Image Generation

Generate and edit images through the chat completions endpoint using LLMs that natively support image output.

Overview

Some LLMs can produce images directly as part of a chat response — no separate endpoint required. Instead of calling a dedicated image API, you send a normal chat completion request and the model returns a base64-encoded image in the assistant message. The main image-capable LLM available through Eden AI is Google’s Gemini Flash Image family:

Model	Model string	Provider
Gemini 2.5 / 3.1 Flash Image	`google/gemini-2.5-flash-image`, `google/gemini-3.1-flash-image-preview`	Google

It supports:

Text-to-image — generate an image from a prompt
Image editing — modify a provided input photo

The endpoint stays the same:

POST /v3/chat/completions

Image-capable LLMs are different from Expert Model image generation — which wraps dedicated image APIs like DALL-E or Stable Diffusion. Use LLM image generation when you want the model to reason about the prompt and output an image in the same turn.

Google Gemini Image Models

Gemini image models accept a standard text prompt plus an optional image_config block that controls the output resolution and aspect ratio.

Text-to-image

import requests

url = "https://api.edenai.run/v3/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
}

payload = {
    "model": "google/gemini-3.1-flash-image-preview",
    "messages": [
        {"role": "user", "content": "A nano banana dish in a fancy restaurant, cinematic lighting"}
    ],
    "image_config": {
        "aspect_ratio": "16:9",
        "image_size": "2K"
    }
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

curl -X POST https://api.edenai.run/v3/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-3.1-flash-image-preview",
    "messages": [
      {"role": "user", "content": "A nano banana dish in a fancy restaurant"}
    ],
    "image_config": {"aspect_ratio": "16:9", "image_size": "2K"}
  }'

Supported `image_config` values

Field	Description	Allowed values
`aspect_ratio`	Output shape	`1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `21:9`, `2:3`, `3:2`, `4:5`, `5:4`, `1:4`, `4:1`, `1:8`, `8:1`
`image_size`	Output resolution	`512` (Gemini 3.1 Flash only), `1K`, `2K`, `4K`

Allowed values vary slightly per model — check the Gemini image generation docs for per-model support.

Image editing (with input photo)

Pass an existing image to the model alongside your edit instruction. Use the standard OpenAI multimodal content format:

import base64
import requests

url = "https://api.edenai.run/v3/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
}

# Encode a local image as base64
with open("input.jpg", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode("utf-8")

payload = {
    "model": "google/gemini-3.1-flash-image-preview",
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Turn this photo into a watercolor painting, keep the composition"},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}
                }
            ]
        }
    ],
    "image_config": {
        "aspect_ratio": "1:1",
        "image_size": "2K"
    }
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])

You can also pass a remote URL directly instead of base64:

{
  "type": "image_url",
  "image_url": {"url": "https://example.com/photo.jpg"}
}

Response Format

The generated image is returned as a base64-encoded data URL inside the assistant message content.

{
  "id": "chatcmpl-...",
  "object": "chat.completion",
  "model": "google/gemini-3.1-flash-image-preview",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUg..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 14,
    "completion_tokens": 512,
    "total_tokens": 526
  },
  "cost": 0.042
}

Saving the image to disk

import base64
import re

content = result["choices"][0]["message"]["content"]
# Strip the "data:image/...;base64," prefix if present
match = re.match(r"data:image/\w+;base64,(.*)", content)
image_b64 = match.group(1) if match else content

with open("output.png", "wb") as f:
    f.write(base64.b64decode(image_b64))

V3 Documentation

Quick Start

Overview

LLMs

Expert Models

General

Data Governance

Integrations

Overview

Google Gemini Image Models

Text-to-image

Supported `image_config` values

Image editing (with input photo)

Response Format

Saving the image to disk

Next Steps

Expert Image Generation

Chat Completions

​Overview

​Google Gemini Image Models

​Text-to-image

​Supported image_config values

​Image editing (with input photo)

​Response Format

​Saving the image to disk

​Next Steps

Expert Image Generation

Chat Completions

Overview

Google Gemini Image Models

Text-to-image

Supported `image_config` values

Image editing (with input photo)

Response Format

Saving the image to disk

Next Steps