LTX 2.3 Dual Digital Human | IC Edit No-Subtitle Dialogue Workflow

This workflow is designed for LTX 2.3 dual-person digital human dialogue generation, with IC Edit-style control and a strong focus on clean subtitle-free output. Its main purpose is to take a two-person reference image or character scene, generate a controlled dialogue-style video, and keep the final result clean without unwanted subtitles, fake captions, random text, watermark-like marks, overlays, or UI-style artifacts appearing on the screen.

Compared with a single-person digital human workflow, this setup is more demanding because it needs to maintain two character identities at the same time. A good dual-person dialogue video must preserve left-right placement, facial consistency, clothing, body proportion, camera framing, background stability, and interaction logic. If the workflow is not controlled well, the two characters may swap positions, merge faces, duplicate body parts, drift away from the original image, or create random mouth movement that does not match the intended dialogue structure.

The workflow uses LTX 2.3 as the main video generation backbone, with LTX video VAE, LTX audio VAE, image resizing, image-to-video conditioning, LTXVConditioning, LTXVImgToVideoConditionOnly, LTXVConcatAVLatent, LTXVSeparateAVLatent, SamplerCustomAdvanced, ManualSigmas, latent upscaling, tiled VAE decoding, audio decoding, CreateVideo, SaveVideo, and VRAM cleanup logic. This makes it a more complete production workflow rather than a simple one-pass image animation graph.

The core generation design follows a staged rendering structure. The first stage builds the base motion, character presence, camera structure, and dialogue performance from the reference image and prompt conditioning. Later stages continue from the generated latent result with lower sigma values, refining motion stability, facial detail, clothing texture, background consistency, and final visual quality. This staged approach is especially useful for two-person digital human scenes because both subjects need to remain coherent across the full video.

A key feature of this workflow is its dual-character control direction. The workflow is built for restrained dialogue performance rather than chaotic motion. The ideal output should show two people facing the camera or interacting naturally, with subtle head movement, mouth movement, facial expression changes, small hand gestures, and stable body posture. The workflow is not intended to create excessive action; it is designed to create clean, usable talking scenes for AI presenters, short drama dialogue, virtual hosts, product explanation, teaching videos, and social media content.

The no-subtitle direction is another important selling point. Many AI video models may accidentally generate fake subtitles, random text, caption bars, logos, watermarks, or noisy symbols when the prompt contains speech or dialogue. This workflow is designed with clean-output restrictions to reduce those problems and make the generated dialogue video more suitable for direct publishing.

The workflow also uses audio-duration handling, FPS control, frame-count logic, latent preparation, mask / latent routing, latent upscaling, tiled decoding, and final video export. These components help creators generate repeatable dual-person dialogue clips while managing memory pressure and maintaining final output quality.

This workflow is ideal for creators who want to produce two-person AI dialogue videos, dual digital human presentations, character conversation clips, virtual interview scenes, AI short-drama dialogue, Bilibili / YouTube explainers, RunningHub demos, and Civitai workflow previews. If you want to see how LTX 2.3, IC Edit-style control, staged sampling, dual-character stability, no-subtitle restrictions, and final dialogue video export work together, watch the full tutorial from the YouTube link above.

⚙️ Try the Workflow Online

Workflow: https://www.runninghub.ai/post/2054479470995755009?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.

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📺 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/BV1za5y6FE7r/

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/2054479470995755009?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。

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📺 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/BV1za5y6FE7r/

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。