For an additional speed-up, use `torch.compile` to generate multiple images in <1 second:  
```diff
import torch
from diffusers import ConsistencyModelPipeline

device = "cuda"
# Load the cd_bedroom256_lpips checkpoint.
model_id_or_path = "openai/diffusers-cd_bedroom256_lpips"
pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
pipe.to(device)

+ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

# Multistep sampling
# Timesteps can be explicitly specified; the particular timesteps below are from the original GitHub repo:
# https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L83
for _ in range(10):
image = pipe(timesteps=[17, 0]).images[0]
image.show()
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consistency_models.md