Comparison
SimpleTuner vs stable-diffusion
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 stable-diffusion if stable-diffusion is a latent text-to-image diffusion model for generating images from text descriptions, using a conda environment for setup.
Markdown twin · SimpleTuner alternatives · stable-diffusion alternatives
GraphCanon updated Sep 20, 2026
10views this month
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Trust & integrity
| Signal | SimpleTuner | stable-diffusion |
|---|---|---|
| Maintenance | Active (12d since push) As of Sep 19, 2026 · github_public_v1 | Dormant (822d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 19, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · 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
- stable-diffusion
- A latent text-to-image diffusion model
Stars
- SimpleTuner
- 2.9k
- stable-diffusion
- 73k
Forks
- SimpleTuner
- 294
- stable-diffusion
- 11k
Open issues
- SimpleTuner
- 10
- stable-diffusion
- 619
Language
- SimpleTuner
- Python
- stable-diffusion
- Jupyter Notebook
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.
- stable-diffusion
- Stable-diffusion is a latent text-to-image diffusion model for generating images from text descriptions, using a conda environment for setup.
Persona
- SimpleTuner
- -
- stable-diffusion
- -
Runtime
- SimpleTuner
- -
- stable-diffusion
- -
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.
- stable-diffusion
- Other
Last pushed
- SimpleTuner
- Sep 7, 2026
- stable-diffusion
- Jun 18, 2024
Categories
- SimpleTuner
- Computer Vision, Model Training
- stable-diffusion
- Computer Vision, Model Training
Trust and health
Maintenance
- SimpleTuner
- Active (82%)
- stable-diffusion
- Dormant (18%)
Days since push
- SimpleTuner
- 12d
- stable-diffusion
- 822d
Open issues (now)
- SimpleTuner
- 10
- stable-diffusion
- 619
Stars delta
- SimpleTuner
- +39 (30d)
- stable-diffusion
- +188 (30d)
Open issues delta
- SimpleTuner
- -3 (30d)
- stable-diffusion
- +3 (30d)
Owner type
- SimpleTuner
- User
- stable-diffusion
- Organization
Full report
- SimpleTuner
- Trust report
- stable-diffusion
- Trust report
Shared compatibility
- Python · SimpleTuner: Python runtime · stable-diffusion: Python runtime
Choose SimpleTuner if…
- SimpleTuner is primarily Python; stable-diffusion is Jupyter Notebook.
- License: SimpleTuner is AGPL-3.0, stable-diffusion is Other.
- 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 stable-diffusion if…
- stable-diffusion is primarily Jupyter Notebook; SimpleTuner is Python.
- License: stable-diffusion is Other, SimpleTuner is AGPL-3.0.
- Requirements: A conda environment named `ldm` is required, which can be created and activated using the provided commands..
- Tags unique to stable-diffusion: conda environment, diffusion model, image-generation, jupyter-notebook.
- When you need to generate images from text descriptions with a high degree of detail and quality, as stable-diffusion is known for its ability to produce highly detailed images.
When NOT to use stable-diffusion
- If your project requires real-time image generation, as stable-diffusion may not be optimized for real-time performance and could be slower compared to some alternatives.
- When you are looking for a tool that supports a wide range of customization options for the diffusion process, as stable-diffusion might have a more limited set of parameters for customization.
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 Sep 20, 2026
- GitHub forks (bghira/SimpleTuner) · observed Sep 20, 2026
- Last push (bghira/SimpleTuner) · observed Sep 7, 2026
- License file (AGPL-3.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (CompVis/stable-diffusion) · observed Sep 20, 2026
- GitHub forks (CompVis/stable-diffusion) · observed Sep 20, 2026
- Last push (CompVis/stable-diffusion) · observed Jun 18, 2024
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: SimpleTuner 2.9k · stable-diffusion 73k (synced Sep 20, 2026).
Common questions
- What is the difference between SimpleTuner and stable-diffusion?
- SimpleTuner: A Python-based general fine-tuning kit for image/video/audio diffusion models. stable-diffusion: A latent text-to-image diffusion model. See the comparison table for live GitHub stats and shared categories.
- When should I choose SimpleTuner over stable-diffusion?
- Choose SimpleTuner over stable-diffusion when SimpleTuner is primarily Python; stable-diffusion is Jupyter Notebook; License: SimpleTuner is AGPL-3.0, stable-diffusion is Other; 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 stable-diffusion over SimpleTuner?
- Choose stable-diffusion over SimpleTuner when stable-diffusion is primarily Jupyter Notebook; SimpleTuner is Python; License: stable-diffusion is Other, SimpleTuner is AGPL-3.0; Requirements: A conda environment named
ldmis required, which can be created and activated using the provided commands.; Tags unique to stable-diffusion: conda environment, diffusion model, image-generation, jupyter-notebook; When you need to generate images from text descriptions with a high degree of detail and quality, as stable-diffusion is known for its ability to produce highly detailed images. - 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 stable-diffusion?
- If your project requires real-time image generation, as stable-diffusion may not be optimized for real-time performance and could be slower compared to some alternatives. When you are looking for a tool that supports a wide range of customization options for the diffusion process, as stable-diffusion might have a more limited set of parameters for customization.
- Is SimpleTuner or stable-diffusion more popular on GitHub?
- stable-diffusion has more GitHub stars (73,442 vs 2,924). Stars measure visibility, not whether either tool fits your constraints.
- Are SimpleTuner and stable-diffusion open source?
- Yes - both are open-source projects on GitHub (SimpleTuner: AGPL-3.0, stable-diffusion: Other).
- Where can I find alternatives to SimpleTuner or stable-diffusion?
- GraphCanon lists graph-backed alternatives at SimpleTuner alternatives and stable-diffusion alternatives (SimpleTuner markdown twin, stable-diffusion 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 stable-diffusion?
- SimpleTuner: Active. stable-diffusion: Dormant. 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 stable-diffusion?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: SimpleTuner trust report; stable-diffusion trust report.