Ethanfel
e56ece9c1c
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>
29 lines
2.0 KiB
Python
29 lines
2.0 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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}
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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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