# Trellis

> Generate 3D models from multiple images using Trellis. A native 3D generative model enabling versatile and high-quality 3D asset creation.


## Overview

- **Endpoint**: `https://fal.run/fal-ai/trellis/multi`
- **Model ID**: `fal-ai/trellis/multi`
- **Category**: image-to-3d
- **Kind**: inference
**Tags**: stylized



## Pricing

- **Price**: $0.02 per 

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_urls`** (`list<string>`, _required_):
  List of URLs of input images to convert to 3D
  - Array of string
  - Examples: ["https://storage.googleapis.com/falserverless/model_tests/video_models/front.png","https://storage.googleapis.com/falserverless/model_tests/video_models/back.png","https://storage.googleapis.com/falserverless/model_tests/video_models/left.png"]

- **`seed`** (`integer`, _optional_):
  Random seed for reproducibility

- **`ss_guidance_strength`** (`float`, _optional_):
  Guidance strength for sparse structure generation Default value: `7.5`
  - Default: `7.5`
  - Range: `0` to `10`

- **`ss_sampling_steps`** (`integer`, _optional_):
  Sampling steps for sparse structure generation Default value: `12`
  - Default: `12`
  - Range: `1` to `50`

- **`slat_guidance_strength`** (`float`, _optional_):
  Guidance strength for structured latent generation Default value: `3`
  - Default: `3`
  - Range: `0` to `10`

- **`slat_sampling_steps`** (`integer`, _optional_):
  Sampling steps for structured latent generation Default value: `12`
  - Default: `12`
  - Range: `1` to `50`

- **`mesh_simplify`** (`float`, _optional_):
  Mesh simplification factor Default value: `0.95`
  - Default: `0.95`
  - Range: `0.9` to `0.98`

- **`texture_size`** (`TextureSizeEnum`, _optional_):
  Texture resolution Default value: `"1024"`
  - Default: `1024`
  - Options: `512`, `1024`, `2048`

- **`multiimage_algo`** (`MultiimageAlgoEnum`, _optional_):
  Algorithm for multi-image generation Default value: `"stochastic"`
  - Default: `"stochastic"`
  - Options: `"stochastic"`, `"multidiffusion"`



**Required Parameters Example**:

```json
{
  "image_urls": [
    "https://storage.googleapis.com/falserverless/model_tests/video_models/front.png",
    "https://storage.googleapis.com/falserverless/model_tests/video_models/back.png",
    "https://storage.googleapis.com/falserverless/model_tests/video_models/left.png"
  ]
}
```

**Full Example**:

```json
{
  "image_urls": [
    "https://storage.googleapis.com/falserverless/model_tests/video_models/front.png",
    "https://storage.googleapis.com/falserverless/model_tests/video_models/back.png",
    "https://storage.googleapis.com/falserverless/model_tests/video_models/left.png"
  ],
  "ss_guidance_strength": 7.5,
  "ss_sampling_steps": 12,
  "slat_guidance_strength": 3,
  "slat_sampling_steps": 12,
  "mesh_simplify": 0.95,
  "texture_size": 1024,
  "multiimage_algo": "stochastic"
}
```


### Output Schema

The API returns the following output format:

- **`model_mesh`** (`File`, _required_):
  Generated 3D mesh file

- **`timings`** (`Timings`, _required_):
  Processing timings



**Example Response**:

```json
{
  "model_mesh": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  }
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/trellis/multi \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "image_urls": [
       "https://storage.googleapis.com/falserverless/model_tests/video_models/front.png",
       "https://storage.googleapis.com/falserverless/model_tests/video_models/back.png",
       "https://storage.googleapis.com/falserverless/model_tests/video_models/left.png"
     ]
   }'
```

### 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(
    "fal-ai/trellis/multi",
    arguments={
        "image_urls": ["https://storage.googleapis.com/falserverless/model_tests/video_models/front.png", "https://storage.googleapis.com/falserverless/model_tests/video_models/back.png", "https://storage.googleapis.com/falserverless/model_tests/video_models/left.png"]
    },
    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("fal-ai/trellis/multi", {
  input: {
    image_urls: ["https://storage.googleapis.com/falserverless/model_tests/video_models/front.png", "https://storage.googleapis.com/falserverless/model_tests/video_models/back.png", "https://storage.googleapis.com/falserverless/model_tests/video_models/left.png"]
  },
  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);
```


## Additional Resources

### Documentation

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

### fal.ai Platform

- [Platform Documentation](https://docs.fal.ai)
- [Python Client](https://docs.fal.ai/clients/python)
- [JavaScript Client](https://docs.fal.ai/clients/javascript)