TFinv proposes iterative noise alignment and suffix learning to enable training-free inversion and editing for one-step diffusion models, achieving SOTA performance and higher efficiency than multistep methods.
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Training-free image inversion for one-step diffusion models
TFinv proposes iterative noise alignment and suffix learning to enable training-free inversion and editing for one-step diffusion models, achieving SOTA performance and higher efficiency than multistep methods.