This workflow is designed for LTX 2.3 Distill 1.1 image-to-video generation with VBVR 240K enhancement. Its main purpose is to turn a single reference image into a more stable, cinematic, and physically coherent video, while improving motion control, prompt adherence, texture quality, and final visual polish through a multi-stage LTX 2.3 pipeline.
The workflow uses ltx-2.3-22b-distilled-1.1 as the core checkpoint, combined with Ltx2.3-Licon-VBVR-I2V-240K-R32 as the main image-to-video enhancement LoRA. It also includes an additional distilled LoRA route, Gemma-style LTX text encoding, LTX video VAE, spatial latent upscaling, NAG enhancement, manual sigma control, tiled decoding, and final video export. This makes it more advanced than a basic first-frame animation workflow.
The core strength of this setup is controlled image-to-video consistency. Many I2V workflows can animate an image, but the result often drifts: the subject changes, the camera becomes unstable, the object behavior becomes random, or the scene loses physical logic. This workflow is designed to reduce those problems by using the first frame as a strong visual anchor, then reinforcing the generation through VBVR I2V guidance, LTXVImgToVideoConditionOnly, staged sampling, and latent refinement.
In the uploaded workflow, the example prompt focuses on a realistic commercial shot of a woman pushing a domino tile and triggering a clean chain reaction. This is a useful stress test because domino physics are difficult for video models. The prompt requires each domino to fall only after being hit by the previous one, with no skipping, no floating, no simultaneous collapse, and no random falling. This shows the workflow’s intended use: not just generating beautiful motion, but testing whether LTX 2.3 can follow ordered cause-and-effect action.
The workflow also includes a strong negative prompt to suppress subtitles, low resolution, blurry frames, scene cuts, scene transitions, warping, extra limbs, extra hands, static frames, watermarks, overlays, and motion glitches. This is important for I2V production because unwanted cuts or unstable body parts can quickly break the illusion of a continuous video.
The multi-stage structure is another key part. The first stage creates the base video motion. Later stages use LTX spatial upscaling and additional sampler passes to refine detail, improve texture, and make the final frames cleaner. Instead of exporting a rough first pass, the workflow pushes the video through a stronger finishing route, making it more suitable for Civitai previews, RunningHub demos, YouTube tutorials, Bilibili showcases, product videos, commercial-style shots, and AI short video experiments.
This workflow is ideal for creators who want a practical LTX 2.3 I2V setup with better consistency than a simple one-pass graph. If you want to see how Distill 1.1, VBVR 240K, image conditioning, NAG guidance, latent upscaling, and final video export work together, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2047659084744232962/?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/BV1x6oLB8E1d/
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/2047659084744232962/?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/BV1x6oLB8E1d/
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。