Instructions to use Muapi/spooning-concept with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/spooning-concept with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OnomaAIResearch/Illustrious-xl-early-release-v0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/spooning-concept") 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

- Xet hash:
- 9a7553806a8ad0450420746c61ad6cbcd3e94aeb833ca43b3e21ae65a92ee592
- Size of remote file:
- 1.01 MB
- SHA256:
- 746337578f9db661bb5bff23b435eaee04a1d13ee5f66235748f81041d7e5935
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