wan2.2 终极工作流：用Lora打造完美高清数字人，附独家放大技巧+低显存方案！|Create Flawless HD Digital Humans with Wan2.2 and Lora

🔥还在为AI数字人角色不一致、画质模糊而烦恼吗？本期视频将带你深入了解 wan2.2 的终极工作流，彻底解决数字人制作中的两大核心难题：角色一致性与高清画面感。我将毫无保留地分享我制作第一个高清数字人的全过程，从模型训练到最终的高清视频输出，让你也能轻松创造出属于自己的虚拟角色！
#runninghub #comfyui #wan22 #数字人
🔥Tired of inconsistent AI characters and blurry results? This video is the definitive guide you've been waiting for to create stunning, high-definition digital humans using wan2.2. We'll tackle the two biggest challenges in digital avatar creation: character consistency and cinematic visual quality. I'm sharing my complete, end-to-end process for creating my first HD digital human, so you can easily create your own!
🎬 你将学到：
1 训练专属数字人Lora：掌握使用少量数据训练高质量Lora的方法，确保角色形象高度统一。
2 高质量提示词的秘密：了解如何生成富有电影感的提示词，提升视频的整体质感。
3 wan2.2 核心工作流：手把手教你搭建基础视频生成流程，并分享我的优化参数。
4 独家高清放大技巧：揭秘如何巧妙利用低噪点模型，将低分辨率视频“无损”放大至720P甚至更高清。
5 低显存解决方案：电脑配置不高？没关系！教你如何拆分工作流，用时间换空间，在低显存设备上也能玩转高清数字人
🎬 You'll learn:
1 Train Your Own Custom Lora: Master the technique of training a high-quality Lora model with a small dataset for perfect character consistency.
2 The Secret to High-Quality Prompts: Understand how to generate cinematic prompts that elevate the overall look and feel of your videos.
3 The Core wan2.2 Workflow: A step-by-step guide to building the base video generation flow with my optimized settings.
4 Exclusive HD Upscaling Trick: Uncover the magic of using the low-noise model to upscale low-resolution videos to 720P or higher with incredible detail.
5 Low VRAM Solution: Don't have a high-end GPU? No problem! Learn how to split the workflow to run it on less powerful computers by trading time for space.
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galsses_woman lora 下载
https://pan.baidu.com/s/15nRTmUlRy_cz...
https://huggingface.co/sunleyan/wan2....
在线工作流
1 video_wan2_2_数字人工作流1-生成
体验地址：https://www.runninghub.cn/post/195339...
2 video_wan2_2_数字人工作流2-放大
体验地址：https://www.runninghub.cn/post/195338...
runninghub粉丝福利 注册送1000点算力
国外用户
https://www.runninghub.ai/?utm_source...
国内用户
https://www.runninghub.cn/?utm_source...
工作流下载 workflow-download：
https://github.com/amao2001/ganloss-l...
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