Agent skill

ltx2

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.

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Install this agent skill to your Project

npx add-skill https://github.com/digitalsamba/claude-code-video-toolkit/tree/main/.claude/skills/ltx2

SKILL.md

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 tools/ltx2.py --prompt "A sunset over the ocean, golden light on waves, cinematic" --output sunset.mp4

# Image-to-video (animate a still image)
python3 tools/ltx2.py --prompt "Gentle camera drift, soft ambient motion" --input photo.jpg --output animated.mp4

# Custom resolution and duration
python3 tools/ltx2.py --prompt "..." --width 1024 --height 576 --num-frames 161 --output wide.mp4

# Fast mode (fewer steps, quicker)
python3 tools/ltx2.py --prompt "..." --quality fast --output quick.mp4

# Reproducible output
python3 tools/ltx2.py --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

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'"

Video Production Use Cases

B-Roll Clips

Generate atmospheric 5s shots for cutaways between narrated scenes:

bash
python3 tools/ltx2.py --prompt "Futuristic holographic interface, glowing data visualizations, clean workspace, cinematic" --output broll_tech.mp4
python3 tools/ltx2.py --prompt "Aerial view of European city at golden hour, modern architecture" --output broll_europe.mp4

Animated Slide Backgrounds

Feed a slide screenshot and add subtle motion:

bash
python3 tools/ltx2.py --prompt "Gentle particle effects, soft ambient light shifts, very slight camera drift" --input slide.png --output animated_slide.mp4

Animated Portraits

Bring still headshots to life:

bash
python3 tools/ltx2.py --prompt "Subtle natural head movement, warm expression, professional lighting" --input headshot.png --output animated_portrait.mp4

Branded Intro/Outro

Generate abstract motion backgrounds for title cards:

bash
python3 tools/ltx2.py --prompt "Dark moody background with flowing blue and coral light streaks, bokeh particles, cinematic tech atmosphere, no text" --output intro_bg.mp4

Combining with Other Tools

LTX-2 generates raw clips. Combine with the rest of the toolkit:

Workflow Tools
Generate clip → upscale ltx2.py → upscale.py
Generate clip → add to Remotion ltx2.py → use as <OffthreadVideo> in composition
Generate image → animate flux2.py → ltx2.py --input
Generate clip → extract audio ltx2.py → ffmpeg -i clip.mp4 -vn audio.wav
Generate clip → add voiceover ltx2.py → mix with qwen3_tts.py output

Technical Details

  • Model: LTX-2.3 22B DiT (Lightricks), bf16
  • GPU: A100-80GB on Modal (~$4.68/hr)
  • Inference: ~2.5 min per clip (768x512, 121 frames, 30 steps)
  • Cost: ~$0.20-0.25 per 5s clip
  • Cold start: ~60-90s (loading ~55GB weights)
  • Output: H.264 MP4 with synchronized ambient audio (24fps)
  • Max duration: ~8s (193 frames) per clip

Known Limitations

  • Training data artifacts: ~30% of generations may have unwanted logos/text from training data. Re-run with different --seed.
  • Text rendering: Cannot reliably generate readable text in video. Use Remotion overlays instead.
  • Max duration: ~8s per clip. Longer content needs stitching.
  • Audio: Generated audio is ambient/environmental only. Use voiceover/music tools for speech and music.
  • License: Community License — free under $10M revenue, commercial license needed above that.

Setup

bash
# 1. Create Modal secret for HuggingFace (one-time)
modal secret create huggingface-token HF_TOKEN=hf_your_token

# 2. Deploy (downloads ~55GB of weights, takes ~10 min)
modal deploy docker/modal-ltx2/app.py

# 3. Save endpoint URL to .env
echo "MODAL_LTX2_ENDPOINT_URL=https://yourname--video-toolkit-ltx2-ltx2-generate.modal.run" >> .env

# 4. Test
python3 tools/ltx2.py --prompt "A candle flickering on a dark table, cinematic" --output test.mp4

Important: HuggingFace token needs read-access scope. Accept the Gemma 3 license before deploying. Unauthenticated downloads are severely rate-limited.

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