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Create a high-end fashion product advertisement poster in portrait 4:5 ratio, clean minimal editorial style using a reference product image. Background is a crisp blue gradient studio setup, very minimal, soft, airy, with subtle bright tones. Main product: a tangerine orange oversized waffle-knit long sleeve crewneck shirt, ghost-hung on a silver metal clothing rack stand with a visible clamp at the top-right. The shirt hangs naturally with slight drape, front-facing. On the center chest, include white puff embroidery text: “SVG” (first line with 3 small 4-point star inside the V), “STUDIOS Aloony” (second line), “MOTIVATION THROUGH DISCIPLINE” (third line, slightly smaller), “SS26 - 29213” (fourth line, smallest). Add a small white woven label patch on the right hem side seam and a visible white neck tag at the collar. Typography: top center minimal brand logo “SVG STUDIOS” in blue green clean sans-serif, top-left italic cursive “Field” with a small blue green rectangle badge below reading “edition” in white, behind the shirt place massive ultra-condensed midnight purple green text “GREEN TEE” partially hidden and cut by the product, bleeding off edges. lower-left include handwritten “Triple A” and large cursive “Studios” in purple tone. Bottom section features three diagonal detail strips: macro waffle-knit texture, closeup puff embroidery stitching, and closeup white neck label, all with bright studio lighting. Between strips include small text “Built for movement, designed for unity. The SVG Studios WIP Waffle Long sleeve — SS26 Collection” Footer includes 4 small barcode at bottom-left, centered text “• WWW. ALIBABA. COM • • •” in spaced caps blue tone, and small text “MADE IN BANGLADESH • SS26 COLLECTION”. Color palette is crimson red green, white, and bright accents only. Overall style is clean editorial fashion lookbook, military-inspired streetwear campaign, soft diffused lighting, high contrast product focus, photorealistic, ultra detailed, 8K quality.
AutoAGC workflow
转化为可复用工作流
- 复制提示词正文，保留角色、任务、模型、风格和输出约束。
- 在 GPT Image 或同类模型中粘贴，并上传参考材料或工作流 brief。
- 根据输出格式、品牌语气、多语言版本和执行目标替换最后一段限定条件。
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