## Basic model info

- Model name: openai/openai gpt-image-1.5
- Model description: GPT Image 1.5 generates high-fidelity images with strong prompt adherence, preserving composition, lighting, and fine-grained detail.
- Endpoint name: text-to-image


## Model schema

The model schema is defined in the OpenAPI schema: [OpenAPI Schema](https://oapi.sunra.ai/main/openai/gpt-image-1.5/latest.json)

### Model input schema

The model input schema is:



```json
{
  "description": "Input model for text-to-image generation using GPT Image 1.5.",
  "properties": {
    "prompt": {
      "description": "Text description of the desired image.",
      "minLength": 1,
      "title": "Prompt",
      "type": "string",
      "x-sr-order": 201
    },
    "aspect_ratio": {
      "default": "1:1",
      "description": "Aspect ratio of the generated image. Maps to sizes: 1024x1024 (1:1), 1536x1024 (3:2 landscape), 1024x1536 (2:3 portrait).",
      "enum": [
        "1:1",
        "3:2",
        "2:3"
      ],
      "title": "Aspect Ratio",
      "type": "string",
      "x-sr-order": 401
    },
    "background": {
      "default": "auto",
      "description": "Background for the generated image.",
      "enum": [
        "auto",
        "transparent",
        "opaque"
      ],
      "title": "Background",
      "type": "string",
      "x-sr-order": 403
    },
    "output_format": {
      "default": "png",
      "description": "Output format for the generated images.",
      "enum": [
        "jpeg",
        "png",
        "webp"
      ],
      "title": "Output Format",
      "type": "string",
      "x-sr-order": 404
    },
    "quality": {
      "default": "high",
      "description": "The quality of the image that will be generated.",
      "enum": [
        "high",
        "medium",
        "low"
      ],
      "title": "Quality",
      "type": "string",
      "x-sr-order": 402
    }
  },
  "required": [
    "prompt"
  ],
  "title": "TextToImageInput",
  "type": "object"
}
```



### Model output schema

The model output schema is:



```json
{
  "properties": {
    "images": {
      "items": {
        "properties": {
          "content_type": {
            "description": "The mime type of the file.",
            "title": "Content Type",
            "type": "string"
          },
          "file_name": {
            "description": "The name of the file. It will be auto-generated if not provided.",
            "title": "File Name",
            "type": "string"
          },
          "file_size": {
            "description": "The size of the file in bytes.",
            "title": "File Size",
            "type": "integer"
          },
          "url": {
            "description": "The URL where the file can be downloaded from.",
            "title": "Url",
            "type": "string"
          }
        },
        "required": [
          "content_type",
          "file_name",
          "file_size",
          "url"
        ],
        "title": "SunraFile",
        "type": "object"
      },
      "title": "Images",
      "type": "array"
    }
  },
  "required": [
    "images"
  ],
  "title": "ImagesOutput",
  "type": "object"
}
```



## Example inputs and outputs

Use the following example inputs and outputs to understand the model.

### Input example



```json
{
  "prompt": "",
  "aspect_ratio": "1:1",
  "background": "auto",
  "output_format": "png",
  "quality": "high"
}
```



### Output example



```json
{
}
```



## Model code examples

### JavaScript



```javascript
import { sunra } from "@sunra/client";

const result = await sunra.subscribe("openai/gpt-image-1.5/text-to-image", {
  input: {
  	prompt: '',
  	aspect_ratio: '1:1',
  	quality: 'high',
  	background: 'auto',
  	output_format: 'png'
  },
  logs: true,
  onQueueUpdate: (update) => {
    console.log(`Status Update: ${update.status}, Request ID: ${update.request_id}`);
  },
});
console.log(result.data);
console.log(result.requestId);
```



### Python



```python
import sunra_client

result = sunra_client.subscribe(
    "openai/gpt-image-1.5/text-to-image",
    arguments={
        "prompt": "",
        "aspect_ratio": "1:1",
        "quality": "high",
        "background": "auto",
        "output_format": "png"
    },
    with_logs=True,
    on_enqueue=print,
    on_queue_update=print,
)
print(result)
```



### Java



```java
import ai.sunra.client.*;
import java.util.Map;
import com.google.gson.JsonObject;

var client = SunraClient.withEnvCredentials();

var response = client.subscribe(
    "openai/gpt-image-1.5/text-to-image",
    SubscribeOptions.<JsonObject>builder()
        .input(Map.of(
            "prompt", "",
            "aspect_ratio", "1:1",
            "quality", "high",
            "background", "auto",
            "output_format", "png"))
        .resultType(JsonObject.class)
        .onQueueUpdate(update -> System.out.printf(
            "\nStatus Update: %s, Request ID: %s%n",
            update.getStatus(),
            update.getRequestId()
        ))
        .logs(true)
        .build()
);

System.out.println("Completed!");
System.out.println(response.getData());
  
```



### Kotlin



```kotlin
import ai.sunra.client.kt.*
import com.google.gson.JsonObject

val client = createSunraClient()

val response = client.subscribe<JsonObject>(
    endpointId = "openai/gpt-image-1.5/text-to-image",
    input = mapOf(
            "prompt" to "",
            "aspect_ratio" to "1:1",
            "quality" to "high",
            "background" to "auto",
            "output_format" to "png"),
    options = ai.sunra.client.kt.SubscribeOptions(logs = true),
    onUpdate = { update ->
        println("\nStatus Update: ${update.status}, Request ID: ${update.requestId}")
    }
)

println("Completed!")
println(response.data)
  
```



### Curl



```bash

curl --request POST \
  --url https://api.sunra.ai/v1/queue/openai/gpt-image-1.5/text-to-image \
  --header "Authorization: Key $SUNRA_KEY" \
  --header "Content-Type: application/json" \
  --data '{"prompt":"","aspect_ratio":"1:1","quality":"high","background":"auto","output_format":"png"}'
    
```



## Model readme

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