Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models
On the map: Diffusion models → Image generation, text-to-image and editing
Abstract (arXiv)
In image editing employing diffusion models, it is crucial to preserve the reconstruction fidelity to the original image while changing its style. Although existing methods ensure reconstruction fidelity through optimization, a drawback of these is the significant amount of time required for optimization. In this paper, we propose negative-prompt inversion, a method capable of achieving equivalent reconstruction solely through forward propagation without optimization, thereby enabling ultrafast editing processes. We experimentally demonstrate that the reconstruction fidelity of our method is comparable to that of existing methods, allowing for inversion at a resolution of 512 pixels and with 50 sampling steps within approximately 5 seconds, which is more than 30 times faster than null-text inversion. Reduction of the computation time by the proposed method further allows us to use a larger number of sampling steps in diffusion models to improve the reconstruction fidelity with a moderate increase in computation time.
Key facts. Cited by 172 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 31 of its 38 references that are in the map. First posted on arXiv 26 May 2023.
Builds on
31 of 38 references.
Closest ancestors · 8
Same direction, newest first
- 2023Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models23 Feb
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- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation25 Aug
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding23 May
- 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation6 Oct
Other references · 23
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
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- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
- 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 897
- 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
- 2023Zero-shot Image-to-Image Translationcited by 503
- 2023ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generationcited by 414
- 2022Flexible Diffusion Modeling of Long Videoscited by 294
- 2022Diffusion Models for Video Prediction and Infillingcited by 134
- 2021WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesiscited by 65
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Citing papers per year: 2023: 15; 2024: 61; 2025: 60; 2026: 36.
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