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

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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.

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Awnings, if I understand correctly (I just learned this word right now), are purely additive attachments to structure exteriors - so perhaps they wouldn't necessarily need a full inpainting model? Wouldn't it be enough to estimate an affine transform for a quad and blend the image of awning directly (and the same with shadow map to fake shade)? Is classical photogrammetry up to such task these days?
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I'm quite perplexed by this comment. If I'm understanding you correctly, sure, what you describe is possible through significantly more effort, orchestration, and source photos. Or we can grab one still image and throw an inpainting model at it.
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I have no idea but I think you might be onto something.

So you're saying that, if I can calculate from the picture the position (height, inclination and such), and I can render the model (should be doable) for that height and angle, my best course of action could be to combine original + render and only at the end use a visual model? That could be interesting.

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flux klein with LoRa. GPT image and nano often produce high frequency artifacts when editing.
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