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Wan 2.2 New Lightning and Fine-Tune Models - It Fixed All the Old Problems!
🕑 Added 2025-10-02 13:00:15 +0000 UTC
Tutorial Video : https://youtu.be/tIQNxi95AzQ?si=OXKf8nu-4R_WSL7E
In this video, we dive deep into the latest updates for WAN 2.2 AI video generation—specifically the new Lightning models and fine-tuned variants like WAN 2.2 Palingenesis. You’ll see real-world comparisons between older WAN 2.2 Lightning versions and the newest high-noise diffusion model that merges Light X2V technology for dramatically improved motion, camera dynamics, and generation speed. We also test combinations with HPS Reward LoRAs, Sage Attention, and Torch Compile to squeeze out even better quality and performance—all running locally in ComfyUI at 720p.
This content is perfect for AI video creators, hobbyists, and developers who run local AI video workflows using ComfyUI and want faster, higher-quality results without relying on cloud services. If you’ve struggled with stiff motion, poor prompt adherence, or slow render times in earlier WAN models, this update is a game-changer.
Why does it matter? Because WAN 2.2’s new Lightning architecture—especially when paired with fine-tuned models like Palingenesis—delivers near real-time video generation with cinematic camera movement and reliable action sequencing. Whether you're creating short films, concept animations, or experimental AI art, these improvements save time and significantly boost output quality, making advanced AI video more accessible and practical for everyday creators.
Here is what I got from the test , 2 steps high noise, 4 steps low noise
Resources:
Wan2.2-Lightning
https://huggingface.co/lightx2v/Wan2.2-Lightning
https://huggingface.co/lightx2v/Wan2.2-Lightning/tree/main/Wan2.2-T2V-A14B-4steps-250928-dyno
https://huggingface.co/lightx2v/Wan2.2-Lightning/tree/main/Wan2.2-T2V-A14B-4steps-lora-250928
Attached Workflow Used In This Tutorial:
This Workflow Used Comfy-Asset-Downloader, It help you auto download model files in the first run. https://github.com/ServiceStack/comfy-asset-downloader
