LongLive-Plug-Wan2.1-T2V-14B-cfg Video Generation Model: 80GB+ VRAM

September 30, 2026

At a Glance

Values determined by this site’s code at collection time. Dates are JST.

Overview

Efficient-Large-Model has released LongLive-Plug-Wan2.1-T2V-14B-cfg, a LoRA adapter for Wan2.1-T2V-14B. This adapter aims to distill Classifier-Free Guidance (CFG) into conditional-only video generation. This reduces the need to run a separate unconditional inference branch during the inference process. Note that this adapter does not provide a speedup in a few steps on its own; as it functions strictly as an adapter, using it requires the corresponding base model.

Specifications

• Base model: Wan-AI/Wan2.1-T2V-14B
• Architecture: Video Diffusion DiT (Flow Matching framework)
• Base model specifications (14B):

• Output resolution: Supports 480P and 720P
• Distribution format: safetensors (PEFT/LoRA)
• License: apache-2.0
• Recommended settings: It is recommended to use this in combination with the corresponding few-step LoRA. The recommended weight ratio is few-step : CFG = 1 : 0.5. However, this refers to the adapter weight ratio, not the CFG scale itself during inference.

Performance and Quality

The base model targeted by this adapter, Wan2.1-T2V-14B, is reported to outperform open-source and commercial state-of-the-art (SOTA) models across multiple benchmarks. According to the model card, evaluations were conducted using 1,035 internal prompts across 14 major dimensions and 26 sub-dimensions, confirming superior performance compared to existing models.

Furthermore, manual evaluations with Prompt Extension applied show that it yields better results than existing open-source and closed-source models. The base model excels in its ability to generate high-quality visuals and notable motion dynamics, and is also characterized as being the only video model capable of generating text in both Chinese and English.

Strengths and Use Cases

This adapter excels at distilling Classifier-Free Guidance (CFG) into conditional-only generation during the video generation process of the base model, Wan2.1-T2V-14B. This makes it possible to eliminate the overhead of running a separate unconditional branch during inference.

The base model, Wan2.1-T2V-14B, excels in text-to-video tasks at generating high-quality visuals and dynamic motion. It also has the capability to generate both Chinese and English text within videos.

Hardware Requirements

Estimated requirements (calculated by Local Model Watch) — 14.3B parameters (taken from the base model Wan-AI/Wan2.1-T2V-14B)

Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. This release is an adapter (LoRA etc.); the table shows what the base model Wan-AI/Wan2.1-T2V-14B needs. Compare with other models in our VRAM quick reference. What the quantization names mean: glossary.

Can You Run It Locally?

The publisher distributes this model as safetensors.

License — apache-2.0 (Commercial use allowed): Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.

Compiled by this site’s code from the published formats and the license field. License summaries are not legal advice — check the publisher’s original terms before relying on them.

Distributed Files

Weight files published in Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-cfg, listed by this site from the Hugging Face API. Sizes are the actual file sizes.

How to Get It

This adapter is available from the following repository:

• Repository: Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-cfg
• Distribution format: safetensors
• Supported library: peft

Note that using this adapter requires the corresponding base model, Wan-AI/Wan2.1-T2V-14B. Additionally, the publisher has released a few-step LoRA intended to be used in combination with it, and using them together is recommended.

Related Articles

• LongLive-Plug-Wan2.1-T2V-14B-few-step: 80GB+ VRAM, File List
• LongLive-Plug-Wan2.2-TI2V-5B-cfg Video Generation Model: 24GB+ VRAM
• LongLive-Plug-Wan2.2-TI2V-5B-few-step: 24GB+ VRAM, File List
• H3-to-LTX-Latent-Adapter: 4GB+ VRAM, File List

What to Read Next

• Formats this model is available in → Safetensors format guide and models
• Learn about the publisher → Efficient Large Model (NVIDIA and MIT): models, licenses and articles
• Other models for the same task → Other video generation models

Sources

• https://huggingface.co/Efficient-Large-Model/LongLive-Plug-Wan2.1-T2V-14B-cfg