doometernalcum/Qwen-Image-2.1-Uncensored-GGUF

[!IMPORTANT] The uncensored GGUFs are now available! If you encounter any issues or would like to share your generated images, feel free to post them in the Community section.

Qwen-Image-2.1 Uncensored GGUF

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

Benchmark

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Uncensored GGUF Files

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

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)
    ├── 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. 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`).

1. 1.Node Configuration:
2. 2.Diffusion Model: Add the `Unet Loader (GGUF)` node and select your downloaded .gguf file.
3. 3.Text Encoder: Add the standard `CLIPLoader` node, select qwen3vl_8b_bf16.safetensors (or int8), and set `type` to `qwen_image`.
4. 4.VAE: Add the standard `VAELoader` node and select qwen_image_2.1_vae_bf16.safetensors.
5. 5.Official Workflows:
6. 6.You can use the official Comfy-Org workflow templates: Text-to-Image or Image Edit.
7. 7.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-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 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