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GPT Image 2 Image Edit: High-Fidelity Image-to-Image Editing on playground and API | RunComfy

openai/gpt-image-2/edit

Transform source images into polished 4K visuals with multilingual text, precise in-painting, and brand-ready edits for fast product updates, campaigns, and creative design workflows.

Image 1
Reference images to edit, up to 10 images
Output size of the edited image
Idle
The rate is $0.1 per image.

Introduction To GPT Image 2 Image Edit

OpenAI's GPT Image 2 on RunComfy turns reference images plus instructions into production-ready visuals at $0.1 per image, with outputs up to 4K and reliable multilingual text rendering for high-fidelity image-to-image editing. Trading manual masking, reshoots, and layer-heavy retouching for context-aware in-painting, out-painting, and precise typography, it eliminates complex masking and accelerates approvals for e-commerce teams, brand designers, and marketing workflows using GPT Image 2 Image Edit. For developers, GPT Image 2 Image Edit on RunComfy can be used both in the browser and via an HTTP API, so you don’t need to host or scale the model yourself.
Ideal for: Product Photo Enhancement | Campaign Visual Adaptation | Brand Asset Localization

OpenAI / GPT Image 2 Edit#


On RunComfy, GPT Image 2 offers state-of-the-art image generation plus GPT Image 2 Image Edit for precise, instruction-driven transformations using one or more reference images. It accepts images and a text prompt, returning high-fidelity still images suitable for commercial visuals, product shots, UI mockups, and marketing assets.


Highlights#

  • Instruction-following edits: Uses natural language to modify reference images with reliable adherence to complex directions.
  • High text legibility: Often renders labels, UI text, and multilingual typography with greater accuracy than earlier GPT Image models.
  • Flexible input handling: Works with up to 10 input images for batch updates or multi-angle references via GPT Image 2 Image Edit.
  • Coherent composition: Improves lighting, reflections, and scene structure for more consistent, photoreal-like results across iterations.
  • Size presets optimized for workflow: Choose auto or fixed dimensions (1024x1024, 1024x1536, 1536x1024) to balance detail and speed.
  • Robust editing fidelity: GPT Image 2 Image Edit commonly maintains identity, branding elements, and layout while applying guided changes.

Parameters#


ParameterRequiredTypeDefaultRange / OptionsDescription
images*Yes (*)List of images—Up to 10 imagesReference images to edit, up to 10 images
prompt*Yes (*)String——Text description of the desired edit
sizeNoEnum (string)autoauto, 10241024, 10241536, 1536*1024Output size: auto (default), 10241024, 10241536, or 1536*1024

Pricing#


Billing for GPT Image 2 Image Edit on RunComfy is simple:


Billing UnitPrice
Per image$0.1 per image.

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Frequently Asked Questions

What are the maximum resolution and aspect ratio constraints when using GPT Image 2 Image Edit for image-to-image workflows?

GPT Image 2 Image Edit supports output resolutions up to approximately 4K, adhering to pixel budget limits defined by the underlying model. For image-to-image editing, it’s best to maintain aspect ratios similar to the input image to ensure fidelity. Exceeding these parameters may result in automatic resizing or reduced visual coherence.

Are there any prompt or input limitations in GPT Image 2 Image Edit when performing high-fidelity image-to-image updates?

Yes. GPT Image 2 Image Edit typically limits text prompts to a few thousand tokens and may restrict reference inputs to one primary image for image-to-image tasks. Advanced multi-reference control (e.g., ControlNet or IP-Adapter) may become available in later API versions, but current implementations prioritize single-image guidance.

How do I migrate my GPT Image 2 Image Edit image-to-image prototype from the RunComfy Playground to an API-based production workflow?

To move from trial to production, export your GPT Image 2 Image Edit configurations from the RunComfy Playground, replace manual editing steps with the RunComfy API endpoint, and authenticate with your production API keys. Ensure you manage API rate limits and usd balance, as production requests consume paid computation resources, while the Playground trial uses limited free quota.

What distinguishes GPT Image 2 Image Edit from earlier models like GPT Image 1.5 in terms of image-to-image consistency?

GPT Image 2 Image Edit delivers superior visual stability and instruction following compared to GPT Image 1.5. In image-to-image operations, it preserves character likeness, lighting accuracy, and scene coherence more effectively due to the new autoregressive multimodal architecture replacing prior diffusion-based systems.

How well does GPT Image 2 Image Edit render text and multilingual content in image-to-image use cases?

GPT Image 2 Image Edit excels at reproducing accurate text overlays, including non-Latin and multilingual scripts. This represents a notable step up from older models, as it minimizes distortions and retains text legibility even after multiple image-to-image edits within the same session.

Can GPT Image 2 Image Edit handle photorealistic product imagery better than competitors like Nano Banana Pro?

GPT Image 2 Image Edit provides excellent fidelity for photorealistic and compositional tasks, especially where precise text rendering or product labeling is critical. While Nano Banana Pro may generate slightly faster and with smoother skin texture realism in some tests, GPT Image 2 Image Edit offers stronger control and scene accuracy for commercial-grade image-to-image workflows.

How does GPT Image 2 Image Edit maintain visual coherence during iterative refinements in an image-to-image session?

GPT Image 2 Image Edit uses an autoregressive design that tracks contextual coherence across multiple generations. As a result, when performing iterative image-to-image refinements, it better preserves geometric structure, reflections, and lighting consistency compared to diffusion models that may drift across edits.

Are there any latency considerations or performance tips when running GPT Image 2 Image Edit for large-scale image-to-image production batches?

For batch-based use, GPT Image 2 Image Edit can incur slightly higher latency than lightweight generators. Developers should manage request concurrency and consider smaller image-to-image batch sizes for efficiency. Caching reusable references and minimizing redundant uploads can also improve throughput in production.

What types of creative tasks benefit most from GPT Image 2 Image Edit’s image-to-image modality?

GPT Image 2 Image Edit performs exceptionally well for professional product visuals, UX/UI mockups, and branding updates where faithful preservation of composition is essential. Its image-to-image editing mode lets users cleanly transform existing imagery, refine object placement, or modify colors and text overlays while retaining visual fidelity.

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GPT Image 2 Image Edit Examples