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"horizontal": false, "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } } }, { "id": 708, "type": "Reroute", "pos": [ -2496, 1728 ], "size": [ 75, 26 ], "flags": { "pinned": true }, "order": 42, "mode": 0, "inputs": [ { "name": "", "type": "*", "widget": { "name": "value" }, "link": 1522 } ], "outputs": [ { "name": "", "type": "INT", "links": [ 1523 ] } ], "properties": { "showOutputText": false, "horizontal": false, "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } } }, { "id": 713, "type": "Note", "pos": [ -2304, 1152 ], "size": [ 352, 320 ], "flags": { "pinned": true }, "order": 5, "mode": 2, "inputs": [], "outputs": [], "title": "Additional notes LoRa AutoTuner", "properties": { "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } }, "widgets_values": [ "Tips:\n\n• cache_patches keeps the full sweep result in\n RAM — switching output_mode or re-running with\n same LoRAs is instant.\n\n• diff_cache_mode \"auto\" speeds up Phase 2 by\n caching LoRA diffs between candidates. Uses RAM\n up to diff_cache_ram_pct, then spills to disk.\n\n• scoring_svd adds effective rank scoring — more\n thorough but much slower on large models. Leave\n disabled unless you need it.\n\n• The tuner_data output can connect to a Merge\n Selector node to try alternative ranked configs,\n or to a LoRA Optimizer for the bridge workflow." ], "color": "#432", "bgcolor": "#653" }, { "id": 714, "type": "Note", "pos": [ -2272, 2240 ], "size": [ 352, 320 ], "flags": { "pinned": true }, "order": 6, "mode": 2, "inputs": [], "outputs": [], "title": "Additional notes combo", "properties": { "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } }, "widgets_values": [ "Tips:\n\n• Switching modes is instant when cache is enabled\n — the AutoTuner reuses its cached sweep results.\n\n• In Mode A (from_autotuner), the Optimizer's \n widgets update to show the AutoTuner's winning\n config. These values persist when you switch \n to Mode B.\n\n• The LoRA Stack connects to BOTH nodes. The \n AutoTuner always needs it for analysis. The\n Optimizer uses it when in \"manual\" mode.\n\n• You can still use the AutoTuner's report output\n to see the full ranking of tested configs." ], "color": "#432", "bgcolor": "#653" }, { "id": 712, "type": "Note", "pos": [ -3200, 1600 ], "size": [ 576, 704 ], "flags": { "pinned": true }, "order": 7, "mode": 0, "inputs": [], "outputs": [], "title": "Lora AutoTuner + LoRa Optimizer", "properties": { "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } }, "widgets_values": [ "AutoTuner + Optimizer Bridge\n\nBest of both worlds: let the AutoTuner find optimal\nsettings, then fine-tune them manually.\n\nHow to use:\n 1. Run the workflow — AutoTuner finds the best \n config and merges your LoRAs automatically\n 2. The Optimizer shows you what settings won\n 3. Want to tweak? Flip the Optimizer to \"manual\"\n — the AutoTuner automatically steps aside\n 4. Adjust any setting (quality, sparsification...)\n and re-run. You're starting from the best\n config, not from scratch.\n\nThe two switches stay in sync automatically:\n • \"from_autotuner\" = AutoTuner merges, Optimizer\n shows the result (hands-off mode)\n • \"manual\" = You control the merge, AutoTuner\n just passes the model through (hands-on mode)\n\nSwitching between modes is instant thanks to caching.\n\nThis is the recommended workflow if you want to\nunderstand and control how your LoRAs are merged\nwhile still getting a smart starting point." ], "color": "#432", "bgcolor": "#653" }, { "id": 711, "type": "Note", "pos": [ -3168, 544 ], "size": [ 576, 704 ], "flags": { "pinned": true }, "order": 8, "mode": 0, "inputs": [], "outputs": [], "title": "Lora AutoTuner", "properties": { "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } }, "widgets_values": [ "LoRA AutoTuner\n\nFinds the best settings automatically. 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Disable for large video models.\n\n• patch_compression \"smart\" shrinks memory with\n no quality loss. Use \"aggressive\" to save more RAM.\n\n• normalize_keys should stay enabled — it makes\n LoRAs from different trainers compatible.\n\n• strategy_set \"full\" is the latest. 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