aaronjmars/triposg – Run with an API on Replicate

aaronjmars / triposg

TripoSG unofficial implementation

Cold

Public
204 runs
A100 (80GB)
GitHub
Weights
Paper
License

Run with an API

Playground API Examples README Versions

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=aaronjmars/triposg

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run aaronjmars/triposg using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "aaronjmars/triposg:14bcf0ef5a1d638ef5cd78c30a72efb32344cdd9280efb5819fd15275cec85e3",
  {
    input: {
      seed: 42,
      image: "https://replicate.delivery/pbxt/MkfWaszeUKdynXaueF7QDkCW3dfdIOdAfFE4SJeB3QCVOwTa/133.png",
      guidance_scale: 7,
      num_inference_steps: 50
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run aaronjmars/triposg using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "aaronjmars/triposg:14bcf0ef5a1d638ef5cd78c30a72efb32344cdd9280efb5819fd15275cec85e3",
    input={
        "seed": 42,
        "image": "https://replicate.delivery/pbxt/MkfWaszeUKdynXaueF7QDkCW3dfdIOdAfFE4SJeB3QCVOwTa/133.png",
        "guidance_scale": 7,
        "num_inference_steps": 50
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run aaronjmars/triposg using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "14bcf0ef5a1d638ef5cd78c30a72efb32344cdd9280efb5819fd15275cec85e3",
    "input": {
      "seed": 42,
      "image": "https://replicate.delivery/pbxt/MkfWaszeUKdynXaueF7QDkCW3dfdIOdAfFE4SJeB3QCVOwTa/133.png",
      "guidance_scale": 7,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

output.obj

{
  "completed_at": "2025-03-30T21:56:32.611607Z",
  "created_at": "2025-03-30T21:50:55.709000Z",
  "data_removed": false,
  "error": null,
  "id": "et79cm2cvnrma0cnx44v0ky2pc",
  "input": {
    "seed": 42,
    "image": "https://replicate.delivery/pbxt/MkfWaszeUKdynXaueF7QDkCW3dfdIOdAfFE4SJeB3QCVOwTa/133.png",
    "guidance_scale": 7,
    "num_inference_steps": 50
  },
  "logs": "Processing input image: /tmp/tmpytlyzxxw133.png\nUsing 50 inference steps\nUsing guidance scale of 7.0\nUsing seed: 42\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:07,  6.50it/s]\n  6%|▌         | 3/50 [00:00<00:04, 11.46it/s]\n 10%|█         | 5/50 [00:00<00:04, 10.75it/s]\n 14%|█▍        | 7/50 [00:00<00:04, 10.49it/s]\n 18%|█▊        | 9/50 [00:00<00:03, 10.35it/s]\n 22%|██▏       | 11/50 [00:01<00:03, 10.27it/s]\n 26%|██▌       | 13/50 [00:01<00:03, 10.21it/s]\n 30%|███       | 15/50 [00:01<00:03, 10.18it/s]\n 34%|███▍      | 17/50 [00:01<00:03, 10.16it/s]\n 38%|███▊      | 19/50 [00:01<00:03, 10.14it/s]\n 42%|████▏     | 21/50 [00:02<00:02, 10.13it/s]\n 46%|████▌     | 23/50 [00:02<00:02, 10.12it/s]\n 50%|█████     | 25/50 [00:02<00:02, 10.11it/s]\n 54%|█████▍    | 27/50 [00:02<00:02, 10.10it/s]\n 58%|█████▊    | 29/50 [00:02<00:02, 10.09it/s]\n 62%|██████▏   | 31/50 [00:03<00:01, 10.09it/s]\n 66%|██████▌   | 33/50 [00:03<00:01, 10.08it/s]\n 70%|███████   | 35/50 [00:03<00:01, 10.08it/s]\n 74%|███████▍  | 37/50 [00:03<00:01, 10.06it/s]\n 78%|███████▊  | 39/50 [00:03<00:01, 10.06it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 10.06it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 10.06it/s]\n 90%|█████████ | 45/50 [00:04<00:00, 10.06it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 10.06it/s]\n 98%|█████████▊| 49/50 [00:04<00:00, 10.06it/s]\n100%|██████████| 50/50 [00:04<00:00, 10.12it/s]\nstep 1 query num: 16777216\nstep 2 query num: 13218384\nfinal grids shape =  torch.Size([512, 512, 512])",
  "metrics": {
    "predict_time": 19.719734619,
    "total_time": 336.902607
  },
  "output": "https://replicate.delivery/xezq/1ZfJxSKaji1QNygkWPpyAei7Ubpefn1pgE0LeuutD0IFC4ujC/output.obj",
  "started_at": "2025-03-30T21:56:12.891872Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-xxdm4kfuo5mxczqk5tx6w6btoaihv3f74xdtu4dwlgnsvlqmmpfq",
    "get": "https://api.replicate.com/v1/predictions/et79cm2cvnrma0cnx44v0ky2pc",
    "cancel": "https://api.replicate.com/v1/predictions/et79cm2cvnrma0cnx44v0ky2pc/cancel"
  },
  "version": "14bcf0ef5a1d638ef5cd78c30a72efb32344cdd9280efb5819fd15275cec85e3"
}

Generated in

19.7 seconds

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Run time and cost

This model costs approximately $0.077 to run on Replicate, or 12 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 56 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.