Wan 2.7 Text to Video is Wan's text-to-video model on Versely. This page is its structured prompting reference: the 6 parameters its schema actually exposes, the t2v technique that applies to it, copy-ready templates.
Everything here is grounded in the same sources Versely's agent reads — the model's input schema, the wan family rule its prompt enhancer applies. Where a line is general craft advice rather than a documented fact about Wan 2.7 Text to Video, the page says so.
What Wan 2.7 Text to Video wants
The exact input surface, from the same schema the Versely agent fetches with get_model_input_schema before every generation.
| Parameter | What it does | Values |
|---|---|---|
promptreq | Prompt (max 5000 chars per docs) | string (max 5000) |
negative_prompt | Negative prompt (max 500 chars) | string (max 500) |
audio_url | Optional driving audio | string |
resolution | Resolution | 720p · 1080pdefault: 1080p |
duration | Seconds (2-15 per docs) | integer (min 2, max 15)default: 5 |
prompt_extend | LLM rewrite | booleandefault: true |
- — Aspect-ratio key is `ratio` not `aspect_ratio`
- — Verified against docs.kie.ai/market/wan/2-7-text-to-video
The rule Versely's enhancer applies
Versely's prompt enhancer carries a per-family rule for wan models, applied automatically whenever it rewrites a prompt for Wan 2.7 Text to Video. Verbatim:
“Wan models work well with straightforward scene descriptions and style keywords.”
Technique that applies here
Text-to-video: scene narrative, camera movement, temporal flow; negative prompts where supported
- VEO, Kling, and Sora all carry a MODEL_TIPS rule the prompt enhancer applies automatically, and all three converge on naming the camera: VEO's tip says to 'include camera angles, movement types (dolly, crane, steadicam), lighting setup, and temporal flow' and to 'be specific about scene transitions'; Kling's tip asks for 'camera movement (pan, zoom, tracking shot), subject action, and environment details... motion direction and pacing'; Sora's tip wants 'clear scene narratives, camera movements, and temporal progression.' None of these schemas expose a camera-movement parameter — the enhancer is rewriting your prose toward this language, so writing the move by name yourself ('slow dolly-in,' 'tracking shot following the subject') works with the family instead of getting rewritten.
- Wan's tip is the shortest in the whole family list — 'straightforward scene descriptions and style keywords' — and the schema backs that up: Wan 2.7 Text to Video's own params are prompt, negative_prompt, an optional audio_url, resolution, duration and a prompt_extend toggle, with no camera or style-detail field to write toward. Don't over-engineer a Wan prompt with the dense camera-movement language that rewards Kling; a plain sentence plus a style word (e.g. 'cinematic') is the shape the tip is asking for.
- General technique, not model-specific: where a model exposes a real negative_prompt field (Kling, Pixverse, LTX, Wan, VEO's fal variant), put exclusions there instead of writing 'no X' into the scene description — it's a separate parameter, read independently of the prompt. And where a generate_audio-style boolean exists, decide sound as that parameter, not as a prose request — defaults vary even within one family (Kling's O3 tier defaults generate_audio to false while its V2.6/V3 tiers default it to true).
Copy-ready templates
Replace the bracketed slots; each template says when it's the right shape.
[SUBJECT], [SETTING], [ONE-LINE ACTION]. Style: [STYLE KEYWORD — e.g. cinematic / documentary / abstract].
Use when: Wan-family t2v models — the tip explicitly wants straightforward description plus a style keyword, matching a schema with no camera or style-detail params.
[MAIN PROMPT TEXT]. Negative prompt: [ELEMENTS TO EXCLUDE — e.g. text, watermark, blurry, extra limbs, distorted hands].
Use when: models whose schema exposes a separate negative_prompt field (Kling, Pixverse, LTX, Wan, VEO's fal variant) — exclusions belong in that field, not folded into the scene description.
How the Versely agent does this automatically
You can use this page by hand, or let the agent apply the same knowledge. Four real mechanisms — no more, no less:
get_model_input_schema— before generating, the agent looks up Wan 2.7 Text to Video's exact input fields, required fields, allowed values, defaults, and min/max bounds. The parameter table above is that same surface.- The prompt enhancer's family rules — 12 per-family rewrite rules, including the wan rule quoted on this page, shape how a rough prompt gets rewritten.
- The per-provider speech guide — for TTS scripts, the agent follows a provider-specific tag scheme — not relevant to this model, but it's why voiceover scripts come out marked up correctly.
expand_movie_scene— in movie flows, brief scene ideas are rewritten into detailed cinematic descriptions before generation.
Mistakes that waste generations
- Writing a multi-beat script (three different actions or cuts) into one generation call on a model capped well under 15s — VEO tops out at 8s per call, Sora 2's kie variant only offers 10 or 15 frames — and the model compresses everything into a blur or drops the later beats rather than pacing through your scenes.
- Describing aspect ratio or resolution in prose ('shot in glorious 4K widescreen') instead of setting the actual aspect_ratio/resolution enum — the text has no effect on frame shape or output resolution, only the parameter does, and on Sora 2 the valid values are the words 'portrait'/'landscape', not a ratio string at all.
- Using one family's register on another: a dense Kling-style shot list ('slow pan, then a hard cut to a tracking shot') fights a Wan model's 'straightforward... style keywords' tip, and a plain narrative sentence undersells VEO or Sora's named-camera-movement tip.
The long-form guide
This page is the structured reference. For the essay treatment — worked examples, failure modes, and narrative — read Wan 2.7 Prompting: References, Frames, and Voice.
This guide also covers
These siblings share Wan 2.7 Text to Video's prompting-relevant input surface, so their prompting URLs resolve here — tier and pricing differences live on their own model pages:
Frequently asked questions
How long can a Wan 2.7 Text to Video prompt be?+
Wan 2.7 Text to Video's schema caps the prompt at 5,000 characters. Spend them on the subject, motion, and the register its family rewards rather than restating parameters the schema already controls.
Does Wan 2.7 Text to Video support negative prompts?+
Yes — the schema exposes negative_prompt (max 500 characters). Put exclusions there instead of writing "no text, no watermark" into the main prompt.
How long can a Wan 2.7 Text to Video generation be?+
Duration is bounded (min 2, max 15). Write one continuous beat sized to that window rather than a multi-act script.
How does the Versely agent know Wan 2.7 Text to Video's parameters?+
Before generating, the agent calls its get_model_input_schema tool, which looks up the exact input fields, required fields, allowed values, defaults, and min/max bounds for the model; separately, the prompt enhancer applies the wan family rule quoted on this page to the prompt text itself. Nothing on this page is guessed — it is the same schema surface those tools read.
Does this guide also cover Wan V2.6 Text to Video and Wan Video 2.5 Text to Video?+
Yes. Wan V2.6 Text to Video, Wan Video 2.5 Text to Video share the same prompting-relevant input surface as Wan 2.7 Text to Video, so their prompting URLs redirect here instead of duplicating this page. Tier and pricing differences live on each model's own /models page.
Related prompting guides
Generate with Wan 2.7 Text to Video
Wan 2.7 Text to Video is live in Versely — paste a template above, or just describe what you want and let the agent map it onto the schema for you.