An Edit Friendly DDPM Noise Space: Inversion and Manipulations

Inbar Huberman-SpiegelglasVladimir KulikovTomer Michaeli

article2024CVPR308 citations

Proposes an exact, optimization-free DDPM inversion method that maps real images into a structured noise space, enabling diverse and structure-preserving text-guided editing without requiring model fine-tuning or attention manipulation.

Listen

Diffusion models represent the state of the art in image generation, but editing real, existing images with these systems remains difficult. Most current workflows rely on deterministic inversion methods that require either many computational steps, intensive model retraining, or internal attention manipulation to keep the edited output faithful to the original image. Standard probabilistic generation schemes possess a mathematically rich noise space, but their standard internal representations do not naturally retain image structure during editing.

The article demonstrates an alternative mathematical inversion strategy for probabilistic diffusion models that extracts structured, edit-friendly noise maps from any given image without requiring model fine-tuning or internal network modifications.

The researchers evaluated their approach by deriving an exact reconstruction technique that constructs intermediate noisy states independently from the source image, rather than chaining them through regular sequential sampling. They tested this method on two benchmark datasets across hundreds of image-text editing pairs, comparing speed, structural preservation, and text alignment against leading commercial and open-source diffusion editing baselines.

The analysis produced several key findings. First, the proposed inversion extracts noise maps with higher variances and negative temporal correlations, imprinting the underlying image structure far more strongly than standard noise. Second, the method achieves an optimal balance between structural fidelity and prompt compliance: on zero-shot image-to-image translation benchmarks, integrating the technique reduced perceptual error from 0.35 to 0.27 (an approximate 23% improvement) while preserving identical text alignment accuracy. Third, the process operates in 36 seconds per image, delivering a fourfold to fourteenfold speedup compared to optimization-heavy alternatives that require 160 to 520 seconds. Finally, because the inversion is stochastic, it generates diverse variations from a single text prompt while preserving core structures—a capability unavailable in deterministic methods.

These findings indicate that image editing pipelines can achieve superior visual quality and throughput without costly per-image model training. For operational systems, eliminating optimization loops drastically lowers compute costs, reduces user latency, and removes the risk of catastrophic degradation in fine textures and object structures during complex edits.

Organizations developing or deploying diffusion-based editing tools should integrate this inversion method into existing production frameworks to improve image fidelity and reduce processing overhead. Implementation teams should tune the forward skipping and guidance parameters to fit their specific domain requirements and explore batch generation to leverage the inherent output diversity.

Confidence in these findings is high for standard image editing and image-to-image tasks, as validated across multiple benchmark datasets and baseline comparisons. However, performance remains dependent on the underlying base model's comprehension of complex prompts, and users should carefully validate parameter selections when deploying across non-standard visual domains.

Technion – Israel Institute of Technology DDPM inversion edit-friendly noise maps text-guided diffusion models

arXiv: 2304.06140

Paper: Denoising Diffusion Probabilistic Models, Jonathan Ho et al. (2020). This seminal work establishes Denoising Diffusion Probabilistic Models (DDPM) and their stochastic sampling dynamics, which the target paper directly inverts and restructures for image editing.
Paper: Denoising Diffusion Implicit Models, Jiaming Song et al. (2021). This paper introduces Denoising Diffusion Implicit Models (DDIM) and deterministic sampling, which define the standard inversion baselines that the target paper aims to improve upon.
Paper: Null-text Inversion for Editing Real Images using Guided Diffusion Models, Ron Mokady et al. (2022). This paper develops Null-text Inversion for diffusion editing via optimization, providing the primary baseline and motivating the target paper's search for an optimization-free, stochastic alternative.
Paper: Prompt-to-Prompt Image Editing with Cross Attention Control, Amir Hertz et al. (2022). This foundational paper presents cross-attention control for text-guided editing, establishing the internal representation mechanisms and editing paradigms that the target paper builds upon.
Paper: SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations, Chenlin Meng et al. (2022). This paper introduces stochastic differential equation-based image editing by adding noise and denoising, providing key conceptual foundations for manipulating noisy latent trajectories.
Paper: High-Resolution Image Synthesis with Latent Diffusion Models, Robin Rombach et al. (2022). This work introduces Latent Diffusion Models, establishing the latent-space architecture on which modern inversion and text-driven image editing techniques operate.
Paper: Imagic: Text-Based Real Image Editing with Diffusion Models, Bahjat Kawar et al. (2022). This study demonstrates text-based real image editing using diffusion models, highlighting the computational trade-offs of per-image fine-tuning that the target paper circumvents.
Paper: InstructPix2Pix: Learning to Follow Image Editing Instructions, Tim Brooks et al. (2023). This work provides essential context for instruction-based diffusion editing pipelines and zero-shot image-to-image translation benchmarks.

Paper: FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing, Yingying Deng et al. (2025). This paper advances training-free inversion techniques by developing higher-order ODE solvers for fast, high-fidelity semantic editing in rectified flow models.
Paper: Initno: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization, Xiefan Guo et al. (2024). This study builds on manipulating the initial noise distribution by optimizing noise parameters directly to improve text alignment in diffusion models.
Paper: DiffEditor: Boosting Accuracy and Flexibility on Diffusion-Based Image Editing, Chong Mou et al. (2024). This work extends diffusion-based image manipulation by incorporating localized randomness and rollback techniques to achieve precise interactive edits.
Paper: Fast ODE-based Sampling for Diffusion Models in Around 5 Steps, Zhenyu Zhou et al. (2024). This paper investigates geometric trajectories in diffusion noise spaces to enable high-quality ODE-based sampling in as few as five steps.
Paper: Variational Flow Maps: Make Some Noise for One-Step Conditional Generation, Abbas Mammadov et al. (2026). This work extends noise-space reformulation strategies to one-step conditional generation and inverse problems via variational flow maps.

Cover for An Edit Friendly DDPM Noise Space: Inversion and Manipulations

Abstract

Denoising diffusion probabilistic models (DDPMs) employ a sequence of white Gaussian noise samples to generate an image. In analogy with GANs, those noise maps could be considered as the latent code associated with the generated image. However, this native noise space does not possess a convenient structure, and is thus challenging to work with in editing tasks. Here, we propose an alternative latent noise space for DDPM that enables a wide range of editing operations via simple means, and present an inversion method for extracting these edit-friendly noise maps for any given image (real or synthetically generated). As opposed to the native DDPM noise space, the edit-friendly noise maps do not have a standard normal distribution and are not statistically independent across timesteps. However, they allow perfect reconstruction of any desired image, and simple transformations on them translate into meaningful manipulations of the output image (e.g. shifting, color edits). Moreover, in text-conditional models, fixing those noise maps while changing the text prompt, modifies semantics while retaining structure. We illustrate how this property enables text-based editing of real images via the diverse DDPM sampling scheme (in contrast to the popular non-diverse DDIM inversion). We also show how it can be used within existing diffusion-based editing methods to improve their quality and diversity. The code of the method is attached to this submission.

Keywords

DDPM inversion edit-friendly noise maps text-guided diffusion models text-driven image editing DDIM inversion null-text inversion negative-prompt inversion DDPM sampling real image editing latent noise space

1 Introduction
2 Related work
2.1 Inversion of diffusion models
2.2 Image editing using diffusion models
3 The DDPM noise space
3.1 Edit friendly inversion
3.2 Properties of the edit-friendly noise space
4 Text-Guided Image Editing
5 Experiments
6 Conclusion
References
A Shifting the latent code
B The effect of the numerical error
C CycleDiffusion
D The effect of skip and strength parameters
E Integrating to P2P
F Additional details on experiments and further numerical evaluation
F.1 Experiments on the modified ImageNet-R-TI2I
F.2 Experiments on the modified zero-shot I2IT dataset
G Additional results

Knowls

Knowl 1 — Edit-Friendly DDPM Inversion Algorithm
algorithm
Denoising Diffusion Probabilistic Models (DDPM) generate images by reversing a forward diffusion process. Given an image $x_0 \in \mathbb{R}^d$ (or a latent representation in a latent diffusion model), a forward noise schedule $\beta_t$ with $\alpha_t = 1 - \beta_t$ and $\bar{\alpha}_t = \prod_{s=1}^t \alpha_s$ , a noise prediction network $f_t$ , and variance scale $\sigma_t = \eta \beta_t (1 - \bar{\alpha}_{t-1}) / (1 - \bar{\alpha}_t)$ where $\eta \in (0, 1]$ , the edit-friendly inversion algorithm extracts a sequence of noise maps $\{x_T, z_T, \dots, z_1\}$ that reconstructs $x_0$ upon reverse diffusion.

In the forward phase, an auxiliary sequence $\{x_1, \dots, x_T\}$ is generated by perturbing the target image $x_0$ with statistically independent Gaussian noise vectors $\tilde{\epsilon}_t \sim \mathcal{N}(0, I)$ at each step. In the backward inversion phase, the denoiser prediction is used to compute the deterministic drift $\hat{\mu}_t(x_t) = \sqrt{\bar{\alpha}_{t-1}} P(f_t(x_t)) + D(f_t(x_t))$ , where the estimated clean sample is $P(f_t(x_t)) = (x_t - \sqrt{1 - \bar{\alpha}_t} f_t(x_t)) / \sqrt{\bar{\alpha}_t}$ and the directional component is $D(f_t(x_t)) = \sqrt{1 - \bar{\alpha}_{t-1} - \sigma_t^2} f_t(x_t)$ . The noise vector $z_t$ is extracted by isolating it from the reverse transition equation, and numerical error compensation is applied at each step to prevent drift accumulation.
Algorithm: Edit-friendly DDPM inversion
Input: Real image $x_0$ , total timesteps $T$ , parameters $\bar{\alpha}_t, \sigma_t$ , denoiser $f_t$
Output: Latent noise sequence $\{x_T, z_T, \dots, z_1\}$
for $t = 1$ to $T$ do
    Sample $\tilde{\epsilon}_t \sim \mathcal{N}(0, I)$ independently
     $x_t \leftarrow \sqrt{\bar{\alpha}_t} x_0 + \sqrt{1 - \bar{\alpha}_t} \tilde{\epsilon}_t$
end for
for $t = T$ down to $1$ do
    Compute drift $\hat{\mu}_t(x_t) = \sqrt{\bar{\alpha}_{t-1}} P(f_t(x_t)) + D(f_t(x_t))$
     $z_t \leftarrow (x_{t-1} - \hat{\mu}_t(x_t)) / \sigma_t$
     $x_{t-1} \leftarrow \hat{\mu}_t(x_t) + \sigma_t z_t$
end for
return $\{x_T, z_T, \dots, z_1\}$
Knowl 2 — Structural Imprinting via Edit-Friendly Noise Space Statistics
theoretical result
In standard DDPM sampling, the forward trajectory constructs $x_t = \sqrt{\bar{\alpha}_t} x_0 + \sqrt{1 - \bar{\alpha}_t} \epsilon_t$ via the cumulative addition of step noises $n_1, \dots, n_t$ , which makes consecutive noise maps $\epsilon_t$ and $\epsilon_{t-1}$ highly correlated and leaves the generative reverse noise vectors $z_t \sim \mathcal{N}(0, I)$ mutually independent with standard deviation $1$ .

In contrast, the edit-friendly inversion constructs the auxiliary sequence as: $x_t = \sqrt{\bar{\alpha}_t} x_0 + \sqrt{1 - \bar{\alpha}_t} \tilde{\epsilon}_t$ where $\tilde{\epsilon}_t \sim \mathcal{N}(0, I)$ are statistically independent for all $t \in \{1, \dots, T\}$ . Because each $x_t$ is perturbed independently, consecutive auxiliary states $x_t$ and $x_{t-1}$ are farther apart than in standard diffusion trajectories. Consequently, the extracted noise vectors: $z_t = \frac{x_{t-1} - \hat{\mu}_t(x_t)}{\sigma_t}$ exhibit two distinct statistical properties:
1. Higher Variance: The per-pixel standard deviation of $z_t$ is elevated significantly above $1$ (typically ranging between $2$ and $5$ ).
2. Negative Correlation: Consecutive noise vectors $(z_t, z_{t-1})$ exhibit strong negative correlation, and the empirical distribution of angles between $z_t$ and $z_{t-1}$ shows a sharp peak at $180^\circ$ (obtuse angles), rather than the uniform $[0^\circ, 180^\circ]$ distribution observed in native i.i.d. noise.
These properties encode the structural identity of the input image $x_0$ directly into the latent trajectory $\{x_T, z_T, \dots, z_1\}$ , enabling downstream structural preservation under conditioning changes.
Knowl 3 — Text-Guided Image Editing via Noise-Preserving Reverse Diffusion
model/method
Text-guided image editing is performed using edit-friendly DDPM noise maps without model fine-tuning or attention-map overrides. Given a real input image $x_0$ , a source text prompt $p_{\text{src}}$ , and a target text prompt $p_{\text{tar}}$ :
1. Inversion: Extract the edit-friendly latent sequence $\{x_T, z_T, \dots, z_1\}$ using the denoiser conditioned on $p_{\text{src}}$ .
2. Generation with Timestep Skipping: Fix the inverted noise maps $\{z_t\}$ and initialize reverse diffusion at an intermediate timestep $T - T_{\text{skip}}$ , where $T_{\text{skip}} \in [0, T]$ is a hyperparameter balancing structural fidelity to $x_0$ against adherence to $p_{\text{tar}}$ . The reverse process proceeds iteratively from $t = T - T_{\text{skip}}$ down to $1$ : $x_{t-1} = \hat{\mu}_t(x_t; p_{\text{tar}}) + \sigma_t z_t$ where $\hat{\mu}_t(x_t; p_{\text{tar}})$ evaluates the denoiser conditioned on $p_{\text{tar}}$ with classifier-free guidance.
Because the auxiliary forward sequence uses stochastic independent draws $\tilde{\epsilon}_t$ , independent runs of the inversion produce different valid noise sequences that all reconstruct $x_0$ perfectly while yielding diverse semantic variations under $p_{\text{tar}}$ .
Knowl 4 — Mask-Guided Color Manipulation in DDPM Latent Space
model/method
Local color editing is achieved in the DDPM latent space by directly modifying the extracted noise vectors across a specified timestep interval $[T_1, T_2] \subseteq [1, T]$ . Given an input image $x_0$ , a binary spatial mask $B \in \{0, 1\}^d$ , a target color mask $M \in \mathbb{R}^d$ , and an editing strength parameter $s > 0$ :
1. Extract the latent noise maps $\{z_1, \dots, z_T\}$ using edit-friendly DDPM inversion.
2. For each timestep $t \in [T_1, T_2]$ , replace $z_t$ with: $z_t^{\text{edited}} = z_t + s \cdot B \odot \left( M - P(f_t(x_t)) \right)$ where $\odot$ denotes element-wise multiplication, and $P(f_t(x_t)) = (x_t - \sqrt{1 - \bar{\alpha}_t} f_t(x_t)) / \sqrt{\bar{\alpha}_t}$ is the predicted clean image at step $t$ .
3. Execute the reverse diffusion process using $z_t^{\text{edited}}$ .
The residual term $M - P(f_t(x_t))$ drives the predicted clean image towards the color profile $M$ within the masked region $B$ during the specified timesteps, preserving underlying image texture and structures inside and outside $B$ without introducing border artifacts.
Knowl 5 — Spatial Image Shifting via Latent Noise Code Translation
empirical result
Applying a horizontal spatial translation of $d$ pixels (tested for $d \in \{1, 2, 4, 8, 12, 16\}$ ) to all $T+1$ latent noise maps $\{x_T, z_T, \dots, z_1\}$ before executing reverse DDPM sampling shifts the synthesized image by $d$ pixels while maintaining texture, object identity, and structural integrity.

In contrast, applying the same spatial shift to:
1. The native DDPM noise maps that generated the original image, or
2. The noise maps extracted by CycleDiffusion,
causes immediate collapse of the image structure and severe visual artifacts across all non-zero shifts $d$ . This demonstrates that edit-friendly noise maps uniquely preserve spatial semantics under geometric transformations.
Knowl 6 — Comparative Evaluation on Real-Image Text-Guided Editing Benchmarks
empirical result
On the modified ImageNet-R-TI2I benchmark (48 real images, 212 image-text pairs) using Stable Diffusion with classifier-free guidance (default parameters: strength $= 15$ , $T_{\text{skip}} = 36$ , $\eta = 1$ , 100 inference steps), edit-friendly DDPM inversion achieves an LPIPS score of $\approx 0.23$ (measuring structural preservation; lower is better) with a CLIP score of $\approx 0.312$ (measuring text alignment; higher is better) at a runtime of 36 seconds per image.

Under identical evaluation conditions:
- Null-text inversion achieves LPIPS $\approx 0.32$ and CLIP $\approx 0.295$ , taking 160 seconds per image.
- Plug-and-Play (PnP) achieves LPIPS $\approx 0.35$ and CLIP $\approx 0.310$ , taking 206 seconds per image.
- EDICT achieves LPIPS $\approx 0.25$ and CLIP $\approx 0.288$ , taking 520 seconds per image.
- CycleDiffusion (with $\eta = 1.0$ ) achieves LPIPS $\approx 0.20$ but lower CLIP scores ( $\approx 0.292$ ), failing to make strong semantic edits.
- Deterministic DDIM inversion yields LPIPS $\approx 0.62$ and mid-way DDIM inversion yields LPIPS $\approx 0.50$ , reflecting severe loss of source image fidelity.
When integrated into Prompt-to-Prompt (P2P), edit-friendly inversion improves the LPIPS score from $\approx 0.62$ down to $\approx 0.23$ while maintaining a CLIP score of $\approx 0.305$ .
Knowl 7 — Enhancement of Zero-Shot Image-to-Image Translation with Edit-Friendly Inversion
empirical result
Integrating edit-friendly DDPM inversion into Zero-Shot Image-to-Image Translation (Zero-Shot I2I) replaces its default DDIM inversion on the modified Zero-Shot I2IT dataset (60 image-text pairs spanning Cat, Dog, Horse, and Zebra translations, evaluated with 50 diffusion steps and cross-attention scale reduced from 0.1 to 0.03).

While both the baseline Zero-Shot I2I and the edit-friendly integrated version achieve identical CLIP text-classification accuracy of 0.88, baseline Zero-Shot I2I yields blurry outputs that lose fine texture details (e.g., fur patterns and surrounding background elements), obtaining an LPIPS distance of 0.35. Integrating edit-friendly DDPM inversion recovers fine textures and sharp details, reducing the LPIPS distance from 0.35 down to 0.27 relative to the input image.

Coverage note — Supplementary hyperparameter sweeps on guidance strength and skip timesteps, as well as the 2D synthetic toy demonstration details, were omitted as they detail secondary analyses.

References

1.Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 18208–18218, 2022.
2.Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu. eDiff-I: Text-to-image diffusion models with ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022.
3.Tim Brooks, Aleksander Holynski, and Alexei A Efros. InstructPix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 18392–18402, 2023.
4.Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 22560–22570, 2023.
5.Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. DiffEdit: Diffusion-based semantic image editing with mask guidance. In International Conference on Learning Representations (ICLR), 2023.
6.Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems (NeurIPS), 34:8780–8794, 2021.
7.Adham Elarabawy, Harish Kamath, and Samuel Denton. Direct inversion: Optimization-free text-driven real image editing with diffusion models. arXiv preprint arXiv:2211.07825, 2022.
8.Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Yuxiao Chen, Di Liu, Qilong Zhangli, et al. Improving negative-prompt inversion via proximal guidance. arXiv preprint arXiv:2306.05414, 2023.
9.Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross-attention control. In International Conference on Learning Representations (ICLR), 2023.
10.Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021.
11.Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NeurIPS), pages 6840–6851, 2020.
12.Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. Journal of Machine Learning Research, 23:47:1–47:33, 2021.
13.Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. In Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
14.Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. DiffusionCLIP: Text-guided diffusion models for robust image manipulation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2416–2425, 2022.
15.Gihyun Kwon and Jong Chul Ye. Diffusion-based image translation using disentangled style and content representation. arXiv preprint arXiv:2209.15264, 2022.
16.Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. RePaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11451–11461, 2022.
17.Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations (ICLR), 2022.
18.Daiki Miyake, Akihiro Iohara, Yu Saito, and Toshiyuki Tanaka. Negative-prompt inversion: Fast image inversion for editing with text-guided diffusion models. arXiv preprint arXiv:2305.16807, 2023.
19.Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6038–6047, 2023.
20.Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning (ICLR), pages 16784–16804. PMLR, 2022.
21.Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. Zero-shot image-to-image translation. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1–11, 2023.
22.Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125, 2022.
23.Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684–10695, 2022.
24.Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. ACM SIGGRAPH 2022 Conference Proceedings, 2021.
25.Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems (NeurIPS), 35:36479–36494, 2022.
26.Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations (ICLR), 2021.
27.Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. Plug-and-play diffusion features for text-driven image-to-image translation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1921–1930, 2023.
28.Dani Valevski, Matan Kalman, Yossi Matias, and Yaniv Leviathan. UniTune: Text-driven image editing by fine tuning an image generation model on a single image. arXiv preprint arXiv:2210.09477, 2022.
29.Bram Wallace, Akash Gokul, and Nikhil Naik. EDICT: Exact diffusion inversion via coupled transformations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 22532–22541, 2023.
30.Chen Henry Wu and Fernando De la Torre. A latent space of stochastic diffusion models for zero-shot image editing and guidance. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2023.
31.Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 586–595, 2018.
32.Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris N Metaxas, and Jian Ren. Sine: Single image editing with text-to-image diffusion models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6027–6037, 2023.

Citation

MLA

Huberman-Spiegelglas, I., et al. “An Edit Friendly DDPM Noise Space: Inversion and Manipulations”. arXiv, 2023, http://arxiv.org/abs/2304.06140v3.

APA

Huberman-Spiegelglas, I., Kulikov, V., & Michaeli, T. (2023). An Edit Friendly DDPM Noise Space: Inversion and Manipulations. arXiv. http://arxiv.org/abs/2304.06140v3

Chicago

Huberman-Spiegelglas, I., V. Kulikov, and T. Michaeli. 2023. “An Edit Friendly DDPM Noise Space: Inversion and Manipulations”. arXiv. http://arxiv.org/abs/2304.06140v3.

Harvard

Huberman-Spiegelglas, I., Kulikov, V. and Michaeli, T. (2023) “An Edit Friendly DDPM Noise Space: Inversion and Manipulations”, arXiv [Preprint]. Available at: http://arxiv.org/abs/2304.06140v3.

Vancouver

1. Huberman-Spiegelglas I, Kulikov V, Michaeli T (2023) An Edit Friendly DDPM Noise Space: Inversion and Manipulations. arXiv

BibTeX

@article{hubermanspiegelglas2023edit,
  title = {An Edit Friendly DDPM Noise Space: Inversion and Manipulations},
  author = {Huberman-Spiegelglas, Inbar and Kulikov, Vladimir and Michaeli, Tomer},
  year = {2023},
  journal = {arXiv},
  url = {http://arxiv.org/abs/2304.06140v3},
  eprint = {2304.06140}
}

Metadata:arXiv

Access the Paper

This paper is available from its original source. Click below to access the PDF.

Open PDF

License: IEEE

An Edit Friendly DDPM Noise Space: Inversion and Manipulations

Abstract

Keywords

Table of Contents

Knowls

Knowl 1 — Edit-Friendly DDPM Inversion Algorithm

Knowl 2 — Structural Imprinting via Edit-Friendly Noise Space Statistics

Knowl 3 — Text-Guided Image Editing via Noise-Preserving Reverse Diffusion

Knowl 4 — Mask-Guided Color Manipulation in DDPM Latent Space

Knowl 5 — Spatial Image Shifting via Latent Noise Code Translation

Knowl 6 — Comparative Evaluation on Real-Image Text-Guided Editing Benchmarks

Knowl 7 — Enhancement of Zero-Shot Image-to-Image Translation with Edit-Friendly Inversion

References

Citation

BibTeX

Access the Paper