# FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers

arXiv: https://arxiv.org/abs/2412.09611 · HTML: https://mlsanity.com/paper/2412.09611/

## Key facts

- Cited by 28 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 15 of its 45 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## 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, 15 of 15)

- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models](https://mlsanity.com/paper/2108.08877/) (2021)
- [Diffusion Models already have a Semantic Latent Space](https://mlsanity.com/paper/2210.10960/) (2022)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models](https://mlsanity.com/paper/2311.12092/) (2023)
- [NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models](https://mlsanity.com/paper/2312.05390/) (2023)
- [Sparse Overcomplete Word Vector Representations](https://mlsanity.com/paper/1506.02004/) (2015)
- [Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions](https://mlsanity.com/paper/2403.17064/) (2024)
- [Interpreting the Weight Space of Customized Diffusion Models](https://mlsanity.com/paper/2406.09413/) (2024)
- [Learn Once, Edit Anywhere: Visual Direction Transfer for Diffusion Models](https://mlsanity.com/paper/2403.19645/) (2024)
- [SEGA: Instructing Text-to-Image Models using Semantic Guidance](https://mlsanity.com/paper/2301.12247/) (2023)
- [The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP](https://mlsanity.com/paper/2406.00457/) (2024)

## Built on by (most cited first, 28 of 28)

- [ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features](https://mlsanity.com/paper/2502.04320/) (2025)
- [EEdit: Rethinking the Spatial and Temporal Redundancy for Efficient Image Editing](https://mlsanity.com/paper/2503.10270/) (2025)
- [UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models](https://mlsanity.com/paper/2504.13109/) (2025)
- [Exploring Multimodal Diffusion Transformers for Enhanced Prompt-based Image Editing](https://mlsanity.com/paper/2508.07519/) (2025)
- [WordCon: Word-level Typography Control in Scene Text Rendering](https://mlsanity.com/paper/2506.21276/) (2025)
- [LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers](https://mlsanity.com/paper/2505.23758/) (2025)
- [PairEdit: Learning Semantic Variations for Exemplar-based Image Editing](https://mlsanity.com/paper/2506.07992/) (2025)
- [Flow Straight and Fast in Hilbert Space: Functional Rectified Flow](https://mlsanity.com/paper/2509.10384/) (2025)
- [The Unreasonable Effectiveness of Text Embedding Interpolation for Continuous Image Steering](https://mlsanity.com/paper/2603.17998/) (2026)
- [Taming Flow-based I2V Models for Creative Video Editing](https://mlsanity.com/paper/2509.21917/) (2025)
- [Continuous Control of Editing Models via Adaptive-Origin Guidance](https://mlsanity.com/paper/2602.03826/) (2026)
- [DFVEdit: Conditional Delta Flow Vector for Zero-shot Video Editing](https://mlsanity.com/paper/2506.20967/) (2025)
- [TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models](https://mlsanity.com/paper/2601.08011/) (2026)
- [SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Models](https://mlsanity.com/paper/2506.00562/) (2025)
- [On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers](https://mlsanity.com/paper/2603.28762/) (2026)
- [Omni123: Exploring 3D Native Foundation Models with Limited 3D Data by Unifying Text to 2D and 3D Generation](https://mlsanity.com/paper/2604.02289/) (2026)
- [Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling](https://mlsanity.com/paper/2506.08796/) (2025)
- [The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion](https://mlsanity.com/paper/2506.21008/) (2025)
- [RFMI: Estimating Mutual Information on Rectified Flow for Text-to-Image Alignment](https://mlsanity.com/paper/2503.14358/) (2025)
- [Token-to-Token Alignment of Text Embeddings for Semantic Blending](https://mlsanity.com/paper/2606.24021/) (2026)
- [FusionEdit: Semantic Fusion and Attention Modulation for Training-Free Image Editing](https://mlsanity.com/paper/2602.08725/) (2026)
- [Rethinking Global Text Conditioning in Diffusion Transformers](https://mlsanity.com/paper/2602.09268/) (2026)
- [Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation](https://mlsanity.com/paper/2606.06813/) (2026)
- [Attribute Token Arithmetic: Disentangled and Continuous Semantic Control for Visual Autoregressive Models](https://mlsanity.com/paper/2608.28082/) (2026)
- [AcFlow: Controlling Text-to-Image Diffusion Transformers via Learned Conditional Activation Flow](https://mlsanity.com/paper/2609.10723/) (2026)
- [SliderEdit++: Improved Slider Trajectories in Instruction-Based Image Editing](https://mlsanity.com/paper/2610.14253/) (2026)
- [Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers](https://mlsanity.com/paper/2610.16892/) (2026)
- [Not Just a Subject: Capturing Adaptive Scene-Specific Variance for Visual Storytelling](https://mlsanity.com/paper/2610.22320/) (2026)

## Abstract (arXiv)

Rectified flow models have emerged as a dominant approach in image generation, showcasing impressive capabilities in high-quality image synthesis. However, despite their effectiveness in visual generation, rectified flow models often struggle with disentangled editing of images. This limitation prevents the ability to perform precise, attribute-specific modifications without affecting unrelated aspects of the image. In this paper, we introduce FluxSpace, a domain-agnostic image editing method leveraging a representation space with the ability to control the semantics of images generated by rectified flow transformers, such as Flux. By leveraging the representations learned by the transformer blocks within the rectified flow models, we propose a set of semantically interpretable representations that enable a wide range of image editing tasks, from fine-grained image editing to artistic creation. This work offers a scalable and effective image editing approach, along with its disentanglement capabilities.