license: other
license_name: qwen-research
base_model:

• abenzerps/Qwen-Image-2.1-Uncensored-GGUF
  base_model_relation: quantized
  pipeline_tag: text-to-image
  library_name: gguf
  tags:
• gguf
• qwen
• image-generation
• comfyui
• comfyui-gguf
• genesis

Qwen-Image-2.1-Uncensored-GGUF -> Genesis

⚡ If you like this Genesis LLM release you can donate to me via Hipolink or:

USDT (TRC20): TGa4KTwHfF6zDBsLUBEjd1f1KdeAFwYUks

USDT (ERC20): 0x93F4019E0aa85d8078F56B3D3176Fab5Dfa79924

USDT (SOL): BorkkyPDG4aRN2op8c5SQF5NithX38U4sh7wDAWQhYyX

and support future Genesis LLM development.

⚡ Why Genesis project exists? During training, ALL models don't just learn knowledge - they also accumulate random noise in their tensors. This noise builds up and creates something I call the Noise Gate - a fundamental barrier that stops LLM models from learning further and makes them unstable, verbose, and prone to hallucinations. My approach reduces this noise. It repairs the signal in tensors without touching the learned knowledge and gradient using Marchenko–Pastur distribution as a core criteria. The result is a model that consistent in performance, context clarity and following instructions, because it's no longer fighting its own internal chaos.

What is Genesis? Genesis is post training data regeneration and calibrarion algorythm for neural networks (LLM) in GGUF format that I made with AI help during almost half a year of development. It's optimized, architecture independent, works with any model in GGUF format and based on mathematical statistics. I don't train or finetune models, I repair purity of signal in them instead on Google Collab Free on Tesla T4 GPU via Python based on how models learns information. On first stage I scan ssm_conv1d tensors in model, they handle long context memory. I repair balance between heads in them. On second stage, I scan blocks in model via chunks via 3 parameters and pick best one that fits to weight distribution in tensor. Best picked chunk replaces zero chunks in broken tensor without touching learned structure in model. On third stage I scan model and detect noise in tensors via custom SVD. During scanning I exclude token_embd.weight, output.weight, 1D tensors, bias and norms. Then I reduce training noise in tensors via custom SVD based on Marchenko–Pastur law with preserved training data, 99% of siginal and learned gradient.

Many tensors in this model in original weights shared by Alibaba were singular with huge condition number for matrices. They distorted the signal distribution between tensors instead of transmitting it correctly. I fixed it as much as I can for base model and text encoder and reduced condition number for matrices for stable inference during image generation.

Any questions?

Contact: [email protected], [email protected]

My Telegram: @LuffyTheFox

GGUF quantizations of abenzerps/Qwen-Image-2.1-Uncensored-GGUF for local image generation using the modified via Genesis base weights.

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: NVFP4
  • Text Encoder: NVFP4
• Low VRAM Mode: If you experience VRAM out-of-memory errors, start ComfyUI with the --lowvram argument.

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