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>
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>
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>
- 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>
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>
- 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>
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>
Teaches the model new/partial sound classes from custom video+audio pairs.
Only ~10 MB of adapter weights are trained vs ~4.4 GB for the full model.
selva_core/model/lora.py
LoRALinear: wraps nn.Linear with frozen base + trainable A/B matrices.
B initialised to zero → zero adapter contribution at init.
apply_lora(): walks named_modules, replaces matching nn.Linear in-place.
Default target: "attn.qkv" (all 21 SelfAttention QKV projections in
large_44k). Add "linear1" to also wrap post-attention output projections.
get_lora_state_dict() / load_lora() for ~10 MB save/load.
train_lora.py (standalone script, no ComfyUI dependency)
Data format: directory of video files + optional prompts.txt
("filename: description"). Falls back to directory name as prompt.
Pre-extracts features for all clips into RAM, then trains from those.
Training loop: encode audio→latent (need_vae_encoder=True), flow
matching MSE loss on velocity prediction, backward on LoRA params only.
Saves adapter_stepNNNNN.pt checkpoints + adapter_final.pt with metadata.
Key verified interfaces used:
encode_audio() → DiagonalGaussianDistribution; .mode().clone() required
normalize() is in-place
forward(latent, clip_f, sync_f, text_f, t) takes raw tensors
nodes/selva_lora_loader.py (SelVA LoRA Loader ComfyUI node)
Loads .pt adapter, deep-copies the generator, applies LoRA, loads weights.
strength param scales lora_B to adjust adapter contribution at inference.
Reads rank/alpha/target from embedded metadata if present.
Returns a patched SELVA_MODEL bundle for use with the existing Sampler.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- nodes/__init__.py: fix [PrismAudio] leftover label in error print
- selva_feature_extractor: hash beginning, middle and end of video tensor
instead of just first 1MB, avoiding collisions on videos with same opening frames
- selva_sampler: derive SequenceConfig from model template via dataclasses.replace
instead of hardcoding sampling_rate/spectrogram_frame_rate per mode
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This branch registers only the three SelVA nodes. PrismAudio nodes stay
on master/feature/lora-trainer.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ethanfel