Updated: May 11, 2026
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This workflow is designed for LTX 2.3 AI video text artifact cleanup, focusing on repairing unwanted subtitles, random AI-generated letters, watermark-like text pollution, overlay captions, ghost text, logo artifacts, and other visual contamination inside video frames. Its main purpose is to help creators clean their own generated videos or authorized materials by reconstructing the damaged area instead of simply blurring, cropping, or covering it.
The workflow uses an LTX 2.3 video inpainting route based on a GGUF LTX 2.3 distilled model, LTX23 video VAE, LTX23 audio VAE, Gemma-style text conditioning, custom sampler control, and LoRA-assisted repair. The key repair direction is built around LTX 2.3 Edit Anything and inpaint-style LoRA logic, allowing the model to understand that the masked area should be regenerated while the unmasked region should remain unchanged.
The core prompt is very direct: remove the subtitle, watermark, or text inside the masked area, reconstruct the occluded background naturally and seamlessly, and keep the original scene, camera angle, lighting, motion, composition, and all unmasked regions unchanged. This is exactly what makes the workflow useful for AI video cleanup. It is not trying to redesign the whole shot. It is trying to surgically repair the polluted area.
The negative prompt is also targeted for this use case. It suppresses subtitles, captions, text, Chinese subtitles, watermarks, logos, overlay text, random letters, unreadable text, ghost text, flicker, color shift, inconsistent background, blurry patches, and duplicated edges. These negative controls are important because text-removal workflows often fail by leaving behind soft stains, repeated edges, or new unreadable letters. This setup tries to reduce those artifacts during regeneration.
The workflow also contains an audio latent route, using LTXVAudioVAEEncode and LTXVConcatAVLatent. Even when the repair task is mainly visual, keeping the LTX audio / video latent structure makes the pipeline more suitable for actual video production. The video latent is sampled through SamplerCustomAdvanced, then separated, cropped, decoded, and prepared for output. This makes it closer to a real repair workflow rather than a single-frame test.
This setup is useful for fixing AI-generated video mistakes, removing accidental prompt text, cleaning subtitle pollution, repairing logo-like artifacts in authorized footage, restoring damaged visual regions, and preparing cleaner examples for Civitai, RunningHub, YouTube, and Bilibili. The most important usage rule is mask quality: the mask should cover the unwanted text area cleanly while leaving enough surrounding context for the model to reconstruct the background.
If you want to see how the mask, prompt, LTX 2.3 repair LoRA, audio/video latent route, and final reconstruction are connected, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2050245386517921794/?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/BV1uMRFBJEPu/
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/2050245386517921794/?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/BV1uMRFBJEPu/
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。