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"pinned": true }, "order": 15, "mode": 0, "inputs": [ { "name": "", "type": "*", "widget": { "name": "value" }, "link": 1439 } ], "outputs": [ { "name": "", "type": "STRING", "links": [ 1440 ] } ], "properties": { "showOutputText": false, "horizontal": false, "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } } }, { "id": 713, "type": "Note", "pos": [ -1696, 1152 ], "size": [ 352, 320 ], "flags": {}, "order": 0, "mode": 0, "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": 711, "type": "Note", "pos": [ -3168, 544 ], "size": [ 576, 704 ], "flags": { "pinned": true }, "order": 1, "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. Instead of\nguessing which merge settings work best for your\nspecific LoRA combination, it tries many different\nconfigurations and picks the winner.\n\nWhen to use:\n You want the best possible merge quality and are\n willing to wait for the tuner to test options.\n First run takes a while, but results are cached\n so subsequent runs are instant.\n\nWhat to connect:\n • model — Your base model\n • lora_stack — Your LoRAs from a LoRA Stack node\n • clip — Your text encoder\n\nWhat you get:\n • The best merged model it found\n • A ranked report showing all tested configs\n • tuner_data — The winning settings, which you \n can pass to a Merge Selector to try runner-up\n configs, or to an Optimizer for fine-tuning\n\nGood starting settings:\n • top_n: 3 (tests 3 best candidates)\n • scoring_speed: turbo (fast, good enough)\n • cache_patches: enabled (re-runs are instant)\n\nThe report shows you which config won and why —\nuseful even if you later want to tweak manually." ], "color": "#432", "bgcolor": "#653" }, { "id": 710, "type": "Note", "pos": [ -3168, -512 ], "size": [ 576, 704 ], "flags": { "pinned": true }, "order": 2, "mode": 0, "inputs": [], "outputs": [], "title": "LoRa Optimizer", "properties": { "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } }, "widgets_values": [ "LoRA Optimizer\n\nCombines multiple LoRAs together intelligently.\nInstead of just stacking them (which often causes\nmuddy or oversaturated results), it analyzes where\nyour LoRAs conflict and picks the best way to blend\nthem for each part of the model.\n\nWhen to use:\n You have 2+ style/character LoRAs and want them\n to work well together without fighting each other.\n\nWhat to connect:\n • model — Your base model (SDXL, Flux, etc.)\n • lora_stack — Your LoRAs from a LoRA Stack node\n • clip — Your text encoder (so LoRAs can also \n affect how prompts are understood)\n\nWhat you get:\n • A merged model ready for sampling\n • An analysis report explaining what it did\n\nGood starting settings:\n • output_strength: 1.0 (lower if too strong)\n • auto_strength: enabled (prevents oversaturation)\n • Everything else: defaults work well for most cases\n\nRead the analysis_report output to understand how\nyour LoRAs interact — it shows which layers had\nconflicts and how they were resolved." ], "color": "#432", "bgcolor": "#653" }, { "id": 550, "type": "PrimitiveStringMultiline", "pos": [ -4512, -96 ], "size": [ 384, 384 ], "flags": { "pinned": true }, "order": 3, "mode": 0, "inputs": [], "outputs": [ { "name": "STRING", "type": "STRING", "links": [ 1439 ] } ], "properties": { "cnr_id": "comfy-core", "ver": "0.15.1", "Node name for S&R": "PrimitiveStringMultiline", "ue_properties": { "widget_ue_connectable": { "value": true }, "version": "7.7", "input_ue_unconnectable": {} } }, "widgets_values": [ "dark atmosphere, (cosplay:1.35) , very beautiful and sexy woman wearing (Basic bodysuit and high-rise leggings:1.2) , smiling to the viewer Stirring a pot , sexy pose, in New Yor" ], "color": "#232", "bgcolor": "#353" }, { "id": 709, "type": "Note", "pos": [ -2304, 128 ], "size": [ 352, 320 ], "flags": { "pinned": true }, "order": 4, "mode": 2, "inputs": [], "outputs": [], "title": "Additional notes LoRa Optimizer", "properties": { "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } }, "widgets_values": [ "Tips:\n\n• cache_patches keeps the result in RAM — re-runs\n are instant. 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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"widgets_values": [ "(1+2) + 3" ] }, { "id": 783, "type": "Note", "pos": [ -2016, 960 ], "size": [ 256, 1088 ], "flags": {}, "order": 14, "mode": 0, "inputs": [], "outputs": [], "properties": { "ue_properties": { "widget_ue_connectable": {}, "version": "7.7", "input_ue_unconnectable": {} } }, "widgets_values": [ "======================================================\n LoRA AutoTuner Results\n======================================================\n\n Analysis Summary:\n LoRAs: 3 | Prefixes: 240 | Avg conflict: 49.8%\n Excess conflict: 0.0% | Subspace overlap: 0.04\n Avg cosine similarity: 0.03 | Importance ratio: 2.8x\n Output strength: -1.0\n Suggested max output_strength: 1.00\n Scoring speed: turbo (subsampled prefix scoring)\n Decision smoothing: 0.25\n\n --------------------------------------\n Top 10 Configurations\n --------------------------------------\n\n #1 ★ (applied to output) Score: 0.41\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: disabled\n Auto-strength: enabled | Optimization: per_prefix | Strategy: no_slerp\n Sparsity: 28.9% | Energy: 1.08x\n\n #2 Score: 0.39\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: disabled\n Auto-strength: enabled | Optimization: per_prefix | Strategy: basic\n Sparsity: 30.6% | Energy: 1.08x\n\n #3 Score: 0.20\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: disabled\n Auto-strength: enabled | Optimization: per_prefix | Strategy: full\n Sparsity: 49.6% | Energy: 1.62x\n\n #4 Score: 0.09\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.9)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: no_slerp\n Sparsity: 52.7% | Energy: 1.11x\n\n #5 Score: 0.09\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.9)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: basic\n Sparsity: 52.7% | Energy: 1.11x\n\n #6 Score: 0.09\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.9)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: full\n Sparsity: 53.0% | Energy: 1.66x\n\n #7 Score: 0.07\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.7)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: no_slerp\n Sparsity: 56.1% | Energy: 1.21x\n\n #8 Score: 0.07\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.7)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: basic\n Sparsity: 56.1% | Energy: 1.21x\n\n #9 Score: 0.07\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della (density=0.7)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: full\n Sparsity: 56.5% | Energy: 1.81x\n\n #10 Score: 0.07\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.7)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: full\n Sparsity: 56.5% | Energy: 1.81x\n\n To use a different config: change selection=N\n or connect TUNER_DATA to a Merge Selector node\n======================================================\n\n==================================================\nANALYSIS REPORT\n==================================================\n==================================================\nLORA OPTIMIZER - ANALYSIS REPORT\n==================================================\nArchitecture preset: dit (DiT (Flux/WAN/Z-Image/LTX/HunyuanVideo))\nDetected architecture: Z-Image Turbo (Lumina2)\nKey normalization: enabled\n\n\n--- Per-LoRA Analysis ---\n ZImageTurbo/character/xcl_Ivy_ZIT.safetensors:\n Strength: 1.0\n Keys: 240\n Avg rank: 32\n L2 norm (mean): 1.0335\n ZImageTurbo/character/MisterMR_Pippa_Belrose_Zimageturbo.safetensors:\n Strength: 1.0\n Keys: 240\n Avg rank: 32\n L2 norm (mean): 2.2177\n ZImageTurbo/concept/zimage-igbaddie.safetensors:\n Strength: 1.0\n Keys: 240\n Avg rank: 32\n L2 norm (mean): 0.9188\n\n--- Auto-Strength Adjustment ---\n ZImageTurbo/character/xcl_Ivy_ZIT.safetensors: model 1.0 -> 0.8536, clip 1.0 -> 1.0000\n ZImageTurbo/character/MisterMR_Pippa_Belrose_Zimageturbo.safetensors: model 1.0 -> 0.8536, clip 1.0 -> 1.0000\n ZImageTurbo/concept/zimage-igbaddie.safetensors: model 1.0 -> 0.8536, clip 1.0 -> 1.0000\n Model: scale factor 0.8536\n Model: exact streamed energy 58.4934 (orthogonal baseline 58.4864, avg cos 0.000 — mostly orthogonal (independent))\n\n--- Pairwise Analysis ---\n ZImageTurbo/character/xcl_Ivy_ZIT.safetensors vs ZImageTurbo/character/MisterMR_Pippa_Belrose_Zimageturbo.safetensors:\n Overlapping positions: 23973797\n Sign conflicts: 11910524 (49.7%)\n Magnitude-weighted conflict: 48.3%\n Excess conflict over cosine baseline: 0.0% (expected 49.1%)\n Cosine similarity: 0.032\n Subspace overlap: 0.04\n ZImageTurbo/character/xcl_Ivy_ZIT.safetensors vs ZImageTurbo/concept/zimage-igbaddie.safetensors:\n Overlapping positions: 23973739\n Sign conflicts: 11910758 (49.7%)\n Magnitude-weighted conflict: 48.3%\n Excess conflict over cosine baseline: 0.0% (expected 49.0%)\n Cosine similarity: 0.034\n Subspace overlap: 0.04\n ZImageTurbo/character/MisterMR_Pippa_Belrose_Zimageturbo.safetensors vs ZImageTurbo/concept/zimage-igbaddie.safetensors:\n Overlapping positions: 23975682\n Sign conflicts: 11962779 (49.9%)\n Magnitude-weighted conflict: 49.5%\n Excess conflict over cosine baseline: 0.1% (expected 49.7%)\n Cosine similarity: 0.010\n Subspace overlap: 0.03\n\n--- Collection Statistics ---\n Total LoRAs: 3\n Total target groups: 240\n Avg sign conflict ratio: 49.8%\n Avg weighted conflict ratio: 48.7%\n Avg excess conflict: 0.0%\n Avg subspace overlap: 0.04\n Importance ratio (max/min frobenius): 2.75x\n Decision smoothing: 0.25\n\n--- Auto-Selected Parameters ---\n Merge mode: weighted_average\n (global fallback — each target group uses its own parameters)\n Strategy set: no_slerp (full detection, no SLERP upgrade)\n\n--- Per-Group Strategy ---\n weighted_average (orthogonal): 240 groups (100%)\n Total: 240 groups\n\n--- Block Strategy Map ---\n layers.0 ---- avg 0% conflict (8 avg) (8x)\n layers.1 ---- avg 0% conflict (8 avg) (8x)\n layers.10 ---- avg 0% conflict (8 avg) (8x)\n layers.11 ---- avg 0% conflict (8 avg) (8x)\n layers.12 ---- avg 0% conflict (8 avg) (8x)\n layers.13 ---- avg 0% conflict (8 avg) (8x)\n layers.14 ---- avg 0% conflict (8 avg) (8x)\n layers.15 ---- avg 0% conflict (8 avg) (8x)\n layers.16 ---- avg 0% conflict (8 avg) (8x)\n layers.17 ---- avg 0% conflict (8 avg) (8x)\n layers.18 ---- avg 0% conflict (8 avg) (8x)\n layers.19 ---- avg 0% conflict (8 avg) (8x)\n layers.2 ---- avg 0% conflict (8 avg) (8x)\n layers.20 ---- avg 0% conflict (8 avg) (8x)\n layers.21 ---- avg 0% conflict (8 avg) (8x)\n layers.22 ---- avg 0% conflict (8 avg) (8x)\n layers.23 ---- avg 0% conflict (8 avg) (8x)\n layers.24 ---- avg 0% conflict (8 avg) (8x)\n layers.25 ---- avg 0% conflict (8 avg) (8x)\n layers.26 ---- avg 0% conflict (8 avg) (8x)\n layers.27 ---- avg 0% conflict (8 avg) (8x)\n layers.28 ---- avg 0% conflict (8 avg) (8x)\n layers.29 ---- avg 0% conflict (8 avg) (8x)\n layers.3 ---- avg 0% conflict (8 avg) (8x)\n layers.4 ---- avg 0% conflict (8 avg) (8x)\n layers.5 ---- avg 0% conflict (8 avg) (8x)\n layers.6 ---- avg 0% conflict (8 avg) (8x)\n layers.7 ---- avg 0% conflict (8 avg) (8x)\n layers.8 ---- avg 0% conflict (8 avg) (8x)\n layers.9 ---- avg 0% conflict (8 avg) (8x)\n Legend: ==== sum ~~~~ slerp ---- avg ++++ cons #### TIES\n\n--- Reasoning ---\n Cosine similarity 0.03 near zero (orthogonal LoRAs) — sign conflict 49.8% is base-rate noise, not real conflict\n Using weighted_average to preserve both signals (SLERP upgrade disabled by profile)\n\n--- Merge Summary ---\n Keys processed: 240\n Model patches: 240\n CLIP patches: 0\n Output strength: 1.00 (auto — suggested max)\n CLIP strength: 1.0\n\n==================================================" ], "color": "#432", "bgcolor": 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"widgets_values": [ "======================================================\n LoRA AutoTuner Results\n======================================================\n\n Analysis Summary:\n LoRAs: 3 | Prefixes: 240 | Avg conflict: 49.8%\n Excess conflict: 0.0% | Subspace overlap: 0.04\n Avg cosine similarity: 0.03 | Importance ratio: 2.8x\n Output strength: -1.0\n Suggested max output_strength: 1.00\n Decision smoothing: 0.25\n\n --------------------------------------\n Top 10 Configurations\n --------------------------------------\n\n #1 ★ (applied to output) Score: 0.43\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: disabled\n Auto-strength: enabled | Optimization: per_prefix | Strategy: basic\n Sparsity: 27.5% | Energy: 1.02x\n\n #2 Score: 0.42\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: disabled\n Auto-strength: enabled | Optimization: per_prefix | Strategy: no_slerp\n Sparsity: 28.0% | Energy: 1.02x\n\n #3 Score: 0.22\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: disabled\n Auto-strength: enabled | Optimization: per_prefix | Strategy: full\n Sparsity: 48.8% | Energy: 1.54x\n\n #4 Score: 0.10\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.9)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: full\n Sparsity: 51.0% | Energy: 1.58x\n\n #5 Score: 0.10\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.9)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: no_slerp\n Sparsity: 50.7% | Energy: 1.04x\n\n #6 Score: 0.10\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.9)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: basic\n Sparsity: 50.7% | Energy: 1.04x\n\n #7 Score: 0.09\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della (density=0.7)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: full\n Sparsity: 54.5% | Energy: 1.72x\n\n #8 Score: 0.09\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.7)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: full\n Sparsity: 54.5% | Energy: 1.72x\n\n #9 Score: 0.08\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.7)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: no_slerp\n Sparsity: 54.1% | Energy: 1.14x\n\n #10 Score: 0.08\n Mode: per-prefix (auto) | Refinement: none\n Sparsification: della_conflict (density=0.7)\n Auto-strength: enabled | Optimization: per_prefix | Strategy: basic\n Sparsity: 54.1% | Energy: 1.14x\n\n To use a different config: change selection=N\n or connect TUNER_DATA to a Merge Selector node\n======================================================\n\n==================================================\nANALYSIS REPORT\n==================================================\n==================================================\nLORA OPTIMIZER - ANALYSIS REPORT\n==================================================\nArchitecture preset: dit (DiT (Flux/WAN/Z-Image/LTX/HunyuanVideo))\nDetected architecture: Z-Image Turbo (Lumina2)\nKey normalization: enabled\n\n\n--- Per-LoRA Analysis ---\n ZImageTurbo/character/xcl_Ivy_ZIT.safetensors:\n Strength: 1.0\n Keys: 240\n Avg rank: 32\n L2 norm (mean): 1.0335\n ZImageTurbo/character/MisterMR_Pippa_Belrose_Zimageturbo.safetensors:\n Strength: 1.0\n Keys: 240\n Avg rank: 32\n L2 norm (mean): 2.2177\n ZImageTurbo/concept/zimage-igbaddie.safetensors:\n Strength: 1.0\n Keys: 240\n Avg rank: 32\n L2 norm (mean): 0.9188\n\n--- Auto-Strength Adjustment ---\n ZImageTurbo/character/xcl_Ivy_ZIT.safetensors: model 1.0 -> 0.8536, clip 1.0 -> 1.0000\n ZImageTurbo/character/MisterMR_Pippa_Belrose_Zimageturbo.safetensors: model 1.0 -> 0.8536, clip 1.0 -> 1.0000\n ZImageTurbo/concept/zimage-igbaddie.safetensors: model 1.0 -> 0.8536, clip 1.0 -> 1.0000\n Model: scale factor 0.8536\n Model: exact streamed energy 58.4934 (orthogonal baseline 58.4864, avg cos 0.000 — mostly orthogonal (independent))\n\n--- Pairwise Analysis ---\n ZImageTurbo/character/xcl_Ivy_ZIT.safetensors vs ZImageTurbo/character/MisterMR_Pippa_Belrose_Zimageturbo.safetensors:\n Overlapping positions: 23973797\n Sign conflicts: 11910524 (49.7%)\n Magnitude-weighted conflict: 48.3%\n Excess conflict over cosine baseline: 0.0% (expected 49.1%)\n Cosine similarity: 0.032\n Subspace overlap: 0.04\n ZImageTurbo/character/xcl_Ivy_ZIT.safetensors vs ZImageTurbo/concept/zimage-igbaddie.safetensors:\n Overlapping positions: 23973739\n Sign conflicts: 11910758 (49.7%)\n Magnitude-weighted conflict: 48.3%\n Excess conflict over cosine baseline: 0.0% (expected 49.0%)\n Cosine similarity: 0.034\n Subspace overlap: 0.04\n ZImageTurbo/character/MisterMR_Pippa_Belrose_Zimageturbo.safetensors vs ZImageTurbo/concept/zimage-igbaddie.safetensors:\n Overlapping positions: 23975682\n Sign conflicts: 11962779 (49.9%)\n Magnitude-weighted conflict: 49.5%\n Excess conflict over cosine baseline: 0.1% (expected 49.7%)\n Cosine similarity: 0.010\n Subspace overlap: 0.03\n\n--- Collection Statistics ---\n Total LoRAs: 3\n Total target groups: 240\n Avg sign conflict ratio: 49.8%\n Avg weighted conflict ratio: 48.7%\n Avg excess conflict: 0.0%\n Avg subspace overlap: 0.04\n Importance ratio (max/min frobenius): 2.75x\n Decision smoothing: 0.25\n\n--- Auto-Selected Parameters ---\n Merge mode: weighted_average\n (global fallback — each target group uses its own parameters)\n Strategy set: basic (TIES vs weighted_average only)\n\n--- Per-Group Strategy ---\n weighted_average (orthogonal): 240 groups (100%)\n Total: 240 groups\n\n--- Block Strategy Map ---\n layers.0 ---- avg 0% conflict (8 avg) (8x)\n layers.1 ---- avg 0% conflict (8 avg) (8x)\n layers.10 ---- avg 0% conflict (8 avg) (8x)\n layers.11 ---- avg 0% conflict (8 avg) (8x)\n layers.12 ---- avg 0% conflict (8 avg) (8x)\n layers.13 ---- avg 0% conflict (8 avg) (8x)\n layers.14 ---- avg 0% conflict (8 avg) (8x)\n layers.15 ---- avg 0% conflict (8 avg) (8x)\n layers.16 ---- avg 0% conflict (8 avg) (8x)\n layers.17 ---- avg 0% conflict (8 avg) (8x)\n layers.18 ---- avg 0% conflict (8 avg) (8x)\n layers.19 ---- avg 0% conflict (8 avg) (8x)\n layers.2 ---- avg 0% conflict (8 avg) (8x)\n layers.20 ---- avg 0% conflict (8 avg) (8x)\n layers.21 ---- avg 0% conflict (8 avg) (8x)\n layers.22 ---- avg 0% conflict (8 avg) (8x)\n layers.23 ---- avg 0% conflict (8 avg) (8x)\n layers.24 ---- avg 0% conflict (8 avg) (8x)\n layers.25 ---- avg 0% conflict (8 avg) (8x)\n layers.26 ---- avg 0% conflict (8 avg) (8x)\n layers.27 ---- avg 0% conflict (8 avg) (8x)\n layers.28 ---- avg 0% conflict (8 avg) (8x)\n layers.29 ---- avg 0% conflict (8 avg) (8x)\n layers.3 ---- avg 0% conflict (8 avg) (8x)\n layers.4 ---- avg 0% conflict (8 avg) (8x)\n layers.5 ---- avg 0% conflict (8 avg) (8x)\n layers.6 ---- avg 0% conflict (8 avg) (8x)\n layers.7 ---- avg 0% conflict (8 avg) (8x)\n layers.8 ---- avg 0% conflict (8 avg) (8x)\n layers.9 ---- avg 0% conflict (8 avg) (8x)\n Legend: ==== sum ~~~~ slerp ---- avg ++++ cons #### TIES\n\n--- Reasoning ---\n Excess conflict 0.0% <= 25% threshold -> weighted_average mode selected\n Low conflict means LoRAs are mostly compatible, simple averaging works well\n\n--- Merge Summary ---\n Keys processed: 240\n Model patches: 240\n CLIP patches: 0\n Output strength: 1.00 (auto — suggested max)\n CLIP strength: 1.0\n\n==================================================" ] }, { "id": 786, "type": "LoRAAutoTunerSettings", "pos": [ -1984, 608 ], "size": [ 288, 320 ], "flags": {}, "order": 16, "mode": 0, "inputs": [ { "name": "merge_settings", "shape": 7, "type": "MERGE_SETTINGS", "link": 1709 }, { "name": "evaluator", "shape": 7, "type": "AUTOTUNER_EVALUATOR", "link": null } ], "outputs": [ { "name": "settings", "type": "OPTIMIZER_SETTINGS", "links": [ 1710 ] } ], "properties": { "cnr_id": 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