This workflow is designed for LTX 2.3 1.1 + VBVR image-to-video generation. Its main purpose is to take a single still image and turn it into a controlled cinematic video, while using VBVR to improve motion logic, prompt obedience, scene stability, and character consistency.
The workflow is built around LTX 2.3 Distilled 1.1 as the main video generation base, with VBVR I2V LoRA support, Gemma 3 text encoding, LTX video VAE, spatial latent upscaling, multi-stage sampling, NAG-style enhancement, and final video export. Compared with a basic image-to-video workflow, this setup is more focused on making the model follow clear action instructions instead of producing random movement.
A key point in the uploaded workflow is the VBVR prompt rule note. VBVR is not mainly used to change the visual style. Its value is that it helps the model execute motion logic more literally. The prompt should not only describe a beautiful scene; it should describe what moves, what stays still, what happens first, what happens next, and what the final pose should be. In other words, the best prompt structure is: starting state, action process, and ending state.
This makes the workflow especially useful for creators who want more predictable image-to-video results. Instead of writing vague words such as “cinematic, dreamy, dynamic,” the workflow encourages direct instructions: the character tilts her head, smiles, steps forward, raises the fan, leans toward the camera, keeps eye contact, and then stops in a stable final pose. This kind of ordered action description is exactly where VBVR is most useful.
The example prompt in the workflow animates a woman standing in a warm Japanese-style room. The room, doorway, lantern light, wooden floor, and paper sliding doors are kept stable, while only the woman, hair, sleeves, fan, mouth, and subtle camera movement are allowed to move. This shows the intended production logic: keep the background locked, control the subject motion, and avoid unnecessary scene drift.
The workflow also includes a strong negative prompt to suppress subtitles, Chinese captions, low resolution, blur, static frames, watermarks, scene cuts, transitions, warping, extra hands, extra limbs, and unstable body parts. These restrictions are important for LTX image-to-video work because even one unwanted cut or body distortion can ruin a short cinematic clip.
The pipeline uses multiple sampler and refinement stages rather than only one generation pass. The first pass creates the main motion from the image and prompt. Later stages use latent refinement and spatial upscaling to improve detail, texture, and final image quality. This makes the final video more suitable for Civitai previews, RunningHub demos, YouTube tutorials, Bilibili showcases, and social media publishing.
This workflow is ideal for AI character animation, cinematic image-to-video tests, fantasy portraits, talking-style motion prompts, fan movement, subtle body performance, controlled camera push-ins, and prompt research around VBVR. If you want to see how VBVR prompt logic, LTX 2.3 image conditioning, staged refinement, 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/2045068954359570434/?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/BV17Td5BgETn/
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/2045068954359570434/?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/BV17Td5BgETn/
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。