by Lightricks · AI Tool · ★ 2.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is LTX-Desktop safe to install? View the security audit →
Featured in: AI Video Generation & Editing: Seedance, Kling & Subtitles
LTX Desktop LTX Desktop is an open-source desktop app for generating videos with LTX models — locally on supported Windows/Linux NVIDIA GPUs, with an API mode for unsupported hardware and macOS. Status: Beta. Expect breaking changes. Frontend architecture is under active refactor; large UI PRs may be declined for now (see ). Features Text-to-video generation Image-to-video generation Audio-to-video generation Video edit generation (Retake) Video Editor Interface Video Editing Projects Local vs API mode In API-only mode, available resolutions/durations may be limited to what the API supports. System requirements Win
No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 2,080 |
| Forks | 435 |
| Language | TypeScript |
| Category | AI Tool |
| License | Apache-2.0 |
| Quality Score | 73.4836265275776/100 |
| Open Issues | 65 |
| Last Updated | 2026-10-09 |
| Created | 2026-03-04 |
| Platforms | node |
| Est. Tokens | ~15k |
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LTX-Desktop is An open-source desktop app for generating videos with LTX models. It is categorized as a AI Tool with 2.1k GitHub stars.
LTX-Desktop is primarily written in TypeScript. It covers topics such as generative-ai, ltx, ltx-2.
You can find installation instructions and usage details in the LTX-Desktop GitHub repository at github.com/Lightricks/LTX-Desktop. The project has 2.1k stars and 435 forks, indicating an active community.
LTX-Desktop is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to LTX-Desktop on Agent Skills Hub include agent-squad, cursor-talk-to-figma-mcp, mcp-context-forge. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: