# KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing

arXiv: https://arxiv.org/abs/2309.16608 · HTML: https://mlsanity.com/paper/2309.16608/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 50 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 30 of 48)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [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)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [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)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)

## Built on by (most cited first, 22 of 22)

- [Diffusion Model-Based Image Editing: A Survey](https://mlsanity.com/paper/2402.17525/) (2024)
- [DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization](https://mlsanity.com/paper/2402.09812/) (2024)
- [Conditional Image Synthesis with Diffusion Models: A Survey](https://mlsanity.com/paper/2409.19365/) (2024)
- [BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models](https://mlsanity.com/paper/2410.07273/) (2024)
- [LEGO: Learning EGOcentric Action Frame Generation via Visual Instruction Tuning](https://mlsanity.com/paper/2312.03849/) (2023)
- [FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior](https://mlsanity.com/paper/2407.04947/) (2024)
- [LocInv: Localization-aware Inversion for Text-Guided Image Editing](https://mlsanity.com/paper/2405.01496/) (2024)
- [FastEdit: Fast Text-Guided Single-Image Editing via Semantic-Aware Diffusion Fine-Tuning](https://mlsanity.com/paper/2408.03355/) (2024)
- [Dual-Schedule Inversion: Training- and Tuning-Free Inversion for Real Image Editing](https://mlsanity.com/paper/2412.11152/) (2024)
- [DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation](https://mlsanity.com/paper/2411.14295/) (2024)
- [SpecRef: A Fast Training-free Baseline of Specific Reference-Condition Real Image Editing](https://mlsanity.com/paper/2401.03433/) (2024)
- [Move and Act: Enhanced Object Manipulation and Background Integrity for Image Editing](https://mlsanity.com/paper/2407.17847/) (2024)
- [ColorEdit: Training-free Image-Guided Color editing with diffusion model](https://mlsanity.com/paper/2411.10232/) (2024)
- [Attention in Diffusion Model: A Survey](https://mlsanity.com/paper/2504.03738/) (2025)
- [Exploring the latent space of diffusion models directly through singular value decomposition](https://mlsanity.com/paper/2502.02225/) (2025)
- [InstructAttribute: Fine-grained Object Attributes editing with Instruction](https://mlsanity.com/paper/2505.00751/) (2025)
- [Personalized Image Generation via Human-in-the-loop Bayesian Optimization](https://mlsanity.com/paper/2602.02388/) (2026)
- [Training-free image inversion for one-step diffusion models](https://mlsanity.com/paper/2606.01380/) (2026)
- [Seal2Real: Prompt Prior Learning on Diffusion Model for Unsupervised Document Seal Data Generation and Realisation](https://mlsanity.com/paper/2310.00546/) (2023)
- [Latent Inversion with Timestep-aware Sampling for Training-free Non-rigid Editing](https://mlsanity.com/paper/2402.08601/) (2024)
- [Towards LLM-centric Affective Visual Customization via Efficient and Precise Emotion Manipulating](https://mlsanity.com/paper/2602.18016/) (2026)
- [QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing](https://mlsanity.com/paper/2610.17829/) (2026)

## Abstract (arXiv)

Text-conditioned image editing is a recently emerged and highly practical task, and its potential is immeasurable. However, most of the concurrent methods are unable to perform action editing, i.e. they can not produce results that conform to the action semantics of the editing prompt and preserve the content of the original image. To solve the problem of action editing, we propose KV Inversion, a method that can achieve satisfactory reconstruction performance and action editing, which can solve two major problems: 1) the edited result can match the corresponding action, and 2) the edited object can retain the texture and identity of the original real image. In addition, our method does not require training the Stable Diffusion model itself, nor does it require scanning a large-scale dataset to perform time-consuming training.