Community pipelines are a really fun and creative way to extend the capabilities of the original pipeline with new and unique features. You can find all community pipelines in the [diffusers/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) folder with inference and training examples for how to use them.  
This section showcases a couple of the community pipelines and hopefully it'll inspire you to create your own (feel free to open a PR for your community pipeline and ping us for a review)!  
> [!TIP]
> The [`~DiffusionPipeline.from_pipe`] method is particularly useful for loading community pipelines because many of them don't have pretrained weights and add a feature on top of an existing pipeline like Stable Diffusion or Stable Diffusion XL. You can learn more about the [`~DiffusionPipeline.from_pipe`] method in the [Load with from_pipe](custom_pipeline_overview#load-with-from_pipe) section.  
<hfoptions id="community">
<hfoption id="Marigold">  
[Marigold](https://marigoldmonodepth.github.io/) is a depth estimation diffusion pipeline that uses the rich existing and inherent visual knowledge in diffusion models. It takes an input image and denoises and decodes it into a depth map. Marigold performs well even on images it hasn't seen before.  
```py
import torch
from PIL import Image
from diffusers import DiffusionPipeline
from diffusers.utils import load_image

pipeline = DiffusionPipeline.from_pretrained(
"prs-eth/marigold-lcm-v1-0",
custom_pipeline="marigold_depth_estimation",
torch_dtype=torch.float16,
variant="fp16",
)

pipeline.to("cuda")
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/community-marigold.png")
output = pipeline(
image,
denoising_steps=4,
ensemble_size=5,
processing_res=768,
match_input_res=True,
batch_size=0,
seed=33,
color_map="Spectral",
show_progress_bar=True,
)
depth_colored: Image.Image = output.depth_colored
depth_colored.save("./depth_colored.png")
```  
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/community-marigold.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/marigold-depth.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">colorized depth image</figcaption>
</div>
</div>  
</hfoption>
<hfoption id="HD-Painter">  
[HD-Painter](https://hf.co/papers/2312.14091) is a high-resolution inpainting pipeline. It introduces a *Prompt-Aware Introverted Attention (PAIntA)* layer to better align a prompt with the area to be inpainted, and *Reweighting Attention Score Guidance (RASG)* to keep the latents more prompt-aligned and within their trained domain to generate realistc images.  
```py
import torch
from diffusers import DiffusionPipeline, DDIMScheduler
from diffusers.utils import load_image

pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5-inpainting",
custom_pipeline="hd_painter"
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter.jpg")
mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter-mask.png")
prompt = "football"
image = pipeline(prompt, init_image, mask_image, use_rasg=True, use_painta=True, generator=torch.manual_seed(0)).images[0]
image
```  
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter.jpg"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hd-painter-output.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image</figcaption>
</div>
</div>  
</hfoption>
</hfoptions>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/custom_pipeline_overview.md