realrebelai/Qwen-Image-2.1_GGUFs

Qwen-Image 2.1 GGUFs

High-quality mixed-precision GGUF quantizations of Qwen-Image 2.1 for use in ComfyUI with ComfyUI-GGUF.

These files were converted from the Comfy-Org Qwen-Image 2.1 BF16 diffusion model and preserve the native Comfy tensor naming/layout. No additional tensor-name remapping is required after conversion.

This repository contains the diffusion model only. You still need the normal Qwen-Image 2.1 supporting models required by your ComfyUI workflow, including the text encoder and VAE.

Why these are mixed-precision GGUFs

Rebels Q4

Comfy INT8

A standard generic Q4_K_M conversion showed visible quality loss in fine structure and anatomy compared with the reference INT8 model.

To preserve quality, these GGUFs use a Qwen-Image 2.1-specific mixed-precision policy. The requested quant level is still the base quant for the model, but precision-sensitive transformer projections are kept at higher precision.

The corrected Q4_K_M build was A/B tested against the INT8 reference with identical generation settings and showed a substantial restoration of anatomy, face detail, and overall structural consistency.

Quant ladder

File	Base quant	Attention Q/K/V/Out	img_mlp.out	img_mlp.gate_up
Q8_0-HQv3	Q8_0	Q8_0	Q8_0	Q8_0
Q6_K-HQv3	Q6_K	Q8_0	Q8_0	Q8_0
Q5_K_M-HQv3	Q5_K_M	Q8_0	Q8_0	Q6_K
Q4_K_M-HQv3	Q4_K_M	Q8_0	Q8_0	Q5_K
Q3_K_M-HQv3	Q3_K_M	Q6_K	Q6_K	Q4_K
Q2_K-HQv3	Q2_K	Q5_K	Q5_K	Q3_K

The following top-level Qwen modules remain at their original higher precision where applicable:

img_in
txt_in
time_text_embed
modulation
norm_out
proj_out

Norm and other small tensors remain high precision where required by the GGUF conversion path.

Source

BF16 diffusion model source:

Comfy-Org/Qwen-Image-2.1

diffusion_models/qwen_image_2.1_bf16.safetensors

Architecture

The GGUF files use:

general.architecture = qwen_image

The source transformer uses:

• 32 transformer blocks
• 4096 hidden dimension
• Qwen-Image 2.1 native Comfy tensor naming
• attn.to_q
• attn.to_k
• attn.to_v
• attn.to_out.0
• img_mlp.gate_up
• img_mlp.out

The Comfy-Org BF16 checkpoint already uses the tensor layout expected by ComfyUI, so these conversions preserve those names rather than remapping them to a separate llama.cpp-style naming scheme.

GGUF shape handling

Where a tensor must be physically reshaped to satisfy GGUF quantization requirements, the original logical shape is stored using:

comfy.gguf.orig_shape.*

ComfyUI-GGUF uses this metadata when loading the model.

Recommended quant

For most users, start with:

Qwen-Image-2.1-Q4_K_M-HQv3.gguf

This is the main balanced release: substantially smaller than higher-precision variants while retaining much better structural quality than a generic Q4 conversion.

If you have more memory available, Q5_K_M, Q6_K, or Q8_0 provide additional precision.

Q3_K_M and Q2_K are intended for more memory-constrained systems and may still show increasing quality loss despite the protected high-precision layers.

ComfyUI installation

Install ComfyUI-GGUF in your ComfyUI custom nodes directory.

Portable Windows example:

cd /d D:\AI_Tools\ComfyUI_windows_portable\ComfyUI\custom_nodes && git clone https://github.com/city96/ComfyUI-GGUF

Install its requirements:

D:\AI_Tools\ComfyUI_windows_portable\python_embeded\python.exe -m pip install -r D:\AI_Tools\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-GGUF\requirements.txt

If ComfyUI-GGUF is already installed, update your existing installation instead of cloning a second copy.

Model placement

Place the GGUF diffusion model in:

ComfyUI/models/diffusion_models/

Load it using the GGUF diffusion-model / UNet loader supplied by ComfyUI-GGUF.

Continue using the standard Qwen-Image 2.1 text encoder, VAE, conditioning, sampler, and workflow components around it.

Notes

These are unofficial community quantizations.

The HQv3 suffix identifies the Qwen-specific high-quality mixed-precision policy used for these releases. It is intentionally different from a generic llama.cpp Q2/Q3/Q4/Q5/Q6/Q8 conversion.

Lower-bit quantization can still affect:

• fine detail
• hands and anatomy
• typography
• prompt adherence
• edit fidelity
• texture consistency

If a lower quant shows noticeable degradation, move up one quant level.

Credits

• Qwen — Qwen-Image / Qwen-Image 2.1
• Comfy-Org — ComfyUI-compatible Qwen-Image 2.1 model packaging
• City96 / ComfyUI-GGUF — GGUF loading and image-model quantization infrastructure
• llama.cpp / ggml — GGUF quantization infrastructure

Quantizations and Qwen-Image 2.1 mixed-precision work by RealRebelAI.

Links

• Qwen-Image 2.1: https://huggingface.co/Comfy-Org/Qwen-Image-2.1
• ComfyUI-GGUF: https://github.com/city96/ComfyUI-GGUF