Updated: May 18, 2026
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This ComfyUI workflow is designed for LTX 2.3 long MV generation, audio-driven video creation, and for-loop style automatic music video production. Unlike a simple image-to-video workflow that only generates one short clip, this workflow focuses on longer video output by combining audio duration detection, automatic frame calculation, image-to-video conditioning, audio-video latent processing, latent upscaling, multi-stage sampling, and final video assembly.
The workflow is built around LTX 2.3, using ltx-2.3-22b-dev as the main video model, Gemma 3 12B as the text encoder, LTX 2.3 spatial upscaler for latent enhancement, and motion/control LoRA support for stronger video consistency. It can take image input, audio input, prompts, and automatically calculate the number of frames needed for the video. The frame logic follows the LTX-compatible 8n+1 rule, helping users avoid frame-count errors when matching video duration to music or narration.
A key part of this workflow is the automatic duration system. The audio duration is read, converted into frame length, and aligned with the required LTX frame structure. This makes the workflow more practical for MV production because users do not need to manually calculate every segment. The workflow also uses LTXVConditioning, LTXVImgToVideoConditionOnly, LTXVConcatAVLatent, and LTXVSeparateAVLatent to connect image guidance, audio-video latent logic, and video generation.
The workflow is structured for long-form generation. Instead of forcing the whole MV into one single heavy render, it uses a staged process. The first stage creates the base motion and visual direction. Later stages can continue, refine, upscale, and improve the latent video result. This makes it easier to build longer music videos, character MVs, digital idol clips, cinematic visual loops, and stylized AI video segments.
The workflow also includes LTXVLatentUpsampler for higher-quality output. This allows the video to be generated more efficiently at a manageable stage first, then enhanced later through latent upscaling and additional refinement. This is useful for balancing speed, quality, and VRAM usage.
Final output is handled through VHS_VideoCombine, which combines the generated frames with the audio track into a finished MP4 video. This makes the workflow suitable for actual publishing, not just frame preview. It can be used for YouTube, Bilibili, RunningHub demonstrations, Civitai workflow showcases, social media clips, and AI music video experiments.
Main features:
- LTX 2.3 long MV generation workflow
- Audio-driven video creation
- Automatic audio duration detection
- 8n+1 compatible frame calculation
- Image-to-video conditioning
- Multi-image input support
- Audio-video latent processing
- ManualSigmas and SamplerCustomAdvanced control
- LTXVLatentUpsampler for quality improvement
- Final MP4 output with audio
- Suitable for long MV, music video, digital idol, character video, and AI visual storytelling
Suggested workflow:
Prepare your audio first, then prepare one or more reference images. Use a clear prompt describing the subject, scene, camera movement, lighting, mood, and MV atmosphere. Let the workflow calculate the frame count automatically. Start with a shorter test segment, confirm the motion and identity are stable, then use the loop-style structure to extend and refine the full MV.
🎥 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/eqTjKOURnF0
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/2055888057307549697?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/BV1CSLJ6vEMQ/
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🎥 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/eqTjKOURnF0
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/2055888057307549697?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/BV1CSLJ6vEMQ/
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。