Instructions to use Kev09/Makimamodel1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kev09/Makimamodel1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lykon/AnyLoRA", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Kev09/Makimamodel1") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 4d5c241425b7357b0632320c02be48dc7ec0e76a14758fc86305b10e6e225656
- Size of remote file:
- 151 MB
- SHA256:
- bff9e6bed8327bef78b754fd565ebf17394da1c08a8d618a0ecf5eeb9b885158
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