Instructions to use alkiskoudounas/sd-aurora-256px with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alkiskoudounas/sd-aurora-256px with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("alkiskoudounas/sd-aurora-256px", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("alkiskoudounas/sd-aurora-256px", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Model Card for Stable Diffusion - Aurora Borealis, 256px
Model developed for the Unit 1 of the Diffusion Models Class ๐งจ.
This model is a diffusion model for unconditional image generation of Aurora Borealis ๐.
It is trained on a small collection of aurora pictures and trained for 50 epochs, with ๐ค Accelerate.
Usage
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('alkiskoudounas/sd-aurora-256px')
Example
Here you can find an example of the output of the model, in a batch of 8 images:
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