Seedream 4.5 Face Consistency: Preserve Identity in Every Edit

I kept seeing creators post great Seedream 4.5 portraits… that looked like cousins from frame to frame. I wanted the same person, not a family reunion. So I ran a week of tests to lock in Seedream 4.5 face consistency and make it reliable for real work, brand shoots, ads, and thumbnails where identity can't drift. If you care about realistic AI images for marketing or need AI images with accurate text on posters or packaging, the same rules that stabilize text also help you hold a face steady. Here's what actually worked for me, minus the fluff.

Why Faces Change in Seedream 4.5

Seedream 4.5 is good at style, lighting, and skin realism. But like most diffusion models, it balances two forces: your prompt and its learned "priors." When prompts are broad ("young woman, soft light"), the model leans on its priors and happily morphs identity across generations. That's why your first render looks perfect, then shot two looks like a sibling.

From my runs, three things drive most shifts:

Prompt entropy: Vague descriptors let the model swap facial ratios while keeping overall vibe.
Unlocked seed: New seeds invite new faces. Same seed + small changes = better continuity.
Aggressive edits: High denoise, heavy style switches, and big pose shifts break identity anchors.

Technical note in plain English: diffusion iterates noise toward an image. If you change the noise (seed) or push the guidance (CFG) too hard with competing style terms, the face "snaps" to a nearby but different identity. The fix is constraint: pin the seed, provide strong visual anchors, and keep edit strength low. I treat face consistency like I treat on-image text accuracy, tight constraints beat poetic prompts. If you're evaluating AI tools for designers, test this first: can it keep the same person across five angles?

Reference Strategy for Better Seedream 4.5 Face Consistency

I get the best results when I combine a locked seed with a solid reference workflow.

Here's my baseline setup that kept identity across 6–8 shots:

Seed: locked (reuse the exact seed for the whole set)
CFG (guidance): 4.5–6.0 for portraits: higher than 6.5 started nudging identity on my rig
Steps: 28–36 (fewer steps = more variance: more steps = subtle drift toward priors)
Denoise strength (for edits/variations): 0.25–0.40 sweet spot: 0.5+ often changes bone structure
Reference strength: medium to medium-high: too high = plasticity, too low = drift
Lighting consistency: keep a core lighting phrase constant: adjust only small modifiers

I'll also keep a short identity clause in the prompt: "oval face, high cheekbones, slightly wide-set eyes, subtle cupid's bow", this acts like a textual anchor without boxing me into a celebrity look.

How to Use Reference Images to Stabilize Facial Identity

Start with a clean, sharp headshot as your primary reference (frontal or 3/4). Avoid heavy makeup or occlusions.
Feed one strong anchor image, not a collage. Multiple weak references confused Seedream 4.5 more often than not.
Generate your first "master" shot. Save it. Then create variations by changing only one thing at a time: angle, focal length, or lighting, not all three.
For each variation, keep the seed and identity clause, lower denoise to ~0.3, and nudge the camera note: "35mm, slight 3/4 turn, chin down."
If the jaw or eyes begin to drift, re-inject the master shot as a secondary reference at low weight. Think of it like a facial checkpoint.

Seven minutes later, I had already exported my first production-ready image set with consistent identity across three backgrounds.

Identity Anchors That Improve Seedream 4.5 Face Consistency

You don't need a 40-line prompt. You need the right anchors.

The four that mattered most in my tests:

Bone structure: jawline shape, cheekbone height, chin length
Eye geometry: spacing, lid fold description, iris size
Nose profile: bridge height/width, tip shape
Mouth specifics: lip fullness, cupid's bow, corner downturn/upturn

I write short, concrete descriptors rather than vibe words. Example: "soft round jaw, medium bridge, slightly wide-set almond eyes, defined cupid's bow." That beats "beautiful model, cinematic."

I also fix three contextual anchors:

Focal length range (35–50mm feels natural and stable)
Consistent color science (neutral film look or daylight white balance)
Hairstyle baseline (length, part side). Wild hair changes fooled the model into new identities.

Key Facial Features That Prevent Identity Drift

Pick two to three features to lock per set:

Eyes: "slightly wide-set, almond, mild epicanthic fold"
Nose: "straight medium bridge, narrow tip"
Mouth: "medium-full upper, fuller lower, well-defined cupid's bow"
Jaw: "soft oval jaw, subtle point at chin"

I've found that when these are present, Seedream 4.5 behaves. Remove them and the model leans into style over person. If you're producing realistic AI images for marketing where the same talent appears across banners, these anchors are non-negotiable.

Editing Without Drift

Most identity loss happens during edits, background swaps, wardrobe changes, expression tweaks. My rule: low-strength, single-purpose passes.

Working pattern that held up:

For background changes, run an inpaint or region edit with denoise 0.25–0.35. Keep the face area masked out or weighted down.
For expression changes, nudge with micro-phrases: "subtle smile" vs "big smile with teeth." Large teeth reveals often reshape jaw and lips.
For angle shifts, adjust camera terms first before re-posing the head. "Slight 3/4" lands better than "profile," which frequently resets the nose and chin.
For style filters, keep a neutral base and apply grade outside generation (e.g., Lightroom). Heavy in-model stylization can rewrite facial geometry.

Safe Editing Techniques That Preserve the Same Face

Lock seed for the session: if you must change it, re-inject your master reference at higher weight for one pass.
Keep CFG in the 4.5–6 zone for edits: high guidance pulled Seedream 4.5 toward archetypes in my tests.
Use small, literal edits: hair pin, earring, collar change. Avoid hats covering the forehead, occlusion is a top drift trigger.
When text is in frame (posters, packaging), I treat it like a second anchor. Clear, legible text correlates with lower denoise and steadier identity, the same knobs help both. If you're hunting the best AI image generator for text, those conservative settings pay dividends here too.

If an edit goes sideways, don't fight it for ten more passes. Roll back to the last stable image, reapply a single change, and move on.

Examples of Strong Seedream 4.5 Face Consistency

Three quick scenarios I reproduced reliably:

Close-up to medium: Start with a tight headshot (50mm, soft window light). Duplicate with 35mm, shoulders visible, same lighting phrase. Seed locked, denoise 0.3. Result: same person, natural scale change.
Indoor to outdoor: Keep identity clause and focal length. Change only light: "overcast open shade." Reuse master as low-weight reference. Result: skin tone shift without bone-structure drift.
Wardrobe swap for a banner set: Inpaint sweater to blazer at low denoise. Keep hair part and color constant. Result: clean change, facial features intact, and AI images with accurate text on the mock poster stayed readable.

Where it still struggles: extreme profile shots (full 90°), heavy occlusions (big sunglasses, masks), and exaggerated laughter. I mark those as separate character briefs, not variations. That honesty saves hours and keeps clients happy.

If you need a quick takeaway: lock your seed, anchor two facial features, edit gently, and treat lighting like a constant. It's not flashy, it's just dependable. And dependable is what ships.

By the way, if you need both consistent faces and readable text in your shots (posters, product labels, etc.), check out Z-Image.ai. It's fast, handles bilingual text really well, and they give you free daily credits to mess around with. Worth a try.