LTX 2.3 Multi-Image Keyframe Smooth Transition Workflow

This workflow is designed for LTX 2.3 multi-image first-frame / last-frame video generation with high consistency and smoother visual transitions. Its main purpose is to help creators connect multiple reference images into one coherent video sequence, while reducing random drift, abrupt visual jumps, identity changes, and unstable motion between keyframes.

Unlike a basic image-to-video workflow that only uses one starting image, this workflow is built around a multi-stage keyframe guidance structure. The input images can act as strong visual anchors across the video timeline, helping LTX 2.3 understand where the video should start, how the visual state should develop, and what target image or final frame it should move toward. This makes the workflow especially useful for storyboard animation, character transformation, scene transition shots, before-and-after effects, product motion sequences, cinematic loops, and AI short-video production.

The workflow uses LTX 2.3 as the core video generation system, with LTX video VAE, LTX audio VAE, LTXVEmptyLatentAudio, EmptyLTXVLatentVideo, LTXVImgToVideoConditionOnly, LTXVConcatAVLatent, LTXVSeparateAVLatent, SamplerCustomAdvanced, LTXVLatentUpsampler, tiled VAE decoding, and multi-stage refinement logic. This gives the graph a complete audio-video latent structure instead of only producing silent image motion. The audio latent route can remain connected through the generation pipeline, making the output more suitable for real video publishing.

A key strength of this workflow is the staged sampling design. The first sampling stage builds the base video motion from the keyframe structure and prompt conditioning. Later stages reconnect video and audio latents, apply additional sampling passes, and use LTX latent upscaling to improve detail and visual continuity. This layered approach helps the final output feel more polished than a single-pass generation.

The workflow also uses manual sigma schedules, such as a longer first pass with high-to-low sigma values and shorter refinement passes using lower sigma ranges. This matters because multi-image transition generation needs balance: too much denoise may destroy the input references, while too little denoise may not create enough motion. The staged sigma design gives the workflow a more controlled path from rough motion to refined transition.

The LTXVImgToVideoConditionOnly nodes are important for reference-image control. They allow image conditioning to be injected into the video latent process, helping the model preserve visual identity and transition toward the target frame more reliably. This is useful when the user wants the character, object, camera composition, color palette, or environment to remain recognizable across the full clip.

The workflow also includes latent upscaling and tiled decoding. This helps improve the final output quality while keeping memory usage more manageable. For AI video workflows, high consistency is not only about motion. It also depends on whether the texture, face, clothing, background, lighting, and fine details remain stable after enhancement. The upscaling and tiled decode route helps push the result toward a cleaner final video.

This workflow is ideal for creators who want to test LTX 2.3 multi-image keyframe control, first-frame / last-frame video generation, smooth transition clips, high-consistency character motion, scene evolution, and cinematic AI video production. If you want to see how the reference images, LTX 2.3 conditioning, staged sampler passes, latent upscaling, and final smooth transition output are connected, watch the full tutorial from the YouTube link above.

⚙️ Try the Workflow Online

👉 Workflow: https://www.runninghub.ai/post/2039960294918721537?inviteCode=rh-v1111

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 & 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: https://www.bilibili.com/video/BV18B9gB1Eni/

I will continue updating model resources on Quark Drive:

👉 https://pan.quark.cn/s/20c6f6f8d87b

These resources are mainly prepared for local users, making creation and learning more convenient.

⚙️ 在线体验工作流

👉 工作流： https://www.runninghub.ai/post/2039960294918721537?inviteCode=rh-v1111

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： https://www.bilibili.com/video/BV18B9gB1Eni/

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。