image = pipe(image=image, prompt="<prompt>", strength=0.3).images
```  
You can also use [`torch.compile`](../../optimization/torch2.0). Note that we have not exhaustively tested `torch.compile`
with IF and it might not give expected results.  
```py
from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")