Remix 3.0 Image-to-Video Workflow

This workflow is designed for Remix 3.0 image-to-video generation. Its main purpose is to take a prepared reference image, analyze its visual content, expand the user’s simple motion idea into a structured video prompt, and then generate a dynamic video through a Remix / Wan-style I2V pipeline with NAG enhancement, VRAM optimization, and frame interpolation.

Compared with a pure text-to-video workflow, this setup starts from an existing image. That makes it more practical when the creator already has a character design, AI cover image, anime-style scene, commercial visual, or cinematic frame and wants to animate it without losing the original composition. The input image is resized through image_scale_pixel_v2, passed into a prompt-analysis route, and then used as the visual anchor for the video generation stage.

A key part of this workflow is the built-in prompt-director logic. The graph uses a local llama.cpp / Qwen-style vision-language model route to read the input image and rewrite the user’s short idea into a more complete video prompt. The prompt is structured around “initial state anchoring,” ordered action steps, final freeze-frame state, and professional camera instructions. This is useful because video models usually perform better when the prompt describes exact motion order instead of only describing a beautiful scene.

The example prompt in the workflow describes a wet-haired adult Asian female character standing in front of a convenience store at night, wearing a dark biker-style outfit and holding a transparent umbrella. The action is broken into a logical sequence: she holds her hair, shifts her posture, swings her hair, rotates her waist, moves her weight forward, raises her chin, and ends in a stable dynamic pose. The camera follows the action with a medium focal length, lateral movement, and a smooth push-in toward the face. This shows the workflow’s main strength: turning a static character image into a directed, physically coherent motion shot.

The main generation route uses Remix 3.0 image-to-video models, including high-lighting and low-lighting branches. These two branches help handle different lighting conditions and give the workflow more flexibility for night scenes, neon lighting, dark environments, cinematic contrast, and stylized character shots. The pipeline also applies SageAttention optimization and WanVideoNAG guidance to improve control, reduce drift, and strengthen prompt adherence during generation.

The video generation itself uses a two-stage KSamplerAdvanced structure. The first stage builds the rough motion and latent direction, while the second stage continues from the first latent result to complete the video more cleanly. This staged approach helps reduce random motion, weak subject control, and unstable final frames.

The workflow also includes VRAM management nodes such as PainterVRAM, PurgeVRAM, and clean GPU utilities, making it more suitable for heavier ComfyUI video workflows. After generation, GIMMVFI frame interpolation is used to make the video smoother by inserting additional intermediate frames.

This workflow is ideal for AI character animation, anime image-to-video, cinematic portrait motion, social media video covers, short-form character shots, RunningHub demos, Civitai previews, and prompt-to-motion testing. If you want to see how the image input, prompt rewrite, Remix 3.0 I2V route, NAG control, VRAM optimization, and interpolation are connected, watch the full tutorial from the YouTube link above.

⚙️ Try the Workflow Online

👉 Workflow: https://www.runninghub.ai/post/2036428488873353218?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/2036428488873353218?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。