Instructions to use Muapi/seiko-ayase-dandadan-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/seiko-ayase-dandadan-lora with Diffusers:
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/seiko-ayase-dandadan-lora") 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
Seiko Ayase | DANDADAN LoRa
Base model: SDXL 1.0 Trained words: seiko_wz, white hair, long hair, hair bun, single hair bun, large breasts, jacket, letterman jacket, sweater, yellow sweater, clavage, open clothes, open jacket, red glasses, two-tone jacket, multicolored jacket, blue jacket, black cape, cape, hair ornament, hoop earrings
🧠 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": "seiko-ayase-dandadan-lora",
"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/seiko-ayase-dandadan-lora
Base model
stabilityai/stable-diffusion-xl-base-1.0