The `sigmas` parameter is the amount of noise added at each timestep according to the timestep schedule. Like the `timesteps` parameter, you can customize the `sigmas` parameter to control how much noise is added at each step. When you use a custom `sigmas` value, the `timesteps` are calculated from the custom `sigmas` value and the default scheduler configuration is ignored.  
For example, you can manually pass the [sigmas](https://github.com/huggingface/diffusers/blob/6529ee67ec02fcf58d2fd9242164ea002b351d75/src/diffusers/schedulers/scheduling_utils.py#L55) for something like the 10-step AYS schedule from before to the pipeline.  
```py
import torch

from diffusers import DiffusionPipeline, EulerDiscreteScheduler

model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.0]
prompt = "anthropomorphic capybara wearing a suit and working with a computer"
generator = torch.Generator(device='cuda').manual_seed(123)
image = pipeline(
prompt=prompt,
num_inference_steps=10,
sigmas=sigmas,
generator=generator
).images[0]
```  
When you take a look at the scheduler's `timesteps` parameter, you'll see that it is the same as the AYS timestep schedule because the `timestep` schedule is calculated from the `sigmas`.  
```py
print(f" timesteps: {pipe.scheduler.timesteps}")
"timesteps: tensor([999., 845., 730., 587., 443., 310., 193., 116.,  53.,  13.], device='cuda:0')"
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/scheduler_features.md