SimpleTuner
A Python-based general fine-tuning kit for image/video/audio diffusion models
GraphCanon updated 2d · GitHub synced 2d
Decision brief
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.
Good fit when
- Use SimpleTuner when you need specialized fine-tuning capabilities for diffusion models involving image, video, or audio data.
- Consider it if your project is licensed in a manner that is compatible with AGPL-3.0, benefiting from its open-source community and contributions.
Avoid when
- 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.
- Requirements:
- SimpleTuner does not have a stated requirement for Docker, making deployment more flexible.
Observed Jul 14, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 2d
- Provenance
- Not a fork · Personal account
- As of 2d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install SimpleTunerSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
SimpleTuner provides utilities and scripts to facilitate the fine-tuning process of various diffusion models related to machine learning tasks including image, video, and audio processing.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 23, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 23, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 23, 2026
- MCP server
- No MCP server detected
Source: repo_scan · Aug 23, 2026
- Languages
- python, javascript
Source: github.language+package.json+pyproject.toml · Aug 23, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 23, 2026)
pip install simpletunerSource link
Tags
README
General Requirements
- NVIDIA: RTX 3080+ recommended (tested up to H200)
- AMD: 7900 XTX 24GB and MI300X verified (higher memory usage vs NVIDIA)
- Apple: M3 Max+ with 24GB+ unified memory for LoRA training
Base installation (CPU-only PyTorch)
pip install simpletuner
For agents
This page has a .md twin and JSON over the API.