Wan AI Prompt From a Video: Open-Weights Workflow

Wan-family models occupy a different position from the hosted generators: open weights, local or self-hosted runs, and a training set whose captions were written in a particular plain style. That has two consequences for this workflow — the prompts you write look different, and the video-to-prompt step can be done entirely on your own hardware if you want it that way.

Why open weights change the workflow

With a hosted generator you pay per generation and accept a queue; with an open model you pay in time and hardware and keep everything. That matters most in three situations: confidential footage that must not leave your machine, volume work where per-credit pricing stops making sense, and long experiments where you want to render forty variants of a shot without counting pennies. Text-to-video models in the open-weights category — Alibaba’s Wan family among them — behave like their hosted counterparts for prompting purposes, with a distinct caption dialect.

The caption style Wan responds to

These models are typically trained on short, dense, observational captions, so prompts work best as one or two sentences with the structure:

[what the subject is doing] + [where] + [how it is shot: one camera verb, framing] + [one style cue]

"Barista tamps espresso on a marble counter, shoulders turning into frame; slow push-in from
 chest height, soft window light, shallow focus."

• Present-tense action verbs beat noun stacks.
• One style cue is enough; three fight each other.
• Do not write negatives — describe the state you want.
• Movement words behave inconsistently across local implementations, so test the verb once per setup rather than assuming portability.

Extracting and compressing

Reference clips are still the fast path to a good prompt, and extraction is where the hosted tool earns its keep even in a local pipeline: paste a link (27 named platforms) or upload MP4/MOV/WebM into SocialToPrompt, and its analysis returns subject, camera, lighting, pacing and style in a run it states takes 20–40 seconds. Then cut it down to the two-sentence shape above — most extraction output is roughly twice as long as an open-weights prompt should be.

If you want the whole loop local, the DIY version is described in our comparison: SocialToPrompt replaced by a video LLM running locally, at the cost of the temporal reasoning that still-frame sampling cannot do.

A local pipeline

# 1. grab the reference clip (where you are entitled to)
yt-dlp -f "bv*[height<=720]+ba/b" --output ref.mp4 "URL"
# 2. excerpt the shot you care about
ffmpeg -ss 00:00:04 -t 6 -i ref.mp4 -c copy shot.mp4
# 3. extract the prompt (hosted tool, or a local video-LLM if privacy requires it)
# 4. generate locally with the two-sentence prompt + shot.mp4 frame as image conditioning
ffmpeg -i shot.mp4 -vf "select=eq(n\,30)" -vframes 1 cond.png

The conditioning image is the trick that makes open-weights work feel closer to reference-driven hosted products: one frame carries the look, so your text only has to carry the action. Frame selection and filters are covered in the ffmpeg documentation.

Cost, privacy and control

Three trade-offs to decide knowingly. Privacy: local is unbeatable — nothing leaves the machine, which is the only acceptable answer for unreleased product footage or client material. Cost shape: a hosted extractor charges credits (1 extraction + 3 prompt generation, per the site’s pricing, with 3 free credits valid 24 hours) while a local run costs electricity and your GPU’s time; below roughly thirty clips a month the hosted side is cheaper than the hardware. Quality ceiling: hosted models still lead on temporal coherence, so a hybrid is what most people should run — extract and generate locally, and reach for a hosted model only when a shot needs the extra polish.

Related: Hailuo prompting, working from files you already own, and converting MP4 files to prompts.

Frequently asked questions

How do I write a Wan prompt from an existing video?

Extract the clip's fields — subject, camera, lighting, pacing, style — then compress them into one or two present-tense sentences: what the subject does, where, how it is shot with one camera verb, and a single style cue. Open-weights captions were short and observational, so dense prose outperforms long descriptions.

Can the whole pipeline run locally?

Yes, and that is the point for confidential footage: download and excerpt with yt-dlp and ffmpeg, describe the clip with a local video model, and generate locally with a frame from the clip as image conditioning. What you give up is the temporal analysis a hosted service does, since sampling still frames cannot measure motion.

Does image conditioning replace detailed prompts?

For look and subject, largely. One conditioning frame carries palette, lighting and identity, so your text should stop describing those and instead carry the action and the camera behaviour — which is exactly what a frame cannot express.

When is a hosted extractor worth paying for in a local workflow?

When motion and camera language are what you need. Frame-sampling a clip locally gives you content and colour but guesses at movement, so most people run a hybrid: extract the shot description hosted, then generate locally.