Ethanfel
f745e241c4
- Replace all BJ references with generic "target style/audio" in activation steering, DITTO optimizer, and BigVGAN trainer - Add latent_mixup_alpha/latent_noise_sigma to LoRA scheduler defaults - Add bigvgan_disc_fm_retest.json and lora_optimized_dataset.json Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
65 lines
2.3 KiB
JSON
65 lines
2.3 KiB
JSON
{
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"name": "lora_optimized_dataset",
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"description": "LoRA training on optimized dataset (134 clips: resampled 44.1kHz, LUFS-normalized, spectral matched, HF smoothed, gain-augmented). Tests latent augmentation and schedule variants on top of known-best config (PiSSA, rank=128, lr=3e-4).",
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"data_dir": "/media/unraid/davinci/Selva/BJ/features_v2_improved/",
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"output_root": "/media/unraid/davinci/Selva/BJ/experiment/lora_optimized_dataset",
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"base": {
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"rank": 128,
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"lr": 3e-4,
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"steps": 5000,
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"batch_size": 4,
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"warmup_steps": 100,
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"save_every": 1000,
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"seed": 42,
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"init_mode": "pissa",
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"use_rslora": true,
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"target": "attn.qkv",
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"timestep_mode": "uniform",
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"lr_schedule": "constant"
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},
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"experiments": [
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{
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"id": "baseline",
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"description": "Control: known-best config (PiSSA r128 lr=3e-4) on the optimized dataset. No latent augmentation."
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},
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{
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"id": "latent_mixup",
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"description": "Latent mixup alpha=0.4 (MusicLDM). Tests if mixing training latents reduces memorization on 134 clips.",
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"latent_mixup_alpha": 0.4
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},
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{
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"id": "latent_noise",
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"description": "Latent noise sigma=0.02. Mild Gaussian noise on training latents for regularization.",
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"latent_noise_sigma": 0.02
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},
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{
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"id": "mixup_and_noise",
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"description": "Both latent mixup (0.4) and noise (0.02). Combined regularization.",
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"latent_mixup_alpha": 0.4,
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"latent_noise_sigma": 0.02
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},
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{
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"id": "cosine_schedule",
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"description": "Cosine LR decay. lr=3e-4 was stable with constant, but cosine may extract more from 5k steps.",
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"lr_schedule": "cosine"
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},
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{
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"id": "cosine_mixup",
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"description": "Cosine LR + latent mixup. Best regularization combo candidate.",
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"lr_schedule": "cosine",
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"latent_mixup_alpha": 0.4
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},
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{
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"id": "logit_normal",
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"description": "Logit-normal timestep sampling (sigma=1.0). Concentrates training near t=0.5 where flow matching is hardest.",
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"timestep_mode": "logit_normal"
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},
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{
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"id": "curriculum_mixup",
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"description": "Curriculum timesteps (logit_normal first 60%, then uniform) + latent mixup. Full regularization stack.",
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"timestep_mode": "curriculum",
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"latent_mixup_alpha": 0.4
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}
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]
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}
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