使用扩散模型的迭代多粒度图像编辑
Iterative Multi-granular Image Editing using Diffusion Models
AI总结：
本文提出EMILIE迭代多粒度图像编辑器，通过潜在迭代策略和梯度控制操作，使扩散模型支持从局部到全局的迭代编辑，并构建新基准数据集验证其效果。
摘要：
文本引导图像合成的最新进展极大地改变了创意专业人士生成艺术性和美观视觉素材的方式。为充分支持此类创意工作，该过程应具备以下能力：1）对生成结果进行迭代编辑；2）控制所需变化的空间范围（全局、局部或介于两者之间的任意范围）。我们将这一实际问题形式化为迭代多粒度编辑。尽管基于扩散模型的图像合成与编辑已取得显著进展，但现有方法均为一次性操作（即不具备迭代编辑能力），并且无法自然产生多粒度控制（即无法覆盖从局部到全局编辑的完整谱系）。为克服这些不足，我们提出了EMILIE：迭代多粒度图像编辑器。EMILIE引入了一种新颖的潜在迭代策略，通过重新利用预训练扩散模型来实现迭代编辑，并辅以梯度控制操作以实现多粒度控制。我们引入了一个新的基准数据集来评估所提出的新设定。我们针对适配到本任务的最新先进方法进行了详尽的定量和定性评估，以展现EMILIE的出色能力。我们希望本工作能引起对这一新发现的实用问题设定的关注。
英文摘要：
Recent advances in text-guided image synthesis has dramatically changed how creative professionals generate artistic and aesthetically pleasing visual assets. To fully support such creative endeavors, the process should possess the ability to: 1) iteratively edit the generations and 2) control the spatial reach of desired changes (global, local or anything in between). We formalize this pragmatic problem setting as Iterative Multi-granular Editing. While there has been substantial progress with diffusion-based models for image synthesis and editing, they are all one shot (i.e., no iterative editing capabilities) and do not naturally yield multi-granular control (i.e., covering the full spectrum of local-to-global edits). To overcome these drawbacks, we propose EMILIE: Iterative Multi-granular Image Editor. EMILIE introduces a novel latent iteration strategy, which re-purposes a pre-trained diffusion model to facilitate iterative editing. This is complemented by a gradient control operation for multi-granular control. We introduce a new benchmark dataset to evaluate our newly proposed setting. We conduct exhaustive quantitatively and qualitatively evaluation against recent state-of-the-art approaches adapted to our task, to being out the mettle of EMILIE. We hope our work would attract attention to this newly identified, pragmatic problem setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
