reviews: 
Comments: In this study, the authors investigate the limitations of Text-guided diffusion models (TDMs) and propose a novel approach called SAGE to systematically explore and understand these failures. TDMs are commonly used for image generation but can exhibit unexpected issues. The study identifies four key failure modes that have not been extensively studied before: 
1) TDMs can generate images that fail to accurately represent the semantics of the input text prompts. The authors discuss the causes and potential solutions for this issue.
2) Some regions in the latent space of TDMs lead to distorted images, regardless of the text prompt. This suggests that certain parts of the latent space are not well-structured. 
3) Latent samples can produce natural-looking images that are unrelated to the given text prompt, indicating a potential misalignment between the latent and prompt spaces.
4) The addition of a single adversarial token embedding to input prompts can lead to the generation of various specified target objects with minimal impact on CLIP scores, highlighting the fragility of language representations.

Overall, the SAGE method efficiently explores both the discrete language space and the complex latent space, shedding light on these TDM failure modes and offering insights into potential solutions.
Strengths: 1. This work is interesting and systemically studying the failure mode of text to image generation model is an important and sometimes overlooked area of research.
2. Comprehensive human study provides important validation on the results.
Weaknesses: 1. It's well known that text image generative model can not handle multiple objects especially when keywords are relational and containing novel actions, for instance, “A photo of a cat fighting a fish”. If it's a novel scenario that was not seen in the training set, text to image models often produce only one of the subjects or some blended versions.

2. Most of the proposed failures seem contrived, which are not really issues concerning day to day usage of text-to-image models, especially the ones in latent space, where explicitly optimization/distortion need to be performed on the latent vector, such that it will produce distort the image. If sampled naturally, this event is very unlikely to happen.

3. The arguments are very hand-waving. Not enough evidence/details are provided to support the claims. 
    1) "Furthermore, we demonstrate that these failure examples are not isolated spots in the latent space; rather, they exist as connected regions, wherein any sample will generate similar distorted images (see Supp. B.3). It shows that these distorted images can be generated with meaningful probability under Gaussian sampling, and the probability can be computed by the probability density function."
    No calculation has been shown/referred to neither in the main body nor the appendix on the probability. In addition, the paper demonstrated only QQ-plot and statistic of three prompts. It's unclear whether it's a generalized phenomenon. 
     2)  "Tab. 2 compares the normalized size of these failure regions and the probability of ..." No details are provided on how the failure regions were calculated/estimated. How exactly the algorithms in PoseExaminer is adopted remains questionable.



One side note but not the main concerns I have on this paper: the presentation lacks structure and appears to be very messy. The authors seem to be ambitious in delivering many things all at once but failed to fufill any of the promises.
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 1 poor