feat: LoRA fine-tuning for SelVA generator

Teaches the model new/partial sound classes from custom video+audio pairs.
Only ~10 MB of adapter weights are trained vs ~4.4 GB for the full model.

selva_core/model/lora.py
  LoRALinear: wraps nn.Linear with frozen base + trainable A/B matrices.
  B initialised to zero → zero adapter contribution at init.
  apply_lora(): walks named_modules, replaces matching nn.Linear in-place.
  Default target: "attn.qkv" (all 21 SelfAttention QKV projections in
  large_44k). Add "linear1" to also wrap post-attention output projections.
  get_lora_state_dict() / load_lora() for ~10 MB save/load.

train_lora.py (standalone script, no ComfyUI dependency)
  Data format: directory of video files + optional prompts.txt
  ("filename: description"). Falls back to directory name as prompt.
  Pre-extracts features for all clips into RAM, then trains from those.
  Training loop: encode audio→latent (need_vae_encoder=True), flow
  matching MSE loss on velocity prediction, backward on LoRA params only.
  Saves adapter_stepNNNNN.pt checkpoints + adapter_final.pt with metadata.
  Key verified interfaces used:
    encode_audio() → DiagonalGaussianDistribution; .mode().clone() required
    normalize() is in-place
    forward(latent, clip_f, sync_f, text_f, t) takes raw tensors

nodes/selva_lora_loader.py (SelVA LoRA Loader ComfyUI node)
  Loads .pt adapter, deep-copies the generator, applies LoRA, loads weights.
  strength param scales lora_B to adjust adapter contribution at inference.
  Reads rank/alpha/target from embedded metadata if present.
  Returns a patched SELVA_MODEL bundle for use with the existing Sampler.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/__init__.py

@@ -5,6 +5,7 @@ _NODES = {
     "SelvaModelLoader":      (".selva_model_loader",      "SelvaModelLoader",      "SelVA Model Loader"),
     "SelvaFeatureExtractor": (".selva_feature_extractor", "SelvaFeatureExtractor", "SelVA Feature Extractor"),
     "SelvaSampler":          (".selva_sampler",           "SelvaSampler",          "SelVA Sampler"),
+    "SelvaLoraLoader":       (".selva_lora_loader",       "SelvaLoraLoader",       "SelVA LoRA Loader"),
 }
 
 for key, (module_path, class_name, display_name) in _NODES.items():