Stable Diffusion “CUDA Out of Memory” Error: Fixes That Actually Work (2026)

Why this error happens (simple explanation)

The “CUDA out of memory” error appears when your GPU (NVIDIA, typically) doesn’t have enough VRAM (video memory) for what Stable Diffusion is trying to do.

Stable Diffusion doesn’t just store the model. It also needs VRAM for:

• your image resolution (biggest factor)
• batch size and number of images at once
• model type (SDXL uses more VRAM than SD 1.5)
• extra features like high-res fix, ControlNet, LoRAs, ADetailer, upscalers
• your sampler and internal attention method

The good news: most of the time, you can fix it without buying a new GPU—by changing a few settings.

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Step 1) Identify your VRAM (don’t guess)

Before you change anything, confirm how much VRAM you have.

Quick check (Windows)

• Open Task Manager → Performance → GPU
• Look for Dedicated GPU memory

Typical scenarios:

• 4GB VRAM: SD 1.5 works with strict settings; SDXL will be hard
• 6GB VRAM: SD 1.5 comfortable; SDXL possible with compromises
• 8GB VRAM: SD 1.5 easy; SDXL workable with tuned settings
• 12GB+ VRAM: fewer issues, can push higher resolutions and SDXL

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Step 2) The fastest fixes (work in 2 minutes)

If you’re in the middle of generating and it crashes, do these first.

1) Lower resolution (most important)

If you’re using SD 1.5:

• Start at 512×512 (or 512×768 / 768×512)

If you’re using SDXL:

• Start at 1024×1024 only if you have enough VRAM
• Otherwise try 832×832 or 768×768 and upscale later

Why: VRAM usage grows fast with pixels. Going from 512→768 can be the difference between success and failure.

2) Set batch size to 1

• Batch size = 1
• Batch count can be higher if you want more images (it runs sequentially)

Why: batch size multiplies VRAM usage.

3) Turn off High-Res Fix / Hires Upscale (for now)

High-res fix is a VRAM killer on low cards. Generate a normal image first, then upscale later.

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Step 3) Use the right model for your GPU

SDXL vs SD 1.5 (VRAM reality check)

• SDXL generally needs more VRAM
• SD 1.5 is much more forgiving and often looks great with good prompts + upscaling

If you have 4–6GB VRAM, SD 1.5 is usually the best choice for stability.

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Step 4) Enable memory-efficient attention (A1111 / ComfyUI)

This is one of the biggest “free” performance boosts.

If you use Automatic1111 (A1111)

In Settings → Optimizations (labels vary by version), look for:

• xFormers (if available)
• SDPA (PyTorch scaled dot product attention)
• “medvram” / “lowvram” style optimizations (if provided)

Then Apply settings and Restart UI.

What to expect: fewer OOM errors and smoother generations, sometimes faster too.

If you use ComfyUI

ComfyUI tends to be more memory-efficient than A1111 in many setups, but OOM can still happen from:

• big resolutions
• SDXL pipelines
• too many nodes loaded at once

Fixes still apply: reduce resolution, batch size, and consider using more efficient attention options depending on your install.

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Step 5) Reduce VRAM-heavy features (one by one)

If you still hit OOM, disable these in this order:

1. ControlNet (each enabled model consumes VRAM)
2. ADetailer (extra passes = extra memory)
3. Multiple LoRAs at high strength
4. Restore faces + heavy post-processing
5. High steps (less common cause, but can contribute)

Tip: Don’t debug everything at once. Turn off all extras, get a baseline working, then re-enable features one at a time.

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Step 6) Use “tiled” options (huge help for low VRAM)

If you’re using VAE or upscalers that cause memory spikes, “tiled” processing can help.

Look for:

• Tiled VAE
• Tiled diffusion / tiled upscaler
• Or upscalers that support tile size (in some UIs)

Tradeoff: slightly slower, but dramatically more stable on low VRAM.

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Step 7) Close GPU-hungry apps (hidden culprit)

Your GPU VRAM may already be consumed by:

• Chrome tabs with video
• games / launchers
• OBS recording
• Discord hardware acceleration

Fix: close these apps and try again.

On Windows, check Task Manager → GPU and see what’s using VRAM before starting Stable Diffusion.

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Step 8) If you’re on 4GB/6GB VRAM: stable “starter settings”

These settings aim for “it works every time.”

SD 1.5 recommended baseline (4–6GB VRAM)

• Resolution: 512×512 (or 512×768)
• Batch size: 1
• Steps: 20–28
• CFG: 5–8
• Sampler: use a stable default (depends on UI; start simple)
• Extras: OFF initially
• High-res fix: OFF
• Enable memory-efficient attention (xFormers/SDPA)

SDXL on 6–8GB (only if needed)

• Resolution: 768×768 or 832×832
• Batch size: 1
• Steps: 20–30
• Keep extras minimal
• Upscale afterwards instead of hires fix

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Step 9) Advanced: When nothing works (clean reset)

If OOM happens even at low settings, the issue might be a messy environment.

Try:

1. Restart your PC
2. Update GPU driver
3. Make sure you’re using the correct Python / torch build (depends on install)
4. Consider reinstalling using a clean guide

Internal link (perfect fit):

• How to Install Stable Diffusion Locally (Complete 2026 Guide)
  https://logixcontact.site/install-stable-diffusion-locally/