Wan 2.7 Image to Video is Wan's image-to-video model on Versely. This page is its structured prompting reference: the 8 parameters its schema actually exposes, the i2v 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 Image to Video, the page says so.
What Wan 2.7 Image 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) |
first_frame_url | First-frame image URL (optional per docs) | string |
last_frame_url | Optional last-frame image | string |
driving_audio_url | Optional driving audio | string |
resolution | Resolution | 720p · 1080pdefault: 1080p |
duration | Seconds (2-15) | integer (min 2, max 15)default: 5 |
prompt_extend | LLM rewrite | booleandefault: true |
- — Image keys are `first_frame_url`/`last_frame_url` (NOT `image_url`)
- — Verified against docs.kie.ai/market/wan/2-7-image-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 Image to Video. Verbatim:
“Wan models work well with straightforward scene descriptions and style keywords.”
Technique that applies here
Image-to-video: animating a start frame; motion description relative to the input image; UGC hook pool applies
- Aspect ratio usually isn't a settable parameter in image-to-video mode — it's inherited from your source image. Vidu Q3's runpod schema note is explicit: 'No style/aspect_ratio fields in I2V — aspect ratio is inferred from the input image.' Pixverse 5.6 I2V's note says the same thing: 'I2V infers aspect_ratio from image (no aspect_ratio field).' Wan's i2v schemas (2.7, and the folded Wan 2.5 variants) all omit an aspect_ratio param entirely; two of its provider variants confirm this directly in their own schema notes — kie's says 'inferred from image', replicate's says 'aspect is derived from the input image.' Crop or compose your source image to the aspect you want before you upload it; asking for '16:9 widescreen' in the prompt text does nothing on these schemas.
- Kling and Wan's MODEL_TIPS carry over unchanged from their text-to-video siblings, because the enhancer matches by model name, not by mode. Kling's tip ('camera movement (pan, zoom, tracking shot), subject action, and environment details... motion direction and pacing') and Wan's ('straightforward scene descriptions and style keywords') apply identically to Kling Video V3 Standard Image to Video and Wan 2.7 Image to Video as they do to each family's t2v page.
- Several i2v schemas quietly accept a second, optional 'end' image alongside the required start image — Vidu Q3's fal variant (end_image_url, described as generating a transition), Kling Video V3 Standard Image to Video's fal variant (end_image_url), LTX 2.3 Image to Video Pro (a model folded into the LTX 2 Pro page; end_image_url), and Wan 2.7 Image to Video (last_frame_url). If you supply one, write the prompt as the path between the two states, not as 'what happens next' from a single photo — you're now effectively writing a first-last-frame prompt inside an i2v call.
Copy-ready templates
Replace the bracketed slots; each template says when it's the right shape.
[WHAT MOVES — e.g. 'she turns her head toward camera and smiles'], [SECONDARY MOTION DETAIL — e.g. 'hair drifts in a light breeze']. Camera: [static / slow push-in / slight handheld].
Use when: i2v models whose schema infers aspect ratio from the source image (Vidu, Pixverse, Wan) — crop the source image to your target aspect first, then spend the whole prompt on motion, not framing.
[SUBJECT], [ONE-LINE ACTION]. Style: [STYLE KEYWORD].
Use when: Wan-family i2v models — the tip wants straightforward description plus a style keyword, matching a schema with no dedicated style or camera params.
[STATE A DESCRIPTION] transitions into [STATE B DESCRIPTION] as [WHAT CHANGES IN BETWEEN].
Use when: you've supplied both a start image and an optional end/tail image (Vidu's end_image_url, Kling Video V3's end_image_url, LTX 2.3 Image to Video Pro's end_image_url, Wan 2.7's last_frame_url) — describe the whole path, not a single continuation.
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 Image 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
- Asking for a specific aspect ratio in the prompt when the schema doesn't expose an aspect_ratio param — it's silently ignored; on Vidu, Pixverse, and Wan's i2v variants the frame shape comes entirely from the source image you upload.
- Re-describing the subject or scene the source image already shows instead of focusing on the motion — wastes prompt budget and can conflict with the photo (a different outfit or background than what's actually in frame).
- Treating every 'image-to-video' model as the same shape: sending a HeyGen-style voice/talking_style prompt to Vidu, or a plain motion-description prompt to HeyGen, targets a control surface that model doesn't expose.
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 Image 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 Image to Video prompt be?+
Wan 2.7 Image 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 Image 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 Image 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 Image 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 2.1 Image to Video and Wan 2.6 Image to Video and others?+
Yes. Wan 2.1 Image to Video, Wan 2.6 Image to Video, Wan 2.6 Image to Video Flash, Wan Video 2.5 Image to Video share the same prompting-relevant input surface as Wan 2.7 Image 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 Image to Video
Wan 2.7 Image 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.