CIVITAI / Workflows

LTX 2.3 Subtitle Remover | AI Video Cleanup 0.5 Workflow

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 v