Diffusers How to use rzgar/ltx-2.3-22b-distilled-lora-384-1.1-n4w 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("Lightricks/LTX-2.3", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("rzgar/ltx-2.3-22b-distilled-lora-384-1.1-n4w")
prompt = "A man with short gray hair plays a red electric guitar."
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png")
image = pipe(image=input_image, prompt=prompt).frames[0]
export_to_video(output, "output.mp4") LTX-2 How to use rzgar/ltx-2.3-22b-distilled-lora-384-1.1-n4w 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 rzgar/ltx-2.3-22b-distilled-lora-384-1.1-n4w --local-dir models/ltx-2.3-22b-distilled-lora-384-1.1-n4w
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/ltx-2.3-22b-distilled-lora-384-1.1-n4w/<weights>.safetensors 1.0 \
--prompt "your prompt here" \
--output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8 
