pipeline = AutoPipelineForText2Image.from_pretrained("path/to/saved/model", torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()

prompt="A robot naruto, 4k photo"
image = pipeline(prompt=prompt).images[0]
```  
For the decoder model, you can also perform inference from a saved checkpoint which can be useful for viewing intermediate results. In this case, load the checkpoint into the UNet:  
```py
from diffusers import AutoPipelineForText2Image, UNet2DConditionModel