How to Fix CUDA Out of Memory Errors in Stable Diffusion

That dreaded red error halfway through a render almost always means one thing: your graphics card ran out of video memory. To fix CUDA out of memory errors in Stable Diffusion you free up VRAM, either by asking the software to use less or by giving it less to do. Lower the resolution, drop the batch size, add a low-memory launch flag, and the error usually clears in minutes.

Quick Answer

A CUDA out of memory error fires when the data Stable Diffusion needs exceeds your VRAM. Fix it by lowering output resolution, reducing batch size to one, and adding the --medvram flag to your launch command (or --lowvram on a 4GB card). Closing other GPU apps and switching to a smaller checkpoint frees more headroom. These steps resolve the vast majority of cases without new hardware.

Why The Error Happens

The message is literal: Stable Diffusion tried to allocate more video memory than your card has. VRAM holds the model, the working image data and the intermediate tensors during generation, and when their combined size overflows, CUDA throws the error and the render stops. The larger your image, the bigger your batch, and the heavier your checkpoint, the more VRAM you demand, so the fix is always about reducing one of those or making the software spread its memory use over time.

The Fastest Fixes

Work through these in order, easiest first.

Start by lowering resolution and batch size. Drop back to 512x512 and set batch size to one, since VRAM use climbs sharply with both. Next, add a memory-saving launch flag. In AUTOMATIC1111 WebUI, --medvram splits the model so the U-Net, VAE and text encoder are not all resident at once, cutting peak VRAM for a small speed cost of roughly 10 to 15 percent. On a 4GB card use the more aggressive --lowvram, which keeps generation working by shuffling data between system RAM and VRAM, though it adds 50 to 70 percent to render time. Crucially, neither flag changes image quality; they only manage memory, so your output is identical.

Free Up More Headroom

A few more moves reclaim memory. Close other applications using the GPU, browsers with hardware acceleration, games and video players all hold VRAM you could be using. Switch to a smaller or more efficient checkpoint, since a lighter model leaves more room for the actual generation. Adding --xformers improves memory efficiency on supported cards and pairs well with --medvram as a solid default for a 6GB GPU. If you keep hitting the wall on bigger work, more VRAM is the real answer, and the top-selling graphics cards are where to compare options with more memory.

Frequently Asked Questions

Does --medvram reduce image quality?

No. The flag only changes how VRAM is managed by moving model components between the GPU and system RAM. The generated image is identical to one made without it; you only pay a small speed penalty of around 10 to 15 percent.

When should I use --lowvram instead of --medvram?

Use --lowvram on very low-memory cards, around 4GB, where --medvram still is not enough. It is more aggressive and keeps generation working, but it adds 50 to 70 percent to render time because it constantly shuffles data between RAM and VRAM.

Why does batch size cause out of memory errors?

Each image in a batch needs its own working memory, so generating four at once uses roughly four times the VRAM of a single image. Dropping batch size to one is one of the quickest ways to clear the error.

Will closing other programs really help?

Yes. Browsers with hardware acceleration, games and video players hold VRAM in the background. Closing them returns that memory to Stable Diffusion, which can be enough to push a borderline render through.

Is the real fix just a better GPU?

For consistently large or high-resolution work, more VRAM removes the limit entirely. The flags and settings above stretch what you have, but a card with more video memory is the lasting answer if you keep hitting the ceiling.