Watch the full video first if you want to understand how this LTX 2.3 image-to-video workflow works in practice. The video shows how one image can be turned into a complete video clip, how the staged rendering pipeline improves stability, and how to launch the workflow online without rebuilding the full ComfyUI environment locally.
This ComfyUI workflow is designed for LTX 2.3 image-to-video generation, using OmniNFT, Relay-style prompt control, and the distilled 1.1 model route to turn a single image into a finished video clip. The main purpose of this workflow is to make one-image video generation more stable, more controllable, and more production-ready than a basic image-to-video graph.
The workflow starts from a single input image. The image is resized and prepared through Image_Resize_longsize and LTXVPreprocess, then passed into LTXVImgToVideoConditionOnly as the main visual condition. This allows the source image to guide the video identity, composition, subject placement, and overall visual style while still giving the LTX model enough freedom to generate motion.
The workflow is built around the LTX 2.3 distilled 1.1 route. It uses LTX video conditioning, LTX audio-video latent logic, an LTX Audio VAE, LTX2_NAG negative guidance, a universal negative prompt, and a three-stage rendering pipeline. The graph also includes Seed Everywhere, fps control, EmptyLTXVLatentVideo, LTXVEmptyLatentAudio, LTXVConcatAVLatent, LTXVSeparateAVLatent, ManualSigmas, CFGGuider, SamplerCustomAdvanced, LTXVLatentUpsampler, VAEDecodeTiled, and final video output.
The key generation structure is divided into three stages. The first stage focuses on initial composition and base motion. It uses the image condition to establish the main character or scene and generate the first stable video latent. The second stage performs latent-space expansion, reconditioning, and continuation, helping the result gain more structure and detail. The third stage performs high-resolution refinement after latent upscaling, making the final output cleaner and more suitable for publishing.
Compared with ordinary image-to-video workflows, this graph is more structured. A simple one-pass workflow may create motion but often struggles with identity drift, weak detail, inconsistent lighting, or unstable composition. This version uses staged sampling, manual sigma control, image conditioning, negative guidance, latent upscaling, and tiled decoding to improve control and final quality.
This workflow is suitable for turning portraits, character designs, AI illustrations, cinematic stills, product visuals, anime scenes, fantasy concepts, and cover images into short video clips. It is especially useful for creators who want to make AI video previews, social media clips, MV fragments, Bilibili demonstrations, YouTube shorts, RunningHub showcases, or Civitai workflow examples from a single strong image.
The final output is decoded through tiled VAE decoding and assembled into a playable video. This makes the workflow practical for both testing and actual content production.
Main features:
LTX 2.3 image-to-video workflow
One image to complete video clip
OmniNFT + Relay-style prompt control
Distilled 1.1 model route
LTXVImgToVideoConditionOnly image guidance
LTXVPreprocess input preparation
LTX2_NAG negative guidance support
Universal negative prompt structure
Three-stage rendering pipeline
ManualSigmas and SamplerCustomAdvanced control
LTXVLatentUpsampler high-resolution refinement
Tiled VAE decoding and final video output
Suggested workflow:
Prepare one clean source image first. The subject should be clear, visually stable, and not too small in the frame. Load the image into the workflow, then write a prompt that describes the desired motion, camera behavior, lighting, atmosphere, and video style. Run a short test first to check whether the image identity is preserved and whether the motion direction is correct. If the video is too static, strengthen the motion description. If the subject changes too much, reduce aggressive prompt wording and keep the image guidance stronger. Once the first stage looks stable, continue through latent upscaling and final high-resolution refinement.
⚙️ RunningHub Workflow
Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2058326590685274113?inviteCode=rh-v1111
If the results meet your expectations, you can later deploy it locally for customization.
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📺 Bilibili Updates (Mainland China & Asia-Pacific)
If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1yRGj6XEaM/
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⚙️打开下方链接即可在线体验,无需安装。
👉 工作流: https://www.runninghub.ai/post/2058326590685274113?inviteCode=rh-v1111
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/BV1yRGj6XEaM/
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。
