Maximum-control K-pop choreography — 13-tag DSL Seedance prompt

@Kashberg_09,271 followers·2,935 views48 likes23 bookmarks·2026-04-29View original tweet ↗

Polar opposite of @Ciri_ai's one-paragraph Seedance prompt. Same template family, different control strategy: instead of trusting Seedance to infer motion, lock every dimension (character/wardrobe/camera/music/timing/restrictions). Useful when you need exact reproducibility for ads or branded content. Source: EvoLinkAI/GPT-Image-2-Seedance2-Workflow Case 25.

GPT Image 2 generated reference sheet
Step 1 — GPT Image 2 reference sheet
Step 2 — Seedance 2.0 result video
The prompt chain

Copy these prompts, swap your character and topic.

Step 1GPT Image 2· tagged
Prompt not published.GPT Image 2 step generates the 4×4 grid of 16 dance steps; author re-used the canonical movement-sheet prompt skeleton (see technique step 1).
Step 2Seedance 2.0· tagged· 94 lines
K-Pop Dance Sequence (16 Steps, Korean Street)

[PROJECT TYPE]
Cinematic K-pop dance video (instruction-to-performance translation)

[CORE REQUIREMENT — STRICT]
The video must faithfully follow the exact 16-step choreography shown in the reference sheet, in the same order, with accurate poses and transitions.
No steps added, removed, or rearranged.

🧍‍♀️ [CHARACTER]
Korean female dancer (K-pop idol aesthetic)
Slim, athletic build
Same consistent face and proportions throughout
Expressive, confident stage presence
Natural, fluid but sharp K-pop movement quality

👕 [WARDROBE — K-POP STYLE]
Fitted crop top
Loose high-waisted jeans
Sneakers
Modern idol styling (clean, trendy)
Fabric reacts naturally to movement (denim weight, subtle folds)

📍 [LOCATION / ENVIRONMENT]
Empty aesthetic Korean street (Seoul-inspired)
Verified — full prompt published verbatim by @Kashberg_0. Demonstrates the most maximal Seedance prompt shape we have observed: 13 bracketed sections covering character / wardrobe / location / 16-step sequence / camera / movement / timing / music / visual style / output / restrictions. Counterexample to the @Iancu_ai 'short wins' rule — this case uses an exhaustive prompt and gets exactly the controlled output the author wanted. The trade-off: short prompt = let model improvise; long prompt = lock in every parameter.