Gracefully recover from VRAM out of memory errors (next branch version)

[lstein]

What type of PR is this? (check all applicable)

• Refactor
• Feature
• Bug Fix
• Optimization
• Documentation Update
• Community Node Submission

Have you discussed this change with the InvokeAI team?

• Yes
• No, because: straightforward fix

Have you updated all relevant documentation?

• Yes
• No

Description

At least on my system, if the model manager runs out of VRAM while moving a model into the GPU, the partial model gets stuck in VRAM and can't easily be removed. This makes the model unusable, and uses precious VRAM.

I encountered this when playing with large language models on the same system, but I suspect it will also happen if a video game is being played. I tried various approaches to recover from this state, including clearing the vram cache, deleting the model object, and running garbage collection, but without success.

This PR avoids the issue by implementing a check for sufficient available VRAM before trying to move a model to a CUDA GPU. If there is insufficient room, it raises a torch.cuda.OutOfMemoryError. This message is propagated to the front end. If more VRAM becomes available later, invocations will begin to work again.

Note: This pull request is against next. The model manager code has changed a bit, so I'm making a separate PR for main.

Related Tickets & Documents

• Related Issue #
• Closes #

QA Instructions, Screenshots, Recordings

Launch InvokeAI web service and another application that uses a lot of GPU VRAM. For my testing, I used ollama with a large model loaded. Run a generation and see if it generates an out of memory error. Try this repeatedly - should get the same error each time. Now kill the other application to free up VRAM and try to generate an image. It should work!

Merge Plan

Can merge when approved.

Added/updated tests?

• Yes
• No : please replace this line with details on why tests
  have not been included

[optional] Are there any post deployment tasks we need to perform?