Z-Image-ControlNet second-order Refiner + Pose / Depth / Line

This workflow uses the Z-Image model together with ControlNet to achieve reliable control over pose skeletons, depth information, and line-based structure. Z-Image’s fast inference makes it responsive to these conditioning signals, producing an initial output that follows the intended structure while still carrying the model’s vivid rendering style. However, ControlNet naturally reduces aesthetic richness, so the workflow incorporates a second refinement stage: the generated image is re-encoded into latent space and passed through another diffusion round. This latent-space pass restores color depth, detail, and visual cohesion more effectively than pixel-space upscaling, giving the final image a more polished look.

In practice, switching to SD-Pose improves skeleton accuracy when the automatically detected pose is incomplete, and adjusting preprocessing strength lets you fine-tune how strongly the structure affects the result. Line control and depth control both behave predictably, though stylized cases may still require refinement to maintain visual appeal. LoRA models can be loaded, but ControlNet will weaken their influence, so the workflow favors simpler stylistic adjustments. Despite its early stage of development, Z-Image already shows strong potential—its image-to-image quality is striking, and the two-stage approach offers a balanced combination of structural control and aesthetic refinement.

🎥 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：
https://youtu.be/7GtFnr8ixnM

Before 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：
https://www.runninghub.ai/post/1996508277796126722/?inviteCode=rh-v1111

If 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：
https://www.bilibili.com/video/BV1XW2BBVEqW/

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If you find my content helpful and want to support future creations, you can buy me a coffee ☕。
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👉 Ko-fi：
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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：
https://youtu.be/7GtFnr8ixnM

开始前建议尽量完整地观看视频 —— 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：
https://www.runninghub.ai/post/1996508277796126722/?inviteCode=rh-v1111

打开上方链接即可直接运行该工作流，实时查看生成效果。
如果觉得效果理想，你也可以在本地进行自定义部署。

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