Large-scale text-to-image diffusion models have been a ground-breaking development in generating convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques predominantly hinge on DDIM inversion as a prevalent practice rooted in Latent Diffusion Models (LDM). However, the large pretrained T2I models working on the latent space suffer from losing details due to the first compression stage with an autoencoder mechanism. Instead, other mainstream T2I pipeline working on the pixel level, such as Imagen and DeepFloyd-IF, circumvents the above problem. They are commonly composed of multiple stages, typically starting with a text-to-image stage and followed by several super-resolution stages. In this pipeline, the DDIM inversion fails to find the initial noise and generate the original image given that the super-resolution diffusion models are not compatible with the DDIM technique. According to our experimental findings, iteratively concatenating the noisy image as the condition is the root of this problem. Based on this observation, we develop an iterative inversion (IterInv) technique for this category of T2I models and verify IterInv with the open-source DeepFloyd-IF model.Specifically, IterInv employ NTI as the inversion and reconstruction of low-resolution image generation. In stages 2 and 3, we update the latent variance at each timestep to find the deterministic inversion trace and promote the reconstruction process. By combining our method with a popular image editing method, we prove the application prospects of IterInv. The code will be released upon acceptance. The code is available at \url{https://github.com/Tchuanm/IterInv.git}.

翻译：大规模文本到图像扩散模型在依据输入文本提示生成逼真图像方面取得了突破性进展。图像编辑研究的目标是通过修改文本提示，赋予用户对生成图像的控制能力。当前的图像编辑技术主要依赖基于潜在扩散模型（LDM）的DDIM反演作为主流方法。然而，在潜在空间工作的大规模预训练T2I模型，由于采用自编码器机制进行第一阶段压缩，会导致细节损失。相比之下，在像素级工作的其他主流T2I流水线（如Imagen和DeepFloyd-IF）可规避上述问题。这类流水线通常由多个阶段组成，典型流程为文本到图像生成阶段后接多个超分辨率阶段。在该流水线中，由于超分辨率扩散模型与DDIM技术不兼容，DDIM反演无法找到初始噪声并重建原始图像。我们的实验发现，迭代地将带噪图像作为条件进行拼接是问题的根源。基于这一观察，我们为此类T2I模型开发了一种迭代反演技术（IterInv），并通过开源DeepFloyd-IF模型进行验证。具体而言，IterInv采用NTI实现低分辨率图像生成的反演与重建。在第二和第三阶段，我们更新每个时间步的潜在方差以寻找确定性反演轨迹，从而促进重建过程。通过将我们的方法与流行的图像编辑方法相结合，我们证明了IterInv的应用前景。代码将在录用后开源，目前可见于\url{https://github.com/Tchuanm/IterInv.git}。