{ "name": "tier1_thorough", "description": "Full overnight Tier 1 ablation on 49-clip BJ dataset. 4 groups: rank, alpha, regularisation, and best combinations. ~10-12h depending on GPU.", "data_dir": "/media/unraid/davinci/Selva/BJ/features", "output_root": "/media/unraid/davinci/Selva/BJ/experiment/tier1_thorough", "base": { "steps": 4000, "rank": 16, "alpha": 0.0, "lr": 1e-4, "batch_size": 16, "warmup_steps": 100, "grad_accum": 1, "save_every": 1000, "seed": 42, "target": "attn.qkv", "timestep_mode": "uniform", "logit_normal_sigma": 1.0, "curriculum_switch": 0.6, "lora_dropout": 0.0, "lora_plus_ratio": 1.0 }, "experiments": [ { "id": "g1_rank_16", "group": "rank", "description": "Rank 16 baseline — reference point for all groups." }, { "id": "g1_rank_32", "group": "rank", "description": "Rank 32 — midpoint. Does doubling rank improve quality without overfitting?", "rank": 32 }, { "id": "g1_rank_64", "group": "rank", "description": "Rank 64 — MMAudio LoRA guide default. Maximum expressiveness at 49 clips.", "rank": 64 }, { "id": "g2_alpha_half_r16", "group": "alpha", "description": "Alpha=8 with rank 16 (scale=0.5). Reduces intruder singular dimensions (arXiv:2410.21228).", "alpha": 8.0 }, { "id": "g2_alpha_half_r64", "group": "alpha", "description": "Alpha=32 with rank 64 (scale=0.5). Best-practice scaling for high-rank adapters.", "rank": 64, "alpha": 32.0 }, { "id": "g3_lora_plus_4", "group": "regularisation", "description": "LoRA+ ratio=4 — conservative asymmetric LR. Lower bound for the technique.", "lora_plus_ratio": 4.0 }, { "id": "g3_lora_plus_16", "group": "regularisation", "description": "LoRA+ ratio=16 — standard from FLUX LoRA literature. Faster early convergence.", "lora_plus_ratio": 16.0 }, { "id": "g3_dropout_0.05", "group": "regularisation", "description": "LoRA dropout 0.05 only. Light sparsity regularisation (arXiv:2404.09610).", "lora_dropout": 0.05 }, { "id": "g3_dropout_0.1", "group": "regularisation", "description": "LoRA dropout 0.1 only. Stronger regularisation — may prevent overfitting past step 2000.", "lora_dropout": 0.1 }, { "id": "g3_curriculum", "group": "regularisation", "description": "Curriculum sampling only: logit_normal steps 1-2400, then uniform (arXiv:2603.12517).", "timestep_mode": "curriculum" }, { "id": "g4_full_r16", "group": "combined", "description": "All Tier 1 at rank 16: LoRA+ 16 + dropout 0.05 + curriculum.", "lora_plus_ratio": 16.0, "lora_dropout": 0.05, "timestep_mode": "curriculum" }, { "id": "g4_full_r64", "group": "combined", "description": "All Tier 1 at rank 64 + alpha=32. Best expressiveness + best regularisation.", "rank": 64, "alpha": 32.0, "lora_plus_ratio": 16.0, "lora_dropout": 0.05, "timestep_mode": "curriculum" }, { "id": "g5_lr_low", "group": "lr", "description": "LR=3e-5 — 3× lower than baseline. Tests if 1e-4 is overshooting.", "lr": 3e-5 }, { "id": "g5_lr_high", "group": "lr", "description": "LR=3e-4 — 3× higher than baseline. Tests if 1e-4 is too conservative.", "lr": 3e-4 }, { "id": "g6_target_full_r16", "group": "target", "description": "Rank 16 targeting attn.qkv + linear1 (FFN projections). Doubles LoRA coverage.", "target": "attn.qkv linear1" }, { "id": "g6_target_full_r64", "group": "target", "description": "Rank 64 + alpha=32 targeting attn.qkv + linear1. Maximum coverage + expressiveness.", "rank": 64, "alpha": 32.0, "target": "attn.qkv linear1" }, { "id": "g4_full_r64_6k", "group": "combined", "description": "All Tier 1 at rank 64 + alpha=32, extended to 6000 steps. Checks if convergence is done at 4000.", "rank": 64, "alpha": 32.0, "lora_plus_ratio": 16.0, "lora_dropout": 0.05, "timestep_mode": "curriculum", "steps": 6000, "save_every": 1000 } ] }