Remix 3.0 + Z-Image Turbo Text-to-Video Workflow

This workflow is designed for Remix 3.0 + Z-Image Turbo text-to-video creation. Its main purpose is to help creators start from a written idea, generate or prepare a strong visual reference, transform the prompt into a more video-ready motion description, and then produce a dynamic video through a Remix / Wan-style video generation route with VRAM optimization, NAG enhancement, and frame interpolation.

The workflow combines several practical stages. First, it includes a Z-Image Turbo text-to-image section for generating a visual starting point from a text prompt. This is useful when the creator does not already have a perfect first frame and wants to quickly create a strong image asset before entering the video stage. The Z-Image side uses a lightweight generation route with LoRA support, VAE decoding, and a simple prompt structure, making it suitable for fast image concept creation.

The second important part is the prompt-director section. The workflow includes a local Qwen3-VL / llama.cpp instruction route that can read the image and rewrite the idea into a more complete video prompt. Instead of using a short vague sentence, the generated prompt describes scene background, character identity, lighting type, lens behavior, composition, subject motion, action sequence, camera movement, and final pose. This helps the video model understand not only what should appear, but also how the shot should move over time.

The main video generation section uses Remix 3.0 style high-lighting and low-lighting model branches. The workflow loads both high-lighting and low-lighting routes, applies SageAttention optimization, adds WanVideoNAG guidance, and then processes the latent video through two-stage KSamplerAdvanced sampling. This structure is useful because video generation often needs better separation between motion construction and final detail refinement. The first stage builds the main motion, while the second stage continues from the latent result to complete the clip more cleanly.

The workflow is especially useful for text-to-video shots that require clear action. The example prompt describes a character on a city rooftop at night, turning from a back-facing pose, lifting a helmet, stepping toward the camera, performing a body turn and hair movement, and ending with a stable confident close-up. This type of prompt is much stronger than a simple “woman turns around” prompt because it defines the beginning, middle, and ending states of the shot.

The workflow also includes a negative prompt to reduce common video failures such as oversaturated colors, overexposure, static frames, blurred details, subtitles, low quality, malformed hands, broken faces, messy backgrounds, extra limbs, and wrong walking direction. This is important for Remix-style video generation because complex body motion, hair movement, camera push-in, and character posing can easily become unstable if the prompt is too loose.

Another practical feature is VRAM management. The workflow uses PainterVRAM, PurgeVRAM, clean GPU utilities, tiled decoding, and model cleanup logic so creators can run a heavier multi-stage pipeline more safely. It also includes GIMMVFI frame interpolation, which can make the final video feel smoother by inserting intermediate frames after generation.

This workflow is ideal for AI video creators who want a more complete text-to-video pipeline: generate a first visual idea with Z-Image Turbo, rewrite the prompt into a usable motion script, run Remix 3.0 video generation, enhance guidance with NAG, optimize VRAM usage, and smooth the final output with interpolation. If you want to see how the full prompt-to-image-to-video pipeline is connected, watch the full tutorial from the YouTube link above.

⚙️ Try the Workflow Online

👉 Workflow: https://www.runninghub.ai/post/2036368662243844097?inviteCode=rh-v1111

Open the link above to run the workflow directly online and view the generation results in real time.

If the results meet your expectations, you can also deploy it locally for further customization.

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📺 Bilibili Updates (Mainland China & Asia-Pacific)

If you are in Mainland China or the Asia-Pacific region, you can watch the video below for workflow demos and a detailed creative breakdown.

📺 Bilibili Video: https://www.bilibili.com/video/BV1GQQ6BhEcc/

I will continue updating model resources on Quark Drive:

👉 https://pan.quark.cn/s/20c6f6f8d87b

These resources are mainly prepared for local users, making creation and learning more convenient.

⚙️ 在线体验工作流

👉 工作流： https://www.runninghub.ai/post/2036368662243844097?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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📺 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： https://www.bilibili.com/video/BV1GQQ6BhEcc/

我会在 夸克网盘 持续更新模型资源：

👉 https://pan.quark.cn/s/20c6f6f8d87b

这些资源主要面向本地用户，方便进行创作与学习。