fix: lower RMS normalization target from -23/-20 to -27 dBFS

Training clips at -23 LUFS measure -25 to -31 dBFS RMS (avg ~-27).
Normalizing output to -23 dBFS was 4-8 dB too loud, causing saturation
on clips with high crest factor and peaks near 0 dBFS.

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

nodes/selva_lora_trainer.py

@@ -139,7 +139,7 @@ def _eval_sample(generator, feature_utils_orig, dataset, seq_cfg, device, dtype,
         elif audio.dim() == 3 and audio.shape[1] != 1:
             audio = audio.mean(dim=1, keepdim=True)
 
-        target_rms = 10 ** (-23.0 / 20.0)   # -23 dBFS matches training data
+        target_rms = 10 ** (-27.0 / 20.0)   # -27 dBFS matches measured RMS of training clips
         rms = audio.pow(2).mean().sqrt().clamp(min=1e-8)
         audio = audio * (target_rms / rms)
         peak = audio.abs().max().clamp(min=1e-8)

nodes/selva_sampler.py

@@ -36,8 +36,8 @@ class SelvaSampler:
                     "tooltip": "Normalize output level. Uses RMS normalization to target_lufs rather than peak normalization, so level matches typical audio content.",
                 }),
                 "target_lufs": ("FLOAT", {
-                    "default": -20.0, "min": -40.0, "max": -6.0, "step": 1.0,
-                    "tooltip": "Target RMS level in dBFS when normalize=True. -20 matches typical processed audio. Increase toward -14 for louder output, decrease toward -30 for quieter.",
+                    "default": -27.0, "min": -40.0, "max": -6.0, "step": 1.0,
+                    "tooltip": "Target RMS level in dBFS when normalize=True. -27 matches the measured RMS of LUFS-normalized training clips. Increase toward -20 for louder output.",
                 }),
             },
         }
@@ -49,7 +49,7 @@ class SelvaSampler:
     CATEGORY = SELVA_CATEGORY
     DESCRIPTION = "Generates audio from video features using SelVA's flow matching ODE. Supports text prompts and negative prompts via classifier-free guidance."
 
-    def generate(self, model, features, prompt, negative_prompt, duration, steps, cfg_strength, seed, normalize=True, target_lufs=-20.0):
+    def generate(self, model, features, prompt, negative_prompt, duration, steps, cfg_strength, seed, normalize=True, target_lufs=-27.0):
         import dataclasses
         from selva_core.model.flow_matching import FlowMatching