Hi, I'm Dora. I've been generating and testing with Wan 2.6 for short-form content to see if it can deliver photorealistic 9:16 clips with readable, stable text overlays. In this guide, I'll share the exact settings, prompts, and composition tricks I'm using to get production-ready results fast. If you're after realistic AI images for marketing and need AI images with accurate text, this is the workflow I'd use today.

Why Vertical Video Matters

If you're making content for TikTok, Shorts, or Reels, 9:16 isn't optional, it's the default. The screen is small, attention is shorter, and any text that's even slightly warped or cropped will tank performance.

Wan 2.6 Vertical Video Best Practices 2.png

Here's what I see in data and real projects:

  • The first 2–3 seconds decide whether people stay. If the subject isn't clear and text isn't crisp, retention collapses.
  • Platform UI covers edges. If you place key copy at the bottom, a caption bar will eat it.
  • Vertical composition is unforgiving. Bad headroom or off-center framing looks amateur in 9:16.

So when I test AI tools for designers, I treat vertical video like a separate medium. Wan 2.6 can generate sharp motion and believable light, but the win comes from setting it up for vertical from the start. If you need the best AI image generator for text, the tool matters, but settings and composition matter more.

Setting Up 9:16 in Wan 2.6

Wan 2.6 Vertical Video Best Practices 3.png

I start with aspect ratio and stability. My baseline when I'm creating AI images with accurate text in motion:

  • Aspect ratio: 9:16 (1080 × 1920). If your system uses presets, pick "Vertical/Story." If it's param-based, set ar=9:16.
  • Duration: 5–8 seconds for quick hooks: 12 seconds max for story beats. Shorter clips render faster and keep energy high.
  • Frame rate: 24 fps for cinematic motion: 30 fps for UI-heavy explainers. 30 fps helps with on-screen text clarity.
  • Seed: lock it once you find a look. Seed consistency makes brand iterations repeatable.
  • Guidance/CFG: medium (around 6–8). Lower = more natural motion: higher = stricter adherence to prompt and layout.
  • Motion strength: keep it conservative for text scenes. I cap at "low–medium" to avoid wobble around typography.
  • Upscale pass: on. It adds just enough sharpness for phone screens without haloing.

My test prompt for a clean vertical baseline: "handheld vertical video, natural morning light, young woman holding a takeaway coffee, city sidewalk, soft bokeh, center framed, 9:16, cinematic, realistic skin, shallow depth of field."

Then I run a second pass with text: "add a clean paper coffee cup label with printed text: ‘Morning Fuel', modern sans-serif, black on white, straight, readable." If Wan 2.6 resists precise text (common), I render the cup plain and composite the label later as an overlay. That combo is faster and more reliable than wrestling the model for perfect glyphs.

Composition for Vertical

Center Your Subject

I've tested off-center frames, but for the first second of a vertical hook, center framing wins. It reads instantly on small screens. I'll drift to the left or right only after the viewer settles. This is especially true when your callout text shares the frame, split attention kills retention.

Vertical Rule of Thirds

Wan 2.6 Vertical Video Best Practices 4.png

In 9:16, I place faces on the upper third and hands/products on the lower third. That keeps eyes above captions and leaves space for UI. With Wan 2.6, I add prompt hints like "subject aligned to upper third, product lower third, negative space for copy at top." The model responds better when I describe the composition plainly.

Headroom & Look Room

Too much headroom looks like a mistake in vertical. Too little chops hair. I nudge Wan 2.6 with: "tight portrait crop, minimal headroom, subject looking slightly off-camera, look room toward right side." If the subject moves, I bias look room toward the direction of motion. Simple, but it prevents awkward cuts later. For realistic AI images for marketing, these tiny composition rules do more than heavier post fixes.

Safe Zones for Text & UI

On phones, platform chrome will cover your edges. I keep my text inside a conservative safe rectangle so captions, buttons, and usernames don't collide with my message. When I can, I use a transparent guide overlay while previewing. If not, I follow these mental margins.

TikTok Safe Zones

  • Top UI: avoid the top 180–220 px (usernames, icons)
  • Bottom UI: avoid the bottom 250–300 px (captions, CTA bar)
  • Side UI: keep key text 80–100 px away from each edge

YouTube Shorts Safe Zones

  • Top: avoid ~150–180 px
  • Bottom: avoid ~200–240 px
  • Sides: 60–80 px padding

Instagram Reels Safe Zones

  • Top: avoid ~160–200 px
  • Bottom: avoid ~220–260 px
  • Sides: 60–80 px padding

I'm conservative because device DPIs and UI updates shift slightly. If I need absolute accuracy, I export a frame, mock UI overlays in Figma, and nudge positions. It takes 3 minutes and saves a reshoot. If you're comparing AI tools for designers, look for ones that let you import guides or add rectangles: Wan 2.6's preview plus an external guide gets me 95% of the way there.

Wan 2.6 Vertical Video Best Practices 5.png

Vertical-Specific Prompts

Vertical isn't just an aspect ratio, it's a storytelling style. I adjust prompts to reinforce tall framing, emphasize hands, and reserve clean space for copy.

What I actually type (and test):

  • "vertical 9:16 framing, center subject, minimal headroom, negative space at top for title"
  • "tight portrait, subject upper third, hands presenting product lower third, soft backlight"
  • "static background, gentle handheld sway, low motion blur, text-safe composition"
  • "label reads: ‘30% OFF', bold sans-serif, flat decal, straight, centered, no warping"
  • Negative prompts: "no distorted letters, no curved labels, no warped signage, avoid busy background text"

When Wan 2.6 struggles with precise wording on props, I pivot: generate the clean object, then composite text in post using a font that matches brand guidelines. It's faster and gives legal clarity. For AI images with accurate text, this two-step is still the most reliable approach across tools.

Portrait Framing Keywords

These help Wan 2.6 keep faces sharp and flattering in 9:16:

  • "portrait lighting, soft key, gentle fill, catchlights in eyes"
  • "85mm look, shallow depth of field, background separation"
  • "skin texture natural, no plastic, subtle pores"

If the model oversharpens, I reduce guidance a notch and add "natural grain." If the face drifts off-frame, I reiterate "center framed, maintain framing throughout." That line alone stabilized multiple tests in my runs.

Platform Optimization Tips

TikTok: Hook in 1 Second

I front-load motion or a reveal. Example workflow in Wan 2.6:

  • Shot 1 (0–1s): fast push-in on the product, bold title appears top-center.
  • Shot 2 (1–3s): human element enters, smile or action beat.
  • Shot 3 (3–5s): benefit line in safe zone, logo bug top-left.

Prompt accents: "instant visual reveal," "bold on-screen title," "high contrast lighting." If I'm chasing reach, I keep captions super short and legible. This is where a best AI image generator for text matters, but I still prefer adding the text overlay in edit to guarantee perfection.

Shorts: Clear Text, Fast Pace

Shorts prefers crisp text and clean cuts. I aim for 30 fps and white text on a soft black plate at 80% opacity. In Wan 2.6, I minimize background clutter: "simple backdrop, neutral texture, no extraneous signage." The result is scannable in under a second, which YouTube rewards.

Reels: Aesthetic First

Reels audiences tolerate slightly slower pacing if the vibe is strong. I push for "golden-hour backlight, soft lens bloom, slow handheld sway." I'll overlay fewer words, just a headline and a price tag, and let the look sell it. Realistic AI images for marketing don't need to shout on Reels: they need to feel premium.

Export Settings Checklist

Wan 2.6 Vertical Video Best Practices 6.png

Here's the checklist I actually use before shipping a Wan 2.6 vertical video:

  • Resolution: 1080 × 1920. If quality allows, 1440 × 2560 for archive/master.
  • Frame rate: 24 fps (cinematic) or 30 fps (text/UI heavy). Match your edit.
  • Bitrate: 10–16 Mbps for H.264: 20–35 Mbps for ProRes Proxy/422 masters.
  • Codec: H.264 High Profile for upload: ProRes/10-bit if you'll grade or remaster.
  • Color: Rec.709, video levels. Avoid unexpected shifts on mobile.
  • Audio: If present, 48 kHz AAC, 192–256 kbps. Normalize to -14 LUFS for socials.
  • Sharpening: light. Over-sharpened vertical looks crunchy on mid-tier Androids.
  • Text legibility: check on a real phone. If you can't read it at arm's length, it's not ready.
  • Safe zones: recheck against platform UI. No key info below ~250 px from bottom.
  • Branding: add a small, non-intrusive logo bug in a safe corner.
  • Legal: if the model printed brand names you don't own, mask or replace. Don't risk it.

One more thing: I compare Wan 2.6 results with a quick pass in Midjourney + After Effects and a Stable Video Diffusion run. Wan tends to give me cleaner motion out of the box: MJ still wins for still-frame photorealism: Stable setups are flexible if you need deep control. Pick the path that gets you to "usable" fastest.

If you're pressed for time and need AI tools for designers that just ship, this workflow keeps you honest: get the shot, keep the text clean, and let the platform do the distribution. When I need to quickly prototype vertical compositions or test text placement on still frames before committing to video generation, I often start with Z-Image— it's fast, free, and reliably delivers crisp typography in 9:16 format.