Prompting AI video models is basically superstition at scale. You write something, hit generate, cross your fingers. When it breaks, you tweak a word and try again. Maybe you add “cinematic” or swap “walking” for “strolling.” Maybe you throw in a filmmaker’s name. You are not solving a problem. You are performing a ritual. One prompt engineer on Reddit traced that pattern back to its actual root cause: coverage gaps.
Seedance 2.0 pays attention to specific dimensions. Subject. Action. Camera. Style. Constraints. Miss any one of them and the model fills the gap with whatever it wants. That gap-filling is where consistency dies. It is not randomness or model failure. It is the model doing exactly what it was trained to do: infer missing information. A five-layer structure removes that ambiguity entirely and forces you to make every decision deliberately instead of leaving it to inference.
Compare these two prompts for the same shot:
Vague: “A girl walking in a room, cinematic.”
Structured: “25-year-old Asian woman, long black hair, white loose shirt and jeans, focused calm expression. She slowly turns and looks out the window. Start from a medium shoulder-back angle, slowly push to a face close-up. Soft pendant lamp warm yellow, slight film grain, cozy living room mood. No text in frame, hands fully visible, eyes open.”
Same scene. Completely different output reliability. The vague version produces a different person every generation. Hair changes. Age shifts. The room is a different room. The “cinematic” tag adds nothing because “cinematic” is not an instruction, it is an aesthetic wish. The structured version gives the model nowhere to improvise. Every major variable is locked before the model touches it.
The Five-Layer Structure 🎬
- 🎯 Subject, Specific over vague. Age, build, clothing, hair, expression, hand position. Vague subjects drift. Specific ones anchor. “A girl” gives you anything. “25-year-old Asian woman, long black hair, white loose shirt, focused calm expression” gives you a person. Subject drift is the most visible failure mode in multi-clip projects: faces shift between generations, clothing changes color mid-sequence, hands disappear or multiply. Locking the subject description cuts most of that drift immediately. If you are building a multi-clip sequence, copy your subject description across every prompt exactly. Even small wording changes can produce a new person.
- Action, One beat, present tense. Compound sequences like “she turns, walks, then notices something” confuse the model’s temporal logic. Single actions render cleanly every time. If you need multiple beats, treat each as a separate generation. The model is solving a spatial and temporal physics problem in a single inference pass. Asking it to choreograph three sequential actions in one clip is asking it to plan a scene, not generate a shot. A single clear action is the difference between a take you can use and a take that needs five regenerations before it reads correctly.
- Camera, Frame type plus movement. Wide/medium/close-up for framing. Push/pan/orbit/handheld for motion. “Cinematic shot” is not a camera instruction. “Start from a medium shoulder-back angle, slowly push to a face close-up” is. If you want a specific feel, describe the physical mechanics: how far the camera moves, how fast, whether there is shake. A slow dolly push reads completely differently from a quick cut or a handheld orbit. Describe what the camera physically does rather than the emotion you want the shot to produce. The model can execute mechanics. It cannot execute feelings.
- Style, Lighting, color palette, film texture, mood. Keep it concrete. Film or photographer references work if kept brief. “Soft pendant lamp warm yellow, slight film grain, cozy living room” lands consistently. Abstract mood words like “melancholy” or “tense” do not anchor the model the same way a light source does. If you reference a film, pair it with a concrete descriptor: “Blade Runner 2049, teal and orange contrast, desaturated shadows” gives the model something measurable to work from. “Kubrick vibes” does not.
- ⚠️ Constraints, The negative list most people skip. “No text in frame. No watermark. Hands fully visible. Eyes open the whole time.” This is the layer that cuts broken-physics generations the hardest. It is also the most underused layer by far. Common constraint failures that this layer actually fixes: fingers merging, text artifacts appearing on surfaces, subjects blinking at the wrong moment, watermark-style overlays on clothing. You cannot always predict which constraints a shot needs before running it, but you can build a base constraint list from your past failures and add shot-specific entries on top. Five to eight constraints is usually enough. More than that and you are over-specifying in ways that create their own conflicts.
The order matters less than the coverage. Drop layers you do not need. Expand the ones that matter for your specific shot. A tight close-up on a face needs significant subject detail but almost no camera instruction. A wide establishing shot is the reverse. Run through all five layers before you start diagnosing why output is inconsistent. Most “random” inconsistency traces back to one uncovered layer, not to model behavior or bad luck.
The structure also transfers to Wan 2.7. That model responds harder to camera language than subject specificity, while Seedance is the reverse. Seedance wants to know exactly who is in the frame. Wan 2.7 wants to know exactly how the camera is moving. Same five layers, different tuning. When you switch models, start by rebalancing: expand your camera layer for Wan, expand your subject layer for Seedance. You can test both on Atlas Cloud without changing your core workflow at all, which makes the comparison fast and the differences obvious.
If you take one thing from this: add the constraints layer to your next Seedance prompt. It has the most unexploited upside for most people, and you will see the difference on the first run!
Frequently Asked Questions
Q: Does the five-layer order actually matter?
Less than coverage does. You can drop or expand layers based on what you need. The exception: don’t skip constraints. That’s the layer that cuts broken physics and weird artifacts most effectively.
Q: Can I use this for other models like Wan?
Yes. It transfers cleanly, but different models have different sensitivities. Wan responds harder to camera language, Seedance to subject specificity. Adjust which layers you emphasize based on what each model cares about most.
Q: Which layer should I prioritize if I’m short on time?
Constraints. The author spent a long time underusing this one and found it made the biggest difference once locked in consistently. It’s what stops broken physics and unwanted elements like text or watermarks.
Q: Why do my multi-action sequences look confused?
Seedance handles single beats better than compound sequences. “She slowly turns and looks out the window” works. “She turns, looks out the window, then walks away” gets muddled. Keep one main action per shot or break complex sequences across multiple shots.
Q: Is “cinematic shot” specific enough for the camera layer?
No. Generic terms like “cinematic” don’t give the model enough detail. Get specific: name the frame type (wide, medium, close-up) and movement (push, pan, orbit, handheld). Example: “medium shoulder-back angle, slowly push in to a face close-up.”
The five-layer prompt structure that fixed my Seedance 2.0 output stability
by u/Fresh-Resolution182 in PromptEngineering