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LTX2.3_comfy Quantized GGUF

06/30/2026 · Inksilopedia

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LTX2.3_comfy Quantized GGUF

Docker offers the quickest path to setting up this model locally.

Use the instructions provided below to complete the setup.

The installer automatically pulls the model (could be multiple GBs).

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

🔗 SHA sum: 4e21ceba3a24c97f8fa01ea1db5af892 | Updated: 2026-06-23
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  • Processor: 6-core 3.5 GHz minimum required
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk: high-speed SSD 120 GB to cache model layers
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The LTX2.3_comfy model represents a significant advancement in generative AI, combining *high‑fidelity* text‑to‑image synthesis with an intuitive user interface. It leverages a refined transformer architecture that balances computational efficiency with detailed visual coherence, making it suitable for both creative professionals and hobbyists. The model has been optimized for *rapid inference*, delivering consistent quality across a wide range of styles while maintaining a modest memory footprint. Users appreciate its seamless integration with popular workflow tools, thanks to built‑in support for common file formats and API endpoints. A quick reference table below outlines the core technical specifications that differentiate LTX2.3_comfy from earlier versions.

Specification Value
Parameters 2.3B
Training Data 500M images
Inference Time <0.1s
Memory Usage <4GB
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