Assemble a song-driven music-video ad from a config — a generated sung track carries the whole narration across N tableaux (one keyframe -> one i2v clip per lyric beat) with NO separate voiceover, captions synced to the song's OWN word timings (script-window, never Whisper) and the hook word landing on the chorus drop, closed on a PIL brand end card. This is the FREE deterministic assembly stage (clip cut-to-timeline + captions + end card + FFmpeg composite); the song, keyframes, and clips come from create-music-elevenlabs / create-image-fal / create-video-fal. Use for the song-driven-music-video format.
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Assemble a song-driven music-video ad from a config: a purpose-written, sung song is
the entire narration (no separate voiceover), and every visual beat is timed to the lyrics.
The delivered song sets the timeline; N tableaux (one keyframe → one image-to-video clip per
lyric beat, all in a single look pack) are cut to their lyric windows and hard-concatenated
on the beat, captions are built from the song's OWN word timings with the hook line landing
on the chorus drop, and the spot closes on a PIL brand end card. It reads like a tiny animated
music video, not a demo. scripts/config.example.json is one worked example (Loóna "Fall In
Love With Sleep Again", 28s paper-craft 9:16) — its style, song, vocalist, mood, protagonist and
setting are that demo's answers, not defaults; scripts/PIPELINE.md maps every config block
to its step and scripts/README.md documents the free assembly.
The creative calls are made upstream by the user (the recipe's choices) and arrive in the config;
this assembly never picks them.
look_pack, clip_engine.motion_opener. The demo used a paper-craft diorama
at night.song.prompt / song.bpm. The demo used a dreamy synth lullaby at ~80 BPM.song.prompt. The demo used a soft breathy female lead.song.prompt, the lyrics, tableaux. The demo went calm/dreamy.look_pack.style_opener. The demo used a young-woman paper-doll.tableaux[].keyframe_prompt. The demo went bedroom at night → moonlit paper village.This is the FREE, deterministic assembly stage — it spends nothing. The three paid
inputs are separate capabilities: the sung song (create-music-elevenlabs, music_v1,
force_instrumental FALSE — the lyrics ARE the script, returns mp3 + words.json), one
keyframe per tableau (create-image-fal), and one Kling 3.0 i2v clip per tableau
(create-video-fal). Given the delivered song + words.json + one clip per beat,
render-song-mv cuts each clip to its lyric window, hard-concats on the beat, builds the
lyric-synced captions, composites the PIL end card, and muxes → the master. Re-cuts reuse
the existing song / keyframes / clips and cost $0.
force_instrumental false); do not add a spoken voiceover or a
second music bed.timeline.json) — never trim the song to a pre-planned grid.audio/words.json (~3 words at lyric boundaries); accent words get captions.accent_color.
Whisper on sung audio returns "🎵 Music Playing 🎵", so it can't caption lyrics.is_hook) is timed so the
payoff word (song.hook_word) sits on the chorus drop; accent that word in the captions.style_opener + negative_tail + palette drives
every keyframe so N beats read as one film; no morph within a clip.audio_mix.climax_beat_id — set it to
this run's hook tableau; the demo's T08 is example-only), loudnorm to −14 LUFS → 1080×1920
h264+aac. No paid calls, no keys.还没有评价。欢迎分享你的使用感受。
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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