Instructions to use capablebear/pokemon-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use capablebear/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("capablebear/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-3000/optimizer.bin from capablebear/pokemon-lora: direct link, hf CLI and curl.
- Browser
- Download file 6.53 MB
-
https://huggingface.co/capablebear/pokemon-lora/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://capablebear/pokemon-lora/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/capablebear/pokemon-lora/resolve/main/checkpoint-3000/optimizer.bin
6.53 MB
- Xet hash:
- 972d89d29333ccdda67d467b0388227839b5039407ae4a9fffd805c4b51ec01b
- Size of remote file:
- 6.53 MB
- SHA256:
- ae7c574cf55354ec96e19c3556a2a7fe5fb67694cae1424568598634c3503ad0
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