Diffusers How to use akshayboora/LTX-2.3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image, export_to_video
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("akshayboora/LTX-2.3", dtype=torch.bfloat16, device_map="cuda")
pipe.to("cuda")
prompt = "A man with short gray hair plays a red electric guitar."
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
)
output = pipe(image=image, prompt=prompt).frames[0]
export_to_video(output, "output.mp4") LTX-2 How to use akshayboora/LTX-2.3 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 akshayboora/LTX-2.3 --local-dir models/LTX-2.3
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/LTX-2.3/<distilled-checkpoint>.safetensors \
--spatial-upsampler-path models/LTX-2.3/<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/LTX-2.3/<checkpoint>.safetensors \
--distilled-lora models/LTX-2.3/<distilled-lora>.safetensors 0.8 \
--spatial-upsampler-path models/LTX-2.3/<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 
