c4ff52263e
The dialog assumed users knew the Studio home layout and that only SDXL is trainable, and hid both facts behind free-text fields. Restructure it around the three real decisions: - Base model is a dropdown of the trainable SDXL picks (Base 1.0, Turbo, the loaded SDXL pipeline when there is one) with a custom repo/path escape hatch, instead of a bare text field defaulting to a repo id. - Training images come from an in-browser upload (new dataset endpoints) or a picker over existing dataset folders with image/caption counts. No shell access or knowledge of the datasets root is needed any more, and the captioning rules are explained inline. - The output field is now Adapter name and the instance prompt is labelled as the trigger prompt, with a no-captions warning wired to the selected dataset's actual caption count. Hyperparameters collapse behind a training settings toggle since the defaults suit a first run. A completed run says where the adapter went and offers Done / Train another, and the top-bar button gets an icon and a plainer description. The dialog title states the SDXL-only scope and that other families load LoRAs but cannot train them yet. |
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| .. | ||
| 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 | ||