Open-Source Tool Enables GGUF Quantization of SDXL for Low-End GPUs

Open-Source Tool Enables GGUF Quantization of SDXL for Low-End GPUs

A breakthrough in accessible AI image generation has emerged from the grassroots AI community, as a developer known online as OldFisherman8 unveiled two open-source tools designed to bring Stable Diffusion XL (SDXL) performance to low-end hardware. The tools — SDXL GGUF Quantize Tool and ComfyUI-DJ_nodes — enable users with modest computing resources, such as laptops equipped with 3GB VRAM GPUs, to run SDXL models locally without relying on cloud services or high-end hardware.

According to the developer’s Reddit post on r/StableDiffusion, the project was born out of necessity. Facing performance bottlenecks on a laptop with a GTX 1050 and only 3GB of VRAM, the developer sought to leverage the GGUF format — a quantized model format popularized by the llama.cpp project — to reduce memory usage while preserving visual fidelity. The resulting tool automates the extraction and quantization of SDXL’s U-Net component into GGUF format, making it compatible with local inference engines like llama.cpp and its derivatives. To overcome CPU bottlenecks during batch processing, the developer also created a Gradio-based Colab notebook, allowing users to offload heavy computations to Google’s cloud infrastructure while continuing other work locally.

Equally significant is the companion project, ComfyUI-DJ_nodes, which introduces custom nodes to load bundled SDXL CLIP text encoders in GGUF format directly within ComfyUI — a popular node-based workflow interface for Stable Diffusion. Previously, users had to rely on full FP16 models or workarounds that consumed excessive memory. By enabling GGUF-compatible CLIP models, the tool allows for end-to-end quantized pipelines, significantly reducing memory footprints and accelerating inference speeds on constrained systems. This innovation is particularly valuable for artists, educators, and hobbyists in regions with limited access to powerful hardware or cloud credits.

The implications of this development extend beyond convenience. Quantization techniques like GGUF have long been used in large language models to enable on-device AI, but their application to image generation models has been fragmented. This toolset represents one of the first comprehensive, user-friendly solutions for bringing SDXL’s advanced image capabilities to consumer-grade hardware. Community feedback on Reddit has been overwhelmingly positive, with users reporting successful deployments on devices as old as the NVIDIA MX150 and AMD Radeon integrated graphics.

While the tools are currently experimental and require some technical familiarity to install and configure, the developer has prioritized portability and documentation. The GitHub repositories include step-by-step installation guides and sample workflows, lowering the barrier to entry for non-experts. The project also avoids proprietary dependencies, aligning with the open-source ethos of the Stable Diffusion community.

Industry analysts note that such grassroots innovations often precede commercial adoption. While companies like Stability AI and Runway have focused on cloud-based APIs and enterprise solutions, tools like these empower individual users to retain control over their AI workflows — a growing demand in an era of increasing data privacy concerns. As quantization becomes a standard in AI deployment, this toolset may serve as a blueprint for future lightweight AI applications across creative industries.

For those interested in testing the tools, both repositories are freely available on GitHub under permissive licenses. The developer has expressed openness to community contributions and plans to expand support for additional model architectures in future updates.