Instructions to use alisa666/modelurpm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alisa666/modelurpm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("alisa666/modelurpm", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0e3c164a2027dae8c63afd9bf3f95ed1bb02a9250cd355404922328c48db7eff
- Size of remote file:
- 492 MB
- SHA256:
- 558cf975c5a4d380544000196d8eec2ed8a40c5c26c1c6297c06ffa7b92fecfb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.