This workflow is designed for LTX 2.3 video inpainting and authorized video cleanup, focusing on removing unwanted subtitles, watermarks, overlay text, random AI letters, logo marks, and visual text artifacts from a video while keeping the original motion and scene continuity as stable as possible. It is not a simple blur or crop solution. The goal is to reconstruct the damaged area frame by frame so the repaired region blends naturally with the original footage.
The workflow uses an LTX 2.3 video repair route with a GGUF-based LTX 2.3 model, LTX23 video VAE, LTX23 audio VAE, LTX conditioning, and a custom sampling structure. It also applies two important LoRA directions: an Edit Anything global LoRA and an inpaint masked R2V LoRA. This combination makes the workflow more suitable for targeted video repair, because the model is guided to understand both the original video context and the masked area that needs to be regenerated.
The main logic is mask-based video restoration. The user provides a video and a mask area that covers the unwanted text, subtitle, watermark, or damaged region. The workflow then prepares the video latent, applies the mask as a noise mask, and lets LTX 2.3 regenerate only the target area while preserving the rest of the frame. This is especially important for video repair, because uncontrolled regeneration can easily change the face, background, lighting, camera motion, or scene details outside the repair zone.
The prompt and negative prompt are also critical. The negative prompt explicitly suppresses subtitles, captions, Chinese subtitles, watermarks, logos, overlay text, unreadable text, ghost text, flicker, color shift, inconsistent background, blurry patches, and duplicated edges. This helps the workflow understand that the target is clean reconstruction, not adding new text or replacing the scene with unrelated content.
The workflow also includes audio-aware routing. Audio can be encoded through the LTX audio VAE and combined with video latent processing, while the final result is exported through VHS_VideoCombine as an MP4 file. This makes the workflow suitable for practical video cleanup instead of isolated frame repair.
This setup is useful for repairing your own AI-generated videos, removing accidental prompt text, fixing subtitle contamination, cleaning logo artifacts from authorized material, restoring damaged video areas, and preparing cleaner Civitai, RunningHub, YouTube, or Bilibili showcase clips. If you want to see how the mask is prepared, how the LTX inpaint LoRAs are connected, and how the repaired video is exported, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2053501073624715266/?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/BV1Qa5s6tESM/
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/2053501073624715266/?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/BV1Qa5s6tESM/
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。