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  • Has anyone determined how to actually set max_split_size_mb . . . - Reddit
    You set it in the webui-user bat file as a starting argument, adding a line like this: set PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0 6,max_split_size_mb:128 But it definitely won't stop OoM errors from appearing completely - perhaps you will only see less of them if lucky
  • RuntimeError: CUDA out of memory. How can I set max_split_size_mb?
    max_split_size_mb prevents the allocator from splitting blocks larger than this size (in MB) This can help prevent fragmentation and may allow some borderline workloads to complete without running out of memory
  • Basic things you might not know: How to avoid CUDA OUT OF MEMORY?
    In most cases, the above arguments are already set to the optimal configuration However, if your VRAM is larger than 24GB, you can consider setting max_split_size_mb to 1024 or higher If your VRAM is smaller, you should decrease it, but not lower than 128
  • Stable Diffusion runtime error - how to fix CUDA out of memory error
    As mentioned in the error message, run the following command first: PYTORCH_CUDA_ALLOC_CONF=garbage_collection_threshold:0 6, max_split_size_mb:128 Then run the image generation command with: --n_samples 1
  • 6 Ways to Fix CUDA out of Memory in Stable Diffusion
    According to Pytorch documentation on memory management: "max_split_size_mb prevents the native allocator from splitting blocks larger than this size (in MB) This can reduce fragmentation and may allow some borderline workloads to complete without running out of memory "
  • CUDA Out of Memory in Stable Diffusion or PyTorch
    A smaller batch size would require less memory on the GPU and may help avoid the out of memory error Try to Set max_split_size_mb to a smaller value to avoid fragmentation There is a DataParallel module in PyTorch, which allows you to distribute the model across multiple GPUs
  • 【Stable Diffusion Web UI】RuntimeError: CUDA out of memory . . . - Kageori
    GPU メモリ容量の使用量がしきい値を超えた時（この場合は0 6=60%）、 GPUのメモリのブロックを再利用してくれる。 max_split_size_mbとは？ ここで設定したサイズ（128MB）より大きなブロックを断片化するのを防いでくれるので、メモリ不足が原因で中断しにくくなる。 参考: CUDA SEMANTICS それでもダメなら？ 上記方法でもダメな場合、他の対処法が以下に書いてありましたので、参考までに。 Stable Diffusion Runtime Error: How To Fix CUDA Out Of Memory Error In Stable Diffusion
  • CUDA Out of memory error for Stable Diffusion 2. 1
    torch cuda OutOfMemoryError: CUDA out of memory Tried to allocate 20 00 MiB (GPU 0; 4 00 GiB total capacity; 3 31 GiB already allocated; 624 00 KiB free; 3 35 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation
  • stabilityai stable-diffusion · RuntimeError: CUDA out of memory.
    My GPU may be too small try using --W 256 --H 256 as part your prompt where put that? Put it in prompt, if you are using the invokeAI web change width-256 and height-256





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