Designed for creative professionals who need fast and efficient workflows.
Generate videos in seconds
Extend videos forward and backward, or use keyframes to guide the generation process
Complete 3D camera control
LTXV now implemented in LTX Studio
The 2B model is recommended for real-time production.
Fully open source - code and weights are available
Choose between full precision and quantized capabilities
Supports I2V | T2V | V2V | Video Extension
Runs on local GPUs, low VRAM support
Native ComfyUI and diffusers integrations
1) Source: Hyung et al., 'Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling', arXiv preprint arXiv:2411.18664, 2024. https://arxiv.org/abs/2411.18664
Spatial-Temporal Guidance (STG)
Enhances denoising control for more stable video output.
Spatial-Temporal Guidance (STG) is a breakthrough in video diffusion that significantly improves denoising control. By optimizing temporal consistency, STG reduces flickering and enhances stability, making it ideal for high-quality, AI-generated video.
2) Source: Liu et al., 'Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model', arXiv preprint arXiv:2411.19108, 2024. https://arxiv.org/abs/2411.19108
TeaCache (2x Inference Speedup)
Caching mechanism for faster processing.
The new TeaCache system dramatically accelerates video generation by leveraging an advanced caching mechanism. By reusing key computational steps, TeaCache reduces inference time by up to 2x, allowing for faster, more efficient AI-driven video creation.
Inversion
Precision noise reduction for higher-quality editing.
FlowEdit enables high-fidelity image reconstruction, allowing users to refine AI-generated content with unmatched precision. By effectively reducing noise while maintaining key structural details, Inversion ensures smoother transitions between real and stylized outputs.
3) Source: 'ComfyUI-LTXTricks', GitHub Repository. https://github.com/logtd/ComfyUI-LTXTricks
4) Source: 'q8-ltx-video', GitHub Repository. https://github.com/sayakpaul/q8-ltx-video
Kernel Optimization
Improves AI efficiency using Q8 kernels for low-resource devices.
The Q8 kernel optimization dramatically enhances efficiency by reducing memory consumption and processing time. By leveraging quantized computation, Q8 enables faster inference speeds while maintaining high-quality outputs, making AI-driven video generation more accessible to low-resource devices.
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