>>> repo_id = "google/ddpm-cat-256"
>>> model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True)
```  
To access the model parameters, call `model.config`:  
```py
>>> model.config
```  
The model configuration is a 🧊 frozen 🧊 dictionary, which means those parameters can't be changed after the model is created. This is intentional and ensures that the parameters used to define the model architecture at the start remain the same, while other parameters can still be adjusted during inference.