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>
Ethanfel