LTX 2.3 + VBVR Image-to-Video | Three-Stage NAG Render & VRAM-Optimized Workflow

This workflow is designed for LTX 2.3 + VBVR image-to-video generation with a three-stage rendering structure, NAG-style enhancement, and VRAM-conscious optimization. Its main purpose is to take a single reference image, preserve its visual identity, and push it through a stronger LTX 2.3 video pipeline so the final result has better motion control, clearer detail, stronger consistency, and a more polished cinematic finish than a simple one-pass I2V workflow.

The workflow uses LTX 2.3 as the main video generation backbone, with ltx-2.3-22b-dev as the core checkpoint route. It also includes Gemma 3 12B text encoding, LTX video VAE, image preprocessing, LTXVImgToVideoConditionOnly, LTXVLatentUpsampler, tiled VAE decoding, manual sigma control, multiple sampler stages, and a distilled LoRA route. This makes the graph suitable for creators who want a more production-ready LTX 2.3 image-to-video setup rather than a quick rough preview.

The key advantage is the three-stage render design. The first stage builds the base motion from the input image and prompt conditioning. This stage is responsible for establishing the main action, camera behavior, character movement, and overall temporal direction. The second stage refines the generated latent video, helping improve structure, texture, and motion stability. The third stage adds another layer of polishing, making the final result cleaner and more suitable for publishing.

VBVR is useful here because it strengthens image-to-video control. In practice, many I2V workflows fail because the model drifts away from the original image: the character changes, the outfit mutates, the background shifts, or the motion becomes random. This workflow is built to reduce that kind of drift by using the source image as a strong visual anchor while letting the prompt describe clear motion, expression, environment movement, and cinematic atmosphere.

The workflow example focuses on a dark fantasy cinematic scene: an adult silver-haired female warrior riding a massive black beast, holding the hand of a silver-haired fox-ear woman in a purple-red magic hall. The prompt includes character identity, costume, creature design, lighting, smoke, floating magic cards, mouth movement, dialogue performance, facial reactions, cloth motion, hair motion, and camera stability. This makes it a strong test case for complex character interaction, fantasy atmosphere, and motion consistency.

The workflow also uses strong negative prompting to suppress common video failures such as low resolution, blurry frames, flat lighting, static output, no movement, scene cuts, subtitles, overlays, watermark artifacts, warping, extra hands, extra limbs, and broken body parts. This is especially important for fantasy character video because multiple bodies, props, hair, cloth, creature anatomy, and magical effects can easily become unstable.

Another practical strength is VRAM optimization. The workflow uses tiled decoding and controlled resolution settings so creators can push better quality without making the graph unnecessarily heavy. The use of latent upscaling and tiled VAE decoding helps improve final clarity while keeping the process more manageable for RunningHub and local ComfyUI deployment.

This workflow is ideal for LTX 2.3 image-to-video testing, VBVR motion-control experiments, fantasy character animation, AI short drama clips, cinematic creature scenes, talking character shots, Civitai previews, RunningHub demos, and YouTube / Bilibili workflow tutorials. If you want to see how the source image, VBVR-style I2V control, three-stage render structure, NAG enhancement, latent upscaling, and VRAM-optimized tiled decoding work together, watch the full tutorial from the YouTube link above.

⚙️ Try the Workflow Online

👉 Workflow: https://www.runninghub.ai/post/2039686946766721026?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/BV1UE9uBzE8q/

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/2039686946766721026?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/BV1UE9uBzE8q/

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。