Instructions to use Muapi/tarots-pro-flux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/tarots-pro-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/tarots-pro-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
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Download README.md from Muapi/tarots-pro-flux: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/Muapi/tarots-pro-flux/resolve/main/README.md
- Command line
-
hf download hf://Muapi/tarots-pro-flux/README.md
-
curl -L -o README.md https://huggingface.co/Muapi/tarots-pro-flux/resolve/main/README.md
1.21 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
Tarots PRO Flux
Base model: Flux.1 D Trained words: the_hanged_man, the_world, the_devil, lovers, justice, temperance, the_tower, the_moon, the_hierophant, the_fool, the_chariot, the_hermit, the_sun, the_emperor, judgement, the_strength, the_star, the_magician, death, the_highpriestess, the_wheel_of_fortune, the_empress, celtic, italian1, italian2, woodcut, grl pwr, Marseille, Rider_Waite, tarot card
🧠 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:977598@1094847", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
