The way sample steps are selected in the schedule can affect the quality of the generated image, especially with respect to [rescaling the noise schedule](#rescale-noise-schedule), which can enable a model to generate much brighter or darker images. Diffusers provides three timestep spacing methods:  
- `leading` creates evenly spaced steps
- `linspace` includes the first and last steps and evenly selects the remaining intermediate steps
- `trailing` only includes the last step and evenly selects the remaining intermediate steps starting from the end  
It is recommended to use the `trailing` spacing method because it generates higher quality images with more details when there are fewer sample steps. But the difference in quality is not as obvious for more standard sample step values.  
```py
import torch
from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler

pipeline = StableDiffusionXLPipeline.from_pretrained(
"SG161222/RealVisXL_V4.0",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing")

prompt = "A cinematic shot of a cute little black cat sitting on a pumpkin at night"
generator = torch.Generator(device="cpu").manual_seed(2487854446)
image = pipeline(
prompt=prompt,
negative_prompt="",
generator=generator,
num_inference_steps=5,
).images[0]
image
```  
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/trailing_spacing.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">trailing spacing after 5 steps</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/leading_spacing.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">leading spacing after 5 steps</figcaption>
</div>
</div>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/scheduler_features.md