Downloads · 30 days
118
7% of all-time downloads
diffusion-aurora-256
li-yan/diffusion-aurora-256
diffusion-aurora-256 is a unconditional image generation model from li-yan. Use it for the unconditional image generation task on the model card, and read the license before you ship it in a product. It is set up for diffusers.
All-time downloads
1.8K
Public
Parameters
114M
1.9 GB on disk
Likes
1
Public
Repo makeup
Click a slice to open those files.
.safetensors455 MB · 95%
At a glance
- Task
- Unconditional Image Generation
- Library
- diffusers
- Access
- Public
- Created
- Jun 20, 2024
- Updated
- Jun 21, 2024
- SHA
- a3e389ab
Datasets
Model card
From the Hugging Face model README
<center>
Description
Have you ever seen aurora with your own eyes? Check the above picture I got in Alaska in Winter. Beautiful right?
However, aurora is so rare that we can hardly see it even in the very north places like Alaska.
Don't worry. Now we have generative models!!! Here are the pictures generated by this model:
 |  |  |  |
|---|---|---|---|
 |  |  |  |
Model Details
This model generate 256 * 256 pixel pictures of aurora.
It is trained from dataset huggan/few-shot-aurora.
The training method is modified from this example.
You can check my training source code here: <img src="https://colab.research.google.com/assets/colab-badge.svg">
Usage
Option 1 (Slow)
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('li-yan/diffusion-aurora-256')
image = pipeline().images[0]
image
Option 2 (Fast)
from diffusers import DiffusionPipeline, DDIMScheduler
scheduler = DDIMScheduler.from_pretrained('li-yan/diffusion-aurora-256')
scheduler.set_timesteps(num_inference_steps=40)
pipeline = DiffusionPipeline.from_pretrained(
'li-yan/diffusion-aurora-256', scheduler=scheduler)
images = pipeline(num_inference_steps=40).images
images[0]
- Task
- Unconditional Image Generation
- Library
- diffusers
- Created
- Jun 20, 2024
- Updated
- Jun 21, 2024