AI for Photographers: Edit, Retouch & Enhance Photos

Uploads are standard web image formats: JPEG, PNG and WebP. Camera RAW files such as CR3, NEF, ARW, RAF and DNG are not accepted as direct input. Export a JPEG from Lightroom, Capture One or your usual converter first, then bring that file in. Everything on this page assumes you are working from an exported frame, not from the RAW itself.

No. RAW development works on the image data your sensor captured: white balance, exposure, tone curves, lens corrections and calibrated colour, all deterministic and repeatable. Generative editing interprets an existing image and generates new pixels for the parts you asked to change. The two are complementary. getimg.ai is not a replacement for a RAW development workflow, and it does not claim to be one.

Not guaranteed. For a targeted edit the model can often preserve much of the original subject and composition, but generated details may vary: face details, hair, fingers, jewellery, fabric, reflections, edges and geometry. Reference images and a clear instruction help steer it. Review identity-sensitive facial details closely whenever eyes, expressions, teeth or hair are being generated, and check them at full size before delivery.

Not in the sense a RAW developer means. If a highlight is genuinely clipped, the information is not in the file and nothing can recover it from the sensor data. What AI can do is clean up distracting highlights and reconstruct visually plausible detail where source information is limited. That result is a new interpretation of the area, not recovered data, so treat it as a creative fix rather than a correction.

It can guide a similar visual direction across a set, and saving the look as an Element makes that easier to repeat. It is not a calibrated preset or profile workflow: results vary between images and between runs of the same prompt. For a delivered gallery, review the set together and finish the grade in your usual tools.

It can create concepts. From a single photo the model does not know the unseen sides of a product, the exact ports, hidden labels or real geometry, so anything it generates for those views is invention. Use product references to create lifestyle concepts, alternate compositions and new presentation directions. For catalogue imagery that has to match the physical product exactly, verify geometry, labels, colours and details against real product references. Deeper product workflows are covered on AI for E-Commerce.

Yes. Describe who should go and the model removes them and generatively reconstructs the background behind them. In our own test it also removed the person's cast shadow from the paving. The rebuilt area is new pixels, not recovered ones, so check the repaired patch, nearby hands and clothing edges at full size. You can also edit the frame again with a second prompt if the first rebuild is not clean enough.

In our own tests FLUX 3 held skin texture, pores and freckles best on a subtle blemish cleanup, while redrawing the hairline and tightening the crop slightly. Nano Banana 2.1 was the stronger choice for removing something from a frame and rebuilding the background behind it, though it re-rendered nearby faces. Auto mode is a reasonable default, and running the same frame through two models before a client job is worth the few minutes. Model rankings move as new versions ship, so re-test rather than trusting a fixed list.

We have not run a controlled test on this yet, so we are not going to hand you a universal rule. In practice: if the source is small and soft, upscaling first gives the editing model more detail to work with; if the source is already large, retouch first and upscale only the frames you are delivering. Either way, compare the upscaled file against the source at full size, because creative upscalers can add texture that was not in the original.