A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style
gpt-image-2/api/v1/gpt_image_2/text_to_image
运行信息
模型
gpt-image-2
提供方
OpenAI
服务
Gpt Image 2
Endpoint
Text To Image
1. claude mcp add runapi -s user -- npx -y @runapi.ai/mcp
2. 重启 Claude Code
3. 粘贴这个 prompt：生成一张图像："A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style"
1. codex plugin install runapi-mcp@agents
2. 重启 Codex
3. 粘贴这个 prompt：生成一张图像："A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style"
1. npx @runapi.ai/mcp init cursor
2. 重启 Cursor
3. 粘贴这个 prompt：生成一张图像："A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style"
1. npx @runapi.ai/mcp init windsurf
2. 重启 Windsurf
3. 粘贴这个 prompt：生成一张图像："A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style"
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": "A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style"
}
JSON
import { GptImage2Client } from "@runapi.ai/gpt-image-2";
const client = new GptImage2Client({
apiKey: process.env.RUNAPI_API_KEY,
});
const result = await client.textToImage.run({
"model": "gpt-image-2",
"prompt": "A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style"
});
console.log(result.id);
require "runapi/gpt_image_2"
client = RunApi::GptImage2::Client.new
result = client.text_to_image.run(
model: "gpt-image-2",
prompt: "A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style"
)
puts result.id
package main
import (
"context"
"fmt"
"log"
"net/http"
"os"
"strings"
)
func main() {
body := strings.NewReader("{\"model\":\"gpt-image-2\",\"prompt\":\"A hyper-detailed origami [FOOD ITEM] folded from realistic colored paper with visible creases and paper texture. Complex geometric folds capture the food’s shape, layers, and distinctive features. Placed on clean surface with soft shadows, minimalist food art photography style\"}")
req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, "https://runapi.ai/api/v1/gpt_image_2/text_to_image", body)
if err != nil {
log.Fatal(err)
}
req.Header.Set("Authorization", "Bearer "+os.Getenv("RUNAPI_API_KEY"))
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)
if err != nil {
log.Fatal(err)
}
defer resp.Body.Close()
fmt.Println(resp.Status)
}
gpt-image-2/api/v1/gpt_image_2/text_to_image获取 API Key
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