Proprietary? Either gpt-image-2 or NB2.

I have an example of interior decorating inpainting where I replaced a large floor-to-ceiling window with a mirror, and the result was pretty impressive using NB Pro from nearly a year ago.

https://imgpb.com/ZXkiXV

Locally hostable? For my money I'd argue Flux.2 Klein but Qwen-Edit still puts in the work.

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CharlesW a day ago | parent | next [-]

NB2 means "Nano Banana 2", a Google image generation model. https://blog.google/innovation-and-ai/technology/ai/nano-ban...

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woadwarrior01 a day ago | parent | prev | next [-]

For locally hostable image editing models, the edit variant of the recently released Boogu-Image[1] model is very good. Anecdotally, I'd say way better than Flux.2 Klein 9B and Qwen-Edit.

[1]: https://github.com/boogu-project/Boogu-Image

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IAmGraydon a day ago | parent | prev [-]

As far as I know, gpt-image-2 doesn't even let you define a mask unless you've already run it through one iteration, and once you do define the mask, it just ignores it 90% of the time. It's utterly useless for inpainting. Also, this and other proprietary models are severely limited in their output resolution.

I do agree, however, that the Flux2 family is the SoTA at the moment. Running locally via something like Comfy gets incredible results.

	▲	vunderba a day ago \| parent [-]
		Yeah definitely. You can do workarounds like drawing circles or using highlighters to create pseudo-masks for use with OpenAI or Google models but it’s really just a visual indication more than anything. If you want real precision (especially for complex polygonal masks), or if you’re concerned about image degradation over multiple edit rounds, you'll slam against the limitations of those approaches. Even with SOTA proprietary models, repeatedly editing and re-uploading an image is like making a copy of a copy of a VHS tape: you're gonna see subtle color shifts and quality loss steadily accumulate. At that point, you either need to put in the manual work in something like Photoshop (bringing elements in as layers and masking them properly) or, as you mentioned, use a model or workflow that properly supports masking.