Qwen-Image-Edit_ComfyUI 100% Private PC Quantized GGUF Full Method

Deploying this model locally is quickest when done via a simple curl command.

Review and follow the instructions below.

The client handles the setup, pulling gigabytes of data automatically.

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

đŸ–¹ HASH-SUM: e654ba9fac147216388f940907de57e8 | đŸ“… Updated on: 2026-07-02



  • Processor: high single-core performance needed for token latency
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk: high-speed SSD 120 GB to cache model layers
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

The Qwen-Image-Edit_ComfyUI model leverages a state‑of‑the‑art diffusion framework to deliver precise image editing capabilities directly within the ComfyUI environment. It supports high‑resolution outputs and enables operations such as object removal, inpainting, and style transfer with minimal latency. A conditional guidance mechanism ensures semantic consistency across edited regions, preserving the original context while applying modifications. The architecture employs a dual‑encoder design that combines a vision encoder for detailed feature extraction and a text encoder for contextual understanding. Users can integrate the model into existing node‑based workflows without extensive retraining, making advanced editing accessible to both developers and artists. Below is a quick comparison of key performance metrics that highlight its efficiency and quality relative to similar tools.

Metric Value
Resolution 2048×2048
Inference Time ~120ms
PSNR 38.5 dB