Instructions to use Muapi/horse-riding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/horse-riding 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/horse-riding") 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/horse-riding: direct link, hf CLI and curl.
- Browser
- Download file 931 Bytes
-
https://huggingface.co/Muapi/horse-riding/resolve/main/README.md
- Command line
-
hf download hf://Muapi/horse-riding/README.md
-
curl -L -o README.md https://huggingface.co/Muapi/horse-riding/resolve/main/README.md
931 Bytes
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
Horse Riding
Base model: Flux.1 D Trained words: HORSE RIDING, HORSE STANDING, HORSE WALKING, HORSE RUNNING, EQUESTRIAN HURDLE
🧠 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:683295@802595", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
