Magic-Wan-V2 tiled ultra-resolution upscaling

This workflow is designed specifically to handle ultra-resolution upscaling using a tiled reconstruction method. Instead of enlarging the entire image at once—which often leads to smearing, blurred textures, or broken details—it splits the picture into multiple tiles, enhances each tile independently, and then reassembles them with precise positional data. Magic Wan 2.0 provides the underlying reconstruction ability, while the tile cutting, tile decoding, and tile assembly steps ensure that each section is processed with higher local clarity. The result is an image that keeps the original structure intact but gains significantly sharper textures, more stable edges, and overall a much cleaner high-resolution appearance.

Using the workflow effectively mainly comes down to a few intuitive points. Choosing a reasonable tile count—like a 2×2 or 3×3 split—usually delivers the clearest result without causing visible seams. Lower denoise values help maintain detail consistency between tiles and prevent the model from over-rewriting local regions, especially at very large output sizes. Pre-scaling the image slightly before tiling can improve texture richness, but overscaling will only introduce unnecessary variability. As long as the tiles stay within a size the model handles well, the workflow will take care of the rest, producing an image that is noticeably sharper and more detailed while still preserving the original content and lighting.

🎥 YouTube Video Tutorial

Want to know what this workflow actually does and how to start fast?
This video explains what the tool is, how to launch the workflow instantly, and shares my core design logic — no local setup, no complicated environment. Everything starts directly on RunningHub, so you can experience it in action first.

👉 YouTube Tutorial：
you begin, I recommend watching the video thoroughly — getting the full context helps you understand the tool faster and avoid common detours.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.

👉 Workflow：
the results meet your expectations, you can later deploy it locally for customization.

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📺 Bilibili Updates (Mainland China & Asia-Pacific)

If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.

📺 Bilibili Video：
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Every bit of support helps me keep creating — just like a spark that can ignite a blazing flame.

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For collaboration or inquiries, please contact aiksk95 on WeChat.

🎥 YouTube 视频教程

想了解这个工作流到底是怎样的工具，以及如何快速启动？
视频主要介绍 工具定位、快速启动方法 和 我的构筑思路。
我们会直接在 RunningHub 上进行演示，让你第一时间看到实际效果。

👉 YouTube 教程：
—— 把握整体思路会更快上手，也能少走常见弯路。

⚙️ 在线体验工作流

现在就可以在线体验，无需安装。

👉 工作流：
/>如果觉得效果理想，你也可以在本地进行自定义部署。

🎁 粉丝福利：
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📺 Bilibili 更新（中国大陆及南亚太地区）

如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。

📺 B站视频：
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我会在 夸克网盘 持续更新模型资源：
👉