Flux-12B Abliterated V2 — GGUF Q4_K_M
A
GGUF Q4_K_M quantization of
aoxo/flux.1dev-abliteratedv2, prepared for
local inference with ComfyUI and ComfyUI-GGUF.
This repository provides a compact quantized version of the Flux.1-dev Abliterated V2 model while preserving the original model's image-generation capabilities as closely as possible.
This is a quantized conversion, not the original model.
Please refer to the original repository for the source model, license, and full model information.
✨ Model
Architecture: FLUX.1-dev / 12B
Format: GGUF
Quantization: Q4_K_M
Task: Text-to-Image
Recommended environment: ComfyUI + ComfyUI-GGUF
🚀 Why GGUF?
GGUF provides a convenient way to run large models locally with reduced memory requirements.
The Q4_K_M quantization provides a strong balance between:
- 🧠 Model quality
- 💾 File size
- ⚡ Inference performance
- 🖥️ VRAM requirements
GGUF is supported by a growing ecosystem of local inference tools and includes model metadata directly inside the file.
🖥️ ComfyUI
This model is intended primarily for use with ComfyUI through ComfyUI-GGUF.
Requirements
- ComfyUI
- ComfyUI-GGUF
- A compatible Flux workflow
- Sufficient system RAM / VRAM for Flux inference
Installation
- Install ComfyUI.
- Install the ComfyUI-GGUF custom node.
- Download the
.gguf file from the Files and versions section of this repository.
- Place the GGUF model in the appropriate model directory used by your ComfyUI-GGUF setup.
- Load a Flux workflow that supports GGUF models.
- Select the downloaded
Q4_K_M model.
The exact folder may depend on your ComfyUI-GGUF configuration and workflow.
📦 Quantization
This repository currently provides:
| Quantization | Format |
|---|
| Q4_K_M | GGUF |
Q4_K_M is a 4-bit K-quantization variant designed to provide a useful compromise between model size and quality. Hugging Face documents Q4_K as a 4-bit quantization family, with K-quant variants designed around block/super-block quantization.
🎨 Usage
The model is designed for local image generation.
A typical workflow is:
1Prompt
2 ↓
3Flux Text Encoder
4 ↓
5Flux GGUF Q4_K_M
6 ↓
7Sampler
8 ↓
9VAE
10 ↓
11Generated Image
Use your preferred Flux.1-dev workflow and replace the transformer/model component with the GGUF version where supported.
⚙️ Performance
Actual performance depends heavily on:
- GPU model
- Available VRAM
- System RAM
- ComfyUI configuration
- Resolution
- Number of sampling steps
- CPU/GPU offloading
- Flux workflow
Because of this, VRAM and generation-speed figures are intentionally not specified here unless they have been measured on a particular hardware configuration.
🧪 Model Status
This is an unofficial GGUF conversion of the original abliterated Flux model.
The goal of this release is to make the model more accessible to users who want to run it locally with a smaller memory footprint.
If you encounter:
- corrupted output,
- loading errors,
- unexpected artifacts,
- incorrect tensor loading,
- or other compatibility problems,
please open an issue or leave feedback on the repository.
📜 Credits
Original model
aoxo/flux.1dev-abliteratedv2
All credit for the original model and its underlying work belongs to the original authors.
GGUF conversion
anlord
This repository contains the GGUF quantized conversion published by anlord.
⚠️ License
The original model is distributed under:
Fair Noncommercial Research License
Please read and comply with the original model's license before downloading, using, modifying, or redistributing this model.
The license of the original model takes precedence over any assumptions made from this README.
⭐ Feedback
If this model is useful to you, consider leaving a ⭐ on the repository.
Feedback, generation examples, bug reports, and suggestions for additional quantizations are welcome.
🔗 Links
About this release
Flux-12B Abliterated V2 — GGUF Q4_K_M
A community GGUF quantization prepared by anlord for convenient local inference.
