Image Generation

Import generateImage from std::image:

import { generateImage } from "std::image"

node main() {
  const r = generateImage("a red bicycle in the rain", size: "1024x1024")
  if (isFailure(r)) {
    print("generation failed: ${r.error}")
    return
  }
  const img = r.value          // { base64: string, mimeType: string }
  writeBinary("bike.png", img.base64)
  print("saved bike.png")
}

generateImage returns a Result. On success, its value is a { base64, mimeType } object — the image is returned in memory as base64, not written to disk automatically.

Saving the image

writeBinary(path, base64) decodes base64 and writes the raw bytes — use it for any binary data (images, audio, video, PDFs). It's auto-imported, so no import is needed.

Read a binary file back with readBinary(path), which returns the contents as base64.

Sending a generated image in an LLM call

Images generated by generateImage are not added to the current message thread, so if you want to ask a question about it, for example, you have to send it along with your message. Once it's sent, though, it is part of the message thread, and you don't need to send it again. Note that images do bloat the context of message threads.

How to send the image:

import { generateImage } from "std::image"
import { image } from "std::thread"

node main() {
  const imageResult = generateImage("a red bicycle")
  match(imageResult) {
    failure(error) => print("generation failed: ${error}")
    success(img) => {
      const answer = llm([
        "What color is the bicycle in this image?",
        image(img.base64, img.mimeType, base64: true),
      ])
      print(answer)
    }
  }
}

Editing images

You can pass images in to edit them. Each entry is a path, an http(s) URL, or a data: URI.

const edited = generateImage("make it nighttime", images: ["bike.png"])

Choosing a provider and model

By default, generateImage uses OpenAI's image model. You can override the model or provider per call.

// OpenAI (default)
generateImage("a red bike")

// Google's "nano banana" (Gemini 2.5 Flash Image). The provider is derived
// from the model name, so you only need `model`. Set GEMINI_API_KEY.
generateImage("a red bike", model: "gemini-2.5-flash-image")

// The higher-tier "nano banana pro"
generateImage("a red bike", model: "nano-banana-pro-preview")

// An open-source model via LiteLLM
generateImage("a red bike", provider: "litellm", model: "flux-pro",
              baseUrl: "https://your-litellm-host")

// Together AI (OpenAI-compatible images endpoint)
generateImage("a red bike", provider: "openai-compat",
              model: "black-forest-labs/FLUX.1-schnell",
              baseUrl: "https://api.together.ai/v1")

Options:

Option	Description
`model`	Image model (default: the provider's default image model).
`provider`	eg "google", "openai-compat", "litellm"
`size`	Image size, e.g. `"1024x1024"` (provider-dependent).
`quality`	`"low"` / `"medium"` / `"high"` / `"auto"`.
`images`	Input images to edit/vary, as path / URL / data-URI strings.
`apiKey`	API key (reads env vars `OPENAI_API_KEY`, `GEMINI_API_KEY`, etc. if not set)
`baseUrl`	Base URL for `openai-compat` / `litellm` providers.

Limitations

Local image generation is not yet supported.
One image per call.
No mask-based inpainting yet.

Image Generation ​

Saving the image ​

Sending a generated image in an LLM call ​

Editing images ​