Conversational Image Editing with Multimodal Models

There are two broad ways AI edits images. Classic diffusion inpainting begins with a mask. You paint over the region you want changed, and the model regenerates only that area to fit the surroundings and your prompt. Pixels outside the mask stay untouched, but you have to know and mark exactly where the change goes. Research models such as InstructPix2Pix, published in 2022, showed that an image could be edited from a written instruction alone, with no mask. Natively multimodal models go further. With GPT-4o image generation in ChatGPT, launched in March 2025, and Google's Gemini 2.5 Flash Image, released in August 2025 and nicknamed Nano Banana, one model reads your uploaded image, your text and the earlier conversation, then produces a new image. Because it sees the whole conversation, it can follow requests like 'go back to the jacket color from two edits ago' or 'keep everything, just add rain'. It can also reason about what is in the picture, such as which object is the car or where the light comes from. The tradeoff is that these models usually generate a whole new image rather than patching pixels. Parts you did not mention may still shift slightly: a face may lose some likeness, small text may change, textures may soften. Across many turns these small changes add up, which is often called drift. Newer models preserve detail much better than early versions, but preservation is something the model learned, not a guarantee. Three misconceptions are common. The chat model is not editing your original file or its layers. It does not remember earlier versions perfectly. The output may not match your original resolution. For provenance, Google adds an invisible SynthID watermark to images from its models, and OpenAI attaches C2PA metadata to generated images.