Qwen-Image-2.1 Uncensored Quantized

GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights.

Benchmark

Uncensored Quantized Files

Q4_K_M is recommended for the best balance of size and quality for GGUF. INT8 ConvRot and NVFP4 offer high performance on supported GPUs, while MLX formats are optimized for Apple Silicon.

GGUF files

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-UC-Q4_K_M.gguf      # Choose one GGUF quantization (Q4_K_M recommended) or .safetensors
    ├── 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 (for .gguf files) or standard UNETLoader (for .safetensors files) and select your downloaded 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) when using GGUF files.

Memory & Performance Notes

• Optimal Setup (GPU + RAM): Keep the GGUF / Quantized 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-UC-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 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 & comfy-kitchen
• License: Qwen Research License
• Checksums: SHA256SUMS