Qwen-Image-Edit_ComfyUI Locally via LM Studio Quantized GGUF Easy Build

Qwen-Image-Edit_ComfyUI Locally via LM Studio Quantized GGUF Easy Build

The fastest method for installing this model locally is by using Docker.

Check out the detailed setup guide below to begin.

The installer auto-downloads and deploys the entire model pack.

An automated hardware sweep ensures the system will select the best tuning parameters.

🔍 Hash-sum: 4b8d1c806e9be7f6f7bf0513f122af33 | 🕓 Last update: 2026-06-29



  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • GPU: high memory bandwidth GPU for next-gen local AI pipeline

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
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