LTX-2.5-Distilled-GGUF

LTX-2.5 Distilled GGUF

<video controls autoplay loop muted style="width: 100%; border-radius: 8px;" src="https://huggingface.co/Abiray/LTX-2.5-Distilled-GGUF/resolve/main/video/LTX_2.5_t2v.mp4"></video> This repository provides quantized GGUF formats of the distilled transformer from Lightricks/LTX-2.5. These weights are highly optimized for local execution, allowing you to run high-fidelity video and audio generation workflows on hardware with memory constraints while retaining the core visual fidelity of the original base model.

Available Quantizations

Required Supporting Components (Text Encoders & VAEs)

LTX-2.5 operates on a split-component architecture. To run these GGUF diffusion models in environments like ComfyUI or local Python pipelines, you must also fetch the official Text Encoders and VAEs directly from the upstream Lightricks/LTX-2.5 repository.

Text Encoders

Download these from the text_encoders directory:

• gemma4-12b-with-proj-ltx-2.5-bf16.safetensors (26.3 GB) — The full unquantized Gemma 4 12B text encoder.
• gemma4-12b-with-proj-ltx-2.5-comfy-int8-convrot.safetensors (15.4 GB) — An optimized INT8 text encoder specifically designed for lower VRAM usage in ComfyUI.

VAEs (Video & Audio)

Download these from the vae directory:

• ltx-2.5-video-vae-bf16.safetensors (1.47 GB) — The primary DiffVAE for highest-quality video decoding.
• ltx-2.5-video-vae-conv-bf16.safetensors (1.45 GB) — Convolutional video VAE for faster, lighter processing.
• ltx-2.5-audio-vae-bf16.safetensors (365 MB) — Audio VAE and vocoder required for synchronized audio generation.

📈 Latent Upscalers (Recommended)

To achieve maximum visual fidelity and smoothness, it is highly recommended to use the official Stage 2 latent upscalers alongside this GGUF model.

You can download them from the official Lightricks LTX-2.5 Repository here.

Available Upscaler Models

• ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0.safetensors (996 MB)
  • Function: Doubles the pixel resolution of your generated video for sharp, 4K-like crispness.
• ltx-2.5-latent-temporal-upscaler-x2-bf16-1.0.safetensors (262 MB)
  • Function: Doubles the framerate (e.g., 24fps to 48fps) by interpolating frames, resulting in buttery-smooth motion.

ComfyUI T2V and I2V GGUF Workflows

To make running this GGUF model as plug-and-play as possible, I have included two pre-configured ComfyUI workflows in this repository.

You can view and download them directly here:

• 🎥 Text-to-Video Workflow (T2V)
  • Best for: Generating videos from scratch using only text prompts and audio conditioning.
• 🖼️ Image-to-Video Workflow (I2V)
  • Best for: Animating a starting input image (like a portrait or landscape) while using a text prompt to guide the motion and camera tracking.

About the Original Model

LTX-2.5 is an open-world model built for local execution and fine-tuning. It specializes in generating synchronized, high-fidelity video and audio from text, image, and video inputs.

Key Features of LTX-2.5

• Native Multishot Generation: Generate connected scenes in a single pass holding character identity, environment, lighting, voice, and visual style across cuts.
• Diffusion Fidelity Rendering: The model dynamically allocates compute based on scene complexity, rendering flawless detail where needed.
• Distilled Efficiency: These GGUF checkpoints are derived from the distilled model, capturing much of the full 22B model's capabilities in a significantly faster, smaller package.

Usage Requirements

Ensure your inference engine (such as llama.cpp or compatible ComfyUI GGUF loader nodes) supports the LTX-2.5 DiT architecture. Depending on the quantization tier, appropriate system RAM or GPU VRAM size must be allocated to accommodate the file sizes listed above.

License & Limitations

These weights fall under the original LTX-2.x Community License. Commercial and production use is permitted at no cost for entities under $10M annual revenue.

• This model is not intended or able to provide factual information.
• Prompt following is heavily influenced by prompting style.
• The model may fail to generate videos that match the prompt perfectly or may generate artifacts in highly complex scenes.

For full architectural details, official multishot prompting guides, and citation information, please refer to the Original LTX-2.5 Model Card.