I've been stress‑testing WAN 2.6 for image‑to‑video (i2v) specifically to answer one question: can we get stable, believable faces without the "morphing monster" moments? The image was right, but the face was wrong. That's the problem I'm here to solve. If you're an independent creator or designer who needs fast, realistic AI images for marketing clips, client reels, or social ads, this breakdown shows exactly what's working for me now with WAN 2.6 i2v face stability, from prompt phrasing to parameters and post passes. Along the way, I'll also note where I still reach for other AI tools for designers when WAN 2.6 hits its limits.

Why Face Issues Happen in AI Video

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Understanding Face Drift

Face drift is when the identity slowly slides frame to frame, eyebrows migrate, jawline reshapes, or the nose nudges sideways. In i2v, drift typically comes from:

  • Weak identity anchoring in the prompt (no consistent descriptors)
  • High motion strength/denoise causing the model to re‑imagine features each frame
  • Large perspective or scale changes across the clip
  • Low‑detail input source (soft, compressed, or small faces)

In WAN 2.6, you'll see drift most when you push dynamic camera moves on a single still. If your use case is realistic AI images for marketing where the face must match a brand talent or model, start by reducing motion and locking identifiers.

Understanding Face Melting

"Melting" is that uncanny sagging or warping during motion. It's less about identity and more about structural integrity:

  • Overly aggressive upscaling or sharpening between passes
  • Extreme lighting changes the model can't reconcile
  • Occlusions (hands, hair, glasses) that confuse edges
  • Strong expression changes when motion strength is high

I can sometimes trigger melting by asking for a smile to widen while the head turns. WAN 2.6 can handle either action alone, but both together can tip it. Keep expressions subtle if you need absolute stability.

Wan 2.6 i2v Face Stability3.png

Understanding Flickering

Flicker is rapid, small inconsistencies, pores appear/disappear, lips vary in hue, teeth switch geometry. Causes include:

  • Inconsistent noise seed across frames
  • Texture hallucination at high resolution
  • Post filters (contrast, clarity) applied inconsistently per frame

If you're aiming for AI images with accurate text overlays in the same shot, flicker becomes more noticeable because stable text contrasts with a jittery face. The fix is to control randomness and keep finishing steps temporal-aware.

Input Image Best Practices

Ideal Face Position & Size

I get the most stable identity in WAN 2.6 when the face is 20–35% of the vertical frame height and centered or slightly rule-of-thirds. Too small, and identity cues vanish: too big, and micro‑detail noise creeps in.

  • Framing: chest‑up or head‑and‑shoulders. Avoid extreme close‑ups for your first pass.
  • Angle: 0–20° yaw works best. Profiles (>45°) raise drift risk.
  • Expression: neutral or soft smile to start. You can add subtle expression in motion prompts later.

Lighting Requirements

Faces stabilize when lighting is clean and directional but not harsh.

  • Key light: soft, from 30–45° to one side: shadows with gentle roll‑off.
  • Avoid: heavy backlight, colored gels on skin, and mixed color temps.
  • Skin detail: a bit of texture helps the model "latch on." Over‑airbrushed sources are risky.

I treat the still like I'm shooting a passport‑adjacent portrait. It's not glamorous, but it's reliable.

Avoid These Input Mistakes

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  • Sunglasses or heavy frames obscuring the eyes (spikes drift)
  • Hair covering half the face
  • Busy, high‑contrast backgrounds competing with the face
  • JPEGs crushed by social compression
  • Low‑res crops that require 2×–4× upscales before i2v

If you're building brand visuals, this prep matters as much as prompts. It's the boring edge that saves you hours. And if your project includes typography (product shots, signs), this same discipline helps when you later need the best AI image generator for text in the pipeline.

Prompt Techniques for Stable Faces

Face-Locking Keywords

In WAN 2.6, identity phrases carry weight. I stack descriptors from general to specific:

  • "consistent identity, same person, photorealistic portrait, clean skin texture, natural pores, neutral expression"
  • Add age range, ethnicity, and defining markers sparingly: "25–35, short dark hair, almond eyes"
  • For i2v: "face remains consistent across frames"

Template I use:

"head and shoulders portrait, consistent identity, same person, neutral expression, soft key light, shallow depth of field, face remains consistent across frames, realistic skin texture, cinematic color."

Motion Constraint Phrases

You can request motion without inviting chaos:

  • "subtle camera drift only, minimal head movement"
  • "slight blink, micro‑expression only"
  • "no dramatic rotation, no sudden tilt"

When I need movement, I write it like a shot list: "very slow dolly in, 5%, steady: eyes blink once near middle: hair movement subtle." WAN 2.6 responds better to concrete, small motions than vague "dynamic shot" language.

Negative Prompt for Face Issues

I keep a standard negative block to fight deformities:

"deformed face, warped features, melting, extra teeth, extra eyes, lopsided eyes, asymmetrical pupils, glitch skin, harsh sharpening, over‑smoothed skin, plastic look, unstable identity, jitter, flicker."

If I'm placing text in‑frame later (lower thirds, packaging), I also add: "no text, no watermark" to reduce random glyphs, then composite accurate copy afterward with dedicated tools for AI images with accurate text.

Parameter Settings That Help

Motion Strength / CFG Scale

Exact names vary by UI, but the dials map roughly to "how much to change per frame" and "how strongly to follow the prompt." My stable ranges in WAN 2.6 i2v:

  • Motion/Denoise Strength: 0.18–0.32 for faces. Start at 0.25.
  • CFG/Guidance Scale: 6.5–8.5. Start at 7.5 for descriptive prompts: drop toward 6.5 if you see over‑sharpened or plastic skin.
  • Seed/Temporal Consistency: lock the seed if the UI allows per‑sequence stability.

If you crave more movement, nudge motion to 0.34–0.38 and compensate by tightening motion prompts: "no head rotation, no expression change."

Duration Sweet Spot

Shorter clips are naturally steadier. My sweet spot for faces is 3–6 seconds at 24 fps. Past ~8 seconds on a single still, WAN 2.6 starts to reinterpret details unless you:

  • Split into two shorter beats with a tiny overlap
  • Re‑seed and stitch with a dissolve or cutaway
  • Or run a two‑pass workflow (base stable pass, then texture‑only pass)

For ads and social, that 3–6s beat is enough for a hero moment and plays nicely with captions, especially if you're producing realistic AI images for marketing reels with quick cuts.

Resolution Trade-offs

  • Base generation: 720p is the safest starting point for face integrity.
  • 1080p is doable but needs careful denoise (≤0.28) and clean input.
  • 4K: generate at 720–1080p, then upscale with a face‑aware model (keep it subtle).

I prefer a two‑step: stable 720p → mild temporal denoise → 1.5×–2× upscale with face protection. Oversharpening at 4K is where "melting" masquerades as detail.

Post-Processing Fixes

Here's the practical stack I use when WAN 2.6 gets me 80–90% there.

  • Temporal Deflicker: Apply first. Tools like After Effects' Deflicker or DaVinci Resolve's Temporal NR set low. Goal: even out micro‑contrast shifts without smearing pores.
  • Face Enhancement (gentle): CodeFormer or GFPGAN at low strength on keyframes only, then propagate via optical flow or EBSynth. Don't blanket‑apply every frame.
  • Motion Stabilization: If you asked for a slow dolly but got micro‑jitters, stabilize the background/crop slightly. Keep it subtle.
  • Texture Pass: A tiny grain layer (2–4%) hides residual flicker and sells realism.
  • Compositing Text After: If the shot includes packaging or signage, add copy in post with tracking. This is faster and more reliable than trusting any generator, even the best AI image generator for text.

If a single feature breaks (e.g., teeth), I patch just that region using a clean frame and a tracked mask. Small surgical fixes beat regenerating the whole sequence.

Before/After Examples

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Let me share three quick scenarios I ran last week with WAN 2.6 i2v.

1. Neutral portrait, micro‑motion

  • Input: 1024×1536 portrait, face ~30% frame, soft key from camera left.
  • Prompt: "head and shoulders portrait, consistent identity, neutral expression, soft key light, shallow DOF, face remains consistent across frames: subtle camera drift only, minimal head movement." Negative: "deformed face, melting, flicker, extra teeth."
  • Params: motion 0.25, CFG 7.5, 24 fps, 4 seconds, seed locked.
  • Result BEFORE fixes: Stable identity, minor eyelash flicker.
  • AFTER: Deflicker + 2% grain. Rock‑solid. This passes for a brand cutaway shot.

2. Smile + slight head turn (harder)

  • Input: Similar framing: asked for "gentle smile forms, 10° head turn to camera right."
  • Params: motion 0.28, CFG 7.2.
  • BEFORE: Subtle mouth warp at peak smile: one frame of tooth geometry weirdness.
  • AFTER: Patched teeth on a 6‑frame window using a clean mid‑smile frame and a tracked mask in AE. Acceptable for social: I'd avoid for close‑up TV. If a client insists, I'd recut to hide the peak.

3. Hair movement + shallow DOF

  • Input: Outdoor portrait, backlight, wind.
  • Prompt included: "hair movement subtle, no face deformation, no strong rotation."
  • Params: motion 0.32 (pushed), CFG 7.0.
  • BEFORE: Good identity, slight skin texture pumping.
  • AFTER: Temporal NR low + grain. Works in a montage but not as a hero shot.

Where I compare tools: If I need larger head turns or emotional changes, I sometimes prototype in WAN 2.6 for timing, then test a shot in another i2v model with stronger temporal priors. For static or gentle moves, WAN 2.6 is efficient and predictable, which is what most AI tools for designers should aim for.

Practical takeaways you can copy today:

  • Keep motion ≤0.28 for faces, ≤6s duration.
  • Use identity/consistency phrases and explicit motion limits.
  • Fix the last 10% with deflicker, tiny grain, and surgical patches.

If you're layering product text in the same shot, composite it in post. It's faster to get AI images with accurate text that way, and you won't sacrifice face stability.

Wan 2.6 i2v Face Stability6.png

That's my current working recipe for WAN 2.6 i2v face stability. If you've got a tougher shot (glasses, strong angles, fast smiles), send it my way, I'll break it down and share settings that actually save you time. That's my current working recipe for WAN 2.6 i2v face stability. When you need high-quality input images for your i2v projects, Z-Image is worth exploring—strong source material makes everything downstream easier.