Instructions to use rzgar/ltx-2.3-22b-distilled-lora-384-1.1-n4w with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- 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.io
How to use rzgar/ltx-2.3-22b-distilled-lora-384-1.1-n4w 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 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 - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
LTX-2.3 Distilled LoRA Enhanced
Model description
The official distilled LoRA more or less acts as a negative strength when stacked with N54W LoRAs, fighting against their appearance and animation characteristics. This lora is an attempt to solve that conflict. Instead of counteracting, it works with N54W LoRAs, improving motion and appearance to varying degrees.
Usage
- Strength: 0.5 - 0.65
- Model: LTX-2.3 Dev | KJ - transformer_only (none-distilled checkpoints only)
- Stack it with your preferred N54W LoRA
Variants
| File | Size | Description |
|---|---|---|
| ltx-2.3-22b-distilled-lora-384-1.1_n4w.safetensors | 7.1 GB | same rank as the official |
| ltx-2.3-22b-distilled-lora-384-1.1_rank_128_n4w.safetensors | 2.4 GB | low VRAM, expect lower quality |
Expected Quality
When stacked with N54W LoRAs listed on Civitai and used with prompts and input images that follow LTX-2.3 prompting guidelines, roughly 6 out of 10 videos are usable. Body deformity and broken animation can still occur, this is standard LTX-2.3 behaviour. Prompt deviation from the input image increases the error rate.
Download model
Download them in the Files & versions tab.
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Model tree for rzgar/ltx-2.3-22b-distilled-lora-384-1.1-n4w
Base model
Lightricks/LTX-2.3