Instructions to use Muapi/schoolgirl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/schoolgirl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/schoolgirl") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from Muapi/schoolgirl: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/Muapi/schoolgirl/resolve/main/README.md
- Command line
-
hf download hf://Muapi/schoolgirl/README.md
-
curl -L -o README.md https://huggingface.co/Muapi/schoolgirl/resolve/main/README.md
1.03 kB
metadata
license: openrail++
library_name: diffusers
base_model: black-forest-labs/FLUX.1-dev
tags:
- lora
- text-to-image
- stable-diffusion
- flux
- flux.1-d
pipeline_tag: text-to-image
Schoolgirl
Base model: Flux.1 D Trained words: Sch00lg1rl, american, african, caribbean, indian, australian, chinese, japanese, korean, polynesian, european, latin american, arabic, hispanic, canadian, mexican, cuban, cheerleder
🧠 Usage (Python)
🔑 Get your MUAPI key from muapi.ai/access-keys
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:940001@1892401", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
