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

fal-ai/trellis

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

Inference

Commercial use

Schema

LLMs

Playground API

Input

Image Url*

Hint: Drag and drop image files from your computer, images from web pages, paste from clipboard (Ctrl/Cmd+V), or provide a URL. Accepted file types: jpg, jpeg, png, webp, gif, avif, heic, heif

Additional Settings

Customize your input with more control.

Your request will cost $0.02 per generation.

Result

Idle

What would you like to do next?

Your request will cost $0.02 per generation.

Readme

Trellis - Advanced AI Model for 3D Generation

Transform your creative workflows with Trellis, a powerful AI model designed for reliable, high-quality 3D asset generation from images. Built for developers who need production-ready 3D models with minimal complexity.

Overview

Trellis provides a streamlined API for generating 3D models from images, utilizing a native 3D generative model based on Structured LATents (SLAT) representation. Whether you're building creative tools, content generation systems, or enhancing existing applications, Trellis delivers consistent, high-quality 3D assets with simple integration.

Getting Started

Setting up Trellis takes just a few minutes. Choose your preferred SDK:

For JavaScript/TypeScript projects:

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

For Python applications:

bash
pip install fal-client

Configure your authentication:

JavaScript:

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

fal.config({
  credentials: "YOUR_FAL_KEY_HERE"
});

Python:

python
import fal_client
import os

os.environ["FAL_KEY"] = "YOUR_FAL_KEY_HERE"

Basic Usage

Generate your first 3D model with just a few lines of code:

JavaScript:

javascript
const result = await fal.subscribe("fal-ai/trellis", {
  input: {
    image_url: "https://example.com/your-image.jpg"
  }
});

Python:

python
result = fal_client.subscribe("fal-ai/trellis", {
    "image_url": "https://example.com/your-image.jpg"
})

Advanced Features

Trellis supports advanced configuration options to fine-tune your results:

3D Output Formats

Generate multiple 3D representations (Radiance Fields, 3D Gaussians, meshes)
Export to GLB format for universal compatibility
Maintain high detail in both geometry and texture

Quality Control

Intricate shape and texture detail preservation
Flexible output format selection
Support for local 3D editing capabilities

API Parameters

Input Parameters:

image_url (required): URL of the input image
Additional configuration options available for advanced use cases

Output:

3D model in multiple formats
GLB file export capability
High-quality mesh and texture data

Best Practices

Maximize your success with Trellis by following these guidelines:

Error Handling

javascript
try {
  const result = await fal.subscribe("fal-ai/trellis", {
    input: { image_url: "your-image-url" }
  });
} catch (error) {
  console.error("Generation failed:", error.message);
}

Image Requirements

Use clear, well-lit images with distinct subjects
Supported formats: JPG, JPEG, PNG, WebP, GIF, AVIF
Best results with images showing full object visibility

Technical Specifications

Model Architecture:

Based on Structured LATents (SLAT) representation
Powered by Rectified Flow Transformers
Up to 2 billion parameters
Trained on 500K diverse 3D objects

Performance Metrics:

Fast 3D generation from single images
High-quality output with intricate details
Multiple output format support

File Upload Support

Upload local images using the file storage API:

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

const file = new File([imageData], "image.jpg", { type: "image/jpeg" });
const url = await fal.storage.upload(file);

const result = await fal.subscribe("fal-ai/trellis", {
  input: {
    image_url: url
  }
});

Queue-Based Processing

For asynchronous workflows:

javascript
// Submit request
const { request_id } = await fal.queue.submit("fal-ai/trellis", {
  input: {
    image_url: "your-image-url"
  },
  webhookUrl: "https://optional.webhook.url/for/results"
});

// Check status
const status = await fal.queue.status("fal-ai/trellis", {
  requestId: request_id,
  logs: true
});

// Get result
const result = await fal.queue.result("fal-ai/trellis", {
  requestId: request_id
});

Integration Support

Our documentation includes complete reference implementations for:

React/Next.js applications
Python backend services
Node.js environments
REST API clients

Use Cases

3D Asset Creation

Game development assets
Product visualization
Digital art and design
AR/VR content creation

Professional Applications

E-commerce product models
Architecture visualization
Educational content
Creative prototyping

Pricing and Usage

Cost: $0.02 per unit
Transparent, usage-based pricing
No subscription necessary
No hidden fees or minimum commitments

View detailed pricing or contact sales for enterprise solutions.

Support and Resources

Access comprehensive support through multiple channels:

Documentation

API Reference
General fal.ai Documentation
Code examples and tutorials

Community Support

Active developer community
Regular updates and improvements
Direct technical support

Get started with Trellis today and experience the next generation of AI-powered 3D asset generation. Sign up for an API key at fal.ai.

Readme

Trellis - Advanced AI Model for 3D Generation

Overview

Getting Started

Setting up Trellis takes just a few minutes. Choose your preferred SDK:

For JavaScript/TypeScript projects:

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

For Python applications:

bash
pip install fal-client

Configure your authentication:

JavaScript:

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

fal.config({
  credentials: "YOUR_FAL_KEY_HERE"
});

Python:

python
import fal_client
import os

os.environ["FAL_KEY"] = "YOUR_FAL_KEY_HERE"

Basic Usage

Generate your first 3D model with just a few lines of code:

JavaScript:

javascript
const result = await fal.subscribe("fal-ai/trellis", {
  input: {
    image_url: "https://example.com/your-image.jpg"
  }
});

Python:

python
result = fal_client.subscribe("fal-ai/trellis", {
    "image_url": "https://example.com/your-image.jpg"
})

Advanced Features

Trellis supports advanced configuration options to fine-tune your results:

3D Output Formats

Generate multiple 3D representations (Radiance Fields, 3D Gaussians, meshes)
Export to GLB format for universal compatibility
Maintain high detail in both geometry and texture

Quality Control

Intricate shape and texture detail preservation
Flexible output format selection
Support for local 3D editing capabilities

API Parameters

Input Parameters:

image_url (required): URL of the input image
Additional configuration options available for advanced use cases

Output:

3D model in multiple formats
GLB file export capability
High-quality mesh and texture data

Best Practices

Maximize your success with Trellis by following these guidelines:

Error Handling

javascript
try {
  const result = await fal.subscribe("fal-ai/trellis", {
    input: { image_url: "your-image-url" }
  });
} catch (error) {
  console.error("Generation failed:", error.message);
}

Image Requirements

Use clear, well-lit images with distinct subjects
Supported formats: JPG, JPEG, PNG, WebP, GIF, AVIF
Best results with images showing full object visibility

Technical Specifications

Model Architecture:

Based on Structured LATents (SLAT) representation
Powered by Rectified Flow Transformers
Up to 2 billion parameters
Trained on 500K diverse 3D objects

Performance Metrics:

Fast 3D generation from single images
High-quality output with intricate details
Multiple output format support

File Upload Support

Upload local images using the file storage API:

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

const file = new File([imageData], "image.jpg", { type: "image/jpeg" });
const url = await fal.storage.upload(file);

const result = await fal.subscribe("fal-ai/trellis", {
  input: {
    image_url: url
  }
});

Queue-Based Processing

For asynchronous workflows:

javascript
// Submit request
const { request_id } = await fal.queue.submit("fal-ai/trellis", {
  input: {
    image_url: "your-image-url"
  },
  webhookUrl: "https://optional.webhook.url/for/results"
});

// Check status
const status = await fal.queue.status("fal-ai/trellis", {
  requestId: request_id,
  logs: true
});

// Get result
const result = await fal.queue.result("fal-ai/trellis", {
  requestId: request_id
});

Integration Support

Our documentation includes complete reference implementations for:

React/Next.js applications
Python backend services
Node.js environments
REST API clients

Use Cases

3D Asset Creation

Game development assets
Product visualization
Digital art and design
AR/VR content creation

Professional Applications

E-commerce product models
Architecture visualization
Educational content
Creative prototyping

Pricing and Usage

Cost: $0.02 per unit
Transparent, usage-based pricing
No subscription necessary
No hidden fees or minimum commitments

View detailed pricing or contact sales for enterprise solutions.

Support and Resources

Access comprehensive support through multiple channels:

Documentation

API Reference
General fal.ai Documentation
Code examples and tutorials

Community Support

Active developer community
Regular updates and improvements
Direct technical support

Get started with Trellis today and experience the next generation of AI-powered 3D asset generation. Sign up for an API key at fal.ai.

fal-ai/trellis, details and endpoints

fal-ai/trellis

Input

Result

What would you like to do next?

Readme

Trellis - Advanced AI Model for 3D Generation

Overview

Getting Started

Basic Usage

Advanced Features

API Parameters

Best Practices

Technical Specifications

File Upload Support

Queue-Based Processing

Integration Support

Use Cases

Pricing and Usage

Support and Resources

Readme

Trellis - Advanced AI Model for 3D Generation

Overview

Getting Started

Basic Usage

Advanced Features

API Parameters

Best Practices

Technical Specifications

File Upload Support

Queue-Based Processing

Integration Support

Use Cases

Pricing and Usage

Support and Resources