> ## Documentation Index > Fetch the complete documentation index at: https://www.edenai.co/docs/llms.txt > Use this file to discover all available pages before exploring further. # Image Generation > Generate and edit images through the chat completions endpoint using LLMs that natively support image output. export const TechArticleSchema = ({title, description, path, articleSection, about, proficiencyLevel = "Beginner", dependencies, keywords = [], datePublished, dateModified, image, inLanguage = "en"}) => { const baseUrl = "https://www.edenai.co/docs"; const canonicalUrl = `${baseUrl}/${path}`.replace(/\/+$/, ""); const ogParams = new URLSearchParams({ division: articleSection || "", title: title || "", description: description || "" }); const resolvedImage = image || `https://edenai.mintlify.app/_mintlify/api/og?${ogParams.toString()}`; const data = { "@context": "https://schema.org", "@type": "TechArticle", "@id": `${canonicalUrl}#techarticle`, mainEntityOfPage: { "@type": "WebPage", "@id": canonicalUrl }, headline: title, name: title, description: description, url: canonicalUrl, inLanguage: inLanguage, isPartOf: { "@type": "WebSite", name: "Eden AI Documentation", url: baseUrl }, author: [{ "@type": "Organization", name: "Eden AI", url: "https://www.edenai.co/" }], publisher: { "@type": "Organization", name: "Eden AI", url: "https://www.edenai.co/", logo: { "@type": "ImageObject", url: "https://www.edenai.co/assets/logo.png" } } }; if (articleSection) data.articleSection = articleSection; if (about) data.about = { "@type": "Thing", name: about }; if (proficiencyLevel) data.proficiencyLevel = proficiencyLevel; if (dependencies) data.dependencies = dependencies; if (keywords && keywords.length) data.keywords = keywords; if (datePublished) data.datePublished = datePublished; if (dateModified) data.dateModified = dateModified; data.image = Array.isArray(resolvedImage) ? resolvedImage : [resolvedImage]; const json = JSON.stringify(data); const schemaId = `techarticle-${canonicalUrl}`; React.useEffect(() => { if (typeof document === "undefined") return; document.querySelectorAll(`script[data-schema-id="${schemaId}"]`).forEach(n => n.remove()); const script = document.createElement("script"); script.type = "application/ld+json"; script.dataset.schemaId = schemaId; script.textContent = json; document.head.appendChild(script); return () => script.remove(); }, [json, schemaId]); return null; }; 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](/v3/expert-models/features/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 ```python Python theme={null} 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"]) ``` ```bash cURL theme={null} 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](https://ai.google.dev/gemini-api/docs/image-generation) 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: ```python Python theme={null} 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: ```json theme={null} { "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. ```json theme={null} { "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 ```python Python theme={null} 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)) ``` ## Next Steps Use dedicated image APIs like DALL-E or Stable Diffusion Standard text chat completions reference