Sabrina Carpenter Wimbledon Tennis Fashion AI Image Prompt
Subject: {argument name="identity" default="Sabrina Carpenter, mid-20s, petite athletic feminine figure"}, soft blonde hair in loose flowing waves, delicate oval face, expressive eyes, groomed brows, soft glossy lips, subtle blush, natural soft structure, realistic proportions, visible skin detail, natural posture, wearing {argument name="attire" default="pastel pink ribbed halter crop top, white pleated tennis skirt, white wristband, glossy white stiletto heels"}, accessories: white and pink athletic visor, Yonex tennis racket, 'LOVE' branding on heels. Pose: forward-leaning athletic stance, leaning over tennis net, left hand on top tape, body angled in profile, right hand holding racket, slightly arched posture. Environment: Wimbledon grass court during tournament, pristine grass with white lines, Wimbledon net, wooden umpire chair, blurred spectator stands, green hedges and floral arrangements. Camera: full body shot, 85mm prime lens, eye-level neutral perspective. Lighting: bright natural daylight with professional tournament lighting, soft diffused overhead, gentle shadows, wrap-around bounce light on skin and fabric. Mood and expression: dynamic high-fashion athletic editorial, focused and confident, direct gaze with neutral inquisitive brow. Style and realism: ultra-realistic, unfiltered texture, natural skin details and fabric quality. Colors and tone: soft pink and white tones with green accents. Quality: 8k UHD, ISO 100, zero noise reduction. Aspect ratio: 2:3. ControlNet: pose control with OpenPose weight 1.0 for exact skeletal lock, depth control with MiDaS weight 0.8 for volume preservation. Negative prompt: proportion averaging, flattened body shape, over-smoothed skin, beauty filters, plastic skin, depth flattening, wide-angle distortion, unrealistic proportions.
Content: Structure formula: Subject (identity+appearance+attire+accessories) → Pose → Environment → Camera → Lighting → Mood → Style → Color → Quality → ControlNet → Negative prompt.
Pros: Rich details, precise control (ControlNet weights).
Cons: Too complex, may constrain generation; lengthy prompt.
Reference image: No obvious reference-image dependency
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