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. GGUF quantizations of [Qwen/Qwen-Image-2.1](https://huggingface.co/Qwen/Qwen-Image-2.1) for local image generation using the original upstream base weights. **Q4_K_M** is recommended for the best balance of size and quality. Companion model files packaged for ComfyUI: Use the model with [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [ComfyUI-GGUF](https://github.com/leejet/ComfyUI-GGUF). All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository. 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 1. **Install ComfyUI-GGUF**: Use the maintained fork with native Qwen-Image 2.1 support by cloning [leejet/ComfyUI-GGUF](https://github.com/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](https://github.com/Comfy-Org/workflow_templates/blob/main/templates/image_qwen_image_2_1_t2i.json) or [Image Edit](https://github.com/Comfy-Org/workflow_templates/blob/main/templates/image_qwen_image_2_1_image_edit.json). - In the workflow, simply replace the default `UNETLoader` node with **`Unet Loader (GGUF)`**. - **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. 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 model:** [Qwen/Qwen-Image-2.1](https://huggingface.co/Qwen/Qwen-Image-2.1) - **Text encoder & VAE source:** [Comfy-Org/Qwen-Image-2.1](https://huggingface.co/Comfy-Org/Qwen-Image-2.1) - **Source revision:** `b3179ad355be050328e483a9dfdd9e60cd62adfa` - **Conversion:** [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) commit `1330cebae8f2ba99249df846cc0c9444fcbd4308` - **License:** Qwen Research License - **Checksums:** [SHA256SUMS](./SHA256SUMS)