Instructions to use Muapi/mita-miside with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/mita-miside 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/mita-miside") 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
Mita - MiSide
Base model: Illustrious Trained words: Mita, 1girl, low twintails, red shirt, headband, hair scunchie, pleated skirt, thighhighs, Mila, 1girl, glasses, jacket, necktie, pleated skirt, hair ornament, ShortHairMita, 1girl, short hair, hair bow, red shirt, pleated skirt, thighhighs, KindMita, 1girl, long hair, red shirt, pleated skirt, thighhighs, SleepyMita, 1girl, sleep mask, messy hair, pajamas, shorts, CoolMita, 1girl, hat, gloves, red shirt, blue pleated skirt, red thighhighs, ponytail
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": "mita-miside",
"lora_strength": 1.0,
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
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Model tree for Muapi/mita-miside
Base model
KBlueLeaf/kohaku-xl-beta5