HomeStoreWAN 2.2 14B Text/Image To Video(6GB) - ComfyUI Workflow - One Click Installer
WAN 2.2 14B Text/Image To Video(6GB) - ComfyUI Workflow - One Click Installer
⚡ AI Installer Package

WAN 2.2 14B Text/Image To Video(6GB) - ComfyUI Workflow - One Click Installer

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// About this product

Run the best video generation model locally on devices with as little as 6GB VRAM using ComfyUI.


WAN 2.2 14B models have just been released, bringing state-of-the-art video generation capabilities in a format optimized for low VRAM devices. The quantized GGUF versions allow high-quality results even on consumer GPUs as low as 6GB VRAM, while standard FP16/FP8 variants are available for those with more powerful hardware.

 

One-Click Installer & Custom Workflow:
I’ve developed a one-click installer and a custom workflow enabling you to run WAN 2.2 quantized GGUF models for high-quality video generation on lower VRAM setups. The installer automatically sets up the Wan2.2 T2V A14B Low Noise Q3_K_S GGUF model, but if your system allows, you can easily add higher quant GGUF or the standard FP16/FP8 models for even better results.

 

Preloaded Models within the Installer (Low VRAM)

  • umt5-xxl-encoder-Q5_K_M.gguf (ComfyUI\models\clip) - https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main

  • wan_2.1_vae.safetensors (ComfyUI\models\vae) - https://huggingface.co/Kijai/WanVideo_comfy/tree/main

  • Wan2.2-T2V-A14B-LowNoise-Q3_K_S.gguf (ComfyUI\models\unet) - https://huggingface.co/QuantStack/Wan2.2-T2V-A14B-GGUF/tree/main/LowNoise

  • 2xLexicaRRDBNet_Sharp.pth Upscale model (ComfyUI\models\upscale_models) - https://huggingface.co/Thelocallab/2xLexicaRRDBNet_Sharp/blob/main/2xLexicaRRDBNet_Sharp.pth

  • Wan21 T2V 14B lightx2v (ComfyUI\models\loras) - https://huggingface.co/Thelocallab/WAN-2.1-loras/tree/main

 

The standard Wan2.2 Low Noise 14B diffusion models (FP16 and FP8) are not packaged with the installer, but you can download them directly from the Comfy Org Hugging Face repository and add them to your ComfyUI/models/diffusion_models folder:
Comfy Org Wan2.2 Diffusion Models

 

Speed:
Generate 480p resolution videos in about 10–15 minutes on an RTX 4050 with 6GB VRAM. Faster performance is possible on higher-end GPUs.

 

System Requirements:

  • Nvidia RTX 30XX, 40XX, or 50XX series GPU (FP16 support required; GTX 10XX/20XX not tested)

  • CUDA-compatible GPU with at least 6 GB VRAM

  • Windows OS

  • At least 40 GB free storage

 

What’s Included:

  • Portable ComfyUI Windows Installer, pre-configured for WAN 2.2 text-to-video & image-to-video

  • Custom workflow supporting text-to-video & image-to-video generation

  • Automatic downloads for all required nodes and models

 

Usage Notes:

  • You can utilize either the WAN 2.2 14B GGUF models or the standard WAN 2.2 14B diffusion models in the workflow.

  • Enable or disable workflow sections using the levers in the Fast Groups Bypasser node on the left side of the workflow.

  • Enter your detailed descriptive text prompt for the scene you want to generate.

  • For best results, enhance your prompt with an LLM.

 

Support and More Information

  • Community Support: For troubleshooting or to connect with other users, join the Discord server.

// Frequently asked

Common questions

What do I get when I buy this?
A one-click Windows installer you download instantly after checkout. It sets up the app and all its dependencies for you — no manual Python, CUDA, or environment wrangling.
Do I need to be technical to use it?
No. The installer is fully automated — run it and it handles the setup for you. Basic familiarity with Windows is all you need.
What are the system requirements?
Windows 10 or 11. Most tools run best on an NVIDIA GPU, and many are optimized for 6–8GB VRAM. See the description above for this tool's specific requirements.
Should I buy this or get Local Lab Pro?
Buy this if you only need this one tool. If you want several, Local Lab Pro ($10/month) includes this and all 75+ installers plus every future release — cheaper than buying a handful individually.
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