Create README.md

README.md

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+---
+language:
+- en
+- de
+- es
+- fr
+- ja
+- ko
+- zh
+- it
+- pt
+license: other
+license_name: ltx-2-community-license-agreement
+license_link: https://github.com/Lightricks/LTX-2/blob/main/LICENSE.md
+pipeline_tag: image-to-video
+tags:
+- gguf
+- quantized
+- image-to-video
+- text-to-video
+- video-to-video
+- audio-to-video
+- ltx-video
+- lightricks
+- ltx-2.5
+base_model: Lightricks/LTX-2.5
+---
+
+# LTX-2.5 Distilled GGUF
+
+This repository provides quantized GGUF formats of the distilled transformer from [Lightricks/LTX-2.5](https://huggingface.co/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
+
+| File | Size | Description |
+|---|---|---|
+| **LTX-2.5-Distilled-Q3_K_M.gguf** | 11.5 GB | Smallest footprint with the highest quantization loss. Best for strict memory limits. |
+| **LTX-2.5-Distilled-Q4_K_S.gguf** | 13.9 GB | Slightly smaller than Q4_K_M, balancing speed and limited VRAM/RAM. |
+| **LTX-2.5-Distilled-Q4_K_M.gguf** | 15.1 GB | Recommended baseline. Good balance of visual fidelity, motion consistency, and memory footprint. |
+| **LTX-2.5-Distilled-Q5_K_M.gguf** | 16.8 GB | Higher precision, retaining strong prompt adherence with minimal degradation. |
+| **LTX-2.5-Distilled-Q6_K.gguf** | 18.7 GB | Near-unquantized visual quality, very low quantization loss. |
+| **LTX-2.5-Distilled-Q8_0.gguf** | 23.6 GB | Largest quantized footprint, nearly indistinguishable from the original bf16 weights. |
+
+## 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](https://github.com/Lightricks/LTX-2/blob/main/LICENSE.md). 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](https://huggingface.co/Lightricks/LTX-2.5).