<div data-youtube-video><iframe width="640" height="480" allowfullscreen="true" autoplay="false" disablekbcontrols="false" enableiframeapi="false" endtime="0" ivloadpolicy="0" loop="false" modestbranding="false" origin playlist src="https://www.youtube.com/embed/wh-UA7B0q3c?rel=1" start="0"></iframe></div>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 short videos, character clips, music-driven scenes, product demos, cinematic loops, and social media publishing, keeping audio and visual timing together is a major advantage.This workflow is especially useful for creators who already have a video structure and want to improve it with LTX 2.3 rather than starting completely from zero. It can be used for video enhancement, controlled video regeneration, pose-guided clips, depth-guided cinematic shots, AI video cleanup, higher-resolution remakes, and RunningHub / Civitai workflow demonstrations.If you want to see how the control video, LTX 2.3 model route, Depth / Canny / Pose preprocessing, latent upscaling, tiled decoding, and final HD video export are connected, watch the full tutorial from the YouTube link above.⚙️ Try the Workflow Online👉 Workflow: <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2040812593555709953?inviteCode=rh-v1111">https://www.runninghub.ai/post/2040812593555709953?inviteCode=rh-v1111</a>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.🎁 Fan Benefits: Register now to get 1000 points, plus 100 daily login points — enjoy 4090-level performance and 48 GB of powerful compute!📺 Bilibili Updates (Mainland China &amp; 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: <a target="_blank" rel="ugc" href="https://www.bilibili.com/video/BV1gaSfBgEqz/">https://www.bilibili.com/video/BV1gaSfBgEqz/</a>I will continue updating model resources on Quark Drive:👉 <a target="_blank" rel="ugc" href="https://pan.quark.cn/s/20c6f6f8d87b">https://pan.quark.cn/s/20c6f6f8d87b</a>These resources are mainly prepared for local users, making creation and learning more convenient.⚙️ 在线体验工作流👉 工作流： <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2040812593555709953?inviteCode=rh-v1111">https://www.runninghub.ai/post/2040812593555709953?inviteCode=rh-v1111</a>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： <a target="_blank" rel="ugc" href="https://www.bilibili.com/video/BV1gaSfBgEqz/">https://www.bilibili.com/video/BV1gaSfBgEqz/</a>我会在 夸克网盘 持续更新模型资源：👉 <a target="_blank" rel="ugc" href="https://pan.quark.cn/s/20c6f6f8d87b">https://pan.quark.cn/s/20c6f6f8d87b</a>这些资源主要面向本地用户，方便进行创作与学习。

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