ReNoise is a novel inversion method designed to enhance the reconstruction accuracy of real images using diffusion models. The method addresses the challenge of accurately inverting images into the domain of the pretrained diffusion model, which is crucial for text-guided image editing. By employing an iterative renoising mechanism, ReNoise refines the approximation of a predicted point along the forward diffusion trajectory at each inversion sampling step. This approach improves the quality of inversions while maintaining a high quality-to-operation ratio, making it suitable for both deterministic and non-deterministic samplers. The paper evaluates ReNoise using various sampling algorithms and models, including recent accelerated diffusion models like SDXL Turbo and LCM. The results demonstrate that ReNoise achieves superior reconstruction accuracy and editability compared to existing methods. Specifically, ReNoise allows for prompt-driven image editing, as shown through examples where specific words or phrases are replaced or added to the original prompt. Key contributions of ReNoise include: 1. **Enhanced Reconstruction Accuracy**: ReNoise improves the reconstruction quality of real images using a fixed number of UNet operations. 2. **Editability**: The method preserves the editability of inverted images, enabling a wide range of text-driven image manipulations. 3. **Versatility**: ReNoise is effective across different models and samplers, including few-step diffusion models. The paper also includes a detailed convergence analysis and ablation studies to validate the effectiveness and stability of the method. Overall, ReNoise provides a robust and efficient solution for real image inversion, paving the way for advanced image editing techniques.ReNoise is a novel inversion method designed to enhance the reconstruction accuracy of real images using diffusion models. The method addresses the challenge of accurately inverting images into the domain of the pretrained diffusion model, which is crucial for text-guided image editing. By employing an iterative renoising mechanism, ReNoise refines the approximation of a predicted point along the forward diffusion trajectory at each inversion sampling step. This approach improves the quality of inversions while maintaining a high quality-to-operation ratio, making it suitable for both deterministic and non-deterministic samplers. The paper evaluates ReNoise using various sampling algorithms and models, including recent accelerated diffusion models like SDXL Turbo and LCM. The results demonstrate that ReNoise achieves superior reconstruction accuracy and editability compared to existing methods. Specifically, ReNoise allows for prompt-driven image editing, as shown through examples where specific words or phrases are replaced or added to the original prompt. Key contributions of ReNoise include: 1. **Enhanced Reconstruction Accuracy**: ReNoise improves the reconstruction quality of real images using a fixed number of UNet operations. 2. **Editability**: The method preserves the editability of inverted images, enabling a wide range of text-driven image manipulations. 3. **Versatility**: ReNoise is effective across different models and samplers, including few-step diffusion models. The paper also includes a detailed convergence analysis and ablation studies to validate the effectiveness and stability of the method. Overall, ReNoise provides a robust and efficient solution for real image inversion, paving the way for advanced image editing techniques.