5f65f01e2e
Run backend.start_training off the event loop with asyncio.to_thread so the synchronous diffusion/video unload calls (which wait on engine generation locks) cannot freeze concurrent requests; guard against overlapping starts with a _start_in_progress compare-and-set under the service lock. Resolve bare diffusion dataset names directly under datasets_root() before falling back to the generic resolver, so an unrelated LLM upload file or recipe folder sharing the name cannot shadow the image dataset. Reject exact duplicate filenames within one multipart upload batch: two parts staged to the same destination would let the later tmp.replace silently discard the earlier file. Case variants stay exempt per the existing stem-guard contract. Require an instance prompt in the train panel when only some images have captions, since backend discovery silently skips uncaptioned images. |
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| .. | ||
| data_recipe | ||
| export | ||
| inference | ||
| rag | ||
| training | ||
| __init__.py | ||
| _torchao_stub.py | ||
| import_guards.py | ||
| tool_healing.py | ||