abenzerps/Qwen-Image-2.1-GGUF — new model trending #14 on Hugging Face

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.

### 1. Download & File Placement

Download the files and place them in their respective ComfyUI directories:

```text

ComfyUI/

└── models/

├── diffusion_models/

│ └── qwen-image-2.1-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](https://github.com/leejet/ComfyUI-GGUF) into your custom nodes:

```bash

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)`**.

### 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-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](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)

Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/abenzerps/Qwen-Image-2.1-GGUF