OOM with 32GB 5090 when trying Qwen-Image training

[NeedForSpeed73]

This is for bugs only

Did you already ask in the discord?

Yes

You verified that this is a bug and not a feature request or question by asking in the discord?

Yes

Describe the bug

When trying to replicate the workflow in this official Ostris YouTube channel video, I get an OOM error before the actual training starts, when just creating samples.
According to another Discord help request, seems like the thing is common and has arisen since a few weeks (some update broke it?).

I'm running Ubuntu 24.04 (kernel 6.14.0-33). python 3.13.9, nvidia-driver-580-open (CUDA 13.0) and pytorch 2.9.0+cu128.

�[1;35mtorch.OutOfMemoryError�[0m: �[35mCUDA out of memory. Tried to allocate 5.06 GiB. GPU 0 has a total capacity of 31.36 GiB of which 336.62 MiB is free. Including non-PyTorch memory, this process has 30.68 GiB memory in use. Of the allocated memory 27.17 GiB is allocated by PyTorch, and 2.92 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)�[0m

[kesslerdev]

Yes it seems, look at this comment #457 (comment)

[NeedForSpeed73]

Yes it seems, look at this comment #457 (comment)

I've tried the suggested workaround but still get the same exact error.

What I did was:
git reset --hard c6edd71

then I launched the ui again:

cd ui npm run build_and_start

I double checked the changed python source files to be sure I was actually using the older commit and they are indeed the old ones.

[NeoLoger]

I'm having the same issue, I'm running this on Arch Linux in a container, specifically CachyOS if this makes any difference.
It was working fine on windows11 when I was using the automatic installer.

I'm using the Dockerfile that is located this git.

Error running job: CUDA out of memory. Tried to allocate 4.45 GiB. GPU 0 has a total capacity of 31.35 GiB of which 1.61 GiB is free. Including non-PyTorch memory, this process has 28.64 GiB memory in use. Of the allocated memory 22.70 GiB is allocated by PyTorch, and 5.34 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

I have RTX 5090

Also tried to revert to c6edd71 build but still getting the same issue.