#!pip install -q diffusers transformers accelerate Kandinsky 2.1 and 2.2 usage is very similar! The only difference is Kandinsky 2.2 doesn’t accept prompt as an input when decoding the latents. Instead, Kandinsky 2.2 only accepts image_embeds during decoding.  Kandinsky 3 has a more concise architecture and it doesn’t require a prior model. This means it’s usage is identical to other diffusion models like Stable Diffusion XL.  Text-to-image To use the Kandinsky models for any task, you always start by setting up the prior pipeline to encode the prompt and generate the image embeddings. The prior pipeline also generates negative_image_embeds that correspond to the negative prompt "". For better results, you can pass an actual negative_prompt to the prior pipeline, but this’ll increase the effective batch size of the prior pipeline by 2x.