## Basic model info

- Model name: tencent-hunyuan/tencent-hunyuan hunyuan3d-v3.1
- Model description: Rapidly generate 3D models from images using Hunyuan 3D.
- Endpoint name: smart-topology


## Model schema

The model schema is defined in the OpenAPI schema: [OpenAPI Schema](https://oapi.sunra.ai/main/tencent-hunyuan/hunyuan3d-v3.1/latest.json)

### Model input schema

The model input schema is:



```json
{
  "description": "Input schema for Hunyuan3D V3.1 smart topology optimization.",
  "properties": {
    "face_level": {
      "default": "medium",
      "description": "Target polygon density. high: more detail, medium: balanced, low: fewer polygons.",
      "enum": [
        "low",
        "medium",
        "high"
      ],
      "title": "Face Level",
      "type": "string",
      "x-sr-order": 403
    },
    "file_type": {
      "default": "glb",
      "description": "Input 3D file format.",
      "enum": [
        "glb",
        "obj"
      ],
      "title": "File Type",
      "type": "string",
      "x-sr-order": 401
    },
    "model_file": {
      "anyOf": [
        {
          "format": "uri",
          "maxLength": 2083,
          "minLength": 1,
          "type": "string"
        },
        {
          "type": "string"
        }
      ],
      "description": "URL of GLB or OBJ file to optimize topology. Max size: 200MB.",
      "title": "Model File",
      "x-sr-order": 301
    },
    "polygon_type": {
      "default": "triangle",
      "description": "Output polygon type. triangle: triangular faces only. quadrilateral: mixed quad and triangle faces.",
      "enum": [
        "triangle",
        "quadrilateral"
      ],
      "title": "Polygon Type",
      "type": "string",
      "x-sr-order": 402
    }
  },
  "required": [
    "model_file"
  ],
  "title": "SmartTopologyInput",
  "type": "object"
}
```



### Model output schema

The model output schema is:



```json
{
  "description": "Unified output schema for Hunyuan3D V3.1.",
  "properties": {
    "model": {
      "description": "URL of the generated 3D model",
      "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"
    },
    "score": {
      "description": "Billing score for image-to-3d: base + add-ons",
      "title": "Score",
      "type": "number"
    }
  },
  "required": [
    "model"
  ],
  "title": "Hunyuan3DV31Output",
  "type": "object"
}
```



## Example inputs and outputs

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

### Input example



```json
{
  "face_level": "medium",
  "file_type": "glb",
  "model_file": "",
  "polygon_type": "triangle"
}
```



### Output example



```json
{
}
```



## Model code examples

### JavaScript



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

const result = await sunra.subscribe("tencent-hunyuan/hunyuan3d-v3.1/smart-topology", {
  input: {
  	model_file: '',
  	file_type: 'glb',
  	polygon_type: 'triangle',
  	face_level: 'medium'
  },
  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(
    "tencent-hunyuan/hunyuan3d-v3.1/smart-topology",
    arguments={
        "model_file": "",
        "file_type": "glb",
        "polygon_type": "triangle",
        "face_level": "medium"
    },
    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(
    "tencent-hunyuan/hunyuan3d-v3.1/smart-topology",
    SubscribeOptions.<JsonObject>builder()
        .input(Map.of(
            "model_file", "",
            "file_type", "glb",
            "polygon_type", "triangle",
            "face_level", "medium"))
        .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 = "tencent-hunyuan/hunyuan3d-v3.1/smart-topology",
    input = mapOf(
            "model_file" to "",
            "file_type" to "glb",
            "polygon_type" to "triangle",
            "face_level" to "medium"),
    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/tencent-hunyuan/hunyuan3d-v3.1/smart-topology \
  --header "Authorization: Key $SUNRA_KEY" \
  --header "Content-Type: application/json" \
  --data '{"model_file":"","file_type":"glb","polygon_type":"triangle","face_level":"medium"}'
    
```



## Model readme

undefined