# Download the weights from this repo, plus the Gemma text encoder
hf download Muapi/ltx2-hopping --local-dir models/ltx2-hopping
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/ltx2-hopping/<weights>.safetensors 1.0 \
--prompt "your prompt here" \
--output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8LTX2 Hopping
Base model: LTXV2 Trained words: h1pping
🧠 Usage (Python)
🔑 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": "ltx2-hopping",
"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/ltx2-hopping
Base model
Lightricks/LTX-Video
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen