LTX 2.3 v1.1 in ComfyUI: 8GB setup and comparison

This tutorial compares LTX 2.3 v1.0 and v1.1, then explains a ComfyUI text-to-video workflow for an 8GB GPU. The speaker uses a Gigabyte A16 laptop with an RTX 5060 and 32GB of DDR5 RAM. The examples use the raw models without LoRAs. In these samples, the speaker finds that v1.1 follows lighting and style prompts more closely and produces steadier movement.

The local AI setup section covers Python 3.12, ComfyUI updates, and the speaker's recommendation to use CUDA 13 or above with a matching PyTorch installation and current Nvidia Studio drivers. The workflow supports either GGUF or FP8 weights. The speaker prefers FP8 on the RTX 5060 and reports faster generation with Sage attention, but warns that it must be installed before enabling it. Older GPUs may need default settings.

For 8GB VRAM, the recommended order is frame interpolation before upscaling. A 2x multiplier converts 25fps footage to 50fps. The speaker reports roughly four to five minutes to interpolate a ten-second 1080p clip. Upscaling 50fps footage to 4K took 15 to 18 minutes and caused substantial SSD swapping with 32GB RAM.

The hardware limits remain relevant: the speaker could not consistently generate FP8 clips beyond 12 seconds at 1080p and 25fps. The comparison also documents distorted hands, unstable faces, changing objects, and failures during fast action.