PROMPT 详情

isometric view, low poly 3d render, cozy coffee shop interio

图像 Illustration and 2D Art 
isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount
/api/v1/runapi/text_to_image
运行信息
模型
提供方
Runapi
服务
Runapi
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："isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount"
1. codex plugin install runapi-mcp@agents
2. 重启 Codex
3. 粘贴这个 prompt：生成一张图像："isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount"
1. npx @runapi.ai/mcp init cursor
2. 重启 Cursor
3. 粘贴这个 prompt：生成一张图像："isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount"
1. npx @runapi.ai/mcp init windsurf
2. 重启 Windsurf
3. 粘贴这个 prompt：生成一张图像："isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount"
curl -X POST https://runapi.ai/api/v1/runapi/text_to_image \
  -H "Authorization: Bearer $RUNAPI_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'JSON'
{
  "prompt": "isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount"
}
JSON
import { RunapiClient } from "@runapi.ai/runapi";

const client = new RunapiClient({
  apiKey: process.env.RUNAPI_API_KEY,
});

const result = await client.textToImage.run({
  "prompt": "isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount"
});
console.log(result.id);
require "runapi/runapi"

client = RunApi::Runapi::Client.new
result = client.text_to_image.run(
  prompt: "isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount"
)
puts result.id
package main

import (
  "context"
  "fmt"
  "log"
  "net/http"
  "os"
  "strings"
)

func main() {
  body := strings.NewReader("{\"prompt\":\"isometric view, low poly 3d render, cozy coffee shop interior, cute miniature furniture, warm ambient lighting, pastel color palette, small potted plants on windowsill, wooden tables with tiny coffee cups, detailed espresso machine, game asset style, clean and organized, blender render, octane render quality, trending on polycount\"}")
  req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, "https://runapi.ai/api/v1/runapi/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)
}
/api/v1/runapi/text_to_image 获取 API Key
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常见问题

使用这个 prompt

如何安全复制这个 prompt？

使用完整 prompt 区块旁边的复制按钮。它只复制 prompt 文本，不包含页面标签，所以可以直接粘贴到 agent 指令、JSON 请求体或你自己的 prompt 库。

为什么详情页会显示参数？

有些 prompt 采集时带有画幅、时长或声音控制等生成设置。存在参数时，页面会单独列出，方便你同时复用 prompt 文本和结构化设置。

应该调用哪个 endpoint？

使用 API 代码块里显示的 endpoint 路径。这个路径由 prompt 的 RunAPI 服务和 endpoint 名称生成，并渲染成 curl 与 SDK 示例使用的公开 API URL。

可以不用代码，直接在 agent 里用吗？

可以。在标签切换器里选择 Claude Code、Codex、Cursor 或 Windsurf，安装 RunAPI MCP Server，然后粘贴生成好的指令。指令会包含完整 prompt 文本。