Better Stable Diffusion Inpainting by Learning to Remove Real Objects
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Inserting novel AI-generated objects into images only through text-based instructions is a tricky task, and many of the best current models have used synthetic data to generate the necessary datasets. However, a new work from Israel attacks the problem from another angle: by REMOVING objects from training images, and telling the training system that the original images are actually the modified/edited images. Tests indicate that this use of real-world imagery obtains superior results.
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