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AI image editing uses an existing image, a written prompt and, optionally, additional reference images to generate a changed image. The result depends on how those inputs and any model-specific settings work together; a prompt describes the goal but cannot guarantee that every requested detail will be followed or every other detail preserved.
What happens in an AI image edit?
At the user-facing level, an editor takes a visual input and an instruction about what to change. Depending on the product, you may also provide multiple images, a selection or mask, and settings for the model or output. The exact controls vary: an API request can expose parameters that a consumer app does not, and not every editor supports the same inputs.
These inputs guide generation; they are not a set of pixel-level commands. The public API documentation describes how to submit an edit and its constraints, not every model’s internal architecture. OpenAI’s image prompting guide notes that results depend on the prompt, reference images, output dimensions and settings.
How to use reference images clearly
A reference can communicate appearance, style, scene or object details that are difficult to convey in words alone. With multiple images, assign each one a distinct job and explain how they should relate. For example, identify one image as the scene to edit and another as the subject whose appearance should be carried into that scene. OpenAI’s prompt guide demonstrates explicit reference roles; its image edit API supports multiple source images.
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- Edit target: Say which image is the base to change.
- Subject or identity: Name the person, character or product whose distinctive features matter.
- Style: Specify which image supplies the palette, texture or medium, separately from the subject to depict.
- Object or scene: Identify the item to add, remove or reposition, and where it belongs.
For a composite, label the scene image and subject image, state what should move and where, and describe how the lighting or composition should match. Attaching several images without explaining their roles can make the intended relationship unclear.
How to request subject consistency
When changing a person or character’s surroundings, describe the subject and name the features or proportions to retain. Then describe the new setting or styling separately. For a product, specify the details that matter—such as its shape, color or visible markings—and distinguish those from the elements you want changed.
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Consistency is a capability to assess, not a guarantee of pixel-identical identity. OpenAI says that “Images 2.5 is better at working from reference photos to transform familiar subjects across new settings, visual styles, and compositions.” That is a claim about its named product, not an independent comparison or a universal consistency rate. Google’s Gemini image-generation documentation also describes character resemblance and object-fidelity capabilities for specific models. Neither claim establishes that every generated image will preserve a subject exactly.
How to limit unwanted changes
Make the requested change explicit and name the context that should remain stable. For example: “Remove the red cup from the table. Keep the person’s pose, the lighting and the rest of the composition unchanged.” This makes the intended scope clearer, but it does not create a hard lock on all other pixels.
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Use a selection or mask for localized edits
If the editor supports it, select or mask the region to change, and explain the change in words as well. OpenAI’s edit API documents mask input for supported workflows. In ChatGPT, the Images help page cautions that highlights are not always precise and that edits may extend beyond the selected area. Inspect the whole image, not only the selected region.
Break complex changes into smaller edits
For a complicated result, make one meaningful change at a time and inspect it before continuing. OpenAI’s prompt guide recommends keeping the references, prompt and output dimensions stable when comparing models, checking both instruction-following and unintended changes, and repeating requests to evaluate consistency. A successful first edit does not establish that a longer sequence will preserve every detail.
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Why prompts cannot guarantee exact preservation
An image prompt is guidance for generation, not a deterministic command that guarantees every requested detail. The outcome reflects the source image, any references, the wording and model-specific settings. A selection can help define the intended area, but boundaries may spill; a reference can guide a subject’s appearance, but cannot ensure an identical result.
That is why a useful edit request has two parts: what should change, and what should stay the same. The second part helps communicate priorities; it is not a promise that unmentioned or supposedly preserved details will remain untouched.
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How to evaluate an image editor for your task
When comparing systems, use the same task, source images, prompt and output dimensions wherever the products allow it. Judge results against the work you actually need to do, rather than relying on a broad quality label.
- Instruction following: Did the requested change happen?
- Subject or product preservation: Did important features, proportions and details remain recognizable?
- Text accuracy: Are signs, labels or other requested words correct?
- Unwanted changes: What changed outside the requested area?
- Repeatability: Do repeated runs and a multi-turn edit sequence stay consistent?
- Practical limits: Does the product support your reference count, file types, image sizes, masks and output controls?
OpenAI’s prompt guide recommends workload-specific evaluation and cautions that matching quality labels do not imply equal image quality or response time across models. Keep an untouched original and review every result before using it.
Check each product’s current limits
Supported formats, image counts, file-size ceilings, mask behavior, fidelity controls, output settings, availability and watermarking differ by model and product. Consult the relevant current documentation before building a workflow around a limit; do not assume a number or feature applies to every editor. For example, Google’s Gemini image-generation documentation gives model-specific reference-image and fidelity information and says generated images include SynthID watermarking. That is a Google documentation statement, not a claim about image editors generally.
OpenAI’s edit API reference likewise documents supported input formats, a per-image size ceiling, multiple images, and model-specific fidelity and mask behavior. Check that live reference for the model you plan to use rather than treating its limits as permanent or universal.
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