The simplest optimization to run IF faster is to move all model components to the GPU.  
```py
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")
```  
You can also run the diffusion process for a shorter number of timesteps.  
This can either be done with the `num_inference_steps` argument:  
```py
pipe("<prompt>", num_inference_steps=30)
```  
Or with the `timesteps` argument:  
```py
from diffusers.pipelines.deepfloyd_if import fast27_timesteps

pipe("<prompt>", timesteps=fast27_timesteps)
```  
When doing image variation or inpainting, you can also decrease the number of timesteps
with the strength argument. The strength argument is the amount of noise to add to the input image which also determines how many steps to run in the denoising process.
A smaller number will vary the image less but run faster.  
```py
pipe = IFImg2ImgPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")

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")

pipe.text_encoder = torch.compile(pipe.text_encoder, mode="reduce-overhead", fullgraph=True)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/deepfloyd_if.md