ltx2
AI video generation with LTX-2.3 22B — text-to-video, image-to-video clips for video production.
Skill by xaralis
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: writing
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What's inside
- Skill name
ltx2- Description
- AI video generation with LTX-2.3 22B — text-to-video, image-to-video clips for video production. Use when generating video clips, animating images, creating b-roll, animated backgrounds, or motion content. Triggers include video generation, animate image, b-roll, motion, video clip, text-to-video, image-to-video.
- License
- MIT
- Instructions
- 196 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# LTX-2.3 Video Generation Generate ~5 second video clips from text prompts or images using the LTX-2.3 22B DiT model. Runs on Modal (A100-80GB). Requires `MODAL_LTX2_ENDPOINT_URL` in `.env`. ## Quick Reference ```bash # Text-to-video python3 -m video_toolkit.ltx2 --prompt "A sunset over the ocean, golden light on waves, cinematic" --output sunset.mp4 # Image-to-video (animate a still image) python3 -m video_toolkit.ltx2 --prompt "Gentle camera drift, soft ambient motion" --input photo.jpg --output animated.mp4 # Custom resolution and duration python3 -m video_toolkit.ltx2 --prompt "..." --width 1024 --height 576 --num-frames 161 --output wide.mp4 # Fast mode (fewer steps, quicker) python3 -m video_toolkit.ltx2 --prompt "..." --quality fast --output quick.mp4 # Reproducible output python3 -m video_toolkit.ltx2 --prompt "..." --seed 42 --output reproducible.mp4 ``` ## Parameters | Parameter | Default | Description | |-----------|---------|-------------| | `--prompt` | (required) | Text description of the video | | `--input` | - | Input image for image-to-video | | `--width` | 768 | Video width (divisible by 64) | | `--height` | 512 | Video height (divisible by 64) | | `--num-frames` | 121 | Frame count, must satisfy `(n-1) % 8 == 0` | | `--fps` | 24 | Frames per second | | `--quality` | standard | `standard` (30 steps) or `fast` (15 steps) | | `--steps` | 30 | Override inference steps directly | | `--seed` | random | Seed for reproducibility | | `--output` | auto | Output file path | | `--negative-prompt` | sensible default | What to avoid | | `--lora` | none | Style LoRA preset. Currently: `crt-terminal`. | ## Style LoRAs Style LoRAs bias the output toward a specific visual aesthetic. They're baked into the Modal image and selected per-request; switching LoRAs forces a pipeline rebuild (~60s one-time cost per container lifetime per switch). ### `crt-terminal` — CRT / pixel-art terminals Base: LTX-2.3 22B, trained by [@lovis93](https://huggingface.co/lovis93/crt-animation-terminal-ltx-2.3-lora) (Apache 2.0). ```bash # Trigger word is auto-prepended — write the prompt normally python3 -m video_toolkit.ltx2 --lora crt-terminal \ --prompt "a terminal typing out \"\\$ claude --continue\" character by character in glowing green pixel font, scanlines, phosphor glow, low choppy frame rate, hacker mood" \ --output crt_claude.mp4 ``` **What the preset changes:** - Prepends `crtanim,` to the prompt (the LoRA's trigger word) - Defaults to 1024×1024, 121 frames (the ratio it was trained on) - Relaxes the default negative prompt so on-screen text isn't filtered out **Prompt pattern:** `<CRT aesthetic> → <color palette> → <animation style> → <subject> → <literal text in quotes> → <mood>`. Keep on-screen text to 1–3 words — the model can't render long strings reliably. The LoRA prefers static framing; ask for camera moves explicitly if you want them. ## Valid Frame Counts `(n - 1) % 8 == 0`: 25 (~1s), 49 (~2s), 73 (~3s), 97 (~4s), **121 (~5s default)**, 161 (~6.7s), 193 (~8s max practical). ## Common Resolutions | Resolution | Ratio | Notes | |------------|-------|-------| | 768x512 | 3:2 | Default, good balance | | 512x512 | 1:1 | Square, fastest | | 1024x576 | 16:9 | Widescreen | | 576x1024 | 9:16 | Portrait/vertical | ## Prompting Guide LTX-2 responds well to cinematographic descriptions. Layer these dimensions: - **Camera:** "Slow dolly forward", "Aerial drone shot", "Tracking shot", "Static wide angle" - **Lighting:** "Golden hour", "Cinematic lighting", "Neon-lit", "Soft diffused light" - **Motion:** "Timelapse of...", "Slow motion", "Gentle camera drift", "Gradually transitions" - **Style:** "Shot on 35mm film", "Documentary style", "Clean minimal aesthetic" - **Negative:** Always implicitly avoids "worst quality, blurry, jittery, watermark, text, logo" Keep prompts under 200 words. Be specific about the scene. ### Good Prompts ``` # Atmospheric b-roll "Aerial drone shot slowly flying over turquoise ocean waves breaking on white sand, golden hour sunlight, cinematic" # Product/tech scene "Close-up of hands typing on a mechanical keyboard, shallow depth of field, soft desk lamp lighting, cozy atmosphere" # Abstract background "Dark moody abstract background with flowing blue light streaks, subtle geometric grid, bokeh particles floating, cinematic tech atmosphere" # Animate a portrait "Professional headshot, subtle natural head movement, confident warm expression, studio lighting, shallow depth of field" # Animate a slide/screenshot "Gentle subtle particle effects floating across a presentation slide, soft ambient light shifts, very slight camera drift" ``` ### Bad Prompts ``` # Too vague "A cool video" # Too many competing ideas "A cat riding a skateboard while juggling fire on the moon during a thunderstorm" # Describing text/UI (model can't render text reliably) "A website showing the text 'Welcome to our platform'" ```
From xaralis/video-toolkit, MIT.
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Signals
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- Last commit (this folder)
- Jul 18, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/xaralis/video-toolkit
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
ltx2 skill. Agent Mart. https://agentmart.dev/skills/xaralis-ltx2 (last updated 2026-10-24)