Diagnosis: learned tokens grew to norm ~3.2 while real CLIP content tokens
sit at ~1.0. Model never trained on embeddings that large — activates buzz
artifact instead of semantic style shift.
Fix: measure mean token norm from content positions (1–20) of dataset CLIP
embeddings at startup, clamp learned_tokens per-token after every optimizer
step to max 1.5× that reference (50% headroom). Token norm is now logged
as current/limit for easy monitoring.
ti_sweep_1.json: rebuild around norm_clamp group — n4_clamped (primary
diagnostic), prefix_clamped, n8_prefix_clamped, warm_clamped.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
n4_baseline showed token_norm growing linearly without plateau — classic
sign of lr too high relative to parameter count. With only K×1024 params,
gradient signal per param is already high-magnitude; high lr causes
overshoot rather than convergence.
- Default lr: 1e-3 → 2e-4 (matches LoRA working regime)
- Default batch_size: 16 → 4 (more diverse gradients, helps norm saturate)
- ti_sweep_1.json: add lr_batch group (lr_low_b4, lr_mid_b8,
lr_low_b4_prefix, lr_2e3), restructure with clearer groups,
annotate n4_baseline as completed with findings
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Observation: n4_baseline loss barely moved (1.025→0.965 over 3000 steps),
token_norm grew linearly without plateau — generator likely ignores last-K
CLIP positions (EOS/padding zone) where suffix injects.
Fix: add inject_mode parameter throughout the pipeline:
- "suffix": replace last K positions (original behavior, model may ignore)
- "prefix": replace positions 1:1+K right after BOS — highest attention
weight in CLIP, much stronger gradient signal expected
Changes:
- selva_textual_inversion_trainer.py: _inject_tokens() helper centralises
the torch.cat construction for both modes; used in training loop and eval;
inject_mode stored in checkpoint files
- selva_textual_inversion_loader.py: reads inject_mode from checkpoint,
includes in TEXTUAL_INVERSION bundle
- selva_sampler.py: uses _inject_tokens() via bundle's inject_mode field
- selva_ti_scheduler.py: inject_mode in _PARAM_DEFAULTS, config, and
_train_inner call
- ti_sweep_1.json: updated with prefix_inject group (n4, n8, n4+warm);
n4_baseline marked completed; suffix experiments retained for comparison
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
First TI sweep covering the three most impactful axes:
- token_count group: n_tokens 4 / 8 / 16 (capacity vs overfitting)
- learning_rate group: 5e-4 / 1e-3 / 2e-3 with n_tokens=4
- warm_init group: n4 and n8 seeded from 'mechanical impact sound design'
7 experiments total, 3000 steps each, same data_dir as LoRA sweeps.
n4_baseline (lr=1e-3, random init) is the primary reference point.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previous sweep used alpha=rank (scale=1.0) which at rank 128/256 drowned
base model priors — spectral flatness went from 0.013 (baseline) to 0.094.
This sweep tests alpha dramatically below rank across r16/r32/r128 to find
the scale where LoRA nudges rather than overwrites.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add lr_schedule param (constant|cosine) to SelvaLoraTrainer
- Cosine decays LR from initial value to ~0 after warmup, preventing
the oscillation observed at steps 6000-8000 with lr=2e-4 flat
- Wire lr_schedule through scheduler _PARAM_DEFAULTS and _train_inner call
- Add g5_r128_lr_2e4_cosine and g5_r128_lr_3e4_cosine to r128_sweet_spot sweep
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
9 experiments targeting loss 0.25-0.35 without LoRA+ noise.
Tests higher base LR (2e-4/3e-4/5e-4), curriculum combos, conservative
LoRA+ ratio=4, and rank 256 baseline + lr=3e-4.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
15 experiments across rank (64/128), alpha, regularisation, LR, target
layers, and combined stacks. Based on tier1_thorough early results
confirming rank 64 sounds best perceptually.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Scheduler: on re-run, reads existing experiment_summary.json and skips
already-completed experiments — safe to stop and restart mid-sweep.
tier1_thorough: adds g5 (lr 3e-5/3e-4), g6 (full target attn.qkv+linear1
at r16 and r64), and g4_full_r64_6k (6000-step extended run) — 17 total.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Dataset browser: audio/features now resolve through features/ subdir
- tier1_sweep.json: update data_dir to BJ dataset path
- tier1_thorough.json: 12-experiment overnight sweep across 4 groups
(rank 16/32/64, alpha scaling, LoRA+/dropout/curriculum isolation,
full Tier 1 stack at r16 and r64) — output to BJ/experiment/
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
6 experiments: baseline, LoRA+ (ratio=16), dropout 0.05, dropout 0.1,
curriculum sampling, and all three combined. bf16 batch 16, 2000 steps,
seed 42. data_dir placeholder needs to be updated before running.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ethanfel