Kandinsky            The Kandinsky models are a series of multilingual text-to-image generation models. The Kandinsky 2.0 model uses two multilingual text encoders and concatenates those results for the UNet. Kandinsky 2.1 changes the architecture to include an image prior model (CLIP) to generate a mapping between text 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 prior model with a larger CLIP-ViT-G model to improve quality. The image prior model was also retrained on images with different resolutions and aspect ratios to generate higher-resolution images and different image sizes. Kandinsky 3 simplifies the architecture and shifts away from the two-stage generation process involving the prior model and diffusion model. Instead, Kandinsky 3 uses Flan-UL2 to encode text, a UNet with BigGan-deep blocks, and Sber-MoVQGAN to decode the latents into images. Text understanding and generated image quality are primarily achieved by using a larger text encoder and UNet. This guide will show you how to use the Kandinsky models for text-to-image, image-to-image, inpainting, interpolation, and more. Before you begin, make sure you have the following libraries installed:   Copied # uncomment to install the necessary libraries in Colab