LTX 2.3 Audio-Reactive Animation Workflow

Watch the full video first if you want to understand how this LTX 2.3 audio-reactive animation workflow works in practice. The video shows how one image and one audio track can be connected into a staged animation pipeline, how the video length follows the audio duration, and how to launch the workflow online without rebuilding the full ComfyUI environment locally. This ComfyUI workflow is designed for LTX 2.3 audio-reactive animation generation. Its main purpose is to take a source image and an audio file, then generate a video clip whose duration and visual rhythm are organized around the audio input. Instead of creating a silent image-to-video clip and manually matching it to music later, this workflow brings audio into the generation structure from the beginning, making it more suitable for music animation, MV fragments, sound-driven visual clips, and social media video production. The workflow is built around the LTX 2.3 video generation route. It uses image reference preparation, audio duration detection, automatic frame calculation, LTXVImgToVideoConditionOnly, LTXVConditioning, CFGGuider, ManualSigmas, SamplerCustomAdvanced, LTXVLatentUpsampler, AV latent combination, tiled VAE decoding, and CreateVideo output. The graph also includes VRAM purge tools, fps control, latent size checking, image resizing, mask handling, and multi-stage refinement. The audio side is one of the most important parts of this workflow. The input audio is measured through an Audio Duration node, then converted into a frame count through a math expression. This keeps the generated video length aligned with the audio length and reduces manual calculation errors. The workflow uses 24 fps logic and LTX-friendly temporal length rules, so the video can follow a cleaner generation structure instead of using arbitrary frame counts. The image side provides the visual identity. The source image is resized and prepared, then injected into the video process through LTXVImgToVideoConditionOnly. This allows the generated animation to preserve the original character, object, scene, or visual style while still producing motion. The same image reference can be reused across later refinement stages, helping the workflow maintain continuity after latent upscaling. The generation pipeline uses a three-stage structure. The first stage builds the initial animation and base composition. The second stage performs latent-space upscaling and refinement. The third stage applies final high-resolution polish before decoding and video output. This is more stable than a single-pass workflow because each stage has a clear purpose: establish motion, improve structure, then refine quality. Compared with ordinary image-to-video workflows, this graph is more useful for audio-based content. A normal I2V workflow may create motion, but the clip length and final output often need to be adjusted manually afterward. This workflow connects audio duration, frame calculation, image guidance, staged sampling, and final video output into one pipeline. It is especially useful for short MV visuals, music-reactive character clips, animated cover art, rhythm-based visual experiments, AI music videos, Bilibili demonstrations, YouTube content, RunningHub showcases, and Civitai workflow examples. Main features: LTX 2.3 audio-reactive animation workflow One image + one audio input Audio duration detection Automatic frame count calculation 24 fps generation logic Image-guided video animation LTXVImgToVideoConditionOnly reference control Three-stage rendering structure ManualSigmas and SamplerCustomAdvanced control LTXVLatentUpsampler high-resolution refinement Mask and latent handling for staged consistency CreateVideo final output with audio Suggested workflow: Prepare a clean source image and a clear audio file first. The image should have a readable subject, strong composition, and enough visual detail for animation. The audio should have stable volume and a clear rhythm or atmosphere. Load the image and audio into the workflow, then let the audio duration node calculate the target video length. Write a prompt that describes the motion, camera behavior, visual mood, and how the subject should respond to the music. Start with a short test to check whether the animation length, identity preservation, and motion direction are correct. If the motion feels too weak, make the prompt more explicit. If the image drifts too much, strengthen the reference structure and simplify the motion language. Once the first stage is stable, continue through latent upscaling and final refinement. ⚙️ RunningHub Workflow Try the workflow online right now — no installation required. 👉 Workflow: https://www.runninghub.ai/post/2058938733910650882?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/BV1gLGo6kEbF/ ☕ 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/2058938733910650882?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/BV1gLGo6kEbF/ 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。