Kati8/ICLR_2023_2025 huggingface dataset:
authors: Tsiry Mayet
authors: Pourya Shamsolmoali
authors: Simon Bernard
authors: Eric Granger
authors: Romain HÉRAULT
authors: Clement Chatelain
reviews: 
Comments: The authors proposed a new training scheme of diffusion-based inpainting model that can perfectly maintain the regions inside the mask.
Strengths: 1. The training strategy is novel and the performance is good.
Weaknesses: 1. Any evaluations about how good the unchanged regions are maintained?
2. I admit that the training scheme is novel, but I am not sure if the novelty is enough for publishing. Since the entire method is a slight modification of existing diffusion model.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 4
reviews: 
Comments: This paper proposes a method that can do faster inpainting in diffusion models by introducing time-aware denoising process.
Strengths: 1. It can generate high-quality inpainting on various mask sizes and shapes.
2. It is faster compared to other stare-of-art methods.
3. It has extensive evaluations
Weaknesses: 1. The visual result is not much better than other baselines
2. Even though they have many evaluation results, the improvement in image quality with scores in not much.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2
reviews: 
Comments: The paper presents a novel method for speeding up diffusion-based inpainting. It changes the noised known region condition in the RePaint paper into a clean one for condition. To support this change, it introduces a trained diffusion model that supports a spatial-varying timestep condition to mark the known and missing part of the image. This new design also allows the method to avoid the repeated sampling process in RePaint, greatly increasing the speed. Quantitative results also demonstrate the proposed method performs on par or better with existing methods.
Strengths: 1. Training the diffusion model to directly use clean known parts as the diffusion condition, avoiding complex modifications to the diffusion process. This approach significantly speeds up the method compared to existing approaches, enhancing efficiency without compromising quality.
2. Introduction of a novel spatial-varying timestep condition for injecting the known region mask. This innovative technique allows for more precise control over the inpainting process, resulting in improved output quality.
3. Comprehensive testing on multiple diverse datasets, demonstrating superior performance (for both quality and diversity) across various scenarios. The extensive evaluation provides strong evidence for the method's robustness and effectiveness in different contexts.
Weaknesses: The paper is in general good, I only have concerns on its generality: 

Since the proposed method requires training and is not trained with data besides CelebA-HQ, ImageNet1K, and Places2, it probably cannot generalize to open-world images like LaMa.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper proposes a way to perform inpainting by leveraging pre-trained pixel diffusion model. The authors fine-tune a diffusion model such that instead of inputting a single scalar that represents the tilmestep of the noise in the diffusion process, the model inputs a map, representing per-pixel tilmesteps. Then, at inference, this allows them to always input the fully denoised image in the known regions while iteratively denoising the masked areas. Unlike the main baseline, RePaint, this process is much faster, as it does not require multiple diffusion passes to synchronize the known and unknown regions.
Strengths: The proposed approach is quite simple but at the same time intuitive and effective. The idea of varying the diffusion at a pixel level is a promising one, both for novel applications (such as inpainting) as well as for a more general approach to accelerating diffusion inference.
Weaknesses: My main concern has to do with lack of comparison to or discussion of some more recent and relevant SOTA diffusion-based inpainting approaches. For instance, LatentPaint [Corneanu et al., WACV 2024] also a propose an approach that requires fine-tuning with a fast inference pass (compared to RePaint). Additionally, "Blended Latent Diffusion" [Avrahami et al., SIGGRAPH 2023] and "Uni-paint" [Yang et al., MM 2023] leverage latent diffusion models for an inpainting task (while the former does not explicitly target unconditional inapinting, it ca be used for that purpose).  It would be very helpful to see comparisons to some of these recent works as well as a high-level discussion of their differences in order to evaluate the proposed method.

On a more minor note, the presentation and exposition could be improved. Figure 1, which illustrates the proposed approach in contrast to RePaint, is somewhat difficult to follow and includes notation that is not defined until much later. In contrast, the idea behind RePaint is explained multiple times in various sections throughout the paper and could be truncated.

I would also encourage the authors to cite and discuss "Differential Diffusion: Giving Each Pixel Its Strength" [Levin and Fried, 2023]. Given that this work does not appear in a peer-reviewed publication, I am not considering it in my rating, but given its close relevance to the topic, it would be good to mention.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3