Instructions to use Muapi/wan_fusionx_facenaturalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/wan_fusionx_facenaturalizer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wan-ai/Wan2.1-T2V-14B-Diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/wan_fusionx_facenaturalizer") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
metadata
license: openrail++
library_name: diffusers
base_model: wan-ai/Wan2.1-T2V-14B-Diffusers
tags:
- lora
- text-to-video
- wan
- wan2.1
- wan-video-14b-t2v
pipeline_tag: text-to-video
Wan_FusionX_FaceNaturalizer
Base model: Wan Video 14B t2v Trained words:
🧠 Usage (Python)
🔑 Get your MUAPI key from muapi.ai/access-keys
import requests, os
url = "https://api.muapi.ai/api/v1/wan21_t2v"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality",
"lora_model": "wan_fusionx_facenaturalizer",
"lora_strength": 1.0,
"width": 832,
"height": 480,
"num_frames": 81
}
print(requests.post(url, headers=headers, json=payload).json())
