lightly-train? SimpleTuner: A Python-based general fine-tuning kit for image/video/audio diffusion models. lightly-train: All-in-one training for vision models: pretraining, fine-tuning, distillation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose SimpleTuner over lightly-train? Choose SimpleTuner over lightly-train when Requirements: SimpleTuner does not have a stated requirement for Docker, making deployment more flexible.; Tags unique to SimpleTuner: diffusers, diffusion-models, fine-tuning, flux-dev; SimpleTuner ships Docker support for self-hosted deployment; Use SimpleTuner when you need specialized fine-tuning capabilities for diffusion models involving image, video, or audio data. ### When should I choose lightly-train over SimpleTuner? Choose lightly-train over SimpleTuner when Requirements: Min 8 GB RAM; Tags unique to lightly-train: computer-vision, contrastive-learning, deep-learning, depth-estimation; Lightly-train is a Python-based framework focused on training vision models including YOLO, ViTs, RT-DETR, and DINOv3, offering comprehensive features like pretraining, fine-tuning, and distillation. ### When should I avoid SimpleTuner? Do not use SimpleTuner if your project requires proprietary licensing, since it is released under AGPL-3.0 which may impose conditions that could be incompatible with commercial projects. Avoid SimpleTuner for tasks unrelated to diffusion models such as natural language processing, as it was designed specifically for image, video, and audio data. ### When should I avoid lightly-train? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is SimpleTuner or lightly-train more popular on GitHub? SimpleTuner has more GitHub stars (2,906 vs 1,650). Stars measure visibility, not whether either tool fits your constraints. ### Are SimpleTuner and lightly-train open source? Yes - both are open-source projects on GitHub (SimpleTuner: AGPL-3.0, lightly-train: AGPL-3.0). ### Where can I find alternatives to SimpleTuner or lightly-train? GraphCanon lists graph-backed alternatives at [SimpleTuner alternatives](/tools/bghira-simpletuner/alternatives) and [lightly-train alternatives](/tools/lightly-ai-lightly-train/alternatives) ([SimpleTuner markdown twin](/tools/bghira-simpletuner/alternatives.md), [lightly-train markdown twin](/tools/lightly-ai-lightly-train/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/bghira-simpletuner-vs-lightly-ai-lightly-train.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, SimpleTuner or lightly-train? SimpleTuner: Very active. lightly-train: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for SimpleTuner and lightly-train? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [SimpleTuner trust report](/tools/bghira-simpletuner/trust); [lightly-train trust report](/tools/lightly-ai-lightly-train/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bghira-simpletuner`](/api/graphcanon/graph?tool=bghira-simpletuner) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_