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-4000/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-4000/pytorch_model.bin
- Command line
-
hf download hf://pcuenq/pokemon-lora/checkpoint-4000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pcuenq/pokemon-lora/resolve/main/checkpoint-4000/pytorch_model.bin
3.29 MB
- Xet hash:
- 56e84eb4973108c13add3e543078248e8fb29a29ebb1cb1ffd6778677b7d5def
- Size of remote file:
- 3.29 MB
- SHA256:
- 9cd565b5354c3b714a1e468cb6e4d96db4712287823c3194586ef8f9dca2d73d
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