Comparison
SimpleTuner vs lightly-train
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.
Markdown twin · SimpleTuner alternatives · lightly-train alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | SimpleTuner | lightly-train |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2d · github_public_v1 | Active (7d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2d · github_public_v1 | Not a fork · Organization account As of 3d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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.
Stars
- SimpleTuner
- 2.9k
- lightly-train
- 1.6k
Forks
- SimpleTuner
- 289
- lightly-train
- 107
Open issues
- SimpleTuner
- 5
- lightly-train
- 68
Language
- SimpleTuner
- Python
- lightly-train
- Python
Adopt for
- SimpleTuner
- 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
- 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
- SimpleTuner
- -
- lightly-train
- -
Runtime
- SimpleTuner
- -
- lightly-train
- -
License
- SimpleTuner
- 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.
- lightly-train
- AGPL-3.0
Last pushed
- SimpleTuner
- Aug 23, 2026
- lightly-train
- Aug 14, 2026
Categories
- SimpleTuner
- Computer Vision, Model Training
- lightly-train
- Computer Vision, Model Training
Trust and health
Maintenance
- SimpleTuner
- Very active (96%)
- lightly-train
- Active (82%)
Days since push
- SimpleTuner
- 0d
- lightly-train
- 7d
Open issues (now)
- SimpleTuner
- 5
- lightly-train
- 68
Stars delta
- SimpleTuner
- +21 (30d)
- lightly-train
- +28 (30d)
Open issues delta
- SimpleTuner
- -8 (30d)
- lightly-train
- +5 (30d)
Owner type
- SimpleTuner
- User
- lightly-train
- Organization
Full report
- SimpleTuner
- Trust report
- lightly-train
- Trust report
Shared compatibility
- Python · SimpleTuner: Python runtime · lightly-train: Python runtime
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.
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.
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 lightly-train
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (bghira/SimpleTuner) · observed Aug 23, 2026
- GitHub forks (bghira/SimpleTuner) · observed Aug 23, 2026
- Last push (bghira/SimpleTuner) · observed Aug 23, 2026
- License file (AGPL-3.0) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (lightly-ai/lightly-train) · observed Aug 22, 2026
- GitHub forks (lightly-ai/lightly-train) · observed Aug 22, 2026
- Last push (lightly-ai/lightly-train) · observed Aug 14, 2026
- License file (AGPL-3.0) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: SimpleTuner 2.9k · lightly-train 1.6k (synced Aug 23, 2026).
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 and lightly-train alternatives (SimpleTuner markdown twin, lightly-train markdown twin), 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 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; lightly-train trust report.