[D] How does the UNet use cross attention with CLIP text embeddings to produce the final noisy image?

At the moment, I have a decent understanding I feel as to how cross attention is actually employed in a Unet at each upsampling and downsampling block. Eventually, the cross attention seems to produce these attention-like heat maps which basically indicate the relevancy of each pixel in the image to the words in the prompt.

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My confusion lies in how this attention map is used to produce the final image. I.e how is the cross attention integrated with the …

attention block clip downsampling embeddings eventually heat image machinelearning maps moment text understanding unet