CIVITAI / Workflows

LTX 2.3 Video Control & HD Enhancement Workflow

This workflow is designed for LTX 2.3 video control and high-definition enhancement. Its main purpose is to take a video or image-guided video input, preserve the original motion structure, and enhance the final result through LTX 2.3 generation, control preprocessing, latent upscaling, audio-video latent routing, and tiled decoding. It is built for creators who want a cleaner, sharper, more stable LTX 2.3 video output instead of a rough low-resolution generation pass. The workflow uses LTX 2.3 as the main video generation backbone, with ltx-2.3-22b-dev_transformer_only_fp8_scaled as the core model route. It also includes Gemma-style LTX text encoding, LTX23 video VAE, LTX23 audio VAE, LTXVPreprocess, EmptyLTXVLatentVideo, LTXVEmptyLatentAudio, LTXVConcatAVLatent, LTXVSeparateAVLatent, SamplerCustomAdvanced, LTXVLatentUpsampler, VAEDecodeTiled, LTXVAudioVAEDecode, and final video output logic. This makes the graph more advanced than a simple image-to-video or video-to-video workflow because it is structured around both control and enhancement. A major strength of this workflow is its video-control preparation section. The graph includes VideoHelperSuite video information reading, frame rate extraction, frame count handling, image resizing, and LTX preprocessing. It also includes optional control preprocessing routes such as DepthCrafter, Canny edge extraction, and DW pose preprocessing. These control modules are useful when the creator wants the generated result to follow the source video’s structure, depth, body movement, edge layout, or camera rhythm more closely. The workflow is also designed for HD improvement. Instead of decoding only the first latent result, it uses LTXVLatentUpsampler and additional refinement sampling stages to push the video toward a higher-quality output. This helps improve detail density, texture clarity, subject sharpness, and final frame polish. The tiled VAE decoding route is important here because high-resolution video decoding can easily become memory-heavy or unstable. Tiled decoding allows the workflow to decode large frames more safely and cleanly. The audio-video route is another practical part of the graph. Audio latent and video latent are connected, separated, decoded, and preserved through the generation process. This makes the workflow suitable for real video production rather than silent visual testing. For AI s