Instructions to use Muapi/star-trek-twok-uniforms-ltx2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/star-trek-twok-uniforms-ltx2 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("Lightricks/LTX-Video", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/star-trek-twok-uniforms-ltx2") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - LTX.io
How to use Muapi/star-trek-twok-uniforms-ltx2 with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download Muapi/star-trek-twok-uniforms-ltx2 --local-dir models/star-trek-twok-uniforms-ltx2 hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Text/image-to-video with the LoRA on the HQ two-stage base pipeline uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path path/to/checkpoint.safetensors \ --distilled-lora path/to/distilled_lora.safetensors 0.8 \ --spatial-upsampler-path path/to/spatial_upsampler.safetensors \ --gemma-root models/gemma-3-12b \ --lora models/star-trek-twok-uniforms-ltx2/<weights>.safetensors 1.0 \ --prompt "your prompt here" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Star Trek TWOK uniforms (LTX2)
Base model: LTXV2 Trained words: a red twok uniform with black pants with white collar and white clasp and black piping on one side and black belt and with the uniform jacket and black piping extending below the belt
๐ง Usage (Python)
๐ Get your MUAPI key from muapi.ai/access-keys
import requests, os
url = "https://api.muapi.ai/api/v1/ltx_lora_video"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality",
"lora_model": "star-trek-twok-uniforms-ltx2",
"lora_strength": 1.0,
"width": 768,
"height": 512,
"num_frames": 97
}
print(requests.post(url, headers=headers, json=payload).json())
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Model tree for Muapi/star-trek-twok-uniforms-ltx2
Base model
Lightricks/LTX-Video