Disclosure: I work on API Crest, the API platform used below. I checked the endpoint and parameters against the public API documentation on October 14, 2026. This is a documentation-based walkthrough: I did not execute paid generation requests, benchmark latency, or verify output quality or prices.

A product-photo editing pipeline starts with an existing image. The tricky engineering parts are uploading files, decoding the response, and not accidentally paying twice when a client times out. This guide demonstrates API Crest's synchronous image-editing route using Python and cURL. It differs from the platform's asynchronous media-generation endpoint.

1. Endpoint and preparation

The API docs document:

  • Base URL: https://api.vv666.ai
  • Image-edit endpoint: POST /v1/images/edits
  • Model ID: gpt-image-2.5 (a provider-documented ID)
  • Auth: Authorization: Bearer API_KEY
  • Multipart image field: repeated image[]
  • Required text fields: model, prompt
  • Default response: data[0].b64_json; an optional response_format=url also exists.

These are documented API Crest values, not universal guarantees of all OpenAI-compatible APIs. Check the live model catalog and account pricing before spending credits.

Install Python requests and place your secret in a local environment variable:

python -m pip install requests export APICREST_API_KEY="YOUR_API_KEY"

In PowerShell, use $env:APICREST_API_KEY = "YOUR_API_KEY". Never hard-code or publish a live key.

2. Multipart cURL request

If you own or are authorized to process product.png:

curl --request POST "https://api.vv666.ai/v1/images/edits" \ --header "Authorization: Bearer APICREST_API_KEY" \ --form "model=gpt-image-2.5" \ --form "image[]=@product.png" \ --form "prompt=Place this product on a clean wooden desk in morning light. Keep its packaging colors and label as close to the reference as possible." \ --form "size=1536x1024" \ --form "quality=high" \ --form "response_format=b64_json"

cURL builds the multipart boundary automatically; do not set Content-Type: application/json for this upload. A prompt cannot guarantee text, logo or packaging fidelity: manually review generated commercial assets.

3. Python: send one edit and save the image

This script deliberately does not automatically retry a possibly billable POST.

import base64 import os from pathlib import Path import requests source = Path("product.png") if not source.is_file(): raise FileNotFoundError(source) endpoint = "https://api.vv666.ai/v1/images/edits" headers = {"Authorization": f"Bearer {os.environ['APICREST_API_KEY']}"} with source.open("rb") as image: response = requests.post( endpoint, headers=headers, files={"image[]": (source.name, image, "image/png")}, data={ "model": "gpt-image-2.5", "prompt": "Place this product on a wooden desk in morning light. Preserve packaging as closely as possible.", "size": "1536x1024", "quality": "high", "response_format": "b64_json", }, timeout=(15, 360), # illustrative connect/read timeouts; not an SLA  ) task_id = response.headers.get("X-Task-Id") if task_id: print("Task ID:", task_id) if not response.ok: try: error = response.json().get("error", {}) if isinstance(error, dict) and error.get("task_id"): print("Task ID for follow-up:", error["task_id"]) except (ValueError, AttributeError): pass response.raise_for_status() image_b64 = response.json()["data"][0]["b64_json"] output = Path("edited.png") output.write_bytes(base64.b64decode(image_b64, validate=True)) print("Saved:", output.resolve())

The endpoint is synchronous; the 360-second example read timeout is a client choice, not a claimed measurement. With the documented response_format=url, handle data[0].url instead; don't assume those URLs last forever.

4. Two reference images

The documented multipart API accepts repeated image inputs:

--form "image[]=@product.png" \ --form "image[]=@background.png"

To send multiple files in Python requests, pass a list of repeated ("image[]", (filename, stream, mimetype)) entries, not a dictionary with duplicate keys. The docs describe up to 16 references, though individual channels may have lower limits. Validate permissions, file types and sizes.

5. Timeout and billing safety

A client timeout does not prove that the provider canceled processing. Blindly retrying the same POST could create a second billable edit. Don't implement automatic POST retries without a documented idempotency guarantee.

API Crest documents a response X-Task-Id header, possible error.task_id values, and a status/cost lookup:

GET https://api.vv666.ai/v1/skills/task-status?task_id=YOUR_TASK_ID Authorization: Bearer YOUR_API_KEY

When is_final=true, the docs describe the settled cost as available in the task result. If the connection drops before any headers, you may have no ID: treat the outcome as unknown, reconcile activity/usage, and avoid infinite retries.

Production checklist:

  • Secrets: keep keys server-side, redact logs, and restrict access.
  • Cost: image edits use documented per-image billing; Enhanced channels can cost more. Check live channel/model prices and settled account charges; no invented dollar amounts or savings claims here.
  • Inputs: validate file type, size, image rights, and user authorization.
  • Errors: inspect sanitized status/error objects; IP allowlist restrictions may return 403 / ip_not_allowed.
  • Capabilities: this documented interface does not support mask or n > 1; input_fidelity is ignored.
  • Visual QA: review logos, ingredient text, labels, proportions, and advertising claims before publication.
  • Protocol: the asynchronous POST /v1/media/generate workflow is separate from synchronous /v1/images/edits.

6. What to validate yourself

Before integrating with production, make one authorized low-volume request, check that the response can be decoded, record returned task IDs, and examine actual billing. I have not run that paid test for this article.

Docs: API Crest public API documentation · API Crest platform

For developers working with product images, what is hardest: reference fidelity, long-running requests, or avoiding duplicate charges?