TL;DR

Gemma 4 31B expands a single-line idea into a 10-beat structure. HiDream generates 11 images at 2048², LTX-2 A2V/I2V renders 11 clips, Irodori-TTS handles dialogue and a male narrator, and ffmpeg burns in subtitles and a Hook title overlay — all fully automated. End-to-end: a 40-second portrait video (512×768) in 25–30 minutes. One local GPU (96 GB Blackwell), zero API cost.

Finished video (already published):

@youtube

Who This Is For

Individual developers who want to mass-produce AI comedy shorts on a local GPU. The focus isn't on any single model — it's on the design of chaining multiple models into one operational pipeline.

What I Built

I automated a dark-comedy format — a short-video style I called consent_dilemma — from a one-line idea all the way to a finished 40-second video.

Finished structure:

• Hook (0–5s): Extreme close-up of a beautiful woman + narrator "The fate of the man who answered 'You're a guy, aren't you'——" + large title overlay
• Main section (5–37s): Movie theater date → "Can I kiss you?" → "No… stop it…" → dejection → "Why aren't you more assertive? You're a guy, aren't you?" → realization → kiss
• Punchline (37–40s): Courtroom — "The defendant is sentenced to 3 years for non-consensual intercourse" + gavel "Knock!" + tears in a jail cell

Before / after:

Architecture

[Stage A] Gemma 4 31B (vllm, port 8894) → plan.json (10 beats + hook)
[Stage B] HiDream-O1-Image (port 8895) → 11 images at 2048²
          + Gemma 4 31B multimodal visual judge (--judge --max-retries 2)
[Stage C] Irodori-TTS (port 8880) + LTX-2 A2V (port 8892) / I2V (port 8891)
          → 11 clips + Hook clip → ffmpeg concat → subtitle burn-in

Implementation lives under llm_server/storyboard/ (pipeline.py / visual.py / judge.py / video.py / render.py / run.py).

The 10-Beat consent_dilemma Format

Fixed as a system prompt via CONSENT_DILEMMA_SYSTEM in prompts.py:

Three key structural choices:

1. Don't make the refusal a flat "No": Stretch it into something like "No… stop it…" with trailing inflection, conveying the "performative No that doesn't mean No" nuance. This is what makes the gaslight's contradiction land later.
2. Don't jump straight from gaslight to kiss: Insert a "pause" (realization beat) of ~1.5 seconds. This controls tempo and the emotional curve.
3. Two-stage punchline — verdict then jail: The verdict alone feels abrupt. Showing him crying in a cell makes "he actually got convicted" click.

Hook Design (The TikTok 3-Second Problem)

On portrait short-form video, drop-off is decided in the first 3 seconds. A Hook segment is prepended before the 10 main beats:

"hook": {
  "title_overlay": "No Means Yes?",
  "narrator_line": "The fate of the man who answered 'You're a guy, aren't you'——",
  "image_prompt": "ultra close-up of beautiful Japanese woman, half-lidded eyes, ...",
  "duration_sec": 3.5
}

Two implementation pitfalls:

Pitfall 1: narrator TTS duration exceeds duration_sec, cutting the audio. The final syllable of the narrator line got clipped. Fix: generate TTS first → measure with ffprobe → pass max(plan_duration, narrator + 0.6) as the I2V duration.

narrator_dur = _ffprobe_duration(narrator_wav)
duration = max(float(hook.get("duration_sec", 0.0)), narrator_dur + 0.6)
ltx_i2v_clip(portrait, i2v_prompt, duration, silent_video, keep_audio=False)

Pitfall 2: drawtext y position. y=h*0.30 (one-third down the screen) overlapped the face. Changed to y=20 (absolute 20 px) to pin the title to the very top.

Subtitle Burn-In (Silent Viewing Support)

Burned-in subtitles for users watching without sound on the train, and for cross-platform reliability.

style = (
    "FontName=Noto Sans CJK JP,FontSize=18,PrimaryColour=&H00FFFFFF,"
    "OutlineColour=&H00000000,Outline=2,Shadow=0,BorderStyle=1,"
    "Alignment=2,MarginV=60,Bold=1"
)
# ffmpeg -i raw.mp4 -vf "subtitles=subs.srt:force_style='..."

Alignment=2 = bottom center. MarginV=60 gives breathing room from the bottom edge.

Long-line splitting: A line of 30+ characters within one beat covers the face. _split_subtitle splits on 。．！？ → greedy-packs into chunks of ≤28 characters → distributes beat duration evenly across chunks:

Input:

言葉で確認するのなんてロマンチックじゃないよね。ねえ、もっと積極的になってよ。男の子でしょ？

Output (one 8.9s beat split into 2 timed chunks):

Using LTX-2 I2V as a Sound Effect Generator (gavel_se)

LTX-2 distilled embeds AI-generated audio (ambient sound / sound effects) directly into the I2V output mp4. Unless you explicitly drop it with ffmpeg -map 0:v:0 -map 1:a:0, whatever the prompt describes comes with sound.

I repurposed this as an SFX generator:

def render_se_tail_beat(sb_dir, beat, prior_clip, work_dir):
    # 1. Extract the last frame of the previous beat
    extract_last_frame(prior_clip, last_frame_png)
    # 2. Feed that image into I2V, request SFX via prompt
    prompt = build_gavel_se_prompt(beat)
    return ltx_i2v_clip(last_frame_png, prompt, duration, clip_path, keep_audio=True)

Added a keep_audio=True flag to ltx_i2v_clip so the audio isn't dropped during ffmpeg re-encoding.

Prompt for gavel_se:

"Single decisive arm motion of the judge bringing the gavel down sharply "
"onto the wooden bench. Loud sharp wood-on-wood thwack impact sound. "
"Brief, contained, no other motion in the frame."

Last frame of the judge + gavel prompt → "Knock!" sound. If that misses, the design falls back to something like the Ace Attorney SFX.

Pitfall Log

Five major pitfalls hit during development:

1. Codex CLI hangs with vLLM 0.20.2

Sending a system prompt + idea via codex exec -p gemma4 hung at 0% CPU for 20+ minutes during the /v1/responses handshake. Piping subprocess output through tail -200 was also suppressing early stderr.

Fix: Dropped Codex entirely, hit /v1/chat/completions directly with urllib.request. Used response_format={"type":"json_object"} to force JSON. plan.json generated in 25 seconds.

2. HiDream won't remove the cinema screen

Even with "The movie screen is BEHIND the camera and NOT VISIBLE in frame" in the setting prompt, the screen persisted in the background through 2048/50 steps.

Fix: Generate scene_base via T2I → feed that same image into I2I edit with a prompt to "replace screen with dark wall, keep character positions identical" → gone in one shot. Two-stage pipeline: low-res → I2I fix → regenerate all beats at full resolution.

3. HiDream turns lips-on-lips into a cheek kiss

With standard prompting, HiDream tends to interpret kiss as a cheek kiss. You need directives at the level of "CRITICAL: their LIPS meet directly — mouth-to-mouth contact at the CENTER of the frame. NOT a cheek kiss". Added a dedicated early-return block in _beat_edit_prompt for the kiss beat.

4. CAST / CROP_BOX / SPEAKER_A2V_PROMPT are hardcoded for two characters

Three dictionaries — CAST, CROP_BOX, SPEAKER_A2V_PROMPT — only know a (Kenta) and b (Misaki). Adding judge/narrator requires updating all three simultaneously (you find out via KeyError). Also added branching in render_speech_beat_ltx_a2v so beats with setting_override crop from the beat's own image rather than scene_base.

5. Gemma 4 multimodal judge has too many false positives

storyboard/judge.py sends beat images + expected expressions to Gemma 4 31B for YES/NO visual judgment. It does catch obvious failures like wrong finger count, open-mouth pose on a silent beat, or scene geometry mismatch — but hammers FAIL on subtle cases like "subtle shy expression."

In practice: accept and proceed after 3 consecutive FAILs with max-retries 2. Automating the threshold for escalating to a frontier reviewer (Gemini 3.1 Pro) is still a TODO.

VRAM Layout

Breakdown on a 96 GB Blackwell Max-Q:

All at peak simultaneously = 109 GiB → OOM. Operational flow:

1. Stage A: Gemma 31B + HiDream idle → peak ~62 GiB
2. Stage B with judge: Gemma 31B + HiDream peak → ~73 GiB
3. Before final render: pkill -f "vllm.*gemma" kills Gemma → 38 GiB freed
4. Stage B final render (2048/50): HiDream peak ~33 GiB
5. Before Stage C: lsof -ti tcp:8895 | xargs kill kills HiDream → 16 GiB freed
6. Stage C: LTX-2 + TTS + Ditto → peak ~32 GiB

Explicit kills at stage transitions, and everything fits on one card.

Iteration Loop (Cache Strategy)

Partial regeneration — not "rebuild everything" — is what keeps iteration fast:

# Regen a single beat image (HiDream only)
python -m storyboard.visual --plan ... --out ... --only-beat 7 --steps 50 --resolution 2048

# Partial video regen (TTS + LTX-2)
python -m storyboard.video --dir ... --regen-beats 5,6,7 --skip-review

# Adjust only subtitle or Hook title position
rm _video_work/clip_00_hook.mp4 _video_work/subs_irodori.srt
python -m storyboard.video --dir ... --regen-beats none --skip-review   # ~30 seconds

Cache hierarchy:

• HiDream beat images (beat_NN_<type>.png) — regenerate individually with --only-beat in ~80 seconds
• A2V / I2V clips (clip_NN_*.mp4) — invalidated when beat type / speaker / line changes
• Finished Hook clip (clip_00_hook.mp4) — delete just this when adjusting title position (the heavy LTX-2 I2V hook_silent.mp4 is reused)
• Subtitle SRT — regenerated every time (~10 seconds)

Title position / subtitle style / Hook copy tweaks re-render in 30 seconds. The 100-second LTX-2 I2V portion stays cached.

How This Fits Into Kotonia

Videos generated by this pipeline feed the SNS distribution layer (TikTok / YouTube Shorts / IG Reels) — the top of the funnel for attention → conversion for Kotonia (kotonia.ai).

Technically, it's an extension of the /studio/ stack (HiDream image generation) into the video direction. The plan is to eventually expose this as /video-studio/ — a one-click Web UI over the same pipeline. Right now it's CLI only.

Related Articles / Want to Try It?

• Running HiDream-O1-Image's 5 modes resident on 1 GPU — backend design for Studio (/studio/)
• Fitting LTX-2 onto a single 95 GB GPU with fp8-cast quantization — the Stage C video generation foundation
• Reproducing language-learning short videos with Claude Code — earlier 6-beat "mango incident" format implementation
• Want to try the image generation side? /studio/ lets you do it in one click (video pipeline CLI is self-host only for now)