Wan 2.2 Complete Training Tutorial - Text to Image, Text to Video, Image to Video, Windows & Cloud
This article provides a comprehensive tutorial on training Wan 2.2 models for text-to-image, text-to-video, and image-to-video generation. It covers local training on Windows PCs as well as cloud-based training options.
Why it matters
This tutorial provides a comprehensive and accessible guide for training and using the powerful Wan 2.2 AI models, which have applications in various creative and generative tasks.
Key Points
- 1Detailed tutorial on training Wan 2.2 models locally and on cloud platforms
- 2Covers text-to-image, text-to-video, and image-to-video generation capabilities
- 3Provides optimized presets and configurations for different GPU and VRAM requirements
- 4Explains dataset preparation, training settings, and troubleshooting tips
- 5Demonstrates inference and upscaling workflows using SwarmUI and ComfyUI
Details
The article presents a complete training tutorial for the Wan 2.2 model, which can be used for text-to-image, text-to-video, and image-to-video generation. It covers both local training on Windows PCs with as little as 6GB of GPU VRAM, as well as cloud-based training options on platforms like RunPod and Massed Compute. The tutorial provides optimized presets and configurations for different training scenarios, and explains the research logic behind the recommended settings. It also covers dataset preparation, training hyperparameters, troubleshooting, and monitoring tools like nvitop. The article then demonstrates the inference and upscaling workflows using SwarmUI and ComfyUI, showcasing the capabilities of the trained Wan 2.2 models.
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