74e7854e15
Four review findings on the diffusion training start path: - It spawned the SDXL trainer without checking the LLM TrainingBackend, so a start while an LLM run was active put two trainers on the same GPU. Add a symmetric interlock: diffusion start returns 409 when LLM training is active, and LLM start refuses while a diffusion job is active. - It went straight to service.start() without freeing GPU residents. Add a pre-spawn free of the export subprocess, the resident Images pipeline (with an arbiter release), and chat models, mirroring the LLM start path. - data_dir / output_dir were passed through unresolved, so Studio-relative names failed and absolute paths bypassed containment. Resolve them with resolve_dataset_path / resolve_output_dir before spawn (400 on an uncontained path). - The request model dropped max_grad_norm and lora_target_modules, so runs that set them trained with defaults. Add both fields. The gemini pump-join deadlock was already fixed earlier (join outside the lock + proc-identity fence). Note: honoring a stop DURING model load is a trainer-loop change owned by the diffusion training engine PR (should_stop polled before the first optimizer step). Adds route + model regression tests. |
||
|---|---|---|
| .. | ||
| backend | ||
| frontend | ||
| src-tauri | ||
| __init__.py | ||
| install_llama_prebuilt.py | ||
| install_node_prebuilt.py | ||
| install_python_stack.py | ||
| install_sd_cpp_prebuilt.py | ||
| LICENSE.AGPL-3.0 | ||
| node_prebuilt_pins.json | ||
| package-lock.json | ||
| package.json | ||
| setup.bat | ||
| setup.ps1 | ||
| setup.sh | ||
| Unsloth_Studio_Colab.ipynb | ||