# Product Packshot

> Turn a messy product photo into a clean, commercial-grade packshot on a studio or lifestyle background, preserving the product 1:1.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/modelrunner/product-packshot`
- **Wrapper ID**: `modelrunner/product-packshot`
- **Category**: image-to-image
- **Kind**: wrapper inference
- **Tags**: product-photography, ecommerce, packshot, image-to-image, background

## Pricing

- **Estimated Price**: $0.08766667 average per output

## Request Lifecycle

This wrapper runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/modelrunner/product-packshot` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the wrapper; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>` (exact key=value matches, AND-ed). The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/modelrunner/product-packshot/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/modelrunner/product-packshot/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/modelrunner/product-packshot/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`prompt`** (`string`, _optional_):
  Optional art direction (props, mood, lighting). Refines the look but cannot override the product-identity constraints.

- **`shadow`** (`string`, _optional_):
  auto uses the background's natural shadow; otherwise override it.
  - Default: `"auto"`
  - Options: `"auto"`, `"soft_contact"`, `"reflection"`, `"floating"`, `"none"`

- **`framing`** (`string`, _optional_):
  standard ≈80% fill with even margins, fill ≈90% tight, breathing_room ≈65% with negative space for text/ad overlays.
  - Default: `"standard"`
  - Options: `"standard"`, `"fill"`, `"breathing_room"`

- **`background`** (`string`, _optional_):
  The studio or lifestyle look. Each preset carries its own natural shadow and composition. Use marketplace_white for a flat #FFFFFF Amazon-style background (see description for the exact-white caveat).
  - Default: `"studio_white"`
  - Options: `"marketplace_white"`, `"studio_white"`, `"soft_gradient"`, `"marble"`, `"light_wood"`, `"concrete"`, `"kitchen_counter"`, `"bathroom_vanity"`

- **`image_size`** (`string`, _optional_):
  Output size. Square (1_1_1k) is the marketplace standard; 4_5 / 3_4 suit Shopify & social, 16_9 suits ads, auto matches the source aspect.
  - Default: `"1_1_1k"`
  - Options: `"auto"`, `"1_1_512"`, `"1_1_1k"`, `"1_1_2k"`, `"4_5_1k"`, `"4_5_2k"`, `"3_4_1k"`, `"3_4_2k"`, `"2_3_1k"`, `"16_9_1k"`, `"16_9_2k"`, `"9_16_1k"`, `"9_16_2k"`, `"4_3_1k"`, `"3_2_1k"`

- **`product_images`** (`array<string>`, _required_):
  1–3 photos of the SAME product. One works; 2–3 different views sharpen fidelity and reduce label hallucination. Supports public URLs or data URIs.

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Output**

```json
"QTpRXJclj6m2q6ekVPnY8"
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/modelrunner/product-packshot \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "product_images": []
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

```javascript
import { modelrunner } from "@modelrunner/client";

const result = await modelrunner.subscribe("modelrunner/product-packshot", {
  input: {
  "product_images": []
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "modelrunner/product-packshot",
        arguments={
  "product_images": []
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/wrappers/modelrunner/product-packshot)
- [OpenAPI Schema](https://modelrunner.ai/wrappers/modelrunner/product-packshot/openapi.json)
- [LLM Instructions](https://modelrunner.ai/wrappers/modelrunner/product-packshot/llms.txt)