Instructions to use Plaguekind/ltx2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX-2
How to use Plaguekind/ltx2.3 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 Plaguekind/ltx2.3 \ 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/ltx2.3 # 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/ltx2.3/diffusion_models/<distilled-transformer>.safetensors \ --text-encoder-path models/ltx2.3/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \ --video-vae-path models/ltx2.3/vae/<video-vae>.safetensors \ --audio-vae-path models/ltx2.3/vae/<audio-vae>.safetensors \ --spatial-upsampler-path models/ltx2.3/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/ltx2.3/diffusion_models/<distilled-transformer>.safetensors \ --text-encoder-path models/ltx2.3/text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors \ --video-vae-path models/ltx2.3/vae/<video-vae>.safetensors \ --audio-vae-path models/ltx2.3/vae/<audio-vae>.safetensors \ --spatial-upsampler-path models/ltx2.3/latent_upscale_models/<spatial-upsampler>.safetensors \ --temporal-upsampler-path models/ltx2.3/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
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Verified signatureMy workflow for ltx 2.3 - WIP PlagueKind - LTX2.3 Workflow - I2V - NVFP4 | MXFP8 | Ease of use  ## V1 Notes - Focused Mainly on Speed and ease of usability while retaining single pass quality and likeness. - First pass is more or less optimized. - Identity Retention is pretty good. - First pass balanced for speed and quality. - Upscale and 2x fps work but will be optimized later. - I would avoid using RTX VSR with the whole chain active unless you have like 96gb ram. - Nvfp4 models are only for Blackwell, using them will not save you speed if you don't have Blackwell, but they should still work. - Default settings of the first pass should produce good quality and sharp. I personally like to use this for a single pass and just render straight at 1280. - Bumped FPS to 30 because it looks way bettwen imo, quality seems clearer overall and it …a6b4f3f -
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