Instructions to use abenzerps/Qwen-Image-2.1-Uncensored-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use abenzerps/Qwen-Image-2.1-Uncensored-Quantized with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download abenzerps/Qwen-Image-2.1-Uncensored-Quantized --local-dir Qwen-Image-2.1-Uncensored-Quantized
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Qwen-Image-2.1 Uncensored Quantized
GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights.
Benchmark
Uncensored Quantized 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 for GGUF. INT8 ConvRot and NVFP4 offer high performance on supported GPUs, while MLX formats are optimized for Apple Silicon.
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/
βββ models/
βββ diffusion_models/
β βββ qwen-image-2.1-UC-Q4_K_M.gguf # Choose one GGUF quantization (Q4_K_M recommended) or .safetensors
βββ text_encoders/
β βββ qwen3vl_8b_bf16.safetensors # Or qwen3vl_8b_int8_convrot.safetensors (recommended for lower memory)
βββ vae/
βββ qwen_image_2.1_vae_bf16.safetensors
2. ComfyUI Setup
- Install ComfyUI-GGUF: Use the maintained fork with native Qwen-Image 2.1 support by cloning leejet/ComfyUI-GGUF into your custom nodes:
(Note: If you have the oldercd ComfyUI/custom_nodes git clone https://github.com/leejet/ComfyUI-GGUFcity96/ComfyUI-GGUFinstalled and encounter anUnknown model architecture!error, update to theleejetfork above or addModelQwenImagetotools/convert.py). - Node Configuration:
- Diffusion Model: Add the
Unet Loader (GGUF)node (for.gguffiles) or standardUNETLoader(for.safetensorsfiles) and select your downloaded file. - Text Encoder: Add the standard
CLIPLoadernode, selectqwen3vl_8b_bf16.safetensors(orint8), and settypetoqwen_image. - VAE: Add the standard
VAELoadernode and selectqwen_image_2.1_vae_bf16.safetensors.
- Diffusion Model: Add the
- Official Workflows:
- You can use the official Comfy-Org workflow templates: Text-to-Image or Image Edit.
- In the workflow, simply replace the default
UNETLoadernode withUnet Loader (GGUF)when using GGUF files.
Memory & Performance Notes
- Optimal Setup (GPU + RAM): Keep the GGUF / Quantized 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 9β17 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)
- Diffusion:
- Low VRAM Mode: If you experience VRAM out-of-memory errors, start ComfyUI with the
--lowvramargument.
Uncensored
This 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& comfy-kitchen - License: Qwen Research License
- Checksums: SHA256SUMS
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Model tree for abenzerps/Qwen-Image-2.1-Uncensored-Quantized
Base model
Qwen/Qwen-Image-2.1