This is a simplified guide to an AI model called Qwen-Image-2.1-Uncensored-Quantized maintained by Abenzerps. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Overview
Qwen-Image-2.1-Uncensored-Quantized is a set of quantized local image-generation weights based on the upstream Qwen-Image-2.1 model. Maintained by abenzerps, it provides GGUF, Safetensors, and Apple Silicon–oriented MLX files for use with ComfyUI. Its defining practical feature is the absence of a built-in safety checker or content filter: prompts can produce adult, NSFW, and sensitive imagery without refusals or blacked-out outputs. The README does not specify the model architecture beyond identifying it as an image-generation model, nor does it give a parameter count, training data, training steps, output resolution, or measured generation speed. Plan around the full component set, not just the diffusion file: ComfyUI also needs a Qwen3-VL 8B text encoder and a VAE. The repository reports 2,188,658 downloads, but that figure does not establish image quality or performance.
Best use cases
Local generation of adult or sensitive imagery. The release explicitly removes built-in safety filtering, so it suits workflows where a safety checker would block intended prompts. That also means the operator is responsible for prompt handling, access controls, and compliance with applicable laws and platform rules.
ComfyUI workflows with limited GPU memory. The GGUF options include a 4.60 GB Q4_K_M diffusion file, and the README recommends keeping the diffusion model in GPU VRAM while running or offloading the text encoder to system RAM. This separates the component that runs during sampling from text encoding, which runs once per prompt.
Apple Silicon image-generation setups. The repository includes MLX 4-bit, 6-bit, and 8-bit variants, described as optimized for Apple Silicon. These provide a platform-specific option rather than requiring a GGUF or GPU-oriented Safetensors file.
Local experimentation across quality and memory tradeoffs. The available formats range from 4.00 GB MLX 4-bit and 4.05 GB base Q4_0 GGUF to 14.23 GB BF16 GGUF. This lets practitioners compare quantization levels in their own workflow, though the README provides no measured quality or speed results for each variant.
Text-to-image and image-editing workflows in ComfyUI. The README points to official Comfy-Org templates for both tasks. It does not provide task-specific benchmark scores, so test the intended prompts and edit cases before relying on it in production.
Limitations
The README does not report output resolution, parameter count, training data, training compute, inference speed, or benchmark methodology. The benchmark image is provided, but no numerical results or hardware details accompany it. Do not infer a resolution, quality level, or generation time from the file sizes.
The smallest diffusion files reduce storage and memory needs, but quantization can involve quality tradeoffs; the repository recommends Q4_K_M as a size-quality balance for GGUF without publishing comparative measurements. The full setup also has substantial memory and storage demands: the text encoder is 17.53 GB in BF16 or 9.35 GB in INT8, and the VAE is 676 MB. The recommended lower-memory arrangement places the INT8 text encoder in system RAM and the Q4_K_M diffusion model in VRAM. The README’s description of this as saving “9–17 GB” of VRAM is consistent with the listed encoder sizes; it does not give a minimum system RAM or GPU VRAM requirement.
The model has no built-in content filter. That is a deliberate property, not a guarantee that every prompt will produce a particular result. The README does not document other known image-quality failure modes, biases, or safety mitigations.
The stated license is the Qwen Research License, described in the repository as “other.” The provided information does not establish commercial-use rights or the license terms, so review the license before commercial deployment.
How it compares
Qwen-Image-2.1-GGUF is the better fit if you want the same upstream Qwen-Image-2.1 base weights in GGUF without choosing this release’s explicitly uncensored package. This model is the clearer choice when the absence of a built-in safety checker is a requirement and when you want additional formats such as Safetensors and MLX; the supplied information does not provide measured speed or quality differences between them. The alternative’s notice says a fully uncensored version is in development, so check its current status before choosing it for an uncensored workflow.
Qwen-Image-2.1-Uncensored-Abenzerps-GGUF is another GGUF distribution of uncensored Qwen-Image-2.1 weights. Choose this model if you need the documented Safetensors or MLX variants, companion text encoder and VAE files, and ComfyUI setup guidance in one repository; choose the alternative if its distribution better matches your existing GGUF workflow. The provided information does not establish a quality, speed, or cost advantage for either distribution.
Qwen-Image-2.1-Uncensored-GGUF is another uncensored GGUF option based on the same upstream model. This repository is a stronger fit when you want multiple non-GGUF quantization formats and explicit companion-file instructions. The alternative’s notice reports a Q8_0 tensor shape mismatch in ComfyUI during sampling and recommends Q4_K_M, Q5_K_M, or Q6_K instead; the supplied information does not confirm whether that issue applies to this model’s Q8_0 file.
Qwen-Image-2.1-Uncensored-GGUF is also described as an uncensored GGUF distribution using the upstream base weights. Pick this model for the documented MLX and Safetensors choices, or the alternative if its repository and files suit your existing setup. No comparative benchmark, inference-speed, or cost data is provided, so there is no supported basis to claim that one produces better images or runs faster.
Qwen-Image-2.1-GGUF targets low-VRAM ComfyUI use with GGUF quantizations and describes its quantization as reducing memory pressure while preserving detail, composition, and prompt alignment. This model is the better fit when you need an explicitly uncensored release and Apple Silicon MLX or Safetensors options; the alternative may suit a workflow centered on its stated low-VRAM GGUF profile. The supplied information does not provide comparable hardware tests or measured quality results.
Technical specifications
Qwen-Image-2.1-Uncensored-Quantized packages quantized versions of the upstream Qwen-Image-2.1 weights. The source revision is b3179ad355be050328e483a9dfdd9e60cd62adfa. The README identifies conversion using stable-diffusion.cpp commit 1330cebae8f2ba99249df846cc0c9444fcbd4308 and comfy-kitchen. The text encoder and VAE source is Comfy-Org/Qwen-Image-2.1. The stated license is the Qwen Research License.
Available uncensored diffusion files:
| Format or quantization | File size |
|---|---|
| BF16 GGUF | 14.23 GB |
| FP8 Safetensors | 6.63 GB |
| INT8 ConvRot Safetensors | 6.76 GB |
| NVFP4 Safetensors | 4.20 GB |
| MLX 4-bit Safetensors | 4.00 GB |
| MLX 6-bit Safetensors | 5.78 GB |
| MLX 8-bit Safetensors | 7.56 GB |
| Q8_0 GGUF | 7.59 GB |
| Q6_K GGUF | 5.88 GB |
| Q5_K_M GGUF | 5.22 GB |
| Q4_K_M GGUF | 4.60 GB |
| Q4_0 GGUF | 4.15 GB |
The repository also lists non-UC GGUF files: Q8_0 at 7.59 GB, Q6_K at 5.88 GB, Q5_K_M at 5.22 GB, Q4_K_M at 4.60 GB, and Q4_0 at 4.05 GB. It recommends Q4_K_M for the best balance of GGUF size and quality. It describes INT8 ConvRot and NVFP4 as high-performance options on supported GPUs, without naming supported GPU models or providing speed measurements. MLX files target Apple Silicon.
ComfyUI companion files are a Qwen3-VL 8B text encoder in BF16 (17.53 GB) or INT8 ConvRot (9.35 GB), plus a BF16 VAE (676 MB). The README recommends the INT8 encoder for lower memory use. It suggests keeping the diffusion model in GPU VRAM and running or offloading the text encoder to system RAM; text encoding runs once per prompt. For VRAM out-of-memory errors, it recommends starting ComfyUI with --lowvram.
Supported workflow components include ComfyUI, the leejet/ComfyUI-GGUF custom node, ComfyUI’s UNETLoader, CLIPLoader, and VAELoader nodes, and official Comfy-Org text-to-image and image-edit workflow templates. For GGUF, use Unet Loader (GGUF); for Safetensors, use UNETLoader. Set the text encoder’s CLIPLoader type to qwen_image. The README warns that the older city96/ComfyUI-GGUF may produce an “Unknown model architecture!” error; it recommends the leejet fork or adding ModelQwenImage to tools/convert.py.
The README does not specify architecture details beyond the image-generation task, parameter count, training dataset, training steps, compute, maximum resolution, inference speed, or batch-size guidance.
Model inputs and outputs
Inputs
- Text prompts: The model is presented for text-to-image generation. The README does not specify prompt syntax, token limits, or supported languages.
- Image inputs: An official image-edit workflow template is listed, but the README does not document accepted image formats, dimensions, or editing constraints.
- Runtime components: A diffusion-model file, a Qwen3-VL 8B text encoder, and a VAE are required for the documented ComfyUI setup. Choose a compatible diffusion quantization and encoder precision.
Outputs
- Generated images: The repository describes local image generation and provides text-to-image and image-edit workflow templates. It does not specify output resolution, image format, or post-processing requirements.
- Content filtering: The release has no built-in safety checker or content filter; output behavior depends on prompts and the execution environment.
Getting started
The README documents a ComfyUI workflow rather than a Python inference API, so it does not provide a supported Python model-loading or generation call. To run the documented setup, place the selected diffusion file in ComfyUI/models/diffusion_models/, the text encoder in ComfyUI/models/text_encoders/, and the VAE in ComfyUI/models/vae/. For example, use qwen-image-2.1-UC-Q4_K_M.gguf, qwen3vl_8b_int8_convrot.safetensors, and qwen_image_2.1_vae_bf16.safetensors.
Install the maintained ComfyUI-GGUF fork in ComfyUI/custom_nodes/:
cd ComfyUI/custom_nodes
git clone https://github.com/leejet/ComfyUI-GGUF
In ComfyUI, load the GGUF diffusion file with Unet Loader (GGUF). Load the text encoder with CLIPLoader and set its type to qwen_image; load the VAE with VAELoader. For a Safetensors diffusion file, use UNETLoader instead. If VRAM runs out, start ComfyUI with --lowvram. The README also points to official text-to-image and image-edit workflow templates.
Frequently asked questions
Q: Can I use Qwen-Image-2.1-Uncensored-Quantized commercially?
A: The repository states the Qwen Research License. The provided information does not specify commercial-use permissions, so review the license terms before commercial use.
Q: How much VRAM do I need to run it?
A: No minimum VRAM requirement is stated. The recommended setup uses a Q4_K_M diffusion file of about 4.6 GB in VRAM and an INT8 text encoder of about 9.35 GB in system RAM; the VAE is 676 MB. Use --lowvram if ComfyUI reports a VRAM out-of-memory error.
Q: Which quantization should I choose for ComfyUI?
A: The README recommends Q4_K_M as the best balance of GGUF size and quality. It lists Q4_K_M at 4.60 GB, Q5_K_M at 5.22 GB, Q6_K at 5.88 GB, and Q8_0 at 7.59 GB, but does not publish comparative quality or speed measurements.
Q: How does it compare with Qwen-Image-2.1-GGUF for uncensored generation?
A: This release explicitly has no built-in safety checker or content filter. The similar model’s notice says a fully uncensored version is in development, so this release is the documented choice when uncensored behavior is required now; the supplied information does not establish a quality or speed difference.
Q: What are the known image-quality issues?
A: The README provides a benchmark image but no numerical results, documented failure modes, or output-resolution details. It recommends Q4_K_M for a GGUF size-quality balance, but does not quantify quality loss across quantizations.
Q: Can I fine-tune this model?
A: The README documents quantized files for local inference through ComfyUI, not a fine-tuning workflow. It does not specify fine-tuning support or a training framework.
Q: How fast is inference, and what batch size should I use?
A: The README gives no inference-time, throughput, or batch-size measurements. It says to keep the diffusion model in GPU VRAM because speed matters during sampling, and to run or offload the text encoder to system RAM because it encodes once per prompt.
Q: Does the model filter NSFW prompts?
A: No. The release has no built-in safety checker or content filter and can generate adult, NSFW, and sensitive imagery without prompt refusals or blacked-out images.
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