We thank the reviewer for the comments and suggestions. We appreciate the time you spent on the paper. Below we address the concerns and comments that you have provided.
**Q**: *For Q1, I appreciate the potential of TGDP to adapt to more complex tasks and the discussion on their similarities and differences. But I'm still confused about technique details. Could the authors provide more details about the design of $c_w(x,y)$ in case when $c_f\notin\{c_t\}$? In this case, should $y$ stands for the embedding of $c_f$ or $\{c_f,c_t\}$? Since [1] is also applicible to the case with limited data, I expect the authors could provide a comprehensive solution or discuss it in the limitation section.*
**A**: Thank you very much for your question. In [1], text prompts $c_f$ and a task-specific condition $c_t$ can have different modalities, e.g., image condition. We believe TGDP is still general enough to deal with this case. We propse a possible way in the following:
1) When $c_t$ has the same data modality as $c_f$, we can use the same pre-trained Language Model to generate the embedding, i.e., $y_{\text{source}}=\text{Encoder}(c_f)$ for the data from the source domain and $y_{\text{target}}=\text{Encoder}(c_f)+\text{Encoder}(c_t)$ for the data from the target domain.
2) When $c_t$ has different data modalities as $c_f$, e.g., image condition, we rely on the aligned encoder to generate the embedding, e.g., ALIGN [Jia et. al. 2021] in vision-language tasks, i.e., $y_{\text{source}}=\text{Encoder}_1(c_f)$ for the data from the source domain and $y _{\text{target}}=\text{Encoder} _1(c_f)+\text{Encoder} _2(c_t)$ for the data from the target domain.
Then, the domain classifier $c_w(x,y)$ is used to distinguish the $(x_{\text{source}}, y_{\text{source}})$ and $(x_{\text{target}}, y_{\text{target}})$, thereby estimating the density ratio.
The approach in [1] addresses limited data scenarios by using zero convolution layers (i.e., 1×1 convolution layers with both weight and bias initialized to zero), which mitigates instability during fine-tuning. This is distinct from our methodology, which leverages the smaller sample complexity of the classifier/density ratio estimator compared with the diffusion model. We will include a discussion of the similarities and differences with [1] in the revised version.
**Q**: *For Q7, I understand which encoder to use, but still concerned why Figure 3(b) and Figure 3(c) looks similar (even if there is an improvement over both diversity and FID). Could the authors provide more details?*
**A**: Thank you very much for your question. We observe a significant difference in the distribution patterns of the purple and black classes. In Figure 3(c), the black class is more concentrated compared to Figure 2(c), suggesting that the synthetic data may lie on the correct manifold. However, given that T-SNE reduces a 128-dimensional feature space to 2 dimensions, we must be careful not to overinterpret these visualizations. The numerical results will provide a more reliable basis for conclusions.
[Jia et. al. 2021]: Jia, C., Yang, Y., Xia, Y., Chen, Y., Parekh, Z., Pham, H., Le, Q.V., Sung, Y., Li, Z., \& Duerig, T. Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. ICML 2021.