To let visitors create images from text, put a prompt form in your website, send the prompt to a server-side route, call an image-generation API from that route, then return and display the result. Keep the API key on the server—not in browser JavaScript. For a one-prompt, one-image experience, the Image API is the direct fit; use the Responses API when the interface needs conversational, multi-turn editing. OpenAI’s image-generation documentation describes these two patterns.
Choose the image-generation flow that fits your site
Start by deciding what the visitor should be able to do after entering a prompt. That interaction determines which API pattern to build around; a simple image form does not need to behave like a chat application.
| Visitor experience | API pattern | Best fit |
|---|---|---|
| Enter one prompt and receive an image | Image API | A generator form with a prompt field and a result view. |
| Refine an image through follow-up instructions or a multi-step exchange | Responses API with image generation as a tool | A conversational editor or a workflow that builds on prior turns. |
OpenAI documents the Image API for generating or editing from a prompt, and the Responses API for image-generation workflows with multi-turn editing. Choose based on the interaction you actually want to support, not just because one API offers more conversational capabilities. See the image-generation guide for current model and API details.
Build the website as a browser-to-server flow
The browser should collect the prompt and display the result; your server should validate the request, call the image service using a protected credential, and handle the returned image data. If visitors need a durable image URL—for example, to revisit or share a result—save the image to storage you control and return a safe reference. The exact storage, authentication, and access-control design depends on your application.
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- Collect input. Provide a prompt field and any options you intend to expose, such as aspect or output preferences supported by the model you select.
- Submit to your server. Send the prompt and supported choices to a backend route over HTTPS. Do not send the service key with the browser request.
- Validate and generate. Check that the prompt is present and within limits you set, then call the selected image-generation API from the server.
- Handle the result. Process the returned image data. Store it if the site needs a persistent asset; otherwise, return it in a form your client can render.
- Render an honest state. Show that a request is in progress, display the image when it succeeds, and provide a useful error state if it fails.
The server-side credential pattern follows the official JavaScript SDK guidance: create and store an API key securely in the server environment, and make the request from server-side JavaScript. OpenAI’s quickstart covers the SDK and server-side environments including Node.js, Deno, and Bun: API quickstart.
A minimal server-side JavaScript example
This illustrates the boundary that matters: a browser sends a prompt to your own route, and the route calls the image API using server configuration. Install and configure the current SDK and use the current image model and response fields documented for your account. Model names, access, and response details can change, so confirm them in the official documentation before shipping.
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// Server-side route example (Node.js; adapt to your web framework)
import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
export async function POST(request) {
try {
const { prompt } = await request.json();
if (typeof prompt !== "string" || !prompt.trim()) {
return Response.json({ error: "Enter a prompt." }, { status: 400 });
}
// Use a currently supported image model and response format for your account.
const result = await client.images.generate({
model: process.env.IMAGE_MODEL,
prompt: prompt.trim()
});
// Process the image result according to the current API response format.
return Response.json({ image: result.data[0] });
} catch (error) {
return Response.json({ error: "Image generation failed." }, { status: 502 });
}
}Set OPENAI_API_KEY and IMAGE_MODEL in server-side environment configuration, not in a file served to visitors. The example intentionally leaves image response processing dependent on the response format for the model and API version you choose. In production, map errors to appropriate status codes, avoid returning secret or internal error details, and ensure the frontend knows how to render the specific image data returned.
A minimal browser form can call that route and display its result once your server returns an image URL or another renderable representation:
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async function generateImage(prompt) {
const response = await fetch("/api/generate-image", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ prompt })
});
const result = await response.json();
if (!response.ok) throw new Error(result.error || "Request failed");
return result.image;
}Design prompts and output controls separately
A prompt communicates what the image should depict and how it should look. Useful prompts state the intended deliverable, composition or visual hierarchy, important text or data, and visual style. For example, specify whether the result is a product illustration, a banner, or a square social post, then describe its main subject, placement, palette, and any exact wording that must appear.
Do not rely on prompt prose for settings the API exposes directly. Dimensions, quality, output format, compression, and background are request parameters where supported; availability and valid combinations depend on the chosen model. Some supported models can return transparent backgrounds when transparency is requested and PNG or WebP is selected. Check the model-specific constraints before showing those controls in your interface. See the current image-generation guide.
- Canvas and dimensions: Offer only sizes supported by the selected model; make the intended use clear to visitors.
- Format: Choose an output format compatible with your display, download, or storage path.
- Quality and compression: Expose these only if the model supports them and visitors have a reason to choose.
- Background: If transparency matters, verify the model and format combination rather than assuming every image request supports it.
Keep the prompt and settings distinct in your server request. That makes options predictable, easier to validate, and easier to update when model capabilities change.
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Prepare the visitor experience and deployment
Generation is an asynchronous network operation from the visitor’s point of view. Decide what the page does while waiting, what happens after a failure, and whether a visitor can submit another request while one is running. Give the result a clear next action—such as viewing, downloading, or saving—based on what your site actually supports.
Before launch, decide whether visitors must sign in, whether you need request quotas, what content rules apply to prompts, and whether results need durable storage. These are product and deployment choices rather than features automatically supplied by an image API. A public form can be abused or generate unexpected volume, so apply validation and access controls appropriate to your audience. No particular authentication, quota, moderation, or storage setup is universal.
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Troubleshoot common integration failures
- The request fails before generation: Check that the browser is calling your own route, the route accepts the submitted content type, and the server has the required environment configuration.
- The API rejects the request: Verify model access and confirm that the model name, parameters, and combinations are currently supported for your account. Image options vary by model.
- The server returns a result but no image appears: Inspect the actual response shape and adapt the server and client to the selected model’s documented image-data format. Do not assume a response is already a public URL.
- The page appears stuck: Show a pending state and handle network failures and server errors. Avoid leaving the submit control in a permanent loading state after an unsuccessful request.
- A generated image disappears later: If the application needs persistent access, save the result to storage you control and return a durable reference rather than relying on transient response data.
- Your key is visible in browser code: Remove it from client-delivered files, rotate it if exposed, and move API calls to the server. The browser should call your route, not receive the service credential.
Or skip the browser setup
ScreenshotNeo does not generate images from prompts; it captures a web page as an image or PDF. It can be useful after you have built your generator, when you need a clean screenshot of its page or result. Its website screenshot API accepts a URL in one GET request. The API also offers capture controls and an MCP server for AI agents; see the ScreenshotNeo API documentation.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpScreenshotNeo removes cookie and consent banners, newsletter popups, and chat widgets before capture; each of those steps can be disabled. Bot checks, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers report the page verdict and billing status. Its MCP server provides take_screenshot, get_page_info, and capture_pdf for MCP clients such as Claude and Cursor. The Free plan includes 1,000 shots per month without a card; paid plans start at $5 for 3,000 shots. Every feature is on every plan.
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Can visitors edit an image after it is generated?
Yes. Build an iterative editing experience with the Responses API image-generation tool; the Image API is the direct pattern for a single prompt and result.
Does the generated image automatically have a permanent URL?
Not necessarily. If your site needs durable access, save the image to storage you control and return a reference your application can use.
Can every model generate transparent PNGs?
No universal support is established. Transparency and valid output formats depend on the model, so check the current constraints before offering that option.
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