reviews: 
Comments: This study presents an architecture designed for efficient text-to-image generation. The first text-conditional LDM produces a low-resolution latent map (Stage C), which is used for the second LDM for a high-resolution latent map (Stage B). This map is fed into a VQGAN-based decoder to produce a final image (Stage A), as performed in other LDM and SD models.
Strengths: - The key distinction of this work from previous LDM and SD lies in the introduction of a two-stage latent diffusion process, facilitated by the Semantic Compressor. The authors argue that the additional guidance from low-resolution latent maps (Stage C) can help yield good results under a smaller training budget, compared to the conventional LDM framework's Stage B and Stage A.
- I appreciate the efforts put into designing and training the Semantic Compressor and Stage C. This appears to be far from straightforward, representing methodological and empirical contributions.
Weaknesses: - I'm uncertain about the inference efficiency of this approach, as it appears to add an "extra" computation (Stage C) on top of the conventional LDM and SD (Stage B and Stage A). In particular, how could the proposed method achieve better inference time than SD-v2.1 in Figure 4? A detailed computational comparison would be beneficial for different components in the system (the text encoder, LDM(s), and image decoder) instead of just an overall process. 
- I think the Baseline LDM (trained for 25,000 GPU-hours (same as Stage C)) needs to be trained for GPU hours of Stage B + Stage C, given that both stages contribute to the final latent representation of the proposed method. More importantly, a baseline with the same architecture of the upper part in Stage B, Figure 3 (i.e., a conventional LDM obtained by just removing Stage C and the below part of Stage B, Figure 3) seems necessary to show the benefit of the proposed approach.
- The parameter values in Table 2 might confuse readers due to inconsistencies in their presentation. For some models, like LDM, the table seems to consider all the parameters, including the text encoder. Yet, for other models such as the proposed method and SD, only the diffusion parameters are listed. I strongly suggest presenting the "total" parameters (because several components work together for a single text-to-image system) or, preferably, detailing both the "total" and diffusion parameters separately.
- The popular MS-COCO benchmark has been conducted at the resolution of 256x256. Why did the authors change the resolution for IS in Table 2? In my experience, the resolution affects the metric scores. Furthermore, for some models (LDM, DALL-E, CogView), the IS results at 256x256 were reported. I also highly recommend including CLIP score.
- I think the description “By conditioning Stage B on low-dimensional latent representations, we can effectively decode images from a 16x24x24 latent space to a resolution of 3x1024x1024, resulting in a total spatial compression of 42:1” in page 5 looks incorrect or overclaimed, because Stage B also takes a high-resolution latent map, 4x256x256, as input.
- The behavior of the proposed model seems less explored. The representative analysis with different classifier-free guidance scales to show the tradeoff between FID-CLIP score [SD, GLIDE, Imagen] is missing. Furthermore, it would be interesting to analyze the tradeoff between the number of sampling steps and generation quality.
- Minors: The paper is fairly easy to follow, but I think a careful proofreading is necessary: many typos exist.
  - x -> × (in many parts)
  - In stage B, we utilize a -> Stage B
  - Inception Score (IC) -> IS
Recommendation: 8: accept, good paper
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: 3 good
Clarity: 3 good