reviews: 
Comments: The authors mainly proposed to use the same T2I architecture to do inpainting. To properly feed in the known image conditions, they propose to use a pixel-wise noisy injection with different levels, and a spatial-variant timesteps map. Therefore, during training and inference, it allows to add minimum levels of noises to known image regions, while only diffuse the unknown regions following the normal diffusion process. The idea is simple, effective and easy to implement. Extensive experiments also demonstrate its effectiveness in maintaining good inpainting quality while reducing inpainting complexity compared to RePaint.
Strengths: - The design of spatial-variant time embedding for inpainting tasks bring two major advantages: (1) no architecture change form T2I, and (2) relative faster inpainting process. 
- The authors provide extensive and scientific evaluation on different masks, and the proposed method achieved good inpainting quality.
Weaknesses: - Repaint seems not the state-of-the-art inpainting model, and recently models based on latest latent diffusion model or DiT framework achieve much better results, for instance, powerpaint(https://powerpaint.github.io/). Some comparison needs to be updated. 
- Since the model is trained on diffusion-based framework, is it still necessary to list ConvNet-based or transformer-based model in the comparison? They will be not comparable given the inference speed and model capacity. The comparisons should focus on latest diffusion-based model in quality and speed. 
- To the best of the reviewer's knowledge, recent diffusion-based inpainting is no longer using the way of RePaint to do inference. The problems mentioned by the authors like 'consistency' are no longer an issue. It can be simply achieved by adding an additional conditions concatenated with the noisy input in UNet, or adding additional tokens for DiT-based framework. Some controlnet-based method do have the composition and harmonization problems.
- It is not clear how the framework can be generalized to image editing, when the mask is soft or we need some levels of content preservation under the mask region. Without having the original image as a condition, it will be very hard to generalize the model to other editing tasks, like color changing. Therefore, the proposed framework will be limited as a foundation model, or used to finetuned for other tasks. 
- The masks used in inpainting literature are mostly not suitable for real practical application because they are not used by the real inpainter users. Better to try some object-shaped masks and use those for user study. 
- The proposed method may lack mask precision in a latent diffusion framework, especially for tasks like background replacement. For latent diffusion, the masks are usually applied to the pixel space, and the masked image will be encoded using the latent encoder for compression, and server as the condition. In this case, the latent diffusion model can still learn to reconstruct the high-precision mask boundary to preserve the details around the boundary. If we apply the low-res mask in the latent and apply different t in the precision of latent mask, it won't be able to leverage the advantages of the high-quality encoding.
Recommendation: 6
Reviewer confidence: 5
Soundness/correctness: 3
Clarity: 2