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

The character consistency in i2v is a disaster.

#30

by lol104 - opened Aug 13

Discussion

lol104

Aug 13

Please fix this issue.

Thanks anyway for your great work. :-)

joffreypersonal

Aug 13

which model did you use？

lol104

Aug 13

which model did you use？

I tested with a photo (half portrait photo head to knees) of a European woman using LTX 2.3 and LTX 2.5 (without ID LoRa). Result: LTX 2.3 rendered the woman correctly, but with LTX 2.5, she looked completely different.
Finally, I tried a version with the ID LoRA "LTX-2.3-ID-LoRA-CelebVHQ-3K.safetensors." The result with LTX 2.5 was that the woman suddenly had an Asian face. I am MacUser (Macmini M4 pro, 25 GB RAM).

joffreypersonal

Aug 13

maybe the face id lora of ltx2.5 will fix it，just wait it

lol104

Aug 13

maybe the face id lora of ltx2.5 will fix it，just wait it

Is one planned? That would be wonderful :-)

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