Diffusers How to use akshan-main/tiny-ltx-modular-pipe with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("akshan-main/tiny-ltx-modular-pipe", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0] LTX-2 How to use akshan-main/tiny-ltx-modular-pipe with LTX-2:
# 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 akshan-main/tiny-ltx-modular-pipe --local-dir models/tiny-ltx-modular-pipe
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/tiny-ltx-modular-pipe/<distilled-checkpoint>.safetensors \
--spatial-upsampler-path models/tiny-ltx-modular-pipe/<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/tiny-ltx-modular-pipe/<checkpoint>.safetensors \
--distilled-lora models/tiny-ltx-modular-pipe/<distilled-lora>.safetensors 0.8 \
--spatial-upsampler-path models/tiny-ltx-modular-pipe/<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 
