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/pytorch_model.bin from pcuenq/pokemon-lora: direct link, hf CLI and curl.
- Browser
- Download file 3.29 MB
-
https://huggingface.co/pcuenq/pokemon-lora/resolve/main/checkpoint-12500/pytorch_model.bin
- Command line
-
hf download hf://pcuenq/pokemon-lora/checkpoint-12500/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pcuenq/pokemon-lora/resolve/main/checkpoint-12500/pytorch_model.bin
3.29 MB
- Xet hash:
- 483e43545304feabde9f1d9028d62f07ec0faeb40cd76f1067fdfcbe04feb928
- Size of remote file:
- 3.29 MB
- SHA256:
- 711eb7067c0eeb1ef57a3eed20cc57c92b1e5a54e20ba3d55941b57283fb0ed6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.