Low-angle fashion campaign photograph of a confident model holding a large [product name] very close to the camera, exaggerated perspective with the hand and product dominating the foreground, full-body pose visible in the background, wide stance, dynamic posture, clean pure white studio background, high-key lighting, sharp focus on product, slight depth of field on the model, bold colorful outfit with strong contrast tones, modern beauty advertising aesthetic, ultra-clean composition, commercial studio photography, glossy packaging detail visible, crisp shadows
curl -X POST https://runapi.ai/api/v1/gpt_image_2/text_to_image \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "gpt-image-2",
"prompt": "Low-angle fashion campaign photograph of a confident model holding a large [product name] very close to the camera, exaggerated perspective with the hand and product dominating the foreground, full-body pose visible in the background, wide stance, dynamic posture, clean pure white studio background, high-key lighting, sharp focus on product, slight depth of field on the model, bold colorful outfit with strong contrast tones, modern beauty advertising aesthetic, ultra-clean composition, commercial studio photography, glossy packaging detail visible, crisp shadows"
}
JSON
Create an infographic image of [LANDMARK], combining a real photograph of the landmark with blueprint-style technical annotations and diagrams overlaid on the image. Include the title “[LANDMARK]” in a hand-drawn box in the corner. Add white chalk-style sketches showing key structural data, important measurements, material quantities, internal diagrams, load-flow arrows, cross-sections, floor plans, and notable architectural or engineering features. Style: blueprint aesthetic with white line drawings on the photograph, technical/architectural annotation style, educational infographic feel, with the real environment visible behind the annotations.
curl -X POST https://runapi.ai/api/v1/gpt_image_2/text_to_image \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "gpt-image-2",
"prompt": "Create an infographic image of [LANDMARK], combining a real photograph of the landmark with blueprint-style technical annotations and diagrams overlaid on the image. Include the title “[LANDMARK]” in a hand-drawn box in the corner. Add white chalk-style sketches showing key structural data, important measurements, material quantities, internal diagrams, load-flow arrows, cross-sections, floor plans, and notable architectural or engineering features. Style: blueprint aesthetic with white line drawings on the photograph, technical/architectural annotation style, educational infographic feel, with the real environment visible behind the annotations."
}
JSON
curl -X POST https://runapi.ai/api/v1/gpt_image_2/text_to_image \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "gpt-image-2",
"prompt": "{ \"global_settings\": { \"resolution\": \"8K ultra high definition\", \"aspect_ratio\": \"3:4\", \"camera_style\": \"studio food photography with cinematic lighting\", \"depth_of_field\": \"shallow depth of field, sharp subject, soft background\", \"lighting\": \"soft directional key light, subtle rim light, controlled highlights\", \"style\": \"hyper-realistic food illustration with editorial infographic overlays\", \"composition_rules\": [ \"no zoom\", \"no crop\", \"center-weighted vertical composition\", \"floating elements frozen in motion\" ], \"text_design\": { \"ingredient_name_color\": \"metallic gold\", \"ingredient_description_color\": \"pure white\", \"font_style\": \"elegant serif for titles, clean sans-serif for descriptions\", \"indicator_lines\": \"long, thin, smooth golden lines with rounded corners\" } }, \"module_1_image_1_style\": { \"scene_description\": \"A vertical stack of assorted cake slices floating above a white ceramic plate against a soft pink gradient background.\", \"background\": { \"color\": \"soft pastel pink\", \"texture\": \"smooth gradient\", \"lighting\": \"even, studio-lit, no harsh shadows\" }, \"main_subjects\": [ \"multiple layered sponge cake slices\", \"white whipped cream layers\", \"raspberry cream layer\", \"chocolate cream topping\" ], \"visible_ingredients\": [ \"vanilla sponge cake\", \"whipped cream\", \"raspberries\", \"blueberries\", \"strawberries\", \"macarons (vanilla and chocolate)\", \"chocolate bar pieces\", \"mint leaves\", \"small nut fragments\" ], \"motion_elements\": [ \"floating fruits\", \"floating macarons\", \"crumbs suspended in air\" ], \"text_labels\": [ \"Vanilla Cake – soft, fluffy sponge cake layered with white cream filling\", \"Macaron – creamy filling between almond meringue shells\", \"Raspberries – juicy, fresh raspberries\", \"Chocolate Bar – chunks of smooth milk chocolate\", \"Raspberry Cream – soft sponge cake layered with creamy, fruity raspberry cream\" ] }, \"module_2_image_2_style\": { \"scene_description\": \"Rolled Syrian dessert presented vertically with syrup pouring from above, placed in a warm, rustic kitchen environment.\", \"background\": { \"environment\": \"traditional kitchen\", \"elements\": [ \"warm lantern light\", \"wooden surfaces\", \"brass and copper utensils\" ], \"lighting\": \"warm ambient lighting with soft highlights\" }, \"main_subjects\": [ \"rolled white dessert dough\", \"cream filling spilling out\", \"golden syrup stream\" ], \"visible_ingredients\": [ \"white cheese dough\", \"cream filling\", \"pistachios\", \"sugar syrup\" ], \"motion_elements\": [ \"syrup dripping vertically\", \"pistachio crumbs falling\" ], \"text_labels\": [ \"White Cheese Dough – soft outer layer made from cheese and semolina\", \"Cream Filling – creamy filling inside the roll\", \"Pistachios – crushed pistachios sprinkled over and inside the rolls\" ] }"
}
JSON
FAQ
Using this gpt-image-2 prompt
How do I copy this prompt safely?
Use the copy button beside the full prompt block. It copies only the prompt text, not surrounding page labels, so you can paste it into an agent instruction, a JSON request body, or your own prompt library.
Why does the detail page show parameters?
Some prompts were collected with saved generation settings such as aspect ratio, duration, or voice controls. When parameters exist, the page lists them separately so you can reuse the prompt text and the structured settings together.
Which endpoint should I call?
Use the endpoint path shown in the API code block. The path is generated from the prompt's RunAPI service and endpoint name, then rendered as the public API URL used by curl and SDK examples.
Can I use this prompt in an agent instead of code?
Yes. Pick Claude Code, Codex, Cursor, or Windsurf in the tab switcher, install the RunAPI MCP Server, and paste the generated instruction. The instruction includes the full prompt text.