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

arXiv: https://arxiv.org/abs/2310.13165 · HTML: https://mlsanity.com/paper/2310.13165/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 46 of its 56 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://mlsanity.com/paper/2210.11427/) (2022)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [Zero-shot Image-to-Image Translation](https://mlsanity.com/paper/2302.03027/) (2023)
- [Text2LIVE: Text-Driven Layered Image and Video Editing](https://mlsanity.com/paper/2204.02491/) (2022)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)

## Built on by (most cited first, 30 of 41)

- [Diffusion Model-Based Image Editing: A Survey](https://mlsanity.com/paper/2402.17525/) (2024)
- [Inversion-Free Image Editing with Natural Language](https://mlsanity.com/paper/2312.04965/) (2023)
- [I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing](https://mlsanity.com/paper/2408.14180/) (2024)
- [SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing](https://mlsanity.com/paper/2312.11392/) (2023)
- [SMooDi: Stylized Motion Diffusion Model](https://mlsanity.com/paper/2407.12783/) (2024)
- [VINCIE: Unlocking In-context Image Editing from Video](https://mlsanity.com/paper/2506.10941/) (2025)
- [UniCtrl: Improving the Spatiotemporal Consistency of Text-to-Video Diffusion Models via Training-Free Unified Attention Control](https://mlsanity.com/paper/2403.02332/) (2024)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion](https://mlsanity.com/paper/2412.11106/) (2024)
- [CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy](https://mlsanity.com/paper/2312.02235/) (2023)
- [ColorEdit: Training-free Image-Guided Color editing with diffusion model](https://mlsanity.com/paper/2411.10232/) (2024)
- [S2ST: Image-to-Image Translation in the Seed Space of Latent Diffusion](https://mlsanity.com/paper/2312.00116/) (2023)
- [ParallelEdits: Efficient Multi-object Image Editing](https://mlsanity.com/paper/2406.00985/) (2024)
- [Difference Inversion: Interpolate and Isolate the Difference with Token Consistency for Image Analogy Generation](https://mlsanity.com/paper/2506.07750/) (2025)
- [PENGUIN: Enhancing Transformer with Periodic-Nested Group Attention for Long-term Time Series Forecasting](https://mlsanity.com/paper/2508.13773/) (2025)
- [Target-aware Image Editing via Cycle-consistent Constraints](https://mlsanity.com/paper/2510.20212/) (2025)
- [MedCycle: Unpaired Medical Report Generation via Cycle-Consistency](https://mlsanity.com/paper/2403.13444/) (2024)
- [Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision](https://mlsanity.com/paper/2507.20976/) (2025)
- [Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge](https://mlsanity.com/paper/2602.16664/) (2026)
- [Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data](https://mlsanity.com/paper/2607.13927/) (2026)
- [FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models](https://mlsanity.com/paper/2410.14429/) (2024)
- [UIP2P: Unsupervised Instruction-based Image Editing via Edit Reversibility Constraint](https://mlsanity.com/paper/2412.15216/) (2024)
- [AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification](https://mlsanity.com/paper/2503.22019/) (2025)
- [CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis](https://mlsanity.com/paper/2505.23444/) (2025)
- [Score-based Diffusion Model for Unpaired Virtual Histology Staining](https://mlsanity.com/paper/2506.23184/) (2025)
- [Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models](https://mlsanity.com/paper/2509.02161/) (2025)
- [Cycle Diffusion Model for Counterfactual Image Generation](https://mlsanity.com/paper/2509.24267/) (2025)
- [TimePerceiver: An Encoder-Decoder Framework for Generalized Time-Series Forecasting](https://mlsanity.com/paper/2512.22550/) (2025)
- [CoCoDiff: Correspondence-Consistent Diffusion Model for Fine-grained Style Transfer](https://mlsanity.com/paper/2602.14464/) (2026)
- [Recursive Flow Matching](https://mlsanity.com/paper/2605.26535/) (2026)

## Abstract (arXiv)

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: https://cyclenetweb.github.io/