LTX.io How to use milo01/INT4-Convrot-Comfy-Models 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 milo01/INT4-Convrot-Comfy-Models --local-dir models/INT4-Convrot-Comfy-Models
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/INT4-Convrot-Comfy-Models/<distilled-checkpoint>.safetensors \
--spatial-upsampler-path models/INT4-Convrot-Comfy-Models/<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/INT4-Convrot-Comfy-Models/<checkpoint>.safetensors \
--distilled-lora models/INT4-Convrot-Comfy-Models/<distilled-lora>.safetensors 0.8 \
--spatial-upsampler-path models/INT4-Convrot-Comfy-Models/<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 
