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Qwen-Image-2.1-Uncensored-GGUF

Text-to-Image
GGUF
qwen
image-generation
comfyui
comfyui-gguf
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Qwen-Image-2.1-Uncensored-GGUF / README.md
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metadata 
license: other
license_name: qwen-research
base_model:
  - Qwen/Qwen-Image-2.1
base_model_relation: quantized
pipeline_tag: text-to-image
library_name: gguf
tags:
  - gguf
  - qwen
  - image-generation
  - comfyui
  - comfyui-gguf

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

Qwen-Image-2.1 benchmark

Uncensored GGUF Files

Quantization File Size
BF16 qwen-image-2.1-UC-BF16.gguf 14.23 GB
FP8 qwen-image-2.1-UC-fp8.safetensors 6.63 GB
INT8 ConvRot qwen-image-2.1-UC-int8_convrot.safetensors 6.76 GB
NVFP4 qwen-image-2.1-UC-NVFP4.safetensors 4.20 GB
MLX 4-bit qwen-image-2.1-UC-MLX-4bit.safetensors 4.00 GB
MLX 6-bit qwen-image-2.1-UC-MLX-6bit.safetensors 5.78 GB
MLX 8-bit qwen-image-2.1-UC-MLX-8bit.safetensors 7.56 GB
Q8_0 qwen-image-2.1-UC-Q8_0.gguf 7.59 GB
Q6_K qwen-image-2.1-UC-Q6_K.gguf 5.88 GB
Q5_K_M qwen-image-2.1-UC-Q5_K_M.gguf 5.22 GB
Q4_K_M qwen-image-2.1-UC-Q4_K_M.gguf 4.60 GB
Q4_0 qwen-image-2.1-UC-Q4_0.gguf 4.15 GB

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

GGUF files

Quantization File Size
Q8_0 qwen-image-2.1-Q8_0.gguf 7.59 GB
Q6_K qwen-image-2.1-Q6_K.gguf 5.88 GB
Q5_K_M qwen-image-2.1-Q5_K_M.gguf 5.22 GB
Q4_K_M qwen-image-2.1-Q4_K_M.gguf 4.60 GB
Q4_0 qwen-image-2.1-Q4_0.gguf 4.05 GB

Text Encoders & VAE

Companion model files packaged for ComfyUI:

Type File Precision Size
Text Encoder text_encoders/qwen3vl_8b_bf16.safetensors BF16 17.53 GB
Text Encoder text_encoders/qwen3vl_8b_int8_convrot.safetensors Int8 9.35 GB
VAE vae/qwen_image_2.1_vae_bf16.safetensors BF16 676 MB

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. 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-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