Instructions to use Muapi/reverse-fellatio-concept with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/reverse-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/reverse-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
Reverse Fellatio - Concept
Base model: Illustrious Trained words: lora:reverse-fellatio-v6-illustriousxl-lora-nochekaiser:1, reverse fellatio, looking at viewer, blush, 1boy, navel, nipples, hetero, heart, sweat, uncensored, lying, penis, tongue, solo focus, tongue out, cum, on back, pussy juice, oral, fellatio, facial, licking, masturbation, fingering, female masturbation, licking penis, completely nude, pov, breasts apart,
๐ง Usage (Python)
๐ Get your MUAPI key from muapi.ai/access-keys
import requests, os
url = "https://api.muapi.ai/api/v1/sdxl-lora-image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality",
"lora_model": "reverse-fellatio-concept",
"lora_strength": 1.0,
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
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