Unconditional image generation
Unconditional image generation is a relatively straightforward task. The model only generates images - without any additional context like text or an image - resembling the training data it was trained on.
The DiffusionPipeline is the easiest way to use a pre-trained diffusion system for inference.
Start by creating an instance of DiffusionPipeline and specify which pipeline checkpoint you would like to download. You can use any of the 🧨 Diffusers checkpoints from the Hub (the checkpoint you’ll use generates images of butterflies).
💡 Want to train your own unconditional image generation model? Take a look at the training guide to learn how to generate your own images.
In this guide, you’ll use DiffusionPipeline for unconditional image generation with DDPM:
from diffusers import DiffusionPipeline generator = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128")
The DiffusionPipeline downloads and caches all modeling, tokenization, and scheduling components. Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on a GPU. You can move the generator object to a GPU, just like you would in PyTorch:
Now you can use the
generator to generate an image:
0]image = generator().images[
The output is by default wrapped into a
You can save the image by calling:
Try out the Spaces below, and feel free to play around with the inference steps parameter to see how it affects the image quality!