drifterAI3000/image-editing-model-notes

Image Editing Model Notes

Working notes on image-generation and prompt-based editing models.

I’m mainly interested in what happens after the first good-looking image:

• —whether the model follows small editing instructions
• —whether faces and expressions remain consistent
• —whether untouched objects quietly change
• —how well models handle text replacement
• —whether exact object counts are respected
• —how lighting edits affect skin and image texture

A visually strong result is not always a reliable edit. Small changes around the target area are often more revealing than obvious failures.

Models currently on my list

• —Nano Banana 2
• —Nano Banana Pro
• —Seedream 5.0 Pro
• —GPT Image 2
• —Qwen Image 2.0 Pro
• —FLUX.2 Max
• —Midjourney V8.1

Editing tasks worth testing

Local object removal

Remove one object while preserving the structure behind it.

Things to inspect:

• —whether nearby furniture changes
• —whether the crop shifts
• —whether image texture becomes smoother
• —whether the subject’s face changes

Text replacement

Replace a short word on an object without modifying the object itself.

Things to inspect:

• —text legibility
• —perspective
• —material integration
• —unrelated facial or lighting changes

Exact object counts

Replace an object with an exact number of repeated items.

This is useful because models may create the correct number of visible objects but an incorrect number of stems, reflections or supporting details.

Identity preservation

Remove jewellery or change clothing while preserving:

• —facial structure
• —expression
• —skin texture
• —hairstyle
• —pose
• —lighting

Time-of-day changes

Change daylight to blue hour or evening without adding new light sources or removing existing scene elements.

Useful external benchmark

I found a practical comparison from These Guys Know that tested seven image generators using the same source task and six independent editing instructions.

The test kept the first result from every model instead of regenerating until the output looked good. It also separated full-image quality from instruction accuracy, which is useful because a polished image can still fail a simple edit.

Full comparison and side-by-side results:

[AI image model comparison on These Guys Know](https://theseguysknow.io/best-ai-image-generator-editor-test)

I’ll add my own tests and structured notes here as I work through more models.

Limitations

These notes are based on practical use rather than a controlled academic benchmark. Model behaviour may change with different providers, inference settings, resolutions and model updates.

External comparison

These notes were partly inspired by a practical TGK test comparing seven image generators across one generation task and six independent edits.

The comparison keeps the first result from each model and looks closely at instruction accuracy, object preservation, text replacement, exact counting, identity consistency and lighting changes.

Full article with side-by-side results:

[Read the complete AI image model comparison on TGK](https://theseguysknow.io/best-ai-image-generator-editor-test)