CIVITAI / Workflows

LTX 2.3 Image to Video OmniNFT + Relay One-Image Film Workflow

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, neg