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Expression Prompt

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Expression Prompt

Master emotion-led facial prompts that produce stable expressions for portrait, avatar, and character workflows.

2026.07.26

把「Expression Prompt」做成你想要的样子做一个你自己的「Expression Prompt」

用大白话说出来就行——Carat 会为你生成合适的图片和视频。

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GPT 이미지 2로 생성한 얼굴 표정 시트

얼굴 표정 시트

GPT Image 2· 图片
photograph, clean editorial style, animation reference sheet aesthetic. Glimmerglass micro-diffusion on skin only. Crystal clear on product. Lifted blacks to soft gray. Overexposed highlights high-key. Skin L0-L1 glass reflection poreless. Flat front lighting no shadows. White or pale pastel background. Ultra-clean digital. No grain. Hasselblad digital medium format, 2x3 grid layout showing six distinct headshots of the same fictional character. Consistent character design: Young Korean woman early 20s, extremely beautiful Korean idol, small head short face, slim sharp V-jaw, large bright double-lid eyes slightly upturned sparkling iris long separated lashes, high straight nose soft rounded tip, small defined lips clear lip line. Expressions from top-left to bottom-right: happy with bright eye-smile, sad with downturned gaze, angry with furrowed brow, surprised with wide eyes and open mouth, neutral with calm direct gaze, and confused with tilted head and raised eyebrow. Clean line art style integrated with realistic textures. Subject centered on white studio seamless. No text, no watermark, no 3D render, no film grain, no harsh shadows.
fal:208076

What expression prompts should do

Expression prompts are for making emotion readable at a glance. In portrait, character, and profile workflows, a small change in eyes or mouth can shift the whole mood, so this page focuses on structured prompts that lock emotional intent while allowing repeatable variation. The goal is not to chase randomness but to define a reliable baseline for each mood.

Build emotional layers in prompts

Instead of using one broad phrase such as 'happy' or 'sad', define layers. You can combine core emotion, eye behavior, mouth geometry, and eyebrow direction so generation follows a shared emotional grammar. For example, 'mild smile with relaxed eyes and gentle cheek lift' makes the expression look natural rather than forced. This pattern reduces mismatched outputs in batch production.

Lighting and camera support for expression

The same expression token can look very different under different lighting. Use explicit constraints like 'soft directional light', 'moderate contrast', and 'clean eye highlights' to keep expression intensity stable. If you want a restrained mood, avoid aggressive shadows and keep the palette neutral. These lighting rules help preserve clarity of facial micro-movements where emotion is actually decided.

Apply across image genres

The same core prompt stack works for realistic portrait and stylized characters if you add a final style suffix. Realistic outputs benefit from texture and eye detail terms, while character outputs may need 'clean linework' or 'animation-friendly features'. Keeping the emotion block unchanged across genres improves speed because you can compare mood quality without rewriting every element.

Separate free copy from paid rendering

All text prompts in this page are free to copy and test in your own planning flow. Actual image rendering remains a paid action that consumes credits each time. The most cost-efficient method is to finalize emotion text and style once, then generate only a small set of shortlisted versions for final selection.

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