Instructions to use pcuenq/pokemon-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pcuenq/pokemon-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pcuenq/pokemon-lora", 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:
- 6e9035292acaa34bb1bca7ee124a9505a4f0a3b3e206019535058124f15cb0a4
- Size of remote file:
- 3.29 MB
- SHA256:
- 9e1c5a696e3f3a2f6e4366394c494bb227767efc5f0a4403c69d9377bdbb4e36
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.