ReNoise：透過迭代加噪實現真實影像反轉

ReNoise: Real Image Inversion Through Iterative Noising

March 21, 2024

作者: Daniel Garibi, Or Patashnik, Andrey Voynov, Hadar Averbuch-Elor, Daniel Cohen-Or

cs.AI

摘要

最近在以文本引導的擴散模型方面取得的進展已經開啟了強大的圖像操作能力。然而，將這些方法應用於真實圖像需要將圖像反轉到預訓練擴散模型的領域。實現忠實的反轉仍然是一個挑戰，特別是對於最近訓練用於生成具有少量去噪步驟圖像的模型而言。在這項工作中，我們介紹了一種具有高質量-操作比的反轉方法，提高了重建準確性而不增加操作次數。基於反轉擴散採樣過程，我們的方法在每個反轉採樣步驟中採用了一種迭代重去噪機制。該機制通過迭代應用預訓練擴散模型並對這些預測進行平均，來改進對前向擴散軌跡上預測點的近似。我們使用各種採樣算法和模型，包括最近的加速擴散模型，來評估我們的ReNoise技術的性能。通過全面的評估和比較，我們展示了它在準確性和速度方面的有效性。此外，我們通過展示在真實圖像上進行以文本驅動的圖像編輯，確認了我們的方法保留了可編輯性。

English

Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities. However, applying these methods to real images necessitates the inversion of the images into the domain of the pretrained diffusion model. Achieving faithful inversion remains a challenge, particularly for more recent models trained to generate images with a small number of denoising steps. In this work, we introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. Building on reversing the diffusion sampling process, our method employs an iterative renoising mechanism at each inversion sampling step. This mechanism refines the approximation of a predicted point along the forward diffusion trajectory, by iteratively applying the pretrained diffusion model, and averaging these predictions. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models. Through comprehensive evaluations and comparisons, we show its effectiveness in terms of both accuracy and speed. Furthermore, we confirm that our method preserves editability by demonstrating text-driven image editing on real images.