digitalsamba/claude-code-video-toolkit1 file

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.

Specification
Skill ID
digitalsamba/claude-code-video-toolkit/ltx2
Publisher
digitalsamba
Repository
claude-code-video-toolkit
Installs
0
Files
1
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

digitalsamba/claude-code-video-toolkit/ltx2Installs these files
  • SKILL.md

What this skill tells the agent

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

# Text-to-video
uv run tools/ltx2.py --prompt "A sunset over the ocean, golden light on waves, cinematic" --output sunset.mp4

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

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

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

# Reproducible output
uv run tools/ltx2.py --prompt "..." --seed 42 --output reproducible.mp4

Parameters

ParameterDefaultDescription
--prompt(required)Text description of the video
--input-Input image for image-to-video
--width768Video width (divisible by 64)
--height512Video height (divisible by 64)
--num-frames121Frame count, must satisfy (n-1) % 8 == 0
--fps24Frames per second
--qualitystandardstandard (30 steps) or fast (15 steps)
--steps30Override inference steps directly
--seedrandomSeed for reproducibility
--outputautoOutput file path
--negative-promptsensible defaultWhat to avoid
--loranoneStyle 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 (Apache 2.0).

# Trigger word is auto-prepended — write the prompt normally
uv run tools/ltx2.py --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

ResolutionRatioNotes
768x5123:2Default, good balance
512x5121:1Square, fastest
1024x57616:9Widescreen
576x10249:16Portrait/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:

uv run tools/ltx2.py --prompt "Futuristic holographic interface, glowing data visualizations, clean workspace, cinematic" --output broll_tech.mp4
uv run 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:

uv run 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:

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

Stylized Character Cameo (SadTalker Alternative)

For non-realistic faces — fantasy characters, masked figures, heavy beards, helmets, illustrations — SadTalker often produces uncanny or broken lip sync because it's trained on photoreal humans. LTX-2 image-to-video is frequently a better choice when lip-sync precision isn't critical (the viewer's brain fills in the gap as long as something is moving). Prompt for motion + atmosphere, not phonemes:

uv run tools/ltx2.py \
  --input character_portrait.png \
  --prompt "Ancient warrior speaks slowly with gravitas, beard shifts subtly, glowing aura pulses, embers drift past, slow head movement, cinematic close-up, mystical atmosphere" \
  --width 768 --height 768 \
  --output character_speaking.mp4

When LTX-2 wins over SadTalker:

  • Stylized / illustrated / fantasy characters
  • Heavy facial hair or accessories obscuring the mouth
  • Masked or helmeted figures
  • Short cameo lines where atmosphere matters more than precision
  • Dramatic VO rather than dialogue

When SadTalker still wins:

  • Photoreal human presenters
  • Full sentences where mouth shape needs to match phonemes
  • Tutorials / talking-head explainers where the viewer is effectively reading lips

Branded Intro/Outro

Generate abstract motion backgrounds for title cards:

uv run 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:

WorkflowTools
Generate clip → upscaleltx2.py → upscale.py
Generate clip → add to Remotionltx2.py → use as <OffthreadVideo> in composition
Generate image → animateflux2.py → ltx2.py --input
Generate clip → extract audioltx2.py → ffmpeg -i clip.mp4 -vn audio.wav
Generate clip → add voiceoverltx2.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

Use this skill in a project

Open any RiverX project, search the Skills panel for digitalsamba/claude-code-video-toolkit/ltx2, and install it in one click.

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