Ethanfel
f206a1b38c
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>
20 lines
980 B
Python
20 lines
980 B
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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}
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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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