[!IMPORTANT] Notice: A fully uncensored version is currently in development and will be added to this repository soon.

Qwen-Image-2.1 GGUF

GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights.

Benchmark

GGUF files

Q4_K_M is recommended for the best balance of size and quality.

Text Encoders & VAE

Companion model files packaged for ComfyUI:

Usage

Use the model with ComfyUI and ComfyUI-GGUF.

All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository.

1. Download & File Placement

Download the files and place them in their respective ComfyUI directories:

ComfyUI/
└── models/
    ├── diffusion_models/
    │   └── qwen-image-2.1-Q4_K_M.gguf         # Choose one GGUF quantization (Q4_K_M recommended)
    ├── text_encoders/
    │   └── qwen3vl_8b_bf16.safetensors        # Or qwen3vl_8b_int8_convrot.safetensors (recommended for lower memory)
    └── vae/
        └── qwen_image_2.1_vae_bf16.safetensors

2. ComfyUI Setup

1. Install ComfyUI-GGUF: Use the maintained fork with native Qwen-Image 2.1 support by cloning leejet/ComfyUI-GGUF into your custom nodes:

   cd ComfyUI/custom_nodes
   git clone https://github.com/leejet/ComfyUI-GGUF
   

   (Note: If you have the older city96/ComfyUI-GGUF installed and encounter an Unknown model architecture! error, update to the leejet fork above or add ModelQwenImage to tools/convert.py).
2. Node Configuration:
   • Diffusion Model: Add the Unet Loader (GGUF) node and select your downloaded .gguf file.
   • Text Encoder: Add the standard CLIPLoader node, select qwen3vl_8b_bf16.safetensors (or int8), and set type to qwen_image.
   • VAE: Add the standard VAELoader node and select qwen_image_2.1_vae_bf16.safetensors.
3. Official Workflows:
   • You can use the official Comfy-Org workflow templates: Text-to-Image or Image Edit.
   • In the workflow, simply replace the default UNETLoader node with Unet Loader (GGUF).

Memory & Performance Notes

• Optimal Setup (GPU + RAM): Keep the GGUF diffusion model in GPU VRAM (where speed is crucial during sampling) and let the text encoder run in / offload to System RAM (CPU). Because text encoding only runs once per prompt, this saves 9–17 GB of VRAM with virtually zero impact on generation speed.
• Recommended Configuration:
  • Diffusion: qwen-image-2.1-Q4_K_M.gguf (~4.6 GB in VRAM)
  • Text Encoder: qwen3vl_8b_int8_convrot.safetensors (~9.35 GB in RAM)
• Low VRAM Mode: If you experience VRAM out-of-memory errors, start ComfyUI with the --lowvram argument.

Uncensored

This GGUF release has no built-in safety checker or content filter. It generates adult, NSFW, and sensitive imagery directly without prompt refusals or blacked-out images. Output behavior depends solely on the input prompts and the environment in which the model is executed.

Source and build

• Source model: Qwen/Qwen-Image-2.1
• Text encoder & VAE source: Comfy-Org/Qwen-Image-2.1
• Source revision: b3179ad355be050328e483a9dfdd9e60cd62adfa
• Conversion: stable-diffusion.cpp commit 1330cebae8f2ba99249df846cc0c9444fcbd4308
• License: Qwen Research License
• Checksums: SHA256SUMS