How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Muapi/javelin_-azur-lane")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

javelin_(azur lane) 标枪(碧蓝航线)

preview

Base model: SDXL 1.0 Trained words: javelin_(azur_lane),purple hair,ponytail, single_glove, hair_ornament, ribbon, mini_crown,hair_ribbon, plaid_skirt, purple_skirt, hood, white_camisole, javelin_(azur_lane),purple hair,ponytail,hair_flower, swimsuit, javelin_(azur_lane),purple hair,ponytail,wedding_dress, hair_ornament, bow,choker, bridal_gauntlets, bridal_veil,, javelin_(azur_lane),purple hair,ponytail, midriff, thighband_pantyhose, hair_bow, plaid_skirt, thigh_gap, headset, sleeveless_shirt, idol, suspender_skirt, plaid_bow , frills, star_hair_ornament, arm_warmers,, javelin_(azur_lane),purple hair,ponytail,crown,wrist_cuffs, sleeveless, hair_ornament, bow,blue_shirt, blue_skirt, white_sailor_collar,waitress,, javelin_(azur_lane),purple hair,ponytail, triped_thighhighs, hair_ribbon, navel, sleep_mask, striped, frilled_shorts, hair_ornament, ribbon, camisole,, javelin_(azur_lane),long hair, mini_crown, cross_hair_ornament, sweater_vest, bowtie, short_sleeves, collared_shirt, school_uniform, skirt,, javelin_(azur_lane),purple hair,ponytail, black_dress,hair_ornament,arm_garter,bare legs,

🧠 Usage (Python)

🔑 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": "javelin_azur-lane-标枪碧蓝航线",
    "lora_strength": 1.0,
    "width": 1024,
    "height": 1024,
    "num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
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