Teach the chat and image load guards about an active diffusion (SDXL) LoRA job: a chat load is refused (its footprint cannot be fit-checked against the trainer) and an image load is refused outright, mirroring the existing LLM training guards, so a load can no longer allocate GPU memory alongside the trainer and undo the pre-start cleanup. Bind the diffusion trainer subprocess to the parent's lifetime and scrub the native path lease secret from it by running the child through run_without_native_path_secret, matching the inference/export/LLM workers, so a Studio crash or kill no longer leaves the trainer holding the GPU. Reset in_model_load on the complete and error terminal events: a stop or failure during model loading otherwise leaves the status reporting a stale loading indicator after the job has ended. |
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| .. | ||
| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||