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
Download checkpoint-12500/optimizer.bin from pcuenq/pokemon-lora: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/pcuenq/pokemon-lora/resolve/main/checkpoint-12500/optimizer.bin
- Command line
-
hf download hf://pcuenq/pokemon-lora/checkpoint-12500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/pcuenq/pokemon-lora/resolve/main/checkpoint-12500/optimizer.bin
6.59 MB
- Xet hash:
- b8960233c41dc292499c0e8c13b911a865e72dfc3432301d386950e3c73d7db2
- Size of remote file:
- 6.59 MB
- SHA256:
- fb92f640ff93ca7b5f5bfeced0ca9204500a571151e7ebe29124e555179301da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.