Instructions to use Lightricks/LTX-2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lightricks/LTX-2.5 with Diffusion Single File:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

LTX-2

How to use Lightricks/LTX-2.5 with LTX-2:

# Install the LTX-2 pipelines
git clone https://github.com/Lightricks/LTX-2.git
cd LTX-2
uv sync --extra natten

# Download weights from this repo
# Substitute filenames from this repo's "Files and versions" if they differ
hf download Lightricks/LTX-2.5 \
    diffusion_models/<distilled-transformer>.safetensors \
    text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \
    vae/<video-vae>.safetensors \
    vae/<audio-vae>.safetensors \
    latent_upscale_models/<spatial-upsampler>.safetensors \
    latent_upscale_models/<temporal-upsampler>.safetensors \
    --local-dir models/LTX-2.5
# DFR requires the detailing IC-LoRA (separate repo; strength is fixed at 0.5)
hf download Lightricks/LTX-2.5-22b-IC-LoRA-Pixel-Spatial-Upscaler --local-dir models/LTX-2.5-22b-IC-LoRA-Pixel-Spatial-Upscaler

# Distilled LTX-2.5 pipeline (fast)
uv run python -m ltx_pipelines.distilled \
    --transformer-path models/LTX-2.5/diffusion_models/<distilled-transformer>.safetensors \
    --text-encoder-path models/LTX-2.5/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \
    --video-vae-path models/LTX-2.5/vae/<video-vae>.safetensors \
    --audio-vae-path models/LTX-2.5/vae/<audio-vae>.safetensors \
    --spatial-upsampler-path models/LTX-2.5/latent_upscale_models/<spatial-upsampler>.safetensors \
    --num-frames 121 \
    --prompt "A beautiful sunset over the ocean" \
    --output-path output.mp4
# For image-to-video, add: --image path/to/image.jpg 0 0.8

# DFR pipeline (higher detail fidelity; optional temporal 2x/4x)
uv run python -m ltx_pipelines.dfr_pipeline \
    --transformer-path models/LTX-2.5/diffusion_models/<distilled-transformer>.safetensors \
    --text-encoder-path models/LTX-2.5/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \
    --video-vae-path models/LTX-2.5/vae/<video-vae>.safetensors \
    --audio-vae-path models/LTX-2.5/vae/<audio-vae>.safetensors \
    --spatial-upsampler-path models/LTX-2.5/latent_upscale_models/<spatial-upsampler>.safetensors \
    --temporal-upsampler-path models/LTX-2.5/latent_upscale_models/<temporal-upsampler>.safetensors \
    --detailing-lora models/LTX-2.5-22b-IC-LoRA-Pixel-Spatial-Upscaler/ltx-2.5-22b-ic-lora-pixel-spatial-upscaler-x2-1.0.safetensors \
    --spatial-upscalings 1 \
    --temporal-upscalings 1 \
    --height 1088 \
    --width 1920 \
    --num-frames 121 \
    --prompt "A beautiful sunset over the ocean" \
    --output-path output.mp4
# For 4K: --spatial-upscalings 2 --width 3840 --height 2176
# For image-to-video, add: --image path/to/image.jpg 0 0.8

Notebooks
Google Colab
Kaggle

LTX-2.5 distilled NVFP4 fails in ComfyUI

#16

by YieumYoon - opened Aug 12

Discussion

YieumYoon

Aug 12

Is anyone else able to run the officialltx-2.5-22b-distilled-transformer-nvfp4.safetensorscheckpoint in ComfyUI?

I used ComfyUI’s built-in LTX 2.5 Image to Video template. Sampling fails immediately with:

RuntimeError: mat1 and mat2 shapes cannot be multiplied (3520x4096 and 2048x4096)

My environment:

ComfyUI 0.32.0
Ubuntu 24.04.4 LTS
NVIDIA RTX 5090
PyTorch 2.13.0+cu130

The official ltx-2.5-22b-distilled-transformer-comfy-int8-convrot.safetensors
checkpoint works with the same template.

I’m not sure whether this is related to the checkpoint, ComfyUI’s loading path,
or something specific to my setup. Can anyone confirm whether the NVFP4
checkpoint works?