reviews: 
Comments: This paper focuses on scaling diffusion transformers for text-to-image synthesis. It proposes EC-DIT, a new family of Mixture-of-Experts (MoE) models with adaptive expert-choice routing. By leveraging the computational heterogeneity of image generation, EC-DIT adaptively allocates compute to understand input texts and generate image patches, enabling heterogeneous computation aligned with text-image complexity. It scales up to 97 billion parameters and shows significant improvements in training convergence, text-to-image alignment, and overall generation quality compared to dense and conventional MoE models. Its contributions include introducing EC-DIT for adaptive computation in text-to-image synthesis, achieving promising performance at scale with faster loss convergence and better quality, and conducting comprehensive experiments to validate its superiority and demonstrate effective adaptive compute allocation.
Strengths: 1. EC-DIT can scale up to 97 billion parameters, achieving a state-of-the-art GenEval score of 71.68% in text-to-image synthesis.
2. The adaptive expert-choice routing is a novel design. It enables efficient compute allocation by having experts select relevant tokens, ensuring load balance without an extra load-balancing loss. This approach better utilizes computational resources, adapting to the complexity of different image regions and text, and improving overall performance and efficiency in handling inputs.
3. By embedding timestep and using cross-attention modules, EC-DIT effectively integrates global information, which allows the model to understand the context of the image and its relationship with the text, guiding compute allocation at different stages. It results in hierarchical processing, enhancing image quality and text-to-image alignment, and making the generation process more context-aware.
Weaknesses: 1. The best score for SD3 (Esser et al., 2024) w/ DPO is 74% in the original paper, which is actually higher than that of EC-DIT (71.68%). Even with the value 71% listed in Table 2,  EC-DIT is only gaining marginally. Given its additional overhead, it is hard to see the value of the gain. 
2. The difference between the theoretical and actual inference overhead (e.g., for EC-DIT-M, the theoretical is around 3% but the actual is 23%) indicates that there may be room for optimization in the inference process. The authors may want to explore the factors contributing to this overhead or propose specific strategies for mitigating it, which could further enhance the practical applicability of the model, especially for applications where real-time or fast inference is crucial.
3. The generalizability of the adaptive expert-choice routing is unclear. The performance of EC-DIT is mainly demonstrated in the context of text-to-image synthesis. How would it fit into the task of pre-training mixed-modal, early-fusion language models? It would be interesting to explore its effectiveness and efficiency in other related tasks. 
4. This paper could benefit from more detailed ablation studies on specific components of the proposed model. For example, understanding the individual contributions of the timestep embedding, cross-attention modules, and the expert-choice routing mechanism in more detail. This lack of in-depth analysis makes it harder to understand which parts are truly driving its performance.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3