--- title: "LlamaFactory vs StyleTTS2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-yl4579-styletts2" tools: ["hiyouga-llamafactory", "yl4579-styletts2"] --- # LlamaFactory vs StyleTTS2 *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when license: LlamaFactory is Apache-2.0, StyleTTS2 is MIT; pick StyleTTS2 when license: StyleTTS2 is MIT, LlamaFactory is Apache-2.0. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [StyleTTS2](https://github.com/yl4579/StyleTTS2) has 6.3k stars, 694 forks, and 118 open issues, last pushed Aug 10, 2024. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [StyleTTS2's repository](https://github.com/yl4579/StyleTTS2). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [StyleTTS2](/tools/yl4579-styletts2.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models | | Stars | 73,157 | 6,306 | | Forks | 8,937 | 694 | | Open issues | 1,067 | 118 | | Language | Python | Python | | Adopt for | LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [StyleTTS2](/tools/yl4579-styletts2.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 700d | | Open issues (now) | 1.1k | 118 | | Security scan | No lockfile | No criticals | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/yl4579-styletts2/trust.md) | ## Decision facts: LlamaFactory - **Adopt for:** LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. ## Choose when ### Choose LlamaFactory if… - License: LlamaFactory is Apache-2.0, StyleTTS2 is MIT. - Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose StyleTTS2 if… - License: StyleTTS2 is MIT, LlamaFactory is Apache-2.0. - Tags unique to StyleTTS2: adversarial-training, deep-learning, diffusion-models, gan. - Also covers Vector Databases. ## When NOT to use LlamaFactory - When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. - If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa ## When NOT to use StyleTTS2 - Last GitHub push was 701 days ago (dormant maintenance, Aug 10, 2024). Validate activity before betting a new project on StyleTTS2. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between LlamaFactory and StyleTTS2? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. StyleTTS2: StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over StyleTTS2? Choose LlamaFactory over StyleTTS2 when License: LlamaFactory is Apache-2.0, StyleTTS2 is MIT; Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose StyleTTS2 over LlamaFactory? Choose StyleTTS2 over LlamaFactory when License: StyleTTS2 is MIT, LlamaFactory is Apache-2.0; Tags unique to StyleTTS2: adversarial-training, deep-learning, diffusion-models, gan; Also covers Vector Databases. ### When should I avoid LlamaFactory? When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa ### When should I avoid StyleTTS2? Last GitHub push was 701 days ago (dormant maintenance, Aug 10, 2024). Validate activity before betting a new project on StyleTTS2. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is LlamaFactory or StyleTTS2 more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 6,306). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and StyleTTS2 open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, StyleTTS2: MIT). ### Where can I find alternatives to LlamaFactory or StyleTTS2? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [StyleTTS2 alternatives](/tools/yl4579-styletts2/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [StyleTTS2 markdown twin](/tools/yl4579-styletts2/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/hiyouga-llamafactory-vs-yl4579-styletts2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or StyleTTS2? LlamaFactory: Very active. StyleTTS2: 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 LlamaFactory and StyleTTS2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [StyleTTS2 trust report](/tools/yl4579-styletts2/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-llamafactory`](/api/graphcanon/graph?tool=hiyouga-llamafactory) - 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/_