Wan 2.2 Complete Training Tutorial - Text to Image, Text to Video, Image to Video, Windows & Cloud
Full tutorial link > https://www.youtube.com/watch?v=ocEkhAsPOs4
Wan 2.2 training is now so easy. I have done over 64 different unique Wan 2.2 trainings to prepare the very best working training configurations for you. The configurations are fully working locally with as low as 6 GB GPUs. So you will be able to train your awesome Wan 2.2 image or video generation LoRAs on your Windows computer with easiness. Moreover, I have shown how to train on cloud platforms RunPod and Massed Compute so even if you have no GPU or you want faster training, you can train on cloud for very cheap prices fully privately.
📂 Resources & Links:
Download the One-Click Installer & Configs: [ https://www.patreon.com/posts/Musubi-Tuner-Trainer-App-Configs-137551634 ]
Qwen Image Model Training Tutorial (Prerequisite): [ https://youtu.be/DPX3eBTuO_Y ]
SwarmUI & ComfyUI Setup Guide for Windows: [ https://youtu.be/c3gEoAyL2IE ]
SwarmUI Installer and Model Downloader : [ https://www.patreon.com/posts/SwarmUI-Install-Download-Models-114517862 ]
ComfyUI Installer : [ https://www.patreon.com/posts/ComfyUI-Installers-105023709 ]
SwarmUI & ComfyUI Setup Guide for RunPod & Massed Compute: [ https://youtu.be/bBxgtVD3ek4 ]
Upload / Download Big Files Guide for RunPod & Massed Compute: [ https://youtu.be/X5WVZ0NMaTg ]
⏱️ Video Chapters:
00:00:00 Introduction to Wan 2.2 Training & Capabilities
00:00:56 Installing & Updating Musubi Tuner Locally
00:02:20 Explanation of Optimized Presets & Research Logic
00:04:00 Differences Between T2I, T2V, and I2V Configs
00:05:36 Extracting Files & Running Update Batch File
00:06:14 Downloading Wan 2.2 Training Models via Script
00:07:30 Loading Configs: Selecting GPU & VRAM Options
00:09:33 Using nvitop to Monitor RAM & VRAM Usage
00:10:28 Preparing Image Dataset & Trigger Words
00:11:17 Generating Dataset Config & Resolution Logic
00:12:55 Calculating Epochs & Checkpoint Save Frequency
00:13:40 Troubleshooting: Fixing Missing VAE Path Error
00:15:12 VRAM Cache Behavior & Training Speed Analysis
00:15:51 Trade-offs: Learning Rate vs Resolution vs Epochs
00:16:29 Installing SwarmUI & Updating ComfyUI Backend
00:18:13 Importing Latest Presets into SwarmUI
00:19:25 Downloading Inference Models via Script
00:20:33 Generating Images with Trained Low Noise LoRA
00:22:22 Upscaling Workflow for High-Fidelity Results
00:24:15 Increasing Base Resolution to 1280x1280
00:27:26 Text-to-Video Generation with Lightning LoRA
00:30:12 Image-to-Video Generation Workflow & Settings
00:31:35 Restarting Backend to Clear VRAM for Model Switching
00:33:45 Fixing RAM Crashes with Cache-None Argument
00:35:13 Dual Model (High & Low Noise) Training Setup
00:36:54 Preparing Hybrid Datasets (Images + Videos)
00:37:40 Manually Editing Dataset TOML for Resolution Control
00:39:53 Setting High Noise Model Paths for Dual Training
00:41:50 Optimization: Block Swap vs CPU Offload
00:43:10 Generating Video with Dual-Model Trained LoRA
00:45:35 Massed Compute: Server Setup & Coupon Code
00:47:00 Connecting via ThinLinc & File Transfer Methods
00:49:12 Massed Compute: Fast UV Installation & Downloads
00:50:27 Loading Configurations on Massed Compute
00:52:18 Troubleshooting: Fixing Config Version Error
00:53:20 Dual Model Training Speed Analysis on Cloud
00:55:40 RunPod: Selecting the Correct Template & GPU
00:57:45 RunPod: Uploading Files & Extracting Archive
00:58:38 RunPod: Terminal Installation & Model Downloads
01:00:26 RunPod: Correct Pathing Syntax & Backslash Fix
01:01:28 Setting Dataset Paths on RunPod
01:03:34 Installing nvitop on RunPod Terminal
01:03:54 Speed Hack: Disabling Numpy Memory Mapping
01:06:00 Terminating Instances & Final Remarks
Greetings everyone! Today I am presenting an epic tutorial on how to train the Wan 2.2 model to generate extremely high-quality, realistic images and videos. This is currently the most advanced model for generating life-like textures and details.
In this comprehensive guide, I cover everything you need to know to train Wan 2.2 on your local Windows computer, as well as on cloud platforms like RunPod and Massed Compute. We utilize the SECourses Musubi Tuner with fully optimized, 1-click presets designed for every GPU range (from 6GB to 192GB VRAM).
🚀 What You Will Learn in This Tutorial:
Wan 2.2 Text-to-Image Training: How to train the Low Noise model for massive detail and realism.
Wan 2.2 Text-to-Video Training: Mastering Dual Model training (Low Noise + High Noise) for superior video consistency.
Image-to-Video Workflow: How to use your trained LoRAs to animate static images.
Cloud Training: Step-by-step guides for Massed Compute (ultra-fast disk speeds) and RunPod.
Performance Optimization: Using FP8 scaling, Block Swapping, and CPU offloading to train on consumer GPUs.
Inference & Upscaling: Using SwarmUI and ComfyUI to generate and upscale content to 4K resolution.
💡 Key Features of Our Workflow:
Auto-Resume & Speed: New UV package installers for lightning-fast setup.
Presets for All GPUs: Configurations included for 6GB, 12GB, 24GB, 48GB, and 80GB+ cards.
Dataset Automation: Auto-resizing and captioning for both image and video datasets.