---

license: other

license_name: qwen-research

license_link: >-

https://huggingface.co/Qwen/Qwen-Image-2.1-PE-T2I/blob/f3ed7985c788ad75b3ab7223e0c4c51e2a43545b/LICENSE

base_model: Qwen/Qwen-Image-2.1-PE-T2I

base_model_relation: quantized

library_name: gguf

pipeline_tag: text-generation

tags:

• gguf
• llama-cpp
• prompt-rewriting
• text-to-image

---

Qwen-Image-2.1-PE-T2I GGUF Q8_0

This repository provides the Q8_0 GGUF quantization of Qwen-Image-2.1-PE-T2I and the files needed to run it with llama.cpp.

Original model: Qwen/Qwen-Image-2.1-PE-T2I.

Download llama.cpp binaries

Download a binary package for your operating system and processor architecture from the llama.cpp releases page.

For Apple Silicon MacBooks and Macs, download the b11564 macOS arm64 binary package.

Run

Set the environment variables below, replacing the example paths with the actual absolute paths to your model and binary directories. You can run the commands from any directory.

export MODEL_DIR="/path/to/Qwen-Image-2.1-PE-T2I-GGUF-Q8_0"
export LLAMA_BIN="/path/to/llama-b11564"

"${LLAMA_BIN}/llama-completion" \
  --model "${MODEL_DIR}/Qwen-Image-2.1-PE-T2I-Q8_0.gguf" \
  --system-prompt-file "${MODEL_DIR}/system_prompt.txt" \
  --jinja --chat-template "$(cat "${MODEL_DIR}/chat_template.jinja")" \
  --conversation --single-turn --no-display-prompt --simple-io --color off \
  --ctx-size 32768 --n-gpu-layers 99 --threads 8 \
  --n-predict 16256 --seed 42 \
  --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0 --repeat-penalty 1.0 \
  --verbosity 1 \
  --prompt "A corgi playing guitar in the rain, 16:9"

LightX2V APP

A native macOS app for local image generation with Qwen-Image-2.1-Turbo on Apple Silicon. It uses LightX2V for image inference and llama.cpp for optional prompt enhancement, with a simple interface for text-to-image and image-to-image creation.

Download LightX2V APP and try local inference with Qwen-Image-2.1-Turbo and prompt enhancement (PE) on your MacBook!

LightX2V APP is open source—we welcome you to explore and build upon LightX2V APP and LightX2V, and appreciate your feedback and contributions.