CIVITAI / Workflows

LTX 2.3 Text Artifact Remover | AI Video Cleanup Workflow

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