torch.inference_mode is thread-local, but the inference flag lives on the
tensor object. Operations on inference tensors always propagate it, even in
a clean thread. The only escape is .clone() called outside inference_mode.
At thread entry (inference_mode disabled): clone clips and mel_converter
buffers to get clean normal tensors before any training computation.
Vocoder parameter clone() also now works correctly in this thread context.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
torch.inference_mode is thread-local. ComfyUI sets it on the node-execution
thread; inference_mode(False) alone is insufficient to escape it in some
environments (e.g. async wrappers, lora-manager hook). A new thread always
starts clean. Moved all training logic into _do_train() called via
threading.Thread so every tensor is a normal autograd tensor by default.
Simplified parameter cloning: clone().detach().requires_grad_(True).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previous fix only iterated mel_converter._buffers (direct buffers). Submodules
(e.g. Spectrogram.window) still held inference tensors. Switch to .modules()
to cover all nested buffers, matching the vocoder parameter sanitization.
Also add a zeros+copy_ safety net on target_mel output so conv can save it.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The clips list is built inside ComfyUI's inference_mode context, so every
element is an inference tensor. torch.stack().clone() propagates the flag.
Use zeros+copy_ (same pattern as params/buffers) to get a normal tensor,
so mel_converter(target_flat) inside no_grad produces a saveable input.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
param.data.clone() and tensor.detach() on inference tensors both produce
inference tensors — the flag propagates through all operations on them.
Inside inference_mode(False), torch.zeros() creates genuine normal tensors.
Use zeros+copy_ to sanitize both vocoder parameters and mel_converter
buffers once before training, so autograd can save inputs for backward.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
param.data = clone() only replaces storage — the nn.Parameter object itself
retains the inference tensor flag set when the model was loaded. Replace each
parameter with a fresh nn.Parameter(data.clone()) created inside
inference_mode(False) so both the object and its data are normal tensors.
Move optimizer creation to after re-creation so it references the new objects.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The vocoder is loaded inside ComfyUI's torch.inference_mode(), making all
its parameters inference tensors. Autograd cannot save inference tensors
for backward even with requires_grad=True. Clone all parameters inside
torch.inference_mode(False) before training to get normal tensors.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
mel_converter buffers (mel_basis, hann_window) are inference tensors
because the model was loaded inside ComfyUI's torch.inference_mode().
Operations on them propagate the flag to outputs. Clone both target_mel
and pred_mel to get normal autograd-compatible tensors. .clone() is
differentiable so the grad graph to vocoder parameters is preserved.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Clips loaded outside torch.inference_mode(False) are inference tensors.
Autograd cannot save them for backward. .clone() creates a normal tensor,
same fix pattern as selva_lora_trainer's dist.mode().clone().
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Model loaded in bf16 causes mel_basis buffer to be bf16. Audio loaded
from disk is float32, causing matmul dtype mismatch. Cast all audio
tensors to model["dtype"] before passing to mel_converter/vocoder.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
torchcodec/libavutil soname mismatch causes torchaudio to fail on every
file load, silently emptying clips. Add _load_wav() that tries torchaudio
first then falls back to soundfile (handles wav/flac without ffmpeg).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
After the finally block, offload_to_cpu moves the vocoder to CPU while
ref_mel stays on GPU. Fix: detect vocoder's current device via
next(vocoder.parameters()).device and move ref_mel there before vocoding.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Saves baseline.wav (ground truth roundtrip before training), stepN.wav
at each save_every checkpoint, and final.wav after training completes.
All use the same fixed reference segment (clip 0, position 0) for
direct comparison across checkpoints.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
44k variants use BigVGANv2 directly as the vocoder (no wrapper, no
@inference_mode decorator), accessible at feature_utils.tod.vocoder.
16k wraps BigVGANVocoder inside BigVGAN, accessed at .vocoder.vocoder.
Both trainer and loader now branch on model["mode"].
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Spectral-loss-only fine-tuning of the BigVGAN vocoder (mel→waveform)
on BJ audio clips. DiT and VAE are completely frozen. Losses: mel L1
reconstruction + multi-resolution STFT magnitude L1 (same three
resolutions as the BigVGAN discriminator config). Saves in
{'generator': state_dict} format compatible with the original BigVGAN
checkpoint. Loader replaces vocoder weights in the loaded SELVA_MODEL
in-place so no full model reload is needed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two improvements for stronger steering effect:
1. Apply steering only during the conditional predict_flow pass by
monkey-patching predict_flow to set a flag via identity check
(cond is conditions). Hooks skip the unconditional pass, so
steering is amplified by cfg_strength (~4.5x) instead of canceling
out in the CFG guidance term.
2. Restore per-position [seq, hidden] steering vectors instead of
seq-averaged [hidden]. More spatially specific — captures positional
activation patterns rather than a global mean. Seq length mismatch
at inference time handled via linear interpolation.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Implements per-block DiT activation steering as an alternative to textual
inversion. Extractor runs frozen generator on dataset with BJ vs empty
conditions, records mean hidden-state delta per block, saves [hidden_dim]
vectors (seq-averaged so they broadcast to any inference duration). Loader
reads the bundle. Sampler registers forward hooks during the ODE that add
strength × vec to each block output, cleaned up in a finally block.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
TI via text conditioning produces buzz because SelVA's text path is
mean-pooled into a global DiT bias — not rich per-token cross-attention
like SD. The optimizer learns a constant spectral artifact rather than
semantic style shift.
ti_strength=1.0 (default) = full injection as before.
ti_strength<1.0 = lerp between original and injected text_clip,
allowing the effect to be dialled back without retraining.
Applies to both text_clip and neg_text_clip symmetrically.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
Previously the comparison PNG was only written at the very end of the sweep,
so an interrupted run produced no image at all. Now _save_comparison() is
called right after _write_summary() for every successful experiment, keeping
loss_comparison.png current throughout the sweep.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Saves baseline.wav + baseline.png in the checkpoint dir using the same
seed as the TI eval samples — direct A/B comparison at every checkpoint
without re-generating the baseline each time.
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>
Reuses _draw_loss_curve + _smooth_losses + _pil_to_tensor from the LoRA
trainer — raw loss in light blue, smoothed overlay in blue, matches the
LoRA trainer's visual style.
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>
- SelvaTiScheduler: runs a JSON-defined sweep of TI training experiments,
loading the dataset once and reusing it across runs
- Collects per-experiment loss history, final/min loss, stability metric
(loss_std_last_quarter), and duration — written to experiment_summary.json
after each completed run so partial sweeps survive interruption
- Resume-aware: skips experiments already marked completed in an existing
summary file
- Outputs smoothed loss comparison chart (same axes, one curve per experiment)
- SelvaTextualInversionTrainer._train_inner now returns a dict
{embeddings_path, loss_history} so the scheduler can read results;
train() extracts just the path for ComfyUI
JSON format: name, description, data_dir, output_root, base config,
experiments list with id + param overrides
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Wrap _spectral_metrics + _save_spectrogram in try-except so a matplotlib
or STFT error doesn't abort the checkpoint save (matches LoRA trainer)
- Remove unused `import math` and `_pil_to_tensor` import
- Drop dead `img` variable (_save_spectrogram returns None)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Replace in-place text_clip assignment with torch.cat so the computation
graph correctly links text_input → learned_tokens; in-place assignment
into a requires_grad=False leaf severs the graph and learned_tokens
receives no gradients
- _spectral_metrics(wav, sr): was passing wav.unsqueeze(0) [1,1,L] instead
of wav [1,L]; stft mean(dim=1) would return wrong shape [1,T] not [n_freqs]
- _save_spectrogram(wav, sr, ...): was passing wav.squeeze(0) [L] (1D)
instead of wav [1,L] as the function expects
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Learns K CLIP token embeddings ([K, 1024]) with all model weights frozen,
keeping generated latents on the decoder's natural manifold — avoids the
quality degradation that affects LoRA on BJ's audio dataset.
- selva_textual_inversion_trainer.py: trains learned_tokens via AdamW,
injects into last K positions of 77-token CLIP embedding, checkpoints
with eval audio + spectral metrics
- selva_textual_inversion_loader.py: loads .pt bundle, returns
TEXTUAL_INVERSION dict for sampler
- selva_sampler.py: optional textual_inversion input; injects into both
text_clip and neg_text_clip before preprocess_conditions
- __init__.py: registers both new nodes
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two ComfyUI nodes to reduce domain mismatch between custom training audio
and the MMAudio VAE's expected spectral distribution:
SelvaHfSmoother: blends a low-pass filtered copy (biquad) with the original
at a configurable cutoff and blend ratio. Attenuates extreme HF content that
BigVGANv2 handles poorly. RMS-preserving.
SelvaSpectralMatcher: computes the log-mel energy profile of the clip,
compares it per-band to the VAE's normalization means (DATA_MEAN_80D/128D),
and applies a smooth STFT-domain gain correction to match the codec's training
distribution. Configurable strength and max_gain_db clamp. RMS-preserving.
Recommended workflow: SpectralMatcher → HfSmoother → feature extraction.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
AutoEncoderModule unconditionally asserts vocoder_ckpt_path is not None
even when need_vae_encoder=True. Pass best_netG.pt to satisfy the assert;
the vocoder weights are not actually used since decode+vocode go through
model["feature_utils"].
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Load fresh FeaturesUtils only for encoding; use model["feature_utils"] for
decode+vocode to mirror the exact path the sampler takes
- Apply generator.normalize() → unnormalize() around the encoded latent so the
decoder receives latents in the same space it expects from inference
- Log both encoded and norm→unnorm latent stats to diagnose round-trip fidelity
- Normalize output to -27 dBFS (matching training clip RMS) and clamp to [-1, 1]
to prevent clipping artifacts in the output waveform
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Without this the decoder produced 7s instead of 8s due to STFT rounding.
Same fix as _prepare_dataset uses for training data.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Encodes audio through the VAE then decodes straight back, bypassing the
diffusion model entirely. Use this to isolate whether saturation artifacts
are introduced by the codec reconstruction (VAE/DAC) or by the LoRA.
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>
- _eval_sample gains clip_idx param (default 0, backward compatible)
- Evaluator loops over all dataset clips per adapter, saves one WAV per clip
- Reference metrics computed for all clips and averaged
- Comparison chart and summary use avg_metrics across all clips
- Eliminates bias from evaluating on an unrepresentative single clip
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
shutil.copy2 was writing FLAC binary to reference.wav — unplayable.
Now copies as reference{.flac/.wav/etc} matching the source extension.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Loads the first clip's original audio (same clip used for inference),
copies it to output_dir/reference.wav, runs spectral metrics and
saves a spectrogram. Appears first in the comparison chart so generated
samples can be judged against the target sound.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Generates audio samples from a list of adapters against a fixed reference
clip, collects spectral metrics for each, and outputs a comparison bar
chart + eval_summary.json. Useful for comparing sweep candidates before
committing to a next round of training.
JSON format: name, data_dir, output_dir, steps, seed, adapters[{id, path}].
Empty path = baseline (no LoRA).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Training clips at -23 LUFS measure -25 to -31 dBFS RMS (avg ~-27).
Normalizing output to -23 dBFS was 4-8 dB too loud, causing saturation
on clips with high crest factor and peaks near 0 dBFS.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
normalize() uses in-place ops so it worked, but reading the return value
makes the intent clear and guards against future refactors.
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>
- New node: SelVA Skip Experiment — writes skip_current.flag from UI,
queue in a second workflow tab while scheduler is running
- SkipExperiment now attaches partial loss/grad/spectral data to the
exception so the scheduler saves all collected scalars in the summary
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Create the flag file in the sweep output_root to skip the running
experiment at the next log interval (every 50 steps):
touch /path/to/experiment/skip_current.flag
Scheduler marks it as 'skipped' in the summary and continues.
Skipped experiments are NOT resumed on restart (unlike failed ones).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Computes hf_energy_ratio (>4kHz), spectral_centroid_hz, spectral_rolloff_hz
at each save_every checkpoint. Logged to console and stored in
experiment_summary.json under results.spectral_metrics[step].
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Log-frequency dB spectrogram (inferno colormap, 100Hz–16kHz) saved as
step_XXXXX.png next to step_XXXXX.wav in samples/ subfolder.
Makes high-frequency rolloff (low bitrate signature) immediately visible.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
RMS normalize to target then scale back if peaks exceed 1.0,
preserving dynamics instead of hard-clipping transients.
Eval sample target updated to -23 dBFS to match training data.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Peak norm was slamming output to full scale regardless of content level,
making generated audio several times louder than training clips.
RMS norm to -20 dBFS matches typical processed audio level.
Sampler exposes target_lufs (-40 to -6, default -20) for user control.
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>
Inference is fast on RTX PRO 6000 — 8 steps was washing out quality
differences between experiments.
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>
- Always sample dataset[0] with fixed noise seed so checkpoints are
directly comparable (hear the model improve step by step)
- Save to output_dir/samples/step_XXXXX.wav instead of alongside checkpoints
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>
Companion node for inspecting dataset.json entries by integer index.
Outputs video (.mp4), audio (.wav/.flac), features (.npz), frames dir,
mask dir, label, and max_index for constraining the index widget range.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Architecture comparison, capability matrix, integration cost estimate,
LoRA training difficulty analysis, and license implications.
Verdict: SelVA remains preferred for V2A + LoRA fine-tuning; AudioX
adds value for music generation, inpainting, and text-to-audio tasks.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
New section 1.1 covers aspect ratio (16:9 landscape preferred), resolution
(≥480p), frame rate (any, use VHS_VIDEOINFO), and portrait handling
(center-crop to square). Based on CLIP 384px and Synchformer 224px internals.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Full research notes on cleaning, augmentation, and quality metrics for
generative model training. Covers LUFS normalization, AudioSep, waveform
augmentation (pitch shift, RIR, EQ), latent mixup, DNSMOS gating, tool
install commands, and key paper references.
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>
- trainer: raise ValueError early when remaining steps < log_interval (50)
instead of UnboundLocalError on smoothed_img/final_path at return
- trainer: use None in grad_norm_history instead of silent 0.0 when
grad_accum > log_interval and no optimizer step fired in the interval
- trainer: include start_step in _train_inner return dict
- scheduler: use start_step from result dict for min_loss_step and
loss_at_steps (fixes wrong step labels on resumed experiments)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
trainer:
- Track gradient norm before clipping at each optimizer step
- Log avg grad_norm per log_interval alongside loss in console output
- Include grad_norm_history in _train_inner return dict
scheduler:
- Add system block to summary (GPU name, VRAM, torch/CUDA version)
- Include full loss_history and grad_norm_history arrays in each
experiment result (50-step resolution, not just save_every checkpoints)
- Add loss_std_last_quarter stability metric (std dev of raw loss over
last 25% of steps — high value indicates unstable training)
- Add log_interval field so consumers know the x-axis resolution
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Extract _prepare_dataset() from SelvaLoraTrainer.train() as a module-level
function so the dataset can be encoded once and reused across experiments
- Change _train_inner() return value from tuple to dict (adds loss_history,
meta, completed; train() unpacks for ComfyUI — no change to node outputs)
- New SelvaLoraScheduler node: reads a JSON sweep file, runs N experiments
sequentially, writes experiment_summary.json (updated after each run) and
loss_comparison.png with all smoothed curves overlaid on the same axes
- Register SelvaLoraScheduler in nodes/__init__.py
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- LoRA dropout: applied to the LoRA path only (not frozen base weights),
0.05–0.1 helps regularize on small datasets (arXiv:2404.09610)
- LoRA+: separate optimizer param groups for lora_A and lora_B with
configurable LR ratio; ratio=16 enables LoRA+ (arXiv:2402.12354)
- Curriculum mode: logit_normal for first N% of steps then uniform,
directly addresses early convergence + fine-detail degradation at
boundaries (arXiv:2603.12517)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Work-in-progress empirical notes: fp32 batch 32 reaches same quality as
bf16 batch 16 in 1/3 the steps but overfits past ~2000 steps on 10 clips.
Lower loss does not reliably mean better audio on small datasets.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Poor prompts and missing masks are a common source of white noise in LoRA
training — imprecise sync features force the adapter to compensate with noise.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
logit_normal reaches lower loss but perceptual improvement over uniform
is dataset-dependent. Keeping uniform as default to match original MMAudio
training behavior; logit_normal remains available as an option.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
On Windows, /folder is drive-relative (no drive letter) rather than a real
absolute path. Redirect these to ComfyUI's output directory so files don't
land at C:\folder. Also redirects plain relative paths (e.g. lora_output)
to output/ instead of the process working directory.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Wraps training loop in try/finally so adapter_final.pt and loss PNGs are
always written. On cancellation the adapter is named
adapter_cancelled_stepXXXXX.pt so it can be used with --resume.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Clips from shorter videos produce fewer CLIP frames (e.g. 2s → 16 frames,
8s → 64 frames). Mixed-length datasets would cause torch.stack() to fail
during batching. Normalize to seq_cfg.clip_seq_len / sync_seq_len at load,
same as latents are already normalized to latent_seq_len.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Uniform timestep sampling undertrained t>0.8 (the final denoising steps),
leaving residual noise that CFG amplifies at inference. Logit-normal sampling
concentrates training near t=0.5 while still covering the full range, improving
high-t coverage and reducing noise floor in generated audio.
Default changed from uniform to logit_normal (sigma=1.0). Previous behavior
available with timestep_mode=uniform.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Added batch_size VRAM table and updated step recommendations for batched training
- Added adapter strength section with practical guidance (0.6-0.7 for noise)
- Added ComfyUI node as Option A for training (not just CLI)
- Noted .mp3 as not recommended, soundfile fallback implied
- Added output files section with sample_*.wav and loss curve PNGs
- Added "LoRA has no effect" troubleshooting (wrong node wired)
- Updated loss convergence targets based on observed training runs
- Clarified linear1 target: 150+ clips recommended
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces single-sample steps with batched sampling via random.choices().
Tensors are stacked to [B, T, C] before the forward pass; t is now [B].
Default grad_accum lowered to 1 since real batching gives stable gradients.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Raw curve shown in light blue, EMA-smoothed (beta=0.9) overlay in darker
blue. Both saved as PNG at end of training. The node IMAGE output now
returns the smoothed version. Live preview also uses the smoothed overlay.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
500 warmup steps is 25% of a 2000-step run — too long. 100 steps lets
the full lr kick in much earlier without sacrificing stability.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The third element in ComfyUI's preview tuple is max_size in pixels, not
JPEG quality. Passing 85 was capping the live loss curve at 85×40px.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
torch.enable_grad() alone is insufficient: operations on inference tensors
(created inside ComfyUI's outer inference_mode context) produce inference
tensors even inside enable_grad, breaking autograd. inference_mode(False)
exits the inference context so the deepcopy, apply_lora, and training loop
run with a fully clean autograd context.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
torch.enable_grad() re-enables grad tracking but nn.Parameters created while
torch.inference_mode() is active are inference tensors that can't enter autograd
regardless. Splitting into _train_inner() and calling it inside enable_grad()
ensures the deepcopy, apply_lora, and the training loop all run with a clean
autograd context.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ComfyUI executes all nodes inside torch.no_grad(), which prevents gradient
tracking and makes loss.backward() fail. torch.enable_grad() overrides it.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
apply_lora() is called after generator.to(device), so lora_A/lora_B were
being created on CPU while the rest of the model was on CUDA.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
STFT hop-size rounding produces ±1 latent frame vs the expected seq length.
Clamp to seq_cfg.latent_seq_len after transpose so generator.forward assertion passes.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Recent torchaudio defaults to torchcodec as the audio backend, which requires
FFmpeg shared libraries. Falls back to soundfile for envs where torchcodec
can't load (e.g. containerised ComfyUI without system FFmpeg).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
torch.stft requires float32 input — casting vae_utils to bf16 caused silent
failures during dataset pre-loading. Also adds traceback.print_exc() so future
clip-load errors are visible in the ComfyUI log.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
At every save_every steps, run a quick 8-step no-CFG inference pass on
a random training clip and save the decoded waveform as
sample_stepXXXXX.wav next to the checkpoint. Uses the existing
generator.unnormalize + feature_utils.decode + vocode pipeline from
the sampler. Failure is non-fatal (logged and skipped).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Send updated loss curve to ComfyUI frontend every 50 steps via
pbar_train.update_absolute() with a JPEG preview tuple — same
mechanism as KSampler's denoising previews.
- Fix x-axis step labels for resumed runs (previously always started
at 0; now correctly shows start_step + offset).
- Split _draw_loss_curve (returns PIL Image) from _pil_to_tensor
(converts for ComfyUI IMAGE output).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Runs the full training loop inside ComfyUI. Reuses the already-loaded
CLIP model from the inference model for text encoding; loads only a
minimal VAE encoder separately (freed after dataset pre-loading).
Outputs:
- SELVA_MODEL with LoRA applied (ready to connect directly to Sampler)
- adapter_path STRING (for SelVA LoRA Loader in future sessions)
- loss_curve IMAGE (PIL-rendered line chart of training loss per 50 steps)
Progress is shown via ComfyUI ProgressBar (two phases: dataset loading,
then training steps). Resume is supported via resume_path input.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
