Video Toolkit

Create professional explainer videos from a text brief. The toolkit uses open-source AI models on cloud GPUs (Modal or RunPod) for voiceover, image generation, music, and talking head animation. Remotion (React) handles composition and rendering.

CRITICAL: Toolkit Path

The toolkit lives at a fixed path. ALWAYS cd here before running any tool command.

TOOLKIT=~/.openclaw/workspace/claude-code-video-toolkit
cd $TOOLKIT

NEVER run tool commands from inside a project directory. Tools resolve paths relative to the toolkit root.

CRITICAL: Progress Reporting

ALWAYS add --progress json to every cloud GPU tool command. This gives you structured JSON Lines on stderr so you can monitor job status, detect stuck jobs, and report progress to the user in real-time.

# CORRECT — always include --progress json
python3 tools/music_gen.py --preset corporate-bg --duration 60 --output bg.mp3 --progress json

# WRONG — no visibility into job status
python3 tools/music_gen.py --preset corporate-bg --duration 60 --output bg.mp3

Tools that support --progress json: music_gen.py, qwen3_tts.py, flux2.py, upscale.py, sadtalker.py, image_edit.py, dewatermark.py, ltx2.py, chain_video.py.

See the Progress Reporting section below for output format and stage definitions.

CRITICAL: Long-Running Tasks — Use yieldMs, Not background:true

Any tool command that takes more than 30 seconds MUST use exec with yieldMs so you can report progress to the user live. This includes: batch FLUX generation, chain_video, SadTalker, music generation, and any multi-scene pipeline.

exec command:"cd ~/.openclaw/workspace/claude-code-video-toolkit && python3 tools/chain_video.py --output-dir /path/ --progress json ..." yieldMs:10000

The polling loop:

1. exec with yieldMs:10000 starts the command and returns control to you every 10 seconds
2. Read the --progress json output — look for "stage":"item" (scene complete) or "stage":"complete" (all done)
3. Report progress to the user ("Scene 05/30 complete, 17%")
4. Poll again: process action:poll sessionId:<id>
5. Repeat until "stage":"complete"

Why: Your agent run ends when you finish responding. If you use bash background:true, you lose the ability to report progress — the user sees silence until they nudge you. With yieldMs, you stay in the loop.

NEVER do this:

• bash background:true command:"long running thing" then promise to "monitor" — you can't, your run ends
• Break a batch into individual tool calls across separate messages — your run ends between each one
• Promise to "continue autonomously" — you literally cannot without an external trigger

Setup

Step 1: Check Current State

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/verify_setup.py

If everything shows [x], skip to "Quick Test" below. Otherwise continue setup.

Step 2: Install Python Dependencies

cd ~/.openclaw/workspace/claude-code-video-toolkit
pip3 install --break-system-packages -r tools/requirements.txt

Note: --break-system-packages is needed on Debian/Ubuntu with managed Python (PEP 668). Safe inside containers.

Step 3: Configure Cloud GPU Endpoints

The toolkit needs cloud GPU endpoint URLs in .env. Check if .env exists and has Modal endpoints:

cat ~/.openclaw/workspace/claude-code-video-toolkit/.env | grep MODAL

If Modal endpoints are configured, you're ready. If not, ask the user to provide Modal endpoint URLs or set up Modal:

pip3 install --break-system-packages modal
python3 -m modal setup   # Opens browser for authentication

# Deploy each tool — capture the endpoint URL from output
cd ~/.openclaw/workspace/claude-code-video-toolkit
modal deploy docker/modal-qwen3-tts/app.py
modal deploy docker/modal-flux2/app.py
modal deploy docker/modal-music-gen/app.py
modal deploy docker/modal-sadtalker/app.py
modal deploy docker/modal-image-edit/app.py
modal deploy docker/modal-upscale/app.py
modal deploy docker/modal-propainter/app.py
modal deploy docker/modal-ltx2/app.py      # Requires: modal secret create huggingface-token HF_TOKEN=hf_...

LTX-2 prerequisite: Before deploying LTX-2, create a HuggingFace secret and accept the Gemma 3 license:

modal secret create huggingface-token HF_TOKEN=hf_your_read_access_token

Add each URL to .env:

ACEMUSIC_API_KEY=...                          # Free key from acemusic.ai/api-key (best music quality)
MODAL_QWEN3_TTS_ENDPOINT_URL=https://...modal.run
MODAL_FLUX2_ENDPOINT_URL=https://...modal.run
MODAL_MUSIC_GEN_ENDPOINT_URL=https://...modal.run
MODAL_SADTALKER_ENDPOINT_URL=https://...modal.run
MODAL_IMAGE_EDIT_ENDPOINT_URL=https://...modal.run
MODAL_UPSCALE_ENDPOINT_URL=https://...modal.run
MODAL_DEWATERMARK_ENDPOINT_URL=https://...modal.run
MODAL_LTX2_ENDPOINT_URL=https://...modal.run

Optional but recommended — Cloudflare R2 for reliable file transfer:

R2_ACCOUNT_ID=...
R2_ACCESS_KEY_ID=...
R2_SECRET_ACCESS_KEY=...
R2_BUCKET_NAME=video-toolkit

Step 4: Verify and Quick Test

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/verify_setup.py

All tools should show [x]. Then run a quick test to confirm the GPU pipeline works:

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/qwen3_tts.py --text "Hello, this is a test." --speaker Ryan --tone warm --output /tmp/video-toolkit-test.mp3 --cloud modal

If you get a valid .mp3 file, setup is complete. If it fails, check:

• .env has the correct MODAL_QWEN3_TTS_ENDPOINT_URL
• Run python3 tools/verify_setup.py --json and check modal_tools for which endpoints are missing

Cost: Modal includes $30/month free compute. A typical 60s video costs $1-3.

---

Creating a Video

Step 1: Create Project

cd ~/.openclaw/workspace/claude-code-video-toolkit
cp -r templates/product-demo projects/PROJECT_NAME
cd projects/PROJECT_NAME
npm install

Templates: product-demo (marketing/explainer), sprint-review, sprint-review-v2 (composable scenes).

Step 2: Write Config

Edit projects/PROJECT_NAME/src/config/demo-config.ts:

export const demoConfig: ProductDemoConfig = {
  product: {
    name: 'My Product',
    tagline: 'What it does in one line',
    website: 'example.com',
  },
  scenes: [
    { type: 'title', durationSeconds: 9, content: { headline: '...', subheadline: '...' } },
    { type: 'problem', durationSeconds: 14, content: { headline: '...', problems: ['...', '...'] } },
    { type: 'solution', durationSeconds: 13, content: { headline: '...', highlights: ['...', '...'] } },
    { type: 'stats', durationSeconds: 12, content: { stats: [{value: '99%', label: '...'}, ...] } },
    { type: 'cta', durationSeconds: 10, content: { headline: '...', links: ['...'] } },
  ],
  audio: {
    backgroundMusicFile: 'audio/bg-music.mp3',
    backgroundMusicVolume: 0.12,
  },
};

Scene types: title, problem, solution, demo, feature, stats, cta.

Duration rule: Estimate durationSeconds as ceil(word_count / 2.5) + 2. You will adjust this after generating audio in Step 4.

Step 3: Write Voiceover Script

Create projects/PROJECT_NAME/VOICEOVER-SCRIPT.md:

## Scene 1: Title (9s, ~17 words)
Build videos with AI. The product name toolkit makes it easy.

## Scene 2: Problem (14s, ~30 words)
The problem statement goes here. Keep it punchy and relatable.

Word budget per scene: (durationSeconds - 2) * 2.5 words. The -2 accounts for 1s audio delay + 1s padding.

Step 4: Generate Assets

CRITICAL: All commands below MUST be run from the toolkit root, not the project directory.

cd ~/.openclaw/workspace/claude-code-video-toolkit

4a. Background Music

Default provider is acemusic (official cloud API, free key). No GPU required. Falls back to Modal/RunPod for self-hosted.

cd ~/.openclaw/workspace/claude-code-video-toolkit

# Using acemusic cloud API (default — best quality, XL Turbo 4B model)
python3 tools/music_gen.py \
  --preset corporate-bg \
  --duration 90 \
  --output projects/PROJECT_NAME/public/audio/bg-music.mp3 \
  --progress json

# Or with custom prompt and thinking mode
python3 tools/music_gen.py \
  --prompt "Subtle ambient tech, soft synth pads" \
  --duration 90 \
  --output projects/PROJECT_NAME/public/audio/bg-music.mp3 \
  --progress json

# Fall back to self-hosted Modal if no acemusic key
python3 tools/music_gen.py \
  --preset corporate-bg \
  --duration 90 \
  --output projects/PROJECT_NAME/public/audio/bg-music.mp3 \
  --cloud modal --progress json

Presets: corporate-bg, upbeat-tech, ambient, dramatic, tension, hopeful, cta, lofi.

Setup: echo "ACEMUSIC_API_KEY=your_key" >> .env (get free key at acemusic.ai/api-key).

4b. Voiceover (per-scene)

Generate ONE .mp3 file PER SCENE. Do NOT generate a single voiceover file.

cd ~/.openclaw/workspace/claude-code-video-toolkit

# Scene 01
python3 tools/qwen3_tts.py \
  --text "The voiceover text for scene one." \
  --speaker Ryan --tone warm \
  --output projects/PROJECT_NAME/public/audio/scenes/01.mp3 \
  --cloud modal --progress json

# Scene 02
python3 tools/qwen3_tts.py \
  --text "The voiceover text for scene two." \
  --speaker Ryan --tone warm \
  --output projects/PROJECT_NAME/public/audio/scenes/02.mp3 \
  --cloud modal --progress json

# ... repeat for each scene

Speakers: Ryan, Aiden, Vivian, Serena, Uncle_Fu, Dylan, Eric, Ono_Anna, Sohee Tones: neutral, warm, professional, excited, calm, serious, storyteller, tutorial

For voice cloning (needs a reference recording):

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/qwen3_tts.py \
  --text "Text to speak" \
  --ref-audio assets/voices/reference.m4a \
  --ref-text "Exact transcript of the reference audio" \
  --output projects/PROJECT_NAME/public/audio/scenes/01.mp3 \
  --cloud modal --progress json

4c. Scene Images

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/flux2.py \
  --prompt "Dark tech background with blue geometric grid, cinematic lighting" \
  --width 1920 --height 1080 \
  --output projects/PROJECT_NAME/public/images/title-bg.png \
  --cloud modal --progress json

Image presets (use --preset instead of --prompt --width --height): title-bg, problem, solution, demo-bg, stats-bg, cta, thumbnail, portrait-bg

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/flux2.py \
  --preset title-bg \
  --output projects/PROJECT_NAME/public/images/title-bg.png \
  --cloud modal --progress json

4d. Video Clips — B-Roll & Animated Backgrounds (optional)

Generate AI video clips for b-roll cutaways, animated slide backgrounds, or intro/outro sequences:

cd ~/.openclaw/workspace/claude-code-video-toolkit

# B-roll clip from text
python3 tools/ltx2.py \
  --prompt "Aerial drone shot over a European city at golden hour, cinematic wide angle" \
  --output projects/PROJECT_NAME/public/videos/broll-europe.mp4 \
  --cloud modal --progress json

# Animate a slide/screenshot (image-to-video)
python3 tools/ltx2.py \
  --prompt "Gentle particle effects, soft ambient light shifts, very slight camera drift" \
  --input projects/PROJECT_NAME/public/images/title-bg.png \
  --output projects/PROJECT_NAME/public/videos/animated-title.mp4 \
  --cloud modal --progress json

# Abstract intro/outro background
python3 tools/ltx2.py \
  --prompt "Dark moody abstract background with flowing blue light streaks, bokeh particles, cinematic" \
  --output projects/PROJECT_NAME/public/videos/intro-bg.mp4 \
  --cloud modal --progress json

Use in Remotion compositions with <OffthreadVideo>:

<OffthreadVideo src={staticFile('videos/broll-europe.mp4')} />

LTX-2 rules:

• Max ~8 seconds per clip (193 frames at 24fps). Default is ~5s (121 frames).
• Width/height must be divisible by 64. Default: 768x512.
• ~$0.20-0.25 per clip, ~2.5 min generation time.
• Cold start ~60-90s. Subsequent clips on warm GPU are faster.
• Generated audio is ambient only — use voiceover/music tools for speech and music.
• ~30% of generations may have training data artifacts (logos/text). Re-run with --seed to vary.

4d-chain. Chained Video Sequences (visual continuity)

Generate a sequence of video clips where each scene flows from the last frame of the previous one. This runs as a single command — no manual nudging between scenes.

cd ~/.openclaw/workspace/claude-code-video-toolkit

# Chain scenes 1-30 from a directory of FLUX images
python3 tools/chain_video.py \
  --scenes-dir projects/PROJECT_NAME/public/images/scenes/ \
  --output-dir projects/PROJECT_NAME/public/videos/chain/ \
  --prompt "Cinematic continuation, flowing transition" \
  --start 1 --end 30 \
  --progress json

# Resume from scene 10 (skips existing files automatically)
python3 tools/chain_video.py \
  --scenes-dir projects/PROJECT_NAME/public/images/scenes/ \
  --output-dir projects/PROJECT_NAME/public/videos/chain/ \
  --start 10 --end 30 \
  --progress json

# Per-scene prompts from JSON file
python3 tools/chain_video.py \
  --scenes-dir projects/PROJECT_NAME/public/images/scenes/ \
  --output-dir projects/PROJECT_NAME/public/videos/chain/ \
  --prompts-file projects/PROJECT_NAME/scenes.json \
  --progress json

# Chain from an existing clip (no scene images needed)
python3 tools/chain_video.py \
  --first-clip output/chain-04.mp4 \
  --output-dir output/ \
  --start 5 --end 30 \
  --prompt "Celtic mythology, flowing transition" \
  --progress json

Prompts file format (scenes.json):

{"1": "Ancient stone circle at dawn", "2": "Celtic spirals emerge from stone", "3": "Portal opens with golden light"}

Chain rules:

• Extracts last frame from scene N, feeds as --input to scene N+1 via LTX-2
• Skips scenes that already exist on disk (safe to resume)
• Falls back to scene images from --scenes-dir if chaining fails
• Use --prefix to set output filename prefix (default: chain)
• ~2.5 min per scene, ~$0.20-0.25 per clip
• Extra args (e.g. --negative-prompt, --seed) are passed through to ltx2.py

CRITICAL: Style drift in chained sequences. LTX-2 has ~30% training data contamination (anime/Asian content). Generic prompts like "cinematic transition" will drift toward anime aesthetics within 5-10 chained scenes. To prevent this:

1. ALWAYS use --prompts-file with specific per-scene prompts — never a single generic prompt for the whole chain
2. ALWAYS add --negative-prompt to exclude unwanted styles:

   --negative-prompt "anime, manga, asian, cartoon, illustration, watermark, text, logo"
   

3. Each per-scene prompt should include strong style anchors (e.g. "Irish landscape, Celtic knotwork, oil painting style") not just subject descriptions

CRITICAL: Run with yieldMs for live progress reporting. Don't break it into per-scene tool calls — OpenClaw's agent run ends between calls, causing the sequence to stall. Instead, use exec with yieldMs so you stay in the loop and can relay progress to the user:

exec command:"cd ~/.openclaw/workspace/claude-code-video-toolkit && python3 tools/chain_video.py --scenes-dir /path/to/images/ --output-dir /path/to/output/ --prompts-file scenes.json --progress json" yieldMs:10000

How this works:

• yieldMs:10000 returns control to you every 10 seconds
• You read the --progress json output (JSON Lines on stderr with stage/pct/msg)
• Report progress to the user ("Scene 05/30 complete, 17%")
• Then poll again: process action:poll sessionId:<id>
• Repeat until "stage":"complete" appears

This is the correct pattern for ALL long-running tool commands (chain_video, batch flux, batch sadtalker, etc.). Never use bash background:true and forget about it — use exec + yieldMs + process poll loop so you can report progress live.

4e. Talking Head Narrator (optional)

Generate a presenter portrait, then animate per-scene clips:

cd ~/.openclaw/workspace/claude-code-video-toolkit

# 1. Generate portrait
python3 tools/flux2.py \
  --prompt "Professional presenter portrait, clean style, dark background, facing camera, upper body" \
  --width 1024 --height 576 \
  --output projects/PROJECT_NAME/public/images/presenter.png \
  --cloud modal --progress json

# 2. Generate per-scene narrator clips (one per scene, NOT one long video)
python3 tools/sadtalker.py \
  --image projects/PROJECT_NAME/public/images/presenter.png \
  --audio projects/PROJECT_NAME/public/audio/scenes/01.mp3 \
  --preprocess full --still --expression-scale 0.8 \
  --output projects/PROJECT_NAME/public/narrator-01.mp4 \
  --cloud modal --progress json

# Repeat for each scene that needs a narrator

SadTalker rules — follow these exactly:

• ALWAYS use --preprocess full (default crop outputs a square, wrong aspect ratio)
• ALWAYS use --still (reduces head movement, looks professional)
• ALWAYS generate per-scene clips (6-15s each), NEVER one long video
• Processing: ~3-4 min per 10s of audio on Modal A10G
• --expression-scale 0.8 keeps expressions subtle (range 0.0-1.5)

4e. Image Editing (optional)

Create scene variants from existing images:

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/image_edit.py \
  --input projects/PROJECT_NAME/public/images/title-bg.png \
  --prompt "Make it darker with red tones, more ominous" \
  --output projects/PROJECT_NAME/public/images/problem-bg.png \
  --cloud modal --progress json

4f. Upscaling (optional)

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/upscale.py \
  --input projects/PROJECT_NAME/public/images/some-image.png \
  --output projects/PROJECT_NAME/public/images/some-image-4x.png \
  --scale 4 --cloud modal --progress json

Step 5: Sync Timing

ALWAYS do this after generating voiceover. Audio duration differs from estimates.

cd ~/.openclaw/workspace/claude-code-video-toolkit
for f in projects/PROJECT_NAME/public/audio/scenes/*.mp3; do
  echo "$(basename $f): $(ffprobe -v error -show_entries format=duration -of csv=p=0 "$f")s"
done

Update each scene's durationSeconds in demo-config.ts to: ceil(actual_audio_duration + 2).

Example: if 01.mp3 is 6.8s, set scene 1 durationSeconds to 9 (ceil(6.8 + 2) = 9).

Step 6: Review Still Frames

cd ~/.openclaw/workspace/claude-code-video-toolkit/projects/PROJECT_NAME
npx remotion still src/index.ts ProductDemo --frame=100 --output=/tmp/review-scene1.png
npx remotion still src/index.ts ProductDemo --frame=400 --output=/tmp/review-scene2.png

Check: text truncation, animation timing, narrator PiP positioning, background contrast.

Step 7: Render

cd ~/.openclaw/workspace/claude-code-video-toolkit/projects/PROJECT_NAME
npm run render

Output: out/ProductDemo.mp4

---

Composition Patterns

Per-Scene Audio

Use per-scene audio with a 1-second delay (from={30} = 30 frames = 1s at 30fps):

<Sequence from={30}>
  <Audio src={staticFile('audio/scenes/01.mp3')} volume={1} />
</Sequence>

Per-Scene Narrator PiP

<Sequence from={30}>
  <OffthreadVideo
    src={staticFile('narrator-01.mp4')}
    style={{ width: 320, height: 180, objectFit: 'cover' }}
    muted
  />
</Sequence>

ALWAYS use <OffthreadVideo>, NEVER <video>. Remotion requires its own component for frame-accurate rendering.

Transitions

import { TransitionSeries, linearTiming } from '@remotion/transitions';
import { fade } from '@remotion/transitions/fade';
import { glitch } from '../../../lib/transitions/presentations/glitch';
import { lightLeak } from '../../../lib/transitions/presentations/light-leak';

NEVER import from lib/transitions barrel — import custom transitions from lib/transitions/presentations/ directly.

---

Progress Reporting

All cloud GPU tools support structured progress output for automated monitoring.

Usage

Add --progress json to any tool command to get JSON Lines on stderr:

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/music_gen.py \
  --preset corporate-bg --duration 60 \
  --output projects/PROJECT_NAME/public/audio/bg-music.mp3 \
  --progress json

Output Format

Each line on stderr is a JSON object:

{"ts":"14:23:15","stage":"submit","msg":"Sending to acemusic.ai (XL Turbo 4B, thinking: on)...","pct":null,"elapsed":0.0}
{"ts":"14:23:30","stage":"waiting","msg":"Waiting for acemusic.ai response... (15s)","pct":null,"elapsed":15.0}
{"ts":"14:23:45","stage":"waiting","msg":"Waiting for acemusic.ai response... (30s)","pct":null,"elapsed":30.0}
{"ts":"14:24:02","stage":"complete","msg":"Saved: bg-music.mp3 (245 KB, 60.1s)","pct":100,"elapsed":47.3}

Stages

Stage	Meaning
submit	Job sent to provider
queue	RunPod: waiting for GPU
processing	RunPod: GPU processing
waiting	Heartbeat during synchronous calls (acemusic, Modal)
complete	Job finished successfully
error	Something failed — check msg for details
item	Multi-item progress (e.g., scene 3/7) — pct is populated
cost	Estimated cost for the operation

Behaviour by Provider

• acemusic: Emits submit → periodic waiting heartbeats (every 15s) → complete
• RunPod: Emits submit → queue → processing → complete (on each poll)
• Modal: Emits submit → periodic waiting heartbeats → complete

Default mode (--progress human) shows the same events as colored terminal output — no change to existing behaviour.

---

Error Recovery

Problem	Solution
Tool command fails with "No module named..."	Run pip3 install --break-system-packages -r tools/requirements.txt from toolkit root
"MODAL_*_ENDPOINT_URL not configured"	Check .env has the endpoint URL. Run python3 tools/verify_setup.py
SadTalker output is square/cropped	You forgot --preprocess full. Re-run with that flag
Audio too short/long for scene	Re-run Step 5 (sync timing) and update config
npm run render fails	Make sure you're in the project dir, not toolkit root. Run npm install first
"Cannot find module" in Remotion	Check import paths. Custom components use ../../../lib/ relative paths
Cold start timeout on Modal	First call after idle takes 30-120s. Retry once — second call uses warm GPU
SadTalker client timeout (long audio)	The client HTTP request can time out before Modal finishes. Modal still uploads the result to R2. Check sadtalker/results/ in the video-toolkit R2 bucket for the output. Use python3 -c "import boto3; ..." with the R2 creds from .env to list and generate a presigned URL

---

Cost Estimates (Modal)

Tool	Typical Cost	Notes
Qwen3-TTS	~$0.01/scene	~20s per scene on warm GPU
FLUX.2	~$0.01/image	~3s warm, ~30s cold
ACE-Step	~$0.02-0.05	Depends on duration
SadTalker	~$0.05-0.20/scene	~3-4 min per 10s audio
Qwen-Edit	~$0.03-0.15	~8 min cold start (25GB model)
RealESRGAN	~$0.005/image	Very fast
LTX-2.3	~$0.20-0.25/clip	~2.5 min per 5s clip, A100-80GB

Total for a 60s video: ~$1-3 depending on scenes and narrator clips.

Modal Starter plan: $30/month free compute. Apps scale to zero when idle.