Instructions to use Muapi/implied-fellatio-concept with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/implied-fellatio-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/implied-fellatio-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:
- e99a6d392929909af39698f6ee2cc7a032db4cc06d109ebdabd595e2be9fc8fd
- Size of remote file:
- 1.69 MB
- SHA256:
- 25e9fb19d860e6e2f4712790914c2f0e0b60b64ded43b8aac0b0bd53cae2dcea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.