Instructions to use Muapi/wan14b_detailer-enhancer_t2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/wan14b_detailer-enhancer_t2v 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/wan14b_detailer-enhancer_t2v") 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
| 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 | |
| # Wan14B_Detailer/Enhancer_T2V | |
|  | |
| **Base model**: Wan Video 14B t2v | |
| **Trained words**: cinemoraX | |
| ## 🧠 Usage (Python) | |
| 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) | |
| ```python | |
| 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": "wan14b_detailerenhancer_t2v", | |
| "lora_strength": 1.0, | |
| "width": 832, | |
| "height": 480, | |
| "num_frames": 81 | |
| } | |
| print(requests.post(url, headers=headers, json=payload).json()) | |
| ``` | |