image = pipeline("a cute dog running on the grass", num_inference_steps=30).images[0]
image.save("dog.png")
```  
If placing all the model-level components on the GPU at once is not feasible, use [`~DiffusionPipeline.enable_model_cpu_offload`] to help you:  
```diff
- pipeline.to("cuda")
+ pipeline.enable_model_cpu_offload()
```  
In general, we recommend sharding when a checkpoint is more than 5GB (in fp32).