# Image Generation

### Create an image

`POST /v1/images/generations`

You can also find an Azure-style compatibility route in the proxy (`/openai/deployments/{model}/images/generations`). This docs page focuses on `/v1/images/generations`.

#### Request body

AlphaNeural follows the OpenAI Images API request shape. The most commonly used fields are:

* `model` (string). Image model ID to use.
* `prompt` (string). Text description of the image you want.
* `n` (integer). Number of images to generate.
* `size` (string). Output dimensions (supported values depend on the model).
* `quality` (string). Quality level (supported values depend on the model).
* `style` (string). Style hint (primarily for DALL·E models).
* `response_format` (string). `url` or `b64_json` for DALL·E models. Note. GPT image models always return base64 and do not support `response_format`.
* `user` (string). End-user identifier for abuse monitoring.

{% hint style="warning" %}
`response_format` is **not supported** for GPT image models in OpenAI’s API, because they always return base64-encoded image payloads
{% endhint %}

{% hint style="info" %}
Model availability varies by workspace, teams and provider. Use `GET /v1/models` to discover image-capable models exposed by your AlphaNeural key.
{% endhint %}

### Examples

#### cURL

```bash
curl https://proxy.alfnrl.io/v1/images/generations \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-1",
    "prompt": "A cute baby sea otter in a knitted hat, studio lighting",
    "size": "1024x1024",
    "n": 1
  }'
```

#### Python (OpenAI SDK)

```python
from openai import OpenAI
import os, base64
client = OpenAI(api_key=os.environ["ALPHANEURAL_API_KEY"], base_url="https://proxy.alfnrl.io/v1")
r = client.images.generate(model="gpt-image-1", prompt="A tiny robot making espresso, cinematic", size="1024x1024")
img_b64 = r.data[0].b64_json
open("image.png","wb").write(base64.b64decode(img_b64))
```

### Response

The response matches the OpenAI Images API. You will receive a `data` array with one item per generated image. Depending on the model, each item contains either:

* `b64_json` (base64-encoded image), or
* `url` (a temporary URL)

If you receive URLs, they are time-limited. OpenAI’s reference behaviour is that URLs expire after about 60 minutes. [OpenAI Platform](https://platform.openai.com/docs/api-reference/images)

Example (truncated):

```json
{
  "created": 1730000000,
  "data": [
    { "b64_json": "iVBORw0KGgoAAA..." }
  ]
}
```

#### Decode `b64_json`

```python
import os, base64, requests
r = requests.post(
  "https://proxy.alfnrl.io/v1/images/generations",
  headers={"Authorization": f"Bearer {os.environ['ALPHANEURAL_API_KEY']}",
           "Content-Type": "application/json"},
  json={"model":"gpt-image-1","prompt":"A minimal owl logo","size":"1024x1024"}
)
b64 = r.json()["data"][0]["b64_json"]
open("out.png","wb").write(base64.b64decode(b64))
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.alphaneural.io/image-generation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.