For image-to-image with ControlNet, you'll need to use the:  
- [`KandinskyV22PriorEmb2EmbPipeline`] to generate the image embeddings from a text prompt and an image
- [`KandinskyV22ControlnetImg2ImgPipeline`] to generate an image from the initial image and the image embeddings  
Process and extract a depth map of an initial image of a cat with the `depth-estimation` [`~transformers.Pipeline`] from 🤗 Transformers:  
```py
import torch
import numpy as np