This workflow is designed for LTX 2.3 video subtitle removal and visual text cleanup, built as a reference 0.5 version for repairing unwanted text pollution inside AI-generated or authorized video materials. Its main purpose is to help creators remove subtitles, captions, random text artifacts, watermark-like overlays, and other unwanted visual marks from a video while keeping the original scene, motion, lighting, and unmasked areas as stable as possible.
Unlike a simple crop, blur, or cover-up method, this workflow is based on mask-guided video repair. The unwanted subtitle or text region is isolated through a mask pipeline, then the model reconstructs the damaged area using the surrounding visual context. This makes the result more natural because the repaired area is regenerated to match the original background, rather than being hidden by a flat patch or blurred block.
The workflow uses an LTX 2.3 repair route with video VAE, audio VAE, LTX conditioning, custom sampling, mask processing, and audio-video export logic. It includes structured SetNode / GetNode routing for base model, video VAE, audio VAE, CLIP, FPS, final masks, audio, and resolution management. This makes the graph more modular and easier to reuse in a production environment, especially when the user needs to repeatedly process different videos with similar subtitle or text-contamination problems.
A key part of this workflow is the mask preparation section. The workflow includes mask handling tools such as BlockifyMask, final mask routing, and latent noise mask logic. This matters because video subtitle repair depends heavily on the mask quality. If the mask is too small, the text may remain. If the mask is too large, the model may unnecessarily change clean background areas. A good mask should cover the unwanted text fully while preserving enough surrounding context for the model to rebuild the background naturally.
The workflow also keeps the audio route in the graph. Audio can be carried through the pipeline and reattached to the final output, which makes the workflow more practical for actual video repair instead of isolated frame testing. The graph includes audio retrieval, audio trimming / duration logic, LTXVAudioVAEEncode, LTXVConcatAVLatent, and final video creation. This allows the repaired result to remain usable as a complete video output.
The sampling route uses LTX 2.3 video conditioning, CFG guidance, manual sigma control, SamplerCustomAdvanced, latent conditioning, and final decode / export logic. This gives the workflow stronger control over reconstruction compared with a one-click repair pass. The goal is to keep the unmasked regions unchanged while letting the subtitle region be regenerated in a visually consistent way.
This workflow is useful for cleaning AI-generated videos, removing accidental prompt text, fixing subtitle contamination, repairing unwanted overlay captions, restoring damaged areas in authorized footage, and preparing cleaner results for Civitai, RunningHub, YouTube, and Bilibili publishing. Since this is marked as a reference 0.5 version, it is best understood as a practical test workflow: useful for learning the subtitle-removal structure, mask logic, and LTX 2.3 repair process before building a more polished production version.
If you want to see how the mask is prepared, how the LTX 2.3 repair route handles subtitle regions, and how the final cleaned video is exported with audio, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2048741719289630722/?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/BV1DT9zBbEZu/
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/2048741719289630722/?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/BV1DT9zBbEZu/
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。
