JoaoZaokk/LTX-2.5-22B-distilled-W4A8-ConvRot

text-to-video model by JoaoZaokk

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Model card & documentation

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A asymw4a8int8 build of ltx-2.5-22b-distilled-transformer, measured on a complete 10-second video and its soundtrack against the BF16 original and against Lightricks' own INT8. 39.13 GiB → 11.66 GiB, 3.36x lighter, and 1.95x faster than the original on the same 249-frame render. It is also 1.90x less faithful in the picture and 2.9x less faithful in the audio than Lightricks' own INT8 build — those numbers are here because they are the ones that decide whether you want this file. Method, tools and the full measurement log: https://github.com/JoaoZaokk/comfy-quant-bench LTX 2.5 generates video and audio in one latent. The first version of this card decoded only the video branch, and said so in its "not covered" list as if that were a footnote. It is not: an audio-video model judged on frames alone is judged on half its output. All three arms were re-rendered with the audio branch decoded (LTXVAudioVAEDecode on the…

JoaoZaokk/LTX-2.5-22B-distilled-W4A8-ConvRot on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-09-28T06:39:06.033Z

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Model overview

JoaoZaokk/LTX-2.5-22B-distilled-W4A8-ConvRot is a text-to-video model repository published by JoaoZaokk on Hugging Face. The source reports the diffusion-single-file library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: text-to-video
Library: diffusion-single-file
Recent downloads (30 days): 43,168
Cumulative likes: 0
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: other
Access: Not gated by Hugging Face
Created: 2026-09-13T03:16:36.000Z
Last modified: 2026-09-14T07:48:42.000Z

Compatibility and lineage

Reported languages: en

Reported base models: Lightricks/LTX-2.5

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Model card and usage instructions

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