Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing

Summary

This paper focuses on improving the stability of DDIM inversion to produce a better noise space for image editing. They found that the key issue is the singularity schedule, and try to overcome this issue by adopting the logistic schedule rather than the linear schedule.

Strengths

1 This paper found that the singularity issue leads to error accumulation in DDIM inversion, leading to failed or unstable editing results. 2. This paper proposes to adopt the logistic schedule to solve the singularity issue, which is simple but useful. 3. This paper gives many theoretical analyses and visualization results, demonstrating its superiority.

Weaknesses

1. The original denoising diffusion process has provided the sigmoid schedule, as shown in line 461 of (https://github.com/lucidrains/denoising-diffusion-pytorch/blob/main/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py), which is similar to the proposed logistic schedule. The authors should clarify the diversity between these two schedules. Can the beta sigmoid schedule also solve the proposed singularity problem? 2. The logistic scheduler aims to better preserve the content of the original image. Will the editing ability be decreased if much more original content is preserved? There seems to be a contradiction between the original content preservation and the editing ability for the target prompt. 3. In Fig4, why does the noise map not cover the image for Linear and Cosine schedules? 4. In Tab.2, the results of Null-text, NPI, and Direct should also be provided for better comparison.

Questions

Please refer to the weaknesses

Rating

5

Confidence

4

Soundness

2

Presentation

2

Contribution

2

Limitations

The authors have discussed the limitations and broader impacts.

Summary

This paper introduces a novel noise schedule called Logistic Schedule for text-guided image editing. The authors identify that traditional DDIM inversion methods suffer from accumulated prediction errors, leading to suboptimal content preservation and edit fidelity. To address this, this paper proposes the Logistic Schedule, which eliminates singularities, improves inversion stability, and provides a better noise space for image editing, resulting in higher content preservation and edit fidelity.

Strengths

1. The simple Logistic Schedule method effectively addresses the singularity issues in traditional noise schedules. 2. The method is compatible with various existing editing techniques without the need for additional retraining, making it highly applicable. 3. The proposed method is validated through experiments on eight different editing tasks using approximately 1600 images, demonstrating superior performance in content preservation and edit fidelity. 4. The paper provides a thorough theoretical analysis of the failures in DDIM inversion, offering a well-founded solution.

Weaknesses

1. **Limited improvements**: Although there are improvements in the quantitative comparisons, I am concerned that the generated results do not show significant improvement than other schedule methods. The differences require careful observation to notice. 2. **Limited application scope**: While the method is broadly applicable, the current experimental validation focuses primarily on image editing tasks, leaving its effectiveness in other types of tasks.

Questions

1. Does the Logistic Schedule consistently improve performance across all existing editing methods? Are there specific editing methods that are incompatible with the Logistic Schedule? 2. How much do the parameters of the Logistic Schedule affect the results? Is there a specific method for parameter optimization? 3. Can you provide some inversion results without text input (w/o editing)？

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

See weaknesses.

Summary

The paper proposes a noise schedule "Logistic Schedule" to solve the singularity problem in the traditional noise schedules for text-guided diffusion models for image editing. It aims to improve the stability of the DDIM inversion process, reducing noise prediction errors and enhancing content preservation and edit fidelity without additional retraining. It is validated on eight editing tasks and show performance in content preservation and edit fidelity compared to the original noise schedules.

Strengths

The experiments cover a wide range of editing tasks which demonstrating the versatility of the Logistic Schedule. The method does not require retraining existing models, which making it easily applicable to current workflows. It is an easy solution to a well-defined problem.

Weaknesses

The author mainly compared Logistic Schedule with linear and cosine schedules. Additional comparisons with other noise schedules or inversion techniques could be shown to further strengthen the point.

Questions

Can the Logistic Schedule be used in or combined with training-based methods to further improve the performance?

Rating

6

Confidence

3

Soundness

4

Presentation

4

Contribution

4

Limitations

The authors have discussed the limitations of their work, along with potential negative societal impacts.

Summary

The authors introduce the Logistic Schedule, a novel noise schedule designed to resolve the singularity problem in traditional noise schedules, thereby enhancing inversion stability and reducing noise prediction errors. This new schedule enables more faithful editing that preserves the original content of the source image without requiring additional retraining. Experiments across eight editing tasks demonstrate the Logistic Schedule's superior performance in content preservation and edit fidelity, highlighting its adaptability and effectiveness.

Strengths

1. The paper provides a thorough analysis of the primary contributors to error accumulation in DDIM inversion, offering a detailed explanation of how the Logistic Schedule resolves these issues. 2. Extensive experiments across multiple datasets demonstrate the Logistic Schedule's effectiveness in improving content preservation and edit fidelity, showcasing its robustness and adaptability. 3. The related code and models will be open-sourced.

Weaknesses

1. The authors mentioned they extend current datasets as their training dataset. Will this be open-sourced?

Questions

Refer to weaknesses. No more questions.

Rating

7

Confidence

4

Soundness

3

Presentation

4

Contribution

3

Limitations

1. The authors mentioned they extend current datasets as their training dataset. Will this be open-sourced?

Further Reply to Reviewer j2Zw

Dear Reviewer j2Zw, We greatly appreciate your satisfaction with our work and the effort you invested in your review. We will include the discussion in the final manuscript. We enjoy communicating with you and appreciate your efforts!