Instructions to use dlibf/pokemon-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dlibf/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("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("dlibf/pokemon-lora") 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-2500/optimizer.bin from dlibf/pokemon-lora: direct link, hf CLI and curl.
- Browser
- Download file 204 MB
-
https://huggingface.co/dlibf/pokemon-lora/resolve/main/checkpoint-2500/optimizer.bin
- Command line
-
hf download hf://dlibf/pokemon-lora/checkpoint-2500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/dlibf/pokemon-lora/resolve/main/checkpoint-2500/optimizer.bin
204 MB
- Xet hash:
- 7953bedd9915591f2905379a3c3e02f8cbc9d6f99585595694c4827a45d45042
- Size of remote file:
- 204 MB
- SHA256:
- f0839845238452bd1ebc75588d700ab7b33450049faedfb1b4c03fa23abdf821
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