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QuantFunc/LTX-2.5-QuantFunc-4bit

sourceHugging Faceotherupdated 9d agoView on Hugging Face
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LTX-2.5-QuantFunc-4bit

4-bit quantized [Lightricks LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5) 22B distilled — pure 4-bit inference holds up to BF16 quality while cutting weight/VRAM footprint to roughly 1/3. Quantized and loaded by the QuantFunc inference engine.

Why pure 4-bit holds up

Compressing a 22B joint video/audio DiT down to 4-bit activations × 4-bit weights (pure a4w4) runs into one main enemy: activation outliers, which blow past 4-bit's dynamic range and show up as blur and lost detail. QuantFunc adds two layers on top of the SVDQuant approach:

  1. 1.A much stronger low-rank branch. Algorithmic improvements let the low-rank branch absorb significantly more activation-outlier energy than standard SVDQuant — outliers get siphoned off before the main path is quantized to 4-bit, so the main path sees a much cleaner distribution.
  2. 2.A smoother residual. A combination of in-house techniques further smooths the outlier distribution in what's left after low-rank absorption, so the 4-bit grid covers the remaining distribution with far less error.

Net effect: at pure a4w4 inference, output video (including audio) clarity, detail retention and motion coherence are on par with the BF16 baseline — at roughly 1/3 the memory.

Files

Quantizes the base checkpoint — `ltx-2.5-22b-distilled-transformer-bf16.safetensors` from Lightricks LTX-2.5 (22B, distilled variant):

FileFormatSizeMinimum GPU
ltx-2.5-22b-distilled-quantfunc-int4-r128.safetensorsINT4 weights / INT4 activations (SVDQuant, rank-128)~14.06 GiBNVIDIA SM120+ (Blackwell, e.g. RTX 50-series / RTX 6000D)

Produced & loadable ONLY by QuantFunc

This is not a standard diffusers/safetensors checkpoint. The file is produced by, and only loadable by, the QuantFunc inference engine (native C++/CUDA engine) or the [ComfyUI-QuantFunc](https://github.com/quantfunc) custom-node plugin for ComfyUI. It will not load in vanilla diffusers, ComfyUI's stock LTX loader, or any other inference stack. (This repo declares library_name: diffusers purely so Hugging Face counts real downloads of the flat .safetensors file below — see HF's download-stats docs — not because the file loads via the diffusers Python package.)

Inference settings (required for correct output)

This is the distilled checkpoint, joint video+audio. It must be run with the distilled model's own schedule — a generic/default scheduler will produce degraded output:

  • —Sigma schedule (8 steps, fixed): 1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0
  • —CFG: 1 (no classifier-free guidance — this is a guidance-distilled model)
  • —STG: disabled (no Spatio-Temporal Guidance)

Original / base model

This repository redistributes derived (quantized) weights only, produced from the base checkpoint above via the QuantFunc SVDQuant pipeline. No architecture or training changes were made — only post-training quantization (weight + activation, 4-bit, rank-128 SVD residual).

License

Follows the original model's LTX-2 Community License Agreement (license text). This repository distributes quantized weights only; copyright and licensing of the original model belong to Lightricks.

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