Ethanfel
1e9551152e
BigVGAN trainer (selva_bigvgan_trainer.py): - Add snake_alpha_only train mode: tunes only ~27K per-channel α params (0.024% of 112M) — physically cannot cause harmonic smearing - Add lambda_l2sp: L2-SP anchor regularization toward pretrained weights - Add optional discriminator_path: frozen MPD+MRD feature matching loss replaces mel L1 when a BigVGAN discriminator checkpoint is provided - Inline MPD + MRD discriminator implementations (no extra dependencies) DITTO optimizer (selva_ditto_optimizer.py): - New node: inference-time noise optimization (arXiv:2401.12179) - Optimizes x₀ via mel Gram matrix style loss against BJ reference clips - All model weights frozen — zero quality degradation risk - Truncated BPTT through last n_grad_steps of the ODE (configurable) - Gradient checkpointing on each differentiated step Docs: - README: document all 20 nodes (was 3), add workflow diagrams - STYLE_TRANSFER.md: new guide — DITTO, vocoder fine-tuning tiers, why LoRA/TI fail, combined approach, dataset prep Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
35 lines
2.9 KiB
Python
35 lines
2.9 KiB
Python
NODE_CLASS_MAPPINGS = {}
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NODE_DISPLAY_NAME_MAPPINGS = {}
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_NODES = {
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"SelvaModelLoader": (".selva_model_loader", "SelvaModelLoader", "SelVA Model Loader"),
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"SelvaFeatureExtractor": (".selva_feature_extractor", "SelvaFeatureExtractor", "SelVA Feature Extractor"),
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"SelvaSampler": (".selva_sampler", "SelvaSampler", "SelVA Sampler"),
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"SelvaLoraLoader": (".selva_lora_loader", "SelvaLoraLoader", "SelVA LoRA Loader"),
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"SelvaLoraTrainer": (".selva_lora_trainer", "SelvaLoraTrainer", "SelVA LoRA Trainer"),
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"SelvaLoraScheduler": (".selva_lora_scheduler", "SelvaLoraScheduler", "SelVA LoRA Scheduler"),
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"SelvaDatasetBrowser": (".selva_dataset_browser", "SelvaDatasetBrowser", "SelVA Dataset Browser"),
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"SelvaSkipExperiment": (".selva_skip_experiment", "SelvaSkipExperiment", "SelVA Skip Experiment"),
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"SelvaLoraEvaluator": (".selva_lora_evaluator", "SelvaLoraEvaluator", "SelVA LoRA Evaluator"),
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"SelvaVaeRoundtrip": (".selva_vae_roundtrip", "SelvaVaeRoundtrip", "SelVA VAE Roundtrip"),
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"SelvaHfSmoother": (".selva_audio_preprocessors", "SelvaHfSmoother", "SelVA HF Smoother"),
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"SelvaSpectralMatcher": (".selva_audio_preprocessors", "SelvaSpectralMatcher", "SelVA Spectral Matcher"),
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"SelvaTextualInversionTrainer": (".selva_textual_inversion_trainer", "SelvaTextualInversionTrainer", "SelVA Textual Inversion Trainer"),
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"SelvaTextualInversionLoader": (".selva_textual_inversion_loader", "SelvaTextualInversionLoader", "SelVA Textual Inversion Loader"),
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"SelvaTiScheduler": (".selva_ti_scheduler", "SelvaTiScheduler", "SelVA TI Scheduler"),
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"SelvaActivationSteeringExtractor": (".selva_activation_steering_extractor", "SelvaActivationSteeringExtractor", "SelVA Activation Steering Extractor"),
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"SelvaActivationSteeringLoader": (".selva_activation_steering_loader", "SelvaActivationSteeringLoader", "SelVA Activation Steering Loader"),
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"SelvaBigvganTrainer": (".selva_bigvgan_trainer", "SelvaBigvganTrainer", "SelVA BigVGAN Trainer"),
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"SelvaBigvganLoader": (".selva_bigvgan_loader", "SelvaBigvganLoader", "SelVA BigVGAN Loader"),
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"SelvaDittoOptimizer": (".selva_ditto_optimizer", "SelvaDittoOptimizer", "SelVA DITTO Optimizer"),
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}
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for key, (module_path, class_name, display_name) in _NODES.items():
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try:
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import importlib
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mod = importlib.import_module(module_path, package=__name__)
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NODE_CLASS_MAPPINGS[key] = getattr(mod, class_name)
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NODE_DISPLAY_NAME_MAPPINGS[key] = display_name
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except (ImportError, AttributeError) as e:
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print(f"[SelVA] Skipping {key}: {e}")
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