Why this prompt works
It defines the core subject and headline, specifies typography style, lighting, texture, colors, and composition to guide the model toward a cinematic, high-contrast poster with strong readability.
Use this section when you need the complete GPT Image2 prompt wording, not just the summary. Review the structure, copy the full GPT Image2 prompt as-is, and then replace the subject, brand, style, or use case to fit your own workflow. This full GPT Image2 prompt block is the best place to study how the prompt is assembled before you test your own version. Treat this GPT Image2 prompt section as the source text you refine step by step.
{ "type": "technical infographic and exploded view diagram", "header": { "title": "{argument name=\"main title\" default=\"佳能 EOS R5 成像系统剖面 CANON EOS R5 IMAGING ATLAS\"}", "subtitles": [ "一张照片是如何被制造出来的 HOW AN IMAGE IS ACTUALLY FORMED", "从光,到数据 | FROM PHOTONS TO FILES", "相机不是壳体,而是一条运算链 A camera is not a shell, but a computational chain" ], "top_left_box": { "title": "EOS R5 核心规格 KEY SPECIFICATIONS", "bullet_points_count": 6 }, "top_right_images": { "count": 2, "description": "front and back views of the camera body" } }, "centerpiece": { "description": "highly detailed 3D exploded view of the {argument name=\"camera model\" default=\"Canon EOS R5\"} camera, showing internal components separated vertically", "components_visible": [ "lens mount", "lens elements with glowing blue light rays", "image sensor", "motherboard with glowing {argument name=\"processor name\" default=\"DIGIC X\"} chip", "battery pack", "dual card slots", "electronic viewfinder (EVF)" ] }, "layout": { "numbered_sections": [ { "number": 1, "title": "光学入口 OPTICAL ENTRY", "elements": ["lens cross-section with light rays", "2 line graphs"] }, { "number": 2, "title": "光圈、快门与曝光控制 APERTURE, SHUTTER, EXPOSURE", "elements": ["3 aperture blade diagrams", "4 shutter speed example photos", "depth of field diagram", "exposure triangle diagram"] }, { "number": 3, "title": "对焦系统与成像平面 FOCUS ACQUISITION + IMAGE PLANE", "elements": ["lens alignment diagram", "AF coverage photo of a runner"] }, { "number": 4, "title": "传感器与像素结构 SENSOR + PIXEL ARCHITECTURE", "elements": ["3D pixel array diagram", "single pixel cross-section diagram", "sensor spec table", "quantum efficiency graph"] }, { "number": 5, "title": "防抖系统与机械稳定 IBIS + MECHANICAL STABILIZATION", "elements": ["sensor shift mechanism diagram with yaw/pitch/roll axes", "2 stabilization effect comparison photos"] }, { "number": 6, "title": "模拟信号、模数转换与读出 ANALOG READOUT + A/D CONVERSION", "elements": ["signal flowchart", "3 readout timing graphs", "signal-to-noise ratio graph", "rolling shutter example photo of a car"] }, { "number": 7, "title": "DIGIC X 图像处理链 DIGIC X IMAGE PROCESSING PIPELINE", "elements": ["processing flowchart with central chip", "dynamic range graph", "tone curve graph", "histogram"] }, { "number": 8, "title": "文件生成、显示与存储 FILE OUTPUT, PREVIEW, STORAGE", "elements": ["file output flowchart", "2 storage card icons", "file workflow diagram"] } ], "bottom_comparisons": { "count": 5, "labels": [ "传感器尺寸对比 SENSOR SIZE COMPARISON", "镜头焦距与视角 FOCAL LENGTH & ANGLE OF VIEW", "ISO 与噪点关系 ISO & NOISE RELATIONSHIP", "光圈与景深关系 APERTURE & DEPTH OF FIELD", "RAW vs JPEG" ] }, "footer": "{argument name=\"footer quote\" default=\"光被捕获,数据被解读,影像被记录,记忆被永恒。 Light is captured. Data is interpreted. Image is recorded. Memory is eternal.\"}" }, "style": "clean, technical, highly detailed, photorealistic components, blueprint-style annotations, light gray background, precise typography" }
It defines the core subject and headline, specifies typography style, lighting, texture, colors, and composition to guide the model toward a cinematic, high-contrast poster with strong readability.
Create a premium editorial poster for the headline "[TITLE]" with [TYPOGRAPHY STYLE], [LIGHTING STYLE], [COLOR PALETTE], [TEXTURE], [COMPOSITION], and [MOOD/ATMOSPHERE].
Use this GPT Image2 prompt as a working reference, not just a one-off sample. Keep the structure that already defines the output clearly, then replace the subject, typography, environment, campaign context, or aspect ratio to match your own goal. If you need more examples before rewriting the GPT Image2 prompt, go back to the Infographic category page or continue into the related prompts below. This GPT Image2 prompt workflow works best when you keep the original structure visible while you rewrite each key variable. A stable GPT Image2 prompt baseline usually makes revision work much faster.
Start by copying the full GPT Image2 prompt, then replace the subject, style language, and output goal with your own brief. Keep the visual structure if you want results in the same direction, and reuse the GPT Image2 prompt skeleton before you add brand-specific details. In practice, this GPT Image2 prompt works best when you change a small number of variables per iteration.
You can change the headline, product, mood, lighting, composition, typography, and end use while keeping the core GPT Image2 prompt logic that makes the example useful. In most cases, the best result comes from editing one GPT Image2 prompt variable at a time instead of rewriting everything at once. That keeps the GPT Image2 prompt structure stable while you test new creative directions.
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Explore more from the infographic collection to compare GPT Image2 prompt structures, visual directions, and reusable ideas that stay close to this example. Open another GPT Image2 prompt example if you want a nearby variation before rewriting your final version. That extra GPT Image2 prompt comparison step often helps you refine the final wording with less guesswork.