TripoSR

fal-ai/triposr
Inference
Commercial use

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

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

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import * as fal from "@fal-ai/serverless-client";

const result = await fal.subscribe("fal-ai/triposr", {
  input: {
    image_url: "https://raw.githubusercontent.com/VAST-AI-Research/TripoSR/ea034e12a428fa848684a3f9f267b2042d298ca6/examples/hamburger.png"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.
import * as fal from "@fal-ai/serverless-client";

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

3. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import * as fal from "@fal-ai/serverless-client";

// Upload a file (you can get a file reference from an input element or a drag-and-drop event)
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

// Use the URL in your request
const result = await fal.subscribe("fal-ai/triposr", { image_url: url });

Read more about file handling in our file upload guide.

4. Schema#

Input#

image_url*string

Path for the image file to be processed.

output_formatOutputFormatEnum

Output format for the 3D model. Default value: "glb"

Possible values: "glb", "obj"

do_remove_backgroundboolean

Whether to remove the background from the input image. Default value: true

foreground_ratiofloat

Ratio of the foreground image to the original image. Default value: 0.9

mc_resolutioninteger

Resolution of the marching cubes. Above 512 is not recommended. Default value: 256

{
  "image_url": "https://raw.githubusercontent.com/VAST-AI-Research/TripoSR/ea034e12a428fa848684a3f9f267b2042d298ca6/examples/hamburger.png",
  "output_format": "glb",
  "do_remove_background": true,
  "foreground_ratio": 0.9,
  "mc_resolution": 256
}

Output#

model_mesh*File

Generated 3D object file.

timings*Timings

Inference timings.

remeshing_dirFile

Directory containing textures for the remeshed model.

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