LTX2.3 Multi Reference Image Workflow
A new workflow uses continuous image re-injection to maintain visual consistency across AI-generated video frames.
A developer has introduced a sophisticated new workflow for the LTX 2.3 video generation platform, designed to solve a key challenge in AI video: maintaining visual consistency. The 'Multi Reference Image Workflow' allows creators to input multiple reference images and generate a video where characters, objects, and scenes remain coherent across frames. This is a major improvement over earlier methods where AI-generated videos often suffered from 'morphing' or inconsistent details as the sequence progressed.
The workflow's technical core is a 4-stage sampling process. It first uses an LCM (Latent Consistency Model) sampler for two stages to establish the coarse structure or 'skeleton' of the video. It then switches to an Euler sampler for two final stages to refine fine details. A key innovation is the continuous re-injection of the original reference images throughout the generation and upscaling process, not just at the start, which is crucial for preserving likeness and details.
Finally, the workflow employs the 'LTX Sequencer' node by What Dreams Cost to easily manage multiple input images and uses RIFE interpolation and RTX Super Resolution for final upscaling. The developer has shared the complete workflow files and a tutorial video, emphasizing its complexity and their gratitude to the open-source community. This release represents a significant, practical advancement in user-controlled AI video synthesis.
- Uses a 4-stage hybrid sampling process (LCM for structure, Euler for detail) for stable video generation.
- Continuously re-injects source images during upscaling to maintain visual consistency, a key improvement over standard methods.
- Leverages the 'LTX Sequencer' node and final processing with RIFE interpolation and RTX Super Resolution.
Why It Matters
This provides a practical, open-source method for creators to achieve consistent character and scene continuity in AI-generated videos.