I built this Wan 2.6 prompt template library after a month of back-to-back tests on i2v (image-to-video) runs. If you've ever nailed the look but lost the words, you know the pain: the image is perfect, but the text is wrong. That's the problem I'm here to solve. Below you'll find reusable, production-ready templates that keep motion clean and on-model while preserving readable titles, labels, packaging, and UI. I also call out where these help produce realistic AI images for marketing and, yes, AI images with accurate text (the hardest part).

How to Use Wan 2.6 Prompt Template Library

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I treat Wan 2.6 like a camera with a small, picky crew: it needs structure, continuity, and a clear job. When I followed the structure below, my text accuracy and motion stability jumped.

Here's how I run it every time:

  • Start from a clean reference image (i2v): consistent lighting, minimal lens distortion, and text at comfortable legibility (don't cram tiny fonts).
  • Lock the seed for A/B tests. Change only one variable per iteration (camera, motion, or style).
  • Add a small but strong negative prompt. It does more than a long, messy one.
  • Keep motion short and purposeful (2–4 seconds) before trying longer.

Prompt Structure Explained

I use a labeled block so I can swap parts quickly. This is the backbone I apply across all templates:

Template scaffold

  • Subject: {what/who}, framing {close-up/medium/wide}, environment {indoor/outdoor, time of day}.
  • Style: {cinematic/clean/commercial/brand keywords}, color palette {warm/cool}, lighting {soft/hard, key/fill/rim}.
  • Camera: {lens in mm}, movement {push-in/pan/dolly/orbit}, speed {slow/steady}, composition {rule of thirds/center}.
  • Motion Cues: {hair sway, hand gesture, product rotation, liquid pour}. One motion, one verb.
  • Text Fidelity: "preserve legible text on {object/sign/packaging}: {EXACT TEXT}." Use quotes.
  • Technical Hints: "sharp details, no flicker, stable facial features, clean edges."
  • Negative: see library below (anti-flicker, anti-blur…).
  • Parameters I keep consistent in testing:
  • Duration: 3–4s for stability
  • FPS: 24
  • Guidance/CFG: moderate (around 6–8 tends to behave: I lower if overfitting)
  • Seed: locked for comparisons

Why this matters: Wan 2.6 respects short, clear verbs for motion and gets confused by stacked actions. If I want "AI images with accurate text," I explicitly tell it what to keep legible and where.

If you want to turn these Wan 2.6 templates into repeatable, client-ready results, try running them inside z-image.ai. It’s built for testing i2v variations, comparing seeds, and checking text fidelity before anything goes live.

i2v Portrait Prompts (5 Templates)

These portrait templates aim for expressive but stable motion. I wrote them to work with clean studio or natural light and to avoid the dreaded face-drift. Great for social intros, talking heads, founder clips, and realistic AI images for marketing where captions or name plates must stay readable.

Subtle Movement

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  • Subject: {person} in soft window light, medium close-up, neutral background.
  • Style: natural skin tone, commercial clean retouch, shallow depth of field (85mm look).
  • Camera: slow push-in, steady, centered framing.
  • Motion Cues: gentle head nod + eye blink only.
  • Text Fidelity: preserve legible lower-third name tag: "{NAME – TITLE}".
  • Technical: sharp eyes, smooth skin texture, no face warp.
  • Negative: anti-flicker, anti-face-drift, anti-blur.

Talking Head

  • Subject: {speaker} in studio, key light + soft fill, medium shot.
  • Style: corporate yet friendly, true-to-life color.
  • Camera: static camera.
  • Motion Cues: natural mouth movement (2–3 words), light hand raise.
  • Text Fidelity: keep crisp subtitle card: "{SHORT LINE, 3–5 WORDS}".
  • Technical: clear lip sync impression without over-exaggeration.
  • Negative: anti-flicker, anti-artifact.

Emotional Expression

  • Subject: {person} warm backlight, close-up.
  • Style: cinematic, soft contrast, slight film grain.
  • Camera: slow push-in.
  • Motion Cues: brief inhale + micro-smile (or subtle brow furrow).
  • Text Fidelity: preserve quote card at frame edge: "{QUOTE}".
  • Technical: steady skin detail, no jitter.
  • Negative: anti-face-drift, anti-blur.

Fashion Showcase

  • Subject: {model} full-body in seamless studio.
  • Style: editorial, high key, crisp fabric detail.
  • Camera: left-to-right pan.
  • Motion Cues: one-step turn, fabric sway.
  • Text Fidelity: preserve brand wordmark on T-shirt: "{BRAND}".
  • Technical: sharp edges, accurate garment seams.
  • Negative: anti-artifact, anti-flicker.

Professional Headshot

  • Subject: {professional} medium close-up, neutral gray backdrop.
  • Style: corporate headshot, even light, realistic skin.
  • Camera: static, minimal breathing.
  • Motion Cues: tiny head tilt + blink.
  • Text Fidelity: preserve badge text: "{COMPANY}".
  • Technical: natural pores, no plastic look.
  • Negative: anti-blur, general quality boost.

Testing note: I compared 35mm vs 85mm looks. 85mm framing gave me fewer edge distortions and better read on badges and lower-thirds. If you're hunting for the best AI image generator for text in portraits, lens-equivalent cues and static camera help more than cranking guidance.

i2v Product Prompts (5 Templates)

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Product videos are where text accuracy gets judged the hardest. Labels, packaging, and UI must hold. I kept motion simple and foreground lighting consistent.

Product Rotation

  • Subject: {product} on matte turntable, seamless background.
  • Style: clean commercial, high micro-contrast.
  • Camera: static: product rotates 45–90°.
  • Motion Cues: slow, even rotation only.
  • Text Fidelity: preserve packaging text and logo: "{BRAND} {MODEL}".
  • Technical: shadow grounding, no reflections over text.
  • Negative: anti-flicker, anti-artifact.

Product in Use

  • Subject: {hand model} interacting with {product}.
  • Style: lifestyle, natural window light.
  • Camera: gentle push-in.
  • Motion Cues: one tap, one slide, or one pour (choose one).
  • Text Fidelity: preserve UI/label text on product: "{KEY TEXT}".
  • Technical: keep fingertips sharp, avoid motion smear.
  • Negative: anti-blur, anti-face-drift (if face on screen).

Luxury Reveal

  • Subject: premium {product} with soft top light, dark backdrop.
  • Style: high-end, specular highlights controlled.
  • Camera: slow dolly from shadow to light.
  • Motion Cues: reveal cloth lift or case opening.
  • Text Fidelity: preserve embossed logo: "{BRAND}".
  • Technical: clean edges, no glittering noise.
  • Negative: anti-artifact, anti-flicker.

Tech Demo

  • Subject: device on desk with simple UI.
  • Style: tech clean, cool palette.
  • Camera: static.
  • Motion Cues: finger triggers one UI state change.
  • Text Fidelity: preserve on-screen UI labels: "{MENU}", "{CTA}".
  • Technical: screen moiré minimized, crisp icons.
  • Negative: anti-blur, general quality boost.

Food / Beverage Appeal

  • Subject: drink bottle/can: chilled, condensation beads.
  • Style: appetizing, saturated but believable.
  • Camera: slow orbit 20–30°.
  • Motion Cues: quick spritz or cap twist.
  • Text Fidelity: preserve label nutrition line and brand name: "{BRAND} {FLAVOR}".
  • Technical: keep droplets, avoid smear over text.
  • Negative: anti-flicker, anti-artifact.

Tip from my runs: If label text starts drifting mid-rotation, shorten duration to 2.5–3s and reduce the orbit angle. That alone rescued a lot of clips I'd normally throw away.

Wan 2.6 Multi-shot Story Prompts (5 Templates)

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Multi-shot asks more of the model: continuity, matching light, and text that stays consistent across shots (like a repeating logo). I keep the same seed for each shot block when possible and repeat key phrases.

3-Shot Ad Structure

  • Shot 1 (Hook): close-up detail of {product}, static, sharp logo: "{BRAND}".
  • Shot 2 (Use): mid shot, hand interaction, single action (tap/pour/zip).
  • Shot 3 (CTA): hero angle with lower-third card: "{CTA TEXT}".
  • Style: cohesive color and light across all shots.
  • Negative: anti-flicker, anti-blur.
  • Note: reuse "preserve {BRAND}" in each shot text.

Tutorial Sequence

  • Step 1: top-down desk, tools aligned, overlay label: "Step 1".
  • Step 2: hands perform one simple action, overlay: "Step 2".
  • Step 3: finished result, overlay: "Step 3".
  • Style: minimalist, readable typography cues.
  • Camera: static or tiny push-in per step.
  • Negative: anti-artifact.

Mini Drama

  • Scene 1: person discovers {problem}: label on note reads: "{KEY WORD}".
  • Scene 2: tries {solution} with product: logo visible.
  • Scene 3: reaction close-up, text bubble: "{SHORT LINE}".
  • Style: cinematic but not noisy.
  • Camera: straightforward blocking, no wild moves.
  • Negative: anti-face-drift, anti-flicker.

Day-in-Life

  • Morning: coffee cup with brand text: "{BRAND}".
  • Work: laptop close-up with clean UI labels: "{APP}".
  • Evening: gym bottle label legible: "{BRAND}".
  • Style: natural light shifts, consistent LUT.
  • Camera: pans between frames.
  • Negative: general quality boost.

Transformation Story

  • Before: product on cluttered background, small logo visible.
  • During: wipe/replace action: motion limited.
  • After: clean hero setup, bold label: "{BRAND} {MODEL}".
  • Style: commercial reveal, crisp typography.
  • Camera: dolly out ending.
  • Negative: anti-blur.

I tested variations with and without on-screen text. Repeating the exact logo phrase in each shot ("preserve logo: ‘BRAND'") reduced drift and gave me more consistent clips that are actually usable for campaign edits, exactly what AI tools for designers and marketers need.

Wan 2.6 Style-Specific Prompts (5 Templates)

Style is where people tend to overload prompts. I've had better luck steering with small, strong cues and one lighting statement.

Cinematic / Film Look

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  • Subject: {subject}, moody backlight, soft haze.
  • Style: filmic contrast, subtle grain, rich skin tones.
  • Camera: slow push-in, 50mm look.
  • Motion Cues: one micro-gesture (blink, glance).
  • Text Fidelity: lower-third card: "{TITLE}".
  • Negative: anti-flicker, anti-blur.

Anime / Illustration

  • Subject: {character}, cel-shaded, clean linework.
  • Style: flat colors, controlled highlights, studio anime look.
  • Camera: pan left to right.
  • Motion Cues: hair sway only.
  • Text Fidelity: signage or caption: "{TEXT}".
  • Negative: anti-artifact.

Vintage / Retro

  • Subject: {scene} with period props.
  • Style: 70s photo vibe, warm cast, halation.
  • Camera: static.
  • Motion Cues: light subject motion (turn, smile).
  • Text Fidelity: retro label remains sharp: "{BRAND}".
  • Negative: anti-blur.

Minimalist / Clean

  • Subject: {object/person} on white, soft shadow.
  • Style: high key, clinical sharpness.
  • Camera: slow dolly.
  • Motion Cues: single gesture or rotation.
  • Text Fidelity: crisp product text: "{MODEL}".
  • Negative: anti-flicker, general quality boost.

High Energy / Dynamic

  • Subject: {athlete/product} with rim light, saturated color.
  • Style: punchy contrast, fast cuts feel (but single shot).
  • Camera: quick 15–20° orbit.
  • Motion Cues: snap movement (jump, toss).
  • Text Fidelity: short lockup card: "{TAGLINE}".
  • Negative: anti-artifact.

These give me realistic AI images for marketing without devolving into noisy, over-stylized motion. Less is more here.

Camera Movement Prompts (5 Templates)

Camera is a big lever. In my tests, over-ambitious moves caused text drift. Keep it short and specific.

Push In / Zoom

  • Move: slow push-in 5–8% scale over 3s.
  • Best for: talking heads, hero products.
  • Tip: combine with static subject to keep overlay text stable.

Pan Left / Right

  • Move: lateral pan across static subject.
  • Best for: fashion full-body, desk setups.
  • Tip: keep pan under 20° equivalent to protect edge text.

Dolly / Track

  • Move: forward/backward track with parallax.
  • Best for: luxury reveals, cinematic scenes.
  • Tip: lock label phrase ("preserve {BRAND}") to fight perspective smear.

Static with Subject Motion

  • Move: none. Subject performs one clear action.
  • Best for: UI taps, precise label reads.
  • Tip: this wins when you need the best AI image generator for text clarity.

Orbit / 360

  • Move: gentle 15–30° orbit.
  • Best for: bottles, shoes, gadgets.
  • Tip: shorten duration if label drifts: add anti-flicker + anti-artifact.

Negative Prompt Library (5 Templates)

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I keep these short. They stack well with the templates above and are tuned to Wan 2.6 i2v behavior in my tests.

Anti-Flicker

  • Negative: "no flicker, no jitter, stable exposure, stable colors, consistent lighting."

Anti-Face-Drift

  • Negative: "no face warp, no mouth misalignment, stable facial features, no eye mis-track."

Anti-Blur

  • Negative: "no motion blur, no smear, sharp edges, crisp micro-contrast."

Anti-Artifact

  • Negative: "no ringing, no glitter noise, no compression blocks, clean gradients."

General Quality Boost

  • Negative: "no banding, no aliasing, no ghosting: clean edges: preserve text legibility."

If you try these today, start with a simple push-in, lock your seed, and keep a single verb for motion. The image will be right, and this time, the text will be right too.