Instructions to use mikerjacobi/asteroid-lora-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mikerjacobi/asteroid-lora-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mikerjacobi/asteroid-lora-model") prompt = "a photo of sks asteroids" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- ea190abe686f1ee7971b2c184c48d45fae79031fda266389096281feb6455a7f
- Size of remote file:
- 6.59 MB
- SHA256:
- 52e56614bb9c4bec88344c796d52783ed27271776127d65ab4ce43672b08c9a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.