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Open-weight model · Text to video

LongLive-2.0-5B-Diffusers

by Yixiao Zeng Rabinovich/LongLive-2.0-5B-Diffusers

A diffusers-directory-layout repackaging of Efficient-Large-Model/LongLive-2.0-5B so it loads directly in SGLang Diffusion (sglang.multimodalgen) without any runtime overlay/materialization.

Parameters5B
Context
Weights24.2 GB
License
AccessOpen weights
Monthly Downloads18.7k

Runs On

What it takes to serve LongLive-2.0-5B-Diffusers (5B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 10.0 GB 12.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 5.0 GB 6.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 2.5 GB 3.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

A diffusers-directory-layout repackaging of Efficient-Large-Model/LongLive-2.0-5B so it loads directly in SGLang Diffusion (sglang.multimodalgen) without any runtime overlay/materialization. - transformer/ — the generator weights extracted from the original modelbf16.pt, kept in their original (model.) naming; SGLang's LongLive2Transformer3DModel.paramnamesmapping maps them to the diffusers module names at load (same convention as LingBot-World). - scheduler / textencoder / tokenizer / vae — taken from Wan-AI/Wan2.2-TI2V-5B-Diffusers. - modelindex.json classname = LongLive2Pipeline.

Excerpt from the card by Yixiao Zeng.

Identity and Version

Repository
Rabinovich/LongLive-2.0-5B-Diffusers
Publisher
Yixiao Zeng
Task
Text to video
Modality
Video
Library
diffusers
Parameters
5B parameters
Languages
Not stated by the source
Revision
37664a64cfc38cdc5fabe88576961ef10646e4f1
First published
2026-06-07
Last updated
2026-06-07

Files and Weights

17 files, 24.2 GB in total. The weights are 5 files totalling 24.2 GB in safetensors.

Weights5 files · 24.2 GB
Configuration7 files · 33.9 KB
Tokenizer3 files · 21.4 MB
Documentation1 file · 1.4 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
text_encoder/model-00001-of-00003.safetensorsWeights4.9 GB a8e861969c74
text_encoder/model-00002-of-00003.safetensorsWeights5.0 GB d57d948ece48
text_encoder/model-00003-of-00003.safetensorsWeights1.4 GB 0da9ee284e21
transformer/diffusion_pytorch_model.safetensorsWeights10.0 GB 17c1c2fc0cc8
vae/diffusion_pytorch_model.safetensorsWeights2.8 GB 62cd18f19438
model_index.jsonConfiguration511 B
scheduler/scheduler_config.jsonConfiguration820 B
text_encoder/config.jsonConfiguration855 B
text_encoder/model.safetensors.index.jsonConfiguration22.5 KB
tokenizer/special_tokens_map.jsonConfiguration7.1 KB
transformer/config.jsonConfiguration501 B
vae/config.jsonConfiguration1.7 KB
README.mdDocumentation1.4 KB
.gitattributesRepository1.6 KB
tokenizer/spiece.modelTokenizer4.5 MB e3909a67b780
tokenizer/tokenizer.jsonTokenizer16.8 MB 20a46ac25674
tokenizer/tokenizer_config.jsonTokenizer61.8 KB

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
24.2 GB
Download from Yixiao Zeng

Released by Yixiao Zeng through its official repository on Hugging Face.

Built From

  • Derived from Efficient-Large-Model/LongLive-2.0-5B

Memory Requirements

PrecisionWeights in memory
As published24.2 GB
16-bit10.0 GB
8-bit5.0 GB
4-bit2.5 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About LongLive-2.0-5B-Diffusers

How much GPU memory does LongLive-2.0-5B-Diffusers need?

About 12 GB at 16-bit and 3 GB at 4-bit: the weights (5B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run LongLive-2.0-5B-Diffusers on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

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