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microsoft

TRELLIS

Run Anywhere

MSFT TRELLIS is a 3D AI model that generates high-quality 3D assets from text or image inputs.

Run-on-RTXimage-to-3dtext-to-3d
Get API Key
API Reference
Accelerated by DGX Cloud
Deploying your application in production? Get started with a 90-day evaluation of NVIDIA AI Enterprise

Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Step 1
Get Credentials

Export your personal credentials as environment variables:

export NGC_API_KEY=<PASTE_API_KEY_HERE>

Step 2
Pull and Run the NIM

Login to NVIDIA NGC so that you can pull the NIM container:

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

Pull and run the NIM with the command below.

# Create the cache directory on the host machine.
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
chmod 777 $LOCAL_NIM_CACHE
                        
docker run -it --rm --name=nim-server \
  --runtime=nvidia --gpus='"device=0"' \
  -e NGC_API_KEY=$NGC_API_KEY \
  -p 8000:8000 \
  -v "$LOCAL_NIM_CACHE:/opt/nim/.cache/" \
  nvcr.io/nim/microsoft/trellis:latest

You can specify the desired variant of TRELLIS by adding -e NIM_MODEL_VARIANT=<you variant>. Available variants are base:text, large:text, large:image and large:text+large:image. When you run the preceding command, the container downloads the model, initializes a NIM inference pipeline, and performs a pipeline warm up. A pipeline warm up typically requires up to five minutes. The warm up is complete when the container logs show Pipeline warmup: start/done.

Step 3
Test the NIM

invoke_url="http://localhost:8000/v1/infer"

output_image_path="result.glb"

response=$(curl -X POST $invoke_url \
    -H "Accept: application/json" \
    -H "Content-Type: application/json" \
    -d '{
          "prompt": "A simple coffee shop interior",
          "seed": 0
        }')
response_body=$(echo "$response" | awk '/{/,EOF-1')
echo $response_body | jq .artifacts[0].base64 | tr -d '"' | base64 --decode > $output_image_path

For more details on getting started with this NIM including configuring using parameters, visit the Visual GenAI NIM docs.