# Generating a Hero Frame then Animating It

Source: https://docs.prodia.com/workflows/generating-a-hero-frame-then-animating-it/

Image-to-video models follow a starting frame more faithfully than text-to-video models follow a long prompt — you get more control over the look of the scene. This Workflow generates a hero frame with [Flux Schnell](https://docs.prodia.com/models/flux-2/), then feeds it directly into [Wan 2.2 Lightning](https://docs.prodia.com/models/wan-2-2/) image-to-video, all in a single API call.

| Generated hero frame                                                                                                                                     | Animated 5-second clip                                              |
| -------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- |
| ![Hero frame: tropical beach at sunrise with palm trees and turquoise water](https://docs.prodia.com/llms/assets/769da66fd926-workflow-animate-hero.jpg) | [Example video](https://docs.prodia.com/workflow-animate-after.mp4) |

### Project Setup

```bash
# Create a project directory.
mkdir prodia-animate-hero-workflow
cd prodia-animate-hero-workflow
```

### prodia-js

Install Node (if not already installed):

### macOS

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

### Linux

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

### Windows

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

Create project skeleton:

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

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

### requests

Install Python (if not already installed):

### macOS

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

### Linux

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

### Windows

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

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

### curl

Install curl (if not already installed):

### macOS

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

### Linux

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

### Windows

```bash
# 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.
```

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

> note:
>
> 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:

### prodia-js

```js title="main.js"
const { createProdia } = require("prodia/v2");

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

### requests

Create the following `main.py`

```python title="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}"})
```

### curl

```bash title="main.sh"
set -euo pipefail
```

You're now ready to make some API calls!

### Generate then animate (in a single workflow)

The first job generates the hero frame. The second job receives that image as its starting frame and produces a 5-second 720p MP4. Wan 2.2 Lightning is the fastest image-to-video option on Prodia (\~22s per generation).

### prodia-js

```javascript title="main.js"
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: "a tropical beach at sunrise with calm turquoise waves, palm trees swaying gently, photorealistic, cinematic lighting",
            seed: 42,
          },
        },
        {
          type: "inference.wan2-2.lightning.img2vid.v0",
          config: {
            prompt: "soft waves rolling in, palm tree leaves swaying in the breeze, the sun rising slowly",
            resolution: "720p",
            seed: 42,
          },
        },
      ],
    },
  }, {
    accept: "video/mp4",
  });

  const video = await job.arrayBuffer();
  await fs.writeFile("beach.mp4", new Uint8Array(video));
  // open beach.mp4
})();
```

```bash
node main.js
```

### requests

```python title="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}"})

headers = {
    'Accept': 'video/mp4',
}

job = {
    'type': 'workflow.serial.v1',
    'config': {
        'jobs': [
            {
                'type': 'inference.flux-fast.schnell.txt2img.v2',
                'config': {
                    'prompt': 'a tropical beach at sunrise with calm turquoise waves, palm trees swaying gently, photorealistic, cinematic lighting',
                    'seed': 42,
                },
            },
            {
                'type': 'inference.wan2-2.lightning.img2vid.v0',
                'config': {
                    'prompt': 'soft waves rolling in, palm tree leaves swaying in the breeze, the sun rising slowly',
                    'resolution': '720p',
                    'seed': 42,
                },
            },
        ],
    },
}

res = session.post(prodia_url, headers=headers, json=job, timeout=240)
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)

with open('beach.mp4', 'wb') as f:
    f.write(res.content)
```

```bash
python main.py
```

### curl

```bash title="main.sh"
set -euo pipefail

cat <<EOF > job.json
{
  "type": "workflow.serial.v1",
  "config": {
    "jobs": [
      {
        "type": "inference.flux-fast.schnell.txt2img.v2",
        "config": {
          "prompt": "a tropical beach at sunrise with calm turquoise waves, palm trees swaying gently, photorealistic, cinematic lighting",
          "seed": 42
        }
      },
      {
        "type": "inference.wan2-2.lightning.img2vid.v0",
        "config": {
          "prompt": "soft waves rolling in, palm tree leaves swaying in the breeze, the sun rising slowly",
          "resolution": "720p",
          "seed": 42
        }
      }
    ]
  }
}
EOF

curl -sSf --retry 3 --max-time 240 \
  -H "Authorization: Bearer $PRODIA_TOKEN" \
  -H 'Accept: video/mp4' \
  -H 'Content-Type: application/json' \
  --data-binary @job.json \
  --output beach.mp4 \
  https://inference.prodia.com/v2/job
```

```bash
bash main.sh
```

### macOS

```bash
open beach.mp4
```

### Linux

```bash
xdg-open beach.mp4
```

### Windows

```bash
start beach.mp4
```

### Tips

- *Two prompts, two purposes.* The first prompt describes the *scene* — the second describes the *motion*. Keep the image prompt static and visual ("at sunrise", "palm trees", "cinematic lighting"), and let the video prompt focus on what *moves* ("waves rolling in", "leaves swaying").
- *Resolution.* Wan 2.2 Lightning supports `"720p"` (1280x720) and `"480p"` (832x480). 720p is the default.
- *Pinning seeds.* Both jobs accept a `seed` for reproducibility — useful when you want the same output every time, or when iterating on one prompt while keeping the other fixed.
- *Long-running jobs.* Image-to-video runs end-to-end in \~25–35 seconds for this chain. Set generous timeouts on your HTTP client (the curl example uses `--max-time 240`). If you run many of these concurrently, prefer the [async API](https://docs.prodia.com/reference/async/) and poll for completion.
- *Other video models.* For higher quality at the cost of time, swap in [Seedance Pro](https://docs.prodia.com/models/seedance/) (`inference.seedance.pro.img2vid.v1`, \~60s, 1080p) or [Veo](https://docs.prodia.com/models/veo/) (`inference.veo.fast.img2vid.v2` for fast, `inference.veo.img2vid.v2` for quality).