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2026-05-12

🔥 ZIB + ZIT Simple Workflow V7.0 🔥

V7.0 is finally here — and just like V6.0, this update shifts the balance in a BIG way. 🚀

In this release, I’ve reworked the workflow to become significantly more ZIB-dominant, moving away from the older ZIT-heavy approach. Why?
Because we now have a much more refined and sophisticated undistilled ZIB base — Moody Wild V4.0 — and the results are honestly stunning. 🌌✨

The new balance allows the workflow to preserve far more natural detail, structure, lighting, and overall visual quality while remaining extremely flexible and easy to use.

✨ What’s New in V7.0

🔹 Solid preparation for the upcoming Moody Wild V4.0 release
🔹 Noticeably improved body anatomy consistency (greatly because of wild V4)
🔹 New manual detail anything node group
🔹 Slightly faster generation times ⚡
🔹 Multiple QoL improvements and workflow cleanup
🔹 Better organization inspired by my newer workflow releases
🔹 Overall smoother generation experience

This update may look “simple” on the surface, but internally the workflow has been heavily refined for future scalability and image quality improvements. 👀

Thank you all for the incredible support as always ❤️
Have a fantastic day generating!

🔥 ZIB + ZIT Simple Workflow V7.0 🔥

ZIB 主导时代正式开启 ⚡

V7.0 终于来了！这次更新和 V6.0 一样，再次大幅改变了整个工作流的平衡方式。🚀

在这个版本中，我进一步重构了工作流，让它从过去偏向 ZIT 的结构，转变为更加 以 ZIB 为核心 的方案。为什么这么做？

因为现在我们拥有了更加成熟、更加高级的未蒸馏 ZIB 基底 —— Moody Wild V4.0。
而它带来的生成效果真的非常惊艳。🌌✨

新的平衡方式能够在保持极高灵活性与易用性的同时，更好地保留自然细节、画面结构、光影表现以及整体视觉质量。

✨ V7.0 更新内容

🔹 为即将到来的 Moody Wild V4.0 做好了充分准备
🔹 人体结构稳定性明显提升（很大程度归功于 Wild V4）
🔹 全新手动涂抹Detail Anything 节点组
🔹 生成速度略微提升 ⚡
🔹 大量 QoL（体验优化）与工作流整理
🔹 参考新版工作流进行更好的结构优化
🔹 整体生成体验更加顺滑

这个版本表面上看起来或许变化不算特别巨大，但实际上内部已经进行了大量深度优化，为未来的扩展性以及更高的画质提升打下了非常重要的基础。👀

一如既往，非常感谢大家一直以来的支持 ❤️
祝大家生成愉快！

2026-04-20

🔥 ZIB + ZIT Simple Workflow V6.0

V6.0 is here — and this update shifts the balance in a big way.

In this version, I’ve adjusted the workflow to be more ZIB-dominant instead of the previous ZIT-heavy approach. Why? Because we now have a much more sophisticated undistilled ZIB base — Moody Wild x EASON V3.0 — and it delivers absolutely insane results.

This change brings:

• 🎨 Richer color tones

• 🧬 Higher image diversity

• ✨ More expressive and dynamic outputs

On top of that, this update includes:

• ⚙️ Minor quality-of-life improvements

• 🐛 Bug fixes

• ⚡ Optimized generation time

Overall, V6.0 is cleaner, faster, and pushes visual quality further while fully leveraging the strength of the ZIB base.

Good luck and happy generating 🚀
Your support means a lot ❤️

🔥 ZIB + ZIT 简易工作流 V6.0

V6.0 来了，这次是一次比较明显的方向调整。

在这个版本中，我将整体工作流调成了更偏向 ZIB 主导，而不再像之前那样偏向 ZIT。原因很简单——现在我们有了更成熟、更强大的未蒸馏 ZIB 基底：Moody Wild x EASON V3.0，实际效果已经非常让人满意。

这样的调整带来了：

• 🎨 更丰富的色彩层次

• 🧬 更高的图像多样性

• ✨ 更有表现力和变化的画面输出

同时，这次更新还包含：

• ⚙️ 一些体验优化（QoL）

• 🐛 Bug 修复

• ⚡ 生成速度优化

整体来说，V6.0 更干净、更高效，也更好地发挥了 ZIB 基底的潜力。

祝您玩得开心，出图愉快 🚀
感谢支持 ❤️

2026-04-09

V5.0 ZIB+ZIT Dual Ksampler Enhanced

Minor updates on the newer techniques that delivery better results, also consistent with my other newer workflows.

• New upscaler used for further attempt to eliminate the side effect of SD upscale

• QoL updates as my other newer workflows

小更新：采用了一些较新的技术，可以带来更好的效果，同时也与我其他较新的工作流程保持一致。

• 使用了新的放大器（SD upscaler），进一步尝试消除 SD 放大带来的副作用 （莫名的水渍，鸡皮疙瘩，油光）

• 加入了一些使用体验上的优化（QoL），整体流程更加顺手，并与我其他较新的工作流保持一致

2026-03-19

V4.0 ZIB+ZIT Dual Ksampler Enhanced

V4 Dropped. Some optimization and QoL update for Moody Wild V2 better support.

Preview images generated by Moody Wild V2 BASE 45 STEPS undistilled + Moody Porn Mix V10

V4 版本已发布。针对 Moody Wild V2 进行了一些优化和体验改进，以更好地支持 V2。

预览图由 Moody Wild V2 BASE 45 STEPS undistilled + Moody Porn Mix V10 生成。

V3.0 ZIB+ZIT Dual Ksampler Enhanced

Download URLs 下载地址:

------------------------------------------------------------

ZIB Model / Base 模型:

https://civarchive.com/models/2384856?modelVersionId=2714022

（Warning！ Do not download the distilled model, look for the undistilled model）

（注意！ 不要下错成蒸馏模型了 - 要下载未蒸馏模型 undistilled version)

------------------------------------------------------------

Avaible ZIT Models / 可选 Turbo 模型：

https://civarchive.com/models/620406/moody-porn-mix

https://civarchive.com/models/621441/moody-real-mix

https://civarchive.com/models/2384856/moody-wild-mix?modelVersionId=2681752

------------------------------------------------------------

upscaler model / 放大模型：

4x-ultrasharp:

https://openmodeldb.info/models/4x-UltraSharp

1xSkinContrast-SuperUltraCompact:

https://openmodeldb.info/models/1x-SkinContrast-SuperUltraCompact

Before getting into V3, here’s a quick recap of the foundation:

The core idea behind this workflow has always been combining Zimage Base (ZIB) and Zimage Turbo (ZIT) to leverage the strengths of both models.

Why ZIB + ZIT?

• ZIB excels at prompt adherence, composition stability (especially at low resolution), and seed-to-seed variation. It’s ideal for locking in structure and layout early.

• ZIT is significantly faster and strong at refining details, making it perfect for the finishing pass.

• Both models share the same CLIP, VAE, and prompt ecosystem, allowing seamless switching without reloading major components.

Evolution So Far

• V1 used a two-pass KSampler setup:
  ZIB for base composition → ZIT for refinement.

• V2 upgraded to dual Advanced KSamplers, reducing generation time by ~33% while maintaining composition strength and prompt accuracy.

The result is a workflow that balances:

• Strong structure (ZIB)

• Fast refinement (ZIT)

• Efficient pipeline integration

V3 builds on this foundation.

V3 – 简要回顾 & 为什么选择 ZIB + ZIT

在介绍 V3 之前，先快速回顾一下整个工作流的核心思路。

Before diving into V3, here’s a quick recap of the foundation behind this workflow.

整个流程的核心一直都是将 Zimage Base（ZIB） 与 Zimage Turbo（ZIT） 结合使用，发挥两者各自的优势。

The core idea has always been combining Zimage Base (ZIB) and Zimage Turbo (ZIT) to leverage the strengths of both models.

为什么 ZIB + ZIT？

ZIB 的优势：

• 更强的提示词遵循度

• 低分辨率下更稳定的构图能力

• 不同种子之间更高的变化多样性
  → 非常适合在第一阶段锁定整体结构与画面布局

ZIT 的优势：

• 生成速度更快

• 擅长细节刻画与高分辨率精修
  → 适合在第二阶段完成细节和质感强化

同时，ZIB 与 ZIT 共享相同的 CLIP、VAE 与提示词体系，可以无缝衔接，无需反复加载大型组件，使整个流程更加轻量、高效、统一。

版本演进

V1：
使用双 KSampler 两段式流程
ZIB 负责构图 → ZIT 负责细化

V2：
升级为双 Advanced KSampler 架构
在保持构图与提示词强度的前提下，生成时间缩短约 33%

整体思路始终围绕三点：

• ZIB 负责结构

• ZIT 负责速度与细节

• 两者结合实现效率与质量的平衡

V3 正是在这个基础上继续优化与进化。

V2.0 Dual Ksamlers

This is a classic dual Advanced KSampler build using ZIB + ZIT. Huge thanks to the AI artists in the group for sharing their experiments — based on that, I’ve reworked the flow to use a dual Advanced KSampler instead of the old two-pass KSampler setup.

The good stuff 🔥

• V2 cuts generation time by roughly ~33%

• Still hits strong prompt adherence while keeping most of ZIT’s speed

• Image composition is still on par with V1

The trade-offs ⚖️

• Preview is now a noisy latent only (V1 let you see rough image details on the first pass)

• You’ll get a bit more ZIT and slightly less ZIB detail compared to V1, but ZIB’s composition strengths are still very much there

Also tossed in some misc fixes to clean up a few of my earlier mistakes 😅

这是一个经典的双 Advanced KSampler 架构，使用 ZIB + ZIT。非常感谢群里的 AI 艺术家们分享他们的实验成果——在此基础上，我重新调整了流程，改用双 Advanced KSampler，取代了旧的两次 KSampler方案。

亮点 🔥

• V2 将生成时间缩短了大约 33%

• 在保持 ZIT 速度优势的同时，依然具备很强的提示词贴合度

• 图像构图效果仍然与 V1 持平

取舍 ⚖️

• 预览现在只显示带噪的 latent（V1 在第一遍就能看到大致的图像细节）

• 相比 V1，ZIT 成分稍多、ZIB 细节略少，但 ZIB 在构图方面的优势依然非常明显

另外还顺手修了一些杂项问题，清理了我之前的一些小失误 😅

V1.0 2 Ksampler Passes

All showcased result images are arranged as: ZIB only | ZIB + ZIT | ZIT only.

I’ve been experimenting with a new workflow lately. With the release of Zimage Base (ZIB), it introduces several advantages that Zimage Turbo (ZIT) doesn’t currently offer—most notably stronger prompt adherence, better compositional stability at low resolutions, and significantly higher variation when iterating with different seeds. These strengths make ZIB especially well-suited for defining the overall structure and layout of an image early in the pipeline.

However, until a properly optimized speed-up LoRA for ZIB is released, generation times remain relatively slow—unless you’re running on very high-end hardware. Because of that limitation, I developed a workaround:

I first generate a low-resolution pass using ZIB to quickly establish the base composition. This allows for faster iteration, broader variety across seeds, and more reliable prompt interpretation. Once the foundation is set, I switch to Zimage Turbo (ZIT) to refine and complete the finer details at higher resolution.

Why ZIB instead of SDXL, Pony, or other base models?
You can absolutely use those, but the key advantage of ZIB is ecosystem consistency. ZIB and ZIT can share the same CLIP, prompt structure, and VAE, which means everything integrates smoothly without needing to reload or re-import large components. This keeps the workflow lightweight, efficient, and cohesive.

The drawbacks are fairly obvious as well—ZIB cannot directly generate explicit (XXX) content. While private parts can be redrawn or enhanced during the second pass using specialized models like Moody Porn Mix, poses in those scenarios are still hit-or-miss. That said, with more XXX-focused LoRAs for ZIB expected in the near future, the lack of XXX poses should be resolved shortly.

This is an experimental workflow, and likely a temporary one until an official speed-up LoRA for ZIB is released—but it’s a practical and effective approach that’s absolutely worth trying in the meantime.

所有展示的结果分为三类：仅 ZIB | ZIB + ZIT | 仅 ZIT。

最近我一直在尝试一种新的工作流程。随着 Zimage Omni Base（ZIB）的发布，它带来了一些 Zimage Turbo（ZIT）目前尚未具备的优势——最显著的是更强的提示词遵循度、在低分辨率下更好的构图稳定性，以及在使用不同种子迭代时显著更高的变化多样性。这些优势使 ZIB 特别适合在流程早期用来确立图像的整体结构和布局。

然而，在针对 ZIB 发布真正优化好的加速 LoRA 之前，生成时间仍然相对较慢——除非你使用的是非常高端的硬件。正因如此，我设计了一个变通方案：

先用 ZIB 生成一轮低分辨率图像，快速确立基础构图。这样可以实现更快的迭代、在不同种子间获得更广泛的多样性，以及更可靠的提示词理解。一旦基础构图确定，我就切换到 Zimage Turbo（ZIT），在更高分辨率下精炼并完成细节。

为什么选择 ZIB 而不是 SDXL、Pony 或其他基础模型？

当然可以使用那些模型，但 ZIB 的核心优势在于生态系统一致性。ZIB 和 ZIT 可以共享相同的 CLIP、提示词结构和 VAE，这意味着整个流程无需反复加载或导入大型组件，就能实现无缝衔接，从而保持工作流程的轻量、高效和连贯。

缺点也很明显——ZIB 无法直接生成显式（XXX）内容。虽然在第二轮精炼时可以使用 Moody Porn Mix 等专用模型对私密部位进行重绘或增强，但这类场景下的姿势仍然不够稳定。不过，随着未来不久将推出更多专注于 XXX 内容的 ZIB LoRA，姿势问题应该很快得到解决。

这是一个实验性工作流程，很可能只是暂时的——直到官方的 ZIB 加速 LoRA 发布为止。但在目前阶段，这是一个实用且高效的方法，绝对值得一试。