You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We use the Imagen saharia2022photorealistic  text-guided synthesis model as a backbone. Since the composition and geometry are mostly determined at the 64\times 64 resolution, we only adapt the text-to-image diffusion model, using the super-resolution process as is.Recall that each diffusion step t consists of predicting the noise \epsilon from a noisy image z_{t} and text embedding \psi(\mathcal{P}) using a U-shaped network ronneberger2015u . At the final step, this process yields the generated image \mathcal{I}=z_{0}.Most importantly, the interaction between the two modalities occurs during the noise prediction, where the embeddings of the visual and textual features are fused using Cross-attention layers that produce spatial attention maps for each textual token. More formally, as illustrated in fig. 3(Top), the deep spatial features of the noisy image \phi(z_{t}) are projected to a query matrix Q=\ell_{Q}(\phi(z_{t})), and the textual embedding is projected to a key matrix K=\ell_{K}(\psi(\mathcal{P})) and a value matrix V=\ell_{V}(\psi(\mathcal{P})), via learned linear projections \ell_{Q},\ell_{K},\ell_{V}.The attention maps are thenM=\text{Softmax}\left(\frac{QK^{T}}{\sqrt{d}}\right),(1)where the cell M_{ij} defines the weight of the value of the j-th token on the pixel i, and where d is the latent projection dimension of the keys and queries. Finally, the cross-attention output is defined to be \widehat{\phi}\left(z_{t}\right)=MV, which is then used to update the spatial features \phi(z_{t}). In this paper, we introduce an intuitive and powerful textual editingmethod to semantically edit images in pre-trained text-conditioned diffusion models via Prompt-to-Prompt manipulations. To do so, we dive deep into the cross-attention layers and explore their semantic strength as a handle to control the generated image. Specifically, we consider the internal cross-attention maps, which are high-dimensional tensors that bind pixels and tokens extracted from the prompt text. We find that these maps contain rich semantic relations which critically affect the generated image.

Question: Attention maps are calculated by query of spatial feature of the noisy image (\phi(z_t)) and key of textual embedding (\psi(P)). Is it true?
True, as attention maps are calculated by using deep spatial features of a noisy image which is projected to a "Query Matrix" and the textual embedding is projected to a "Key Matrix" and a "Value Matrix", then finally attentions maps calculated by learned linear projections of Query Matrix, Key Matrix and Value Matrix.