Instructions to use Muapi/aurora-borealistyler-flux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/aurora-borealistyler-flux 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/aurora-borealistyler-flux") 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
Aurora BorealiStyler [FLUX]
Base model: Flux.1 D
Trained words: Digital artwork showcasing a breathtaking aurora borealis display in a nighttime landscape. The central subject is (SUBJECT), stylized in glowing, ethereal colors, rendered in vibrant hues of (COLORS), appearing to be formed by the swirling aurora lights.
🧠 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:976774@1093900", "weight": 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/aurora-borealistyler-flux
Base model
black-forest-labs/FLUX.1-dev