t and image embeddings. The mapping provides better text-image alignment and it is used with the text embeddings during training, leading to higher quality results. Finally, Kandinsky 2.1 uses a Modulating Quantized Vectors (MoVQ) decoder - which adds a spatial conditional normalization layer to increase photorealism - to decode the latents into images. Kandinsky 2.2 improves on the previous model by replacing the image encoder of the image pri