New #1 open-source AI video generator is here! Fast + 4K + audio + low vram

Overview

The video introduces LTX-2, an open-source video-generation model, and demonstrates how to use it in ComfyUI. The presenter describes it as free to run offline, fast, capable of native audio generation, and able to produce video at resolutions up to 4K. They also say it can maintain quality and consistency beyond 10 seconds, with generations of up to 20 seconds.

The presenter cites an Artificial Analysis leaderboard that ranked LTX-2 as the leading open-source video model at the time of recording. Demonstrations include spoken dialogue, sound effects, varied accents and artistic styles, and longer scenes. The presenter notes that Japanese dialogue can be unreliable.

Features and Workflow

• ComfyUI setup: Update ComfyUI, install the ComfyUI-LTXVideo custom nodes, and use the repository’s ready-made workflows. The tutorial assumes ComfyUI is already installed.
• Full and distilled models: The full model is presented as higher quality but slower. Distilled models generate in fewer steps and run faster, with a potential quality trade-off. A distilled LoRA can also speed up the full workflow.
• Text-to-video: Load a model, spatial upscaler, and text encoder, then enter a prompt and choose the video dimensions and duration. One example follows a dialogue prompt and depicts the requested emotional action.
• Image-to-video: Provide a starting image and prompt. Examples include a speaking person, an opera performance with orchestral accompaniment, and an anime scene.
• LoRAs: Add fine-tunes for specific styles, characters, actions, effects, or camera movements. The camera-movement example uses a “dolly left” LoRA. The presenter notes that LoRAs may need to be applied in both generation stages.
• Reference guidance: A supplied video can guide the output using extracted pose, edges (Canny), or depth. The tutorial demonstrates pose and edge guidance, including replacing a missing Canny node with a compatible edge-detector node.
• Video detailer and upscaler: The V2V workflow adds detail to low-resolution video. The presenter finds the results limited compared with dedicated upscalers but presents it as another option.

Low-VRAM Guidance

The tutorial explains that the model files do not all need to fit in GPU memory; VRAM plus system RAM must be sufficient. Its example estimates roughly 36 GB total for a distilled FP8 model, the upscaler, and a quantized Gemma text encoder.

The presenter says the workflow can run with very little VRAM—including around 2 GB—if the computer has enough RAM, though some work may be offloaded to system memory. Suggested optimizations include:

• Using a smaller, 4-bit quantized Gemma encoder.
• Bypassing prompt enhancement when it is unnecessary.
• Reserving some VRAM for other applications.
• Using ComfyUI’s no-VRAM mode to offload models.
• Applying a code change shared by the presenter to reduce memory use.

Speaker and Source

The tutorial and model assessment are presented by a single speaker from the AI Search channel. The video also includes generated audio and sample dialogue as demonstrations.