Stable Diffusion Out of Memory Error Fix

Updated June 2026

Stable Diffusion uses your computer’s Graphics Processing Unit (GPU) to generate images. The GPU has dedicated memory called VRAM (Video Random Access Memory).

Understanding Why “Out of Memory” Happens

Stable Diffusion uses your computer’s Graphics Processing Unit (GPU) to generate images. The GPU has dedicated memory called VRAM (Video Random Access Memory). An “out of memory” error, often displayed as CUDA out of memory or RuntimeError: CUDA out of memory, means Stable Diffusion tried to use more VRAM than your GPU has available.

This typically occurs when:

• Generating high-resolution images.
• Using a large batch size (generating multiple images simultaneously).
• Running complex models or many extensions.
• Your GPU simply has limited VRAM (e.g., 4GB, 6GB, 8GB).

Immediate Fixes: Reduce Resolution and Batch Size

The quickest way to free up VRAM is to reduce the demand on your GPU.

1. Lower Image Resolution:
   • In your Stable Diffusion UI (e.g., Automatic1111), find the “Width” and “Height” sliders.
   • Reduce these values. Start with standard sizes like 512×512 or 768×768.
   • Avoid generating images larger than your GPU’s VRAM can comfortably handle (e.g., 1024×1024+ on 8GB VRAM often requires optimizations).
2. Decrease Batch Size:
   • Locate the “Batch size” or “Batch count” setting.
   • Set “Batch size” to 1. Generating images one by one significantly reduces VRAM usage compared to generating multiple at once.
   • You can still generate multiple images by increasing “Batch count” (how many single images to generate sequentially), which doesn’t impact VRAM as much as “Batch size.”
3. Reduce Steps and CFG Scale:
   • While less impactful than resolution or batch size, slightly reducing “Sampling steps” or “CFG scale” can sometimes offer minor VRAM relief, especially when on the edge of an OOM error.

Optimize Stable Diffusion Launch Arguments

Many Stable Diffusion UIs, particularly Automatic1111, allow you to add launch arguments that specifically optimize VRAM usage.

1. Edit webui-user.bat (Windows) or webui.sh (Linux/Mac):
   • Navigate to your Stable Diffusion installation folder.
   • Right-click webui-user.bat (or webui.sh) and select “Edit.”
   • Find the line that starts with set COMMANDLINE_ARGS=.
2. Add VRAM Optimization Flags:
   • --medvram: Recommended for GPUs with 8GB-12GB VRAM. This flag enables a memory-efficient attention layer. Add it like this: set COMMANDLINE_ARGS=--medvram.
   • --lowvram: For GPUs with 4GB-6GB VRAM. This is a more aggressive VRAM optimization. Use it: set COMMANDLINE_ARGS=--lowvram.
   • --xformers: If you have an NVIDIA GPU, xformers is a highly efficient attention mechanism that dramatically reduces VRAM usage and can speed up generation. Install it first if prompted, then add: set COMMANDLINE_ARGS=--xformers. If using --medvram or --lowvram, combine them: set COMMANDLINE_ARGS=--medvram --xformers.
   • --no-half: Prevents the use of half-precision (FP16) floats, which can sometimes resolve issues but increases VRAM usage. Only use if --medvram or --lowvram causes other problems.
3. Save and Relaunch:
   • Save the edited file.
   • Run webui-user.bat (or webui.sh) again to apply the changes.

Update Graphics Drivers and Software

Outdated drivers or software can sometimes lead to inefficient VRAM management or compatibility issues.

1. Update GPU Drivers:
   • NVIDIA: Download the latest drivers from the official NVIDIA website (GeForce Experience or direct download).
   • AMD: Download the latest drivers from the official AMD website (Adrenalin Software).
   • Perform a clean installation if possible.
2. Update Stable Diffusion:
   • If you installed via a Git clone, open a terminal in your Stable Diffusion folder and run git pull to get the latest updates.
   • Newer versions often include performance improvements and VRAM optimizations.
3. Close Background Applications:
   • Ensure no other memory-intensive applications (games, video editors, browsers with many tabs) are running, as they can consume significant system RAM or VRAM.

Diagnostic checklist before you escalate

Image generation failures usually come from prompt moderation, account limits, unsupported settings, browser state, or a temporary queue problem. Save the prompt and parameters before retrying. Then simplify one variable at a time so you can identify whether the trigger is the wording, reference image, model, aspect ratio, or service availability.

1. Try a short neutral prompt with default dimensions and no reference image.
2. Remove artist names, protected characters, ambiguous age terms, and sensitive wording.
3. Confirm the selected model supports the requested resolution, ratio, and editing feature.
4. Check usage credits, generation history, service status, and account notices.
5. Test a private browser window or another network if the interface itself is frozen.

Verify that image generation is genuinely working

Once Stable Diffusion Out of Memory Error produces an image, do not immediately restore every advanced setting. Generate a second neutral test with the same known-good configuration. Confirm that the result opens at full size, downloads correctly, and appears in generation history. This distinguishes a real recovery from a cached thumbnail or one lucky queue attempt.

Add complexity back in stages: first the intended prompt, then the aspect ratio, reference image, style controls, seed, or editing mode. When the failure returns, the last addition is your strongest lead. Save the working prompt and parameters as a baseline so future tests start from a configuration you know the current model accepts.

• Two simple generations complete without duplicate charges.
• The full-resolution file opens and downloads.
• Generation history records the jobs correctly.
• Advanced controls are restored one at a time.
• The final prompt complies with the provider’s current rules.

Keep a short note of the working configuration and the date of the test. Products, models, browser versions, limits, and safety policies change over time, so a previously successful workaround may later become obsolete. Prefer current official documentation over old forum instructions, and reverse temporary diagnostic changes once testing is complete. This gives you a reliable baseline without leaving extensions disabled, security controls weakened, or experimental settings enabled indefinitely. Recheck the baseline after major updates before assuming an older failure has returned for the same reason. When possible, save a screenshot or sanitized log from the successful test so you can compare future behavior without relying on memory alone during later troubleshooting.

When none of the fixes work

Repeat the smallest failing action once and record the exact local time and time zone. Note the product, model or feature, account plan, browser or app version, operating system, and whether the same action works in a private window, on another device, or on another network. This evidence is much more useful than saying the tool is “still broken.”

Use the provider’s official support channel. Include a screenshot with sensitive information removed and list the steps already tested. For developer tools, add sanitized request and response details, correlation IDs, and SDK versions. Never send passwords, one-time codes, API keys, session cookies, private repository contents, or complete payment information.

FAQ

• Q: Can I really use Stable Diffusion with only 4GB of VRAM?
  • A: Yes, but with significant limitations. You’ll likely need to use --lowvram, generate at 512×512 resolution, and keep the batch size at 1. Performance will be slower.
• Q: What if none of these fixes work and I still get the error?
  • A: If you’ve tried all software optimizations and are still facing frequent OOM errors, especially when trying to generate at moderate resolutions, your GPU might genuinely lack sufficient VRAM for your desired usage. Consider generating images on cloud platforms or upgrading your hardware.
• Q: Does system RAM (main memory) affect “out of memory” errors?
  • A: While the CUDA out of memory error specifically refers to VRAM, having ample system RAM (16GB or more is recommended) can prevent other bottlenecks that might indirectly impact overall system stability and performance when running demanding AI models.

To fix Stable Diffusion “out of memory” errors, reduce image resolution and batch size, apply VRAM optimization flags like --medvram or --lowvram and --xformers, and keep your drivers and software updated.

Bottom line: Work from the least disruptive test to the most specific one. Confirm service health, isolate session and network variables, then escalate with clean evidence instead of repeating the same failing action.