Can I Run Stable Diffusion Without a GPU

Stable Diffusion has revolutionized the field of generative AI, making high-quality text-to-image synthesis accessible to a wide range of users. Traditionally, running Stable Diffusion locally has required a discrete graphics processing unit (GPU) due to the model’s heavy computational demands. However, recent developments in software toolkits, hardware architectures, and community-driven optimizations have begun to shift this paradigm. This article explores whether—and how—you can run Stable Diffusion without a dedicated GPU, synthesizing the latest news and research to provide a comprehensive, professional guide.

What is Stable Diffusion and why does it usually require a GPU?

Overview of the Stable Diffusion architecture

Stable Diffusion is a latent diffusion model introduced in 2022, capable of generating high-fidelity images from textual prompts. It operates by iteratively refining noise in a latent representation using a UNet-based neural network, guided by a text encoder (often CLIP-based). The process involves thousands of denoising steps, each requiring large matrix multiplications and convolutions across high-dimensional tensors .

The role of GPUs in machine‐learning inference

GPUs excel at parallel processing, featuring thousands of cores optimized for matrix and vector operations. This architecture dramatically accelerates the tensor computations central to diffusion-based models. Without a GPU, inference on a CPU can be orders of magnitude slower, often making real‐time or interactive use impractical. As an illustrative benchmark, early CPU‐only implementations of Stable Diffusion could take over 30 seconds per denoising step compared to under two seconds on modern GPUs .

Can I run Stable Diffusion without a GPU?

Traditional CPU‐only approaches

In the model’s early days, community members attempted to run Stable Diffusion on CPUs using the default PyTorch “diffusers” library. While functionally possible, this approach suffered from extreme latency: generating a single 512×512 image could take several minutes on a high‐end multicore CPU, rendering it impractical for most users .

Recent toolkit enhancements

OpenVINO 2025.2 support for Stable Diffusion

Intel’s OpenVINO AI toolkit released version 2025.2 in June 2025, adding support for several generative AI models—including Stable Diffusion 3.5 Large Turbo and SD‑XL Inpainting—on both CPUs and integrated NPUs. This update enables optimized inference with quantization and graph optimizations tailored for Intel architectures .

PyTorch Inductor CPP backend improvements

The PyTorch development community has been actively enhancing CPU inference performance. The Inductor CPP backend now targets state‐of‐the‐art (SOTA) execution of key models, including Stable Diffusion, on Intel CPUs. Benchmarks indicate competitive GEMM performance and improved memory utilization, narrowing the gap to GPU‐based inference.

Dedicated CPU‐acceleration projects

FastSD CPU, an open‐source project, reimplements Stable Diffusion inference using Latent Consistency Models and Adversarial Diffusion Distillation. It achieves significant speedups by distilling the sampling process into fewer, more efficient steps, tailored for multi‐core CPUs.

What hardware and software support CPU‐only Stable Diffusion?

Intel OpenVINO and on‑die NPUs

OpenVINO™ streamlines model conversion from PyTorch or ONNX into an optimized format for CPU inference, leveraging vector instructions (e.g., AVX‑512) and graph optimizations. Additionally, Intel’s recent mobile and desktop SoCs integrate neural processing units (NPUs) capable of offloading tensor workloads, further boosting performance on compatible hardware .

AMD Ryzen AI Max+395 APU

AMD’s Ryzen AI Max+395—codenamed Strix Halo—blends high‐performance CPU cores with a dedicated NPU and large unified memory. This APU targets generative AI applications, claiming best‐in‐class performance for local Stable Diffusion inference without discrete GPUs .

Community‐driven projects: stable‑diffusion.cpp and hybrid inference

The lightweight C++ implementation, stable‑diffusion.cpp, designed for CPU, has seen academic enhancements such as Winograd‐based 2D convolution optimizations, yielding up to 4.8× speedups on Apple M1 Pro devices. Such cross‐platform, minimal‐dependency tools make CPU‐only deployment more feasible (arxiv.org). Hybrid strategies that combine CPU and small‐scale GPU or NPU resources are also gaining traction for balanced cost and performance .

OEM and motherboard utility support

OEM utilities like ASRock AI QuickSet v1.0.3i now provide one‐click installation of Stable Diffusion WebUI with OpenVINO optimizations, simplifying setup on Intel‐based motherboards for users without deep technical expertise.

What are the performance trade‐offs of running without a GPU?

Speed and throughput comparisons

Even with optimized toolkits, CPU inference remains slower than GPU. For example, using OpenVINO 2025.2 on a 16‐core Intel Xeon may yield 0.5–1 images per minute, compared to 5–10 images per minute on an RTX 4090. FastSD CPU and specialized NPUs can narrow this gap somewhat, but real‐time interactive generation is still out of reach .

Quality and precision considerations

CPU‐optimized pipelines often rely on quantization (e.g., FP16, INT8) to reduce memory bandwidth, which can introduce minor artifacts compared to full‐precision GPU runs. OpenVINO’s FP16 precision on Xeon CPUs has shown up to 10% latency degradation in certain token operations, indicating ongoing tuning is required .

Cost and accessibility considerations

While GPUs can carry significant upfront costs—especially at the high end—modern CPUs come standard in most desktops and laptops. Leveraging existing CPU hardware reduces barriers for hobbyists, educators, and privacy‐conscious users who cannot or prefer not to use cloud GPU services.

When is CPU‐only inference appropriate?

Prototyping and experimentation

Early experimentation or low‐volume generation tasks can tolerate the slower speeds of CPU inference, especially when exploring prompt engineering or model modifications without incurring extra hardware costs.

Low‑cost or edge deployment

Edge devices lacking discrete GPUs—such as industrial PCs, embedded systems, and mobile workstations—benefit from CPU‐only setups. NPUs and specialized instruction sets further enable deployment in constrained environments.

Privacy and offline requirements

Running entirely locally on CPU ensures that sensitive data never leaves the device, crucial for applications in healthcare, defense, or any context requiring strict data governance.

How to set up and optimize Stable Diffusion for CPU inference?

Environment setup with Diffusers and PyTorch

Install PyTorch with CPU support:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

Install Hugging Face Diffusers:

pip install diffusers transformers accelerate

Converting models with OpenVINO

Export the model to ONNX:

 from diffusers import StableDiffusionPipeline 
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3-5-large-turbo") pipe.save_pretrained("sd-3.5-turbo") 
pipe.to_onnx("sd3.5_turbo.onnx", opset=14, provider="CPUExecutionProvider")

Optimize with OpenVINO:

mo --input_model sd3.5_turbo.onnx --data_type FP16 --output_dir openvino_model

Leveraging mixed precision and quantization

• Use FP16 where supported; fall back to BF16 or INT8 on older CPUs.
• Tools like ONNX Runtime and OpenVINO include quantization toolkits to minimize accuracy loss.

Threading and memory optimization

• Pin thread affinity to physical cores.
• Increase intra_op_parallelism_threads and inter_op_parallelism_threads in PyTorch’s torch.set_num_threads() to match the CPU’s core count.
• Monitor memory usage to avoid swapping, which can severely degrade performance.

Getting Started

CometAPI is a unified API platform that aggregates over 500 AI models from leading providers—such as OpenAI’s GPT series, Google’s Gemini, Anthropic’s Claude, Midjourney, Suno, and more—into a single, developer-friendly interface. By offering consistent authentication, request formatting, and response handling, CometAPI dramatically simplifies the integration of AI capabilities into your applications. Whether you’re building chatbots, image generators, music composers, or data‐driven analytics pipelines, CometAPI lets you iterate faster, control costs, and remain vendor-agnostic—all while tapping into the latest breakthroughs across the AI ecosystem.

Developers can access Stable Diffusion API (Stable Diffusion 3.5 Large API etc)  through CometAPI.

More details about Stable-Diffusion XL 1.0 API and Stable Diffusion 3.5 Large API etc,For more Model information in Comet API please see API doc.Price in CometAPI:

• stability-ai/stable-diffusion-3.5-large: $0.208 per create API call. ​
• stability-ai/stable-diffusion-3.5-medium: $0.112 per call.​
• stability-ai/stable-diffusion-3.5-large-turbo: $0.128 per create API call.​
• stability-ai/stable-diffusion-3: $0.112 per call
• stability-ai/stable-diffusion: $0.016 per call

This pricing structure allows developers to scale their projects efficiently without overspending.

Conclusion

Running Stable Diffusion without a GPU was once a theoretical exercise; today, it is a practical reality for many users. Advances in toolkits like Intel’s OpenVINO 2025.2, PyTorch’s Inductor backend, AMD’s AI‐empowered APUs, and community projects such as FastSD CPU and stable‑diffusion.cpp have collectively democratized access to generative AI. While performance and precision trade‐offs remain, CPU‐only inference unlocks new possibilities where cost, accessibility, and privacy are paramount. By understanding the available hardware, software toolkits, and optimization strategies, you can tailor a CPU‐only Stable Diffusion deployment that meets your specific needs—bringing the power of AI‐driven image synthesis to virtually any device.