Diffusion models (DMs) have enabled breakthroughs in image synthesis tasks
but lack an intuitive interface for consistent image-to-image (I2I)
translation. Various methods have been explored to address this issue,
including mask-based methods, attention-based methods, and image-conditioning.
However, it remains a critical challenge to enable unpaired I2I translation
with pre-trained DMs while maintaining satisfying consistency. This paper
introduces Cyclenet, a novel but simple method that incorporates cycle
consistency into DMs to regularize image manipulation. We validate Cyclenet on
unpaired I2I tasks of different granularities. Besides the scene and object
level translation, we additionally contribute a multi-domain I2I translation
dataset to study the physical state changes of objects. Our empirical studies
show that Cyclenet is superior in translation consistency and quality, and can
generate high-quality images for out-of-domain distributions with a simple
change of the textual prompt. Cyclenet is a practical framework, which is
robust even with very limited training data (around 2k) and requires minimal
computational resources (1 GPU) to train. Project homepage:
this https URL

Cyclenet 是一种新颖而简单的方法，将循环一致性引入扩散模型 (DMs) 以规范图像处理，通过引入循环一致性提高了翻译一致性和质量，并能使用简单的文本提示生成高质量的超领域分布图像。

CycleNet：对图像操控中基于文本引导扩散的循环一致性的重新思考

CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for  Image Manipulation

In this paper, we investigate the problem of unpaired video-to-video
translation. Given a video in the source domain, we aim to learn the
conditional distribution of the corresponding video in the target domain,
without seeing any pairs of corresponding videos. While significant progress
has been made in the unpaired translation of images, directly applying these
methods to an input video leads to low visual quality due to the additional
time dimension. In particular, previous methods suffer from semantic
inconsistency (i.e., semantic label flipping) and temporal flickering
artifacts. To alleviate these issues, we propose a new framework that is
composed of carefully-designed generators and discriminators, coupled with two
core objective functions: 1) content preserving loss and 2) temporal
consistency loss. Extensive qualitative and quantitative evaluations
demonstrate the superior performance of the proposed method against previous
approaches. We further apply our framework to a domain adaptation task and
achieve favorable results.

本文研究了无配对视频到视频翻译的问题，提出了一种新的框架，包括经过精心设计的生成器和鉴别器，以及两个核心目标函数：内容保持损失和时间一致性损失。经过广泛的定性和定量评估，证明了该方法相对于先前的方法具有更优越的性能。