Instructions to use Muapi/star-trek-tng-uniforms-ltx2-2-variants with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Muapi/star-trek-tng-uniforms-ltx2-2-variants 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", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Muapi/star-trek-tng-uniforms-ltx2-2-variants") 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-tng-uniforms-ltx2-2-variants 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-tng-uniforms-ltx2-2-variants --local-dir models/star-trek-tng-uniforms-ltx2-2-variants 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-tng-uniforms-ltx2-2-variants/<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
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", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("Muapi/star-trek-tng-uniforms-ltx2-2-variants")
prompt = "A man with short gray hair plays a red electric guitar."
output = pipe(prompt=prompt).frames[0]
export_to_video(output, "output.mp4")Star Trek TNG uniforms (LTX2)(2 variants)
Base model: LTXV2 Trained words: blue tng uniform with long sleeves and black pants and black shoulders, yellow tng uniform with long sleeves and black pants and black shoulders, red tng uniform with long sleeves and black pants and black shoulders
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-tng-uniforms-ltx2-2-variants",
"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-tng-uniforms-ltx2-2-variants
Base model
Lightricks/LTX-Video