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. 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: What does "interaction between the pixels to the text embedding through the diffusion process" mean?
To answer this question we need to recall the diffusion process, which is in order to predict the noise of an image we have two inputs 1- noisy image and 2- text embedding, and the interaction between the two inputs are fused using Cross-attention layers that produce spatial attention maps for each textual token. and that is what is meant by the interaction between pixels to text embedding.