FLUX.1-dev

Get Credentials

To access FLUX.1-dev model read and accept FLUX.1-dev, FLUX.1-Canny-dev, FLUX.1-Depth-dev and FLUX.1-dev-onnx License Agreements and Acceptable Use Policy.

Create a new Hugging Face token with Read access to contents of all public gated repos you can access permission.

Export your personal credentials as environment variables:

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export NGC_API_KEY=<PASTE_API_KEY_HERE>
export HF_TOKEN=<PASTE_HUGGING_FACE_TOKEN_HERE>

Pull and Run the NIM

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

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

Pull and run the NIM with the command below.

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# 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=$NGC_API_KEY \
  -e HF_TOKEN=$HF_TOKEN \
  -p 8000:8000 \
  -v "$LOCAL_NIM_CACHE:/opt/nim/.cache/" \
  nvcr.io/nim/black-forest-labs/flux.1-dev:1.0.0

You can specify the desired variant of FLUX by adding -e NIM_MODEL_VARIANT=<you variant>. Available variants are base, canny, depth and their combinations, such as base+depth.

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 three minutes. The warm up is complete when the container logs show Pipeline warmup: start/done.

Test the NIM

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

output_image_path="result.jpg"

response=$(curl -X POST $invoke_url \
    -H "Accept: application/json" \
    -H "Content-Type: application/json" \
    -d '{
          "prompt": "A simple coffee shop interior",
          "mode": "base",
          "seed": 0, 
          "steps": 50 
        }')
response_body=$(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.