LTX 2.3 Video Extension OmniNFT + Relay Vertical Widening Workflow

Watch the full video first if you want to understand how this LTX 2.3 video extension workflow works in practice. The video shows how a vertical video can be expanded into a wider frame, why OmniNFT + Relay guidance matters, and how to launch the workflow online without rebuilding the full ComfyUI setup locally. This ComfyUI workflow is designed for LTX 2.3 video extension, vertical-to-wide frame expansion, and guided video outpainting. The main purpose of this workflow is to take a narrow or vertical video-style input and expand it into a wider composition while keeping the original subject, motion, and visual identity as stable as possible. Instead of simply stretching the image or cropping the video, this workflow uses LTX 2.3 generation to synthesize new side areas and create a more natural wide-frame result. The workflow is built around the LTX 2.3 distilled 1.1 route. It uses ltx-2.3-22b-dev-dare-merged-distilled-1.1.safetensors as the main checkpoint, Gemma-based text encoding, LTX Audio VAE, LTXVConditioning, LTXAddVideoICLoRAGuide, LTXVCropGuides, LTXVConcatAVLatent, LTXVSeparateAVLatent, ManualSigmas, CFGGuider, SamplerCustomAdvanced, VAEDecodeTiled, and VHS_VideoCombine. The graph also includes image resizing, color correction, image switching, and image concatenation nodes, which are important for comparing and assembling the expanded output. The key idea is guided extension. The source frame or reference image is first prepared through resizing and LTXVPreprocess. Then LTXAddVideoICLoRAGuide injects the visual guide into the LTX generation process, helping the model preserve the original content while expanding beyond the initial frame boundary. LTXVCropGuides helps manage the guided area so the model can focus on the extension region instead of freely changing the whole image. The workflow also uses audio-video latent logic. Empty video latent and empty audio latent are created, then combined through LTXVConcatAVLatent before sampling. After generation, LTXVSeparateAVLatent separates the video and audio latent streams again. This makes the workflow compatible with LTX 2.3 audio-video generation structure and final video output. Compared with ordinary video resizing, this workflow does not only change the canvas size. It generates new visual content for the expanded region. Compared with basic image outpainting, it works in a video pipeline, so it is more suitable for motion clips, MV fragments, portrait-to-landscape conversion, social media repurposing, and cinematic reframing. It is useful when you have a vertical clip but want to create a wider version for YouTube, Bilibili, horizontal previews, cover videos, or cinematic presentation. The final output is decoded through tiled VAE decoding, optionally adjusted through color correction, assembled through ImageConcanate, and exported through VHS_VideoCombine as an MP4 video. This makes the workflow practical for direct online testing and publishing. Main features: LTX 2.3 video extension workflow Vertical-to-wide video expansion OmniNFT + Relay-style guided generation Distilled 1.1 model route LTXAddVideoICLoRAGuide visual guidance LTXVCropGuides guided area control ImageResizeKJv2 and LTXVPreprocess input preparation ManualSigmas and SamplerCustomAdvanced sampling AV latent concatenation and separation Color correction and image switching support ImageConcanate for side-by-side assembly VHS_VideoCombine MP4 export Suggested workflow: Prepare a vertical or narrow source image/video frame first. Make sure the main subject is clear and not too close to the edge unless you intentionally want strong side expansion. Load the source into the workflow, check the resize and guide settings, then run a short test first. If the expanded area looks too weak, strengthen the guide or adjust the prompt. If the original subject changes too much, reduce aggressive prompt wording and keep the guide structure stable. Once the expansion looks natural, use the final video combine section to export the widened MP4 result. ⚙️ RunningHub Workflow Try the workflow online right now — no installation required. 👉 Workflow: https://www.runninghub.ai/post/2058327114209910786?inviteCode=rh-v1111 If the results meet your expectations, you can later deploy it locally for customization. 🎁 Fan Benefits: Register to get 1000 points + daily login 100 points — enjoy 4090 performance and 48 GB super power! 📺 Bilibili Updates (Mainland China & Asia-Pacific) If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown. 📺 Bilibili Video: https://www.bilibili.com/video/BV1yRGj6XEaM/ ☕ Support Me on Ko-fi If you find my content helpful and want to support future creations, you can buy me a coffee ☕. Every bit of support helps me keep creating — just like a spark that can ignite a blazing flame. 👉 Ko-fi: https://ko-fi.com/aiksk 💼 Business Contact For collaboration or inquiries, please contact aiksk95 on WeChat. ⚙️打开下方链接即可在线体验,无需安装。 👉 工作流: https://www.runninghub.ai/post/2058327114209910786?inviteCode=rh-v1111 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/BV1yRGj6XEaM/ 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。