1.	Note that the paper mainly focuses on SD-based (SD 2.1, SDXL) models. These models are mostly the same styles, e.g., similar network structures and traditional denoising training strategies. Is there any possibility that the MI tuning incorporated with flow-based models like DiT-based models (SD3, Pixart series or so). And it is interesting to see if the proposed MI tuning behaves different with different types of models.
2.	The evaluations on MI mainly focus on only simple semantic concepts like color, shape and texture. Is MI-tuning sensitive to object numbers or so?
3.	The paper fixes the denoising steps to 50 when inferencing an image, are there any differences in performance of MI-tuning when using different steps except 50?
4.	In quantitative analysis of Sect. 3.1, the paper mentions that the point-wise MI ranks images and select 1st, 25th and 50th as the representative images. Why the three images are representative? This needs more detailed explanations. Also, the reason of the selection needs quantitative analysis.
5. Some of the ablations mentioned in previous sections are hard to locate in the following contents, the writing can be improved in this part.
5. Some of the ablations mentioned in previous sections are hard to locate in the following contents, the writing can be improved in this part.