What inputs does the image-to-image API need?

A source image URL or base64 image plus a text instruction describing the requested change.

Can I run batch edits through the API?

Yes. Queue multiple API calls, track job IDs, and route outputs through storage and review systems.

How do I control cost at scale?

Cache source images, limit output quality and resolution, and monitor token spend per job.

What edits should go through human review?

Logos, small text, faces, regulated creative, and brand-sensitive assets should always be reviewed.

When should I use the playground instead?

Use the playground for one-off tests, prompt discovery, and visual approval before writing automation.

Image-to-Image API Workflow

Add Image to Image Automation at Scale

Add image to image editing to your product pipeline with GPT Image 2 Edit. Build automated workflows that take a source image, apply a text instruction, and return an edited output—at batch scale and without manual design tools.

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Sleek black octopus with glowing blue cable-tentacles routing source images through automated image-to-image API workflow, futuristic infrastructure aesthetic

Image-to-image API workflow snapshot

Batch automation

Process hundreds of edits with queue-based API calls

Prompt-driven edits

Replace backgrounds, objects, or styles from text instructions

OpenAI-compatible

Use existing SDKs with OpenOctopus base URL and key

Cost controls

Cache inputs, cap output quality, and monitor token spend

Clean blue image-to-image API workflow diagram showing source image, text instruction, queue, and edited output flowing through octopus connector nodes

Add image to image steps to any pipeline

An image-to-image API workflow turns manual edits into repeatable production steps. Upload a source image, send an instruction, and receive the edited result through a standard REST call. GPT Image 2 Edit handles local changes, background swaps, object replacements, and style shifts while preserving the original subject.

For a deeper model review including benchmark findings and limitation analysis, see the GPT Image 2 Edit Review. For browser-based testing before automation, use the GPT Image 2 Edit Tool.

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Structured blue code integration diagram showing SDK setup, image upload, prompt composition, and edited image delivery with octopus connector nodes

Quick start: image-to-image API call

Send a source image and instruction to the GPT Image 2 Edit endpoint. The response returns an edited image URL that your workflow can store, review, or pass to the next stage.

import requests

response = requests.post(
    "https://api.openoctopus.com/v1/images/generations",
    headers={"Authorization": "Bearer $OPENOCTOPUS_API_KEY"},
    json={
        "model": "gpt-image-2",
        "image": "https://your-cdn.com/source.jpg",
        "prompt": "Replace the background with a clean white studio setting."
    }
)
print(response.json()["data"][0]["url"])

For multi-step edits, cache the source image reference and track each generation job. This keeps batch workflows deterministic and retryable.

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What an image-to-image workflow handles

Batch catalog edits

Standardize product photos across marketplaces

Creative variants

Generate ad and social assets from approved source images

Background replacement

Move subjects into studio, seasonal, or branded scenes

Object cleanup

Remove props, reflections, or clutter automatically

Style transfer

Convert photos to illustration, cinematic, or editorial looks

Multi-round pipelines

Chain edits with source reference caching

Review routing

Escalate logos, text, and faces for human approval

Cost monitoring

Track input, output, and cached-image tokens per job

Pricing snapshot for image-to-image API workflows

GPT Image 2 Edit pricing follows OpenAI's token model. Input images, cached image references, output images, and text instructions all consume tokens. A typical 1024×1024 edit costs approximately $0.03–$0.08 depending on complexity and quality settings. Batch workflows benefit from input caching when the same source image is edited multiple times.

For current rates, OpenAI lists image input at $8 per 1M tokens, cached image input at $2 per 1M tokens, and image output at $30 per 1M tokens.

Trust and source note

OpenAI's GPT Image 2 announcement introduces the model family, and the Images and Vision API documentation covers request patterns and parameters. Use these sources as reference context, then validate output quality against your own images before shipping.

Image-to-image API workflow FAQ

It removes manual editing steps from repeatable workflows such as catalog refreshes, ad variants, and creative pipelines.

Build image-to-image API workflows with OpenOctopus

Connect GPT Image 2 Edit through OpenOctopus for automated editing, batch processing, and scalable image generation pipelines.

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Start with $1 credit.