Ethanfel
3ec380a27e
- 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>
21 lines
1.1 KiB
Python
21 lines
1.1 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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}
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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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