[[open-in-colab]]  
Getting the [`DiffusionPipeline`] to generate images in a certain style or include what you want can be tricky. Often times, you have to run the [`DiffusionPipeline`] several times before you end up with an image you're happy with. But generating something out of nothing is a computationally intensive process, especially if you're running inference over and over again.  
This is why it's important to get the most *computational* (speed) and *memory* (GPU vRAM) efficiency from the pipeline to reduce the time between inference cycles so you can iterate faster.  
This tutorial walks you through how to generate faster and better with the [`DiffusionPipeline`].  
Begin by loading the [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) model:  
```python
from diffusers import DiffusionPipeline

model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipeline = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
```  
The example prompt you'll use is a portrait of an old warrior chief, but feel free to use your own prompt:  
```python
prompt = "portrait photo of a old warrior chief"
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/stable_diffusion.md