Diffusers How to use linoyts/ltx2-audio-video-conditioning with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("linoyts/ltx2-audio-video-conditioning", torch_dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0] LTX.io How to use linoyts/ltx2-audio-video-conditioning 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 linoyts/ltx2-audio-video-conditioning --local-dir models/ltx2-audio-video-conditioning
hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Fast pipeline (distilled model, no distilled LoRA needed)
uv run python -m ltx_pipelines.distilled \
--distilled-checkpoint-path models/ltx2-audio-video-conditioning/<distilled-checkpoint>.safetensors \
--spatial-upsampler-path models/ltx2-audio-video-conditioning/<spatial-upsampler>.safetensors \
--gemma-root models/gemma-3-12b \
--prompt "A beautiful sunset over the ocean" \
--output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8 # HQ pipeline (two-stage, higher quality)
uv run python -m ltx_pipelines.ti2vid_two_stages_hq \
--checkpoint-path models/ltx2-audio-video-conditioning/<checkpoint>.safetensors \
--distilled-lora models/ltx2-audio-video-conditioning/<distilled-lora>.safetensors 0.8 \
--spatial-upsampler-path models/ltx2-audio-video-conditioning/<spatial-upsampler>.safetensors \
--gemma-root models/gemma-3-12b \
--prompt "A beautiful sunset over the ocean" \
--output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8 
