# Sperid Labs Idol

> Idol by Sperid Labs turns a single image of an object into a 3D Gaussian splat, with a GLB point-cloud preview.


## Overview

- **Endpoint**: `https://fal.run/speridlabs/idol`
- **Model ID**: `speridlabs/idol`
- **Category**: image-to-3d
- **Kind**: inference
**Tags**: 3D, gaussian-splat, image-to-3d



## Pricing

- **Price**: $0.1 per generations

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`image_url`** (`string`, _required_):
  URL of the image of a single object to convert into a 3D Gaussian splat.
  - Examples: "https://raw.githubusercontent.com/VAST-AI-Research/TripoSplat/36b6deb0a1613e43c6910de441cb57d50352e44a/static/example_inputs/building_stone_house.webp"

- **`image_type`** (`ImageTypeEnum`, _optional_):
  Kind of input image. `synthetic` for renders and generated images (tight crop), `real` for photographs (looser crop). Default value: `"synthetic"`
  - Default: `"synthetic"`
  - Options: `"synthetic"`, `"real"`

- **`num_gaussians`** (`integer`, _optional_):
  Target number of Gaussians in the output splat. Rounded down to a multiple of 32. Default value: `262144`
  - Default: `262144`
  - Range: `32768` to `1048576`

- **`num_inference_steps`** (`integer`, _optional_):
  Number of sampling steps. Fewer are faster but lose some detail. Default value: `50`
  - Default: `50`
  - Range: `1` to `50`

- **`guidance_scale`** (`float`, _optional_):
  Classifier-free guidance: how strictly the object follows the image. Default value: `3`
  - Default: `3`
  - Range: `0` to `10`

- **`seed`** (`integer`, _optional_):
  Random seed for reproducibility. If None, a random seed is generated.

- **`enable_safety_checker`** (`boolean`, _optional_):
  Enable safety checking of the input image. Default value: `true`
  - Default: `true`



**Required Parameters Example**:

```json
{
  "image_url": "https://raw.githubusercontent.com/VAST-AI-Research/TripoSplat/36b6deb0a1613e43c6910de441cb57d50352e44a/static/example_inputs/building_stone_house.webp"
}
```

**Full Example**:

```json
{
  "image_url": "https://raw.githubusercontent.com/VAST-AI-Research/TripoSplat/36b6deb0a1613e43c6910de441cb57d50352e44a/static/example_inputs/building_stone_house.webp",
  "image_type": "synthetic",
  "num_gaussians": 262144,
  "num_inference_steps": 50,
  "guidance_scale": 3,
  "enable_safety_checker": true
}
```


### Output Schema

The API returns the following output format:

- **`model_mesh`** (`File`, _required_):
  Generated 3D Gaussian splat (.ply).

- **`model_glb`** (`File`, _required_):
  Colored point-cloud preview (.glb) of the splat centers, viewable in standard 3D model viewers.

- **`preprocessed_image`** (`Image`, _required_):
  The background-removed, cropped image the model saw.

- **`camera`** (`Camera`, _required_):
  Predicted input camera: yaw, pitch (radians), radius, fov (degrees).

- **`num_gaussians`** (`integer`, _required_):
  The actual number of Gaussians produced.

- **`seed`** (`integer`, _required_):
  The seed used for generation.

- **`timings`** (`Timings`, _required_):
  Per-stage timings in seconds.



**Example Response**:

```json
{
  "model_mesh": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "model_glb": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "preprocessed_image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  }
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/speridlabs/idol \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "image_url": "https://raw.githubusercontent.com/VAST-AI-Research/TripoSplat/36b6deb0a1613e43c6910de441cb57d50352e44a/static/example_inputs/building_stone_house.webp"
   }'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "speridlabs/idol",
    arguments={
        "image_url": "https://raw.githubusercontent.com/VAST-AI-Research/TripoSplat/36b6deb0a1613e43c6910de441cb57d50352e44a/static/example_inputs/building_stone_house.webp"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

```bash
npm install --save @fal-ai/client
```

Then use the API client to make requests:

```javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("speridlabs/idol", {
  input: {
    image_url: "https://raw.githubusercontent.com/VAST-AI-Research/TripoSplat/36b6deb0a1613e43c6910de441cb57d50352e44a/static/example_inputs/building_stone_house.webp"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Deploy your own model

To run your own weights, a fine-tune or a custom pipeline on the GPUs that serve this API, deploy it on fal Serverless. GPUs from H100 (80GB) to B300 (288GB), billed per second; an H100 is $4.50/h list, as low as $2.49/h. Prices, GPU specs and deploy steps: [fal Serverless](https://fal.ai/serverless.md).

## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/speridlabs/idol)
- [API Documentation](https://fal.ai/models/speridlabs/idol/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=speridlabs/idol)

### fal.ai Platform

- [Platform Documentation](https://fal.ai/docs/documentation)
- [Python Client](https://fal.ai/docs/api-reference/client-libraries/python)
- [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript)

### Other agent-readable surfaces

This file covers one model. To find anything else:

- [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs
- [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page
- [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined
- [Pricing](https://fal.ai/pricing.md): Model API and Serverless GPU prices
- [fal Serverless](https://fal.ai/serverless.md): Deploy your own models: GPU prices, specs and steps
- Any other model: `https://fal.ai/models/<endpoint-id>/llms.txt`