feat: add gram_weight param to DITTO, reduce default style_weight to 0.1

White noise on output was caused by the Gram matrix loss pushing the latent
into incoherent regions. Now gram_weight defaults to 0 (mean spectrum only)
and style_weight defaults to 0.1 instead of 1.0. Users can enable Gram
gradually once mean-only optimization converges cleanly.

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

nodes/selva_ditto_optimizer.py

@@ -42,28 +42,30 @@ def _load_wav(path):
     return wav, sr
 
 
-def _mel_style_loss(mel_gen, ref_mean, ref_gram):
+def _mel_style_loss(mel_gen, ref_mean, ref_gram, gram_weight=0.0):
     """Style loss between generated mel and precomputed reference statistics.
 
-    mel_gen:  [1, n_mels, T]  generated mel spectrogram (with grad)
+    mel_gen:     [1, n_mels, T]  generated mel spectrogram (with grad)
-    ref_mean: [n_mels]         mean spectrum of BJ reference clips (detached)
+    ref_mean:    [n_mels]         mean spectrum of reference clips (detached)
-    ref_gram: [n_mels, n_mels] Gram matrix of BJ reference clips  (detached)
+    ref_gram:    [n_mels, n_mels] Gram matrix of reference clips  (detached)
-
+    gram_weight: weight for Gram matrix component — 0 = mean spectrum only.
-    Mean spectrum loss captures the spectral envelope (which harmonics are
+                 Start at 0; enable only if mean-only optimization converges
-    boosted). Gram matrix loss captures timbral texture — covariance between
+                 without noise, then increase slowly (0.01–0.1).
-    frequency bands — without requiring temporal alignment.
     """
     m = mel_gen.squeeze(0)  # [n_mels, T]
 
-    # Mean spectrum loss
+    # Mean spectrum loss — captures spectral envelope
     gen_mean = m.mean(dim=-1)  # [n_mels]
     loss_mean = F.l1_loss(gen_mean, ref_mean)
 
-    # Gram matrix loss (texture, position-invariant)
+    if gram_weight <= 0.0:
+        return loss_mean
+
+    # Gram matrix loss — captures timbral texture (can add noise if too high)
     gram_gen = (m @ m.T) / m.shape[-1]  # [n_mels, n_mels]
     loss_gram = F.mse_loss(gram_gen, ref_gram)
 
-    return loss_mean + 0.1 * loss_gram
+    return loss_mean + gram_weight * loss_gram
 
 
 class SelvaDittoOptimizer:
@@ -115,9 +117,15 @@ class SelvaDittoOptimizer:
                                "Must be ≤ n_ode_steps. 5 is a good default.",
                 }),
                 "style_weight": ("FLOAT", {
-                    "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1,
+                    "default": 0.1, "min": 0.0, "max": 10.0, "step": 0.05,
-                    "tooltip": "Weight of the BJ style loss. Increase to push harder toward "
+                    "tooltip": "Weight of the BJ style loss. High values push harder toward "
-                               "BJ style at the cost of coherence with the video.",
+                               "BJ style but add noise. Start at 0.1 and increase slowly.",
+                }),
+                "gram_weight": ("FLOAT", {
+                    "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01,
+                    "tooltip": "Weight of the Gram matrix (timbral texture) loss relative to "
+                               "the mean spectrum loss. 0 = mean spectrum only (less noise). "
+                               "0.1 adds texture matching but can introduce white noise.",
                 }),
                 "steps": ("INT", {
                     "default": 25, "min": 1, "max": 200,
@@ -148,7 +156,7 @@ class SelvaDittoOptimizer:
 
     def optimize(self, model, features, prompt, negative_prompt,
                  reference_dir, n_opt_steps, opt_lr, n_ode_steps, n_grad_steps,
-                 style_weight, steps, cfg_strength, seed,
+                 style_weight, gram_weight, steps, cfg_strength, seed,
                  normalize=True, target_lufs=-27.0):
         import traceback
 
@@ -244,7 +252,7 @@ class SelvaDittoOptimizer:
                     ref_mean, ref_gram,
                     seq_cfg, sample_rate, device, dtype,
                     n_opt_steps, opt_lr, n_ode_steps, n_grad_steps,
-                    style_weight, steps, cfg_strength, seed,
+                    style_weight, gram_weight, steps, cfg_strength, seed,
                     normalize, target_lufs, pbar,
                 )
             except Exception as e:
@@ -270,7 +278,7 @@ def _do_optimize(net_generator, feature_utils, mel_converter,
                  ref_mean, ref_gram,
                  seq_cfg, sample_rate, device, dtype,
                  n_opt_steps, opt_lr, n_ode_steps, n_grad_steps,
-                 style_weight, steps, cfg_strength, seed,
+                 style_weight, gram_weight, steps, cfg_strength, seed,
                  normalize, target_lufs, pbar):
     """Optimization loop — runs in a fresh thread (no inference_mode active)."""
 
@@ -411,7 +419,7 @@ def _do_optimize(net_generator, feature_utils, mel_converter,
         mel_gen = feature_utils.tod.vae.decode(x_un.transpose(1, 2))
 
         # ── Style loss ───────────────────────────────────────────────────────
-        loss = style_weight * _mel_style_loss(mel_gen, ref_mean, ref_gram)
+        loss = style_weight * _mel_style_loss(mel_gen, ref_mean, ref_gram, gram_weight)
 
         optimizer.zero_grad()
         loss.backward()  # gradient flows through Phase 2 + STE back to x0.grad