Instructions to use Muapi/tied-to-bed-tied-to-bed-posts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/tied-to-bed-tied-to-bed-posts with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cocktailpeanut/pony-diffusion-v6-xl", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/tied-to-bed-tied-to-bed-posts") 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
Tied to bed (tied to bed posts)
Base model: Pony Trained words: TiedToBed, Xbex, Ankles tied, wrists tied, arms up, arms spread, open arms, legs spread, tied, bondage, rope, x pose, laying down, on bed, laying flat, arms outstretched and legs spread out <
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": "tied-to-bed-tied-to-bed-posts",
"lora_strength": 1.0,
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
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