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Context: To circumvent this, LLI-based methods nichol2021glide ; avrahami2022blendedlatent ; ramesh2022hierarchical require the user to explicitly mask a part of the image to be inpainted, and drive the edited image to change in the masked area only, while matching the background of the original image. This approach has provided appealing results, however, the masking procedure is cumbersome, hampering quick and intuitive text-driven editing. Moreover, masking the image content removes important structural information, which is completely ignored in the inpainting process. Therefore, some editing capabilities are out of the inpainting scope, such as modifying the texture of a specific object. As can be seen in fig. 6, our method is not confined to modifying only textures, and it can perform structural modifications, e.g., change a “bicycle” to a “car”. To analyze our attention injection, in the left column we show the results without cross-attention injection, where changing a single word leads to an entirely different outcome. From left to right, we then show the resulting generated image by injecting attention to an increasing number of diffusion steps. Note that the more diffusion steps in which we apply cross-attention injection, the higher the fidelity to the original image.However, the optimal result is not necessarily achieved by applying the injection throughout all diffusion steps. Therefore, we can provide the user with even better control over the fidelity to the original image by changing the number of injection steps.

Question: What are the examples in which important structural information is removed when masking the image content?
Examples such as modifying textures of specific objects or changing bicycles in an image to a car.