This workflow is designed for IC Edit-style subtitle and watermark removal, built on an LTX 2.3 video restoration pipeline. Its main purpose is to take an existing video with hardcoded subtitles, captions, random AI text, logo overlays, signatures, platform watermarks, or semi-transparent marks, and reconstruct a cleaner video result while preserving the original motion, subject identity, camera movement, lighting, and scene composition.
Unlike a simple blur, crop, mosaic, or overlay method, this workflow uses a generative restoration approach. It does not just cover the unwanted text area. Instead, it uses LTX 2.3, subtitle-removal IC LoRA, watermark-removal IC LoRA, video frame extraction, prompt-guided reconstruction, latent inpainting-style processing, tiled VAE decoding, and final video recombination to rebuild the hidden background more naturally.
The workflow uses ltx-2.3-22b-dev-dare-ties-distilled-1.1 as the main model route, with LTXVAudioVAELoader, CheckpointLoaderSimple, LTXAVTextEncoderLoader, LTXVConditioning, LTXAddVideoICLoRAGuide, LTXVImgToVideoConditionOnly, VAEEncodeForInpaint, SamplerCustomAdvanced, LTXVCropGuides, VAEDecodeTiled, VHS_LoadVideo, and VHS_VideoCombine. It also loads two dedicated IC LoRA models: ltx2.3-ic-subtitles-remove-general-lora and ltx2.3-ic-watermark-remove-general-lora. This makes the workflow focused on cleanup and reconstruction rather than ordinary image-to-video generation.
The positive prompt is specifically written for removal tasks. It asks the model to remove subtitles, captions, hardcoded text, AI-generated garbled text, platform watermarks, logo overlays, signatures, and semi-transparent marks. At the same time, it asks the model to restore the underlying image using surrounding visual context while preserving facial features, body shape, object boundaries, lighting, texture continuity, camera motion, and scene composition. This is the correct logic for video cleanup: erase the unwanted layer, but do not destroy the original scene.
The negative prompt suppresses common restoration failures such as blur, oversaturation, pixelation, low resolution, grain, distortion, noise, compression artifacts, JPEG artifacts, glitches, watermark, text, logo, signature, copyright marks, subtitles, distorted sound, saturated sound, and loud audio artifacts. This helps reduce the chance that the workflow removes one text artifact but creates another.
The workflow also keeps the audio and timing structure through the VideoHelperSuite route. VHS_LoadVideo extracts video frames and audio, while VHS_VideoCombine recombines the repaired frames with the original audio into a final MP4. This is important for real publishing use because many frame-only cleanup workflows lose the audio or break the original timing.
This workflow is ideal for AI video cleanup, subtitle removal, watermark removal, logo removal, random text cleanup, hardcoded caption repair, social-media video restoration, AI-generated video polishing, RunningHub demos, Civitai previews, YouTube examples, and Bilibili workflow showcases. If you need a clean video result without visible subtitles, fake text, logo overlays, or watermark artifacts, this workflow provides a practical IC Edit-style restoration route.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2054546081706463234?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/BV1za5y6FE7r/
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/2054546081706463234?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。
🎁 粉丝福利: 注册即送 1000 积分,每日登录 100 积分,畅玩 4090 体验 48 G 超级性能!
📺 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/BV1za5y6FE7r/
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。
