Running Stable Diffusion on L4 GPUs in the Cloud: A How-To Guide

Stable Diffusion has revolutionized the world of AI-generated art, offering creators, developers, and researchers a powerful tool for generating high-quality images from text prompts. But to harness its full potential—especially with advanced models like SDXL and ControlNet—hardware matters.

NVIDIA’s L4 GPUs provide an ideal balance of affordability and performance for running Stable Diffusion in the cloud. In this guide, we’ll walk you through everything you need to know to deploy and run Stable Diffusion on L4 GPUs using Runpod, from choosing the right container to real-world performance benchmarks.

Whether you're a hobbyist looking for real-time generation speeds or a developer deploying a production pipeline, this guide has you covered.

Why Choose L4 GPUs for Stable Diffusion?

The NVIDIA L4 is designed for high-performance inference tasks and offers:

24GB of VRAM – enough to comfortably run SDXL and ControlNet models.
Low latency & high throughput – perfect for real-time image generation.
Energy efficiency – making it a cost-effective option for long-term use.

In our internal benchmarks, L4 GPUs consistently delivered high performance across different Stable Diffusion workflows, including those using Automatic1111 and ComfyUI.

Stable Diffusion Performance on L4 GPUs

Before diving into the how-to, here’s what you can expect in terms of performance:

ModelStepsResolutionInference Time (per image)Stable Diffusion 1.525512x512~2.5 secondsSDXL Base301024x1024~4.5 secondsControlNet + SDXL30768x768~6 seconds

Note: These benchmarks were run on Runpod L4 cloud instances using the Automatic1111 container with xformers enabled.

Recommended Containers: A1111 vs. ComfyUI

Runpod supports multiple containers for Stable Diffusion. Here are two popular choices:

1. Automatic1111 Web UI

Great for beginners and advanced users alike.
Robust plugin system and model support.
Easy to use with ControlNets, LoRAs, and custom checkpoints.
Recommended for real-time prompting and experimentation.

👉 Launch Stable Diffusion A1111 on L4 GPUs

2. ComfyUI

Modular, node-based interface.
Ideal for complex workflows and batch processing.
Better optimization for SDXL workflows.
Recommended for developers and pipeline automation.

👉 Try ComfyUI on Runpod

Step-by-Step: Launching Stable Diffusion on L4 with Runpod

Here’s how to get Stable Diffusion up and running in minutes.

Step 1: Sign Up or Log In

Visit Runpod.io and create a free account or log in to your existing one.

👉 Sign Up for Runpod

Step 2: Navigate to the Stable Diffusion Template

Go to the Stable Diffusion Templates page and select either A1111 or ComfyUI.

![Template selection screenshot]

Step 3: Choose L4 GPU

Under the GPU options, select L4 (24GB) for the best balance of performance and cost.

![L4 GPU selection screenshot]

Step 4: Configure Your Container

Container Image: Choose the pre-configured container (e.g., runpod/stable-diffusion-a1111)
Volume: Allocate storage for models and outputs (20GB+ recommended)
Ports: Expose 3000 or 7860 for web UI access

Optional: Enable auto-shutdown to save on costs when idle.

Step 5: Launch and Connect

Click “Deploy” and wait a minute for the container to start. Once running, click “Connect” to access the Web UI.

![Web UI screenshot]

And that’s it! You’re now ready to generate images, fine-tune models, or experiment with ControlNet workflows.

Advanced: Using ControlNet and SDXL on L4

Thanks to the L4’s generous VRAM, you can comfortably run advanced models like SDXL and multiple ControlNets concurrently.

Tips:

Use --xformers for memory efficiency.
Load models in 8-bit or use vae_tiled for better performance.
For SDXL, consider enabling refiner pass for higher quality.

👉 Learn more about SDXL and advanced model support

Pricing: How Much Does It Cost?

Runpod’s L4 GPU instances start at just $0.50/hr, making it one of the most affordable ways to run Stable Diffusion in the cloud.

👉 View Full GPU Pricing

You only pay for what you use, and auto-shutdown keeps costs low when idle.

Frequently Asked Questions

Q: Is SDXL supported on L4 GPUs?

Yes. L4’s 24GB VRAM is more than enough to run SDXL base and refiner models, even at high resolutions.

Q: Can I run ControlNet on L4?

Absolutely. You can run multiple ControlNet models simultaneously at 768x768 resolution with consistent performance.

Q: Can I use Runpod for real-time generation?

Yes! L4 GPUs offer fast inference times (2-6 seconds per image), making them ideal for real-time prompting, live demos, and creative iteration.

Q: What if I want to run batch jobs or automate?

ComfyUI is ideal for batch workflows. You can also use Runpod’s API and serverless features to automate image generation at scale.

Ready to Get Started?

Stable Diffusion is more accessible than ever with L4 GPUs on Runpod. Whether you’re experimenting with AI art, building a SaaS product, or training fine-tuned models, Runpod provides the tools you need—fast, affordable, and reliable.

👉 Launch Stable Diffusion Now

👉 See Pricing & GPU Options