Generating Product Shots with Transparent Backgrounds

This Workflow generates a studio product photograph and removes its background in a single API call, returning a transparent PNG ready to drop into a website or marketing asset.

Generated by Flux Schnell	Background removed

Project Setup

# Create a project directory.
mkdir prodia-product-shots-workflow
cd prodia-product-shots-workflow

Install Node (if not already installed):

brew install node
# Close the current terminal and open a new one so that node is available.

apt install node
# Close the current terminal and open a new one so that node is available.

winget install -e --id OpenJS.NodeJS.LTS
# Close the current terminal and open a new one so that node is available.

Create project skeleton:

# Requires node --version >= 18
# Initialize the project with npm.
npm init -y

# Install the prodia-js library.
npm install prodia --save

Install Python (if not already installed):

brew install python
# Close the current terminal and open a new one so that python is available.

apt install python3 python3-venv python-is-python3
# Close the current terminal and open a new one so that python is available.

winget install -e --id Python.Python.3.12
# Close the current terminal and open a new one so that python is available.

# Requires python --version >= 3.12
python -m venv venv
source venv/bin/activate
pip install requests

Install curl (if not already installed):

brew install curl
# Close the current terminal and open a new one so that curl is available.

apt install curl
# Close the current terminal and open a new one so that curl is available.

# NOTE: Windows 10 and up have curl installed by default and this can be
# skipped.
winget install -e --id cURL.cURL
# Close the current terminal and open a new one so that curl is available.

# Export your token so it can be used by the main code.
export PRODIA_TOKEN=your-token-here

Your token is exported to an environment variable. If you close or switch your shell you’ll need to run export PRODIA_TOKEN=your-token-here again.

Create a main file for your project:

const { createProdia } = require("prodia/v2");

const prodia = createProdia({
    token: process.env.PRODIA_TOKEN // get it from environment
});

Create the following main.py

from requests.adapters import HTTPAdapter, Retry
import os
import requests
import sys


prodia_token = os.getenv('PRODIA_TOKEN')
prodia_url = 'https://inference.prodia.com/v2/job'

session = requests.Session()
retries = Retry(allowed_methods=None, status_forcelist=Retry.RETRY_AFTER_STATUS_CODES)
session.mount('http://', HTTPAdapter(max_retries=retries))
session.mount('https://', HTTPAdapter(max_retries=retries))
session.headers.update({'Authorization': f"Bearer {prodia_token}"})

set -euo pipefail

You’re now ready to make some API calls!

Generate and remove the background

The chain has two jobs: a text-to-image generation, then inference.remove-background.v1 which replaces the background with transparency.

The remove-background processor returns two outputs — foreground (the transparent PNG you want) and mask (the binary alpha map). The Prodia SDK gives you the foreground automatically. For Python and curl, you need a multipart parser to pull foreground from the response — examples below.

const { createProdia } = require("prodia/v2");
const fs = require("node:fs/promises");

const prodia = createProdia({
  token: process.env.PRODIA_TOKEN,
});

(async () => {
  const job = await prodia.job({
    type: "workflow.serial.v1",
    config: {
      jobs: [
        {
          type: "inference.flux-fast.schnell.txt2img.v2",
          config: {
            prompt: "studio product photograph of a stainless-steel coffee tumbler on a plain white seamless background, soft even lighting, centred composition",
            seed: 42,
          },
        },
        {
          type: "inference.remove-background.v1",
        },
      ],
    },
  }, {
    accept: "image/png",
  });

  const image = await job.arrayBuffer();
  await fs.writeFile("product.png", new Uint8Array(image));
  // open product.png
})();

node main.js

pip install requests-toolbelt

from requests.adapters import HTTPAdapter, Retry
from requests_toolbelt.multipart.decoder import MultipartDecoder
import os
import requests
import sys


prodia_token = os.getenv('PRODIA_TOKEN')
prodia_url = 'https://inference.prodia.com/v2/job'

session = requests.Session()
retries = Retry(allowed_methods=None, status_forcelist=Retry.RETRY_AFTER_STATUS_CODES)
session.mount('http://', HTTPAdapter(max_retries=retries))
session.mount('https://', HTTPAdapter(max_retries=retries))
session.headers.update({'Authorization': f"Bearer {prodia_token}"})

headers = {
    'Accept': 'multipart/form-data; image/png',
}

job = {
    'type': 'workflow.serial.v1',
    'config': {
        'jobs': [
            {
                'type': 'inference.flux-fast.schnell.txt2img.v2',
                'config': {
                    'prompt': 'studio product photograph of a stainless-steel coffee tumbler on a plain white seamless background, soft even lighting, centred composition',
                    'seed': 42,
                },
            },
            {
                'type': 'inference.remove-background.v1',
            },
        ],
    },
}

res = session.post(prodia_url, headers=headers, json=job)
print(f"Request ID: {res.headers['x-request-id']}")
print(f"Status: {res.status_code}")

if res.status_code != 200:
    print(res.text)
    sys.exit(1)

# remove-background returns two outputs (foreground + mask) — pick the foreground.
for part in MultipartDecoder.from_response(res).parts:
    cd = part.headers[b'Content-Disposition'].decode()
    if 'filename="foreground"' in cd:
        with open('product.png', 'wb') as f:
            f.write(part.content)
        break

python main.py

set -euo pipefail

cat <<EOF > job.json
{
  "type": "workflow.serial.v1",
  "config": {
    "jobs": [
      {
        "type": "inference.flux-fast.schnell.txt2img.v2",
        "config": {
          "prompt": "studio product photograph of a stainless-steel coffee tumbler on a plain white seamless background, soft even lighting, centred composition",
          "seed": 42
        }
      },
      {
        "type": "inference.remove-background.v1"
      }
    ]
  }
}
EOF

curl -sSf --retry 3 \
  -H "Authorization: Bearer $PRODIA_TOKEN" \
  -H 'Accept: multipart/form-data; image/png' \
  -H 'Content-Type: application/json' \
  --data-binary @job.json \
  --output response.bin \
  --dump-header response.headers \
  https://inference.prodia.com/v2/job

# remove-background returns two outputs (foreground + mask) — extract the foreground.
python3 - <<'PY'
import re
boundary = re.search(r'boundary=(\S+)', open('response.headers').read()).group(1).strip()
raw = open('response.bin', 'rb').read()
for part in raw.split(('--' + boundary).encode())[1:-1]:
    part = part.lstrip(b'\r\n')
    head_end = part.find(b'\r\n\r\n')
    head = part[:head_end].decode()
    body = part[head_end+4:].rstrip(b'\r\n')
    if 'filename="foreground"' in head:
        open('product.png', 'wb').write(body)
PY

bash main.sh

open product.png

xdg-open product.png

start product.png

Tips

Prompt for a clean background. Phrases like “plain white seamless background” or “studio backdrop” give the cut-out crisper edges than busy or photographic backgrounds.
Output is always PNG. inference.remove-background.v1 requires PNG output to preserve the alpha channel — JPEG would flatten it onto an opaque background.
Need only the mask? The chain still works if you read the mask part instead of foreground from the multipart response. See Removing Backgrounds for using the mask alone.