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Image editing approaches with diffusion models have been rapidly developed,yet their applicability are subject to requirements such as specific editingtypes (e.g., foreground or background object editing, style transfer), multipleconditions (e.g., mask, sketch, caption), and time consuming fine-tuning ofdiffusion models. For alleviating these limitations and realizing efficientreal image editing, we propose a novel editing technique that only requires aninput image and target text for various editing types including non-rigid editswithout fine-tuning diffusion model. Our method contains three novelties:(I)Target-text Inversion Schedule (TTIS) is designed to fine-tune the input targettext embedding to achieve fast image reconstruction without image caption andacceleration of convergence.(II) Progressive Transition Scheme appliesprogressive linear interpolation between target text embedding and itsfine-tuned version to generate transition embedding for maintaining non-rigidediting capability.(III) Balanced Attention Module (BAM) balances the tradeoffbetween textual description and image semantics.By the means of combiningself-attention map from reconstruction process and cross-attention map fromtransition process, the guidance of target text embeddings in diffusion processis optimized.In order to demonstrate editing capability, effectiveness andefficiency of the proposed BARET, we have conducted extensive qualitative andquantitative experiments. Moreover, results derived from user study andablation study further prove the superiority over other methods.

BARET : Balanced Attention based Real image Editing driven by Target-text Inversion