lllyasviel / lllyasviel/ControlNet
OOM for high resolution image
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 34.1k
- Forks
- 3k
- PR merge metrics
- No merged PRs in 30d
Description
awesome work!!
I'm testing the pipeline for high-resolution images (2048*2048), which takes 128G GPU. Any suggestions on how to make it work for high-resolution inputs? Thanks!
`RuntimeError: CUDA out of memory. Tried to allocate 128.00 GiB (GPU 0; 47.52 GiB total capacity; 5.77 GiB already allocated; 38.53 GiB free; 7.01 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF`
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the pipeline with the reported 2048×2048 input and inspect where CUDA memory is allocated. The issue provides no file or test entry point; done means high-resolution inputs can run without the reported CUDA out-of-memory failure.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100